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Quantitative Research – Methods, Types and Analysis

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Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

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What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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Quantitative Research Methods

What is quantitative research, about this guide, introduction, quantitative research methodologies.

• Key Resources
• Quantitative Software
• Finding Qualitative Studies

 The purpose of this guide is to provide a starting point for learning about quantitative research. In this guide, you'll find:

• Resources on diverse types of quantitative research.
• An overview of resources for data, methods & analysis
• Popular quantitative software options
• Information on how to find quantitative studies

Research involving the collection of data in numerical form for quantitative analysis. The numerical data can be durations, scores, counts of incidents, ratings, or scales. Quantitative data can be collected in either controlled or naturalistic environments, in laboratories or field studies, from special populations or from samples of the general population. The defining factor is that numbers result from the process, whether the initial data collection produced numerical values, or whether non-numerical values were subsequently converted to numbers as part of the analysis process, as in content analysis.

Citation: Garwood, J. (2006). Quantitative research. In V. Jupp (Ed.), The SAGE dictionary of social research methods. (pp. 251-252). London, England: SAGE Publications. doi:10.4135/9780857020116

Watch the following video to learn more about Quantitative Research:

(Video best viewed in Edge and Chrome browsers, or click here to view in the Sage Research Methods Database)

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y, and Z are also playing a role.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses.

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Quantitative and Qualitative Research

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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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• What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

• Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
• Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
• Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
• Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
• Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
• Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

• Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
• Multiple samples can be analyzed and compared using a cross-sectional survey research method.
• Multiple variables can be evaluated using this type of survey research.
• The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

• In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
• Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
• In situations where the sequence of events is highly essential, longitudinal surveys are used.
• Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

• The relationship between stress and depression.
• The equation between fame and money.
• The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

• Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
• Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
• Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
• Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

• Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
• Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
• Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
• Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
• Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
• Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
• Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
• SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
• Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

• SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
• Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
• Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
• TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

• Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
• Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
• Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
• Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
• Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

• A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
• Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

• Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
• Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
• Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
• Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

• Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
• Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
• Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
• Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
• Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
• Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
• Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

• Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
• Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
• Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
• Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
• Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
• Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
• Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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Article Contents

Introduction, what is fieldwork, purpose of fieldwork, physical safety, mental wellbeing and affect, ethical considerations, remote fieldwork, concluding thoughts, acknowledgments, funder information.

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Field Research: A Graduate Student's Guide

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Ezgi Irgil, Anne-Kathrin Kreft, Myunghee Lee, Charmaine N Willis, Kelebogile Zvobgo, Field Research: A Graduate Student's Guide, International Studies Review , Volume 23, Issue 4, December 2021, Pages 1495–1517, https://doi.org/10.1093/isr/viab023

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What is field research? Is it just for qualitative scholars? Must it be done in a foreign country? How much time in the field is “enough”? A lack of disciplinary consensus on what constitutes “field research” or “fieldwork” has left graduate students in political science underinformed and thus underequipped to leverage site-intensive research to address issues of interest and urgency across the subfields. Uneven training in Ph.D. programs has also left early-career researchers underprepared for the logistics of fieldwork, from developing networks and effective sampling strategies to building respondents’ trust, and related issues of funding, physical safety, mental health, research ethics, and crisis response. Based on the experience of five junior scholars, this paper offers answers to questions that graduate students puzzle over, often without the benefit of others’ “lessons learned.” This practical guide engages theory and praxis, in support of an epistemologically and methodologically pluralistic discipline.

¿Qué es la investigación de campo? ¿Es solo para académicos cualitativos? ¿Debe realizarse en un país extranjero? ¿Cuánto tiempo en el terreno es “suficiente”? La falta de consenso disciplinario con respecto a qué constituye la “investigación de campo” o el “trabajo de campo” ha causado que los estudiantes de posgrado en ciencias políticas estén poco informados y, por lo tanto, capacitados de manera insuficiente para aprovechar la investigación exhaustiva en el sitio con el objetivo de abordar los asuntos urgentes y de interés en los subcampos. La capacitación desigual en los programas de doctorado también ha provocado que los investigadores en las primeras etapas de su carrera estén poco preparados para la logística del trabajo de campo, desde desarrollar redes y estrategias de muestreo efectivas hasta generar la confianza de las personas que facilitan la información, y las cuestiones relacionadas con la financiación, la seguridad física, la salud mental, la ética de la investigación y la respuesta a las situaciones de crisis. Con base en la experiencia de cinco académicos novatos, este artículo ofrece respuestas a las preguntas que desconciertan a los estudiantes de posgrado, a menudo, sin el beneficio de las “lecciones aprendidas” de otras personas. Esta guía práctica incluye teoría y praxis, en apoyo de una disciplina pluralista desde el punto de vista epistemológico y metodológico.

En quoi consiste la recherche de terain ? Est-elle uniquement réservée aux chercheurs qualitatifs ? Doit-elle être effectuée dans un pays étranger ? Combien de temps faut-il passer sur le terrain pour que ce soit « suffisant » ? Le manque de consensus disciplinaire sur ce qui constitue une « recherche de terrain » ou un « travail de terrain » a laissé les étudiants diplômés en sciences politiques sous-informés et donc sous-équipés pour tirer parti des recherches de terrain intensives afin d'aborder les questions d'intérêt et d'urgence dans les sous-domaines. L'inégalité de formation des programmes de doctorat a mené à une préparation insuffisante des chercheurs en début de carrière à la logistique du travail de terrain, qu'il s'agisse du développement de réseaux et de stratégies d’échantillonnage efficaces, de l'acquisition de la confiance des personnes interrogées ou des questions de financement, de sécurité physique, de santé mentale, d’éthique de recherche et de réponse aux crises qui y sont associées. Cet article s'appuie sur l'expérience de cinq jeunes chercheurs pour proposer des réponses aux questions que les étudiants diplômés se posent, souvent sans bénéficier des « enseignements tirés » par les autres. Ce guide pratique engage théorie et pratique en soutien à une discipline épistémologiquement et méthodologiquement pluraliste.

Days before embarking on her first field research trip, a Ph.D. student worries about whether she will be able to collect the qualitative data that she needs for her dissertation. Despite sending dozens of emails, she has received only a handful of responses to her interview requests. She wonders if she will be able to gain more traction in-country. Meanwhile, in the midst of drafting her thesis proposal, an M.A. student speculates about the feasibility of his project, given a modest budget. Thousands of miles away from home, a postdoc is concerned about their safety, as protests erupt outside their window and state security forces descend into the streets.

These anecdotes provide a small glimpse into the concerns of early-career researchers undertaking significant projects with a field research component. Many of these fieldwork-related concerns arise from an unfortunate shortage in curricular offerings for qualitative and mixed-method research in political science graduate programs ( Emmons and Moravcsik 2020 ), 1 as well as the scarcity of instructional materials for qualitative and mixed-method research, relative to those available for quantitative research ( Elman, Kapiszewski, and Kirilova 2015 ; Kapiszewski, MacLean, and Read 2015 ; Mosley 2013 ). A recent survey among the leading United States Political Science programs in Comparative Politics and International Relations found that among graduate students who have carried out international fieldwork, 62 percent had not received any formal fieldwork training and only 20 percent felt very or mostly prepared for their fieldwork ( Schwartz and Cronin-Furman 2020 , 7–8). This shortfall in training and instruction means that many young researchers are underprepared for the logistics of fieldwork, from developing networks and effective sampling strategies to building respondents’ trust. In addition, there is a notable lack of preparation around issues of funding, physical safety, mental health, research ethics, and crisis response. This is troubling, as field research is highly valued and, in some parts of the field, it is all but expected, for instance in comparative politics.

Beyond subfield-specific expectations, research that leverages multiple types of data and methods, including fieldwork, is one of the ways that scholars throughout the discipline can more fully answer questions of interest and urgency. Indeed, multimethod work, a critical means by which scholars can parse and evaluate causal pathways, is on the rise ( Weller and Barnes 2016 ). The growing appearance of multimethod research in leading journals and university presses makes adequate training and preparation all the more significant ( Seawright 2016 ; Nexon 2019 ).

We are five political scientists interested in providing graduate students and other early-career researchers helpful resources for field research that we lacked when we first began our work. Each of us has recently completed or will soon complete a Ph.D. at a United States or Swedish university, though we come from many different national backgrounds. We have conducted field research in our home countries and abroad. From Colombia and Guatemala to the United States, from Europe to Turkey, and throughout East and Southeast Asia, we have spanned the globe to investigate civil society activism and transitional justice in post-violence societies, conflict-related sexual violence, social movements, authoritarianism and contentious politics, and the everyday politics and interactions between refugees and host-country citizens.

While some of us have studied in departments that offer strong training in field research methods, most of us have had to self-teach, learning through trial and error. Some of us have also been fortunate to participate in short courses and workshops hosted by universities such as the Consortium for Qualitative Research Methods and interdisciplinary institutions such as the Peace Research Institute Oslo. Recognizing that these opportunities are not available to or feasible for all, and hoping to ease the concerns of our more junior colleagues, we decided to compile our experiences and recommendations for first-time field researchers.

Our experiences in the field differ in several key respects, from the time we spent in the field to the locations we visited, and how we conducted our research. The diversity of our experiences, we hope, will help us reach and assist the broadest possible swath of graduate students interested in field research. Some of us have spent as little as ten days in a given country or as much as several months, in some instances visiting a given field site location just once and in other instances returning several times. At times, we have been able to plan weeks and months in advance. Other times, we have quickly arranged focus groups and impromptu interviews. Other times still, we have completed interviews virtually, when research participants were in remote locations or when we ourselves were unable to travel, of note during the coronavirus pandemic. We have worked in countries where we are fluent or have professional proficiency in the language, and in countries where we have relied on interpreters. We have worked in settings with precarious security as well as in locations that feel as comfortable as home. Our guide is not intended to be prescriptive or exhaustive. What we offer is a set of experience-based suggestions to be implemented as deemed relevant and appropriate by the researcher and their advisor(s).

In terms of the types of research and data sources and collection, we have conducted archival research, interviews, focus groups, and ethnographies with diplomats, bureaucrats, military personnel, ex-combatants, civil society advocates, survivors of political violence, refugees, and ordinary citizens. We have grappled with ethical dilemmas, chief among them how to get useful data for our research projects in ways that exceed the minimal standards of human subjects’ research evaluation panels. Relatedly, we have contemplated how to use our platforms to give back to the individuals and communities who have so generously lent us their time and knowledge, and shared with us their personal and sometimes harrowing stories.

Our target audience is first and foremost graduate students and early-career researchers who are interested in possibly conducting fieldwork but who either (1) do not know the full potential or value of fieldwork, (2) know the potential and value of fieldwork but think that it is excessively cost-prohibitive or otherwise infeasible, or (3) who have the interest, the will, and the means but not necessarily the know-how. We also hope that this resource will be of value to graduate programs, as they endeavor to better support students interested in or already conducting field research. Further, we target instructional faculty and graduate advisors (and other institutional gatekeepers like journal and book reviewers), to show that fieldwork does not have to be year-long, to give just one example. Instead, the length of time spent in the field is a function of the aims and scope of a given project. We also seek to formalize and normalize the idea of remote field research, whether conducted because of security concerns in conflict zones, for instance, or because of health and safety concerns, like the Covid-19 pandemic. Accordingly, researchers in the field for shorter stints or who conduct fieldwork remotely should not be penalized.

We note that several excellent resources on fieldwork such as the bibliography compiled by Advancing Conflict Research (2020) catalogue an impressive list of articles addressing questions such as ethics, safety, mental health, reflexivity, and methods. Further resources can be found about the positionality of the researcher in the field while engaging vulnerable communities, such as in the research field of migration ( Jacobsen and Landau 2003 ; Carling, Bivand Erdal, and Ezzati 2014 ; Nowicka and Cieslik 2014 ; Zapata-Barrero and Yalaz 2019 ). However, little has been written beyond conflict-affected contexts, fragile settings, and vulnerable communities. Moreover, as we consulted different texts and resources, we found no comprehensive guide to fieldwork explicitly written with graduate students in mind. It is this gap that we aim to fill.

In this paper, we address five general categories of questions that graduate students puzzle over, often without the benefit of others’ “lessons learned.” First, What is field research? Is it just for qualitative scholars? Must it be conducted in a foreign country? How much time in the field is “enough”? Second, What is the purpose of fieldwork? When does it make sense to travel to a field site to collect data? How can fieldwork data be used? Third, What are the nuts and bolts? How does one get ready and how can one optimize limited time and financial resources? Fourth, How does one conduct fieldwork safely? What should a researcher do to keep themselves, research assistants, and research subjects safe? What measures should they take to protect their mental health? Fifth, How does one conduct ethical, beneficent field research?

Finally, the Covid-19 pandemic has impressed upon the discipline the volatility of research projects centered around in-person fieldwork. Lockdowns and closed borders left researchers sequestered at home and unable to travel, forced others to cut short any trips already begun, and unexpectedly confined others still to their fieldwork sites. Other factors that may necessitate a (spontaneous) readjustment of planned field research include natural disasters, a deteriorating security situation in the field site, researcher illness, and unexpected changes in personal circumstances. We, therefore, conclude with a section on the promise and potential pitfalls of remote (or virtual) fieldwork. Throughout this guide, we engage theory and praxis to support an epistemologically and methodologically pluralistic discipline.

The concept of “fieldwork” is not well defined in political science. While several symposia discuss the “nuts and bolts” of conducting research in the field within the pages of political science journals, few ever define it ( Ortbals and Rincker 2009 ; Hsueh, Jensenius, and Newsome 2014 ). Defining the concept of fieldwork is important because assumptions about what it is and what it is not underpin any suggestions for conducting it. A lack of disciplinary consensus about what constitutes “fieldwork,” we believe, explains the lack of a unified definition. Below, we discuss three areas of current disagreement about what “fieldwork” is, including the purpose of fieldwork, where it occurs, and how long it should be. We follow this by offering our definition of fieldwork.

First, we find that many in the discipline view fieldwork as squarely in the domain of qualitative research, whether interpretivist or positivist. However, field research can also serve quantitative projects—for example, by providing crucial context, supporting triangulation, or illustrating causal mechanisms. For instance, Kreft (2019) elaborated her theory of women's civil society mobilization in response to conflict-related sexual violence based on interviews she carried out in Colombia. She then examined cross-national patterns through statistical analysis. Conversely, Willis's research on the United States military in East Asia began with quantitative data collection and analysis of protest events before turning to fieldwork to understand why protests occurred in some instances but not others. Researchers can also find quantifiable data in the field that is otherwise unavailable to them at home ( Read 2006 ; Chambers-Ju 2014 ; Jensenius 2014 ). Accordingly, fieldwork is not in the domain of any particular epistemology or methodology as its purpose is to acquire data for further information.

