There are, however, many other types of research, often used only in certain narrow fields of research. Further complicating things, many of the types overlap, go by different names depending on the subject area, or are differentiated only by very subtle differences. For more detailed explanations of the types of research commonly used in your field, please consult references related to research in your specific subject area.
Because secondary research is so widely used, even by non-researchers, and because its practice is relatively consistent between disciplines, we will cover it in more detail on other pages of this guide.
Understanding and solving intractable resource governance problems.
When I switched from chemical engineering (my undergraduate degree) to political science and human geography (my doctoral degree), I went through economics of technical change and international marketing (my Masters). But the chemical engineering component was still very strong during my Masters. I remember reading comments from a professor’s marker (yes, my professor didn’t even grade my essay!) saying “ lacks analysis “.
WHAT THE HELL IS ANALYSIS IF NOT WHAT I AM WRITING THEN?! Now, when I read student essays, or Masters/PhD theses, I find myself writing similar comments: “ this is a very good description, but lacks real analysis “. I asked both the Political Scientists Facebook group (of which I’m proud of being part of) and the Research Companion Facebook group (a fantastic resource created by Dr. Petra Boynton, author of the book “The Research Companion”).
I received A LOT of really good feedback on both groups (who said that Facebook was only good for posting photos of your kids?) which I am detailing here (I’ve asked for permission to attribute whoever recommended a particular book or reading).
Political Scientists
I found through Google a few handouts, but these three were the ones that stood out to me, and were also the simplest for me to refer my students for a reading.
Over on The Research Companion Facebook group, I got a few responses.
I then searched my own Mendeley library for examples of good articles I had read that could show my students what analysis looks like, vis-a-vis descriptive text. Here are a few examples I tweeted.
Describing refers to providing details. Analyzing implies comparing, contrasting, weighing the evidence for additional insight, critiquing — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
The first one is from a World Development 2014 article by Alison Post and Veronica Herrera on public service delivery in Latin America (focusing on water and wastewater). Here, I wanted the reader to see how Herrera and Post set up a comparison between what the literature says versus what their own analysis shows.
. @veromsherrera and Alison Post offer an excellent example of the “They Say/I Say” model, showing what literature says vs their analysis pic.twitter.com/SYUMxtd543 — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
. @veromsherrera Note that here @veromsherrera and Post analyze the literature on privatization and offer their own analysis of what it fails to account for. — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
. @veromsherrera This is important when teaching our students: contrast what the literature says with your own empirical findings. Also, model They Say/I Say — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
. @veromsherrera If you’re wondering what I mean by the “They Say/I Say” model, it’s based on Graff & Birkenstein book https://t.co/Nu7SkRPKT4 h/t @owasow — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
This example comes from Kathryn Harrison’s 2002 Governance article comparing US/Canada/Sweden and dioxins control policy. This paper investigates the role of ideas, interests and institutions on policy change. In this example, I wanted to show how Harrison weighs evidence from each one of the three case studies and evaluates the differential impact that ideas, interests and institutions had on policy evolution.
In comparison of pulp and paper policies US/Canada/Sweden, @khar1958 weighs evidence & explanatory power of ideas, interests & institutions pic.twitter.com/ce2tbqxhmb — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
. @khar1958 Note that while @khar1958 finds compelling evidence of impact of ideas, she points out to interplay of ideas, interests AND institutions. — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
. @khar1958 This is important when we teach students to offer evidence. We need to tell them to offer alternative explanations, weigh evidence/results. — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
I then used Josh Cousins and Josh Newell’s article on political-industrial ecology in Los Angeles’ water supply infrastructure to show the reader how Cousins and Newell present descriptive text on Los Angeles and its water supply and then connect it to the literature through analysis.
