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Experimental Research: What it is + Types of designs

Any research conducted under scientifically acceptable conditions uses experimental methods. The success of experimental studies hinges on researchers confirming the change of a variable is based solely on the manipulation of the constant variable. The research should establish a notable cause and effect.

What is Experimental Research?

Experimental research is a study conducted with a scientific approach using two sets of variables. The first set acts as a constant, which you use to measure the differences of the second set. Quantitative research methods , for example, are experimental.

If you don’t have enough data to support your decisions, you must first determine the facts. This research gathers the data necessary to help you make better decisions.

You can conduct experimental research in the following situations:

• Time is a vital factor in establishing a relationship between cause and effect.
• Invariable behavior between cause and effect.
• You wish to understand the importance of cause and effect.

Experimental Research Design Types

The classic experimental design definition is: “The methods used to collect data in experimental studies.”

There are three primary types of experimental design:

• Pre-experimental research design
• True experimental research design
• Quasi-experimental research design

The way you classify research subjects based on conditions or groups determines the type of research design  you should use.

0 1. Pre-Experimental Design

A group, or various groups, are kept under observation after implementing cause and effect factors. You’ll conduct this research to understand whether further investigation is necessary for these particular groups.

You can break down pre-experimental research further into three types:

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

0 2. True Experimental Design

It relies on statistical analysis to prove or disprove a hypothesis, making it the most accurate form of research. Of the types of experimental design, only true design can establish a cause-effect relationship within a group. In a true experiment, three factors need to be satisfied:

• There is a Control Group, which won’t be subject to changes, and an Experimental Group, which will experience the changed variables.
• A variable that can be manipulated by the researcher
• Random distribution

This experimental research method commonly occurs in the physical sciences.

0 3. Quasi-Experimental Design

The word “Quasi” indicates similarity. A quasi-experimental design is similar to an experimental one, but it is not the same. The difference between the two is the assignment of a control group. In this research, an independent variable is manipulated, but the participants of a group are not randomly assigned. Quasi-research is used in field settings where random assignment is either irrelevant or not required.

Importance of Experimental Design

Experimental research is a powerful tool for understanding cause-and-effect relationships. It allows us to manipulate variables and observe the effects, which is crucial for understanding how different factors influence the outcome of a study.

But the importance of experimental research goes beyond that. It’s a critical method for many scientific and academic studies. It allows us to test theories, develop new products, and make groundbreaking discoveries.

For example, this research is essential for developing new drugs and medical treatments. Researchers can understand how a new drug works by manipulating dosage and administration variables and identifying potential side effects.

Similarly, experimental research is used in the field of psychology to test theories and understand human behavior. By manipulating variables such as stimuli, researchers can gain insights into how the brain works and identify new treatment options for mental health disorders.

It is also widely used in the field of education. It allows educators to test new teaching methods and identify what works best. By manipulating variables such as class size, teaching style, and curriculum, researchers can understand how students learn and identify new ways to improve educational outcomes.

In addition, experimental research is a powerful tool for businesses and organizations. By manipulating variables such as marketing strategies, product design, and customer service, companies can understand what works best and identify new opportunities for growth.

Advantages of Experimental Research

When talking about this research, we can think of human life. Babies do their own rudimentary experiments (such as putting objects in their mouths) to learn about the world around them, while older children and teens do experiments at school to learn more about science.

Ancient scientists used this research to prove that their hypotheses were correct. For example, Galileo Galilei and Antoine Lavoisier conducted various experiments to discover key concepts in physics and chemistry. The same is true of modern experts, who use this scientific method to see if new drugs are effective, discover treatments for diseases, and create new electronic devices (among others).

It’s vital to test new ideas or theories. Why put time, effort, and funding into something that may not work?

This research allows you to test your idea in a controlled environment before marketing. It also provides the best method to test your theory thanks to the following advantages:

• Researchers have a stronger hold over variables to obtain desired results.
• The subject or industry does not impact the effectiveness of experimental research. Any industry can implement it for research purposes.
• The results are specific.
• After analyzing the results, you can apply your findings to similar ideas or situations.
• You can identify the cause and effect of a hypothesis. Researchers can further analyze this relationship to determine more in-depth ideas.
• Experimental research makes an ideal starting point. The data you collect is a foundation for building more ideas and conducting more action research .

Whether you want to know how the public will react to a new product or if a certain food increases the chance of disease, experimental research is the best place to start. Begin your research by finding subjects using  QuestionPro Audience  and other tools today.

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Chapter 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 (generalizability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments , conducted in field settings such as in a real organization, and 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 favorably 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 receives 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 assures that each unit in the population has a positive chance of being selected into the sample. Random assignment is however 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 (generalizability) 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 on a measure during a posttest to regress toward the mean of that measure 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 was 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

The simplest true experimental designs are two group designs involving one treatment group and one control group, and are ideally suited for testing the effects of a single independent variable that can be manipulated as a treatment. The two basic two-group designs are the pretest-posttest control group design and the posttest-only control group design, while variations may include covariance designs. These designs are often depicted using a standardized design notation, where R represents random assignment of subjects to groups, X represents the treatment administered to the treatment group, and O represents pretest or posttest observations of the dependent variable (with different subscripts to distinguish between pretest and posttest observations of treatment and control groups).

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.

Figure 10.1. Pretest-posttest control group design

The effect E of the experimental treatment in the pretest posttest design is measured as the difference in the posttest and pretest scores between the treatment and control groups:

E = (O 2 – O 1 ) – (O 4 – O 3 )

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.

Figure 10.2. Posttest only control group design.

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

E = (O 1 – O 2 )

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.

Covariance designs . Sometimes, measures of dependent variables may be influenced by extraneous variables called covariates . Covariates are those variables that are not of central interest to an experimental study, but should nevertheless be controlled in an experimental design in order to eliminate their potential effect on the dependent variable and therefore allow for a more accurate detection of the effects of the independent variables of interest. The experimental designs discussed earlier did not control for such covariates. A covariance design (also called a concomitant variable design) is a special type of pretest posttest control group design where the pretest measure is essentially a measurement of the covariates of interest rather than that of the dependent variables. The design notation is shown in Figure 10.3, where C represents the covariates:

Figure 10.3. Covariance design

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:

Figure 10.4. 2 x 2 factorial design

Factorial designs can also be depicted using a design notation, such as that shown on the right panel of Figure 10.4. R represents random assignment of subjects to treatment groups, X represents the treatment groups themselves (the subscripts of X represents the level of each factor), and O represent observations of the dependent variable. Notice that the 2 x 2 factorial design will have four treatment groups, corresponding to the four combinations of the two levels of each factor. Correspondingly, the 2 x 3 design will have six treatment groups, and the 2 x 2 x 2 design will have eight treatment groups. As a rule of thumb, each cell in a factorial design should have a minimum sample size of 20 (this estimate is derived from Cohen’s power calculations based on medium effect sizes). So a 2 x 2 x 2 factorial design requires a minimum total sample size of 160 subjects, with at least 20 subjects in each cell. As you can see, the cost of data collection can increase substantially with more levels or factors in your factorial design. Sometimes, due to resource constraints, some cells in such factorial designs may not receive any treatment at all, which are called incomplete factorial designs . Such incomplete designs hurt our ability to draw inferences about the incomplete factors.

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 3 hours/week of instructional time than for 1.5 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 randomized bocks design, Solomon four-group design, and switched replications design.

Randomized 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 treatment group (receiving the same treatment) or 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.

Figure 10.5. Randomized blocks design.

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.

Figure 10.6. Solomon four-group design

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 organizational contexts where organizational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.

Figure 10.7. Switched replication design.

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 organization is used as the treatment group, while another section of the same class or a different organization 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 a 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 impact 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.

Many true experimental designs can be converted to quasi-experimental designs by omitting random assignment. For instance, the quasi-equivalent version of pretest-posttest control group design is called nonequivalent groups design (NEGD), as shown in Figure 10.8, with random assignment R replaced by non-equivalent (non-random) assignment N . Likewise, the quasi -experimental version of switched replication design is called non-equivalent switched replication design (see Figure 10.9).

Figure 10.8. NEGD design.

Figure 10.9. Non-equivalent switched replication design.

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 treatment or control group based on a cutoff 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 standardized 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. The design notation can be represented as follows, where C represents the cutoff score:

Figure 10.10. RD design.

Because of the use of a cutoff score, it is possible that the observed results may be a function of the cutoff score rather than the treatment, which introduces a new threat to internal validity. However, using the cutoff 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 does 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.

Figure 10.11. Proxy pretest design.

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, 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 are not available from the same subjects.

Figure 10.12. Separate pretest-posttest samples design.

Nonequivalent dependent variable (NEDV) design . This is a single-group pre-post quasi-experimental design with two outcome measures, where one measure is theoretically expected to be influenced by the treatment and the other measure is not. For instance, if you are designing a new calculus curriculum for high school students, this curriculum is likely to influence students’ posttest calculus scores but not algebra scores. However, the posttest algebra scores may still vary due to extraneous factors such as history or maturation. Hence, the pre-post algebra scores can be used as a control measure, while that of pre-post calculus can be treated as the treatment measure. The design notation, shown in Figure 10.13, indicates the single group by a single N , followed by pretest O 1 and posttest O 2 for calculus and algebra for the same group of students. This design is weak in internal validity, but its advantage lies in not having to use a separate control group.

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.

Figure 10.13. 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, many experimental research use 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 artifact 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 doubt, using tasks that are simpler and familiar for the respondent sample 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. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Educational Research Basics by Del Siegle

Experimental research.

The major feature that distinguishes experimental research from other types of research is that the researcher manipulates the independent variable.  There are a number of experimental group designs in experimental research. Some of these qualify as experimental research, others do not.

• In true experimental research , the researcher not only manipulates the independent variable, he or she also randomly assigned individuals to the various treatment categories (i.e., control and treatment).
• In quasi experimental research , the researcher does not randomly assign subjects to treatment and control groups. In other words, the treatment is not distributed among participants randomly. In some cases, a researcher may randomly assigns one whole group to treatment and one whole group to control. In this case, quasi-experimental research involves using intact groups in an experiment, rather than assigning individuals at random to research conditions. (some researchers define this latter situation differently. For our course, we will allow this definition).
• In causal comparative ( ex post facto ) research, the groups are already formed. It does not meet the standards of an experiment because the independent variable in not manipulated.

The statistics by themselves have no meaning. They only take on meaning within the design of your study. If we just examine stats, bread can be deadly . The term validity is used three ways in research…

• I n the sampling unit, we learn about external validity (generalizability).
• I n the survey unit, we learn about instrument validity .
• In this unit, we learn about internal validity and external validity . Internal validity means that the differences that we were found between groups on the dependent variable in an experiment were directly related to what the researcher did to the independent variable, and not due to some other unintended variable (confounding variable). Simply stated, the question addressed by internal validity is “Was the study done well?” Once the researcher is satisfied that the study was done well and the independent variable caused the dependent variable (internal validity), then the research examines external validity (under what conditions [ecological] and with whom [population] can these results be replicated [Will I get the same results with a different group of people or under different circumstances?]). If a study is not internally valid, then considering external validity is a moot point (If the independent did not cause the dependent, then there is no point in applying the results [generalizing the results] to other situations.). Interestingly, as one tightens a study to control for treats to internal validity, one decreases the generalizability of the study (to whom and under what conditions one can generalize the results).

There are several common threats to internal validity in experimental research. They are described in our text.  I have review each below (this material is also included in the  PowerPoint Presentation on Experimental Research for this unit):

• Subject Characteristics (Selection Bias/Differential Selection) — The groups may have been different from the start. If you were testing instructional strategies to improve reading and one group enjoyed reading more than the other group, they may improve more in their reading because they enjoy it, rather than the instructional strategy you used.
• Loss of Subjects ( Mortality ) — All of the high or low scoring subject may have dropped out or were missing from one of the groups. If we collected posttest data on a day when the honor society was on field trip at the treatment school, the mean for the treatment group would probably be much lower than it really should have been.
• Location — Perhaps one group was at a disadvantage because of their location.  The city may have been demolishing a building next to one of the schools in our study and there are constant distractions which interferes with our treatment.
• Instrumentation Instrument Decay — The testing instruments may not be scores similarly. Perhaps the person grading the posttest is fatigued and pays less attention to the last set of papers reviewed. It may be that those papers are from one of our groups and will received different scores than the earlier group’s papers
• Data Collector Characteristics — The subjects of one group may react differently to the data collector than the other group. A male interviewing males and females about their attitudes toward a type of math instruction may not receive the same responses from females as a female interviewing females would.
• Data Collector Bias — The person collecting data my favors one group, or some characteristic some subject possess, over another. A principal who favors strict classroom management may rate students’ attention under different teaching conditions with a bias toward one of the teaching conditions.
• Testing — The act of taking a pretest or posttest may influence the results of the experiment. Suppose we were conducting a unit to increase student sensitivity to prejudice. As a pretest we have the control and treatment groups watch Shindler’s List and write a reaction essay. The pretest may have actually increased both groups’ sensitivity and we find that our treatment groups didn’t score any higher on a posttest given later than the control group did. If we hadn’t given the pretest, we might have seen differences in the groups at the end of the study.
• History — Something may happen at one site during our study that influences the results. Perhaps a classmate dies in a car accident at the control site for a study teaching children bike safety. The control group may actually demonstrate more concern about bike safety than the treatment group.
• Maturation –There may be natural changes in the subjects that can account for the changes found in a study. A critical thinking unit may appear more effective if it taught during a time when children are developing abstract reasoning.
• Hawthorne Effect — The subjects may respond differently just because they are being studied. The name comes from a classic study in which researchers were studying the effect of lighting on worker productivity. As the intensity of the factor lights increased, so did the work productivity. One researcher suggested that they reverse the treatment and lower the lights. The productivity of the workers continued to increase. It appears that being observed by the researchers was increasing productivity, not the intensity of the lights.
• John Henry Effect — One group may view that it is competition with the other group and may work harder than than they would under normal circumstances. This generally is applied to the control group “taking on” the treatment group. The terms refers to the classic story of John Henry laying railroad track.
• Resentful Demoralization of the Control Group — The control group may become discouraged because it is not receiving the special attention that is given to the treatment group. They may perform lower than usual because of this.
• Regression ( Statistical Regression) — A class that scores particularly low can be expected to score slightly higher just by chance. Likewise, a class that scores particularly high, will have a tendency to score slightly lower by chance. The change in these scores may have nothing to do with the treatment.
• Implementation –The treatment may not be implemented as intended. A study where teachers are asked to use student modeling techniques may not show positive results, not because modeling techniques don’t work, but because the teacher didn’t implement them or didn’t implement them as they were designed.
• Compensatory Equalization of Treatmen t — Someone may feel sorry for the control group because they are not receiving much attention and give them special treatment. For example, a researcher could be studying the effect of laptop computers on students’ attitudes toward math. The teacher feels sorry for the class that doesn’t have computers and sponsors a popcorn party during math class. The control group begins to develop a more positive attitude about mathematics.
• Experimental Treatment Diffusion — Sometimes the control group actually implements the treatment. If two different techniques are being tested in two different third grades in the same building, the teachers may share what they are doing. Unconsciously, the control may use of the techniques she or he learned from the treatment teacher.

When planning a study, it is important to consider the threats to interval validity as we finalize the study design. After we complete our study, we should reconsider each of the threats to internal validity as we review our data and draw conclusions.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

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Experimental Research

Experimental research is commonly used in sciences such as sociology and psychology, physics, chemistry, biology and medicine etc.

This article is a part of the guide:

• Pretest-Posttest
• Third Variable
• Research Bias
• Independent Variable
• Between Subjects

Browse Full Outline

• 1 Experimental Research
• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

It is a collection of research designs which use manipulation and controlled testing to understand causal processes. Generally, one or more variables are manipulated to determine their effect on a dependent variable.

The experimental method is a systematic and scientific approach to research in which the researcher manipulates one or more variables, and controls and measures any change in other variables.

Experimental Research is often used where:

• There is time priority in a causal relationship ( cause precedes effect )
• There is consistency in a causal relationship (a cause will always lead to the same effect)
• The magnitude of the correlation is great.

