experimental study research definition

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10 Experimental research

Experimental research—often considered to be the ‘gold standard’ in research designs—is one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.

Experimental research is best suited for explanatory research—rather than for descriptive or exploratory research—where the goal of the study is to examine cause-effect relationships. It also works well for research that involves a relatively limited and well-defined set of independent variables that can either be manipulated or controlled. Experimental research can be conducted in laboratory or field settings. Laboratory experiments , conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalisability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments are conducted in field settings such as in a real organisation, and are high in both internal and external validity. But such experiments are relatively rare, because of the difficulties associated with manipulating treatments and controlling for extraneous effects in a field setting.

Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs. Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not. Sometimes, we also refer to non-experimental research, which is not really a research design, but an all-inclusive term that includes all types of research that do not employ treatment manipulation or random assignment, such as survey research, observational research, and correlational studies.

Basic concepts

Treatment and control groups. In experimental research, some subjects are administered one or more experimental stimulus called a treatment (the treatment group ) while other subjects are not given such a stimulus (the control group ). The treatment may be considered successful if subjects in the treatment group rate more favourably on outcome variables than control group subjects. Multiple levels of experimental stimulus may be administered, in which case, there may be more than one treatment group. For example, in order to test the effects of a new drug intended to treat a certain medical condition like dementia, if a sample of dementia patients is randomly divided into three groups, with the first group receiving a high dosage of the drug, the second group receiving a low dosage, and the third group receiving a placebo such as a sugar pill (control group), then the first two groups are experimental groups and the third group is a control group. After administering the drug for a period of time, if the condition of the experimental group subjects improved significantly more than the control group subjects, we can say that the drug is effective. We can also compare the conditions of the high and low dosage experimental groups to determine if the high dose is more effective than the low dose.

Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods. Treatment manipulation helps control for the ‘cause’ in cause-effect relationships. Naturally, the validity of experimental research depends on how well the treatment was manipulated. Treatment manipulation must be checked using pretests and pilot tests prior to the experimental study. Any measurements conducted before the treatment is administered are called pretest measures , while those conducted after the treatment are posttest measures .

Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame. This approach is typically employed in survey research, and ensures that each unit in the population has a positive chance of being selected into the sample. Random assignment, however, is a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group prior to treatment administration. Random selection is related to sampling, and is therefore more closely related to the external validity (generalisability) of findings. However, random assignment is related to design, and is therefore most related to internal validity. It is possible to have both random selection and random assignment in well-designed experimental research, but quasi-experimental research involves neither random selection nor random assignment.

Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats. Some of these threats to internal validity are described below, within the context of a study of the impact of a special remedial math tutoring program for improving the math abilities of high school students.

History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment. For instance, students’ post-remedial math score improvement may have been caused by their preparation for a math exam at their school, rather than the remedial math program.

Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects (e.g., a general improvement in their intellectual ability to understand complex concepts) rather than the experimental treatment.

Testing threat is a threat in pre-post designs where subjects’ posttest responses are conditioned by their pretest responses. For instance, if students remember their answers from the pretest evaluation, they may tend to repeat them in the posttest exam.

Not conducting a pretest can help avoid this threat.

Instrumentation threat , which also occurs in pre-post designs, refers to the possibility that the difference between pretest and posttest scores is not due to the remedial math program, but due to changes in the administered test, such as the posttest having a higher or lower degree of difficulty than the pretest.

Mortality threat refers to the possibility that subjects may be dropping out of the study at differential rates between the treatment and control groups due to a systematic reason, such that the dropouts were mostly students who scored low on the pretest. If the low-performing students drop out, the results of the posttest will be artificially inflated by the preponderance of high-performing students.

Regression threat —also called a regression to the mean—refers to the statistical tendency of a group’s overall performance to regress toward the mean during a posttest rather than in the anticipated direction. For instance, if subjects scored high on a pretest, they will have a tendency to score lower on the posttest (closer to the mean) because their high scores (away from the mean) during the pretest were possibly a statistical aberration. This problem tends to be more prevalent in non-random samples and when the two measures are imperfectly correlated.

Two-group experimental designs

Pretest-posttest control group design . In this design, subjects are randomly assigned to treatment and control groups, subjected to an initial (pretest) measurement of the dependent variables of interest, the treatment group is administered a treatment (representing the independent variable of interest), and the dependent variables measured again (posttest). The notation of this design is shown in Figure 10.1.

Statistical analysis of this design involves a simple analysis of variance (ANOVA) between the treatment and control groups. The pretest-posttest design handles several threats to internal validity, such as maturation, testing, and regression, since these threats can be expected to influence both treatment and control groups in a similar (random) manner. The selection threat is controlled via random assignment. However, additional threats to internal validity may exist. For instance, mortality can be a problem if there are differential dropout rates between the two groups, and the pretest measurement may bias the posttest measurement—especially if the pretest introduces unusual topics or content.