In their paper on the urban industrial-political ecology of Los Angeles water supply, @JoshJCousins & Newell link description w/analysis pic.twitter.com/IPesNK1uSx — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
. @JoshJCousins I used pink to denote descriptive text, and orange to show where Cousins & Newell link the description above with theoretical underpinnings. — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
I used Megan Hatch and Elizabeth Rigby’s article on state-level governments as laboratories of democracy and their study of state-level inequality to show how you can use data (quantitative, in this case) to create an argument and dispel previously held beliefs/preconceived ideas/previous theoretical and empirical findings with their own.
Here, @meganehatch & E. Rigby show (graph above screenshot) how their results counter our traditional understanding of inequality in states pic.twitter.com/Ew3wTHcsYc — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
I also used a paper by Melissa Merry on tweeting and the framing of gun policy using the Narrative Policy Framework. In this example I wanted to show how Merry mobilizes her empirical findings to construct a new measure and to explain the theoretical and empirical implications of her findings.
. @melpoague offers good example of ANALYSIS – “here is how I constructed an index, and what my results imply” (on gun policy narratives) pic.twitter.com/yXUyPCijWk — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
From David Carter and Chris Weible’s study of smoking bans in Colorado in 1977 and 2006, I drew an example where I show how Carter and Weible set up an empirical question (a hypothesis) and then use their data to explain differences between both smoking bans.
In their paper comparing Colorado smoking bans 1977 vs 2006 @DCarterSLC and @chris_weible answer 1 of their questions w data & analysis pic.twitter.com/0SY5E1VAhs — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
Another way in which researchers show they’ve done analysis is in case study selection. In this paper by Rob de Leo and Donnelly, they do a study of policy transfer and the adoption of the Affordable Care Act in Massachusetts. De Leo and Donnelly clearly outline the various reasons why choosing this particular case makes sense.
In their paper on policy transfer and implementation of the Affordable Care Act, @r_deLeo and Donelly clearly outline case study selection pic.twitter.com/afeHzT8amM — Dr Raul Pacheco-Vega (@raulpacheco) May 8, 2017
I am thankful to everyone who provided me with links to books, handouts, etc. And I hope this blog post will be useful to anybody who needs to teach analysis vs. description. I certainly will be using it with my own students and research assistants!
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By Raul Pacheco-Vega – May 7, 2017
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Scholars at all levels are expected to write. People who are not students or scholars often engage in writing for work, or to communicate with friends, family, and strangers through email, text messages, and social media. Academia recognizes two major types of writing—descriptive writing and analytical writing—which are both used in non-academic situations as well. As you might expect, descriptive writing focuses on clear descriptions of facts or things that have happened, while analytical writing provides additional analysis.
Descriptive writing is the most straightforward type of academic writing. It provides accurate information about "who", "what", "where", and "when". Examples of descriptive writing include:
High school students and undergraduates are most commonly asked to write descriptively, to show that they understand the key points of a specific topic (e.g. the major causes of World War II).
Analytical writing goes beyond summarizing information and instead provides evaluation, comparison, and possible conclusions. It addresses the questions of "why?", "so what?", and "what next?". Examples of analytical writing include:
High school students and undergraduates are sometimes asked to write analytically to "stretch their thinking". Possible topics might include "Could World War II have been avoided?" and "How can CRISPR-Cas9 technology improve human health?". The value of any such analysis is entirely dependent on the writer's ability to understand and clearly explain relevant information, which would be explained through descriptive writing. For graduate students and professional researchers, the quality of their work is at least partially based on the quality of their analysis.
The following table from The Study Skills Handbook by Stella Cottrell (2013, 4th edition, Palgrave Macmillan, page 198) is commonly used to summarize the differences between descriptive writing and analytical writing.
