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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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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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experimental psychology

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• American Psychological Association - Understanding Experimental Psychology

experimental psychology , a method of studying psychological phenomena and processes. The experimental method in psychology attempts to account for the activities of animals (including humans) and the functional organization of mental processes by manipulating variables that may give rise to behaviour; it is primarily concerned with discovering laws that describe manipulable relationships. The term generally connotes all areas of psychology that use the experimental method.

These areas include the study of sensation and perception , learning and memory , motivation , and biological psychology . There are experimental branches in many other areas, however, including child psychology , clinical psychology , educational psychology , and social psychology . Usually the experimental psychologist deals with normal, intact organisms; in biological psychology, however, studies are often conducted with organisms modified by surgery, radiation, drug treatment, or long-standing deprivations of various kinds or with organisms that naturally present organic abnormalities or emotional disorders. See also psychophysics .

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6.1 Experiment Basics

Learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Explain what internal validity is and why experiments are considered to be high in internal validity.
• Explain what external validity is and evaluate studies in terms of their external validity.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.

What Is an Experiment?

As we saw earlier in the book, an experiment is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. Do changes in an independent variable cause changes in a dependent variable? Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions. For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables. Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words manipulation and control have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate the independent variable by systematically changing its levels and control other variables by holding them constant.

Internal and External Validity

Internal validity.

Recall that the fact that two variables are statistically related does not necessarily mean that one causes the other. “Correlation does not imply causation.” For example, if it were the case that people who exercise regularly are happier than people who do not exercise regularly, this would not necessarily mean that exercising increases people’s happiness. It could mean instead that greater happiness causes people to exercise (the directionality problem) or that something like better physical health causes people to exercise and be happier (the third-variable problem).

The purpose of an experiment, however, is to show that two variables are statistically related and to do so in a way that supports the conclusion that the independent variable caused any observed differences in the dependent variable. The basic logic is this: If the researcher creates two or more highly similar conditions and then manipulates the independent variable to produce just one difference between them, then any later difference between the conditions must have been caused by the independent variable. For example, because the only difference between Darley and Latané’s conditions was the number of students that participants believed to be involved in the discussion, this must have been responsible for differences in helping between the conditions.

An empirical study is said to be high in internal validity if the way it was conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Thus experiments are high in internal validity because the way they are conducted—with the manipulation of the independent variable and the control of extraneous variables—provides strong support for causal conclusions.

External Validity

At the same time, the way that experiments are conducted sometimes leads to a different kind of criticism. Specifically, the need to manipulate the independent variable and control extraneous variables means that experiments are often conducted under conditions that seem artificial or unlike “real life” (Stanovich, 2010). In many psychology experiments, the participants are all college undergraduates and come to a classroom or laboratory to fill out a series of paper-and-pencil questionnaires or to perform a carefully designed computerized task. Consider, for example, an experiment in which researcher Barbara Fredrickson and her colleagues had college students come to a laboratory on campus and complete a math test while wearing a swimsuit (Fredrickson, Roberts, Noll, Quinn, & Twenge, 1998). At first, this might seem silly. When will college students ever have to complete math tests in their swimsuits outside of this experiment?

The issue we are confronting is that of external validity. An empirical study is high in external validity if the way it was conducted supports generalizing the results to people and situations beyond those actually studied. As a general rule, studies are higher in external validity when the participants and the situation studied are similar to those that the researchers want to generalize to. Imagine, for example, that a group of researchers is interested in how shoppers in large grocery stores are affected by whether breakfast cereal is packaged in yellow or purple boxes. Their study would be high in external validity if they studied the decisions of ordinary people doing their weekly shopping in a real grocery store. If the shoppers bought much more cereal in purple boxes, the researchers would be fairly confident that this would be true for other shoppers in other stores. Their study would be relatively low in external validity, however, if they studied a sample of college students in a laboratory at a selective college who merely judged the appeal of various colors presented on a computer screen. If the students judged purple to be more appealing than yellow, the researchers would not be very confident that this is relevant to grocery shoppers’ cereal-buying decisions.