Second, comparative politics and international relations scholars often opine that fieldwork requires leaving the country in which one's institution is based. Instead, we propose that what matters most is the nature of the research project, not the locale. For instance, some of us in the international relations subfield have interviewed representatives of intergovernmental organizations (IGOs) and international nongovernmental organizations (INGOs), whose headquarters are generally located in Global North countries. For someone pursuing a Ph.D. in the United States and writing on transnational advocacy networks, interviews with INGO representatives in New York certainly count as fieldwork ( Zvobgo 2020 ). Similarly, a graduate student who returns to her home country to interview refugees and native citizens is conducting a field study as much as a researcher for whom the context is wholly foreign. Such interviews can provide necessary insights and information that would not have been gained otherwise—one of the key reasons researchers conduct fieldwork in the first place. In other instances, conducting any in-person research is simply not possible, due to financial constraints, safety concerns, or other reasons. For example, the Covid-19 pandemic has forced many researchers to shift their face-to-face research plans to remote data collection, either over the phone or virtually ( Howlett 2021 , 2). For some research projects, gathering data through remote methods may yield the same if not similar information than in-person research ( Howlett 2021 , 3–4). As Howlett (2021 , 11) notes, digital platforms may offer researchers the ability to “embed ourselves in other contexts from a distance” and glimpse into our subjects’ lives in ways similar to in-person research. By adopting a broader definition of fieldwork, researchers can be more flexible in getting access to data sources and interacting with research subjects.

Third, there is a tendency, especially among comparativists, to only count fieldwork that spans the better part of a year; even “surgical strike” field research entails one to three months, according to some scholars ( Ortbals and Rincker 2009 ; Weiss, Hicken, and Kuhonta 2017 ). The emphasis on spending as much time as possible in the field is likely due to ethnographic research traditions, reflected in classics such as James Scott's Weapons of the Weak , which entail year-long stints of research. However, we suggest that the appropriate amount of time in the field should be assessed on a project-by-project basis. Some studies require the researcher to be in the field for long periods; others do not. For example, Willis's research on the discourse around the United States’ military presence in overseas host communities has required months in the field. By contrast, Kreft only needed ten days in New York to carry out interviews with diplomats and United Nations staff, in a context with which she already had some familiarity from a prior internship. Likewise, Zvobgo spent a couple of weeks in her field research sites, conducting interviews with directors and managers of prominent human rights nongovernmental organizations. This population is not so large as to require a whole month or even a few months. This has also been the case for Irgil, as she had spent one month in the field site conducting interviews with ordinary citizens. The goal of the project was to acquire information on citizens’ perceptions of refugees. As we discuss in the next section, when deciding how long to spend in the field, scholars must consider the information their project requires and consider the practicalities of fieldwork, notably cost.

Thus, we highlight three essential points in fieldwork and offer a definition accordingly: fieldwork involves acquiring information, using any set of appropriate data collection techniques, for qualitative, quantitative, or experimental analysis through embedded research whose location and duration is dependent on the project. We argue that adopting such a definition of “fieldwork” is necessary to include the multitude of forms fieldwork can take, including remote methods, whose value and challenges the Covid-19 pandemic has impressed upon the discipline.

When does a researcher need to conduct fieldwork? Fieldwork can be effective for (1) data collection, (2) theory building, and (3) theory testing. First, when a researcher is interested in a research topic, yet they could not find an available and/or reliable data source for the topic, fieldwork could provide the researcher with plenty of options. Some research agendas can require researchers to visit archives to review historical documents. For example, Greitens (2016) visited national archives in the Philippines, South Korea, Taiwan, and the United States to find historical documents about the development of coercive institutions in past authoritarian governments for her book, Dictators and Their Secret Police . Also, newly declassified archival documents can open new possibilities for researchers to examine restricted topics. To illustrate, thanks to the newly released archival records of the Chinese Communist Party's communications, and exchange of visits with the European communist world, Sarotte (2012) was able to study the Party's decision to crack down on Tiananmen protesters, which had previously been deemed as an unstudiable topic due to the limited data.

Other research agendas can require researchers to conduct (semistructured) in-depth interviews to understand human behavior or a situation more closely, for example, by revealing the meanings of concepts for people and showing how people perceive the world. For example, O'Brien and Li (2005) conducted in-depth interviews with activists, elites, and villagers to understand how these actors interact with each other and what are the outcomes of the interaction in contentious movements in rural China. Through research, they revealed that protests have deeply influenced all these actors’ minds, a fact not directly observable without in-depth interviews.

Finally, data collection through fieldwork should not be confined to qualitative data ( Jensenius 2014 ). While some quantitative datasets can be easily compiled or accessed through use of the internet or contact with data-collection agencies, other datasets can only be built or obtained through relationships with “gatekeepers” such as government officials, and thus require researchers to visit the field ( Jensenius 2014 ). Researchers can even collect their own quantitative datasets by launching surveys or quantifying data contained in archives. In a nutshell, fieldwork will allow researchers to use different techniques to collect and access original/primary data sources, whether these are qualitative, quantitative, or experimental in nature, and regardless of the intended method of analysis. 2

But fieldwork is not just for data collection as such. Researchers can accomplish two other fundamental elements of the research process: theory building and theory testing. When a researcher finds a case where existing theories about a phenomenon do not provide plausible explanations, they can build a theory through fieldwork ( Geddes 2003 ). Lee's experience provides a good example. When studying the rise of a protest movement in South Korea for her dissertation, Lee applied commonly discussed social movement theories, grievances, political opportunity, resource mobilization, and repression, to explain the movement's eruption and found that these theories do not offer a convincing explanation for the protest movement. She then moved on to fieldwork and conducted interviews with the movement participants to understand their motivations. Finally, through those interviews, she offered an alternative theory that the protest participants’ collective identity shaped during the authoritarian past played a unifying factor and eventually led them to participate in the movement. Her example shows that theorization can take place through careful review and rigorous inference during fieldwork.

Moreover, researchers can test their theory through fieldwork. Quantitative observational data has limitations in revealing causal mechanisms ( Esarey 2017 ). Therefore, many political scientists turn their attention to conducting field experiments or lab-in-the-field experiments to reveal causality ( Druckman et al. 2006 ; Beath, Christia, and Enikolopov 2013 ; Finseraas and Kotsadam 2017 ), or to leveraging in-depth insights or historical records gained through qualitative or archival research in process-tracing ( Collier 2011 ; Ricks and Liu 2018 ). Surveys and survey experiments may also be useful tools to substantiate a theoretical story or test a theory ( Marston 2020 ). Of course, for most Ph.D. students, especially those not affiliated with more extensive research projects, some of these options will be financially prohibitive.

A central concern for graduate students, especially those working with a small budget and limited time, is optimizing time in the field and integrating remote work. We offer three pieces of advice: have a plan, build in flexibility, and be strategic, focusing on collecting data that are unavailable at home. We also discuss working with local translators or research assistants. Before we turn to these more practical issues arising during fieldwork, we address a no less important issue: funding.

The challenge of securing funds is often overlooked in discussions of what constitutes field research. Months- or year-long in-person research can be cost-prohibitive, something academic gatekeepers must consider when evaluating “what counts” and “what is enough.” Unlike their predecessors, many graduate students today have a significant amount of debt and little savings. 3 Additionally, if researchers are not able to procure funding, they have to pay out of pocket and possibly take on more debt. Not only is in-person fieldwork costly, but researchers may also have to forego working while they are in the field, making long stretches in the field infeasible for some.

For researchers whose fieldwork involves travelling to another location, procuring funding via grants, fellowships, or other sources is a necessity, regardless of how long one plans to be in the field. A good mantra for applying for research funding is “apply early and often” ( Kelsky 2015 , 110). Funding applications take a considerable amount of time to prepare, from writing research statements to requesting letters of recommendation. Even adapting one's materials for different applications takes time. Not only is the application process itself time-consuming, but the time between applying for and receiving funds, if successful, can be quite long, from several months to a year. For example, after defending her prospectus in May 2019, Willis began applying to funding sources for her dissertation, all of which had deadlines between June and September. She received notifications between November and January; however, funds from her successful applications were not available until March and April, almost a year later. 4 Accordingly, we recommend applying for funding as early as possible; this not only increases one's chances of hitting the ground running in the field, but the application process can also help clarify the goals and parameters of one's research.

Graduate students should also apply often for funding opportunities. There are different types of funding for fieldwork: some are larger, more competitive grants such as the National Science Foundation Political Science Doctoral Dissertation Improvement Grant in the United States, others, including sources through one's own institution, are smaller. Some countries, like Sweden, boast a plethora of smaller funding agencies that disburse grants of 20,000–30,0000 Swedish Kronor (approx. 2,500–3,500 U.S. dollars) to Ph.D. students in the social sciences. Listings of potential funding sources are often found on various websites including those belonging to universities, professional organizations (such as the American Political Science Association or the European Consortium for Political Research), and governmental institutions dealing with foreign affairs. Once you have identified fellowships and grants for which you and your project are a good match, we highly recommend soliciting information and advice from colleagues who have successfully applied for them. This can include asking them to share their applications with you, and if possible, to have them, another colleague or set of colleagues read through your project description and research plan (especially for bigger awards) to ensure that you have made the best possible case for why you should be selected. While both large and small pots of funding are worth applying for, many researchers end up funding their fieldwork through several small grants or fellowships. One small award may not be sufficient to fund the entirety of one's fieldwork, but several may. For example, Willis's fieldwork in Japan and South Korea was supported through fellowships within each country. Similarly, Irgil was able to conduct her fieldwork abroad through two different and relatively smaller grants by applying to them each year.

Of course, situations vary in different countries with respect to what kinds of grants from what kinds of funders are available. An essential part of preparing for fieldwork is researching the funding landscape well in advance, even as early as the start of the Ph.D. We encourage first-time field researchers to be aware that universities and departments may themselves not be aware of the full range of possible funds available, so it is always a good idea to do your own research and watch research-related social media channels. The amount of funding needed thereby depends on the nature of one's project and how long one intends to be in the field. As we elaborate in the next section, scholars should think carefully about their project goals, the data required to meet those goals, and the requisite time to attain them. For some projects, even a couple of weeks in the field is sufficient to get the needed information.

Preparing to Enter “the field”

It is important to prepare for the field as much as possible. What kind of preparations do researchers need? For someone conducting interviews with NGO representatives, this might involve identifying the largest possible pool of potential respondents, securing their contact information, sending them study invitation letters, finding a mutually agreeable time to meet, and pulling together short biographies for each interviewee in order to use your time together most effectively. If you plan to travel to conduct interviews, you should reach out to potential respondents roughly four to six weeks prior to your arrival. For individuals who do not respond, you can follow up one to two weeks before you arrive and, if needed, once more when you are there. This is still no guarantee for success, of course. For Kreft, contacting potential interviewees in Colombia initially proved more challenging than anticipated, as many of the people she targeted did not respond to her emails. It turned out that many Colombians have a preference for communicating via phone or, in particular, WhatsApp. Some of those who responded to her emails sent in advance of her field trip asked her to simply be in touch once she was in the country, to set up appointments on short notice. This made planning and arranging her interview schedule more complicated. Therefore, a general piece of advice is to research your target population's preferred communication channels and mediums in the field site if email requests yield no or few responses.

In general, we note for the reader that contacting potential research participants should come after one has designed an interview questionnaire (plus an informed consent protocol) and sought and received, where applicable, approval from institutional review boards (IRBs) or other ethical review procedures in place (both at one's home institution/in the country of the home institution as well as in the country where one plans to conduct research if travelling abroad). The most obvious advantage of having the interview questionnaire in place and having secured all necessary institutional approvals before you start contacting potential interviewees is that you have a clearer idea of the universe of individuals you would like to interview, and for what purpose. Therefore, it is better to start sooner rather than later and be mindful of “high seasons,” when institutional and ethical review boards are receiving, processing, and making decisions on numerous proposals. It may take a few months for them to issue approvals.

On the subject of ethics and review panels, we encourage you to consider talking openly and honestly with your supervisors and/or funders about the situations where a written consent form may not be suitable and might need to be replaced with “verbal consent.” For instance, doing fieldwork in politically unstable contexts, highly scrutinized environments, or vulnerable communities, like refugees, might create obstacles for the interviewees as well as the researcher. The literature discusses the dilemma in offering the interviewees anonymity and requesting signed written consent in addition to the emphasis on total confidentiality ( Jacobsen and Landau 2003 ; Mackenzie, McDowell, and Pittaway 2007 ; Saunders, Kitzinger, and Kitzinger 2015 ). Therefore, in those situations, the researcher might need to take the initiative on how to act while doing the interviews as rigorously as possible. In her fieldwork, Irgil faced this situation as the political context of Turkey did not guarantee that there would not be any adverse consequences for interviewees on both sides of her story: citizens of Turkey and Syrian refugees. Consequently, she took hand-written notes and asked interviewees for their verbal consent in a safe interview atmosphere. This is something respondents greatly appreciated ( Irgil 2020 ).

Ethical considerations, of course, also affect the research design itself, with ramifications for fieldwork. When Kreft began developing her Ph.D. proposal to study women's political and civil society mobilization in response to conflict-related sexual violence, she initially aimed to recruit interviewees from the universe of victims of this violence, to examine variation among those who did and those who did not mobilize politically. As a result of deeper engagement with the literature on researching conflict-related sexual violence, conversations with senior colleagues who had interviewed victims, and critical self-reflection of her status as a researcher (with no background in psychology or social work), she decided to change focus and shift toward representatives of civil society organizations and victims’ associations. This constituted a major reconfiguration of her research design, from one geared toward identifying the factors that drive mobilization of victims toward using insights from interviews to understand better how those mobilize perceive and “make sense” of conflict-related sexual violence. Needless to say, this required alterations to research strategies and interview guides, including reassessing her planned fieldwork. Kreft's primary consideration was not to cause harm to her research participants, particularly in the form of re-traumatization. She opted to speak only with those women who on account of their work are used to speaking about conflict-related sexual violence. In no instance did she inquire about interviewees’ personal experiences with sexual violence, although several brought this up on their own during the interviews.

