analytical definition in research

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Analytical Research: What is it, Importance + Examples

Finding knowledge is a loose translation of the word “research.” It’s a systematic and scientific way of researching a particular subject. As a result, research is a form of scientific investigation that seeks to learn more. Analytical research is one of them.

Any kind of research is a way to learn new things. In this research, data and other pertinent information about a project are assembled; after the information is gathered and assessed, the sources are used to support a notion or prove a hypothesis.

An individual can successfully draw out minor facts to make more significant conclusions about the subject matter by using critical thinking abilities (a technique of thinking that entails identifying a claim or assumption and determining whether it is accurate or untrue).

What is analytical research?

This particular kind of research calls for using critical thinking abilities and assessing data and information pertinent to the project at hand.

Determines the causal connections between two or more variables. The analytical study aims to identify the causes and mechanisms underlying the trade deficit’s movement throughout a given period.

It is used by various professionals, including psychologists, doctors, and students, to identify the most pertinent material during investigations. One learns crucial information from analytical research that helps them contribute fresh concepts to the work they are producing.

Some researchers perform it to uncover information that supports ongoing research to strengthen the validity of their findings. Other scholars engage in analytical research to generate fresh perspectives on the subject.

Various approaches to performing research include literary analysis, Gap analysis , general public surveys, clinical trials, and meta-analysis.

Importance of analytical research

The goal of analytical research is to develop new ideas that are more believable by combining numerous minute details.

The analytical investigation is what explains why a claim should be trusted. Finding out why something occurs is complex. You need to be able to evaluate information critically and think critically. 

This kind of information aids in proving the validity of a theory or supporting a hypothesis. It assists in recognizing a claim and determining whether it is true.

Analytical kind of research is valuable to many people, including students, psychologists, marketers, and others. It aids in determining which advertising initiatives within a firm perform best. In the meantime, medical research and research design determine how well a particular treatment does.

Thus, analytical research can help people achieve their goals while saving lives and money.

Methods of Conducting Analytical Research

Analytical research is the process of gathering, analyzing, and interpreting information to make inferences and reach conclusions. Depending on the purpose of the research and the data you have access to, you can conduct analytical research using a variety of methods. Here are a few typical approaches:

Quantitative research

Numerical data are gathered and analyzed using this method. Statistical methods are then used to analyze the information, which is often collected using surveys, experiments, or pre-existing datasets. Results from quantitative research can be measured, compared, and generalized numerically.

Qualitative research

In contrast to quantitative research, qualitative research focuses on collecting non-numerical information. It gathers detailed information using techniques like interviews, focus groups, observations, or content research. Understanding social phenomena, exploring experiences, and revealing underlying meanings and motivations are all goals of qualitative research.

Mixed methods research

This strategy combines quantitative and qualitative methodologies to grasp a research problem thoroughly. Mixed methods research often entails gathering and evaluating both numerical and non-numerical data, integrating the results, and offering a more comprehensive viewpoint on the research issue.

Experimental research

Experimental research is frequently employed in scientific trials and investigations to establish causal links between variables. This approach entails modifying variables in a controlled environment to identify cause-and-effect connections. Researchers randomly divide volunteers into several groups, provide various interventions or treatments, and track the results.

Observational research

With this approach, behaviors or occurrences are observed and methodically recorded without any outside interference or variable data manipulation . Both controlled surroundings and naturalistic settings can be used for observational research . It offers useful insights into behaviors that occur in the actual world and enables researchers to explore events as they naturally occur.

Case study research

This approach entails thorough research of a single case or a small group of related cases. Case-control studies frequently include a variety of information sources, including observations, records, and interviews. They offer rich, in-depth insights and are particularly helpful for researching complex phenomena in practical settings.

Secondary data analysis

Examining secondary information is time and money-efficient, enabling researchers to explore new research issues or confirm prior findings. With this approach, researchers examine previously gathered information for a different reason. Information from earlier cohort studies, accessible databases, or corporate documents may be included in this.

Content analysis

Content research is frequently employed in social sciences, media observational studies, and cross-sectional studies. This approach systematically examines the content of texts, including media, speeches, and written documents. Themes, patterns, or keywords are found and categorized by researchers to make inferences about the content.

Depending on your research objectives, the resources at your disposal, and the type of data you wish to analyze, selecting the most appropriate approach or combination of methodologies is crucial to conducting analytical research.

Examples of analytical research

Analytical research takes a unique measurement. Instead, you would consider the causes and changes to the trade imbalance. Detailed statistics and statistical checks help guarantee that the results are significant.