(Reference: en.wikipedia.org)

The word experimental research has a range of definitions. In the strict sense, experimental research is what we call a true experiment .

This is an experiment where the researcher manipulates one variable, and control/randomizes the rest of the variables. It has a control group , the subjects have been randomly assigned between the groups, and the researcher only tests one effect at a time. It is also important to know what variable(s) you want to test and measure.

A very wide definition of experimental research, or a quasi experiment , is research where the scientist actively influences something to observe the consequences. Most experiments tend to fall in between the strict and the wide definition.

A rule of thumb is that physical sciences, such as physics, chemistry and geology tend to define experiments more narrowly than social sciences, such as sociology and psychology, which conduct experiments closer to the wider definition.

Aims of Experimental Research

Experiments are conducted to be able to predict phenomenons. Typically, an experiment is constructed to be able to explain some kind of causation . Experimental research is important to society - it helps us to improve our everyday lives.

Identifying the Research Problem

After deciding the topic of interest, the researcher tries to define the research problem . This helps the researcher to focus on a more narrow research area to be able to study it appropriately.  Defining the research problem helps you to formulate a  research hypothesis , which is tested against the  null hypothesis .

The research problem is often operationalizationed , to define how to measure the research problem. The results will depend on the exact measurements that the researcher chooses and may be operationalized differently in another study to test the main conclusions of the study.

An ad hoc analysis is a hypothesis invented after testing is done, to try to explain why the contrary evidence. A poor ad hoc analysis may be seen as the researcher's inability to accept that his/her hypothesis is wrong, while a great ad hoc analysis may lead to more testing and possibly a significant discovery.

Constructing the Experiment

There are various aspects to remember when constructing an experiment. Planning ahead ensures that the experiment is carried out properly and that the results reflect the real world, in the best possible way.

Sampling Groups to Study

Sampling groups correctly is especially important when we have more than one condition in the experiment. One sample group often serves as a control group , whilst others are tested under the experimental conditions.

Deciding the sample groups can be done in using many different sampling techniques. Population sampling may chosen by a number of methods, such as randomization , "quasi-randomization" and pairing.

Reducing sampling errors is vital for getting valid results from experiments. Researchers often adjust the sample size to minimize chances of random errors .

Here are some common sampling techniques :

• probability sampling
• non-probability sampling
• simple random sampling
• convenience sampling
• stratified sampling
• systematic sampling
• cluster sampling
• sequential sampling
• disproportional sampling
• judgmental sampling
• snowball sampling
• quota sampling

Creating the Design

The research design is chosen based on a range of factors. Important factors when choosing the design are feasibility, time, cost, ethics, measurement problems and what you would like to test. The design of the experiment is critical for the validity of the results.

Typical Designs and Features in Experimental Design

• Pretest-Posttest Design Check whether the groups are different before the manipulation starts and the effect of the manipulation. Pretests sometimes influence the effect.
• Control Group Control groups are designed to measure research bias and measurement effects, such as the Hawthorne Effect or the Placebo Effect . A control group is a group not receiving the same manipulation as the experimental group. Experiments frequently have 2 conditions, but rarely more than 3 conditions at the same time.
• Randomized Controlled Trials Randomized Sampling, comparison between an Experimental Group and a Control Group and strict control/randomization of all other variables
• Solomon Four-Group Design With two control groups and two experimental groups. Half the groups have a pretest and half do not have a pretest. This to test both the effect itself and the effect of the pretest.
• Between Subjects Design Grouping Participants to Different Conditions
• Within Subject Design Participants Take Part in the Different Conditions - See also: Repeated Measures Design
• Counterbalanced Measures Design Testing the effect of the order of treatments when no control group is available/ethical
• Matched Subjects Design Matching Participants to Create Similar Experimental- and Control-Groups
• Double-Blind Experiment Neither the researcher, nor the participants, know which is the control group. The results can be affected if the researcher or participants know this.
• Bayesian Probability Using bayesian probability to "interact" with participants is a more "advanced" experimental design. It can be used for settings were there are many variables which are hard to isolate. The researcher starts with a set of initial beliefs, and tries to adjust them to how participants have responded

Pilot Study

It may be wise to first conduct a pilot-study or two before you do the real experiment. This ensures that the experiment measures what it should, and that everything is set up right.

Minor errors, which could potentially destroy the experiment, are often found during this process. With a pilot study, you can get information about errors and problems, and improve the design, before putting a lot of effort into the real experiment.

If the experiments involve humans, a common strategy is to first have a pilot study with someone involved in the research, but not too closely, and then arrange a pilot with a person who resembles the subject(s) . Those two different pilots are likely to give the researcher good information about any problems in the experiment.

Conducting the Experiment

An experiment is typically carried out by manipulating a variable, called the independent variable , affecting the experimental group. The effect that the researcher is interested in, the dependent variable(s) , is measured.

Identifying and controlling non-experimental factors which the researcher does not want to influence the effects, is crucial to drawing a valid conclusion. This is often done by controlling variables , if possible, or randomizing variables to minimize effects that can be traced back to third variables . Researchers only want to measure the effect of the independent variable(s) when conducting an experiment , allowing them to conclude that this was the reason for the effect.

Analysis and Conclusions

In quantitative research , the amount of data measured can be enormous. Data not prepared to be analyzed is called "raw data". The raw data is often summarized as something called "output data", which typically consists of one line per subject (or item). A cell of the output data is, for example, an average of an effect in many trials for a subject. The output data is used for statistical analysis, e.g. significance tests, to see if there really is an effect.

The aim of an analysis is to draw a conclusion , together with other observations. The researcher might generalize the results to a wider phenomenon, if there is no indication of confounding variables "polluting" the results.

If the researcher suspects that the effect stems from a different variable than the independent variable, further investigation is needed to gauge the validity of the results. An experiment is often conducted because the scientist wants to know if the independent variable is having any effect upon the dependent variable. Variables correlating are not proof that there is causation .

Experiments are more often of quantitative nature than qualitative nature, although it happens.

Examples of Experiments

This website contains many examples of experiments. Some are not true experiments , but involve some kind of manipulation to investigate a phenomenon. Others fulfill most or all criteria of true experiments.

Here are some examples of scientific experiments:

Social Psychology

• Stanley Milgram Experiment - Will people obey orders, even if clearly dangerous?
• Asch Experiment - Will people conform to group behavior?
• Stanford Prison Experiment - How do people react to roles? Will you behave differently?
• Good Samaritan Experiment - Would You Help a Stranger? - Explaining Helping Behavior
• Law Of Segregation - The Mendel Pea Plant Experiment
• Transforming Principle - Griffith's Experiment about Genetics
• Ben Franklin Kite Experiment - Struck by Lightning
• J J Thomson Cathode Ray Experiment
• Psychology 101
• Flags and Countries
• Capitals and Countries

Oskar Blakstad (Jul 10, 2008). Experimental Research. Retrieved Apr 29, 2024 from Explorable.com: https://explorable.com/experimental-research

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• Experimental Research Designs: Types, Examples & Methods

Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes.

Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight. Let the plant exposed to sunlight be called sample A, while the latter is called sample B.

If after the duration of the research, we find out that sample A grows and sample B dies, even though they are both regularly wetted and given the same treatment. Therefore, we can conclude that sunlight will aid growth in all similar plants.

What is Experimental Research?

Experimental research is a scientific approach to research, where one or more independent variables are manipulated and applied to one or more dependent variables to measure their effect on the latter. The effect of the independent variables on the dependent variables is usually observed and recorded over some time, to aid researchers in drawing a reasonable conclusion regarding the relationship between these 2 variable types.

The experimental research method is widely used in physical and social sciences, psychology, and education. It is based on the comparison between two or more groups with a straightforward logic, which may, however, be difficult to execute.

Mostly related to a laboratory test procedure, experimental research designs involve collecting quantitative data and performing statistical analysis on them during research. Therefore, making it an example of quantitative research method .

What are The Types of Experimental Research Design?

The types of experimental research design are determined by the way the researcher assigns subjects to different conditions and groups. They are of 3 types, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research Design

In pre-experimental research design, either a group or various dependent groups are observed for the effect of the application of an independent variable which is presumed to cause change. It is the simplest form of experimental research design and is treated with no control group.

Although very practical, experimental research is lacking in several areas of the true-experimental criteria. The pre-experimental research design is further divided into three types

• One-shot Case Study Research Design

In this type of experimental study, only one dependent group or variable is considered. The study is carried out after some treatment which was presumed to cause change, making it a posttest study.

• One-group Pretest-posttest Research Design: 

This research design combines both posttest and pretest study by carrying out a test on a single group before the treatment is administered and after the treatment is administered. With the former being administered at the beginning of treatment and later at the end.

• Static-group Comparison: 

In a static-group comparison study, 2 or more groups are placed under observation, where only one of the groups is subjected to some treatment while the other groups are held static. All the groups are post-tested, and the observed differences between the groups are assumed to be a result of the treatment.

Quasi-experimental Research Design

  The word “quasi” means partial, half, or pseudo. Therefore, the quasi-experimental research bearing a resemblance to the true experimental research, but not the same.  In quasi-experiments, the participants are not randomly assigned, and as such, they are used in settings where randomization is difficult or impossible.

 This is very common in educational research, where administrators are unwilling to allow the random selection of students for experimental samples.

Some examples of quasi-experimental research design include; the time series, no equivalent control group design, and the counterbalanced design.

True Experimental Research Design

The true experimental research design relies on statistical analysis to approve or disprove a hypothesis. It is the most accurate type of experimental design and may be carried out with or without a pretest on at least 2 randomly assigned dependent subjects.

The true experimental research design must contain a control group, a variable that can be manipulated by the researcher, and the distribution must be random. The classification of true experimental design include:

• The posttest-only Control Group Design: In this design, subjects are randomly selected and assigned to the 2 groups (control and experimental), and only the experimental group is treated. After close observation, both groups are post-tested, and a conclusion is drawn from the difference between these groups.
• The pretest-posttest Control Group Design: For this control group design, subjects are randomly assigned to the 2 groups, both are presented, but only the experimental group is treated. After close observation, both groups are post-tested to measure the degree of change in each group.
• Solomon four-group Design: This is the combination of the pretest-only and the pretest-posttest control groups. In this case, the randomly selected subjects are placed into 4 groups.

The first two of these groups are tested using the posttest-only method, while the other two are tested using the pretest-posttest method.

Examples of Experimental Research

Experimental research examples are different, depending on the type of experimental research design that is being considered. The most basic example of experimental research is laboratory experiments, which may differ in nature depending on the subject of research.

Administering Exams After The End of Semester

During the semester, students in a class are lectured on particular courses and an exam is administered at the end of the semester. In this case, the students are the subjects or dependent variables while the lectures are the independent variables treated on the subjects.

Only one group of carefully selected subjects are considered in this research, making it a pre-experimental research design example. We will also notice that tests are only carried out at the end of the semester, and not at the beginning.

Further making it easy for us to conclude that it is a one-shot case study research. 

Employee Skill Evaluation

Before employing a job seeker, organizations conduct tests that are used to screen out less qualified candidates from the pool of qualified applicants. This way, organizations can determine an employee’s skill set at the point of employment.

In the course of employment, organizations also carry out employee training to improve employee productivity and generally grow the organization. Further evaluation is carried out at the end of each training to test the impact of the training on employee skills, and test for improvement.

Here, the subject is the employee, while the treatment is the training conducted. This is a pretest-posttest control group experimental research example.

Evaluation of Teaching Method

Let us consider an academic institution that wants to evaluate the teaching method of 2 teachers to determine which is best. Imagine a case whereby the students assigned to each teacher is carefully selected probably due to personal request by parents or due to stubbornness and smartness.

This is a no equivalent group design example because the samples are not equal. By evaluating the effectiveness of each teacher’s teaching method this way, we may conclude after a post-test has been carried out.

However, this may be influenced by factors like the natural sweetness of a student. For example, a very smart student will grab more easily than his or her peers irrespective of the method of teaching.

What are the Characteristics of Experimental Research?  

Experimental research contains dependent, independent and extraneous variables. The dependent variables are the variables being treated or manipulated and are sometimes called the subject of the research.

The independent variables are the experimental treatment being exerted on the dependent variables. Extraneous variables, on the other hand, are other factors affecting the experiment that may also contribute to the change.

The setting is where the experiment is carried out. Many experiments are carried out in the laboratory, where control can be exerted on the extraneous variables, thereby eliminating them. 

Other experiments are carried out in a less controllable setting. The choice of setting used in research depends on the nature of the experiment being carried out.

• Multivariable

Experimental research may include multiple independent variables, e.g. time, skills, test scores, etc.

Why Use Experimental Research Design?  

Experimental research design can be majorly used in physical sciences, social sciences, education, and psychology. It is used to make predictions and draw conclusions on a subject matter. 

Some uses of experimental research design are highlighted below.

• Medicine: Experimental research is used to provide the proper treatment for diseases. In most cases, rather than directly using patients as the research subject, researchers take a sample of the bacteria from the patient’s body and are treated with the developed antibacterial

The changes observed during this period are recorded and evaluated to determine its effectiveness. This process can be carried out using different experimental research methods.

• Education: Asides from science subjects like Chemistry and Physics which involves teaching students how to perform experimental research, it can also be used in improving the standard of an academic institution. This includes testing students’ knowledge on different topics, coming up with better teaching methods, and the implementation of other programs that will aid student learning.
• Human Behavior: Social scientists are the ones who mostly use experimental research to test human behaviour. For example, consider 2 people randomly chosen to be the subject of the social interaction research where one person is placed in a room without human interaction for 1 year.

The other person is placed in a room with a few other people, enjoying human interaction. There will be a difference in their behaviour at the end of the experiment.

• UI/UX: During the product development phase, one of the major aims of the product team is to create a great user experience with the product. Therefore, before launching the final product design, potential are brought in to interact with the product.

For example, when finding it difficult to choose how to position a button or feature on the app interface, a random sample of product testers are allowed to test the 2 samples and how the button positioning influences the user interaction is recorded.

What are the Disadvantages of Experimental Research?  

• It is highly prone to human error due to its dependency on variable control which may not be properly implemented. These errors could eliminate the validity of the experiment and the research being conducted.
• Exerting control of extraneous variables may create unrealistic situations. Eliminating real-life variables will result in inaccurate conclusions. This may also result in researchers controlling the variables to suit his or her personal preferences.
• It is a time-consuming process. So much time is spent on testing dependent variables and waiting for the effect of the manipulation of dependent variables to manifest.
• It is expensive. 
• It is very risky and may have ethical complications that cannot be ignored. This is common in medical research, where failed trials may lead to a patient’s death or a deteriorating health condition.
• Experimental research results are not descriptive.
• Response bias can also be supplied by the subject of the conversation.
• Human responses in experimental research can be difficult to measure. 

What are the Data Collection Methods in Experimental Research?  

Data collection methods in experimental research are the different ways in which data can be collected for experimental research. They are used in different cases, depending on the type of research being carried out.

1. Observational Study

This type of study is carried out over a long period. It measures and observes the variables of interest without changing existing conditions.

When researching the effect of social interaction on human behavior, the subjects who are placed in 2 different environments are observed throughout the research. No matter the kind of absurd behavior that is exhibited by the subject during this period, its condition will not be changed.

This may be a very risky thing to do in medical cases because it may lead to death or worse medical conditions.

2. Simulations

This procedure uses mathematical, physical, or computer models to replicate a real-life process or situation. It is frequently used when the actual situation is too expensive, dangerous, or impractical to replicate in real life.

This method is commonly used in engineering and operational research for learning purposes and sometimes as a tool to estimate possible outcomes of real research. Some common situation software are Simulink, MATLAB, and Simul8.

Not all kinds of experimental research can be carried out using simulation as a data collection tool . It is very impractical for a lot of laboratory-based research that involves chemical processes.

A survey is a tool used to gather relevant data about the characteristics of a population and is one of the most common data collection tools. A survey consists of a group of questions prepared by the researcher, to be answered by the research subject.

Surveys can be shared with the respondents both physically and electronically. When collecting data through surveys, the kind of data collected depends on the respondent, and researchers have limited control over it.

Formplus is the best tool for collecting experimental data using survey s. It has relevant features that will aid the data collection process and can also be used in other aspects of experimental research.