Finally, if you are conducting research in another country where you have less-than-professional fluency in the language, pre-fieldwork planning should include hiring a translator or research assistant, for example, through an online hiring platform like Upwork, or a local university. Your national embassy or consulate is another option; many diplomatic offices have lists of individuals who they have previously contracted. More generally, establishing contact with a local university can be beneficial, either in the form of a visiting researcher arrangement, which grants access to research groups and facilities like libraries or informally contacting individual researchers. The latter may have valuable insights into the local context, contacts to potential research participants, and they may even be able to recommend translators or research assistants. Kreft, for example, hired local research assistants recommended by researchers at a Bogotá-based university and remunerated them equivalent to the salary they would have received as graduate research assistants at the university, while also covering necessary travel expenses. Irgil, on the other hand, established contacts with native citizens and Syrian gatekeepers, who are shop owners in the area where she conducted her research because she had the opportunity to visit the fieldwork site multiple times.

Depending on the research agenda, researchers may visit national archives, local government offices, etc. Before visiting, researchers should contact these facilities and make sure the materials that they need are accessible. For example, Lee visited the Ronald Reagan Presidential Library Archives to find the United States’ strategic evaluations on South Korea's dictator in the 1980s. Before her visit, she contacted librarians in the archives, telling them her visit plans and her research purpose. Librarians made suggestions on which categories she should start to review based on her research goal, and thus she was able to make a list of categories of the materials she needed, saving her a lot of her time.

Accessibility of and access to certain facilities/libraries can differ depending on locations/countries and types of facilities. Facilities in authoritarian countries might not be easily accessible to foreign researchers. Within democratic countries, some facilities are more restrictive than others. Situations like the pandemic or national holidays can also restrict accessibility. Therefore, researchers are well advised to do preliminary research on whether a certain facility opens during the time they visit and is accessible to researchers regardless of their citizenship status. Moreover, researchers must contact the staff of facilities to know whether identity verification is needed and if so, what kind of documents (photo I.D. or passport) should be exhibited.

Adapting to the Reality of the Field

Researchers need to be flexible because you may meet people you did not make appointments with, come across opportunities you did not expect, or stumble upon new ideas about collecting data in the field. These happenings will enrich your field experience and will ultimately be beneficial for your research. Similarly, researchers should not be discouraged by interviews that do not go according to plan; they present an opportunity to pursue relevant people who can provide an alternative path to your work. Note that planning ahead does not preclude fortuitous encounters or epiphanies. Rather, it provides a structure for them to happen.

If your fieldwork entails travelling abroad, you will also be able to recruit more interviewees once you arrive at your research site. In fact, you may have greater success in-country; not everyone is willing to respond to a cold email from an unknown researcher in a foreign country. In Irgil's fieldwork, she contacted store owners that are known in the area and who know the community. This eased her process of introduction into the community and recruiting interviewees. For Zvobgo, she had fewer than a dozen interviews scheduled when she travelled to Guatemala to study civil society activism and transitional justice since the internal armed conflict. But she was able to recruit additional participants in-country. Interviewees with whom she built a rapport connected her to other NGOs, government offices, and the United Nations country office, sometimes even making the call and scheduling interviews for her. Through snowball sampling, she was able to triple the number of participants. Likewise, snowball sampling was central to Kreft's recruitment of interview partners. Several of her interviewees connected her to highly relevant individuals she would never have been able to identify and contact based on web searches alone.

While in the field, you may nonetheless encounter obstacles that necessitate adjustments to your original plans. Once Kreft had arrived in Colombia, for example, it transpired quickly that carrying out in-person interviews in more remote/rural areas was near impossible given her means, as these were not easily accessible by bus/coach, further complicated by a complex security situation. Instead, she adjusted her research design and shifted her focus to the big cities, where most of the major civil society organizations are based. She complemented the in-person interviews carried out there with a smaller number of phone interviews with civil society activists in rural areas, and she was also able to meet a few activists operating in rural or otherwise inaccessible areas as they were visiting the major cities. The resulting focus on urban settings changed the kinds of generalizations she was able to make based on her fieldwork data and produced a somewhat different study than initially anticipated.

This also has been the case for Irgil, despite her prior arrangements with the Syrian gatekeepers, which required adjustments as in the case of Kreft. Irgil acquired research clearance one year before, during the interviews with native citizens, conducting the interviews with Syrian refugees. She also had her questionnaire ready based on the previously collected data and the media search she had conducted for over a year before travelling to the field site. As she was able to visit the field site multiple times, two months before conducting interviews with Syrian refugees, she developed a schedule with the Syrian gatekeepers and informants. Yet, once she was in the field, influenced by Turkey's recent political events and the policy of increasing control over Syrian refugees, half of the previously agreed informants changed their minds or did not want to participate in interviews. As Irgil was following the policies and the news related to Syrian refugees in Turkey closely, this did not come as that big of a surprise but challenged the previously developed strategy to recruit interviewees. Thus, she changed the strategy of finding interviewees in the field site, such as asking people, almost one by one, whether they would like to participate in the interview. Eventually, she could not find willing Syrian women refugees as she had planned, which resulted in a male-dominant sample. As researchers encounter such situations, it is essential to remind oneself that not everything can go according to plan, that “different” does not equate to “worse,” but that it is important to consider what changes to fieldwork data collection and sampling imply for the study's overall findings and the contribution it makes to the literature.

We should note that conducting interviews is very taxing—especially when opportunities multiply, as in Zvobgo's case. Depending on the project, each interview can take an hour, if not two or more. Hence, you should make a reasonable schedule: we recommend no more than two interviews per day. You do not want to have to cut off an interview because you need to rush to another one, whether the interviews are in-person or remote. And you do not want to be too exhausted to have a robust engagement with your respondent who is generously lending you their time. Limiting the number of interviews per day is also important to ensure that you can write comprehensive and meaningful fieldnotes, which becomes even more essential where it is not possible to audio-record your interviews. Also, be sure to remember to eat, stay hydrated, and try to get enough sleep.

Finally, whether to provide gifts or payments to the subject also requires adapting to the reality of the field. You must think about payments beforehand when you apply for IRB approval (or whatever other ethical review processes may be in place) since these applications usually contain questions about payments. Obviously, the first step is to carefully evaluate whether the gifts and payments provided can harm the subject or are likely to unduly affect the responses they will give in response to your questions. If that is not the case, you have to make payment decisions based on your budget, field situation, and difficulties in recruitment. Usually, payment of respondents is more common in survey research, whereas it is less common in interviews and focus groups.

Nevertheless, payment practices vary depending on the field and the target group. In some cases, it may become a custom to provide small gifts or payments when interviewing a certain group. In other cases, interviewees might be offended if they are provided with money. Therefore, knowing past practices and field situations is important. For example, Lee provided small coffee gift cards to one group while she did not to the other based on previous practices of other researchers. That is, for a particular group, it has become a custom for interviewers to pay interviewees. Sometimes, you may want to reimburse your subject's interview costs such as travel expenses and provide beverages and snacks during the conduct of research, as Kreft did when conducting focus groups in Colombia. To express your gratitude to your respondents, you can prepare small gifts such as your university memorabilia (e.g., notebooks and pens). Since past practices about payments can affect your interactions and interviews with a target group, you want to seek advice from your colleagues and other researchers who had experiences interacting with the target group. If you cannot find researchers who have this knowledge, you can search for published works on the target population to find if the authors share their interview experiences. You may also consider contacting the authors for advice before your interviews.

Researching Strategically

Distinguishing between things that can only be done in person at a particular site and things that can be accomplished later at home is vital. Prioritize the former over the latter. Lee's fieldwork experience serves as a good example. She studied a conservative protest movement called the Taegeukgi Rally in South Korea. She planned to conduct interviews with the rally participants to examine their motivations for participating. But she only had one month in South Korea. So, she focused on things that could only be done in the field: she went to the rally sites, she observed how protests proceeded, which tactics and chants were used, and she met participants and had some casual conversations with them. Then, she used the contacts she made while attending the rallies to create a social network to solicit interviews from ordinary protesters, her target population. She was able to recruit twenty-five interviewees through good rapport with the people she met. The actual interviews proceeded via phone after she returned to the United States. In a nutshell, we advise you not to be obsessed with finishing interviews in the field. Sometimes, it is more beneficial to use your time in the field to build relationships and networks.

Working With Assistants and Translators

A final consideration on logistics is working with research assistants or translators; it affects how you can carry out interviews, focus groups, etc. To what extent constant back-and-forth translation is necessary or advisable depends on the researcher's skills in the interview language and considerations about time and efficiency. For example, Kreft soon realized that she was generally able to follow along quite well during her interviews in Colombia. In order to avoid precious time being lost to translation, she had her research assistant follow the interview guide Kreft had developed, and interjected follow-up questions in Spanish or English (then to be translated) as they arose.

Irgil's and Zvobgo's interviews went a little differently. Irgil's Syrian refugee interviewees in Turkey were native Arabic speakers, and Zvobgo's interviewees in Guatemala were native Spanish speakers. Both Irgil and Zvobgo worked with research assistants. In Irgil's case, her assistant was a Syrian man, who was outside of the area. Meanwhile, Zvobgo's assistant was an undergraduate from her home institution with a Spanish language background. Irgil and Zvobgo began preparing their assistants a couple of months before entering the field, over Skype for Irgil and in-person for Zvobgo. They offered their assistants readings and other resources to provide them with the necessary background to work well. Both Irgil and Zvobgo's research assistants joined them in the interviews and actually did most of the speaking, introducing the principal investigator, explaining the research, and then asking the questions. In Zvobgo's case, interviewee responses were relayed via a professional interpreter whom she had also hired. After every interview, Irgil and Zvobgo and their respective assistants discussed the answers of the interviewees, potential improvements in phrasing, and elaborated on their hand-written interview notes. As a backup, Zvobgo, with the consent of her respondents, had accompanying audio recordings.

Researchers may carry out fieldwork in a country that is considerably less safe than what they are used to, a setting affected by conflict violence or high crime rates, for instance. Feelings of insecurity can be compounded by linguistic barriers, cultural particularities, and being far away from friends and family. Insecurity is also often gendered, differentially affecting women and raising the specter of unwanted sexual advances, street harassment, or even sexual assault ( Gifford and Hall-Clifford 2008 ; Mügge 2013 ). In a recent survey of Political Science graduate students in the United States, about half of those who had done fieldwork internationally reported having encountered safety issues in the field, (54 percent female, 47 percent male), and only 21 percent agreed that their Ph.D. programs had prepared them to carry out their fieldwork safely ( Schwartz and Cronin-Furman 2020 , 8–9).

Preventative measures scholars may adopt in an unsafe context may involve, at their most fundamental, adjustments to everyday routines and habits, restricting one's movements temporally and spatially. Reliance on gatekeepers may also necessitate adopting new strategies, such as a less vehement and cold rejection of unwanted sexual advances than one ordinarily would exhibit, as Mügge (2013) illustratively discusses. At the same time, a competitive academic job market, imperatives to collect novel and useful data, and harmful discourses surrounding dangerous fieldwork also, problematically, shape incentives for junior researchers to relax their own standards of what constitutes acceptable risk ( Gallien 2021 ).

Others have carefully collected a range of safety precautions that field researchers in fragile or conflict-affected settings may take before and during fieldwork ( Hilhorst et al. 2016 ). Therefore, we are more concise in our discussion of recommendations, focusing on the specific situations of graduate students. Apart from ensuring that supervisors and university administrators have the researcher's contact information in the field (and possibly also that of a local contact person), researchers can register with their country's embassy or foreign office and any crisis monitoring and prevention systems it has in place. That way, they will be informed of any possible unfolding emergencies and the authorities have a record of them being in the country.

It may also be advisable to set up more individualized safety protocols with one or two trusted individuals, such as friends, supervisors, or colleagues at home or in the fieldwork setting itself. The latter option makes sense in particular if one has an official affiliation with a local institution for the duration of the fieldwork, which is often advisable. Still, we would also recommend establishing relationships with local researchers in the absence of a formal affiliation. To keep others informed of her whereabouts, Kreft, for instance, made arrangements with her supervisors to be in touch via email at regular intervals to report on progress and wellbeing. This kept her supervisors in the loop, while an interruption in communication would have alerted them early if something were wrong. In addition, she announced planned trips to other parts of the country and granted her supervisors and a colleague at her home institution emergency reading access to her digital calendar. To most of her interviews, she was moreover accompanied by her local research assistant/translator. If the nature of the research, ethical considerations, and the safety situation allow, it might also be possible to bring a local friend along to interviews as an “assistant,” purely for safety reasons. This option needs to be carefully considered already in the planning stage and should, particularly in settings of fragility or if carrying out research on politically exposed individuals, be noted in any ethical and institutional review processes where these are required. Adequate compensation for such an assistant should be ensured. It may also be advisable to put in place an emergency plan, that is, choose emergency contacts back home and “in the field,” know whom to contact if something happens, and know how to get to the nearest hospital or clinic.

We would be remiss if we did not mention that, when in an unfamiliar context, one's safety radar may be misguided, so it is essential to listen to people who know the context. For example, locals can give advice on which means of transport are safe and which are not, a question that is of the utmost importance when traveling to appointments. For example, Kreft was warned that in Colombia regular taxis are often unsafe, especially if waved down in the streets, and that to get to her interviews safely, she should rely on a ride-share service. In one instance, a Colombian friend suggested that when there was no alternative to a regular taxi, Kreft should book through the app and share the order details, including the taxi registration number or license plate, with a friend. Likewise, sharing one's cell phone location with a trusted friend while traveling or when one feels unsafe may be a viable option. Finally, it is prudent to heed the safety recommendations and travel advisories provided by state authorities and embassies to determine when and where it is safe to travel. Especially if researchers have a responsibility not only for themselves but also for research assistants and research participants, safety must be a top priority.

This does not mean that a researcher should be careless in a context they know either. Of course, conducting fieldwork in a context that is known to the researcher offers many advantages. However, one should be prepared to encounter unwanted events too. For instance, Irgil has conducted fieldwork in her country of origin in a city she knows very well. Therefore, access to the site, moving around the site, and blending in has not been a problem; she also has the advantage of speaking the native language. Yet, she took notes of the streets she walked in, as she often returned from the field site after dark and thought she might get confused after a tiring day. She also established a closer relationship with two or three store owners in different parts of the field site if she needed something urgent, like running out of battery. Above all, one should always be aware of one's surroundings and use common sense. If something feels unsafe, chances are it is.