Posttest -only control group design . This design is a simpler version of the pretest-posttest design where pretest measurements are omitted. The design notation is shown in Figure 10.2.

The treatment effect is measured simply as the difference in the posttest scores between the two groups:

The appropriate statistical analysis of this design is also a two-group analysis of variance (ANOVA). The simplicity of this design makes it more attractive than the pretest-posttest design in terms of internal validity. This design controls for maturation, testing, regression, selection, and pretest-posttest interaction, though the mortality threat may continue to exist.

Because the pretest measure is not a measurement of the dependent variable, but rather a covariate, the treatment effect is measured as the difference in the posttest scores between the treatment and control groups as:

Due to the presence of covariates, the right statistical analysis of this design is a two-group analysis of covariance (ANCOVA). This design has all the advantages of posttest-only design, but with internal validity due to the controlling of covariates. Covariance designs can also be extended to pretest-posttest control group design.

Factorial designs

Two-group designs are inadequate if your research requires manipulation of two or more independent variables (treatments). In such cases, you would need four or higher-group designs. Such designs, quite popular in experimental research, are commonly called factorial designs. Each independent variable in this design is called a factor , and each subdivision of a factor is called a level . Factorial designs enable the researcher to examine not only the individual effect of each treatment on the dependent variables (called main effects), but also their joint effect (called interaction effects).

In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for three hours/week of instructional time than for one and a half hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.

Hybrid experimental designs

Hybrid designs are those that are formed by combining features of more established designs. Three such hybrid designs are randomised bocks design, Solomon four-group design, and switched replications design.

Randomised block design. This is a variation of the posttest-only or pretest-posttest control group design where the subject population can be grouped into relatively homogeneous subgroups (called blocks ) within which the experiment is replicated. For instance, if you want to replicate the same posttest-only design among university students and full-time working professionals (two homogeneous blocks), subjects in both blocks are randomly split between the treatment group (receiving the same treatment) and the control group (see Figure 10.5). The purpose of this design is to reduce the ‘noise’ or variance in data that may be attributable to differences between the blocks so that the actual effect of interest can be detected more accurately.

Solomon four-group design . In this design, the sample is divided into two treatment groups and two control groups. One treatment group and one control group receive the pretest, and the other two groups do not. This design represents a combination of posttest-only and pretest-posttest control group design, and is intended to test for the potential biasing effect of pretest measurement on posttest measures that tends to occur in pretest-posttest designs, but not in posttest-only designs. The design notation is shown in Figure 10.6.

Switched replication design . This is a two-group design implemented in two phases with three waves of measurement. The treatment group in the first phase serves as the control group in the second phase, and the control group in the first phase becomes the treatment group in the second phase, as illustrated in Figure 10.7. In other words, the original design is repeated or replicated temporally with treatment/control roles switched between the two groups. By the end of the study, all participants will have received the treatment either during the first or the second phase. This design is most feasible in organisational contexts where organisational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.

Quasi-experimental designs

Quasi-experimental designs are almost identical to true experimental designs, but lacking one key ingredient: random assignment. For instance, one entire class section or one organisation is used as the treatment group, while another section of the same class or a different organisation in the same industry is used as the control group. This lack of random assignment potentially results in groups that are non-equivalent, such as one group possessing greater mastery of certain content than the other group, say by virtue of having a better teacher in a previous semester, which introduces the possibility of selection bias . Quasi-experimental designs are therefore inferior to true experimental designs in interval validity due to the presence of a variety of selection related threats such as selection-maturation threat (the treatment and control groups maturing at different rates), selection-history threat (the treatment and control groups being differentially impacted by extraneous or historical events), selection-regression threat (the treatment and control groups regressing toward the mean between pretest and posttest at different rates), selection-instrumentation threat (the treatment and control groups responding differently to the measurement), selection-testing (the treatment and control groups responding differently to the pretest), and selection-mortality (the treatment and control groups demonstrating differential dropout rates). Given these selection threats, it is generally preferable to avoid quasi-experimental designs to the greatest extent possible.

In addition, there are quite a few unique non-equivalent designs without corresponding true experimental design cousins. Some of the more useful of these designs are discussed next.

Regression discontinuity (RD) design . This is a non-equivalent pretest-posttest design where subjects are assigned to the treatment or control group based on a cut-off score on a preprogram measure. For instance, patients who are severely ill may be assigned to a treatment group to test the efficacy of a new drug or treatment protocol and those who are mildly ill are assigned to the control group. In another example, students who are lagging behind on standardised test scores may be selected for a remedial curriculum program intended to improve their performance, while those who score high on such tests are not selected from the remedial program.