Differences between Experimental and Non-Experimental Research 

1. In experimental research, the researcher can control and manipulate the environment of the research, including the predictor variable which can be changed. On the other hand, non-experimental research cannot be controlled or manipulated by the researcher at will.

This is because it takes place in a real-life setting, where extraneous variables cannot be eliminated. Therefore, it is more difficult to conclude non-experimental studies, even though they are much more flexible and allow for a greater range of study fields.

2. The relationship between cause and effect cannot be established in non-experimental research, while it can be established in experimental research. This may be because many extraneous variables also influence the changes in the research subject, making it difficult to point at a particular variable as the cause of a particular change

3. Independent variables are not introduced, withdrawn, or manipulated in non-experimental designs, but the same may not be said about experimental research.

Conclusion  

Experimental research designs are often considered to be the standard in research designs. This is partly due to the common misconception that research is equivalent to scientific experiments—a component of experimental research design.

In this research design, one or more subjects or dependent variables are randomly assigned to different treatments (i.e. independent variables manipulated by the researcher) and the results are observed to conclude. One of the uniqueness of experimental research is in its ability to control the effect of extraneous variables.

Experimental research is suitable for research whose goal is to examine cause-effect relationships, e.g. explanatory research. It can be conducted in the laboratory or field settings, depending on the aim of the research that is being carried out. 

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Saunders’ Research Onion: Explained Simply

Peeling the onion, layer by layer (with examples).

By: David Phair (PhD) and Kerryn Warren (PhD) | January 2021

If you’re learning about research skills and methodologies, you may have heard the term “ research onion ”. Specifically, the research onion developed by Saunders et al in 2007 . But what exactly is this elusive onion? In this post, we’ll break Saunders’ research onion down into bite-sized chunks to make it a little more digestible.

Saunders’ (2007) Research Onion – What is it?

At the simplest level, Saunders’ research onion describes the different decisions you’ll need to make when developing a  research methodology   – whether that’s for your dissertation, thesis or any other formal research project. As you work from the outside of the onion inwards , you’ll face a range of choices that progress from high-level and philosophical to tactical and practical in nature. This also mimics the general structure for the methodology chapter .

While Saunders’ research onion is certainly not perfect, it’s a useful tool for thinking holistically about methodology. At a minimum, it helps you understand what decisions you need to make in terms of your research design and methodology.

The layers of Saunders’ research onion

The onion is made up of 6 layers, which you’ll need to peel back one at a time as you develop your research methodology:

• Research philosophy
• Research approach
• Research strategy
• Time horizon
• Techniques & procedures

Onion Layer 1: Research Philosophy

The very first layer of the onion is the research philosophy . But what does that mean? Well, the research philosophy is the foundation of any study as it describes the set of beliefs the research is built upon . Research philosophy can be described from either an  ontological  or  epistemological  point of view. “A what?!”, you ask?

In simple terms,  ontology  is the “what” and “how” of what we know – in other words, what is the nature of reality and what are we really able to know and understand. For example, does reality exist as a single objective thing, or is it different for each person? Think about the simulated reality in the film The Matrix.

Epistemology , on the other hand, is about “how” we can obtain knowledge and come to understand things – in other words, how can we figure out what reality is, and what the limits of this knowledge are. This is a gross oversimplification, but it’s a useful starting point (we’ll cover ontology and epistemology another post).

With that fluffy stuff out the way, let’s look at three of the main research philosophies that operate on different ontological and epistemological assumptions:

• Interpretivism

These certainly aren’t the only research philosophies, but they are very common and provide a good starting point for understanding the spectrum of philosophies.

Research Philosophy 1:  Positivism

Positivist research takes the view that knowledge exists outside of what’s being studied . In other words, what is being studied can only be done so objectively , and it cannot include opinions or personal viewpoints – the researcher doesn’t interpret, they only observe. Positivism states that there is only one reality  and that all meaning is consistent between subjects.

In the positivist’s view, knowledge can only be acquired through empirical research , which is based on measurement and observation. In other words, all knowledge is viewed as a posteriori knowledge – knowledge that is not reliant on human reasoning but instead is gained from research.

For the positivist, knowledge can only be true, false, or meaningless . Basically, if something is not found to be true or false, it no longer holds any ground and is thus dismissed.

Let’s look at an example, based on the question of whether God exists or not. Since positivism takes the stance that knowledge has to be empirically vigorous, the knowledge of whether God exists or not is irrelevant. This topic cannot be proven to be true or false, and thus this knowledge is seen as meaningless.

Kinda harsh, right? Well, that’s the one end of the spectrum – let’s look at the other end.

Research Philosophy 2: Interpretivism

On the other side of the spectrum, interpretivism emphasises the influence that social and cultural factors can have on an individual. This view focuses on  people’s thoughts and ideas , in light of the socio-cultural backdrop. With the interpretivist philosophy, the researcher plays an active role in the study, as it’s necessary to draw a holistic view of the participant and their actions, thoughts and meanings.

Let’s look at an example. If you were studying psychology, you may make use of a case study in your research which investigates an individual with a proposed diagnosis of schizophrenia. The interpretivist view would come into play here as social and cultural factors may influence the outcome of this diagnosis.

Through your research, you may find that the individual originates from India, where schizophrenic symptoms like hallucinations are viewed positively, as they are thought to indicate that the person is a spirit medium. This example illustrates an interpretivist approach since you, as a researcher, would make use of the patient’s point of view, as well as your own interpretation when assessing the case study.

Research Philosophy 3: Pragmatism

Pragmatism highlights the importance of using the best tools possible to investigate phenomena. The main aim of pragmatism is to approach research from a practical point of view , where knowledge is not fixed, but instead is constantly questioned and interpreted. For this reason, pragmatism consists of an element of researcher involvement and subjectivity, specifically when drawing conclusions based on participants’ responses and decisions. In other words, pragmatism is not committed to (or limited by) one specific philosophy.

Let’s look at an example in the form of the trolley problem, which is a set of ethical and psychological thought experiments. In these, participants have to decide on either killing one person to save multiple people or allowing multiple people to die to avoid killing one person. 

This experiment can be altered, including details such as the one person or the group of people being family members or loved ones. The fact that the experiment can be altered to suit the researcher’s needs is an example of pragmatism – in other words, the outcome of the person doing the thought experiment is more important than the philosophical ideas behind the experiment.

To recap, research philosophy is the foundation of any research project and reflects the ontological and epistemological assumptions of the researcher. So, when you’re designing your research methodology , the first thing you need to think about is which philosophy you’ll adopt, given the nature of your research.

Onion Layer 2: Research Approach

Let’s peel off another layer and take a look at the research approach . Your research approach is the broader method you’ll use for your research –  inductive  or  deductive . It’s important to clearly identify your research approach as it will inform the decisions you take in terms of data collection and analysis in your study (we’ll get to that layer soon).

Inductive approaches entail generating theories from research , rather than starting a project with a theory as a foundation.  Deductive approaches, on the other hand, begin with a theory and aim to build on it (or test it) through research.

Sounds a bit fluffy? Let’s look at two examples:

An  inductive approach  could be used in the study of an otherwise unknown isolated community. There is very little knowledge about this community, and therefore, research would have to be conducted to gain information on the community, thus leading to the formation of theories.

On the other hand, a  deductive approach  would be taken when investigating changes in the physical properties of animals over time, as this would likely be rooted in the theory of evolution. In other words, the starting point is a well-established pre-existing body of research.

Closely linked to research approaches are  qualitative and  quantitative  research. Simply put, qualitative research focuses on textual , visual or audio-based data, while quantitative research focuses on numerical data. To learn more about qualitative and quantitative research, check out our dedicated post here .

What’s the relevance of qualitative and quantitative data to research approaches? Well, inductive approaches are usually used within qualitative research, while quantitative research tends to reflect a deductive approach, usually informed by positivist philosophy. The reason for using a deductive approach here is that quantitative research typically begins with theory as a foundation, where progress is made through hypothesis testing. In other words, a wider theory is applied to a particular context, event, or observation to see whether these fit in with the theory, as with our example of evolution above.

So, to recap, the two research approaches are  inductive  and  deductive . To decide on the right approach for your study, you need to assess the type of research you aim to conduct. Ask yourself whether your research will build on something that exists, or whether you’ll be investigating something that cannot necessarily be rooted in previous research. The former suggests a deductive approach while the latter suggests an inductive approach.

Need a helping hand?

Onion Layer 3: Research Strategy

So far, we’ve looked at pretty conceptual and intangible aspects of the onion. Now, it’s time to peel another layer off that onion and get a little more practical – introducing research strategy . This layer of the research onion details how, based on the aims of the study, research can be conducted. Note that outside of the onion, these strategies are referred to as research designs.

There are several strategies  you can take, so let’s have a look at some of them.

• Experimental research
• Action research
• Case study research
• Grounded theory
• Ethnography
• Archival research

Strategy 1: Experimental research

Experimental research involves manipulating one variable (the independent variable ) to observe a change in another variable (the dependent variable ) – in other words, to assess the relationship between variables. The purpose of experimental research is to support, refute or validate a  research hypothesis . This research strategy follows the principles of the  scientific method  and is conducted within a controlled environment or setting (for example, a laboratory).

Experimental research aims to test existing theories rather than create new ones, and as such, is deductive in nature. Experimental research aligns with the positivist research philosophy, as it assumes that knowledge can only be studied objectively and in isolation from external factors such as context or culture.

Let’s look at an example of experimental research. If you had a hypothesis that a certain brand of dog food can raise a dogs’ protein levels, you could make use of experimental research to compare the effects of the specific brand to a “regular” diet. In other words, you could test your hypothesis.

In this example, you would have two groups, where one group consists of dogs with no changes to their diet (this is called  the control group) and the other group consists of dogs being fed the specific brand that you aim to investigate (this is called the experimental/treatment group). You would then test your hypothesis by comparing the protein levels in both groups.

Strategy 2: Action research

Next, we have action research . The simplest way of describing action research is by saying that it involves learning through… wait for it… action. Action research is conducted in practical settings such as a classroom, a hospital, a workspace, etc – as opposed to controlled environments like a lab. Action research helps to inform researchers of problems or weaknesses related to interactions within the real-world . With action research, there’s a strong focus on the participants (the people involved in the issue being studied, which is why it’s sometimes referred to as “participant action research” or PAR.

An example of PAR is a community intervention (for therapy, farming, education, whatever). The researcher comes with an idea and it is implemented with the help of the community (i.e. the participants). The findings are then discussed with the community to see how to better the intervention. The process is repeated until the intervention works just right for the community. In this way, a practical solution is given to a problem and it is generated by the combination of researcher and community (participant) feedback.

This kind of research is generally applied in the social sciences , specifically in professions where individuals aim to improve on themselves and the work that they are doing. Action research is most commonly adopted in qualitative studies and is rarely seen in quantitative studies. This is because, as you can see in the above examples, action research makes use of language and interactions rather than statistics and numbers.

Strategy 3: Case study research

A case study is a detailed, in-depth study of a single subject – for example, a person, a group or an institution, or an event, phenomenon or issue. In this type of research, the subject is analysed to gain an in-depth understanding of issues in a real-life setting. The objective here is to gain an in-depth understanding within the context of the study – not (necessarily) to generalise the findings.

It is vital that, when conducting case study research, you take the social context and culture into account, which means that this type of research is (more often than not) qualitative in nature and tends to be inductive. Also, since the researcher’s assumptions and understanding play a role in case study research, it is typically informed by an interpretivist philosophy.

For example, a study on political views of a specific group of people needs to take into account the current political situation within a country and factors that could contribute towards participants taking a certain view.

Strategy 4: Grounded theory

Next up, grounded theory. Grounded theory is all about “letting the data speak for itself”. In other words, in grounded theory, you let the data inform the development of a new theory, model or framework. True to the name, the theory you develop is “ grounded ” in the data. Ground theory is therefore very useful for research into issues that are completely new or under-researched.

Grounded theory research is typically qualitative (although it can also use quantitative data) and takes an inductive approach. Typically, this form of research involves identifying commonalities between sets of data, and results are then drawn from completed research without the aim of fitting the findings in with a pre-existing theory or framework.

For example, if you were to study the mythology of an unknown culture through artefacts, you’d enter your research without any hypotheses or theories, and rather work from the knowledge you gain from your study to develop these.

Strategy 5: Ethnography

Ethnography involves observing people in their natural environments and drawing meaning from their cultural interactions. The objective with ethnography is to capture the subjective experiences of participants, to see the world through their eyes. Creswell (2013) says it best: “Ethnographers study the meaning of the behaviour, the language, and the interaction among members of the culture-sharing group.”

For example, if you were interested in studying interactions on a mental health discussion board, you could use ethnography to analyse interactions and draw an understanding of the participants’ subjective experiences.

For example, if you wanted to explore the behaviour, language, and beliefs of an isolated Amazonian tribe, ethnography could allow you to develop a complex, complete description of the social behaviours of the group by immersing yourself into the community, rather than just observing from the outside.  

Given the nature of ethnography, it generally reflects an interpretivist research philosophy and involves an inductive , qualitative research approach. However, there are exceptions to this – for example, quantitative ethnography as proposed by David Shafer.

Strategy 6: Archival research

Last but not least is archival research. An archival research strategy draws from materials that already exist, and meaning is then established through a review of this existing data. This method is particularly well-suited to historical research and can make use of materials such as manuscripts and records.

For example, if you were interested in people’s beliefs about so-called supernatural phenomena in the medieval period, you could consult manuscripts and records from the time, and use those as your core data set.

Onion Layer 4: Choices

The next layer of the research onion is simply called “choices” – they could have been a little more specific, right? In any case, this layer is simply about deciding how many data types (qualitative or quantitative) you’ll use in your research. There are three options – mono , mixed , and multi-method .

Let’s take a look at them.

Choosing to use a  mono method  means that you’ll only make use of one data type – either qualitative or quantitative. For example, if you were to conduct a study investigating a community’s opinions on a specific pizza restaurant, you could make use of a qualitative approach only, so that you can analyse participants’ views and opinions of the restaurant.

If you were to make use of both quantitative and qualitative data, you’d be taking a  mixed-methods approach. Keeping with the previous example, you may also want to assess how many people in a community eat specific types of pizza. For this, you could make use of a survey to collect quantitative data and then analyse the results statistically, producing quantitative results in addition to your qualitative ones.

Lastly, there’s  multi-method . With a multi-method approach, you’d make use of a wider range of approaches, with more than just a one quantitative and one qualitative approach. For example, if you conduct a study looking at archives from a specific culture, you could make use of two qualitative methods (such as thematic analysis and content analysis ), and then additionally make use of quantitative methods to analyse numerical data.

As with all the layers of the research onion, the right choice here depends on the nature of your research, as well as your research aims and objectives . There’s also the practical consideration of viability – in other words, what kind of data will you be able to access, given your constraints.

Onion Layer 5: Time horizon

What’s that far in the distance? It’s the time horizon. But what exactly is it? Thankfully, this one’s pretty straightforward. The time horizon simply describes how many points in time you plan to collect your data at . Two options exist – the  cross-sectional  and  longitudinal  time horizon.

Imagine that you’re wasting time on social media and think, “Ooh! I want to study the language of memes and how this language evolves over time”. For this study, you’d need to collect data over multiple points in time – perhaps over a few weeks, months, or even years. Therefore, you’d make use of a  longitudinal time horizon. This option is highly beneficial when studying changes and progressions over time.

If instead, you wanted to study the language used in memes at a certain point in time (for example, in 2020), you’d make use of a  cross-sectional  time horizon. This is where data is collected at one point in time, so you wouldn’t be gathering data to see how language changes, but rather what language exists at a snapshot point in time. The type of data collected could be qualitative, quantitative or a mix of both, as the focus is on the time of collection, not the data type.

As with all the other choices, the nature of your research and your research aims and objectives are the key determining factors when deciding on the time horizon. You’ll also need to consider practical constraints , such as the amount of time you have available to complete your research (especially in the case of a dissertation or thesis).

Onion Layer 6: Techniques and Procedures

Finally, we reach the centre of the onion – this is where you get down to the real practicalities of your research to make choices regarding specific techniques and procedures .