We should be careful, however, not to draw the blanket conclusion that experiments are low in external validity. One reason is that experiments need not seem artificial. Consider that Darley and Latané’s experiment provided a reasonably good simulation of a real emergency situation. Or consider field experiments that are conducted entirely outside the laboratory. In one such experiment, Robert Cialdini and his colleagues studied whether hotel guests choose to reuse their towels for a second day as opposed to having them washed as a way of conserving water and energy (Cialdini, 2005). These researchers manipulated the message on a card left in a large sample of hotel rooms. One version of the message emphasized showing respect for the environment, another emphasized that the hotel would donate a portion of their savings to an environmental cause, and a third emphasized that most hotel guests choose to reuse their towels. The result was that guests who received the message that most hotel guests choose to reuse their towels reused their own towels substantially more often than guests receiving either of the other two messages. Given the way they conducted their study, it seems very likely that their result would hold true for other guests in other hotels.

A second reason not to draw the blanket conclusion that experiments are low in external validity is that they are often conducted to learn about psychological processes that are likely to operate in a variety of people and situations. Let us return to the experiment by Fredrickson and colleagues. They found that the women in their study, but not the men, performed worse on the math test when they were wearing swimsuits. They argued that this was due to women’s greater tendency to objectify themselves—to think about themselves from the perspective of an outside observer—which diverts their attention away from other tasks. They argued, furthermore, that this process of self-objectification and its effect on attention is likely to operate in a variety of women and situations—even if none of them ever finds herself taking a math test in her swimsuit.

Manipulation of the Independent Variable

Again, to manipulate an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. The different levels of the independent variable are referred to as conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore not conducted an experiment. This is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating the third-variable problem.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to do an experiment on the effect of early illness experiences on the development of hypochondriasis. This does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this in detail later in the book.

In many experiments, the independent variable is a construct that can only be manipulated indirectly. For example, a researcher might try to manipulate participants’ stress levels indirectly by telling some of them that they have five minutes to prepare a short speech that they will then have to give to an audience of other participants. In such situations, researchers often include a manipulation check in their procedure. A manipulation check is a separate measure of the construct the researcher is trying to manipulate. For example, researchers trying to manipulate participants’ stress levels might give them a paper-and-pencil stress questionnaire or take their blood pressure—perhaps right after the manipulation or at the end of the procedure—to verify that they successfully manipulated this variable.

Control of Extraneous Variables

An extraneous variable is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their shoe size. They would also include situation or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to control extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of Table 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of Table 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in Table 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” , which makes the effect of the independent variable is easier to detect (although real data never look quite that good).

Table 6.1 Hypothetical Noiseless Data and Realistic Noisy Data

One way to control extraneous variables is to hold them constant. This can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, straight, female, right-handed, sophomore psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger straight women would apply to older gay men. In many situations, the advantages of a diverse sample outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable is an extraneous variable that differs on average across levels of the independent variable. For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs at each level of the independent variable so that the average IQ is roughly equal, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants at one level of the independent variable to have substantially lower IQs on average and participants at another level to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse, and this is exactly what confounding variables do. Because they differ across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable. Figure 6.1 “Hypothetical Results From a Study on the Effect of Mood on Memory” shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Figure 6.1 Hypothetical Results From a Study on the Effect of Mood on Memory

Because IQ also differs across conditions, it is a confounding variable.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables.
• Studies are high in external validity to the extent that the result can be generalized to people and situations beyond those actually studied. Although experiments can seem “artificial”—and low in external validity—it is important to consider whether the psychological processes under study are likely to operate in other people and situations.
• Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.

Practice: For each of the following topics, decide whether that topic could be studied using an experimental research design and explain why or why not.

• Effect of parietal lobe damage on people’s ability to do basic arithmetic.
• Effect of being clinically depressed on the number of close friendships people have.
• Effect of group training on the social skills of teenagers with Asperger’s syndrome.
• Effect of paying people to take an IQ test on their performance on that test.