Because of the use of a cut-off score, it is possible that the observed results may be a function of the cut-off score rather than the treatment, which introduces a new threat to internal validity. However, using the cut-off score also ensures that limited or costly resources are distributed to people who need them the most, rather than randomly across a population, while simultaneously allowing a quasi-experimental treatment. The control group scores in the RD design do not serve as a benchmark for comparing treatment group scores, given the systematic non-equivalence between the two groups. Rather, if there is no discontinuity between pretest and posttest scores in the control group, but such a discontinuity persists in the treatment group, then this discontinuity is viewed as evidence of the treatment effect.

Proxy pretest design . This design, shown in Figure 10.11, looks very similar to the standard NEGD (pretest-posttest) design, with one critical difference: the pretest score is collected after the treatment is administered. A typical application of this design is when a researcher is brought in to test the efficacy of a program (e.g., an educational program) after the program has already started and pretest data is not available. Under such circumstances, the best option for the researcher is often to use a different prerecorded measure, such as students’ grade point average before the start of the program, as a proxy for pretest data. A variation of the proxy pretest design is to use subjects’ posttest recollection of pretest data, which may be subject to recall bias, but nevertheless may provide a measure of perceived gain or change in the dependent variable.

Separate pretest-posttest samples design . This design is useful if it is not possible to collect pretest and posttest data from the same subjects for some reason. As shown in Figure 10.12, there are four groups in this design, but two groups come from a single non-equivalent group, while the other two groups come from a different non-equivalent group. For instance, say you want to test customer satisfaction with a new online service that is implemented in one city but not in another. In this case, customers in the first city serve as the treatment group and those in the second city constitute the control group. If it is not possible to obtain pretest and posttest measures from the same customers, you can measure customer satisfaction at one point in time, implement the new service program, and measure customer satisfaction (with a different set of customers) after the program is implemented. Customer satisfaction is also measured in the control group at the same times as in the treatment group, but without the new program implementation. The design is not particularly strong, because you cannot examine the changes in any specific customer’s satisfaction score before and after the implementation, but you can only examine average customer satisfaction scores. Despite the lower internal validity, this design may still be a useful way of collecting quasi-experimental data when pretest and posttest data is not available from the same subjects.

An interesting variation of the NEDV design is a pattern-matching NEDV design , which employs multiple outcome variables and a theory that explains how much each variable will be affected by the treatment. The researcher can then examine if the theoretical prediction is matched in actual observations. This pattern-matching technique—based on the degree of correspondence between theoretical and observed patterns—is a powerful way of alleviating internal validity concerns in the original NEDV design.

Perils of experimental research

Experimental research is one of the most difficult of research designs, and should not be taken lightly. This type of research is often best with a multitude of methodological problems. First, though experimental research requires theories for framing hypotheses for testing, much of current experimental research is atheoretical. Without theories, the hypotheses being tested tend to be ad hoc, possibly illogical, and meaningless. Second, many of the measurement instruments used in experimental research are not tested for reliability and validity, and are incomparable across studies. Consequently, results generated using such instruments are also incomparable. Third, often experimental research uses inappropriate research designs, such as irrelevant dependent variables, no interaction effects, no experimental controls, and non-equivalent stimulus across treatment groups. Findings from such studies tend to lack internal validity and are highly suspect. Fourth, the treatments (tasks) used in experimental research may be diverse, incomparable, and inconsistent across studies, and sometimes inappropriate for the subject population. For instance, undergraduate student subjects are often asked to pretend that they are marketing managers and asked to perform a complex budget allocation task in which they have no experience or expertise. The use of such inappropriate tasks, introduces new threats to internal validity (i.e., subject’s performance may be an artefact of the content or difficulty of the task setting), generates findings that are non-interpretable and meaningless, and makes integration of findings across studies impossible.

The design of proper experimental treatments is a very important task in experimental design, because the treatment is the raison d’etre of the experimental method, and must never be rushed or neglected. To design an adequate and appropriate task, researchers should use prevalidated tasks if available, conduct treatment manipulation checks to check for the adequacy of such tasks (by debriefing subjects after performing the assigned task), conduct pilot tests (repeatedly, if necessary), and if in doubt, use tasks that are simple and familiar for the respondent sample rather than tasks that are complex or unfamiliar.