Specifically, this is where you’ll:

• Decide on what data you’ll collect and what data collection methods you’ll use (for example, will you use a survey? Or perhaps one-on-one interviews?)
• Decide how you’ll go about sampling the population (for example, snowball sampling, random sampling, convenience sampling, etc).
• Determine the type of data analysis you’ll use to answer your research questions (such as content analysis or a statistical analysis like correlation).
• Set up the materials you’ll be using for your study (such as writing up questions for a survey or interview)

What’s important to note here is that these techniques and procedures need to align with all the other layers of the research onion – i.e., research philosophy, research approaches, research strategy, choices, and time horizon.

For example, you if you’re adopting a deductive, quantitative research approach, it’s unlikely that you’ll use interviews to collect your data, as you’ll want high-volume, numerical data (which surveys are far better suited to). So, you need to ensure that the decisions at each layer of your onion align with the rest, and most importantly, that they align with your research aims and objectives.

Let’s Recap: Research Onion 101

The research onion details the many interrelated choices you’ll need to make when you’re crafting your research methodology. These include:

• Research philosophy – the set of beliefs your research is based on (positivism, interpretivism, pragmatism)
• Research approaches – the broader method you’ll use (inductive, deductive, qualitative and quantitative)
• Research strategies – how you’ll conduct the research (e.g., experimental, action, case study, etc.)
• Choices – how many methods you’ll use (mono method, mixed-method or multi-method)
• Time horizons – the number of points in time at which you’ll collect your data (cross-sectional or longitudinal)
• Techniques and procedures (data collection methods, data analysis techniques, sampling strategies, etc.)

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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60 Comments

This is good

Wow this was sooo helpful. I don’t feel so blank about my research anymore. With this information I can conquer my research. Going ‘write’ into it. Get it write not right hahahaha

I am doing research with Bolton University so i would like to empower myself.

Really thoughtful presentation and preparation. I learnt too much to teach my students in a very simple and understandable way

Very useful, thank you.

You’re most welcome. Good luck with your research!

thanks alot for your brief and brilliant notes

I am a Student at Malawi Institute of Management, pursuing a Masters’ degree in Business Administration. I find this to be very helpful

Extremely useful, well explained. Thank you so much

I would like to download this file… I can’t find the attachment file. Thanks

Thank you so much for explaining it in the most simple and precise manner!

Very thoughtful and well expained, thanks.

This is good for upgrade my research knowledge

I have enjoying your videos on YouTube, they are very educative and useful. I have learned a lot. Thanks

Thank you this has really helped me with writing my dissertation methodology !

Thanks so much for this piece. Just to be clear, which layer do interviews fit in?

well explained i found it to be very engaging. now i’m going to pass my research methods course. thank you.

Thank you so much this has really helped as I can’t get this insight from uni due to covid

well explained with more clarity!

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Beautiful, thank you!

Beautiful and helpful. Thank you!

This is well done!

A complex but useful approach to research simplified! I would like to learn more from the team.

A very simplified version of a complex topic. I found it really helpful. I would like to know if this publication can be cited for academic research. Thank you

You’re welcome to cite this page, but it would be better to cite the original work of Saunders.

Thirteen odd years since my MSc in HRM & HRD at UoL. I’d like to say thank you for the effort to produce such an insightful discussion of a rather complex topic.

I am a PhD in Media Studies student. I found this enormously helpful when stringing together the methodology chapter, especially the research philosophy section.

Hello there. Thank you for summarising the work on the onion. A more recent version of the onion (Saunders et al., 2019) refers to ‘methodological choices’ rather than choices. This can be downloaded, along with the chapter dealing with research philosophies at: https://www.researchgate.net/publication/330760964_Research_Methods_for_Business_Students_Chapter_4_Understanding_research_philosophy_and_approaches_to_theory_development or https://www.academia.edu/42304065/Research_Methods_for_Business_Students_Chapter_4_Understanding_research_philosophy_and_approaches_to_theory_development_8th_edition

Thank you Mark Saunders. Your work is very insightful

Thank you for the update and additional reading Mark, very helpful indeed.

THROUGHLY AND SIMPLY BRIEFED TO MAKE SENSE AND A CLEAR INSIGHT. THANK YOU, VERY MUCH.

Thank you for the sharing the recent version of the Onion!

I want to keep it in my reference of my assignment. May I??

Great summary, thank you taking the time to put this together. I’m sure it’s been a big help to lots of people. It definitely was to me.

I love the analysis… some people do not recognize qualitative or quantitative as an approach but rather have inductive, abductive, and deductive.

This has been helpful in the understanding of research . Thank you for this valuable information.

Great summary. Well explained. Thank you, guys.

This makes my fears on methodology go away. I confidently look forward to working on my methodology now. Thank you so much I ma doing a PhD with UNIMA, School of Education

simple and clear

Simple guide to crafting a research methodology. Quite impactful. Thank you

Thank you for this, this makes things very clear. Now I’m off to conquer my research proposal. Thanks again.

Thank you for this very informative and valuable information. What would the best approach be to take if you are using secondary data to form a qualitative study and relying on industry reports and peer journals to distinguish what factors influence the use of say cryptocurrency ?

Thanks for providing the whole idea/knowledge in the simplest way with essential factors which made my entire research process more efficient as well as valuable.

what is about research design such as descriptive, causal-comparative, correlation, developmental where these fall in the research onion?

This is very helpful. Thank you for this wonderful piece. However, it would be nicer to have References to the knowledge provided here. My suggestion

This material is very important for researchers, particularly for PhD scholars to conduct further study.

This was insightful. Thank you for the knowledge.

Thank you for the wonderful knowledge !Easy to understand and grasp.

thanks very much very simple. will need a coach

Hi this is a great article giving much help to my research. I just wanted to mention here that the example where you mentioned that ” schizophrenic symptoms like hallucinations are viewed positively, as they are thought to indicate the person is a spirit medium” is completely false as those are different cases and a bit out of context here. We are medically and psychologically well versed and obviously understand the difference between the two. As much as I am grateful to this article I would like to suggest you to give proper examples.

Thank you very much, sincerely I appreciate your efforts, it is insightful information. Once again I’m grateful .

In short, a complete insight of and for writing research methodology.

This information was very helpful, I was having difficulties in writing my methodology now I can say I have the full knowledge to write a more informative research methodology.

Thank you so much for this amazing explanation. As a person who hasn’t ever done a research project, this video helped me to clear my doubts and approach my research in a clear and concise manner. Great work

very well explained , after going through this there is no need any material to study . a very concise and to the point.

I have one small query. If I choose mixed -methods (quantitative and qualitative techniques), Then, my research Philosophy will also change to both Positivists and Interpretivist. Isn’t?

well explained and thank you

Thanks for this presentation. Quite simple and easy to understand, and to teach others.

Hello! Having made a decision to use a particular research philosophy, how then do we go about justifying that choice with references? Thank you.

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Experimental Research: Meaning And Examples Of Experimental Research

Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every…

Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every government knows that vaccines are a result of experimental research design and it takes years of collected data to make one. It takes a lot of time to compare formulas and combinations with an array of possibilities across different age groups, genders and physical conditions. With their efficiency and meticulousness, scientists redefined the meaning of experimental research when they discovered a vaccine in less than a year.

What Is Experimental Research?

Characteristics of experimental research design, types of experimental research design, advantages and disadvantages of experimental research, examples of experimental research.

Experimental research is a scientific method of conducting research using two variables: independent and dependent. Independent variables can be manipulated to apply to dependent variables and the effect is measured. This measurement usually happens over a significant period of time to establish conditions and conclusions about the relationship between these two variables.

Experimental research is widely implemented in education, psychology, social sciences and physical sciences. Experimental research is based on observation, calculation, comparison and logic. Researchers collect quantitative data and perform statistical analyses of two sets of variables. This method collects necessary data to focus on facts and support sound decisions. It’s a helpful approach when time is a factor in establishing cause-and-effect relationships or when an invariable behavior is seen between the two.  

Now that we know the meaning of experimental research, let’s look at its characteristics, types and advantages.

The hypothesis is at the core of an experimental research design. Researchers propose a tentative answer after defining the problem and then test the hypothesis to either confirm or disregard it. Here are a few characteristics of experimental research:

• Dependent variables are manipulated or treated while independent variables are exerted on dependent variables as an experimental treatment. Extraneous variables are variables generated from other factors that can affect the experiment and contribute to change. Researchers have to exercise control to reduce the influence of these variables by randomization, making homogeneous groups and applying statistical analysis techniques.
• Researchers deliberately operate independent variables on the subject of the experiment. This is known as manipulation.
• Once a variable is manipulated, researchers observe the effect an independent variable has on a dependent variable. This is key for interpreting results.
• A researcher may want multiple comparisons between different groups with equivalent subjects. They may replicate the process by conducting sub-experiments within the framework of the experimental design.

Experimental research is equally effective in non-laboratory settings as it is in labs. It helps in predicting events in an experimental setting. It generalizes variable relationships so that they can be implemented outside the experiment and applied to a wider interest group.

The way a researcher assigns subjects to different groups determines the types of experimental research design .

Pre-experimental Research Design

In a pre-experimental research design, researchers observe a group or various groups to see the effect an independent variable has on the dependent variable to cause change. There is no control group as it is a simple form of experimental research . It’s further divided into three categories:

• A one-shot case study research design is a study where one dependent variable is considered. It’s a posttest study as it’s carried out after treating what presumably caused the change.
• One-group pretest-posttest design is a study that combines both pretest and posttest studies by testing a single group before and after administering the treatment.
• Static-group comparison involves studying two groups by subjecting one to treatment while the other remains static. After post-testing all groups the differences are observed.

This design is practical but lacks in certain areas of true experimental criteria.

True Experimental Research Design

This design depends on statistical analysis to approve or disregard a hypothesis. It’s an accurate design that can be conducted with or without a pretest on a minimum of two dependent variables assigned randomly. It is further classified into three types:

• The posttest-only control group design involves randomly selecting and assigning subjects to two groups: experimental and control. Only the experimental group is treated, while both groups are observed and post-tested to draw a conclusion from the difference between the groups.
• In a pretest-posttest control group design, two groups are randomly assigned subjects. Both groups are presented, the experimental group is treated and both groups are post-tested to measure how much change happened in each group.
• Solomon four-group design is a combination of the previous two methods. Subjects are randomly selected and assigned to four groups. Two groups are tested using each of the previous methods.

True experimental research design should have a variable to manipulate, a control group and random distribution.

With experimental research, we can test ideas in a controlled environment before marketing. It acts as the best method to test a theory as it can help in making predictions about a subject and drawing conclusions. Let’s look at some of the advantages that make experimental research useful:

• It allows researchers to have a stronghold over variables and collect desired results.
• Results are usually specific.
• The effectiveness of the research isn’t affected by the subject.
• Findings from the results usually apply to similar situations and ideas.
• Cause and effect of a hypothesis can be identified, which can be further analyzed for in-depth ideas.
• It’s the ideal starting point to collect data and lay a foundation for conducting further research and building more ideas.
• Medical researchers can develop medicines and vaccines to treat diseases by collecting samples from patients and testing them under multiple conditions.
• It can be used to improve the standard of academics across institutions by testing student knowledge and teaching methods before analyzing the result to implement programs.
• Social scientists often use experimental research design to study and test behavior in humans and animals.
• Software development and testing heavily depend on experimental research to test programs by letting subjects use a beta version and analyzing their feedback.

Even though it’s a scientific method, it has a few drawbacks. Here are a few disadvantages of this research method:

• Human error is a concern because the method depends on controlling variables. Improper implementation nullifies the validity of the research and conclusion.
• Eliminating extraneous variables (real-life scenarios) produces inaccurate conclusions.
• The process is time-consuming and expensive
• In medical research, it can have ethical implications by affecting patients’ well-being.
• Results are not descriptive and subjects can contribute to response bias.

Experimental research design is a sophisticated method that investigates relationships or occurrences among people or phenomena under a controlled environment and identifies the conditions responsible for such relationships or occurrences

Experimental research can be used in any industry to anticipate responses, changes, causes and effects. Here are some examples of experimental research :

• This research method can be used to evaluate employees’ skills. Organizations ask candidates to take tests before filling a post. It is used to screen qualified candidates from a pool of applicants. This allows organizations to identify skills at the time of employment. After training employees on the job, organizations further evaluate them to test impact and improvement. This is a pretest-posttest control group research example where employees are ‘subjects’ and the training is ‘treatment’.
• Educational institutions follow the pre-experimental research design to administer exams and evaluate students at the end of a semester. Students are the dependent variables and lectures are independent. Since exams are conducted at the end and not the beginning of a semester, it’s easy to conclude that it’s a one-shot case study research.
• To evaluate the teaching methods of two teachers, they can be assigned two student groups. After teaching their respective groups on the same topic, a posttest can determine which group scored better and who is better at teaching. This method can have its drawbacks as certain human factors, such as attitudes of students and effectiveness to grasp a subject, may negatively influence results. 

Experimental research is considered a standard method that uses observations, simulations and surveys to collect data. One of its unique features is the ability to control extraneous variables and their effects. It’s a suitable method for those looking to examine the relationship between cause and effect in a field setting or in a laboratory. Although experimental research design is a scientific approach, research is not entirely a scientific process. As much as managers need to know what is experimental research , they have to apply the correct research method, depending on the aim of the study.

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16 Advantages and Disadvantages of Experimental Research

How do you make sure that a new product, theory, or idea has validity? There are multiple ways to test them, with one of the most common being the use of experimental research. When there is complete control over one variable, the other variables can be manipulated to determine the value or validity that has been proposed.

Then, through a process of monitoring and administration, the true effects of what is being studied can be determined. This creates an accurate outcome so conclusions about the final value potential. It is an efficient process, but one that can also be easily manipulated to meet specific metrics if oversight is not properly performed.

Here are the advantages and disadvantages of experimental research to consider.

What Are the Advantages of Experimental Research?

1. It provides researchers with a high level of control. By being able to isolate specific variables, it becomes possible to determine if a potential outcome is viable. Each variable can be controlled on its own or in different combinations to study what possible outcomes are available for a product, theory, or idea as well. This provides a tremendous advantage in an ability to find accurate results.

2. There is no limit to the subject matter or industry involved. Experimental research is not limited to a specific industry or type of idea. It can be used in a wide variety of situations. Teachers might use experimental research to determine if a new method of teaching or a new curriculum is better than an older system. Pharmaceutical companies use experimental research to determine the viability of a new product.

3. Experimental research provides conclusions that are specific. Because experimental research provides such a high level of control, it can produce results that are specific and relevant with consistency. It is possible to determine success or failure, making it possible to understand the validity of a product, theory, or idea in a much shorter amount of time compared to other verification methods. You know the outcome of the research because you bring the variable to its conclusion.

4. The results of experimental research can be duplicated. Experimental research is straightforward, basic form of research that allows for its duplication when the same variables are controlled by others. This helps to promote the validity of a concept for products, ideas, and theories. This allows anyone to be able to check and verify published results, which often allows for better results to be achieved, because the exact steps can produce the exact results.

5. Natural settings can be replicated with faster speeds. When conducting research within a laboratory environment, it becomes possible to replicate conditions that could take a long time so that the variables can be tested appropriately. This allows researchers to have a greater control of the extraneous variables which may exist as well, limiting the unpredictability of nature as each variable is being carefully studied.

6. Experimental research allows cause and effect to be determined. The manipulation of variables allows for researchers to be able to look at various cause-and-effect relationships that a product, theory, or idea can produce. It is a process which allows researchers to dig deeper into what is possible, showing how the various variable relationships can provide specific benefits. In return, a greater understanding of the specifics within the research can be understood, even if an understanding of why that relationship is present isn’t presented to the researcher.

7. It can be combined with other research methods. This allows experimental research to be able to provide the scientific rigor that may be needed for the results to stand on their own. It provides the possibility of determining what may be best for a specific demographic or population while also offering a better transference than anecdotal research can typically provide.

What Are the Disadvantages of Experimental Research?

1. Results are highly subjective due to the possibility of human error. Because experimental research requires specific levels of variable control, it is at a high risk of experiencing human error at some point during the research. Any error, whether it is systemic or random, can reveal information about the other variables and that would eliminate the validity of the experiment and research being conducted.