Descriptive Writing | Critical Analytical Writing |
---|---|
States what happened | Identifies the significance |
States what something is like | Evaluates strengths and weaknesses |
Gives the story so far | Weighs one piece of information against another |
Outlines the order in which things happened | Makes reasoned judgements |
Instructs how to do something | Argues a case according to the evidence |
List the main elements of a theory | Shows why something is relevant or suitable |
Outlines how something works | Indicates why something will work (best) |
Notes the method used | Identifies whether something is appropriate or suitable |
States when something occurred | Identifies why the timing is of importance |
States the different components | Weighs the importance of component parts |
States options | Gives reasons for selecting each option |
Lists details | Evaluates the relative significance of details |
Lists in any order | Structures information in order of importance |
States links between items | Shows the relevance of links between pieces of information |
Gives information or reports findings | Evaluates information and draws conclusions |
Description and analysis are also used in spoken communication such as presentations and conversations, and in visual communication such as diagrams and memes. In all of these cases, it is important to communicate clearly and effectively, and to use reliable sources of information.
Descriptive writing and analytical writing are often used in combination. In job application cover letters and essays for university admission, adding analytical text can provide context for otherwise unremarkable statements.
Combining description and analysis can also be very effective when discussing the significance of research results.
In both of the examples above, the analytical text includes additional facts (e.g. "A" grade for senior science courses; 1 month old to 98 years old) that help strengthen the argument. The student's transcript and the research paper's results section would contain these same facts—along with many others—written descriptively or presented in graphs, tables, or lists. For the analytical text, the author is trying to persuade the reader, and has therefore selected relevant facts to support their argument.
In the example about PFAS, the author's argument is further strengthened by citing additional information from a reputable source (the CDC). In reports where the author is supposed to be unbiased (e.g. a journalist writing descriptively), a similar effect can be obtained by quoting reputable sources. For example, "Professor of environmental science Kim Lee explains that PFAS are. . ." In these situations, it is often appropriate to present opposing views, as long as they come from reputable sources. This strategy of quoting or citing reputable sources can also be effective for students and professionals who do not have strong credentials in the topic under discussion.
People cannot choose their own facts, but the same facts can be used to support very different points of view. Let's consider some different points of view that can be supported by the PFAS example from above.
These three points of view focus on three different fields (science, policy, and law), but all have a negative view of PFAS. The next example shows how the same factual information can be used to support opposing views.
A primary purpose of analytical writing is to show how facts (explained through descriptive writing) support a particular conclusion or a particular path forward. This often requires explaining why an alternative interpretation is less satisfactory. This is how scholarly work—and good discussions in less formal situations—contribute to our collective understanding of the world.
Analytical study designs can be experimental or observational and each type has its own features. In this article, you'll learn the main types of designs and how to figure out which one you'll need for your study.
Updated on September 19, 2022
A study design is critical to your research study because it determines exactly how you will collect and analyze your data. If your study aims to study the relationship between two variables, then an analytical study design is the right choice.
But how do you know which type of analytical study design is best for your specific research question? It's necessary to have a clear plan before you begin data collection. Lots of researchers, sadly, speed through this or don't do it at all.
A study design is a systematic plan, developed so you can carry out your research study effectively and efficiently. Having a design is important because it will determine the right methodologies for your study. Using the right study design makes your results more credible, valid, and coherent.
Study designs can be broadly divided into either descriptive or analytical.
Descriptive studies describe characteristics such as patterns or trends. They answer the questions of what, who, where, and when, and they generate hypotheses. They include case reports and qualitative studies.
Analytical study designs quantify a relationship between different variables. They answer the questions of why and how. They're used to test hypotheses and make predictions.
Analytical study designs can be either experimental or observational. In experimental studies, researchers manipulate something in a population of interest and examine its effects. These designs are used to establish a causal link between two variables.
In observational studies, in contrast, researchers observe the effects of a treatment or intervention without manipulating anything. Observational studies are most often used to study larger patterns over longer periods.
Experimental study designs are when a researcher introduces a change in one group and not in another. Typically, these are used when researchers are interested in the effects of this change on some outcome. It's important to try to ensure that both groups are equivalent at baseline to make sure that any differences that arise are from any introduced change.