For example, it can look into why the value of the Japanese Yen has decreased. This is so that an analytical study can consider “how” and “why” questions.

Another example is that someone might conduct analytical research to identify a study’s gap. It presents a fresh perspective on your data. Therefore, it aids in supporting or refuting notions.

Descriptive vs analytical research

Here are the key differences between descriptive research and analytical research:

The study of cause and effect makes extensive use of analytical research. It benefits from numerous academic disciplines, including marketing, health, and psychology, because it offers more conclusive information for addressing research issues.

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What Is Analytical Research?

Analytical research is a specific type of research that involves critical thinking skills and the evaluation of facts and information relative to the research being conducted. A variety of people including students, doctors and psychologists use analytical research during studies to find the most relevant information. From analytical research, a person finds out critical details to add new ideas to the material being produced.

Research of any type is a method to discover information. Within analytical research articles, data and other important facts that pertain to a project is compiled; after the information is collected and evaluated, the sources are used to prove a hypothesis or support an idea. Using critical thinking skills (a method of thinking that involves identifying a claim or assumption and deciding if it is true or false) a person is able to effectively pull out small details to form greater assumptions about the material.

Some researchers conduct analytical research to find supporting evidence to current research being done in order to make the work more reliable. Other researchers conduct analytical research to form new ideas about the topic being studied. Analytical research is conducted in a variety of ways including literary research, public opinion, scientific trials and Meta-analysis.

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Analytical research methods explained with examples.

Home » Analytical Research Methods Explained with Examples

Analytical Research Techniques are fundamental tools that help researchers make sense of complex data. Imagine trying to decode insights from countless customer interactions without a systematic approach; the task would become overwhelming and inefficient. These techniques offer structured methods to analyze information, derive meaningful interpretations, and ultimately inform better decision-making in various fields.

Understanding these techniques is essential for effectively interpreting data and recognizing patterns. By employing analytical research methods, organizations can transform raw data into actionable insights. This not only fosters informed strategies but also enhances overall organizational performance. As we explore examples and applications, you'll gain insight into how these techniques can be effectively utilized in your research endeavors.

Types of Analytical Research Techniques

Analytical research techniques are essential tools for systematically gathering and interpreting data. Understanding these techniques allows researchers to derive meaningful insights and make informed decisions. Various methods exist, each serving specific purposes. For instance, qualitative techniques focus on understanding deeper motivations and attitudes, while quantitative techniques emphasize numerical data and statistical analysis.

The primary types of analytical research techniques include case studies, surveys, content analysis , and experimental research. Case studies provide in-depth investigations into specific instances, revealing complex dynamics. Surveys are effective for collecting broad data from target populations, enabling the identification of trends. Content analysis systematically evaluates existing materials, such as text or media, to uncover patterns. Experimental research, on the other hand, tests hypotheses through structured setups, providing causal insights.

By mastering these analytical research techniques, researchers can extract valuable insights that inform choices and strategies effectively. Understanding when to apply each technique is vital for optimizing research outcomes.

Quantitative Analytical Research Techniques

Quantitative analytical research techniques involve the systematic collection and analysis of numerical data to uncover patterns and draw conclusions. These methods allow researchers to quantify behaviors, opinions, and phenomena, enabling effective data-driven decision-making. Surveys and experiments are common approaches in this realm, as they allow for the collection of vast amounts of data in a structured manner.

Key techniques include descriptive statistics, which summarize data characteristics, and inferential statistics, which help make predictions or generalizations about a population based on sample data. Additionally, regression analysis can identify relationships between variables, while hypothesis testing provides a framework for validating theories. Collectively, these quantitative techniques form a robust foundation for analytical research methods, yielding actionable insights for various fields, from marketing to healthcare.

Qualitative Analytical Research Techniques

Qualitative analytical research techniques focus on understanding human behavior, emotions, and experiences. These methods gather rich, detailed data through various approaches, such as interviews, focus groups, and observations. Researchers often analyze this data to uncover patterns, themes, and insights that quantitative methods may overlook. By delving into participants' thoughts and feelings, qualitative methods offer a deeper comprehension of underlying motivations.

Several key techniques are commonly used in qualitative research . First, in-depth interviews provide personalized insights, allowing participants to share their stories and experiences openly. Second, focus groups facilitate dynamic discussions among participants, generating diverse perspectives on a topic. Finally, observational research enables researchers to witness behavior in natural settings, providing context to the data collected. Each technique plays a crucial role in shaping an understanding of the subject matter, ultimately enhancing the analytical research techniques available for interpretation and application.