In summary, this chapter introduced key concepts in the experimental design research method and introduced a variety of true experimental and quasi-experimental designs. Although these designs vary widely in internal validity, designs with less internal validity should not be overlooked and may sometimes be useful under specific circumstances and empirical contingencies.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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What is Experimental Research: Definition, Types & Examples

Understand how experimental research enables researchers to confidently identify causal relationships between variables and validate findings, enhancing credibility.

June 16, 2024

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This blog covers the essentials of experimental research and its  application in businesses. It emphasizes the value of controlled and  systematic research design for establishing cause-and-effect relationships,  managing variables, and generating reliable results. The blog explains the  types of experimental research, including pre-experimental, true  experimental, and quasi-experimental designs, each with distinct structures  that fit different research needs.    The importance of experimental research is outlined through examples such  as product testing, marketing optimization, pricing strategies, and customer  experience enhancement. These examples show how businesses can use  experimental research to test hypotheses, improve products, and make informed  decisions. Despite its benefits, the blog also highlights the disadvantages,  like ethical concerns, artificial settings, high costs, and participant  biases.    Finally, the blog suggests leveraging tools like Entropik's Decode for AI-driven  market research, which allows businesses to experiment and analyze consumer  behavior for better decision-making. The post concludes with a brief FAQ  section, answering common questions about experimental research, including  examples and its application in various fields like medicine, psychology, and  education.

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Experimental research is crucial for companies because it allows them to precisely control and measure key factors, identify dependent and independent elements, and set conditions to observe their effects. By changing one variable systematically, it is possible to determine possible cause-and-effect relations and analyze how specific observed effects depend on them. 

Read this blog to learn more about how experimental research design can drive business success and provide practical examples of its application in various industries.

What is Experimental Research?

Experimental research is a systematic and scientific approach in which the researcher manipulates one or more independent variables and observes the effect on a dependent variable while controlling for extraneous variables. This method allows for the establishment of cause-and-effect relationships between variables. 

Experimental research involves using control groups, random assignment, and standardized procedures to ensure the reliability and validity of the results. It is commonly used in psychology, medicine, and the social sciences to test hypotheses and theories under controlled conditions.

Example of Experimental Research

An experimental research example scenario can be a clinical trial for a new medication. This scenario aims to determine whether the new type of drug applies to the patient. Accordingly, patients with hypertension diagnosed by a medical practitioner are randomly assigned to two groups. 

The experimental group is subjected to the new medication the research treatment facility delivers. In contrast, the control group is treated with either a placebo or the medical drugs previously used by the patients. The data will be both quantitative and qualitative . 

Quantitative data will include blood pressure levels or symptom severity scores. Qualitative data will include symptoms reported by the patient or symptoms observed by the practitioner and side effects experienced by the patients. Consequently, the research on which type of drug is effective is tested, and results are obtained by comparing the patient's conditions in the two groups.

Researchers believe a new medication works if the experimental group shows significant symptom improvements compared to a control group and has no immediate side effects. Testing many patients increases confidence that the effects are due to the medication and not a placebo effect.

What Are The Different Types of Experimental Research?

The following are the different types of experimental research: Pre-Experimental Research

• One-Shot Case Study: A single group is exposed to a treatment and then observed for outcomes. There is no control group for comparison.
• One-Group Pretest-Posttest Design: A single group is measured before and after treatment to observe changes.

True Experimental Research

• Randomized Controlled Trials (RCT): Participants are randomly assigned to experimental and control groups to ensure comparability and reduce bias. This design is considered the gold standard in experimental research.
• Pretest-Posttest Control Group Design: Both the experimental and control groups are measured before and after the treatment. The experimental group receives the treatment, while the control group does not.
• Posttest-Only Control Group Design: Participants are randomly assigned to experimental and control groups, but measurements are taken only after the treatment is administered to the experimental group.

Quasi-Experimental Research

• Non-Equivalent Groups Design: Similar to the pretest-posttest control group design, participants are not randomly assigned to groups. This design is often used when random assignment is not feasible.
• Interrupted Time Series Design: Multiple measurements are taken before and after a treatment to observe changes over time. This design helps control time-related variables.
• Matched Groups Design: Participants are matched based on certain characteristics before being assigned to experimental and control groups, ensuring comparable groups.

Factorial Design

• Full Factorial Design: Involves manipulating two or more independent variables simultaneously to observe their interaction effects on the dependent variable. All possible combinations of the independent variables are tested.
• Fractional Factorial Design: A subset of the possible combinations of independent variables is tested, making it more practical when dealing with many variables.

What is the Importance of Experimental Research?

Establishing Causality

Experimental research is essential for establishing correlations between variables of interest and demonstrating causality. It allows researchers to manipulate one or more independent variables considered the cause and record changes in the dependent variable, the effect.