2. Experimental research can create situations that are not realistic. The variables of a product, theory, or idea are under such tight controls that the data being produced can be corrupted or inaccurate, but still seem like it is authentic. This can work in two negative ways for the researcher. First, the variables can be controlled in such a way that it skews the data toward a favorable or desired result. Secondly, the data can be corrupted to seem like it is positive, but because the real-life environment is so different from the controlled environment, the positive results could never be achieved outside of the experimental research.

3. It is a time-consuming process. For it to be done properly, experimental research must isolate each variable and conduct testing on it. Then combinations of variables must also be considered. This process can be lengthy and require a large amount of financial and personnel resources. Those costs may never be offset by consumer sales if the product or idea never makes it to market. If what is being tested is a theory, it can lead to a false sense of validity that may change how others approach their own research.

4. There may be ethical or practical problems with variable control. It might seem like a good idea to test new pharmaceuticals on animals before humans to see if they will work, but what happens if the animal dies because of the experimental research? Or what about human trials that fail and cause injury or death? Experimental research might be effective, but sometimes the approach has ethical or practical complications that cannot be ignored. Sometimes there are variables that cannot be manipulated as it should be so that results can be obtained.

5. Experimental research does not provide an actual explanation. Experimental research is an opportunity to answer a Yes or No question. It will either show you that it will work or it will not work as intended. One could argue that partial results could be achieved, but that would still fit into the “No” category because the desired results were not fully achieved. The answer is nice to have, but there is no explanation as to how you got to that answer. Experimental research is unable to answer the question of “Why” when looking at outcomes.

6. Extraneous variables cannot always be controlled. Although laboratory settings can control extraneous variables, natural environments provide certain challenges. Some studies need to be completed in a natural setting to be accurate. It may not always be possible to control the extraneous variables because of the unpredictability of Mother Nature. Even if the variables are controlled, the outcome may ensure internal validity, but do so at the expense of external validity. Either way, applying the results to the general population can be quite challenging in either scenario.

7. Participants can be influenced by their current situation. Human error isn’t just confined to the researchers. Participants in an experimental research study can also be influenced by extraneous variables. There could be something in the environment, such an allergy, that creates a distraction. In a conversation with a researcher, there may be a physical attraction that changes the responses of the participant. Even internal triggers, such as a fear of enclosed spaces, could influence the results that are obtained. It is also very common for participants to “go along” with what they think a researcher wants to see instead of providing an honest response.

8. Manipulating variables isn’t necessarily an objective standpoint. For research to be effective, it must be objective. Being able to manipulate variables reduces that objectivity. Although there are benefits to observing the consequences of such manipulation, those benefits may not provide realistic results that can be used in the future. Taking a sample is reflective of that sample and the results may not translate over to the general population.

9. Human responses in experimental research can be difficult to measure. There are many pressures that can be placed on people, from political to personal, and everything in-between. Different life experiences can cause people to react to the same situation in different ways. Not only does this mean that groups may not be comparable in experimental research, but it also makes it difficult to measure the human responses that are obtained or observed.

The advantages and disadvantages of experimental research show that it is a useful system to use, but it must be tightly controlled in order to be beneficial. It produces results that can be replicated, but it can also be easily influenced by internal or external influences that may alter the outcomes being achieved. By taking these key points into account, it will become possible to see if this research process is appropriate for your next product, theory, or idea.

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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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Research Strategies and Methods

• First Online: 22 July 2021

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• Paul Johannesson 3 &
• Erik Perjons 3  

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Researchers have since centuries used research methods to support the creation of reliable knowledge based on empirical evidence and logical arguments. This chapter offers an overview of established research strategies and methods with a focus on empirical research in the social sciences. We discuss research strategies, such as experiment, survey, case study, ethnography, grounded theory, action research, and phenomenology. Research methods for data collection are also described, including questionnaires, interviews, focus groups, observations, and documents. Qualitative and quantitative methods for data analysis are discussed. Finally, the use of research strategies and methods within design science is investigated.

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SYSTEMATIC REVIEW article

Effectiveness of intervention programs in reducing plagiarism by university students: a systematic review provisionally accepted.

• 1 National Autonomous University of Mexico, Mexico

The final, formatted version of the article will be published soon.

Plagiarism in universities is a problem with potential academic, social, ethical, and legal implications. Systematic review research on academic integrity programs, including plagiarism, has been conducted, but few studies have assessed plagiarism. Therefore, this review synthesizes knowledge on the effect of educational interventions designed to prevent or reduce plagiarism by university students. Method: A systematic review was performed using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria to analyze experimental or quasiexperimental studies aimed at reducing plagiarism through objective assessments. The search strategy was implemented in Web of Science, PubMed, Scopus, PsycArticles, ProQuest, ERIC, Redalyc, SciELO, and Tesiunam. Results: Six interventions were evaluated, and 1,631 undergraduate students were included pursuing different majors from different universities. The intervention and assessment strategies varied considerably between studies, 5 of which reported a lower plagiarism frequency in the intervention group than in the control group. Conclusions: The results suggest that interventions with practical elements, such as plagiarism detection, paraphrasing, citation skills, in addition to using software to identify similarities, may reduce plagiarism. However, few studies include an objective evaluation, so more research is needed.

Keywords: plagiarism, university students, Academic dishonesty, academic integrity, CHEATING

Received: 19 Dec 2023; Accepted: 29 Apr 2024.

Copyright: © 2024 Miranda-Rodríguez, Sánchez-Nieto and Ruiz-Rodríguez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Rubén Andrés Miranda-Rodríguez, National Autonomous University of Mexico, México City, Mexico

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• Published: 23 April 2024

Tumor-suppressive miR-4732-3p is sorted into fucosylated exosome by hnRNPK to avoid the inhibition of lung cancer progression

• Wanzhen Zhuang 1 , 2   na1 ,
• Chengxiu Liu 3 , 4   na1 ,
• Yilin Hong 3   na1 ,
• Yue Zheng 1 , 2 ,
• Minjian Huang 1 , 5 ,
• Haijun Tang 1 , 5 ,
• Lilan Zhao 1 , 6 ,
• Zhixin Huang 2 , 7 ,
• Mingshu Tu 1 , 2 ,
• Lili Yu 1 , 2 ,
• Jianlin Chen 1 , 2 ,
• Yi Zhang 1 , 2 ,
• Xiongfeng Chen 1 , 8 ,
• Fan Lin 9 , 10 ,
• Chundong Yu   ORCID: orcid.org/0000-0002-4141-8345 1 , 3 , 5 &
• Yi Huang 1 , 2 , 5 , 10 , 11 , 12  

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Aberrant fucosylation observed in cancer cells contributes to an augmented release of fucosylated exosomes into the bloodstream, where miRNAs including miR-4732-3p hold promise as potential tumor biomarkers in our pilot study. However, the mechanisms underlying the sorting of miR-4732-3p into fucosylated exosomes during lung cancer progression remain poorly understood.

A fucose-captured strategy based on lentil lectin-magnetic beads was utilized to isolate fucosylated exosomes and evaluate the efficiency for capturing tumor-derived exosomes using nanoparticle tracking analysis (NTA). Fluorescence in situ hybridization (FISH) and qRT-PCR were performed to determine the levels of miR-4732-3p in non-small cell lung cancer (NSCLC) tissue samples. A co-culture system was established to assess the release of miRNA via exosomes from NSCLC cells. RNA immunoprecipitation (RIP) and miRNA pull-down were applied to validate the interaction between miR-4732-3p and heterogeneous nuclear ribonucleoprotein K (hnRNPK) protein. Cell functional assays, cell derived xenograft, dual-luciferase reporter experiments, and western blot were applied to examine the effects of miR-4732-3p on MFSD12 and its downstream signaling pathways, and the impact of hnRNPK in NSCLC.

We enriched exosomes derived from NSCLC cells using the fucose-captured strategy and detected a significant upregulation of miR-4732-3p in fucosylated exosomes present in the serum, while its expression declined in NSCLC tissues. miR-4732-3p functioned as a tumor suppressor in NSCLC by targeting 3'UTR of MFSD12, thereby inhibiting AKT/p21 signaling pathway to induce cell cycle arrest in G2/M phase. NSCLC cells preferentially released miR-4732-3p via exosomes instead of retaining them intracellularly, which was facilitated by the interaction of miR-4732-3p with hnRNPK protein for selective sorting into fucosylated exosomes. Moreover, knockdown of hnRNPK suppressed NSCLC cell proliferation, with the elevated levels of miR-4732-3p in NSCLC tissues but the decreased expression in serum fucosylated exosomes.

Conclusions

NSCLC cells escape suppressive effects of miR-4732-3p through hnRNPK-mediated sorting of them into fucosylated exosomes, thus supporting cell malignant properties and promoting NSCLC progression. Our study provides a promising biomarker for NSCLC and opens a novel avenue for NSCLC therapy by targeting hnRNPK to prevent the "exosome escape" of tumor-suppressive miR-4732-3p from NSCLC cells.

Introduction

Lung cancer, one of the most prevalent malignancies, has become the foremost cause of cancer-related deaths worldwide [ 1 ], and non-small cell lung cancer (NSCLC) accounts for approximately 82% of all lung cancer cases, comprising subtypes such as adenocarcinoma (LUAD), squamous cell cancers (LUSC), and large cell cancers [ 2 ]. NSCLC patients diagnosed at TNM stage I have been demonstrated to have a significantly higher five-year survival rate of up to 65%, compared with 5% at TNM stage IV [ 3 ]. Therefore, it is of paramount importance to improve patient outcomes by identifying biomarkers for diagnosis and elucidating the mechanisms driving NSCLC progression.

Exosomes, a specific type of extracellular vesicles with a diameter ranging from 40–160 nm, contain a lipid bilayer structure protecting the cargo from degradation [ 4 ]. Various types of cells have been shown to secrete exosomes with diverse cargo to obtain material exchange and cellular communication [ 5 ], of which microRNAs (miRNAs) have been well defined to be abundant in tumor-derived exosomes and participate in the development of many malignancies [ 6 , 7 ]. Especially mentioned, tumor-derived exosomal miRNAs can be released by cancer cells into the serum [ 8 ], thereby providing potential non-invasive biomarkers for NSCLC diagnosis, as well as promising molecular targets for NSCLC therapy.

Previous studies have identified certain tumor-associated glycan chains aberrantly expressed in exosomes derived from cancer cells, serving as key signatures of malignant transformation [ 9 , 10 ]. Fucosylation, one of the most common glycosylation modifications, is closely associated with the development of malignancies [ 11 , 12 ]. Thus, it is reasonable to propose that capturing serum fucosylated exosomes and analyzing miRNA profiles might offer a promising approach for diagnosis and understanding the mechanisms underlying the sorting of miRNAs during the progression of NSCLC.

In our pilot study, we observed an elevated expression of miR-4732-3p in serum fucosylated exosomes from early LUAD patients, in comparison to both healthy controls (HCs) and patients with benign pulmonary nodules (BPNs) utilizing miRNA sequencing and qRT-PCR [ 13 ]. Nevertheless, the potential role of serum fucosylated exosomal miR-4732-3p in NSCLC progression remains unclarified and needs to be elucidated.

In this study, we first utilized the fucose-captured strategy, based on lentil lectin-magnetic beads with an affinity for fucose on the exosome membrane, to enrich tumor-derived exosomes. Our results showed that the upregulation of miR-4732-3p in serum fucosylated exosomes from NSCLC patients, and its suppressive effects on NSCLC cells. Moreover, our study sheds light on the unique behavior of NSCLC cells to escape suppressive effects of miR-4732-3p through hnRNPK-mediated selective sorting into fucosylated exosomes, supporting cell malignant properties and thus promoting NSCLC progression.

miR-4732-3p is highly expressed in serum fucosylated exosomes but downregulated in tumor tissues of NSCLC patients

To discover potential miRNAs participating in intercellular communication and NSCLC progression, we conducted a comprehensive analysis. Initially, we reanalyzed miRNA sequencing data from serum fucosylated exosomes and identified differentially expressed miRNAs (DEmiRs) in the serum fucosylated exosomes from early LUAD patients compared with both the healthy controls (HCs) and patients with benign pulmonary nodules (BPNs). We then identified DEmiRs (|logFC|> 2 and p  < 0.05) in NSCLC via TCGA database analysis. By intersecting these three datasets, three candidate miRNAs (miR-4732-3p, miR-486-5p, and miR-139-3p) stood out (Fig.  1 A). Considering that miR-486-5p and miR-139-3p have been extensively studied in various malignancies [ 14 , 15 ], but the role of miR-4732-3p remains largely unknown, we therefore explored the expression patterns of miR-4732-3p and potential effects of miR-4732-3p on NSCLC progression.

miR-4732-3p is highly expressed in serum fucosylated exosomes but downregulated in NSCLC tissues. A  Venn diagram exhibiting the overlap of DEmiRs in serum fucosylated exosomes from early LUAD patients compared with both BPNs and HCs groups, together with DEmiRs in NSCLC tissues. B  qRT-PCR was performed to determine miR-4732-3p levels in serum fucosylated exosomes from NSCLC patients ( n  = 96), BPNs ( n  = 30), and HCs ( n  = 32). C  ROC curve analysis of serum fucosylated exosomal miR-4732-3p for diagnosing NSCLC patients form BPNs and HCs. D  Levels of miR-4732-3p were detected via qRT-PCR in serum fucosylated exosomes from NSCLC patients at diverse stages: Tis ( n  = 31), Stage I/II ( n  = 34), and Stage III/IV ( n  = 31). E  The ability of serum fucosylated exosomal miR-4732-3p to discriminate NSCLC patients at diverse stages was evaluated using ROC curve analysis. F-G  Expression of miR-4732-3p in cancerous tissues according to the GEDs ( F ) and ENCORI ( G ) databases. H-I  miR-4732-3p levels in NSCLC tissues were evaluated by the fluorescence intensity via FISH ( H ) and relative expression via qRT-PCR ( I ) analysis. J  Kaplan–Meier survival analysis of NSCLC patients according to hsa-miR-4732 expression. Data are shown as the mean ± SD from at least three independent experiments. ** p  < 0.01; *** p  < 0.001; **** p  < 0.0001; ns, not significant

We first investigated the expression of serum fucosylated exosomal miR-4732-3p in NSCLC patients, BPNs, and HCs. Our findings revealed that miR-4732-3p exhibited significantly higher levels in serum fucosylated exosomes from NSCLC patients, compared with both BPN and HC individuals (Fig.  1 B). Subsequently, we determined the diagnostic value of serum fucosylated exosomal miR-4732-3p for NSCLC using receiver operating characteristic (ROC) curve analysis and calculated area under the curve (AUC), sensitivity, specificity, and predictive values (Table  1 ). Especially mentioned, we noticed that AUC was 0.823 (0.758–0.889) when discriminating NSCLC patients from HC and BPN groups, thereby substantiating its diagnostic potential (Fig.  1 C). Meanwhile, the levels of serum fucosylated exosomal miR-4732-3p were increased concomitantly with advanced-stage NSCLC (Fig.  1 D). More importantly, our analysis of ROC curves for serum fucosylated exosomal miR-4732-3p in discriminating different stages of NSCLC patients demonstrated AUC values of 0.823 (0.715–0.930) for distinguishing NSCLC patients in situ from those at stage I/II, 0.808 (0.694–0.923) for differentiating patients at stage I/II from those at stage III/IV, and 0.946 (0.894–0.998) for discriminating between NSCLC patients in situ and those at stage III/IV (Fig.  1 E). Furthermore, the correlation analysis of the clinical characteristics of NSCLC patients revealed a positive association between the expression of serum fucosylated exosomal miR-4732-3p and TNM stages (Table  2 ). Interestingly, both the GEDs and ENCORI databases indicated that miR-4732-3p was dramatically downregulated in NSCLC tissues compared with that in adjacent non-tumor tissues (Fig.  1 F and G). Further fluorescence in situ hybridization (FISH) and qRT-PCR analysis both confirmed the aberrant downregulation of miR-4732-3p in NSCLC tissues with weaker fluorescence intensity and relative expression (Fig.  1 H and I). Taken together, miR-4732-3p was found to be significantly elevated in serum fucosylated exosomes but decreased in the cancerous tissues of NSCLC patients. Notably, our findings suggest its potential capacity to impede NSCLC progression with an association between the expression of miR-4732 and the lower hazard ratio in the predominant subtypes of NSCLC (Fig.  1 J). Additional evidence of other malignancies also demonstrated a positive correlation between the expression of miR-4732 and improved prognosis for these cancer patients (Fig. S 1 ). Consequently, we hypothesized that miR-4732-3p might confer a protective role by the suppression of NSCLC process.