In one study, Reiner and colleagues studied the effects of a mindfulness intervention on pain perception . The researchers randomly assigned participants into an experimental group that received a mindfulness training program for two weeks. The rest of the participants were placed in a control group that did not receive the intervention.
Experimental studies help us establish causality. This is critical in science because we want to know whether one variable leads to a change, or causes another. Establishing causality leads to higher internal validity and makes results reproducible.
Experimental designs include randomized control trials (RCTs), nonrandomized control trials (non-RCTs), and crossover designs. Read on to learn the differences.
In an RCT, one group of individuals receives an intervention or a treatment, while another does not. It's then possible to investigate what happens to the participants in each group.
Another important feature of RCTs is that participants are randomly assigned to study groups. This helps to limit certain biases and retain better control. Randomization also lets researchers pinpoint any differences in outcomes to the intervention received during the trial. RTCs are considered the gold standard in biomedical research and are considered to provide the best kind of evidence.
For example, one RCT looked at whether an exercise intervention impacts depression . Researchers randomly placed patients with depressive symptoms into intervention groups containing different types of exercise (i.e., light, moderate, or strong). Another group received usual medications or no exercise interventions.
Results showed that after the 12-week trial, patients in all exercise groups had decreased depression levels compared to the control group. This means that by using an RCT design, researchers can now safely assume that the exercise variable has a positive impact on depression.
However, RCTs are not without drawbacks. In the example above, we don't know if exercise still has a positive impact on depression in the long term. This is because it's not feasible to keep people under these controlled settings for a long time.
Nonrandomized controlled trials are a type of nonrandomized controlled studies (NRS) where the allocation of participants to intervention groups is not done randomly . Here, researchers purposely assign some participants to one group and others to another group based on certain features. Alternatively, participants can sometimes also decide which group they want to be in.
For example, in one study, clinicians were interested in the impact of stroke recovery after being in an enriched versus non-enriched hospital environment . Patients were selected for the trial if they fulfilled certain requirements common to stroke recovery. Then, the intervention group was given access to an enriched environment (i.e. internet access, reading, going outside), and another group was not. Results showed that the enriched group performed better on cognitive tasks.
NRS are useful in medical research because they help study phenomena that would be difficult to measure with an RCT. However, one of their major drawbacks is that we cannot be sure if the intervention leads to the outcome. In the above example, we can't say for certain whether those patients improved after stroke because they were in the enriched environment or whether there were other variables at play.
In a crossover design, each participant receives a sequence of different treatments. Crossover designs can be applied to RCTs, in which each participant is randomly assigned to different study groups.
For example, one study looked at the effects of replacing butter with margarine on lipoproteins levels in individuals with cholesterol . Patients were randomly assigned to a 6-week butter diet, followed by a 6-week margarine diet. In between both diets, participants ate a normal diet for 5 weeks.
These designs are helpful because they reduce bias. In the example above, each participant completed both interventions, making them serve as their own control. However, we don't know if eating butter or margarine first leads to certain results in some subjects.
In observational studies, researchers watch (observe) the effects of a treatment or intervention without trying to change anything in the population. Observational studies help us establish broad trends and patterns in large-scale datasets or populations. They are also a great alternative when an experimental study is not an option.
Unlike experimental research, observational studies do not help us establish causality. This is because researchers do not actively control any variables. Rather, they investigate statistical relationships between them. Often this is done using a correlational approach.
For example, researchers would like to examine the effects of daily fiber intake on bone density . They conduct a large-scale survey of thousands of individuals to examine correlations of fiber intake with different health measures.
The main observational studies are case-control, cohort, and cross-sectional. Let's take a closer look at each one below.
A case-control is a type of observational design in which researchers identify individuals with an existing health situation (cases) and a similar group without the health issue (controls). The cases and the controls are then compared based on some measurements.
Frequently, data collection in a case-control study is retroactive (i.e., backwards in time). This is because participants have already been exposed to the event in question. Additionally, researchers must go through records and patient files to obtain the records for this study design.