Cialdini, R. (2005, April). Don’t throw in the towel: Use social influence research. APS Observer . Retrieved from http://www.psychologicalscience.org/observer/getArticle.cfm?id=1762 .

Fredrickson, B. L., Roberts, T.-A., Noll, S. M., Quinn, D. M., & Twenge, J. M. (1998). The swimsuit becomes you: Sex differences in self-objectification, restrained eating, and math performance. Journal of Personality and Social Psychology, 75 , 269–284.

Stanovich, K. E. (2010). How to think straight about psychology (9th ed.). Boston, MA: Allyn & Bacon.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Experimental Research

23 Experiment Basics

Learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.
• Define what a control condition is, explain its purpose in research on treatment effectiveness, and describe some alternative types of control conditions.

What Is an Experiment?

As we saw earlier in the book, an  experiment is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. In other words, whether changes in one variable (referred to as an independent variable ) cause a change in another variable (referred to as a dependent variable ). Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions . For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. For a new researcher, it is easy to confuse these terms by believing there are three independent variables in this situation: one, two, or five students involved in the discussion, but there is actually only one independent variable (number of witnesses) with three different levels or conditions (one, two or five students). The second fundamental feature of an experiment is that the researcher exerts control over, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables . Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words  manipulation  and  control  have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate  the independent variable by systematically changing its levels and control  other variables by holding them constant.

Manipulation of the Independent Variable

Again, to  manipulate an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. The different levels of the independent variable are referred to as conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore has not conducted an experiment. This distinction  is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating potential alternative explanations for the results.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to conduct an experiment on the effect of early illness experiences on the development of hypochondriasis. This caveat does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this type of methodology in detail later in the book.

Independent variables can be manipulated to create two conditions and experiments involving a single independent variable with two conditions are often referred to as a single factor two-level design .  However, sometimes greater insights can be gained by adding more conditions to an experiment. When an experiment has one independent variable that is manipulated to produce more than two conditions it is referred to as a single factor multi level design .  So rather than comparing a condition in which there was one witness to a condition in which there were five witnesses (which would represent a single-factor two-level design), Darley and Latané’s experiment used a single factor multi-level design, by manipulating the independent variable to produce three conditions (a one witness, a two witnesses, and a five witnesses condition).

Control of Extraneous Variables

As we have seen previously in the chapter, an  extraneous variable  is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their gender. They would also include situational or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This influencing factor can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to control extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of  Table 5.1 show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of  Table 5.1 . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective recall strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in  Table 5.1 , which makes the effect of the independent variable easier to detect (although real data never look quite  that  good).

One way to control extraneous variables is to hold them constant. This technique can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres [1] . Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, heterosexual, female, right-handed psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger lesbian women would apply to older gay men. In many situations, the advantages of a diverse sample (increased external validity) outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable  is an extraneous variable that differs on average across  levels of the independent variable (i.e., it is an extraneous variable that varies systematically with the independent variable). For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs in each condition so that the average IQ is roughly equal across the conditions, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants in one condition to have substantially lower IQs on average and participants in another condition to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse , and this effect is exactly why confounding variables are undesirable. Because they differ systematically across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable.  Figure 5.1  shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Treatment and Control Conditions

In psychological research, a treatment is any intervention meant to change people’s behavior for the better. This intervention includes psychotherapies and medical treatments for psychological disorders but also interventions designed to improve learning, promote conservation, reduce prejudice, and so on. To determine whether a treatment works, participants are randomly assigned to either a treatment condition , in which they receive the treatment, or a control condition , in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude that the treatment works. In research on the effectiveness of psychotherapies and medical treatments, this type of experiment is often called a randomized clinical trial .

There are different types of control conditions. In a no-treatment control condition , participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient or element that should make it effective, and a placebo effect is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bed sheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008) [2] .

Placebo effects are interesting in their own right (see Note “The Powerful Placebo” ), but they also pose a serious problem for researchers who want to determine whether a treatment works. Figure 5.2 shows some hypothetical results in which participants in a treatment condition improved more on average than participants in a no-treatment control condition. If these conditions (the two leftmost bars in Figure 5.2 ) were the only conditions in this experiment, however, one could not conclude that the treatment worked. It could be instead that participants in the treatment group improved more because they expected to improve, while those in the no-treatment control condition did not.