Steps in Conducting Analytical Research

Conducting analytical research effectively involves a structured approach to gather and analyze data. First, define your research question. This step focuses on clarifying what you aim to uncover through research. An explicit question guides all subsequent steps by maintaining focus. Next, collect relevant data through various methods. This may include surveys, interviews, or secondary data sources, depending on the analytical research techniques you choose to utilize.

Once data is gathered, the next step is analysis. Employ statistical tools or qualitative methods to derive meaningful insights from the collected data. After analyzing, it's crucial to interpret the results. Consider how your findings relate to the initial research question. Finally, communicate your results plainly. Presenting your findings in a clear and actionable format ensures stakeholders can understand and apply the insights. Following these steps will enhance the effectiveness of your analytical research, leading to better-informed decisions.

Defining the Research Problem and Objectives

Defining a clear research problem is essential for any analytical study. It serves as the foundation upon which all elements of research are built. Initially, identifying the core issue helps researchers focus their inquiries and sets the direction for their analytical research techniques. Once the problem is articulated, specific objectives can be formulated that guide the research process and define the expected outcomes.

The objectives should align with the research problem and be measurable, allowing for a systematic approach to data collection and analysis. For instance, researchers might aim to assess user satisfaction, identify market trends, or understand consumer behavior. Establishing well-defined objectives not only clarifies the purpose of the research but also enhances the reliability of the findings. By understanding the problem and setting clear goals, researchers can utilize analytical methods more effectively, ensuring that their results generate meaningful insights.

Data Collection and Analysis Methods

Data collection and analysis methods are fundamental components of analytical research techniques. The process begins with identifying the research objectives, which guide what data needs to be collected. Researchers often employ qualitative methods like interviews or focus groups and quantitative methods such as surveys to gather valuable insights. Each method serves a different purpose, allowing researchers to explore in-depth nuances or identify broader trends.

Analysis follows data collection and typically includes coding qualitative data or employing statistical methods for quantitative data. Researchers can use various tools and techniques to extract meaningful patterns, trends, and anomalies. For instance, employing a matrix to pull specific insights from interviews can help pinpoint common pain points, as evidenced in the data trends discovered during the conversation analysis. Each step in this process is critical for achieving valid and actionable insights that inform decision-making.

Conclusion on Analytical Research Techniques

In conclusion, Analytical Research Techniques are essential for extracting valuable insights from various data sources. These techniques enable researchers to identify patterns and trends that inform decision-making processes across multiple disciplines. By employing these methods, organizations can create reports that convey pertinent findings to stakeholders effectively.

Furthermore, the application of these techniques promotes a deeper understanding of customer behavior and market dynamics. Analyzing data collaboratively improves content accuracy and enhances strategic planning. Ultimately, mastering analytical research techniques equips teams with the tools needed to navigate complex information and make informed decisions that drive success.

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Descriptive vs Analytical Research: Understanding the Difference

Descriptive research aims to accurately and systematically describe a population, situation, or phenomenon, often using quantitative data, without delving into cause-and-effect relationships. In contrast, analytical research analyses the data to understand patterns, relationships, and causal connections between variables. Let's understand the significant difference between Descriptive and Analytical Research. 

Suppose a researcher is studying the effects of online learning on student performance. A descriptive approach might involve collecting data on student grades, attendance, and engagement levels, simply presenting these facts. Conversely, an analytical approach would delve deeper to understand how and why online learning impacts student performance, analyzing the data to identify the underlying factors or causes, such as the effectiveness of virtual communication, the role of self-discipline, and the availability of resources. Through analytical research, the researcher can derive insights and understand the relationships between online learning and student performance, which can then inform educational strategies. 

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Descriptive research is a type of research method that provides a detailed description of the observed phenomenon or characteristics of a particular subject within a population. It seeks to accurately depict or “describe” behaviours, conditions, attitudes, and other variables as they naturally occur, without any manipulation by the researcher. Descriptive research gathers precise information to paint a clear picture of current affairs using various data collection methods such as surveys, interviews, observations, and case studies. Unlike inferential research, which aims to make predictions, descriptive research merely explores what exists in a given scenario. It helps to form a solid foundation for further investigative studies.

Example of Descriptive Research

A local business owner wants to understand their customer’s shopping behaviour and preferences. This is done to improve their store layout and product offerings. 