Controlling Variables

One of the strengths of this type of research is that it allows for controlling the effect of extraneous variables. This means that experimental research reduces alternative explanations of effects. Using control groups and random assignment to conditions, the experimental method can accurately determine whether the observed group differences resulted from manipulating the independent variable or other factors.

Providing Reliable and Valid Results

A structured and rigorous methodology while conducting experimental research minimizes the possibility of measurement errors and biases. In addition, randomized controlled trials are generally accepted as the gold standard in research. Because of these features, the data’s reliability can be confirmed in advance by similar findings, and the results will also be more replicable and generalizable to the broader population.

Informing Decision-Making

Experimental research provides empirical evidence and data to support important organizational decisions, such as product testing and experimentation, marketing strategies, or improving operational processes and activities.

Driving Innovation

Experimental research drives innovation by systematically testing new ideas and interventions. It allows companies and researchers to experiment with novel concepts in a controlled environment, identify successful innovations, and confidently scale them up.

What Are The Disadvantages of Experimental Research?

Ethical concerns.

Experimental research implies ethical dilemmas, especially when human subjects are concerned. Generally speaking, ethical principles prohibit manipulating variables, specifically intentionally causing harm to, offending, and inducing psychological or physical pressure. Ethics guidelines and review boards are expected to curb risks in an experiment, but they could also somewhat restrain findings. 

Artificial Settings

Most experimental studies are conducted in highly controlled artificial conditions, such as laboratories, where external variables are properly controlled and isolated. Thus, the conclusions of the findings might only sometimes be extended to the real world, so they will only sometimes be applicable. The main type of validity under which this problem falls is external validity. Some variables cannot be controlled or do not appear in artificial conditions. 

High Costs and Time Consumption

Experimental research is expensive and time-consuming. There are various reasons for this statement. First, such a type of research requires specialized equipment, controlled conditions of measurement, and large sample sizes, which means increased costs. Second, designing an experiment, preparing all the necessary information and tools for its implementation, running it, and analyzing the data received is usually time-consuming, even in the simplest cases. 

Practical and Logistical Constraints

Some variables or phenomena cannot be either manipulated or controlled. Experimental studies are impractical if processes are complex, large-scale, or long-term. For example, a lab cannot treat anything related to the environment or societal changes. Therefore, due to the inability to conduct experiments based on such phenomena, some questions can only be studied by other experimental research methods, such as observational or correlational.

Participant Behavior and Bias

Experimental studies may be biased based on the participants’ awareness of being observed during the process. Also called the Hawthorne effect, another issue that can hurt the study’s validity, especially in medical research, is using control groups. Although they are necessary to measure the efficiency of a certain treatment, such research may involve not providing some groups with potentially beneficial treatment. 

These two problems may affect the results and make them unethical. In either case, corrective steps should be taken to address this issue and ensure that the results have been obtained properly.

How Businesses Can Leverage Experimental Research?

Product development and testing.

Businesses can use experimental research to test new products or features before launching them. By creating controlled experiments, such as A/B testing , companies can compare different versions of a product to see which one performs better in terms of customer satisfaction, usability, and sales. This approach allows businesses to refine their products based on empirical evidence, reducing the risk of failure upon release.

Marketing Strategy Optimization

Experimental research is invaluable for optimizing marketing strategies. Businesses can test different marketing messages, channels, and tactics to determine which are most effective in engaging their target audience and driving conversions. For example, they can conduct randomized controlled trials to compare the impact of various advertising campaigns on consumer behavior , enabling data-driven decisions that enhance marketing ROI.

Customer Experience Enhancement

Customer experience is increasingly more critical for retention and loyalty. Companies use experimental research to determine the best practices for customer service, website design, and in-store experience. Through experimenting and measuring responses, companies can identify what promotes satisfaction and loyalty and apply these results to enhance customer experience.

Pricing Strategies

Experimental research helps businesses determine optimal pricing strategies. Companies can analyze consumer reactions and willingness to pay by testing different price points in controlled settings. This approach enables businesses to find the price that maximizes revenue without deterring customers, balancing profitability with market competitiveness.

Operational Efficiency

Businesses can use experimental research to enhance operational efficiency. For instance, they can test various processes, workflows, or technologies to identify which ones improve productivity, reduce costs, or enhance quality. Companies can implement the most effective strategies and practices by systematically experimenting with different operational changes, leading to better overall performance.

Final Words

Experimental research has become a powerful instrument for modern business development. It systematically tests assumptions and variables associated with various activities, from product development, marketing strategies, and customer experiences to pricing and operational efficiencies.