Fortunately, there are several solutions to this problem. One is to include a placebo control condition , in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations. This difference is what is shown by a comparison of the two outer bars in Figure 5.4 .

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a wait-list control condition , in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This disclosure allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?

The Powerful Placebo

Many people are not surprised that placebos can have a positive effect on disorders that seem fundamentally psychological, including depression, anxiety, and insomnia. However, placebos can also have a positive effect on disorders that most people think of as fundamentally physiological. These include asthma, ulcers, and warts (Shapiro & Shapiro, 1999) [3] . There is even evidence that placebo surgery—also called “sham surgery”—can be as effective as actual surgery.

Medical researcher J. Bruce Moseley and his colleagues conducted a study on the effectiveness of two arthroscopic surgery procedures for osteoarthritis of the knee (Moseley et al., 2002) [4] . The control participants in this study were prepped for surgery, received a tranquilizer, and even received three small incisions in their knees. But they did not receive the actual arthroscopic surgical procedure. Note that the IRB would have carefully considered the use of deception in this case and judged that the benefits of using it outweighed the risks and that there was no other way to answer the research question (about the effectiveness of a placebo procedure) without it. The surprising result was that all participants improved in terms of both knee pain and function, and the sham surgery group improved just as much as the treatment groups. According to the researchers, “This study provides strong evidence that arthroscopic lavage with or without débridement [the surgical procedures used] is not better than and appears to be equivalent to a placebo procedure in improving knee pain and self-reported function” (p. 85).

• Knecht, S., Dräger, B., Deppe, M., Bobe, L., Lohmann, H., Flöel, A., . . . Henningsen, H. (2000). Handedness and hemispheric language dominance in healthy humans. Brain: A Journal of Neurology, 123 (12), 2512-2518. http://dx.doi.org/10.1093/brain/123.12.2512 ↵
• Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59 , 565–590. ↵
• Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician . Baltimore, MD: Johns Hopkins University Press. ↵
• Moseley, J. B., O’Malley, K., Petersen, N. J., Menke, T. J., Brody, B. A., Kuykendall, D. H., … Wray, N. P. (2002). A controlled trial of arthroscopic surgery for osteoarthritis of the knee. The New England Journal of Medicine, 347 , 81–88. ↵

A type of study designed specifically to answer the question of whether there is a causal relationship between two variables.

The variable the experimenter manipulates.

The variable the experimenter measures (it is the presumed effect).

The different levels of the independent variable to which participants are assigned.

Holding extraneous variables constant in order to separate the effect of the independent variable from the effect of the extraneous variables.

Any variable other than the dependent and independent variable.

Changing the level, or condition, of the independent variable systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times.

An experiment design involving a single independent variable with two conditions.

When an experiment has one independent variable that is manipulated to produce more than two conditions.

An extraneous variable that varies systematically with the independent variable, and thus confuses the effect of the independent variable with the effect of the extraneous one.

Any intervention meant to change people’s behavior for the better.

The condition in which participants receive the treatment.

The condition in which participants do not receive the treatment.

An experiment that researches the effectiveness of psychotherapies and medical treatments.

The condition in which participants receive no treatment whatsoever.

A simulated treatment that lacks any active ingredient or element that is hypothesized to make the treatment effective, but is otherwise identical to the treatment.

An effect that is due to the placebo rather than the treatment.

Condition in which the participants receive a placebo rather than the treatment.

Condition in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Experimental Research

• First Online: 25 February 2021

Cite this chapter

• C. George Thomas 2  

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Experiments are part of the scientific method that helps to decide the fate of two or more competing hypotheses or explanations on a phenomenon. The term ‘experiment’ arises from Latin, Experiri, which means, ‘to try’. The knowledge accrues from experiments differs from other types of knowledge in that it is always shaped upon observation or experience. In other words, experiments generate empirical knowledge. In fact, the emphasis on experimentation in the sixteenth and seventeenth centuries for establishing causal relationships for various phenomena happening in nature heralded the resurgence of modern science from its roots in ancient philosophy spearheaded by great Greek philosophers such as Aristotle.