Fieldwork may negatively affect the researcher's mental health and mental wellbeing regardless of where one's “field” is, whether related to concerns about crime and insecurity, linguistic barriers, social isolation, or the practicalities of identifying, contacting and interviewing research participants. Coping with these different sources of stress can be both mentally and physically exhausting. Then there are the things you may hear, see and learn during the research itself, such as gruesome accounts of violence and suffering conveyed in interviews or archival documents one peruses. Kreft and Zvobgo have spoken with women victims of conflict-related sexual violence, who sometimes displayed strong emotions of pain and anger during the interviews. Likewise, Irgil and Willis have spoken with members of other vulnerable populations such as refugees and former sex workers ( Willis 2020 ).

Prior accounts ( Wood 2006 ; Loyle and Simoni 2017 ; Skjelsbæk 2018 ; Hummel and El Kurd 2020 ; Williamson et al. 2020 ; Schulz and Kreft 2021 ) show that it is natural for sensitive research and fieldwork challenges to affect or even (vicariously) traumatize the researcher. By removing researchers from their regular routines and support networks, fieldwork may also exacerbate existing mental health conditions ( Hummel and El Kurd 2020 ). Nonetheless, mental wellbeing is rarely incorporated into fieldwork courses and guidelines, where these exist at all. But even if you know to anticipate some sort of reaction, you rarely know what that reaction will be until you experience it. When researching sensitive or difficult topics, for example, reactions can include sadness, frustration, anger, fear, helplessness, and flashbacks to personal experiences of violence ( Williamson et al. 2020 ). For example, Kreft responded with episodic feelings of depression and both mental and physical exhaustion. But curiously, these reactions emerged most strongly after she had returned from fieldwork and in particular as she spent extended periods analyzing her interview data, reliving some of the more emotional scenes during the interviews and being confronted with accounts of (sexual) violence against women in a concentrated fashion. This is a crucial reminder that fieldwork does not end when one returns home; the after-effects may linger. Likewise, Zvobgo was physically and mentally drained upon her return from the field. Both Kreft and Zvobgo were unable to concentrate for long periods of time and experienced lower-than-normal levels of productivity for weeks afterward, patterns that formal and informal conversations with other scholars confirm to be common ( Schulz and Kreft 2021 ). Furthermore, the boundaries between “field” and “home” are blurred when conducting remote fieldwork ( Howlett 2021 , 11).

Nor are these adverse reactions limited to cases where the researcher has carried out the interviews themselves. Accounts of violence, pain, and suffering transported in reports, secondary literature, or other sources can evoke similar emotional stress, as Kreft experienced when engaging in a concentrated fashion with additional accounts of conflict-related sexual violence in Colombia and with the feminist literature on sexual and gender-based violence in the comfort of her Swedish office. This could also be applicable to Irgil's fieldwork as she interviewed refugees whose traumas have come out during the interviews or recall specific events triggered by the questions. Likewise, Lee has reviewed primary and secondary materials on North Korean defectors in the national archives and these materials contain violent, intense, emotional narratives.

Fortunately, there are several strategies to cope with and manage such adverse consequences. In a candid and insightful piece, other researchers have discussed the usefulness of distractions, sharing with colleagues, counseling, exercise, and, probably less advisable in the long term, comfort eating and drinking ( Williamson et al. 2020 ; see also Loyle and Simoni 2017 ; Hummel and El Kurd 2020 ). Our experiences largely tally with their observations. In this section, we explore some of these in more detail.

First, in the face of adverse consequences on your mental wellbeing, whether in the field or after your return, it is essential to be patient and generous with yourself. Negative effects on the researcher's mental wellbeing can hit in unexpected ways and at unexpected times. Even if you think that certain reactions are disproportionate or unwarranted at that specific moment, they may simply have been building up over a long time. They are legitimate. Second, the importance of taking breaks and finding distractions, whether that is exercise, socializing with friends, reading a good book, or watching a new series, cannot be overstated. It is easy to fall into a mode of thinking that you constantly have to be productive while you are “in the field,” to maximize your time. But as with all other areas in life, balance is key and rest is necessary. Taking your mind off your research and the research questions you puzzle over is also a good way to more fully soak up and appreciate the context in which you find yourself, in the case of in-person fieldwork, and about which you ultimately write.

Third, we cannot stress enough the importance of investing in social relations. Before going on fieldwork, researchers may want to consult others who have done it before them. Try to find (junior) scholars who have done fieldwork on similar kinds of topics or in the same country or countries you are planning to visit. Utilizing colleagues’ contacts and forging connections using social media are valuable strategies to expand your networks (in fact, this very paper is the result of a social media conversation and several of the authors have never met in person). Having been in the same situation before, most field researchers are, in our experience, generous with their time and advice. Before embarking on her first trip to Colombia, Kreft contacted other researchers in her immediate and extended network and received useful advice on questions such as how to move around Bogotá, whom to speak to, and how to find a research assistant. After completing her fieldwork, she has passed on her experiences to others who contacted her before their first fieldwork trip. Informal networks are, in the absence of more formalized fieldwork preparation, your best friend.

In the field, seeking the company of locals and of other researchers who are also doing fieldwork alleviates anxiety and makes fieldwork more enjoyable. Exchanging experiences, advice and potential interviewee contacts with peers can be extremely beneficial and make the many challenges inherent in fieldwork (on difficult topics) seem more manageable. While researchers conducting remote fieldwork may be physically isolated from other researchers, even connecting with others doing remote fieldwork may be comforting. And even when there are no precise solutions to be found, it is heartening or even cathartic to meet others who are in the same boat and with whom you can talk through your experiences. When Kreft shared some of her fieldwork-related struggles with another researcher she had just met in Bogotá and realized that they were encountering very similar challenges, it was like a weight was lifted off her shoulders. Similarly, peer support can help with readjustment after the fieldwork trip, even if it serves only to reassure you that a post-fieldwork dip in productivity and mental wellbeing is entirely natural. Bear in mind that certain challenges are part of the fieldwork experience and that they do not result from inadequacy on the part of the researcher.

Finally, we would like to stress a point made by Inger Skjelsbæk (2018 , 509) and which has not received sufficient attention: as a discipline, we need to take the question of researcher mental wellbeing more seriously—not only in graduate education, fieldwork preparation, and at conferences, but also in reflecting on how it affects the research process itself: “When strong emotions arise, through reading about, coding, or talking to people who have been impacted by [conflict-related sexual violence] (as victims or perpetrators), it may create a feeling of being unprofessional, nonscientific, and too subjective.”

We contend that this is a challenge not only for research on sensitive issues but also for fieldwork more generally. To what extent is it possible, and desirable, to uphold the image of the objective researcher during fieldwork, when we are at our foundation human beings? And going even further, how do the (anticipated) effects of our research on our wellbeing, and the safety precautions we take ( Gifford and Hall-Clifford 2008 ), affect the kinds of questions we ask, the kinds of places we visit and with whom we speak? How do they affect the methods we use and how we interpret our findings? An honest discussion of affective responses to our research in methods sections seems utopian, as emotionality in the research process continues to be silenced and relegated to the personal, often in gendered ways, which in turn is considered unconnected to the objective and scientific research process ( Jamar and Chappuis 2016 ). But as Gifford and Hall-Clifford (2008 , 26) aptly put it: “Graduate education should acknowledge the reality that fieldwork is scholarly but also intimately personal,” and we contend that the two shape each other. Therefore, we encourage political science as a discipline to reflect on researcher wellbeing and affective responses to fieldwork more carefully, and we see the need for methods courses that embrace a more holistic notion of the subjectivity of the researcher.

Interacting with people in the field is one of the most challenging yet rewarding parts of the work that we do, especially in comparison to impersonal, often tedious wrangling and analysis of quantitative data. Field researchers often make personal connections with their interviewees. Consequently, maintaining boundaries can be a bit tricky. Here, we recommend being honest with everyone with whom you interact without overstating the abilities of a researcher. This appears as a challenge in the field, particularly when you empathize with people and when they share profound parts of their lives with you for your research in addition to being “human subjects” ( Fujii 2012 ). For instance, when Irgil interviewed native citizens about the changes in their neighborhood following the arrival of Syrian refugees, many interviewees questioned what she would offer them in return for their participation. Irgil responded that her primary contribution would be her published work. She also noted, however, that academic papers can take a year, sometimes longer, to go through the peer-reviewed process and, once published, many studies have a limited audience. The Syrian refugees posed similar questions. Irgil responded not only with honesty but also, given this population's vulnerable status, she provided them contact information for NGOs with which they could connect if they needed help or answers to specific questions.

For her part, Zvobgo was very upfront with her interviewees about her role as a researcher: she recognized that she is not someone who is on the frontlines of the fight for human rights and transitional justice like they are. All she could/can do is use her platform to amplify their stories, bringing attention to their vital work through her future peer-reviewed publications. She also committed to sending them copies of the work, as electronic journal articles are often inaccessible due to paywalls and university press books are very expensive, especially for nonprofits. Interviewees were very receptive; some were even moved by the degree of self-awareness and the commitment to do right by them. In some cases, this prompted them to share even more, because they knew that the researcher was really there to listen and learn. This is something that junior scholars, and all scholars really, should always remember. We enter the field to be taught. Likewise, Kreft circulated among her interviewees Spanish-language versions of an academic article and a policy brief based on the fieldwork she had carried out in Colombia.

As researchers from the Global North, we recognize a possible power differential between us and our research subjects, and certainly an imbalance in power between the countries where we have been trained and some of the countries where we have done and continue to do field research, particularly in politically dynamic contexts ( Knott 2019 ). This is why we are so concerned with being open and transparent with everyone with whom we come into contact in the field and why we are committed to giving back to those who so generously lend us their time and knowledge. Knott (2019 , 148) summarizes this as “Reflexive openness is a form of transparency that is methodologically and ethically superior to providing access to data in its raw form, at least for qualitative data.”

We also recognize that academics, including in the social sciences and especially those hailing from countries in the Global North, have a long and troubled history of exploiting their power over others for the sake of their research—including failing to be upfront about their research goals, misrepresenting the on-the-ground realities of their field research sites (including remote fieldwork), and publishing essentializing, paternalistic, and damaging views and analyses of the people there. No one should build their career on the backs of others, least of all in a field concerned with the possession and exercise of power. Thus, it is highly crucial to acknowledge the power hierarchies between the researcher and the interviewees, and to reflect on them both in the field and beyond the field upon return.

A major challenge to conducting fieldwork is when researchers’ carefully planned designs do not go as planned due to unforeseen events outside of our control, such as pandemics, natural disasters, deteriorating security situations in the field, or even the researcher falling ill. As the Covid-19 pandemic has made painfully clear, researchers may face situations where in-person research is simply not possible. In some cases, researchers may be barred entry to their fieldwork site; in others, the ethical implications of entering the field greatly outweigh the importance of fieldwork. Such barriers to conducting in-person research require us to reconsider conventional notions of what constitutes fieldwork. Researchers may need to shift their data collection methods, for example, conducting interviews remotely instead of in person. Even while researchers are in the field, they may still need to carry out part of their interviews or surveys virtually or by phone. For example, Kreft (2020) carried out a small number of interviews remotely while she was based in Bogotá, because some of the women's civil society activists with whom she intended to speak were based in parts of the country that were difficult and/or dangerous to access.

Remote field research, which we define as the collection of data over the internet or over the phone where in-person fieldwork is not possible due to security, health or other risks, comes with its own sets of challenges. For one, there may be certain populations that researchers cannot reach remotely due to a lack of internet connectivity or technology such as cellphones and computers. In such instances, there will be a sampling bias toward individuals and groups that do have these resources, a point worth noting when scholars interpret their research findings. In the case of virtual research, the risk of online surveillance, hacking, or wiretapping may also produce reluctance on the part of interviewees to discuss sensitive issues that may compromise their safety. Researchers need to carefully consider how the use of digital technology may increase the risk to research participants and what changes to the research design and any interview guides this necessitates. In general, it is imperative that researchers reflect on how they can ethically use digital technology in their fieldwork ( Van Baalen 2018 ). Remote interviews may also be challenging to arrange for researchers who have not made connections in person with people in their community of interest.

Some of the serendipitous happenings we discussed earlier may also be less likely and snowball sampling more difficult. For example, in phone or virtual interviews, it is harder to build good rapport and trust with interviewees as compared to face-to-face interviews. Accordingly, researchers should be more careful in communicating with interviewees and creating a comfortable interview environment. Especially when dealing with sensitive topics, researchers may have to make several phone calls and sometimes have to open themselves to establishing trust with interviewees. Also, researchers must be careful in protecting interviewees in phone or virtual interviews when they deal with sensitive topics of countries interviewees reside in.

The inability to physically visit one's community of interest may also encourage scholars to critically reflect on how much time in the field is essential to completing their research and to consider creative, alternative means for accessing information to complete their projects. While data collection techniques such as face-to-face interviews and archival work in the field may be ideal in normal times, there exist other data sources that can provide comparably useful information. For example, in her research on the role of framing in the United States base politics, Willis found that social media accounts and websites yielded information useful to her project. Many archives across the world have also been digitized. Researchers may also consider crowdsourcing data from the field among their networks, as fellow academics tend to collect much more data in the field than they ever use in their published works. They may also elect to hire someone, perhaps a graduate student, in a city or a country where they cannot travel and have the individual access, scan, and send archival materials. This final suggestion may prove generally useful to researchers with limited time and financial resources.

Remote qualitative data collection techniques, while they will likely never be “the gold-standard,” also pose several advantages. These techniques may help researchers avoid some of the issues mentioned previously. Remote interviews, for example, are less time-consuming in terms of travel to the interview site ( Archibald et al. 2019 ). The implication is that researchers may have less fatigue from conducting interviews and/or may be able to conduct more interviews. For example, while Willis had little energy to do anything else after an in-person interview (or two) in a given day, she had much more energy after completing remote interviews. Second, remote fieldwork also helps researchers avoid potentially dangerous situations in the field mentioned previously. Lastly, remote fieldwork generally presents fewer financial barriers than in-person research ( Archibald et al. 2019 ). In that sense, considering remote qualitative data collection, a type of “fieldwork” may make fieldwork more accessible to a greater number of scholars.

Many of the substantive, methodological and practical challenges that arise during fieldwork can be anticipated. Proper preparation can help you hit the ground running once you enter your fieldwork destination, whether in-person or virtually. Nonetheless, there is no such thing as being perfectly prepared for the field. Some things will simply be beyond your control, and especially as a newcomer to field research, and you should be prepared for things to not go as planned. New questions will arise, interview participants may cancel appointments, and you might not get the answers you expected. Be ready to make adjustments to research plans, interview guides, or questionnaires. And, be mindful of your affective reactions to the overall fieldwork situation and be gentle with yourself.