The objective is to describe the shopping behaviours and preferences of customers who visit the store.

Data Collection:

The business owner distributes surveys to customers asking about their intentions regarding product variety, store layout, pricing, and the overall shopping experience. Additionally, they may have suggestion boxes for customers to provide feedback on what they like or dislike about the store.

Observation:

The owner also observes customer behaviours—navigating the store, which sections they spend the most time in, and the products they purchase.

The collected data is analyzed to identify customer preferences and patterns. For example, they might find that customers prefer a wider variety of products or need clarification on the store layout.

A report is generated summarizing the findings:

• average time spent in the store
• popular product categories
• feedback on store layout
• suggestions for improvement

The business owner understands customers’ shopping behaviours and preferences through this descriptive research. This information is valuable as it helps the owner make informed decisions on improving the store layout, which products to stock, and how to enhance the overall customer experience. Unlike inferential research, the aim here is not to make predictions but to provide a detailed description of the current situation, which can inform future business decisions.

Analytical research dives deeper than descriptive research, aiming to understand, interpret, or explain a phenomenon or situation. It involves thoroughly examining elements or structures of the subject under study, often employing statistical, mathematical, or computational techniques. Unlike descriptive research, which merely observes and records, analytical research seeks to understand underlying relationships, causality, and outcomes. It poses a hypothesis and seeks to answer “why” and “how” questions. Analytical research requires critical thinking, rigorous methodology, and a clear framework to draw meaningful conclusions or insights. It makes it integral in science, business, economics, and social studies to inform decision-making and policy.

Example of Analytical Research

A medium-sized eCommerce company wants to improve its sales, so it conducts analytical research. 

Objective: 

To analyze and understand the factors influencing customer purchase behaviour impacting sales and 

Hypothesis:

The hypothesis might be that website usability, product variety, and customer reviews significantly influence purchasing behaviour.

Collect data on website usability metrics, product variety, customer reviews, and sales figures over a specified period.

Employ statistical analysis to examine the relationships between these factors and sales. For instance, using regression analysis to determine how significantly each element affects sales.

Interpretation:

Interpret the results to understand how and to what extent each factor influences customer purchasing behaviour and sales.

Conclusion:

Conclude whether the analysis supports or refutes the hypothesis and provide

• recommendations for improving website usability,
• expanding product variety or
• enhancing the customer review system to increase sales.

In this analytical research example, the e-commerce company goes beyond merely describing the existing situation (as in descriptive research). It delves into understanding the underlying relationships between various factors and sales, aiming to draw actionable insights to improve business performance.

Descriptive Research vs Analytical Research: Key Pointers

• Descriptive research focuses on "what" is happening, whereas analytical research explores "why" it happens.
• Descriptive employs observation and surveys; analytical uses statistical, mathematical, or computational techniques.
• Descriptive aims to identify patterns or trends, while analytical aims to establish causation.
• Descriptive research is often qualitative, whereas analytical can be both qualitative and quantitative.
• Descriptive research results in specific findings, whereas analytical research leads to general conclusions.
• Descriptive is simpler and more straightforward, while analytical is more complex and involves deeper analysis.

Top FAQs on Descriptive vs Analytical Research

What is the main difference between descriptive and analytical research?

The main difference lies in their objectives. Descriptive research aims to accurately describe characteristics of a population or phenomenon, focusing on "what" is occurring. Analytical research, on the other hand, seeks to understand "why" and "how" a phenomenon occurs, often involving a deeper examination of relationships, causes, and effects.

Can descriptive research include statistical analysis?

Yes, descriptive research can include statistical analysis to summarize and describe data. However, the analysis is used to describe the current state of the data rather than to investigate relationships or causality, which is more in the realm of analytical research.

Is analytical research better than descriptive research?

Neither is inherently better; their appropriateness depends on the research question. Analytical research is suited for understanding the reasons behind a phenomenon, making predictions, and establishing causal relationships. Descriptive research is ideal for providing an accurate snapshot of events, conditions, or attitudes at a specific point in time.

How do the methodologies differ between descriptive and analytical research?

Descriptive research often uses methods like surveys, observations, and case studies to collect data about the phenomenon of interest. Analytical research employs more complex methodologies, including experimental designs, longitudinal studies, and statistical analysis, to explore underlying patterns, relationships, or causal links.

Can a research study be both descriptive and analytical?