Get your hands on Decode , an AI-powered market research tool that can help you test hypotheses about consumer behavior and preferences. Companies can determine cause-and-effect relationships by manipulating specific variables, such as pricing or advertising methods, and observing the effects on consumer responses using Decode diary studies . 

This research method collects qualitative data on user behaviors, activities, and experiences over time. This helps them make informed decisions about product development, marketing strategies, and overall business operations.

Frequently Asked Questions (FAQs)

Question 1: what are examples of experimental research.

Answer: Examples of experimental research include drug trials, psychology experiments, and studies testing new teaching methods. These experiments involve manipulating variables and comparing outcomes to establish causal relationships.

Question 2: What is the meaning of experimental design in research?

Answer: Experimental design in research refers to the methodical planning of experiments to control variables, minimize bias, and draw valid conclusions. It involves carefully considering factors like sample size, randomization, and control groups.

Question 3: What are the characteristics of experimental research?

Answer: Characteristics of experimental research include manipulation of variables, random assignment, control groups, and measurement of outcomes. These features ensure that researchers can isolate the effects of specific variables and draw reliable conclusions.

Question 4: Where is experimental research used?

Answer: Experimental research is used in medicine, psychology, education, and natural sciences to investigate cause-and-effect relationships and validate hypotheses. It provides a systematic approach to testing theories and informing evidence-based practices.

Frequently Asked Questions

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Soham is a true Manchester United fan who finds joy in more than just football. Whether navigating the open road, scoring virtual goals in FIFA, reading novels, or enjoying quality time with friends, Soham embraces a life full of diverse passions.

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A Guide to Website Footers: Best Design Practices & Examples

Explore the importance of website footers, design best practices, and how to optimize them using UX research for enhanced user engagement and navigation.

Customer Effort Score: Definition, Examples, Tips

A great customer score can lead to dedicated, engaged customers who can end up being loyal advocates of your brand. Here's what you need to know about it.

How to Detect and Address User Pain Points for Better Engagement

Understanding user pain points can help you provide a seamless user experiences that makes your users come back for more. Here's what you need to know about it.

What is Quota Sampling? Definition, Types, Examples, and How to Use It?

Discover Quota Sampling: Learn its process, types, and benefits for accurate consumer insights and informed marketing decisions. Perfect for researchers and brand marketers!

What Is Accessibility Testing? A Comprehensive Guide

Ensure inclusivity and compliance with accessibility standards through thorough testing. Improve user experience and mitigate legal risks. Learn more.

Maximizing Your Research Efficiency with AI Transcriptions

Explore how AI transcription can transform your market research by delivering precise and rapid insights from audio and video recordings.

Understanding the False Consensus Effect: How to Manage it

The false consensus effect can cause incorrect assumptions and ultimately, the wrong conclusions. Here's how you can overcome it.

5 Banking Customer Experience Trends to Watch Out for in 2024

Discover the top 5 banking customer experience trends to watch out for in 2024. Stay ahead in the evolving financial landscape.

The Ultimate Guide to Regression Analysis: Definition, Types, Usage & Advantages

Master Regression Analysis: Learn types, uses & benefits in consumer research for precise insights & strategic decisions.

EyeQuant Alternative

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EyeSee Alternative

Embrace the Insights AI revolution: Meet Decode, your innovative solution for consumer insights, offering a compelling alternative to EyeSee.

Skeuomorphism in UX Design: Is It Dead?

Skeuomorphism in UX design creates intuitive interfaces using familiar real-world visuals to help users easily understand digital products. Do you know how?

Top 6 Wireframe Tools and Ways to Test Your Designs

Wireframe tools assist designers in planning and visualizing the layout of their websites. Look through this list of wireframing tools to find the one that suits you best.

Revolutionizing Customer Interaction: The Power of Conversational AI

Conversational AI enhances customer service across various industries, offering intelligent, context-aware interactions that drive efficiency and satisfaction. Here's how.

User Story Mapping: A Powerful Tool for User-Centered Product Development

Learn about user story mapping and how it can be used for successful product development with this blog.

What is Research Hypothesis: Definition, Types, and How to Develop

Read the blog to learn how a research hypothesis provides a clear and focused direction for a study and helps formulate research questions.

Understanding Customer Retention: How to Keep Your Customers Coming Back

Understanding customer retention is key to building a successful brand that has repeat, loyal customers. Here's what you need to know about it.

Demographic Segmentation: How Brands Can Use it to Improve Marketing Strategies

Read this blog to learn what demographic segmentation means, its importance, and how it can be used by brands.

Mastering Product Positioning: A UX Researcher's Guide

Read this blog to understand why brands should have a well-defined product positioning and how it affects the overall business.