We recommend approaching fieldwork as a learning experience as much as, or perhaps even more than, a data collection effort. This also applies to your research topic. While it is prudent always to exercise a healthy amount of skepticism about what people tell you and why, the participants in your research will likely have unique perspectives and knowledge that will challenge yours. Be an attentive listener and remember that they are experts of their own experiences.

We encourage more institutions to offer courses that cover field research preparation and planning, practical advice on safety and wellbeing, and discussion of ethics. Specifically, we align with Schwartz and Cronin-Furman's (2020 , 3) contention “that treating fieldwork preparation as the methodology will improve individual scholars’ experiences and research.” In this article, we outline a set of issue areas in which we think formal preparation is necessary, but we note that our discussion is by no means exhaustive. Formal fieldwork preparation should also extend beyond what we have covered in this article, such as issues of data security and preparing for nonqualitative fieldwork methods. We also note that field research is one area that has yet to be comprehensively addressed in conversations on diversity and equity in the political science discipline and the broader academic profession. In a recent article, Brielle Harbin (2021) begins to fill this gap by sharing her experiences conducting in-person election surveys as a Black woman in a conservative and predominantly white region of the United States and the challenges that she encountered. Beyond race and gender, citizenship, immigration status, one's Ph.D. institution and distance to the field also affect who is able to do what type of field research, where, and for how long. Future research should explore these and related questions in greater detail because limits on who is able to conduct field research constrict the sociological imagination of our field.

While Emmons and Moravcsik (2020) focus on leading Political Science Ph.D. programs in the United States, these trends likely obtain, both in lower ranked institutions in the broader United States as well as in graduate education throughout North America and Europe.

As all the authors have carried out qualitative fieldwork, this is the primary focus of this guide. This does not, however, mean that we exclude quantitative or experimental data collection from our definition of fieldwork.

There is great variation in graduate students’ financial situations, even in the Global North. For example, while higher education is tax-funded in most countries in Europe and Ph.D. students in countries such as Sweden, Norway, Denmark, the Netherlands, and Switzerland receive a comparatively generous full-time salary, healthcare and contributions to pension schemes, Ph.D. programs in other contexts like the United States and the United Kingdom have (high) enrollment fees and rely on scholarships, stipends, or departmental duties like teaching to (partially) offset these, while again others, such as Germany, are commonly financed by part-time (50 percent) employment at the university with tasks substantively unrelated to the dissertation. These different preconditions leave many Ph.D. students struggling financially and even incurring debt, while others are in a more comfortable financial position. Likewise, Ph.D. programs around the globe differ in structure, such as required coursework, duration and supervision relationships. Naturally, all of these factors have a bearing on the extent to which fieldwork is feasible. We acknowledge unequal preconditions across institutions and contexts, and trust that those Ph.D. students interested in pursuing fieldwork are best able to assess the structural and institutional context in which they operate and what this implies for how, when, and how long to carry out fieldwork.

In our experience, this is not only the general cycle for graduate students in North America, but also in Europe and likely elsewhere.

For helpful advice and feedback on earlier drafts, we wish to thank the editors and reviewers at International Studies Review , and Cassandra Emmons. We are also grateful to our interlocuters in Argentina, Canada, Colombia, Germany, Guatemala, Japan, Kenya, Norway, the Philippines, Sierra Leone, South Korea, Spain, Sweden, Turkey, the United Kingdom, and the United States, without whom this reflection on fieldwork would not have been possible. All authors contributed equally to this manuscript.

This material is based upon work supported by the Forskraftstiftelsen Theodor Adelswärds Minne, Knut and Alice Wallenberg Foundation(KAW 2013.0178), National Science Foundation Graduate Research Fellowship Program(DGE-1418060), Southeast Asia Research Group (Pre-Dissertation Fellowship), University at Albany (Initiatives for Women and the Benevolent Association), University of Missouri (John D. Bies International Travel Award Program and Kinder Institute on Constitutional Democracy), University of Southern California (Provost Fellowship in the Social Sciences), Vetenskapsrådet(Diarienummer 2019-06298), Wilhelm och Martina Lundgrens Vetenskapsfond(2016-1102; 2018-2272), and William & Mary (Global Research Institute Pre-doctoral Fellowship).

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Quantitative Research

What is Quantitative Research?

Quantitative research is the methodology which researchers use to test theories about people’s attitudes and behaviors based on numerical and statistical evidence. Researchers sample a large number of users (e.g., through surveys) to indirectly obtain measurable, bias-free data about users in relevant situations.

“Quantification clarifies issues which qualitative analysis leaves fuzzy. It is more readily contestable and likely to be contested. It sharpens scholarly discussion, sparks off rival hypotheses, and contributes to the dynamics of the research process.” — Angus Maddison, Notable scholar of quantitative macro-economic history

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See how quantitative research helps reveal cold, hard facts about users which you can interpret and use to improve your designs.

Use Quantitative Research to Find Mathematical Facts about Users

Quantitative research is a subset of user experience (UX) research . Unlike its softer, more individual-oriented “counterpart”, qualitative research , quantitative research means you collect statistical/numerical data to draw generalized conclusions about users’ attitudes and behaviors . Compare and contrast quantitative with qualitative research, below:

Qualitative Research

You Aim to Determine

The “what”, “where” & “when” of the users’ needs & problems – to help keep your project’s focus on track during development

The “why” – to get behind how users approach their problems in their world

Highly structured (e.g., surveys) – to gather data about what users do & find patterns in large user groups

Loosely structured (e.g., contextual inquiries) – to learn why users behave how they do & explore their opinions

Number of Representative Users

Ideally 30+

Often around 5

Level of Contact with Users

Less direct & more remote (e.g., analytics)

More direct & less remote (e.g., usability testing to examine users’ stress levels when they use your design)

Statistically

Reliable – if you have enough test users

Less reliable, with need for great care with handling non-numerical data (e.g., opinions), as your own opinions might influence findings

Quantitative research is often best done from early on in projects since it helps teams to optimally direct product development and avoid costly design mistakes later. As you typically get user data from a distance—i.e., without close physical contact with users—also applying qualitative research will help you investigate why users think and feel the ways they do. Indeed, in an iterative design process quantitative research helps you test the assumptions you and your design team develop from your qualitative research. Regardless of the method you use, with proper care you can gather objective and unbiased data – information which you can complement with qualitative approaches to build a fuller understanding of your target users. From there, you can work towards firmer conclusions and drive your design process towards a more realistic picture of how target users will ultimately receive your product.

Quantitative analysis helps you test your assumptions and establish clearer views of your users in their various contexts.

Quantitative Research Methods You Can Use to Guide Optimal Designs

There are many quantitative research methods, and they help uncover different types of information on users. Some methods, such as A/B testing, are typically done on finished products, while others such as surveys could be done throughout a project’s design process. Here are some of the most helpful methods:

A/B testing – You test two or more versions of your design on users to find the most effective. Each variation differs by just one feature and may or may not affect how users respond. A/B testing is especially valuable for testing assumptions you’ve drawn from qualitative research. The only potential concerns here are scale—in that you’ll typically need to conduct it on thousands of users—and arguably more complexity in terms of considering the statistical significance involved.

Analytics – With tools such as Google Analytics, you measure metrics (e.g., page views, click-through rates) to build a picture (e.g., “How many users take how long to complete a task?”).

Desirability Studies – You measure an aspect of your product (e.g., aesthetic appeal) by typically showing it to participants and asking them to select from a menu of descriptive words. Their responses can reveal powerful insights (e.g., 78% associate the product/brand with “fashionable”).

Surveys and Questionnaires – When you ask for many users’ opinions, you will gain massive amounts of information. Keep in mind that you’ll have data about what users say they do, as opposed to insights into what they do . You can get more reliable results if you incentivize your participants well and use the right format.

Tree Testing – You remove the user interface so users must navigate the site and complete tasks using links alone. This helps you see if an issue is related to the user interface or information architecture.

Another powerful benefit of conducting quantitative research is that you can keep your stakeholders’ support with hard facts and statistics about your design’s performance—which can show what works well and what needs improvement—and prove a good return on investment. You can also produce reports to check statistics against different versions of your product and your competitors’ products.

Most quantitative research methods are relatively cheap. Since no single research method can help you answer all your questions, it’s vital to judge which method suits your project at the time/stage. Remember, it’s best to spend appropriately on a combination of quantitative and qualitative research from early on in development. Design improvements can be costly, and so you can estimate the value of implementing changes when you get the statistics to suggest that these changes will improve usability. Overall, you want to gather measurements objectively, where your personality, presence and theories won’t create bias.

Learn More about Quantitative Research

Take our User Research course to see how to get the most from quantitative research.

See how quantitative research methods fit into your design research landscape .

This insightful piece shows the value of pairing quantitative with qualitative research .

Find helpful tips on combining quantitative research methods in mixed methods research .

Questions related to Quantitative Research

Qualitative and quantitative research differ primarily in the data they produce. Quantitative research yields numerical data to test hypotheses and quantify patterns. It's precise and generalizable. Qualitative research, on the other hand, generates non-numerical data and explores meanings, interpretations, and deeper insights. Watch our video featuring Professor Alan Dix on different types of research methods.

This video elucidates the nuances and applications of both research types in the design field.

In quantitative research, determining a good sample size is crucial for the reliability of the results. William Hudson, CEO of Syntagm, emphasizes the importance of statistical significance with an example in our video. 

He illustrates that even with varying results between design choices, we need to discern whether the differences are statistically significant or products of chance. This ensures the validity of the results, allowing for more accurate interpretations. Statistical tools like chi-square tests can aid in analyzing the results effectively. To delve deeper into these concepts, take William Hudson’s Data-Driven Design: Quantitative UX Research Course . 

Quantitative research is crucial as it provides precise, numerical data that allows for high levels of statistical inference. Our video from William Hudson, CEO of Syntagm, highlights the importance of analytics in examining existing solutions. 

Quantitative methods, like analytics and A/B testing, are pivotal for identifying areas for improvement, understanding user behaviors, and optimizing user experiences based on solid, empirical evidence. This empirical nature ensures that the insights derived are reliable, allowing for practical improvements and innovations. Perhaps most importantly, numerical data is useful to secure stakeholder buy-in and defend design decisions and proposals. Explore this approach in our Data-Driven Design: Quantitative Research for UX Research course and learn from William Hudson’s detailed explanations of when and why to use analytics in the research process.

After establishing initial requirements, statistical data is crucial for informed decisions through quantitative research. William Hudson, CEO of Syntagm, sheds light on the role of quantitative research throughout a typical project lifecycle in this video:

 During the analysis and design phases, quantitative research helps validate user requirements and understand user behaviors. Surveys and analytics are standard tools, offering insights into user preferences and design efficacy. Quantitative research can also be used in early design testing, allowing for optimal design modifications based on user interactions and feedback, and it’s fundamental for A/B and multivariate testing once live solutions are available.

To write a compelling quantitative research question:

Create clear, concise, and unambiguous questions that address one aspect at a time.

Use common, short terms and provide explanations for unusual words.

Avoid leading, compound, and overlapping queries and ensure that questions are not vague or broad.

According to our video by William Hudson, CEO of Syntagm, quality and respondent understanding are vital in forming good questions. 

He emphasizes the importance of addressing specific aspects and avoiding intimidating and confusing elements, such as extensive question grids or ranking questions, to ensure participant engagement and accurate responses. For more insights, see the article Writing Good Questions for Surveys .

Survey research is typically quantitative, collecting numerical data and statistical analysis to make generalizable conclusions. However, it can also have qualitative elements, mainly when it includes open-ended questions, allowing for expressive responses. Our video featuring the CEO of Syntagm, William Hudson, provides in-depth insights into when and how to effectively utilize surveys in the product or service lifecycle, focusing on user satisfaction and potential improvements.

He emphasizes the importance of surveys in triangulating data to back up qualitative research findings, ensuring we have a complete understanding of the user's requirements and preferences.

Descriptive research focuses on describing the subject being studied and getting answers to questions like what, where, when, and who of the research question. However, it doesn’t include the answers to the underlying reasons, or the “why” behind the answers obtained from the research. We can use both f qualitative and quantitative methods to conduct descriptive research. Descriptive research does not describe the methods, but rather the data gathered through the research (regardless of the methods used).

When we use quantitative research and gather numerical data, we can use statistical analysis to understand relationships between different variables. Here’s William Hudson, CEO of Syntagm with more on correlation and how we can apply tests such as Pearson’s r and Spearman Rank Coefficient to our data.

This helps interpret phenomena such as user experience by analyzing session lengths and conversion values, revealing whether variables like time spent on a page affect checkout values, for example.

Random Sampling: Each individual in the population has an equitable opportunity to be chosen, which minimizes biases and simplifies analysis.

Systematic Sampling: Selecting every k-th item from a list after a random start. It's simpler and faster than random sampling when dealing with large populations.

Stratified Sampling: Segregate the population into subgroups or strata according to comparable characteristics. Then, samples are taken randomly from each stratum.

Cluster Sampling: Divide the population into clusters and choose a random sample.

Multistage Sampling: Various sampling techniques are used at different stages to collect detailed information from diverse populations.

Convenience Sampling: The researcher selects the sample based on availability and willingness to participate, which may only represent part of the population.

Quota Sampling: Segment the population into subgroups, and samples are non-randomly selected to fulfill a predetermined quota from each subset.

These are just a few techniques, and choosing the right one depends on your research question, discipline, resource availability, and the level of accuracy required. In quantitative research, there isn't a one-size-fits-all sampling technique; choosing a method that aligns with your research goals and population is critical. However, a well-planned strategy is essential to avoid wasting resources and time, as highlighted in our video featuring William Hudson, CEO of Syntagm.

He emphasizes the importance of recruiting participants meticulously, ensuring their engagement and the quality of their responses. Accurate and thoughtful participant responses are crucial for obtaining reliable results. William also sheds light on dealing with failing participants and scrutinizing response quality to refine the outcomes.

The 4 types of quantitative research are Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research. Descriptive research aims to depict ‘what exists’ clearly and precisely. Correlational research examines relationships between variables. Causal-comparative research investigates the cause-effect relationship between variables. Experimental research explores causal relationships by manipulating independent variables. To gain deeper insights into quantitative research methods in UX, consider enrolling in our Data-Driven Design: Quantitative Research for UX course.