Yes, a study can incorporate both descriptive and analytical elements. Initially, it might use descriptive methods to outline the state of the research subject, followed by analytical techniques to delve into relationships and causations within the data. This approach allows for a comprehensive understanding of the research topic.

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What are Analytical Study Designs?

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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.

When are analytical study designs used?

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.

Descriptive vs. analytical studies

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.

Experimental and observational

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

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.

Randomized control trials

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.

Advantages of RCTs

• It is possible to infer causality
• Everything is properly controlled, so very little is left to chance or bias
• Can be certain that any difference is coming from the intervention

Disadvantages of RCTs

• Expensive and can be time-consuming
• Can take years for results to be available
• Cannot be done for certain types of questions due to ethical reasons, such as asking participants to undergo harmful treatment
• Limited in how many participants researchers can adequately manage in one study or trial
• Not feasible for people to live under controlled conditions for a long time

Nonrandomized controlled trials

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.

Advantages of NRS's

• Good option when randomized control trials are not feasible
• More flexible than RCTs

Disadvantages of NRS's

• Can't be sure if the groups have underlying differences
• Introduces risk of bias and confounds

Crossover study

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.

Advantages of crossover studies

• Each participant serves as their own control, reducing confounding variables
• Require fewer participants, so they have better statistical power

Disadvantages of crossover studies

• Susceptible to order effects, meaning the order in which a treatment was given may have an effect
• Carry-over effects between treatments

Observational studies

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.

Case-control study

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.

Advantages of case-control studies

• Feasible for rare diseases
• Cheaper and easier to do than an RCT

Disadvantages of case-control studies

• Relies on patient records, which could be lost or damaged
• Potential recall and selection bias

Cohort study (longitudinal)

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.

Advantages of cohort studies

• Ethically safe
• Allows you to study multiple outcome variables
• Establish trends and patterns

Disadvantages of cohort studies

• Time consuming and expensive
• Can take many years for results to be revealed
• Too many variables to manage
• Depending on length of study, can have many changes in research personnel

Cross-sectional study

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.

Advantages of cross-sectional studies

• Fast and inexpensive
• Provides a great deal of information for a given time point
• Leaves room for secondary analysis

Disadvantages of cross-sectional studies

• Requires a large sample to be accurate
• Not clear how long results remain true for
• Do not provide information on causality
• Cannot be used to establish long-term trends because data is only for a given time

So, how about your next study?

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Study designs: Part 3 - Analytical observational studies

Priya ranganathan, rakesh aggarwal.

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Address for correspondence: Dr. Priya Ranganathan, Department of Anaesthesiology, Tata Memorial Centre, Ernest Borges Road, Parel, Mumbai - 400 012, Maharashtra, India. E-mail: [email protected]

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

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.

Keywords: Case–control study, cohort study, epidemiologic methods

INTRODUCTION

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.

COHORT STUDIES

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.

Advantages of cohort studies

For an exposure to be causative, it must precede the outcome. In a cohort study, one starts with subjects who are known to have or not have the exposure and are free of the outcome at the start of the study, and the outcome develops later. Hence, one is certain that the exposure preceded the outcome, and temporality (and therefore probable causality) can be established. In the above example, one can be certain that the maternal vitamin D deficiency preceded the bone abnormalities.

For a given exposure, more than one outcome can be studied. In the above example, the authors compared not only bone growth but also the age at which the babies born to low and high vitamin D mothers started walking independently.

In cohort studies, often several exposures can be studied simultaneously. For this, the investigators begin by assessing several 'exposures', for example, age, sex, smoking status, diabetes, and obesity/overweight status in every member of a population. The entire population is then followed for the outcome of interest, for example, coronary artery disease. At the end of the follow-up, the data can then be analyzed for several contrasting cohorts defined by levels of each “exposure” – old/young, male/female, smoker/nonsmoker, diabetic/nondiabetic, and underweight/ideal body weight/overweight/obese, etc.

Limitations of cohort studies

Cohort studies often require a long duration of follow-up to determine whether outcome will occur or not. This duration depends on the exposure-outcome pair. In the above example, a follow-up of at least 14 months was used. An even longer follow-up over several years or decades may be necessary – for instance, in the above example, if the investigators wanted to study whether maternal vitamin D levels influence the final height of a person, they would have needed to follow the babies till adolescence. During such follow-up, losses to follow-up, and logistic and cost issues pose major challenges.