Discrete Vs. Continuous Data: Everything You Need To Know

Explore the differences between discrete and continuous data and their impact on business decisions and customer insights.

50+ Employee Engagement Survey Questions

Understand how an employee engagement survey provides insights into employee satisfaction and motivation, directly impacting productivity and retention.

A Guide to Interaction Design

Interaction design can help you create engaging and intuitive user experiences, improving usability and satisfaction through effective design principles. Here's how.

Exploring the Benefits of Stratified Sampling

Understanding stratified sampling can improve research accuracy by ensuring diverse representation across key subgroups. Here's how.

A Guide to Voice Recognition in Enhancing UX Research

Learn the importance of using voice recognition technology in user research for enhanced user feedback and insights.

The Ultimate Figma Design Handbook: Design Creation and Testing

The Ultimate Figma Design Handbook covers setting up Figma, creating designs, advanced features, prototyping, and testing designs with real users.

The Power of Organization: Mastering Information Architectures

Understanding the art of information architectures can enhance user experiences by organizing and structuring digital content effectively, making information easy to find and navigate. Here's how.

Convenience Sampling: Examples, Benefits, and When To Use It

Read the blog to understand how convenience sampling allows for quick and easy data collection with minimal cost and effort.

What is Critical Thinking, and How Can it be Used in Consumer Research?

Learn how critical thinking enhances consumer research and discover how Decode's AI-driven platform revolutionizes data analysis and insights.

How Business Intelligence Tools Transform User Research & Product Management

This blog explains how Business Intelligence (BI) tools can transform user research and product management by providing data-driven insights for better decision-making.

What is Face Validity? Definition, Guide and Examples

Read this blog to explore face validity, its importance, and the advantages of using it in market research.

What is Customer Lifetime Value, and How To Calculate It?

Read this blog to understand how Customer Lifetime Value (CLV) can help your business optimize marketing efforts, improve customer retention, and increase profitability.

Systematic Sampling: Definition, Examples, and Types

Explore how systematic sampling helps researchers by providing a structured method to select representative samples from larger populations, ensuring efficiency and reducing bias.

Understanding Selection Bias: A Guide

Selection bias can affect the type of respondents you choose for the study and ultimately the quality of responses you receive. Here’s all you need to know about it.

A Guide to Designing an Effective Product Strategy

Read this blog to explore why a well-defined product strategy is required for brands while developing or refining a product.

A Guide to Minimum Viable Product (MVP) in UX: Definition, Strategies, and Examples

Discover what an MVP is, why it's crucial in UX, strategies for creating one, and real-world examples from top companies like Dropbox and Airbnb.

Asking Close Ended Questions: A Guide

Asking the right close ended questions is they key to getting quantitiative data from your users. Her's how you should do it.

Creating Website Mockups: Your Ultimate Guide to Effective Design

Read this blog to learn website mockups- tools, examples and how to create an impactful website design.

Understanding Your Target Market And Its Importance In Consumer Research

Read this blog to learn about the importance of creating products and services to suit the needs of your target audience.

What Is a Go-To-Market Strategy And How to Create One?

Check out this blog to learn how a go-to-market strategy helps businesses enter markets smoothly, attract more customers, and stand out from competitors.

What is Confirmation Bias in Consumer Research?

Learn how confirmation bias affects consumer research, its types, impacts, and practical tips to avoid it for more accurate and reliable insights.

Market Penetration: The Key to Business Success

Understanding market penetration is key to cracking the code to sustained business growth and competitive advantage in any industry. Here's all you need to know about it.

How to Create an Effective User Interface

Having a simple, clear user interface helps your users find what they really want, improving the user experience. Here's how you can achieve it.

Product Differentiation and What It Means for Your Business

Discover how product differentiation helps businesses stand out with unique features, innovative designs, and exceptional customer experiences.

What is Ethnographic Research? Definition, Types & Examples

Read this blog to understand Ethnographic research, its relevance in today’s business landscape and how you can leverage it for your business.

Product Roadmap: The 2024 Guide [with Examples]

Read this blog to understand how a product roadmap can align stakeholders by providing a clear product development and delivery plan.

Product Market Fit: Making Your Products Stand Out in a Crowded Market

Delve into the concept of product-market fit, explore its significance, and equip yourself with practical insights to achieve it effectively.

Consumer Behavior in Online Shopping: A Comprehensive Guide

Ever wondered how online shopping behavior can influence successful business decisions? Read on to learn more.

How to Conduct a First Click Test?

Why are users leaving your site so fast? Learn how First Click Testing can help. Discover quick fixes for frustration and boost engagement.

What is Market Intelligence? Methods, Types, and Examples

Read the blog to understand how marketing intelligence helps you understand consumer behavior and market trends to inform strategic decision-making.