miR-4732-3p is a tumor-suppressive miRNA in vitro and in vivo

To reveal the role of miR-4732-3p in NSCLC cells, we conducted qRT-PCR to assess its expression in several NSCLC cell lines including SK-MES-1, A549, H460, H226, and H1299. We found that H460 and H1299 expressed the lowest and highest miR-4732-3p, respectively (Fig.  2 A). In order to determine the impact of miR-4732-3p on behaviors of NSCLC cells, we transfected H460 cells with miR-4732-3p mimics to overexpress it, but transfected H1299 cells with miR-4732-3p inhibitors to knock it down (Fig.  2 B).

miR-4732-3p inhibits the proliferation of NSCLC in vitro and in vivo. A  qRT-PCR analysis of miR-4732-3p expression in NSCLC cell lines. B  qRT-PCR was performed to examine miR-4732-3p expression in NSCLC cells transfected with miR-4732-3p mimics and inhibitors. C-E  Colony formation assays ( C-D ), and CCK8 assays ( E ) were performed to evaluate the proliferation ability of NSCLC cells transfected with miR-4732-3p mimics and inhibitors. F-G  Cell cycle assays were conducted to examine the effect of miR-4732-3p mimics and inhibitors on NSCLC cell cycle progression. H  Western blot analysis was used to detect the expression of G2/M phase-related protein in NSCLC cells transfected with miR-4732-3p mimics and inhibitors. I  qRT-PCR was performed to assess the expression of miR-4732-3p in H460 cells following infection with lentivirus engineered to overexpress miR-4732-3p (miR-4732-3p OE) and negative control (miR-NC). J  The indicated H460 cells (miR-NC and miR-4732-3p OE) were injected into nude mice and xenograft tumors were harvested. Representative images and measurement of tumor weight in xenograft tumors were shown ( n  = 6). K  Measurement and analysis of tumor volume in xenograft tumors with H460 cells stably overexpressing miR-4732-3p and miR-NC. L  Histological examination of xenograft tumors by hematoxylin and eosin (HE) staining and Ki-67 expression levels examined by immunohistochemistry (IHC) (Scale bars = 50 μm). M  Western blot analysis investigating the impact of miR-4732-3p on G2/M phase-related protein in vivo. Data are shown as the mean ± SD from at least three independent experiments. * p  < 0.05; ** p  < 0.01; *** p  < 0.001; **** p  < 0.0001; ns, not significant

Colony formation assays revealed a 30% decrease in colony numbers for H460 cells transfected with miR-4732-3p mimics compared to that in the control group transfected with non-targeting miR-NC (Fig.  2 C and D). Similarly, overexpression of miR-4732-3p led to a reduction of absorbance values from 1.60 to 1.15 in CCK8 assay, indicating a decreased number of viable H460 cells (Fig.  2 E). Collectively, the results from both colony formation and CCK8 assays underscore the pivotal role of miR-4732-3p in inhibiting the proliferation of NSCLC cells. We also performed phalloidin staining and transwell assays to determine the effects of miR-4732-3p on migration and invasion ability of NSCLC cells. Phalloidin staining assay displayed that NSCLC cells overexpressing miR-4732-3p became rounder and smaller, whereas NSCLC cells with miR-4732-3p knockdown exhibited notable morphological changes and more filopodia (Fig. S 2 A), which have been proven to be crucial for tumor invasion [ 16 ]. Also, transwell assay results suggested a decrease in the number of migratory and invasive H460 cells due to miR-4732-3p overexpression, whereas its depletion led to the opposite trend (Fig. S 2 B).

To elucidate the underlying mechanisms of miR-4732-3p in suppressing NSCLC cells proliferation, we performed a transcriptome sequencing on H460 cells with miR-4732-3p mimics and miR-NC and identified differentially expressed genes (DEGs) for subsequent Gene Ontology (GO) analysis (Fig. S 3 A). GO analysis revealed that the DEGs were primarily associated with the G2/M phase of cell cycle (Fig. S 3 B). Consistently, the results of cell cycle assays showed that cell cycle was arrested at the G2/M phase in NSCLC cells with miR-4732-3p overexpression, but accelerated at the G2/M phase in NSCLC cells with miR-4732-3p knockdown (Fig.  2 F and G). Moreover, the expression of G2/M phase-related protein, including cell division cycle 25C (CDC25C), cyclin dependent kinase 1 (CDK1), and CyclinB1, was noticeably decreased and increased in NSCLC cells with miR-4732-3p overexpression and knockdown, respectively (Fig.  2 H), corroborating the role of miR-4732-3p in G2/M phase arrest.

To evaluate the potential role of miR-4732-3p in vivo, we established a xenograft model by injecting nude mice with H460 cells stably overexpressing miR-4732-3p and miR-NC (Fig.  2 I). Our findings demonstrated that miR-4732-3p significantly impeded tumor growth, as evidenced by a substantial decrease in tumor weight (Fig.  2 J), reduced tumor growth rate (Fig.  2 K), and decreased Ki-67 expression within the tumors (Fig.  2 L). In alignment with our hypotheses, western blot analysis of the xenograft tumors further corroborated the inhibitory effect of stable miR-4732-3p overexpression on the expression of G2/M phase-related proteins (Fig.  2 M). Taken together, these results suggest that miR-4732-3p hinders NSCLC progression by inducing G2/M phase arrest both in vitro and in vivo.

NSCLC cells preferentially release miR-4732-3p into exosomes

The theory of "exosome escape" states that cancer cells must secrete suppressive circRNAs to maintain cancer cell fitness [ 17 ]. Consistent with it, we observed that miR-4732-3p, a tumor-suppressive miRNA, was abundantly expressed in serum fucosylated exosomes from NSCLC patients. We further investigated the secretion ability of NSCLC cells via exosomes for different miRNAs, including miR-4732-3p, as well as two tumor-promoting miRNAs, miR-92b-3p and miR-1180-3p [ 18 , 19 ]. These two miRNAs were found to be highly expressed in serum fucosylated exosomes from early-stage LUAD patients in our preliminary study [ 13 ]. Additionally, these two miRNAs have been elucidated to enhance the proliferation of NSCLC cells (Fig. S 4 A and B).

To determine the proportion of miRNAs secreted into exosomes, we first examined the levels of miR-4732-3p, miR-92b-3p, and miR-1180-3p in NSCLC cells (Fig.  3 A) and evaluated their expression following treatment with GW4869 (10 μM) to hinder exosome secretion. Our results showed that a greater proportion of miR-4732-3p was released extracellularly via exosomes, compared to miR-92b-3p and miR-1180-3p (Fig.  3 B). Moreover, we isolated fucosylated exosomes derived from NSCLC cells and validated their presence through transmission electron microscopy (Fig.  3 C), western blot (Fig.  3 D), and nanoparticle tracking analysis (Fig. S 5 B). Next, as depicted in Fig.  3 E, we separately co-cultured fucosylated exosomes enriched with miR-4732-3p, miR-92b-3p, or miR-1180-3p (Fig.  3 F-H) with NSCLC cells, and confocal microscopy revealed that exosomes were internalized by the NSCLC cells and localized around the nucleus (Fig.  3 I and S 4 C). Furthermore, we examined the expression of intracellular miR-4732-3p in NSCLC cells that took up fucosylated exosomes enriched with miR-4732-3p using qRT-PCR, demonstrating that the expression of intracellular miR-4732-3p did not increase following co-culturing. However, the intracellular expression of miR-4732-3p significantly upregulated in NSCLC cells treated with GW4869 (Fig.  3 J), suggesting the secretion ability of NSCLC cells for miR-4732-3p via exosomes. Furthermore, our data revealed that co-culturing with fucosylated exosomes enriched with miR-92b-3p or miR-1180-3p both led to higher intracellular levels of these two tumor-promoting miRNAs (Fig.  3 K and L). Overall, these findings support that NSCLC cells preferentially release miR-4732-3p via exosomes rather than retaining it intracellularly, in accordance with the "exosome escape" hypothesis.

NSCLC cells preferentially release miR-4732-3p into exosomes. A  Relative expression of miR-4732-3p, miR-92b-3p, and miR-1180-3p in NSCLC cells. B  NSCLC cells was treated with GW4869 (10 μM) to assess the effects of exosome secretion on intracellular miRNA expression via qRT-PCR analysis and the proportion of each miRNA sorted into exosomes was calculated. C  Images of fucosylated exosomes derived from NSCLC cells were photographed by transmission electron microscopy (TEM). Scale bar = 100 nm. D  Western blot analysis was performed to detect typical exosome markers, including TSG101, CD9, and Calnexin (negative control). E  Graphic illustration of co-culture system, in which we isolated fucosylated exosomes enriched with miRNA and added them into conditioned medium (CM) of NSCLC cells. F–H  Fucosylated exosomes enriched with miR-4732-3p ( F ), miR-92b-3p ( G ), and miR-1180-3p ( H ) were obtained following transfecting NSCLC cells with miRNA mimics. I  Internalization of PKH67-labeled fucosylated exosomes (green) by NSCLC cells. Scale bar = 10 μm. J-L  NSCLC cells were co-cultured with blank control (PBS), or exosomes rich in miR-4732-3p, miR-92b-3p, and miR-1180-3p. We further added GW4869 (10 μM) to the co-culture system and analyzed the expression of intracellular miRNA in NSCLC cells co-cultured with exosomes under conditions where exosome secretion was suppressed, but exosome uptake was allowed. qRT-PCR was applied to verify the expression of miR-4732-3p ( J ), miR-92b-3p ( K ), and miR-1180-3p ( L ) in NSCLC cells after co-culturing. Data are shown as the mean ± SD from at least three independent experiments. * p  < 0.05; ** p  < 0.01; *** p  < 0.001; **** p  < 0.0001; ns, not significant

Heterogeneous nuclear ribonucleoprotein K (hnRNPK) mediates selective sorting of miR-4732-3p into fucosylated exosomes

Specific motifs in miRNAs have been identified to influence their cellular retention or allocation into exosomes by interacting with RNA-binding proteins (RBPs) [ 20 ]. By utilizing the RBPsuite website to predict motifs, we discovered a potential binding sequence between hnRNPK and miR-4732-3p (Fig.  4 A). Previous studies have reported that hnRNPK, a member of the hnRNP protein family, might play an important role in sorting specific RNAs into exosomes [ 21 ]. In order to validate this interaction, we conducted RNA immunoprecipitation (RIP) and miRNA pull-down assays to confirm the enrichment of miR-4732-3p using the hnRNPK antibody (Fig.  4 B) and the binding ability of miR-4732-3p with hnRNPK protein (Fig.  4 C), respectively. To investigate the involvement of hnRNPK in sorting miR-4732-3p into fucosylated exosomes, we conducted knockdown experiments targeting hnRNPK using siRNAs (Fig.  4 D). Our results showed that hnRNPK knockdown led to increased levels of miR-4732-3p in NSCLC cells (Fig.  4 E), but decreased expression of miR-4732-3p in fucosylated exosomes derived from NSCLC cells (Fig.  4 F). Furthermore, we determined that NSCLC cells with miR-4732-3p overexpression secreted fucosylated exosomes with elevated levels of miR-4732-3p. However, hnRNPK knockdown could counteract the elevation effects of intracellular miR-4732-3p expression on exosomal miR-4732-3p (Fig.  4 G), further indicating the involvement of hnRNPK in the sorting of miR-4732-3p into fucosylated exosomes.

miR-4732-3p is sorted into fucosylated exosomes via hnRNPK. A  Sequence motifs of hnRNPK binding site predicted by RBPsuite. B  RIP assay using hnRNPK antibody and qRT-PCR were performed, anti-IgG as the negative control. C  miRNA pull-down and western blot analysis were conducted to confirm interaction between miR-4732-3p and hnRNPK protein. D  Western blot analysis was applied to define knockdown efficiency of hnRNPK in NSCLC cells transfected with si-hnRNPK. E–F  qRT-PCR analysis of miR-4732-3p levels in NSCLC cells ( E ), and their corresponding fucosylated exosomes ( F ) following hnRNPK knockdown. G  qRT-PCR analysis was performed to determine the effects of miR-4732-3p mimics and si-hnRNPK on the levels of fucosylated exosomal miR-4732-3p. Data are shown as the mean ± SD from at least three independent experiments. * p  < 0.05; ** p  < 0.01; *** p  < 0.001; **** p  < 0.0001; ns, not significant

hnRNPK promotes NSCLC cell proliferation in vitro and in vivo

Regarding the role of hnRNPK in NSCLC cells proliferation, we initially performed CCK8 (Fig.  5 A) and colony formation assays (Fig.  5 B), which demonstrated a inhibition of NSCLC cell proliferation and colony formation ability upon knockdown of hnRNPK. To further investigate the impact of hnRNPK in vivo, a xenograft model was established by injecting nude mice with H460 cells in which hnRNPK was stably knocked down (Fig.  5 C). Our findings showed that the knockdown of hnRNPK suppressed tumor growth, as evident from a notable reduction in tumor weight (Fig.  5 D), tumor growth rate (Fig.  5 E), and Ki-67 expression in xenograft tumors (Fig.  5 F). Regarding the the sorting of miR-4732-3p into fucosylated exosomes, we detected miR-4732-3p expression in xenograft tumor tissues and serum fucosylated exosomes via qRT-PCR. There were elevated levels of miR-4732-3p in xenograft tumor tissues (Fig.  5 G), but declined miR-4732-3p expression in serum fucosylated exosomes from nude mice with hnRNPK knockdown (Fig.  5 H). Together, these results suggest that hnRNPK promotes NSCLC progression by selectively sorting miR-4732-3p into fucosylated exosomes, which are subsequently released into circulation.