For example, a group of researchers examined whether using sleeping pills puts people at risk of Alzheimer's disease . They selected 1976 individuals that received a dementia diagnosis (“cases”) with 7184 other individuals (“controls”). Cases and controls were matched on specific measures such as sex and age. Patient data was consulted to find out how much sleeping pills were consumed over the course of a certain time.
Case-control is ideal for situations where cases are easy to pick out and compare. For instance, in studying rare diseases or outbreaks.
A cohort is a group of people who are linked in some way. For instance, a birth year cohort is all people born in a specific year. In cohort studies, researchers compare what happens to individuals in the cohort that have been exposed to some variable compared with those that haven't on different variables. They're also called longitudinal studies.
The cohort is then repeatedly assessed on variables of interest over a period of time. There is no set amount of time required for cohort studies. They can range from a few weeks to many years.
Cohort studies can be prospective. In this case, individuals are followed for some time into the future. They can also be retrospective, where data is collected on a cohort from records.
One of the longest cohort studies today is The Harvard Study of Adult Development . This cohort study has been tracking various health outcomes of 268 Harvard graduates and 456 poor individuals in Boston from 1939 to 2014. Physical screenings, blood samples, brain scans and surveys were collected on this cohort for over 70 years. This study has produced a wealth of knowledge on outcomes throughout life.
A cohort study design is a good option when you have a specific group of people you want to study over time. However, a major drawback is that they take a long time and lack control.
Cross-sectional studies are also known as prevalence studies. They look at the relationship of specific variables in a population in one given time. In cross-sectional studies, the researcher does not try to manipulate any of the variables, just study them using statistical analyses. Cross-sectional studies are also called snapshots of a certain variable or time.
For example, researchers wanted to determine the prevalence of inappropriate antibiotic use to study the growing concern about antibiotic resistance. Participants completed a self-administered questionnaire assessing their knowledge and attitude toward antibiotic use. Then, researchers performed statistical analyses on their responses to determine the relationship between the variables.
Cross-sectional study designs are ideal when gathering initial data on a research question. This data can then be analyzed again later. By knowing the public's general attitudes towards antibiotics, this information can then be relayed to physicians or public health authorities. However, it's often difficult to determine how long these results stay true for.
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Scholars who research phenomena of concern to the discipline of nursing are challenged with making wise choices about different qualitative research approaches. Ultimately, they want to choose an approach that is best suited to answer their research questions. Such choices are predicated on having made distinctions between qualitative methodology, methods, and analytic frames. In this article, we distinguish two qualitative research approaches widely used for descriptive studies: descriptive phenomenological and qualitative description. Providing a clear basis that highlights the distinguishing features and similarities between descriptive phenomenological and qualitative description research will help students and researchers make more informed choices in deciding upon the most appropriate methodology in qualitative research. We orient the reader to distinguishing features and similarities associated with each approach and the kinds of research questions descriptive phenomenological and qualitative description research address.
Keywords: phenomenology; qualitative methods.
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Priya ranganathan.
Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India
1 Director, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
In analytical observational studies, researchers try to establish an association between exposure(s) and outcome(s). Depending on the direction of enquiry, these studies can be directed forwards (cohort studies) or backwards (case–control studies). In this article, we examine the key features of these two types of studies.
In a previous article[ 1 ] in this series, we looked at descriptive observational studies, namely case reports, case series, cross-sectional studies, and ecological studies. As compared to descriptive studies which merely describe one or more variables in a sample (or occasionally population), analytical studies attempt to quantify a relationship or association between two variables – an exposure and an outcome. As discussed previously, in observational analytical studies, the exposure is naturally determined as opposed to experimental studies where an investigator assigns each subject to receive or not receive a particular exposure.
