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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Analytical vs. Descriptive

What's the difference.

Analytical and descriptive are two different approaches used in various fields of study. Analytical refers to the process of breaking down complex ideas or concepts into smaller components to understand their underlying principles or relationships. It involves critical thinking, logical reasoning, and the use of evidence to support arguments or conclusions. On the other hand, descriptive focuses on providing a detailed account or description of a particular phenomenon or event. It aims to present facts, observations, or characteristics without any interpretation or analysis. While analytical aims to uncover the "why" or "how" behind something, descriptive aims to provide a comprehensive picture of what is being studied. Both approaches have their own merits and are often used in combination to gain a deeper understanding of a subject matter.

Further Detail

Introduction.

When it comes to research and data analysis, two common approaches are analytical and descriptive methods. Both methods have their own unique attributes and serve different purposes in understanding and interpreting data. In this article, we will explore the characteristics of analytical and descriptive approaches, highlighting their strengths and limitations.

Analytical Approach

The analytical approach focuses on breaking down complex problems or datasets into smaller components to gain a deeper understanding of the underlying patterns and relationships. It involves the use of logical reasoning, critical thinking, and statistical techniques to examine data and draw conclusions. The primary goal of the analytical approach is to uncover insights, identify trends, and make predictions based on the available information.

One of the key attributes of the analytical approach is its emphasis on hypothesis testing. Researchers using this method formulate hypotheses based on existing theories or observations and then collect and analyze data to either support or refute these hypotheses. By systematically testing different variables and their relationships, the analytical approach allows researchers to make evidence-based claims and draw reliable conclusions.

Another important attribute of the analytical approach is its reliance on quantitative data. This method often involves the use of statistical tools and techniques to analyze numerical data, such as surveys, experiments, or large datasets. By quantifying variables and measuring their relationships, the analytical approach provides a rigorous and objective framework for data analysis.

Furthermore, the analytical approach is characterized by its focus on generalizability. Researchers using this method aim to draw conclusions that can be applied to a broader population or context. By using representative samples and statistical inference, the analytical approach allows researchers to make inferences about the larger population based on the analyzed data.

However, it is important to note that the analytical approach has its limitations. It may overlook important contextual factors or qualitative aspects of the data that cannot be easily quantified. Additionally, the analytical approach requires a strong understanding of statistical concepts and techniques, making it more suitable for researchers with a background in quantitative analysis.

Descriptive Approach

The descriptive approach, on the other hand, focuses on summarizing and presenting data in a meaningful and informative way. It aims to provide a clear and concise description of the observed phenomena or variables without necessarily seeking to establish causal relationships or make predictions. The primary goal of the descriptive approach is to present data in a manner that is easily understandable and interpretable.

One of the key attributes of the descriptive approach is its emphasis on data visualization. Researchers using this method often employ charts, graphs, and other visual representations to present data in a visually appealing and accessible manner. By using visual aids, the descriptive approach allows for quick and intuitive understanding of the data, making it suitable for a wide range of audiences.

Another important attribute of the descriptive approach is its flexibility in dealing with different types of data. Unlike the analytical approach, which primarily focuses on quantitative data, the descriptive approach can handle both quantitative and qualitative data. This makes it particularly useful in fields where subjective opinions, narratives, or observations play a significant role.

Furthermore, the descriptive approach is characterized by its attention to detail. Researchers using this method often provide comprehensive descriptions of the variables, including their distribution, central tendency, and variability. By presenting detailed summaries, the descriptive approach allows for a thorough understanding of the data, enabling researchers to identify patterns or trends that may not be immediately apparent.

However, it is important to acknowledge that the descriptive approach has its limitations as well. It may lack the rigor and statistical power of the analytical approach, as it does not involve hypothesis testing or inferential statistics. Additionally, the descriptive approach may be more subjective, as the interpretation of the data relies heavily on the researcher's judgment and perspective.

In conclusion, the analytical and descriptive approaches have distinct attributes that make them suitable for different research purposes. The analytical approach emphasizes hypothesis testing, quantitative data analysis, and generalizability, allowing researchers to draw evidence-based conclusions and make predictions. On the other hand, the descriptive approach focuses on data visualization, flexibility in handling different data types, and attention to detail, enabling researchers to present data in a clear and concise manner. Both approaches have their strengths and limitations, and the choice between them depends on the research objectives, available data, and the researcher's expertise. By understanding the attributes of each approach, researchers can make informed decisions and employ the most appropriate method for their specific research needs.

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Descriptive Vs Analytical Research

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Grab our Descriptive vs. Analytical Research presentation template for MS PowerPoint and Google Slides to highlight the significant difference between these two fundamental types of research methodologies in a comprehensible manner.

Researchers can use this PPT to explain that descriptive research focuses on providing accurate information or description about a specific phenomenon or subject. In contrast, analytical research focuses on the in-depth examination of a particular subject to understand the underlying patterns or cause-and-effect relationships. You can also depict how these two approaches differ based on the methods used for data collection, objectives, etc.