The strongest arguments prove nothing so long as the conclusions are not verified by experience. Experimental science is the queen of sciences and the goal of all speculation . Roger Bacon (1214–1294)

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Thomas, C.G. (2021). Experimental Research. In: Research Methodology and Scientific Writing . Springer, Cham. https://doi.org/10.1007/978-3-030-64865-7_5

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Statistics By Jim

Making statistics intuitive

Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

• Understanding the relationships between variables.
• Identifying the variables that have the largest impact on the outcomes.
• Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

• Lots of preplanning.
• Developing experimental treatments.
• Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

• Null hypothesis : The jumping exercise intervention does not affect bone density.
• Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a  within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

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March 23, 2024 at 2:35 pm

Dear Jim You wrote a superb document, I will use it in my Buistatistics course, along with your three books. Thank you very much! Miguel

March 23, 2024 at 5:43 pm

Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

April 10, 2023 at 4:36 am

What are the purpose and uses of experimental research design?

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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 Sep 12, 2024 from Explorable.com: https://explorable.com/experimental-research

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How to Conduct a Psychology Experiment

Conducting your first psychology experiment can be a long, complicated, and sometimes intimidating process. It can be especially confusing if you are not quite sure where to begin or which steps to take.

Like other sciences, psychology utilizes the  scientific method  and bases conclusions upon empirical evidence. When conducting an experiment, it is important to follow the seven basic steps of the scientific method:

• Ask a testable question
• Define your variables
• Conduct background research
• Design your experiment
• Perform the experiment
• Collect and analyze the data
• Draw conclusions
• Share the results with the scientific community

At a Glance

It's important to know the steps of the scientific method if you are conducting an experiment in psychology or other fields. The processes encompasses finding a problem you want to explore, learning what has already been discovered about the topic, determining your variables, and finally designing and performing your experiment. But the process doesn't end there! Once you've collected your data, it's time to analyze the numbers, determine what they mean, and share what you've found.

Find a Research Problem or Question

Picking a research problem can be one of the most challenging steps when you are conducting an experiment. After all, there are so many different topics you might choose to investigate.

Are you stuck for an idea? Consider some of the following:

Investigate a Commonly Held Belief

Folk knowledge is a good source of questions that can serve as the basis for psychological research. For example, many people believe that staying up all night to cram for a big exam can actually hurt test performance.

You could conduct a study to compare the test scores of students who stayed up all night with the scores of students who got a full night's sleep before the exam.

Review Psychology Literature

Published studies are a great source of unanswered research questions. In many cases, the authors will even note the need for further research. Find a published study that you find intriguing, and then come up with some questions that require further exploration.

Think About Everyday Problems

There are many practical applications for psychology research. Explore various problems that you or others face each day, and then consider how you could research potential solutions. For example, you might investigate different memorization strategies to determine which methods are most effective.

Define Your Variables

Variables are anything that might impact the outcome of your study. An operational definition describes exactly what the variables are and how they are measured within the context of your study.

For example, if you were doing a study on the impact of sleep deprivation on driving performance, you would need to operationally define sleep deprivation and driving performance .

An operational definition refers to a precise way that an abstract concept will be measured. For example, you cannot directly observe and measure something like test anxiety . You can, however, use an anxiety scale and assign values based on how many anxiety symptoms a person is experiencing. 

In this example, you might define sleep deprivation as getting less than seven hours of sleep at night. You might define driving performance as how well a participant does on a driving test.

What is the purpose of operationally defining variables? The main purpose is control. By understanding what you are measuring, you can control for it by holding the variable constant between all groups or manipulating it as an independent variable .

Develop a Hypothesis

The next step is to develop a testable hypothesis that predicts how the operationally defined variables are related. In the recent example, the hypothesis might be: "Students who are sleep-deprived will perform worse than students who are not sleep-deprived on a test of driving performance."