A cohort is defined as a “group of people with a shared characteristic.” In cohort studies, different groups of people with varying levels of exposure are followed over time to evaluate the occurrence of an outcome. These participants have to be free of the outcome at baseline. The presence or absence of the risk factor (exposure) in each subject is recorded. The subjects are then followed up over time (longitudinally) to determine the occurrence of the outcome. Thus, cohort studies are forward-direction studies (moving from exposure to outcome) and are typically prospective studies (the outcome has not occurred at the start of the study).
An example of cohort study design is a study by Viljakainen et al ., which investigated the relation between maternal vitamin D levels during pregnancy and the bone health in their newborns.[ 2 ] Maternal blood vitamin D levels were estimated during pregnancy. Children born to these mothers were then followed up until 14 months of age, and bone parameters were evaluated. Based on the maternal serum 25-hydroxy vitamin D levels during pregnancy, children were divided into two groups – those born to mothers with normal blood vitamin D and those born to mothers with low blood vitamin D. The authors found that children born to mothers with low vitamin D levels had persistent bone abnormalities.
Sometimes, a researcher may look back at data which have already been collected. For example, let us think of a hospital that records every patient's smoking status at the time of the first visit. A researcher may use these records from 10 years ago, and then contact the persons today to check if any of them have already been diagnosed or currently have features of lung cancer. This is still a forward-direction study (exposure traced forward among exposed and unexposed to outcome) but is retrospective (since the outcome may have already occurred). Such studies are known as 'retrospective cohort studies'.
Large cohort studies, such as the Framingham Heart Study or the Nurses' Health Study, have yielded extremely useful information about risk factors for several chronic diseases.
In case-control studies, the researcher first enrolls cases (participants with the outcome) and controls (participants without the outcome) and then tries to elicit a history of exposure in each group. Thus, these are backward-direction studies (looking from outcome to exposure) and are always retrospective (the outcome must have occurred when the study starts). Typically, cases are identified from hospital records, death certificates or disease registries. This is followed by the identification and enrolment of controls.
Identification of appropriate controls is a key element of the case-control study design and can influence the estimate of association between exposure and outcome (selection bias). The controls should resemble cases in all respects, except for the absence of disease. Thus, they should be representative of the population from which the cases were drawn. For instance, if cases are drawn from a community clinic, an outpatient clinic or an inpatient setting, the controls should also ideally be from the same setting.
Sometimes, controls are individually matched with cases for factors (except for the one which is the exposure of interest) which are considered important to the development of the outcome. For example, in a study on relation of smoking with lung cancer, for each case of lung cancer enrolled, one control with similar age and sex is enrolled. This would reduce the risk of confounding by age and sex – the factors used for matching. Sometimes, the number of controls per case may be larger (e.g. two, three, or more).
Furthermore, to minimize assessment bias, it is important that the person assessing the history of exposure (e.g., smoking in this case) is unaware of (blinded to) whether the participant being interviewed is a case or a control.
For example, Anderson et al . conducted a case–control study to look at risk factors for childhood fractures.[ 3 ] They recruited cases from a hospital fracture clinic and individually matched controls (children without fractures) from a primary care research network. The cases and controls were matched on age, sex, height, and season. They found that the history of previous use of vitamin D supplements was significantly higher in the children without fractures, suggesting an inverse association between vitamin D supplementation and incidence of fractures.
Nested case-control design is a special type of case-control study design which is built into a cohort study. From the main cohorts, participants who develop the outcome (irrespective of whether exposed or unexposed) are chosen as cases. From among the remaining study participants who have not developed the outcome, a subset of matched controls are selected. The cases and controls are then compared with respect to exposure. This is still a backward-direction (since the enquiry begins with outcome and then proceeds toward exposure) and retrospective study (since outcomes have already occurred when the study starts). The main advantage is that since one knows that the outcome had not occurred when the cohorts were established, temporal relation of exposure and outcome is ensured.
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Analytical research asks why something happens, while descriptive research shows what it looks like. Learn the difference, examples, and importance of analytical research for various fields of study.