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Analytical vs. Descriptive Writing: Definitions and Examples

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:

• Summarizing an article (without offering additional insight)
• Stating the results of an experiment (without analyzing the implications)
• Describing a newsworthy event (without discussing possible long-term consequences)

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:

• The discussion section of research papers
• Opinion pieces about the likely consequences of newsworthy events and the steps that should be taken in response.

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.

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.

• Descriptive text: "I graduated from Bear University in 2020 with a B.S. in Chemistry and a cumulative GPA of 3.056."
• Analytical text: "While I struggled with some of my introductory courses, I proactively sought help to fill gaps in my understanding, and earned an "A" grade for all five of my senior year science courses. Therefore, I believe I am a strong candidate for . . ."

Combining description and analysis can also be very effective when discussing the significance of research results.

• Descriptive text: "Our study found significant (>2 ug/L) concentrations of polyfluoroalkyl substances (PFAS) in blood samples from all 5,478 study participants."
• Analytical text: "These results are alarming because the sample population included people who range in age from 1 month old to 98 years old, who live on five different continents, who reside in extremely rural areas and in urban areas, and who have little to no direct contact with products containing PFAS. PFAS are called "forever chemicals" because they are estimated to take hundreds or thousands of years to degrade. According to the US Centers for Disease Control (CDC), PFAS can move through soils to contaminate drinking water, and bioaccumulate in animals. Further research is urgently needed to better understand the adverse effects that PFAS have on human health, to identify the source of PFAS in rural communities, and to develop a method to sequester or destroy PFAS that have already entered the environment."

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.

Analytical writing supports a point of view

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.

• Scientific point of view: "Further research is urgently needed to better understand the adverse effects that PFAS have on human health, to identify the source of PFAS in rural communities, and to develop a method to sequester or destroy PFAS that have already entered the environment."
• Policy point of view: "Legislative action is urgently needed to ban the use of all PFAS, instead of banning new PFAS one at a time. Abundant and reliable data strongly indicates that all PFAS have similar effects, even if they have small differences in chemical composition. Given such evidence, the impetus must be on the chemical industry to prove safety, rather than on the general public to prove harm."
• Legal point of view: "Chemical companies have known about the danger of PFAS for years, but hid the evidence and continued to use these chemicals. Therefore, individuals and communities who have been harmed have the right to sue for damages."

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.

• Descriptive text: " According to Data USA , the average fast food worker in 2019 was 26.1 years old, and earned a salary of $12,294 a year."
• Point of view #1: "These data show why raising the minimum wage is unnecessary. Most fast food workers are young, with many being teenagers who are making extra money while living with their parents. The majority will eventually transition to jobs that require more skills, and that are rewarded with higher pay. If we mandate that companies pay low-skill workers more than required by the free market, then more highly skilled workers will also demand a pay raise. This will hurt businesses, contribute to inflation, and have no net benefit."
• Point of view #2: "These data show why raising the minimum wage is so important. On average, for every 16-year-old working in fast food for extra money, there is a 36-year-old trying to make ends meet. As factory jobs have moved overseas, employees without specialized skills have turned to fast food for steady employment. According to the UC Berkeley Labor Center , for families with someone working full-time (40 hours/week) in fast food, more than half are enrolled in public assistance programs. These include Medicaid, food stamps, and the Earned Income Tax Credit. Therefore, taxpayers are subsidizing companies that pay poverty wages, so that their employees can have access to basic necessities like food and healthcare."

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.

Descriptive Research

Oct 22, 2012

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Descriptive Research. Marketing Research – MKTG 446. Angela D’Auria Stanton, Ph.D. Descriptive Research. Descriptive research (often referred to as survey research) is aimed at characterizing phenomena and identifying association among selected variables.  Descriptive research can be used to:

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Descriptive Research Marketing Research – MKTG 446 Angela D’Auria Stanton, Ph.D.

Descriptive Research • Descriptive research (often referred to as survey research) is aimed at characterizing phenomena and identifying association among selected variables.  Descriptive research can be used to: • describe the characteristics of certain groups • estimate the proportion of people in a specified population who behave in a certain way • make specific predictions • Some specific examples of descriptive studies include: • The proportion of companies that have an electronic storefront • Target customer's evaluation of key product attributes • Measuring employee satisfaction

Classification of Descriptive Studies • Longitudinal Surveys • Provides a series of pictures that, when pierced together, provide a movie of the situation the changes that are occurring. • The sample remains relatively constant through time and the sample members are measured repeatedly. • Cross-Sectional Studies • Provides a snapshot of the variables of interest at a single point in time. • The sample is typically selected to be representative of some known universe.