Null Hypothesis

In order to determine if the results of the study are significant, it is essential to also have a null hypothesis. The null hypothesis is the prediction that one variable will have no association to the other variable.

In other words, the null hypothesis assumes that there will be no difference in the effects of the two treatments in our experimental and control groups .

The null hypothesis is assumed to be valid unless contradicted by the results. The experimenters can either reject the null hypothesis in favor of the alternative hypothesis or not reject the null hypothesis.

It is important to remember that not rejecting the null hypothesis does not mean that you are accepting the null hypothesis. To say that you are accepting the null hypothesis is to suggest that something is true simply because you did not find any evidence against it. This represents a logical fallacy that should be avoided in scientific research.  

Conduct Background Research

Once you have developed a testable hypothesis, it is important to spend some time doing some background research. What do researchers already know about your topic? What questions remain unanswered?

You can learn about previous research on your topic by exploring books, journal articles, online databases, newspapers, and websites devoted to your subject.

Reading previous research helps you gain a better understanding of what you will encounter when conducting an experiment. Understanding the background of your topic provides a better basis for your own hypothesis.

After conducting a thorough review of the literature, you might choose to alter your own hypothesis. Background research also allows you to explain why you chose to investigate your particular hypothesis and articulate why the topic merits further exploration.

As you research the history of your topic, take careful notes and create a working bibliography of your sources. This information will be valuable when you begin to write up your experiment results.

Select an Experimental Design

After conducting background research and finalizing your hypothesis, your next step is to develop an experimental design. There are three basic types of designs that you might utilize. Each has its own strengths and weaknesses:

Pre-Experimental Design

A single group of participants is studied, and there is no comparison between a treatment group and a control group. Examples of pre-experimental designs include case studies (one group is given a treatment and the results are measured) and pre-test/post-test studies (one group is tested, given a treatment, and then retested).

Quasi-Experimental Design

This type of experimental design does include a control group but does not include randomization. This type of design is often used if it is not feasible or ethical to perform a randomized controlled trial.

True Experimental Design

A true experimental design, also known as a randomized controlled trial, includes both of the elements that pre-experimental designs and quasi-experimental designs lack—control groups and random assignment to groups.

Standardize Your Procedures

In order to arrive at legitimate conclusions, it is essential to compare apples to apples.

Each participant in each group must receive the same treatment under the same conditions.

For example, in our hypothetical study on the effects of sleep deprivation on driving performance, the driving test must be administered to each participant in the same way. The driving course must be the same, the obstacles faced must be the same, and the time given must be the same.

Choose Your Participants

In addition to making sure that the testing conditions are standardized, it is also essential to ensure that your pool of participants is the same.

If the individuals in your control group (those who are not sleep deprived) all happen to be amateur race car drivers while your experimental group (those that are sleep deprived) are all people who just recently earned their driver's licenses, your experiment will lack standardization.

When choosing subjects, there are some different techniques you can use.

Simple Random Sample

In a simple random sample, the participants are randomly selected from a group. A simple random sample can be used to represent the entire population from which the representative sample is drawn.

Drawing a simple random sample can be helpful when you don't know a lot about the characteristics of the population.

Stratified Random Sample

Participants must be randomly selected from different subsets of the population. These subsets might include characteristics such as geographic location, age, sex, race, or socioeconomic status.

Stratified random samples are more complex to carry out. However, you might opt for this method if there are key characteristics about the population that you want to explore in your research.

Conduct Tests and Collect Data

After you have selected participants, the next steps are to conduct your tests and collect the data. Before doing any testing, however, there are a few important concerns that need to be addressed.

Address Ethical Concerns

First, you need to be sure that your testing procedures are ethical . Generally, you will need to gain permission to conduct any type of testing with human participants by submitting the details of your experiment to your school's Institutional Review Board (IRB), sometimes referred to as the Human Subjects Committee.

Obtain Informed Consent

After you have gained approval from your institution's IRB, you will need to present informed consent forms to each participant. This form offers information on the study, the data that will be gathered, and how the results will be used. The form also gives participants the option to withdraw from the study at any point in time.