Learn how analytical and descriptive approaches differ in their goals, methods, and applications in research and data analysis. Analytical focuses on breaking down complex problems into smaller components and testing hypotheses, while descriptive focuses on summarizing and presenting data in a clear and informative way.
Learn the key differences between descriptive and analytical research methods, their objectives, data analysis, and outcomes. See examples of each type of research and how they are applied in various fields.
Learn about the two types of study designs: descriptive and analytical. Descriptive studies describe characteristics in a population, while analytical studies observe or experiment outcomes. See examples of common observational and experimental designs.
Learn the difference between descriptive and analytical writing in academic research, and see examples of each type. Descriptive writing states what happened, while analytical writing explains the impact or meaning of what happened in relation to the research aims and questions.
Learn how to choose the right type of research design for your project based on the research aims, data, sampling, timescale, and location. Descriptive research gathers data without controlling variables, while experimental research manipulates and controls variables to determine cause and effect.
Learn the key features, purposes, and examples of descriptive and analytical research methods in sociology. Descriptive research captures the characteristics of a population or phenomenon, while analytical research explores the causes and patterns behind them.
Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what, where, when and how questions, but not why questions. A descriptive research design can use surveys, observations or case studies.
Understanding Research Study Designs - PMC
The main difference between analytical and descriptive research lies in their purpose and approach. Here are the key differences between the two: Purpose: Descriptive research aims to describe a situation, problem, or phenomenon accurately, providing a snapshot of specific phenomena at a particular point in time.
Analytical research is a systematic and scientific way of investigating a subject by using critical thinking and data analysis. It aims to identify the causes and mechanisms of phenomena or events and to support or refute hypotheses. Learn the difference between analytical and descriptive research and see examples of analytical methods.
Descriptive vs. Analytical Research. Descriptive. Discovering or describing the state of affairs as they currently exist. No control over variables. Just the facts. Analytical. Evaluation of available facts or data to make or support an argument or test an hypothesis. Uses data discovered or described in descriptive research.
Learn how to conduct descriptive and analytic studies to describe, explain, or predict health outcomes. Compare different types of studies, sampling methods, measures of association, and examples.
This short guide from the University of Birmingham Writing Centre on critical thinking and the differences between analytical and descriptive writing really outlines when you use description, when you should be analyzing and how to differentiate between both. Over on The Research Companion Facebook group, I got a few responses. Dr. Helen Kara ...
Examples. Descriptive writing and analytical writing are often used in combination. In job application cover letters and essays for university admission, adding analytical text can provide context for otherwise unremarkable statements. Descriptive text: "I graduated from Bear University in 2020 with a B.S. in Chemistry and a cumulative GPA of 3 ...
Learn how to choose the best analytical study design for your research question. Compare experimental and observational designs, such as RCTs, NRS, and crossover studies, and their advantages and disadvantages.
Descriptive versus Analytical Descriptive research consists of surveys and fact-finding enquiries of different types. The main objective of descriptive rese...
Qualitative research collects data qualitatively, and the method of analysis is also primarily qualitative. This often involves an inductive exploration of the data to identify recurring themes, patterns, or concepts and then describing and interpreting those categories. Of course, in qualitative research, the data collected qualitatively can ...
Such choices are predicated on having made distinctions between qualitative methodology, methods, and analytic frames. In this article, we distinguish two qualitative research approaches widely used for descriptive studies: descriptive phenomenological and qualitative description.
Study designs: Part 3 - Analytical observational studies
Research Types. The major purpose of descriptive research is to describe the characteristics of a particular phenomenon or population. Surveys and fact-finding enquiries are commonly used in descriptive research. The primary focus of applied research is to find a solution to a practical problem.
Answer and Explanation: 1. Become a Study.com member to unlock this answer! Create your account. View this answer. Difference between descriptive and analytical research: Descriptive research: It is a method used to describe the characteristics of the variable... See full answer below.
Differences between descriptive research and analytical research. Analytical Research. ... There is a difference between Descriptive Research and Analytical Research as discussed here in this ...