The strength of quantitative research is its ability to provide precise numerical data for analyzing target variables.This allows for generalized conclusions and predictions about future occurrences, proving invaluable in various fields, including user experience. William Hudson, CEO of Syntagm, discusses the role of surveys, analytics, and testing in providing objective insights in our video on quantitative research methods, highlighting the significance of structured methodologies in eliciting reliable results.

To master quantitative research methods, enroll in our comprehensive course, Data-Driven Design: Quantitative Research for UX . 

This course empowers you to leverage quantitative data to make informed design decisions, providing a deep dive into methods like surveys and analytics. Whether you’re a novice or a seasoned professional, this course at Interaction Design Foundation offers valuable insights and practical knowledge, ensuring you acquire the skills necessary to excel in user experience research. Explore our diverse topics to elevate your understanding of quantitative research methods.

Answer a Short Quiz to Earn a Gift

What is the primary goal of quantitative research in design?

• To analyze numerical data and identify patterns
• To explore abstract design concepts for implementation
• To understand people's subjective experiences and opinions

Which of the following methods is an example of quantitative research?

• Conduct a focus groups to collect detailed user feedback
• Participate in open-ended interviews to explore user experiences
• Run usability tests and measure task completion times

What is one key advantage of quantitative research?

• It allows participants to express their opinions in a flexible manner.
• It provides researchers with detailed narratives of user experiences and perspectives.
• It produces standardized, comparable data that researchers can statistically analyze.

What is a significant challenge of quantitative research?

• It lacks objectivity which makes its results difficult to reproduce.
• It may oversimplify complex user behaviors into numbers and miss contextual insights.
• It often results in biased or misleading conclusions.

How can designers effectively combine qualitative and quantitative research?

• They can collect quantitative data first, followed by qualitative insights to explain the findings.
• They can completely replace quantitative methods with qualitative approaches.
• They can treat them as interchangeable methods to gather similar data.

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Literature on Quantitative Research

Here’s the entire UX literature on Quantitative Research by the Interaction Design Foundation, collated in one place:

Learn more about Quantitative Research

Take a deep dive into Quantitative Research with our course User Research – Methods and Best Practices .

How do you plan to design a product or service that your users will love , if you don't know what they want in the first place? As a user experience designer, you shouldn't leave it to chance to design something outstanding; you should make the effort to understand your users and build on that knowledge from the outset. User research is the way to do this, and it can therefore be thought of as the largest part of user experience design .

In fact, user research is often the first step of a UX design process—after all, you cannot begin to design a product or service without first understanding what your users want! As you gain the skills required, and learn about the best practices in user research, you’ll get first-hand knowledge of your users and be able to design the optimal product—one that’s truly relevant for your users and, subsequently, outperforms your competitors’ .

This course will give you insights into the most essential qualitative research methods around and will teach you how to put them into practice in your design work. You’ll also have the opportunity to embark on three practical projects where you can apply what you’ve learned to carry out user research in the real world . You’ll learn details about how to plan user research projects and fit them into your own work processes in a way that maximizes the impact your research can have on your designs. On top of that, you’ll gain practice with different methods that will help you analyze the results of your research and communicate your findings to your clients and stakeholders—workshops, user journeys and personas, just to name a few!

By the end of the course, you’ll have not only a Course Certificate but also three case studies to add to your portfolio. And remember, a portfolio with engaging case studies is invaluable if you are looking to break into a career in UX design or user research!

We believe you should learn from the best, so we’ve gathered a team of experts to help teach this course alongside our own course instructors. That means you’ll meet a new instructor in each of the lessons on research methods who is an expert in their field—we hope you enjoy what they have in store for you!

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An Overview of Quantitative Research Methods

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Your ultimate guide to quantitative research.

10 min read You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

What is quantitative research?

Quantitative is the research method of collecting quantitative data – this is data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analysed.

Quantitative research deals with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or  demographic data .

Quantitative data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

To collect numerical data, surveys are often employed as one of the main research methods to source first-hand information in  primary research . Qualitative research can also  come from third-party research studies .

Quantitative research is widely used in the realms of social sciences, such as psychology, economics, sociology, and marketing.

Research teams collect data that is significant to proving or disproving a hypothesis research question – known as the research objective. When they collect quantitative data, researchers will  aim to use a sample size that is representative  of the total population of the target market they’re interested in.

Then the data collected will be manually or automatically stored and compared for insights.

Learn how Qualtrics can enhance & simplify the quantitative research process

Qualitative vs quantitative research

While the quantitative research definition focuses on numerical data, qualitative research is defined as data that supplies non-numerical information.

Qualitative research focuses on the thoughts, feelings, and values of a participant, to understand why people act in the way they do. They result in data types like quotes, symbols, images, and written testimonials.

These data types tell researchers subjective information, which can help us assign people into categories, such as a participant’s religion, gender, social class, political alignment, likely favoured products to buy, or their preferred training learning style.

For this reason, qualitative research is often used in social research, as this gives a window into the behaviour and actions of people.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative methods. If you want to explore ideas, thoughts, and meanings, use qualitative methods.

However, quantitative and qualitative research methods are both recommended when you’re looking to understand a point in time, while also finding out the reason behind the facts.

Quantitative research data collection methods

Quantitative research methods can use structured research instruments like:

A survey is a simple-to-create and easy-to-distribute research method, which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Quantitative questions tend to be closed questions that ask for a numerical result, based on a range of options, or a yes/no answer that can be tallied quickly.

Face-to-face or phone interviews

Interviews are a great way to connect with participants , though they require time from the research team to set up and conduct.

Researchers may also have issues connecting with participants in different geographical regions. The researcher uses a set of predefined close-ended questions, which ask for yes/no or numerical values.

Polls can be a shorter version of surveys, used to get a ‘flavour’ of what the current situation is with participants. Online polls can be shared easily, though polls are best used with simple questions that request a range or a yes/no answer.

Quantitative data is the opposite of qualitative research, another dominant framework for research in the social sciences, explored further below.

Quantitative data types

Quantitative research methods often deliver the following data types:

• Test Scores
• Per cent of training course completed
• Performance score out of 100
• Number of support calls active
• Customer Net Promoter Score (NPS)

When gathering numerical data, the emphasis is on how specific the data is, and whether they can provide an indication of what ‘is’ at the time of collection. Pre-existing statistical data can tell us what ‘was’ for the date and time range that it represented.

Quantitative research design methods (with examples)

Quantitative research has a number of quantitative research designs you can choose from:

Descriptive

This design type describes the state of a data type is telling researchers, in its native environment. There won’t normally be a clearly defined research question to start with. Instead,  data analysis will suggest a conclusion, which can become the hypothesis to investigate further.

Examples of descriptive quantitative design include:

• A description of child’s Christmas gifts they received that year
• A description of what businesses sell the most of during Black Friday
• A description of a product issue being experienced by a customer

Correlational

This design type looks at two or more data types, the relationship between them, and the extent that they differ or align. This does not look at the causal links deeper – instead statistical analysis looks at the variables in a natural environment.

Examples of correlational quantitative design include:

• The relationship between a child’s Christmas gifts and their perceived happiness level
• The relationship between a business’ sales during Black Friday and the total revenue generated over the year
• The relationship between a customer’s product issue and the reputation of the product

Causal-Comparative/Quasi-Experimental

This design type looks at two or more data types and tries to explain any relationship and differences between them, using a cause-effect analysis. The research is carried out in a near-natural environment, where information is gathered from two groups – a naturally occurring group that matches the original natural environment, and one that is not naturally present.

This allows for causal links to be made, though they might not be correct, as other variables may have an impact on results.

Examples of causal-comparative/quasi-experimental quantitative design include:

• The effect of children’s Christmas gifts on happiness
• The effect of Black Friday sales figures on the productivity of company yearly sales
• The effect of product issues on the public perception of a product

Experimental Research

This design type looks to make a controlled environment in which two or more variables are observed to understand the exact cause and effect they have. This becomes a quantitative research study, where data types are manipulated to assess the effect they have. The participants are not naturally occurring groups, as the setting is no longer natural. A quantitative research study can help pinpoint the exact conditions in which variables impact one another.

Examples of experimental quantitative design include:

• The effect of children’s Christmas gifts on a child’s dopamine (happiness) levels
• The effect of Black Friday sales on the success of the company
• The effect of product issues on the perceived reliability of the product

Quantitative research methods need to be carefully considered, as your data collection of a data type can be used to different effects. For example, statistics can be descriptive or correlational (or inferential). Descriptive statistics help us to summarise our data, while inferential statistics help infer conclusions about significant differences.

Advantages of quantitative research

• Easy to do : Doing quantitative research is more straightforward, as the results come in numerical format, which can be more easily interpreted.
• Less interpretation : Due to the factual nature of the results, you will be able to accept or reject your hypothesis based on the numerical data collected.
• Less bias : There are higher levels of control that can be applied to the research, so  bias can be reduced , making your data more reliable and precise.

Disadvantages of quantitative research

• Can’t understand reasons:  Quantitative research doesn’t always tell you the full story, meaning you won’t understand the context – or the why, of the data you see, why do you see the results you have uncovered?
• Useful for simpler situations:  Quantitative research on its own is not great when dealing with complex issues. In these cases, quantitative research may not be enough.

How to use quantitative research to your business’s advantage

Quantitative research methods may help in areas such as:

• Identifying which advert or landing page performs better
• Identifying  how satisfied your customers are
• How many customers are likely to recommend you
• Tracking how your brand ranks in awareness  and customer purchase intent
• Learn what consumers are likely to buy from your brand.

6 steps to conducting good quantitative research

Businesses can benefit from quantitative research by using it to evaluate the impact of data types. There are several steps to this:

• Define your problem or interest area : What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis : Ask yourself what could be the causes for the situation with those data types.
• Plan your quantitative research : Use structured research instruments like surveys or polls to ask questions that test your hypothesis.
• Data Collection : Collect quantitative data and understand what your data types are telling you. Using data collected on different types over long time periods can give you information on patterns.
• Data analysis : Does your information support your hypothesis? (You may need to redo the research with other variables to see if the results improve)
• Effectively present data : Communicate the results in a clear and concise way to help other people understand the findings.

Related resources

Market intelligence 9 min read, qualitative research questions 11 min read, ethnographic research 11 min read, business research methods 12 min read, qualitative research design 12 min read, business research 10 min read, qualitative research interviews 11 min read, request demo.

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Methodology

• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Quantitative Observation: Everything You Need To Know

What’s the best way to gather data that doesn’t leave you second-guessing?

If you’re dealing with research, you know how important it is to get solid, reliable data.

That’s where quantitative observation steps in.

In this article, we’ll look into everything you need to know about quantitative observation.

We’ll cover what it is, how it’s different from qualitative observation, and why it’s so widely used across various fields like education, healthcare, and marketing.

By the end, you’ll see why this method is a go-to for researchers who need precise, measurable results:

What is quantitative observation?

Quantitative observation is a research method that involves collecting and analyzing numerical data about people, objects, or events. It’s often used to measure specific variables, such as frequency, duration, or intensity. Quantitative observation can be conducted in various settings, including laboratories, classrooms, and public places.

Quantitative and qualitative observation – what’s the difference?

When it comes to research, you’ll often hear about two main types of observations: quantitative and qualitative .

Both have their place, but they’re pretty different in what they focus on and how they’re used.

Let’s break it down.

Focus on numbers vs. descriptions

Quantitative observations are all about numbers. If you can count it, measure it, or express it in figures, it falls into the quantitative camp.

Think of things like:

• the temperature of a room,
• the number of people in a line,
• or the speed of a car.

This type of observation gives you hard data that you can analyze and compare.

On the other hand, qualitative observations focus on descriptions. They’re about the qualities of what you’re observing.

For example, instead of saying, “The car is going 60 mph,” you’d say, “The car is moving quickly.” It’s more about what something is like than how much there is of it.

Objectivity vs. subjectivity

Quantitative observations are usually more objective. The data you gather isn’t influenced by opinions or feelings – it’s just numbers . This makes it reliable when you’re looking for facts that can be backed up by statistical analysis.

Qualitative observations, however, are more subjective.

They depend on the observer’s perspective and interpretation. Two people might describe the same event differently, which can make this type of observation more varied and rich, but also less consistent.

Measurable data vs. rich detail

When you gather quantitative data, you’re looking for specific measurements.

This might include things like:

• or quantity.

It’s precise and can be used in graphs, charts, and statistical models.

Qualitative data, though, is more about the details that don’t fit into neat little boxes.

It includes things like colors, textures, feelings, and experiences. This data is harder to measure, but it adds depth and context to your research.

Standardization vs. flexibility

Quantitative observation methods are usually standardized. You use the same tools and processes each time to make sure your data is consistent. This is great for making comparisons across different studies or groups.

Qualitative observation, in contrast, is more flexible. It allows you to explore your subject in a more open-ended way, which can lead to new insights and understanding that you might miss with a more rigid approach.

So, whether you’re counting heads or describing feelings, both quantitative and qualitative observations play important roles in research. Each brings something valuable to the table, helping you see the full picture.

Comparison table

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Qualitative vs quantitative survey data

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The benefits of quantitative observations

Quantitative observation has attractive advantages, and the most important ones are:

It provides objective and reliable data that can be analyzed statistically

When you’re collecting quantitative observation data, you’re gathering facts that are clear-cut and free from personal bias.

This makes the data objective and reliable, which is a big deal in scientific research.

With these numbers in hand, you can engage in statistical analysis, where patterns and relationships start to emerge.

The beauty of this approach is that it strips away guesswork, leaving you with solid evidence that can back up your findings.

Unlike qualitative observation, which leans on descriptions, quantitative observations give you something concrete to work with.

It allows for precise measurement and comparison of variables

When it comes to measuring and comparing variables, quantitative research is the tool of choice.

Quantitative observation methods focus on capturing exact values – whether it’s the height of a plant, the number of customers, or the temperature of a liquid.

This precision is key in the research process because it lets you compare different factors head-to-head.

With standardized observation techniques, the data you gather is consistent and reliable across the board.

It doesn’t matter if you’re working on a big project or just trying to understand a small detail, quantitative observations help you keep everything measured and comparable.

It can be used to test hypotheses and identify patterns and trends

In scientific research, testing hypotheses is a key part of the job.

Quantitative observation research plays a huge role here.

Thanks to gathering quantitative data through systematic observation, you can put your ideas to the test.

The numbers you collect can either support your hypothesis or show you where things aren’t adding up.

Plus, as you gather more data, you start to see patterns and trends that weren’t obvious at first.

This is where quantitative and qualitative observation work hand in hand.