What is a Longitudinal Study? Definition, Types, and Examples

Is your long-term research strategy unclear? Learn how longitudinal studies decode complexity. Read on for insights.

What Is the Impact of Customer Churn on Your Business?

Understanding and reducing customer churn is the key to building a healthy business that keeps customers satisfied. Here's all you need to know about it.

The Ultimate Design Thinking Guide

Discover the power of design thinking in UX design for your business. Learn the process and key principles in our comprehensive guide.

100+ Yes Or No Survey Questions Examples

Yes or no survey questions simplify responses, aiding efficiency, clarity, standardization, quantifiability, and binary decision-making. Read some examples!

What is Customer Segmentation? The ULTIMATE Guide

Explore how customer segmentation targets diverse consumer groups by tailoring products, marketing, and experiences to their preferred needs.

Crafting User-Centric Websites Through Responsive Web Design

Find yourself reaching for your phone instead of a laptop for regular web browsing? Read on to find out what that means & how you can leverage it for business.

How Does Product Placement Work? Examples and Benefits

Read the blog to understand how product placement helps advertisers seek subtle and integrated ways to promote their products within entertainment content.

The Importance of Reputation Management, and How it Can Make or Break Your Brand

A good reputation management strategy is crucial for any brand that wants to keep its customers loyal. Here's how brands can focus on it.

A Comprehensive Guide to Human-Centered Design

Are you putting the human element at the center of your design process? Read this blog to understand why brands must do so.

How to Leverage Customer Insights to Grow Your Business

Genuine insights are becoming increasingly difficult to collect. Read on to understand the challenges and what the future holds for customer insights.

The Complete Guide to Behavioral Segmentation

Struggling to reach your target audience effectively? Discover how behavioral segmentation can transform your marketing approach. Read more in our blog!

Creating a Unique Brand Identity: How to Make Your Brand Stand Out

Creating a great brand identity goes beyond creating a memorable logo - it's all about creating a consistent and unique brand experience for your cosnumers. Here's everything you need to know about building one.

Understanding the Product Life Cycle: A Comprehensive Guide

Understanding the product life cycle, or the stages a product goes through from its launch to its sunset can help you understand how to market it at every stage to create the most optimal marketing strategies.

Empathy vs. Sympathy in UX Research

Are you conducting UX research and seeking guidance on conducting user interviews with empathy or sympathy? Keep reading to discover the best approach.

What is Exploratory Research, and How To Conduct It?

Read this blog to understand how exploratory research can help you uncover new insights, patterns, and hypotheses in a subject area.

First Impressions & Why They Matter in User Research

Ever wonder if first impressions matter in user research? The answer might surprise you. Read on to learn more!

Cluster Sampling: Definition, Types & Examples

Read this blog to understand how cluster sampling tackles the challenge of efficiently collecting data from large, spread-out populations.

Top Six Market Research Trends

Curious about where market research is headed? Read on to learn about the changes surrounding this field in 2024 and beyond.

Lyssna Alternative

Meet Qatalyst, your best lyssna alternative to usability testing, to create a solution for all your user research needs.

What is Feedback Loop? Definition, Importance, Types, and Best Practices

Struggling to connect with your customers? Read the blog to learn how feedback loops can solve your problem!

UI vs. UX Design: What’s The Difference?

Learn how UI solves the problem of creating an intuitive and visually appealing interface and how UX addresses broader issues related to user satisfaction and overall experience with the product or service.

The Impact of Conversion Rate Optimization on Your Business

Understanding conversion rate optimization can help you boost your online business. Read more to learn all about it.

Insurance Questionnaire: Tips, Questions and Significance

Leverage this pre-built customizable questionnaire template for insurance to get deep insights from your audience.

UX Research Plan Template

Read on to understand why you need a UX Research Plan and how you can use a fully customizable template to get deep insights from your users!

Brand Experience: What it Means & Why It Matters

Have you ever wondered how users navigate the travel industry for your research insights? Read on to understand user experience in the travel sector.

Validity in Research: Definitions, Types, Significance, and Its Relationship with Reliability

Is validity ensured in your research process? Read more to explore the importance and types of validity in research.

The Role of UI Designers in Creating Delightful User Interfaces

UI designers help to create aesthetic and functional experiences for users. Here's all you need to know about them.

Top Usability Testing Tools to Try

Using usability testing tools can help you understand user preferences and behaviors and ultimately, build a better digital product. Here are the top tools you should be aware of.

Understanding User Experience in Travel Market Research

Ever wondered how users navigate the travel industry for your research insights? Read on to understand user experience in the travel sector.

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