Longitudinal Data – Advantages

An Example of a Panel Design Number of Households in Panel Purchasing Each Brand of Detergent

Another Way of Looking at It Loyalty Analysis

Survey Research Methods • Personal Interview • Telephone • Self-Administered • Online (computer administered)

Survey Research Methods • Personal Interviews (or Person Administered Surveys) – requires the presence of a trained human interviewer who asks questions and records the subject’s answers • In-home or In-office • Executive interview • Mall intercept • Purchase intercept

By physically being there, the interviewer may persuade the person to supply answers (probably the highest response rate). Information on the situation may be observed, without asking. Best for getting response from specific, identified person. Versatility of questioning methods (and flexibility in sequencing) and use of visual materials. Allows for probing of open-ended questions and clarification of ambiguous questions. Long questionnaires may be used successfully under interviewer's urging. If the respondent is having trouble understanding, interviewer may notice and remedy this. Selection of sample members can be more precise. Expensive and time intensive. Generally narrow distribution. People may be reluctant to talk with strangers. Often difficult to identify individuals to include in the sampling frame. Interviewer's presence, mannerisms and inflections may bias responses. Respondents know that they can be identified, which may inhibit their willingness to give information. Great difficulty in trying to supervise and control field interviewers. Staffing with capable interviewers, especially when conducted in distant places. May inaccurately record respondent’s answers Personal Interviews Advantages Disadvantages

Survey Research Methods • Telephone Interviews – personal interviews conducted via telephone technology typically from a central location Advantages Disadvantages • Relatively low cost • Wide distribution eliminates distance obstacle • Callbacks • Avoids personal travel to interview • Rapid coverage of even widely scattered sample. • Interviewers can be closely supervised • Less interview bias due to anonymity • Allows easy use of computer support (CATI/CAI) • Representative sampling frame difficult to establish due to unlisted numbers and increased use of cell phones • Inability to observe a respondent • Limitation to audio materials • Difficulty of conducting long interviews; no way to prevent hang-ups • More difficult to establish rapport over the telephone than in-person • Difficult to determine that appropriate respondent is being interviewed • Restrictions on types of data collected • Misperceptions and “sugging”

What to do about lack of listed numbers & cell phones? Plus-one dialing Random digit dialing Systematic random digit dialing Telephone Interviews Results of First Dialing Attempts * Probability of occurrence given eligible individual is at home

Survey Research Methods • Self-Administered – the respondent reads the survey questions and record his/her own answers without the presence of an interviewer. Typically done via mail, fax or drop-off. Advantages Disadvantages • Sampling frame easily developed when mailing lists are available • Respondent reads and answers questions without interviewer influence • May respond whenever convenient and without pressure • Any visual materials can be used • Relatively less expensive • More confidential information may be divulged • Anonymity of respondents easier to achieve • Eliminates the need for an interviewer • A majority may not respond, and those who do may not be typical • Significant time lag between the time the survey is mailed and when returned • Nothing can be learned except what is written on the questionnaire • The apparent low cost becomes relatively high when response is poor • Questions may be misunderstood or skipped • A person may read the entire form before answering any questions, so later questions can influence answers to earlier ones. • Significant problems in "pass along" effect.

Survey Research Methods • Online – typically conducted via e-mail or the Internet Advantages Disadvantages • Survey setup and execution can be done very quickly • Visual materials can be used • Respondent responds without being influenced by the interviewer • May respond whenever convenient and without pressure • Typically the least expensive • Can permit the respondent to be interrupted and later resume where he/she left off • Eliminates the cost of the interviewer. • Permits real-time data cleaning • Response rates are becoming an issue • May be confused with spam. • Will not be able to reach people without access or desire to use the Internet • Nothing can be learned except what is written on the questionnaire • No one present to stimulate replies or offer instructions. • Potential for "pass along" effect. • Respondent frustration if questionnaire forces response. • Issues with projectability of the sample

Selecting a Survey Method: Factors to Consider

Errors (or Bias) Affecting Survey Research Total Error = Sampling Error (difference between the actual sample results and the true population results) + Non-Sampling (Systematic) Error

Non-Sampling Errors • Respondent Errors • Non-response error • Response Bias • Deliberate falsification (social desirability error, auspices error, hostility, yea and nay-saying) • Unconscious misrepresentation (faulty recollection, fatigue, acquiescence error, extremity bias)

Non-Sampling Errors • Measurement/Research Design Errors • Construct development error • Scale measurement error (inappropriate questions, scale attributes or scale point descriptors) • Survey instrument error (improper sequence, length, poor or no instructions, etc.) • Data analysis error (use of wrong analytical technique, etc.) • Misinterpretation error (making the wrong inference, using only a selected portion of the study results)

Non-Sampling Errors • Problem Definition Errors • Misinterpreting the true nature of the problem situation • Administrative Errors • Data processing errors (coding, data entry or editing) • Interviewer error (cheating, recording error, misinterpretation, carelessness • Sample design error (sample selection error, sampling frame error, specifying the wrong population, etc.)

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