The hard numbers from quantitative research point you in the right direction, while qualitative observations add the context you need to understand the bigger picture.

How to do survey research?

What is cross-sectional data?

What is nominal data?

Where is quantitative observation applied? Top use cases

Quantitative observation can be used in a variety of fields, including:

Marketing: measuring customer behavior and preferences

Imagine a store tracking how many customers stop to look at a new product display or how long they spend browsing a particular aisle.

These numbers tell a story about what catches people’s attention and what doesn’t.

For instance, a study published in the International Journal of Advertising explored the effectiveness of retail window displays as part of the marketing mix.

The researchers worked with Boots the Chemists and Nottingham Business School to measure how window display design influences consumer-buying behavior.

They found that connecting buying behavior to specific marketing elements, like window displays, made sales forecasting more predictable.

If a lot of people are lingering by a new clothing line but not buying, it might suggest they’re interested but need a nudge, maybe a sale or better positioning.

This kind of data helps businesses tweak their strategies to match customer behavior.

Education: assessing student engagement and learning outcomes

In education, teachers often use quantitative observation to see how students are engaging with their lessons.

For example, a study presented in the Journal of Educational Psychology introduced the Behavioral Engagement Related to Instruction (BERI) protocol.

This protocol was specifically designed for large university classrooms to measure student engagement levels through quantitative observation data.

The BERI protocol involves tracking student behaviors in real-time, offering teachers immediate feedback on how well students are engaging with the material.

For instance, if students are actively participating in discussions or focusing on tasks during lectures, the data collected can show high levels of engagement.

On the other hand, if students appear distracted or disengaged, the data can highlight areas where the teaching method might need adjustment.

These numbers help educators identify which teaching strategies are working and which might need a different approach. If the protocol shows that students are more engaged during interactive lessons compared to traditional lectures, it indicates a need to incorporate more interactive elements into the curriculum.

This kind of targeted feedback helps instructors refine their methods to improve student learning outcomes.

Psychology: studying human behavior and cognition

Psychologists use quantitative observation to dig into the details of human behavior.

For example, a well-known study in the field of memory research conducted by Ebbinghaus in the late 19th century focused on how quickly people forget information.

In this study, participants were asked to memorize lists of nonsense syllables, and then their recall was tested at different time intervals.

The researchers measured how many syllables participants could remember after varying lengths of time, such as immediately after learning, after a few hours, and after several days.

The numbers collected from these tests helped to map out the “forgetting curve,” which shows that memory retention decreases sharply soon after learning but then levels off over time.

This type of quantitative data is often used in psychology, as it helps researchers understand how memory works and how factors like stress or fatigue might impact recall.

Sociology: investigating social phenomena and trends

In sociology, quantitative observation helps researchers understand broader social trends.

A notable study published in the American Political Science Review examined voting behavior across various neighborhoods in a large metropolitan area.

The researchers collected quantitative data on voter turnout by tracking the number of people who participated in elections in different districts over several election cycles.

The study revealed that neighborhoods with lower voter turnout often had higher levels of economic disadvantage, lower educational attainment, and less access to transportation.

These patterns were not immediately obvious without the data. By analyzing the numbers, sociologists were able to identify the social factors that contributed to lower voting rates.

This type of research helps sociologists understand the underlying reasons for such trends and suggests potential interventions.

For instance, the findings might prompt community programs aimed at increasing voter education or improving access to polling stations.

Quantitative observation in sociology is essential for uncovering these hidden patterns and driving efforts to address social inequalities.

Healthcare: evaluating the effectiveness of medical treatments and interventions

In healthcare, quantitative observation is useful for evaluating the effectiveness of medical treatments.

A well-known example is the clinical trial of the drug Streptomycin in the treatment of tuberculosis, conducted in the late 1940s.

This was one of the first randomized controlled trials (RCTs) in medical history, which set the standard for future clinical research.

In this study, researchers quantitatively observed and recorded the number of patients who showed improvement in their tuberculosis symptoms after taking Streptomycin compared to those who received a placebo.

The results showed a statistically significant improvement in the recovery rates among those treated with the drug, confirming its effectiveness.

This study provided clear evidence of the drug’s efficacy, shaping the future of tuberculosis treatment and demonstrating the power of quantitative observation in healthcare.

Thanks to systematically tracking patient outcomes, healthcare professionals were able to make informed decisions about adopting Streptomycin as a standard treatment.

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SurveyLab for quantitative observation – how does it work?

SurveyLab is a tool that takes quantitative observation to the next level.

If you’re looking to gather precise data and gain deep insights, this platform has you covered.

With SurveyLab, you can create online tests that score automatically and make data collection straightforward.

It doesn’t matter if you’re measuring customer satisfaction, employee engagement, or any other metric, the platform’s scoring mechanism helps you keep everything in check.

• One of the standout features is the ability to set up complex satisfaction indicators and key performance indicators (KPIs). These metrics give you a clear picture of what’s working and what needs attention.
• Plus, with the advanced analytical tools that SurveyLab offers, you can engage in data analysis and discover patterns you might have missed otherwise.
• The platform lets you generate graphical reports that make your findings easy to understand and share. And if you need to dig deeper, you can export the data for further analysis.

But SurveyLab isn’t just about gathering data – it’s about making sense of it.

The combination of scoring, metrics, data collection, and data analysis tools means you can conduct quantitative observations that lead to real, actionable insights.

It’s like having a full toolkit at your disposal, ready to help you make informed decisions based on solid data.

Ready to see how SurveyLab can change your quantitative observation efforts?

Try it today and access the insights that will drive your success.

And for more educational content, check our blog out .

Try SurveyLab for free Best survey tool with great features

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The basics of quantitative and qualitative field research

• Planning field research
• Successful field research

Published market information and your own data can tell you a lot about your customers and your market - but it's unlikely to tell you everything.

Field research can be quantitative or qualitative:

• Quantitative research Provides statistical information - for example, how many potential customers there are and what their average incomes are.
• Qualitative research Examines people's feelings and attitudes towards your product or service, and what motivates them.

You'll probably need to carry out some of your own quantitative and qualitative field research - talking, observing or carrying out product tests with customers and potential customers.

This can help you to:

• Test customers' reactions to a new product, and adapt it if necessary
• Investigate attitudes of customers and potential customers
• Find information specific to your business or a local market, rather than the market as a whole

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Speaker 1: Welcome to this overview of quantitative research methods. This tutorial will give you the big picture of quantitative research and introduce key concepts that will help you determine if quantitative methods are appropriate for your project study. First, what is educational research? Educational research is a process of scholarly inquiry designed to investigate the process of instruction and learning, the behaviors, perceptions, and attributes of students and teachers, the impact of institutional processes and policies, and all other areas of the educational process. The research design may be quantitative, qualitative, or a mixed methods design. The focus of this overview is quantitative methods. The general purpose of quantitative research is to explain, predict, investigate relationships, describe current conditions, or to examine possible impacts or influences on designated outcomes. Quantitative research differs from qualitative research in several ways. It works to achieve different goals and uses different methods and design. This table illustrates some of the key differences. Qualitative research generally uses a small sample to explore and describe experiences through the use of thick, rich descriptions of detailed data in an attempt to understand and interpret human perspectives. It is less interested in generalizing to the population as a whole. For example, when studying bullying, a qualitative researcher might learn about the experience of the victims and the experience of the bully by interviewing both bullies and victims and observing them on the playground. Quantitative studies generally use large samples to test numerical data by comparing or finding correlations among sample attributes so that the findings can be generalized to the population. If quantitative researchers were studying bullying, they might measure the effects of a bully on the victim by comparing students who are victims and students who are not victims of bullying using an attitudinal survey. In conducting quantitative research, the researcher first identifies the problem. For Ed.D. research, this problem represents a gap in practice. For Ph.D. research, this problem represents a gap in the literature. In either case, the problem needs to be of importance in the professional field. Next, the researcher establishes the purpose of the study. Why do you want to do the study, and what do you intend to accomplish? This is followed by research questions which help to focus the study. Once the study is focused, the researcher needs to review both seminal works and current peer-reviewed primary sources. Based on the research question and on a review of prior research, a hypothesis is created that predicts the relationship between the study's variables. Next, the researcher chooses a study design and methods to test the hypothesis. These choices should be informed by a review of methodological approaches used to address similar questions in prior research. Finally, appropriate analytical methods are used to analyze the data, allowing the researcher to draw conclusions and inferences about the data, and answer the research question that was originally posed. In quantitative research, research questions are typically descriptive, relational, or causal. Descriptive questions constrain the researcher to describing what currently exists. With a descriptive research question, one can examine perceptions or attitudes as well as more concrete variables such as achievement. For example, one might describe a population of learners by gathering data on their age, gender, socioeconomic status, and attributes towards their learning experiences. Relational questions examine the relationship between two or more variables. The X variable has some linear relationship to the Y variable. Causal inferences cannot be made from this type of research. For example, one could study the relationship between students' study habits and achievements. One might find that students using certain kinds of study strategies demonstrate greater learning, but one could not state conclusively that using certain study strategies will lead to or cause higher achievement. Causal questions, on the other hand, are designed to allow the researcher to draw a causal inference. A causal question seeks to determine if a treatment variable in a program had an effect on one or more outcome variables. In other words, the X variable influences the Y variable. For example, one could design a study that answered the question of whether a particular instructional approach caused students to learn more. The research question serves as a basis for posing a hypothesis, a predicted answer to the research question that incorporates operational definitions of the study's variables and is rooted in the literature. An operational definition matches a concept with a method of measurement, identifying how the concept will be quantified. For example, in a study of instructional strategies, the hypothesis might be that students of teachers who use Strategy X will exhibit greater learning than students of teachers who do not. In this study, one would need to operationalize learning by identifying a test or instrument that would measure learning. This approach allows the researcher to create a testable hypothesis. Relational and causal research relies on the creation of a null hypothesis, a version of the research hypothesis that predicts no relationship between variables or no effect of one variable on another. When writing the hypothesis for a quantitative question, the null hypothesis and the research or alternative hypothesis use parallel sentence structure. In this example, the null hypothesis states that there will be no statistical difference between groups, while the research or alternative hypothesis states that there will be a statistical difference between groups. Note also that both hypothesis statements operationalize the critical thinking skills variable by identifying the measurement instrument to be used. Once the research questions and hypotheses are solidified, the researcher must select a design that will create a situation in which the hypotheses can be tested and the research questions answered. Ideally, the research design will isolate the study's variables and control for intervening variables so that one can be certain of the relationships being tested. In educational research, however, it is extremely difficult to establish sufficient controls in the complex social settings being studied. In our example of investigating the impact of a certain instructional strategy in the classroom on student achievement, each day the teacher uses a specific instructional strategy. After school, some of the students in her class receive tutoring. Other students have parents that are very involved in their child's academic progress and provide learning experiences in the home. These students may do better because they received extra help, not because the teacher's instructional strategy is more effective. Unless the researcher can control for the intervening variable of extra help, it will be impossible to effectively test the study's hypothesis. Quantitative research designs can fall into two broad categories, experimental and quasi-experimental. Classic experimental designs are those that randomly assign subjects to either a control or treatment comparison group. The researcher can then compare the treatment group to the control group to test for an intervention's effect, known as a between-subject design. It is important to note that the control group may receive a standard treatment or may receive a treatment of any kind. Quasi-experimental designs do not randomly assign subjects to groups, but rather take advantage of existing groups. A researcher can still have a control and comparison group, but assignment to the groups is not random. The use of a control group is not required. However, the researcher may choose a design in which a single group is pre- and post-tested, known as a within-subjects design. Or a single group may receive only a post-test. Since quasi-experimental designs lack random assignment, the researcher should be aware of the threats to validity. Educational research often attempts to measure abstract variables such as attitudes, beliefs, and feelings. Surveys can capture data about these hard-to-measure variables, as well as other self-reported information such as demographic factors. A survey is an instrument used to collect verifiable information from a sample population. In quantitative research, surveys typically include questions that ask respondents to choose a rating from a scale, select one or more items from a list, or other responses that result in numerical data. Studies that use surveys or tests need to include strategies that establish the validity of the instrument used. There are many types of validity that need to be addressed. Face validity. Does the test appear at face value to measure what it is supposed to measure? Content validity. Content validity includes both item validity and sampling validity. Item validity ensures that the individual test items deal only with the subject being addressed. Sampling validity ensures that the range of item topics is appropriate to the subject being studied. For example, item validity might be high, but if all the items only deal with one aspect of the subjects, then sampling validity is low. Content validity can be established by having experts in the field review the test. Concurrent validity. Does a new test correlate with an older, established test that measures the same thing? Predictive validity. Does the test correlate with another related measure? For example, GRE tests are used at many colleges because these schools believe that a good grade on this test increases the probability that the student will do well at the college. Linear regression can establish the predictive validity of a test. Construct validity. Does the test measure the construct it is intended to measure? Establishing construct validity can be a difficult task when the constructs being measured are abstract. But it can be established by conducting a number of studies in which you test hypotheses regarding the construct, or by completing a factor analysis to ensure that you have the number of constructs that you say you have. In addition to ensuring the validity of instruments, the quantitative researcher needs to establish their reliability as well. Strategies for establishing reliability include Test retest. Correlates scores from two different administrations of the same test. Alternate forms. Correlates scores from administrations of two different forms of the same test. Split half reliability. Treats each half of one test or survey as a separate administration and correlates the results from each. Internal consistency. Uses Cronbach's coefficient alpha to calculate the average of all possible split halves. Quantitative research almost always relies on a sample that is intended to be representative of a larger population. There are two basic sampling strategies, random and non-random, and a number of specific strategies within each of these approaches. This table provides examples of each of the major strategies. The next section of this tutorial provides an overview of the procedures in conducting quantitative data analysis. There are specific procedures for conducting the data collection, preparing for and analyzing data, presenting the findings, and connecting to the body of existing research. This process ensures that the research is conducted as a systematic investigation that leads to credible results. Data comes in various sizes and shapes, and it is important to know about these so that the proper analysis can be used on the data. In 1946, S.S. Stevens first described the properties of measurement systems that allowed decisions about the type of measurement and about the attributes of objects that are preserved in numbers. These four types of data are referred to as nominal, ordinal, interval, and ratio. First, let's examine nominal data. With nominal data, there is no number value that indicates quantity. Instead, a number has been assigned to represent a certain attribute, like the number 1 to represent male and the number 2 to represent female. In other words, the number is just a label. You could also assign numbers to represent race, religion, or any other categorical information. Nominal data only denotes group membership. With ordinal data, there is again no indication of quantity. Rather, a number is assigned for ranking order. For example, satisfaction surveys often ask respondents to rank order their level of satisfaction with services or programs. The next level of measurement is interval data. With interval data, there are equal distances between two values, but there is no natural zero. A common example is the Fahrenheit temperature scale. Differences between the temperature measurements make sense, but ratios do not. For instance, 20 degrees Fahrenheit is not twice as hot as 10 degrees Fahrenheit. You can add and subtract interval level data, but they cannot be divided or multiplied. Finally, we have ratio data. Ratio is the same as interval, however ratios, means, averages, and other numerical formulas are all possible and make sense. Zero has a logical meaning, which shows the absence of, or having none of. Examples of ratio data are height, weight, speed, or any quantities based on a scale with a natural zero. In summary, nominal data can only be counted. Ordinal data can be counted and ranked. Interval data can also be added and subtracted, and ratio data can also be used in ratios and other calculations. Determining what type of data you have is one of the most important aspects of quantitative analysis. Depending on the research question, hypotheses, and research design, the researcher may choose to use descriptive and or inferential statistics to begin to analyze the data. Descriptive statistics are best illustrated when viewed through the lens of America's pastimes. Sports, weather, economy, stock market, and even our retirement portfolio are presented in a descriptive analysis. Basic terminology for descriptive statistics are terms that we are most familiar in this discipline. Frequency, mean, median, mode, range, variance, and standard deviation. Simply put, you are describing the data. Some of the most common graphic representations of data are bar graphs, pie graphs, histograms, and box and whisker graphs. Attempting to reach conclusions and make causal inferences beyond graphic representations or descriptive analyses is referred to as inferential statistics. In other words, examining the college enrollment of the past decade in a certain geographical region would assist in estimating what the enrollment for the next year might be. Frequently in education, the means of two or more groups are compared. When comparing means to assist in answering a research question, one can use a within-group, between-groups, or mixed-subject design. In a within-group design, the researcher compares measures of the same subjects across time, therefore within-group, or under different treatment conditions. This can also be referred to as a dependent-group design. The most basic example of this type of quasi-experimental design would be if a researcher conducted a pretest of a group of students, subjected them to a treatment, and then conducted a post-test. The group has been measured at different points in time. In a between-group design, subjects are assigned to one of the two or more groups. For example, Control, Treatment 1, Treatment 2. Ideally, the sampling and assignment to groups would be random, which would make this an experimental design. The researcher can then compare the means of the treatment group to the control group. When comparing two groups, the researcher can gain insight into the effects of the treatment. In a mixed-subjects design, the researcher is testing for significant differences between two or more independent groups while subjecting them to repeated measures. Choosing a statistical test to compare groups depends on the number of groups, whether the data are nominal, ordinal, or interval, and whether the data meet the assumptions for parametric tests. Nonparametric tests are typically used with nominal and ordinal data, while parametric tests use interval and ratio-level data. In addition to this, some further assumptions are made for parametric tests that the data are normally distributed in the population, that participant selection is independent, and the selection of one person does not determine the selection of another, and that the variances of the groups being compared are equal. The assumption of independent participant selection cannot be violated, but the others are more flexible. The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the method of analysis for a quasi-experimental design. When choosing a t-test, the assumptions are that the data are parametric. The analysis of variance, or ANOVA, assesses whether the means of more than two groups are statistically different from each other. When choosing an ANOVA, the assumptions are that the data are parametric. The chi-square test can be used when you have non-parametric data and want to compare differences between groups. The Kruskal-Wallis test can be used when there are more than two groups and the data are non-parametric. Correlation analysis is a set of statistical tests to determine whether there are linear relationships between two or more sets of variables from the same list of items or individuals, for example, achievement and performance of students. The tests provide a statistical yes or no as to whether a significant relationship or correlation exists between the variables. A correlation test consists of calculating a correlation coefficient between two variables. Again, there are parametric and non-parametric choices based on the assumptions of the data. Pearson R correlation is widely used in statistics to measure the strength of the relationship between linearly related variables. Spearman-Rank correlation is a non-parametric test that is used to measure the degree of association between two variables. Spearman-Rank correlation test does not assume any assumptions about the distribution. Spearman-Rank correlation test is used when the Pearson test gives misleading results. Often a Kendall-Taw is also included in this list of non-parametric correlation tests to examine the strength of the relationship if there are less than 20 rankings. Linear regression and correlation are similar and often confused. Sometimes your methodologist will encourage you to examine both the calculations. Calculate linear correlation if you measured both variables, x and y. Make sure to use the Pearson parametric correlation coefficient if you are certain you are not violating the test assumptions. Otherwise, choose the Spearman non-parametric correlation coefficient. If either variable has been manipulated using an intervention, do not calculate a correlation. While linear regression does indicate the nature of the relationship between two variables, like correlation, it can also be used to make predictions because one variable is considered explanatory while the other is considered a dependent variable. Establishing validity is a critical part of quantitative research. As with the nature of quantitative research, there is a defined approach or process for establishing validity. This also allows for the findings transferability. For a study to be valid, the evidence must support the interpretations of the data, the data must be accurate, and their use in drawing conclusions must be logical and appropriate. Construct validity concerns whether what you did for the program was what you wanted to do, or whether what you observed was what you wanted to observe. Construct validity concerns whether the operationalization of your variables are related to the theoretical concepts you are trying to measure. Are you actually measuring what you want to measure? Internal validity means that you have evidence that what you did in the study, i.e., the program, caused what you observed, i.e., the outcome, to happen. Conclusion validity is the degree to which conclusions drawn about relationships in the data are reasonable. External validity concerns the process of generalizing, or the degree to which the conclusions in your study would hold for other persons in other places and at other times. Establishing reliability and validity to your study is one of the most critical elements of the research process. Once you have decided to embark upon the process of conducting a quantitative study, use the following steps to get started. First, review research studies that have been conducted on your topic to determine what methods were used. Consider the strengths and weaknesses of the various data collection and analysis methods. Next, review the literature on quantitative research methods. Every aspect of your research has a body of literature associated with it. Just as you would not confine yourself to your course textbooks for your review of research on your topic, you should not limit yourself to your course texts for your review of methodological literature. Read broadly and deeply from the scholarly literature to gain expertise in quantitative research. Additional self-paced tutorials have been developed on different methodologies and techniques associated with quantitative research. Make sure that you complete all of the self-paced tutorials and review them as often as needed. You will then be prepared to complete a literature review of the specific methodologies and techniques that you will use in your study. Thank you for watching.