Knockdown of hnRNPK suppresses NSCLC in vitro and in vivo. A-B  The proliferation ability of NSCLC cells transfected with hnRNPK siRNAs was evaluated through CCK8 (A) and colony formation assays ( B ). C  H460 cells were infected with lentivirus engineered to knock down hnRNPK (sh-hnRNPK) and negative control (sh-NC). The protein expressions were analyzed by western blot. D  The indicated H460 cells (sh-NC and sh-hnRNPK) were injected into nude mice and xenograft tumors were harvested. Representative images and measurement of tumor weight in xenograft tumors were shown ( n  = 6). E  Measurement and analysis of tumor volume in xenograft tumors of sh-NC and sh-hnRNPK groups. F  Histological examination of xenograft tumors through HE staining and analysis of Ki-67 expression levels by IHC (Scale bars = 50 μm). G  qRT-PCR analysis of miR-4732-3p expression in xenograft tumor tissues with stably hnRNPK knockdown. H  qRT-PCR analysis of fucosylated exosomal miR-4732-3p expression in serum from nude mice with stably hnRNPK knockdown. Data are shown as the mean ± SD from at least three independent experiments. * p  < 0.05; ** p  < 0.01; *** p  < 0.001; **** p  < 0.0001; ns, not significant

miR-4732-3p induces G2/M arrest by regulating MFSD12/AKT/p21 axis in NSCLC cells

miRNAs have been proven to regulate gene expression by binding to the 3'UTR of mRNA, thereby resulting in mRNA degradation or translation inhibition. These regulatory mechanisms play a pivotal role in maintaining the balance of gene expression and biological processes. To determine which genes are regulated by miR-4732-3p, we employed the TargetScan website to predict the target genes with the potential binding sites of miR-4732-3p. Among these genes, major facilitator superfamily domain containing 12 (MFSD12) attracted our attention, because MFSD12 showed a significant downregulation with the highest fold change in DEGs of transcriptome sequencing analysis on H460 cells with miR-4732-3p mimics and miR-NC (Fig.  6 A) and MFSD12 has ever been reported to promote cancer cell proliferation [ 22 ]. qRT-PCR results also revealed that miR-4732-3p overexpression in H460 cells led to a significant decrease of MFSD12 mRNA, whereas miR-4732-3p knockdown in H1299 cells caused an increase of MFSD12 mRNA (Fig.  6 B), suggesting that MFSD12 is the potential target of miR-4732-3p. To prove that MFSD12 is a direct target of miR-4732-3p, we generated reporter plasmids by inserting 3'UTR of MFSD12 with intact miR-4732-3p binding site (WT) or mutated miR-4732-3p binding site (MUT) to the 3' terminal of luciferase gene (Fig.  6 C), and then performed dual-luciferase reporter assays in 293T cells. Our results showed that miR-4732-3p could reduce luciferase activity in the MFSD12 WT group, but had no effect on MUT group (Fig.  6 D), providing evidence that miR-4732-3p directly targeted MFSD12 mRNA and inhibited the expression of MFSD12. Furthermore, the function of MFSD12 in NSCLC cells was determined through CCK8 assays, which showed that MFSD12 partially attenuated the inhibitory effects of miR-4732-3p on NSCLC cells proliferation (Fig.  6 E), suggesting that miR-4732-3p suppresses NSCLC cells proliferation by targeting MFSD12.

miR-4732-3p targets 3'UTR of MFSD12 in NSCLC cells. A  Volcano plot presenting significantly differentially expressed genes (DEGs) (|log2FC|>1 and p  < 0.05) between two groups: H460 cells transfected with miR-4732-3p mimics and miR-NC. B  qRT-PCR analysis of MFSD12 mRNA expression in NSCLC cells following transfection of miR-4732-3p mimics and inhibitors. C  Predicted binding sites between miR-4732-3p and the 3'UTR of MFSD12 mRNA. D  Luciferase assay performed in 293T cells to validate the binding interaction between miR-4732-3p and MFSD12. E  CCK8 assays examining the proliferation of NSCLC cells with overexpression or suppression of miR-4732-3p and MFSD12. F-G  Western blot analysis of MFSD12 protein and the AKT/p21 signaling pathway in NSCLC cells after treatment as described above ( F ), and in xenograft tumors with miR-4732-3p overexpression ( G ). H-I  Cell cycle analysis performed to assess the combined effects of miR-4732-3p and MFSD12 in NSCLC cells. J  Western blot analysis conducted to investigate the AKT/p21 signaling pathway and G2/M phase-related protein after the indicated treatment. Data are shown as the mean ± SD from at least three independent experiments. * p  < 0.05; ** p  < 0.01; *** p  < 0.001; **** p  < 0.0001; ns, not significant

Due to the downregulation of AKT expression observed in transcriptome sequencing results and previous studies highlighting the regulatory effects of the AKT/p21 pathway on cell cycle [ 23 ], our further investigation aimed to elucidate how MFSD12 regulates NSCLC progression via the AKT/p21 signaling pathway. We first performed western blot analysis to assess AKT/p21 expression, demonstrating decreased phosphorylation of AKT and significantly elevated p21 expression in H460 cells overexpressing miR-4732-3p (Fig.  6 F). Likewise, western blot analysis of xenograft tumors also confirmed the effects of miR-4732-3p overexpression on the downregulation of protein expression in the AKT/p21 signaling pathway in vivo (Fig.  6 G). Furthermore, cell cycle assays showed that the G2/M arrest induced by miR-4732-3p mimics in H460 cells could be alleviated by MFSD12 overexpression (Fig.  6 H and I). Likewise, the reduced phosphorylation of AKT and the G2/M phase-related proteins were counteracted, and the elevated levels of p21 declined accordingly upon overexpression of both miR-4732-3p and MFSD12 in H460 cells (Fig.  6 J), suggesting that MFSD12 enhances the AKT/p21 signaling pathway and contributes to cell cycle progression. Herein, we believe that intracellular miR-4732-3p induces G2/M arrest and suppresses NSCLC progression via MFSD12/AKT/p21 axis.

MFSD12 is upregulated in NSCLC and associated with unfavorable prognosis

To further investigate MFSD12 mRNA expression in human NSCLC, we utilized the ENCORI database and found increased MFSD12 mRNA expression in both LUAD and LUSC tissues (Fig.  7 A). Moreover, higher levels of MFSD12 in NSCLC tissues were associated with unfavorable outcomes, as observed in the Kaplan-Meier plotter website, which suggests its potential oncogenic role (Fig.  7 B).

MFSD12 is upregulated in NSCLC and associated with unfavorable prognosis. A  ENCORI database analysis depicting the MFSD12 mRNA expression in LUAD and LUSC tissues. B  Kaplan–Meier analysis illustrating the negative relationship between MFSD12 expression and overall survival (OS) rates of LUAD and LUSC patients. C  Proposed working model depicting the mechanism by which tumor-suppressive miR-4732-3p is sorted into fucosylated exosomes through its interaction with hnRNPK protein, thereby promoting NSCLC progression. Data are shown as the mean ± SD from at least three independent experiments. **** p  < 0.0001

Based on the findings in this study, we proposed a working model: tumor-suppressive miR-4732-3p inhibits NSCLC progression by targeting MFSD12 to downregulate AKT signaling, which leads to p21-mediated G2/M cell cycle arrest; NSCLC cells avoid suppressive effects of miR-4732-3p through hnRNPK-mediated sorting of miR-4732-3p into fucosylated exosomes (Fig.  7 C).

Exosomal miRNAs have emerged as essential roles in intercellular communication and tumor progression [ 24 ]. As a potent biological sample recommended for depicting pulmonary health conditions, bronchoalveolar lavage fluid (BALF) entails the invasiveness and patient discomfort during acquisition. Compared with BALF, blood sample presents the advantages of both noninvasion and readily accession. Moreover, there are currently consistent performances of biomarkers between BALF and blood sample. For example, miR-223 and miR-142 expressions have been reported to consistently upregulate in both BALF and sera during pneumonia [ 25 ]; another study revealed 10 cell cycle-regulatory biomarkers with similar expression patterns by analyzing and comparing exosomal miRNA profiles derived from BALF and plasma of Icotinib-resistant NSCLC patients [ 26 ]. Therefore, we opted to collect serum sample as an alternative access of BALF to explore NSCLC-related exosomal miRNAs, aiming to provide an optimal understanding for NSCLC progression by noninvasive liquid biopsy approach.

Especially mentioned, aberrantly high fucosylation in cancer cells usually results in an augmented release of fucosylated exosomes, a specific type of exosomes bearing fucose molecules on their surface, which might yield valuable insights into the occurrence and development of malignancies [ 27 ]. Our pilot study employed the fucose-captured strategy based on lentil lectin-magnetic beads to isolate serum fucosylated exosomes [ 13 ]. Ultracentrifugation (UC) is widely recognized as the "gold standard" and the most commonly employed method for exosome isolation [ 28 ]. In this study, we determined whether the fucose-capture strategy is preferable to the traditional UC for enriching exosomes derived from NSCLC cells. Initially, we isolated exosomes from cell conditioned medium (CM) of two NSCLC cells (H460 and H1299), as well as two normal cells (BEAS-2B and 293T), using both isolation methods, and then performed nanoparticle tracking analysis (NTA) to compare the sizes and concentration of exosomes. To determine the effectiveness of the fucose-captured strategy for capturing tumor-derived exosomes, we utilized the particle number ratio (Fucosylated Exo/UC Exo) as a quantitative measurement (Fig. S 5 A). Notably, we observed a higher ratio in the CM of NSCLC cells compared to normal cells, indicating the selective enrichment of tumor-derived exosomes through the fucose-captured strategy (Fig. S 5). These findings suggest that capturing fucosylated exosomes is conducive to enriching tumor-derived exosomes and reliably depicting the miRNA profiles that reflect tumor progression. In our pilot study, the fucose-captured strategy, which was implemented to isolate serum exosomes derived from tumors, exhibited a promising potential of serum fucosylated exosomal miRNAs including miR-4732-3p for the early diagnosis of LUAD [ 13 ]. In this study, miR-4732-3p was demonstrated to be significantly upregulated in serum fucosylated exosomes from NSCLC patients while declined in NSCLC tissues. Notably, Kaplan–Meier plotter analysis revealed a positive correlation between the expression of miR-4732 and the overall survival (OS) rates of NSCLC patients. Thus, it is reasonable to deduce that miR-4732-3p, which is highly expressed in serum fucosylated exosomes from NSCLC patients, might act as a protective factor for NSCLC patients.

It is well documented that cancer cells could discard certain tumor-suppressive molecules, including circRNAs and miRNAs, through exosomes to maintain tumor development [ 29 , 30 ], a process known as the "exosome escape" theory [ 17 ]. Analogously, we demonstrated that miR-4732-3p, released by NSCLC cells via exosomes, exhibited markedly suppressive effects on the proliferation of NSCLC cells by targeting MFSD12. MFSD12, involved in the import of cysteine into melanosomes and lysosomes [ 31 , 32 ], has been reported to enhance melanoma cell proliferation [ 22 ]. However, its function in NSCLC remains undetermined. Herein, we elucidated the role of MFSD12 in promoting tumor growth, wherein its overexpression partially mitigated the suppressive effects of miR-4732-3p on NSCLC cells. It is currently acknowledged that AKT phosphorylation is initiated by a variety of upstream signals, notably the activation-induced stimulation of phospoinositide 3-kinase (PI3K) stemming from insulin/IGF receptors or the engagement with growth factors. Notably, several negative modulators, including phosphatase and tensin homolog (PTEN), mechanistic target of rapamycin complex 2 (mTORC2), and PH domain leucine-rich repeat protein phosphatase (PHLPP), have been documented to directly suppress AKT protein phosphorylation [ 33 ]. Moreover, there have been reports suggesting that ubiquitin-mediated degradation of heat shock protein 90 (Hsp90) can indeed promote AKT phosphorylation [ 34 ]. Our findings revealed that the miR-4732-3p-mediated downregulation of MFSD12 did not affect total AKT protein abundance; however, it significantly dampened the phosphorylation of the AKT protein. Consequently, MFSD12 protein seems to exert an indirect influence on AKT phosphorylation regulation. It's reasonable to deduce that the reduced expression of MFSD12 could hinder upstream AKT signaling cascades to cause a decline of AKT phosphorylation level, by potentially decreasing PI3K activity or augmenting the expression of negative regulators which will be defined in our future study. Furthermore, dysregulation of AKT is responsible for cancer cell survival, and the AKT/p21 signaling pathway serves as a regulatory mechanism of cell cycle progression at both the G1/S and G2/M checkpoints [ 35 ]. We initially disclosed G2/M arrest according to cell cycle results, along with a corresponding decrease in expression of G2/M-phase related proteins including CDK1 and CyclinB1, which were attributed to miR-4732-3p. Our further research results showed that the upregulated expression of p21 mainly caused the suppression of cyclin-dependent kinase activity during the G2/M phase. In conclusion, miR-4732-3p suppresses NSCLC cell proliferation and tumor growth by modulating the MFSD12/AKT/p21 signaling pathway and inducing G2/M arrest.

Exosomes have gained increasing attentions based on their cargo transportation and biological function [ 36 , 37 ]. Current studies have stated that the levels of miRNAs in exosomes positively correlate with their expression in the originating cells, implying that miRNAs, highly expressed in cells, are likewise abundantly present in exosomes [ 5 ]. Nevertheless, mounting evidence has suggested that cancer cells exhibit a complex miRNA secretion profile. In addition to actively releasing highly expressed tumor-promoting miRNAs, they also discard tumor-suppressive miRNAs, which are believed to contribute to the maintenance and enhancement of malignant properties in cancer cells [ 38 , 39 ]. In our pilot study, we identified certain upregulated serum fucosylated exosomal miRNAs associated with early diagnosis of LUAD, including a combination of tumor-promoting miRNAs and tumor-suppressive miRNAs [ 13 ]. However, the extent to which miRNAs with tumor-promoting or tumor-suppressive effects could be sorted into fucosylated exosomes remains unclear. Herein, we demonstrated that NSCLC cells preferentially secreted tumor-suppressive miR-4732-3p over tumor-promoting miRNAs, including miR-92b-3p and miR-1180-3p via exosomes. Moreover, a large quantity of fucosylated exosomal miR-4732-3p taken up by NSCLC cells did not give rise to intracellular miR-4732-3p levels, indicating the predominant extracellular release of miR-4732-3p via exosomes which serve as irreplaceable carriers in NSCLC cells. However, transfection with miR-4732-3p mimics disrupted the balance between intracellular uptake and sorting processes, causing a surge in the intracellular expression of miR-4732-3p. Furthermore, we observed that the expression of serum fucosylated exosomal miR-4732-3p exhibited an elevation with the development of NSCLC, indicating that NSCLC cells may discard a higher quantity of tumor-suppressive miR-4732-3p into circulation via fucosylated exosomes in order to evade suppressive effects of miR-4732-3p and ultimately foster NSCLC progression.

Accumulating evidence supports the involvement of RBPs in selectively sorting miRNAs into exosomes [ 40 , 41 ]. RBPs have been shown to interact with intracellular miRNAs to form RNA-loaded RBPs which are subsequently recruited to the sites of exosomes budding and sorted into exosomes [ 5 ]. The hnRNPK protein, a member of the heterogeneous nuclear ribonucleoprotein (hnRNP) family of RNA-binding proteins, plays crucial roles in multiple facets of RNA metabolism, including mRNA precursor splicing, transcription regulation, and regulation of miRNA [ 42 , 43 ]. It has also been recognized for its involvement in the sequence-dependent packaging of miRNAs into exosomes [ 21 ]. Our findings validated the interaction between hnRNPK and miR-4732-3p and revealed its role in facilitating the selective packaging of miR-4732-3p into fucosylated exosomes for extracellular release. These results underscore the critical involvement of hnRNPK in the "exosome escape" of tumor-suppressive miR-4732-3p, unveiling a pivotal target for NSCLC therapy.

In summary, these findings shed light on the significant involvement of miR-4732-3p in NSCLC progression. Intracellularly, miR-4732-3p induces G2/M arrest and inhibits proliferation of NSCLC cells by targeting 3'UTR of MFSD12, thereby inhibiting AKT/p21 signaling pathway. In contrast, NSCLC cells preferentially secrete miR-4732-3p via hnRNPK into fucosylated exosomes and discard it extracellularly instead of retaining it intracellularly so as to escape tumor-suppressive effects of miR-4732-3p. Additionally, the knockdown of hnRNPK causes elevated levels of miR-4732-3p in NSCLC tissues and decreased levels of serum fucosylated exosomal miR-4732-3p, thereby suppressing NSCLC cells proliferation. Moreover, a significant amount of miR-4732-3p, discarded by NSCLC cells, is released into the bloodstream via fucosylated exosomes, resulting in a notable increase in the levels of serum fucosylated exosomal miR-4732-3p during NSCLC progression, thus highlighting its potential as a novel serum biomarker for the diagnosis and monitoring of NSCLC. Consequently, further investigation is warranted to elucidate the mechanisms underlying the selective sorting of additional tumor-suppressive miRNAs into fucosylated exosomes, and to devise a strategy targeting hnRNPK to prevent the "exosome escape" of tumor-suppressive miR-4732-3p from NSCLC cells, thereby opening up a new avenue for improving the five-year survival rates of NSCLC patients.

Materials and methods

Patients samples and cell lines.

Human serum samples were obtained from 96 patients diagnosed with NSCLC, 30 patients with BPN (benign pulmonary nodule), and 32 HCs (healthy controls) at Fujian Provincial Hospital between January 2022 and December 2022. All serum samples were preserved at a temperature of -80℃ following centrifugation. In addition, tissue samples were collected from a total of 40 NSCLC patients, all of whom were histologically confirmed [ 44 ]. Written informed consent has been obtained and the study was approved by the Ethics Committee of Fujian Provincial Hospital (approval number: K2018-12–040/02, from January 2019 to December 2023).