It is not uncommon for one or more unknown confounding factors to affect the occurrence of outcome. For example, in a cohort study looking at coffee drinking as a risk factor for pancreatic cancer, people who drink a large amount of coffee may also be consuming alcohol. In such cases, the finding that coffee drinkers have an increased occurrence of pancreatic cancer may lead the investigator to incorrectly conclude that drinking coffee increases the risk of pancreatic cancer, whereas it is the consumption of alcohol which is the true risk factor. Similarly, in the above study, the mothers with low and high vitamin D levels could have been different in another factor, e.g. overall nutrition or socioeconomic status, and that could be the real reason for the differences in the babies' bone health.

Uses of cohort studies

Since cohort study design closely resembles the experimental design with the only difference being lack of random assignment to exposure, it is considered as having a greater validity compared to the other observational study designs.

Since one starts with subjects known to have or not have exposure, one can determine the risk of outcome among exposed persons and unexposed persons, as also the relative risk.

In situations where experimental studies are not feasible (e.g., when it is either unethical to randomize participants to a potentially harmful intervention, such as smoking, or impractical to create an exposure, such as diabetes or hypertension), cohort studies are a reasonable and arguably the best alternative.

Variations of cohort studies

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.

CASE-CONTROL STUDIES

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.

Advantages of case–control studies

Case-control studies are often cheap, and less time-consuming than cohort studies.

Once cases and controls are identified and enrolled, it is often easy to study the relationship of outcome with not one but several exposures.

Limitations of case–control studies

In case-control studies, temporality (whether the outcome or exposure occurred first) is often difficult to establish.

There may be a bias in selecting cases or controls. For instance, if the cases studied differ from the entire pool of cases of a disease in an important characteristic, then the results of the study may apply only to the selected type of cases and not to the entire population of cases. In the above example,[ 3 ] the cases and controls were derived from different sources, and it is possible that the children that attended the hospital fracture clinic had different socioeconomic backgrounds to those attending the primary care facility from where controls were enrolled.

Confounding factors, as discussed in cohort studies, also apply to case-control studies. For instance, the children with fractures and controls could have had different overall food intake, milk intake, and outdoor play time. These factors could influence both the likelihood of prior use of vitamin D supplements (exposure) and the risk of fracture (outcome), affecting the measurement of their association.

The determination of exposure relies on existing records or history taking. Either can be problematic. The records may not contain information on exposure or contain erroneous data (e.g., those collected perfunctorily). This is particularly challenging if the missing or unreliable data are more likely to be present in one of the two groups being compared – cases or controls (misinformation bias). During history taking, cases may be more likely to recall exposure than controls (recall bias), for example, the mother of a child with a congenital anomaly is more likely to recall drugs ingested during pregnancy than a mother with a normal child. In the study by Anderson et al,[ 3 ] the mothers of children with fractures could have underestimated the amount of vitamin D their children have received, believing that this was the reason for the occurrence of fracture.

Finally, since case–control studies are backward-directed, there is no “at risk” group at the start of the study; therefore, the determination of “risk” (and relative risk or risk ratio) is not possible, and one can only estimate “odds” (and odds ratio). For a detailed discussion on this, please refer to a previous article.[ 4 ]

Uses of case–control studies

Case-control studies are ideal for rare diseases, where identifying cases is easier than following up large numbers of exposed persons to determine outcome.

Case-control studies, because of their simplicity and need for fewer resources, are often the initial study design used to assess the relationship of a particular exposure and an outcome. If this study is positive, then a study with more complex and robust study design (cohort or interventional) can be undertaken.

A special variation of case–control study design

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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There are no conflicts of interest.

• 1. Ranganathan P, Aggarwal R. Study designs: Part 1 – An overview and classification. Perspect Clin Res. 2018;9:184–6. doi: 10.4103/picr.PICR_124_18. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 2. Viljakainen HT, Korhonen T, Hytinantti T, Laitinen EK, Andersson S, Mäkitie O, et al. Maternal vitamin D status affects bone growth in early childhood – A prospective cohort study. Osteoporos Int. 2011;22:883–91. doi: 10.1007/s00198-010-1499-4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 3. Anderson LN, Heong SW, Chen Y, Thorpe KE, Adeli K, Howard A, et al. Vitamin D and fracture risk in early childhood: A case-control study. Am J Epidemiol. 2017;185:1255–62. doi: 10.1093/aje/kww204. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 4. Ranganathan P, Aggarwal R, Pramesh CS. Common pitfalls in statistical analysis: Odds versus risk. Perspect Clin Res. 2015;6:222–4. doi: 10.4103/2229-3485.167092. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
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