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Linking network- and neuron-level correlations by renormalized field theory

Michael dick, alexander van meegen, and moritz helias, phys. rev. research 6 , 033264 – published 6 september 2024.

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It is frequently hypothesized that cortical networks operate close to a critical point. Advantages of criticality include rich dynamics well suited for computation and critical slowing down, which may offer a mechanism for dynamic memory. However, mean-field approximations, while versatile and popular, inherently neglect the fluctuations responsible for such critical dynamics. Thus, a renormalized theory is necessary. We consider the Sompolinsky-Crisanti-Sommers model which displays a well studied chaotic as well as a magnetic transition. Based on the analog of a quantum effective action, we derive self-consistency equations for the first two renormalized Greens functions. Their self-consistent solution reveals a coupling between the population level activity and single neuron heterogeneity. The quantitative theory explains the population autocorrelation function, the single-unit autocorrelation function with its multiple temporal scales, and cross correlations.

• Received 30 January 2024
• Accepted 15 August 2024

DOI: https://doi.org/10.1103/PhysRevResearch.6.033264

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• 1 Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre , 52428 Jülich, Germany
• 2 Department of Computer Science 3 - Software Engineering, RWTH Aachen University , 52074 Aachen, Germany
• 3 Peter Grünberg Institut (PGI-1) and Institute for Advanced Simulation (IAS-1), Jülich Research Centre , 52428 Jülich, Germany
• 4 Institute of Zoology, University of Cologne , 50674 Cologne, Germany
• 5 Department of Physics, Faculty 1, RWTH Aachen University , 52065 Aachen, Germany
• * Contact author: [email protected]

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(a) Population-averaged activity R ( t ) for g ¯ = 0.5 (gray) and g ¯ = 1 (red). (b) Auxiliary fields Q and R , proportional to population-averaged output autocorrelation and activity, respectively, binned for each point in time. (c) Time-lagged, population-averaged, stationary autocorrelation Q ( t , t + τ ) simulated for different values of g ¯ (shades of red) for multiple seeds (shaded background shows the standard deviation) and mean field prediction (black) plotted logarithmically. Remaining network parameters: ϕ ( x ) = erf ( π 2 x ) , N = 1000 , g = 0.5 , and D = 0.1 .

Mean-field phase diagram spanned by g ¯ and g for (a) the noiseless case ( D = 0 ) and (b) noise-driven dynamics ( D = 0.1 ). The red shading quantifies the absolute population activity | R ( t ) | , which is the order parameter for ferromagnetic activity, and the gray shading quantifies the dynamic variability Q ( t , t ) , which for D = 0 is the order parameter indicating the onset of chaotic activity. Above the black curves, the population activity R becomes nonzero. The dynamic variability Q ( t , t ) does not vanish in the presence of noise.

Time lagged population-averaged autocorrelation Q ( t , t + τ ) ( 3 ) simulated (red) with standard deviation across seeds (shaded), mean-field theory (gray), fluctuation expansion ( 12 ) (gray dotted) and self-consistent solution ( 25 ) (black dotted) together with autocorrelation 〈 〈 R ( t + τ ) R ( t ) 〉 〉 = β 11 ( τ ) of population-averaged activity R ( 2 ) (dashed, self-consistent in black, empirical in red, exponential decay with time scale ( 1 − g ¯ 〈 ϕ ′ 〉 ) − 1 in gray) plotted logarithmically for g ¯ = 1.0 . Other parameters as in Fig.  1 .

(a) Transient of R in response to a stimulation provided as common input of 0.01 to each neuron [additive constant on right-hand side of Eq. ( 1 )] within the time span indicated by the dashed lines; g ¯ = 1 and different values of g ( g = 0 in gray, g = 0.95 in red) with standard deviation across seeds (shaded). (b) Transient of Q under same conditions as in (a). (c) 2D histogram of Q over R with crosses at the zero time lag predicted as Q * ( t , t ) from theory ( 25 ). g ¯ = 1 ,   g = 0 in gray, g = 0.95 in red, other parameters as in Fig.  1 .

Population averaged cross correlation C ϕ ϕ x ( τ ) , Eq. ( 30 ), over time lag given by Eq. ( 30 ) (black) compared to simulation (red) with standard deviation from averaging over seeds (shaded) for g ¯ = 0.5 . Other parameters as in Fig.  1 .

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The impact of Storm Alex on the Vievola catchment: a quantitative analysis of sediment volume and morphological changes in the Roya River tributaries

• Technical Note
• Published: 06 September 2024

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• Raphaël Kerverdo   ORCID: orcid.org/0009-0008-8984-0749 1 ,
• Sara Lafuerza   ORCID: orcid.org/0000-0003-2126-6505 1 ,
• Christian Gorini   ORCID: orcid.org/0000-0003-3123-4822 1 ,
• Alain Rabaute   ORCID: orcid.org/0000-0003-1369-0218 1 ,
• Didier Granjeon   ORCID: orcid.org/0000-0002-1457-6671 2 ,
• Rémy Deschamps   ORCID: orcid.org/0000-0002-0888-3456 2 ,
• Eric Fouache   ORCID: orcid.org/0000-0002-5392-0615 3 ,
• Mina Jafari 4 &
• Pierre-Yves Lagrée   ORCID: orcid.org/0000-0002-3931-6622 4  

This study investigates the sediment dynamics resulting from the extreme Storm Alex in October 2020 in the Roya Valley and its tributaries in the Alpes-Maritimes region, France. The storm, triggered by a low-pressure system, led to unprecedented rainfall, causing extensive flooding and erosion in the region. Despite limited pre-flood data, the study employs aerial and satellite imagery, digital elevation models, and field surveys to quantify sediment mobilization and its effects on the Viévola alluvial fan in the Roya Valley. The Roya Valley’s complex geomorphology, characterized by steep gradients, gullies, and torrential streams, played a significant role in sediment transport. The study reveals that the Dente and Rabay torrents were major sediment contributors, with gullies in these areas producing substantial erosion. Bank erosion in the Dente valley was particularly prominent, attributed to geological factors and glacial deposits. The analysis, relying on topographical comparisons and digital data, assesses sediment volumes eroded and deposited during the event. Despite challenges in data quality, the study offers valuable insights into sediment dynamics during extreme hydro-sedimentary events. The Viévola catchment area is a focal point, emphasizing the importance of scree and fluvio-glacial deposits as primary sources of sediment. The findings emphasize the need for improved pre-event data and monitoring in mountainous regions susceptible to extreme events. The study’s methodology, despite limitations, contributes to a better understanding of geomorphic responses to extreme events. Expanding similar studies to cover a wider range of catchment areas and incorporating field data offers potential for enhanced hazard assessment and management strategies. The research underscores the critical role of sediment transport in shaping landscapes and impacting human infrastructure during extreme flood events.

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Acknowledgements

We express our sincere gratitude to all those who have contributed to this research. The authors acknowledge the support of the ANR-17-CE03-004-01 project, the “coup de pouce” project of ISTeP Sorbonne University, and the doctoral fellowship from the French Research Ministry for funding this study. Additionally, we would like to extend our thanks to the Institute of Ocean and Environmental Transition of the Alliance Sorbonne University for financing three field campaigns. We are grateful to the Monastery of Saorge (Center for National Monuments) for providing accommodation for researchers. We also recognize the MITI of CNRS for funding the equipment used in this study. Finally, our appreciation goes to Nicoletta BIANCHI from the Musée des Merveilles in Tende (Alpes-Maritimes department) for her invaluable assistance in the field. I express my gratitude to the two reviewers whose constructive comments have significantly enhanced the quality of this work.

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Raphaël Kerverdo, Sara Lafuerza, Christian Gorini & Alain Rabaute

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Laboratoire Médiations, Institut de Géographie, Sorbonne Université, Paris, France

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Kerverdo, R., Lafuerza, S., Gorini, C. et al. The impact of Storm Alex on the Vievola catchment: a quantitative analysis of sediment volume and morphological changes in the Roya River tributaries. Landslides (2024). https://doi.org/10.1007/s10346-024-02361-2

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DOI : https://doi.org/10.1007/s10346-024-02361-2

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