Human NSCLC cell lines, including A549, H460, H226, H1299, and SK-MES-1, along with human normal lung epithelial cells (BEAS-2B) and human renal epithelial cells (293T), were all purchased from Procell (Wuhan, China). Both H460 and H1299 cells were maintained in Roswell Park Memorial Institute (RPMI) 1640 medium (Gibco, USA), enriched with 10% fetal bovine serum (FBS) sourced from Procell (Wuhan, China). Meanwhile, A549, H226, SK-MES-1, BEAS-2B, and 293T cells were cultured in Dulbecco's modified Eagle's medium (DMEM) (Gibco, USA), also supplemented with 10% FBS. All cell lines were authenticated through short tandem repeat profiling and were cultured strictly at 37℃ in a cell culture incubator with a controlled environment of 5% CO 2 .

Fluorescence in situ hybridization (FISH)

The specific fluorescently labeled miR-4732-3p FISH probes were designed and synthesized by Servicebio (Wuhan, China), and the experiment was conducted following the manufacturer's instructions. All images were acquired on Nikon A1Si Laser Scanning confocal microscope (Nikon Instruments Inc., Japan).

Cell transfection and stable cell lines

miR-4732-3p mimics and inhibitors, hnRNPK small interfering RNAs (siRNAs), and pcDNA3.1-MFSD12 overexpression and knockdown plasmids were designed and constructed by Zolgene (Fuzhou, China). EntransterTM-R4000 from Engreen (Beijing, China) was obtained to transfect miR-4732-3p mimics and inhibitors, and hnRNPK siRNAs. EntransterTM-H4000 Transfection reagent (Engreen) was applied to transfect plasmids as per instructions. After 24 h of transient transfection, the cells were harvested for subsequent cell functional assays.

To establish NSCLC cells with stable overexpression or suppression of miR-4732-3p and hnRNPK, lentiviral plasmids were constructed by Hanbio (Shanghai, China). Following lentiviral infection at an appropriate multiplicity of infection (MOI) value for 72 h and selection with puromycin, stably transfected NSCLC cells were obtained. Additionally, qRT-PCR and western blot analysis were conducted to assess the efficiency of transfection and infection. All sequences against specific targets are provided in the Supplementary Tables .

Cell proliferation assays

Colony formation assay and CCK8 assay were carried out to assess the proliferation of NSCLC cells with transient transfection. For colony formation assay, cells were placed at 6-well plates and allowed to grow for weeks. Subsequently, the developed colonies were fixed, stained and counted using a light microscope. Additionally, cell viability was evaluated with cell counting kit-8 (CCK8) according to instructions (Cellcook, China). At specific time points, the CCK8 solution was added into wells and the absorbance was measured at 450 nm (OD450) using a microplate reader following a 1-h of incubation.

Cell cycle assay

To synchronize the cell cycle, NSCLC cells were incubated with nocodazole (100 nM) for 14 h, effectively leading to their arrest at the G2-M phase transition point. Subsequently, cell cycle assay was conducted to determine the distribution of each cell cycle phase in NSCLC cells with transient transfection. Cells were first thoroughly washed with precooled PBS, subsequently fixed with 75% ethanol and incubated overnight at 4℃. On the following day, after centrifugation and another round of washing with PBS, the cell pellets were collected. The staining working solution containing propidium iodide (PI) and RNase A was then prepared (Meilunbio, China). The harvested cell pellets were resuspended in the freshly prepared staining working solution and incubated in the dark at 37℃ for 30 min. Upon completion of the staining process, cells were subjected to flow cytometry analysis for cell cycle determination, and the percentages of cells in each phase were quantified using ModFit LT software version 5.0.

Transwell assays

Cell migration and invasion were assessed using 8.0 µm 24-well transwell plates (Corning, USA). The upper chamber was seeded with 50,000 cells, transfected with miR-4732-3p mimics and inhibitors, in RPMI 1640 medium, whereas the bottom chamber was full of medium containing 12% FBS. After a 48-h incubation, cells that invaded the membrane or migrated to the bottom chamber were fixed in 4% paraformaldehyde (PFA), stained with 0.1% crystal violet, and counted in five randomly selected microscope fields. Of note, the upper surface of the membrane was coated with 60 µL of Matrigel (BD Biosciences, USA) for the invasion assay.

Phalloidin staining

After fixed in 4% PFA, NSCLC cells transfected with miR-4732-3p mimics and inhibitors were permeabilized using 0.1% Triton X-100 and subsequently blocked with 5% bovine serum albumin (BSA) in phosphate buffered saline (PBS) for 30 min. Next, staining for F-actin and DAPI was done to visualize cell structures, followed by images acquisition using confocal microscopy.

Exosome isolation and validation

Exosomes from serum samples and cells CM were isolated using the fucose-captured strategy which bases on the specific affinity of lentil lectin-magnetic beads to fucosylated exosomes according to standard protocols [ 13 ]. Similarly, exosomes in cells CM were also isolated through ultracentrifugation (UC) as described in Figure S 5 A. To visualize the obtained exosomes using transmission electron microscopy (TEM), they were fixed and placed onto grids coated with copper mesh Formvar; 2% phosphotungstic acid was added to the grids for staining, and the exosomes were observed under a JEOL TEM (model JEM1230, Japan). Additionally, the concentration and size distribution of obtained exosomes were assessed using Nanoparticle Tracking Analysis (NTA) with ZetaView (Particle Metrix, Germany) and the obtained data were then analyzed using the accompanying software, ZetaView 8.04.02.

Co-culture and exosome uptake assay

A co-culture assay was conducted to investigate the trafficking of exosomal miRNA derived from NSCLC cells. We first transfected NSCLC cells with miR-4732-3p mimics and isolated their secreted exosomes, thus obtaining exosomes enriched with miR-4732-3p. Using this method, we also obtained exosomes that are enriched with miR-92b-3p or miR-1180-3p. To monitor exosome uptake, exosomes obtained from cell CM were labeled with the PKH67 fluorescent cell linker and suspended in PBS following careful isolation. Subsequently, stained exosomes were incubated with NSCLC cells and observed using confocal fluorescence microscopy after staining cell nuclei with DAPI. During co-culture of NSCLC cells, GW4869 (10 µM, Cayman Chemical, USA), an inhibitor of exosome secretion, was also utilized to evaluate the role of exosome in releasing miR-4732-3p extracellularly.

RNA extraction and qRT-PCR

To extract total RNA from exosomes, we used the miRNeasy® Mini Kit (Qiagen, Germany). Subsequent qRT-PCR was performed using reagent kits from Tiangen (Beijing, China).

For the extraction of total RNA from NSCLC cells, NucleoZOL (Takara, Japan) was utilized following a standard protocol. Similarly, qRT-PCR was performed using reagent kits from Takara. The primers synthesized by Shangya (Fuzhou, China) were listed in the Supplementary materials .

Western blot

Total protein was extracted and protein concentrations were measured using protein assay kits from Solarbio (Beijing, China). The following procedures for western blot were performed: total protein was separated and transferred to a PVDF membrane. After blocking the membrane with 5% BSA, we incubated the membrane with various primary antibodies overnight at 4℃ and then with secondary antibodies. Finally, protein detection was performed using a chemiluminescence instrument.

Transcriptome sequencing

We prepared H460 cells with miR-4732-3p overexpression and miR-NC ( n  = 3). RNA was extracted and used for illumina sequencing technology. The data generated was analyzed using the online platform from Majorbio for further analysis.

Public data and bioinformatics analysis

We analyzed miRNA sequencing data with Sequence Read Archive (SRA) accession number PRJNA847004, consisting of serum fucosylated exosomes from eight HCs, eight BPNs, and eight early LUAD patients. miRNA-seq data specific to NSCLC tissues was obtained from cBioPortal ( http://www.cbioportal.org/public-porta ) to identify DEmiRs in NSCLC tissues. As for the levels of miR-4732-3p and MFSD12 in malignancies, GEDs ( http://bioinfo.life.hust.edu.cn/web/GEDS/ ) and ENCORI ( https://rnasysu.com/encori/index.php ) databases were applied. Moreover, we assessed the correlation between miR-4732/MFSD12 expression and OS rates of patients with LUAD and LUSC, using the Kaplan–Meier plotter ( http://kmplot.com ). To predict binding sites between MFSD12 and miR-4732-3p, we used TargetScan ( http://www.targetscan.org/vert_80/ ), a database that provides information on miRNA-target interactions. In addition, we investigated RBPs that potentially bind to miR-4732-3p by utilized RBPsuite website ( www.csbio.sjtu.edu.cn/bioinf/RBPsuite/ ), which offers tools for predicting potential protein-RNA interactions.

Dual-luciferase reporter assay

Dual-luciferase reporter plasmids, consisting of MFSD12 wild-type (WT), mutant (MUT), and control (CON), were constructed in psicheck2.0 vectors (Zolgene, China). 293T cells were co-transfected with reporter plasmids and miR-4732-3p mimics using EntransterTM-H4000 and EntransterTM-R4000 transfection reagents from Engreen. Cell lysates were obtained to perform luciferase reporter assays following transfection for 24 h.

RNA immunoprecipitation (RIP) assay

Cell lysates were immunoprecipitated with magnetic beads conjugated with hnRNPK antibody using the PureBinding Kit (Geneseed, China), with IgG (ab205718, Abcam) as negative control. Finally, the captured RNA were detected by qRT-PCR.

miRNA pull-down assay

We utilized the miRNA-protein pull-down kit from Geneseed to validate the interaction between hnRNPK and miR-4732-3p. Biotinylated miR-4732-3p and its mutant sequences were incubated with the cell lysates to facilitate binding with their respective interacting proteins. Finally, the eluted protein from the RNA–protein complex was determined based on the results obtained from western blot analysis.

Animal experiments

Specific pathogen-free BALB/c nude mice, aged four to six weeks, were randomly divided into two groups ( n  = 6), and performed subcutaneous injections of 5 × 10 6 cells in 100 μL serum-free RPMI 1640 and Matrigel (1:1) to establish cell line-derived xenograft (CDX) models. One group of mice received subcutaneous injections with H460 cells stably overexpressing miR-4732-3p (miR-4732-3p OE group) while the other group received H460 cells with only vectors (miR-NC group). CDX models were also developed using H460 cells, wherein hnRNPK was stably suppressed (sh-hnRNPK group) or with only vectors (sh-NC group). Tumor size was measured every 3 days and calculated using the formula: (length × width 2 ) × 0.5 after measuring the length and width with the vernier caliper. Three weeks after tumor implantation, the mice were sacrificed for subsequent experiments. Consequently, all xenograft tumors were fixed, embedded, and stained with hematoxylin and eosin (HE).

Besides the aforementioned experiments, total RNA from xenograft tumor tissues and serum fucosylated exosomes of nude mice in sh-hnRNPK and sh-NC groups were extracted for further analysis. All animal experiments have been approved by the Animal Ethics Committee of Fujian Ambri Biotechnology Co., Ltd (approval number: IACUC FJABR 2022101101, from October 2022 to October 2023).

Immunohistochemistry (IHC)

All paraffin sections of xenograft tumor tissues were deparaffinized, rehydrated, and then incubated with Ki-67 antibody after blocking. The following day, images were collected and analyzed following incubation with the secondary antibody and staining with DAB and hematoxylin. As for calculating the Ki-67 staining results, we counted 200 cells in each high-power field of view and recorded the number of Ki-67-positive cells (brown). The percentage obtained by dividing the number of Ki-67-positive cells by the total counted cells is the Ki-67-positive cells (%). Take the average of five fields of view as the Ki-67 expression level of the sample.

Statistics analysis

Data are shown as mean ± standard deviation (SD) from at least three independent experiments. Statistical analysis was performed using GraphPad Prism 9.3. The Student's t-test was employed to assess differences between groups, while one-way analysis of variance (ANOVA) was utilized for analysis involving three or more groups. ROC curve and AUC value were determined to measure the performance of the diagnostic model. Relationship between serum fucosylated exosomal miR-4732-3p expression and clinicopathological feature was analyzed by Chi-square test. The cumulative OS rates were calculated using the Kaplan–Meier method, and significance was evaluated with the log-rank test. A significance level of p  < 0.05 was considered statistically significant, unless specially noted otherwise.

Availability of data and materials

All data pertinent to this study are contained within the article or can be obtained from the corresponding author upon a reasonable request.

Abbreviations

Non-small cell lung cancer

Lung adenocarcinoma

Lung squamous cell cancers

Major facilitator superfamily domain containing 12

Heterogeneous nuclear ribonucleoprotein K

Benign pulmonary nodule

Healthy control

Real-time quantitative PCR

Differentially expressed miRNAs

Differentially expressed genes

Receiver operating characteristic

Area under the curve

Fluorescence in situ hybridization

Overall survival

Negative control

Cell division cycle 25C

Cyclin dependent kinase 1

Conditioned medium

Ultracentrifugation

Transmission electron microscopy

Nanoparticle Tracking Analysis

• Fucosylated exosome

Ultracentrifugation exosome

RNA-binding protein

RNA immunoprecipitation

Phospoinositide 3-kinase

Phosphatase and tensin homolog

Mechanistic target of rapamycin complex 2

PH domain leucine-rich repeat protein phosphatase

Heat shock protein 90

Fetal bovine serum

Roswell Park Memorial Institute

Dulbecco's modified Eagle's medium

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Acknowledgements

This study was supported in part by Medical Vertical Project of Fujian Province (Grant No. 2020CXB001) to Yi Huang, Joint Fund of Science and Technology Innovation of Fujian Province (Grant No. 2021Y9024) to Yi Huang, Key Project of Natural Science Foundation of Fujian Province (Grant No. 2022J02048) to Yi Huang.

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Wanzhen Zhuang, Chengxiu Liu, and Yilin Hong contributed equally to this work.

Authors and Affiliations

Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China

Wanzhen Zhuang, Yue Zheng, Minjian Huang, Haijun Tang, Lilan Zhao, Mingshu Tu, Lili Yu, Jianlin Chen, Yi Zhang, Xiongfeng Chen, Chundong Yu & Yi Huang

Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, 350001, China

Wanzhen Zhuang, Yue Zheng, Zhixin Huang, Mingshu Tu, Lili Yu, Jianlin Chen, Yi Zhang & Yi Huang

State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, 361102, China

Chengxiu Liu, Yilin Hong & Chundong Yu

Institute of Future Technology, Beijing Hotgen Biotech Co., Ltd, Beijing, 102600, China

Chengxiu Liu & Qi Gao

Center for Experimental Research in Clinical Medicine, Fujian Provincial Hospital, Fuzhou, 350001, China

Minjian Huang, Haijun Tang, Chundong Yu & Yi Huang

Department of Thoracic Surgery, Fujian Provincial Hospital, Fuzhou, 350001, China

Integrated Chinese and Western Medicine College, Fujian University of Traditional Chinese Medicine, Fuzhou, 350108, China

Zhixin Huang

Department of Scientific Research, Fujian Provincial Hospital, Fuzhou, 350001, China

Xiongfeng Chen

Department of Geriatric Medicine, Fujian Provincial Hospital, Fuzhou, 350001, China

Fujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, 350001, China

Fan Lin & Yi Huang

Central Laboratory, Fujian Provincial Hospital, Fuzhou, 350001, China

Fujian Provincial Key Laboratory of Critical Care Medicine, Fujian Provincial Key Laboratory of Cardiovascular Disease, Fuzhou, 350001, China

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Contributions

WZZ, CXL, and YLH conducted all experiments and wrote the original draft. YZ, MJH, and HJT collected tissue and serum samples. LLZ, ZXH, YZ, and XFC analyzed data. MST, LLY, JLC, FL, and QG provided experiment assistance. YH and CDY performed manuscript revisions and handled project administration. All authors approved the final version before submission.

Corresponding authors

Correspondence to Chundong Yu or Yi Huang .

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Zhuang, W., Liu, C., Hong, Y. et al. Tumor-suppressive miR-4732-3p is sorted into fucosylated exosome by hnRNPK to avoid the inhibition of lung cancer progression. J Exp Clin Cancer Res 43 , 123 (2024). https://doi.org/10.1186/s13046-024-03048-1

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Received : 19 December 2023

Accepted : 16 April 2024

Published : 23 April 2024

DOI : https://doi.org/10.1186/s13046-024-03048-1

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