Once this step has been completed, you can begin administering your testing procedures and collecting the data.

Analyze the Results

After collecting your data, it is time to analyze the results of your experiment. Researchers use statistics to determine if the results of the study support the original hypothesis and if the results are statistically significant.

Statistical significance means that the study's results are unlikely to have occurred simply by chance.

The types of statistical methods you use to analyze your data depend largely on the type of data that you collected. If you are using a random sample of a larger population, you will need to utilize inferential statistics.

These statistical methods make inferences about how the results relate to the population at large.

Because you are making inferences based on a sample, it has to be assumed that there will be a certain margin of error. This refers to the amount of error in your results. A large margin of error means that there will be less confidence in your results, while a small margin of error means that you are more confident that your results are an accurate reflection of what exists in that population.

Share Your Results After Conducting an Experiment

Your final task in conducting an experiment is to communicate your results. By sharing your experiment with the scientific community, you are contributing to the knowledge base on that particular topic.

One of the most common ways to share research results is to publish the study in a peer-reviewed professional journal. Other methods include sharing results at conferences, in book chapters, or academic presentations.

In your case, it is likely that your class instructor will expect a formal write-up of your experiment in the same format required in a professional journal article or lab report :

• Introduction
• Tables and figures

What This Means For You

Designing and conducting a psychology experiment can be quite intimidating, but breaking the process down step-by-step can help. No matter what type of experiment you decide to perform, always check with your instructor and your school's institutional review board for permission before you begin.

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Purna Singh A, Vadakedath S, Kandi V. Clinical research: A review of study designs, hypotheses, errors, sampling types, ethics, and informed consent .  Cureus . 2023;15(1):e33374. doi:10.7759/cureus.33374

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Miller CJ, Smith SN, Pugatch M. Experimental and quasi-experimental designs in implementation research .  Psychiatry Res . 2020;283:112452. doi:10.1016/j.psychres.2019.06.027

Nijhawan LP, Manthan D, Muddukrishna BS, et. al. Informed consent: Issues and challenges . J Adv Pharm Technol Rese . 2013;4(3):134-140. doi:10.4103/2231-4040.116779

Serdar CC, Cihan M, Yücel D, Serdar MA. Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies .  Biochem Med (Zagreb) . 2021;31(1):010502. doi:10.11613/BM.2021.010502

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Experimental methods: how to create a successful study

Various rules govern every science and it uses different experimental methods. It does so in order to achieve the results. These results have to be able to be ready for publishing in scientific articles. In order to come up with a successful, publishable material a researcher needs to follow specific guidelines. He needs to arrange the experiments in an understandable, easily replicable way. Every future experiment needs to be able to replicate an already published scientific one. Without this ability to replicate, we couldn’t know whether the experiments are valid and transferable to a bigger population. Scientists need to have a clear view of all the guidelines in their mind before beginning a project.

Researchers need to have as much control as possible over all of the variables. They need to try and avoid confounding. Striving for objectivity and clearness in every step of the way is the goal of science. There is no worse thing for a scientist to miss a significant contributing factor to their study. This is due to the fact that this factor could have been able to change the results greatly.

If that happens, in a lot of cases, the research will have to start again. If you are lucky, maybe re-doing one or two experiments will be enough. In many cases, the scientist needs to run the entire set of experiments again. He does that in order to reach statistical significance with the results. Re-gathering all of the data again can sometimes turn research into a walking living nightmare!

Can you imagine running 200 infant participants between the ages of 5-7 months? And then you find out that the entire design of your study is faulty and you need to start over? You will have to rethink the entire design again but also recruit the 200 infants of the required age again! That is the reason why research takes time, patience, serious dedication and on occasion job stress .

In order to prevent these types of mashups, scientists have created guidelines and sets of rules. They aim to help speed the process along and make sure that it is running smoothly.

Experiments – the key to success in experimental methods

As mentioned before, an important part of every scientific study is the experiment itself. An experiment has certain prerequisites. A scientist needs to formulate a clear and concise hypothesis and the objectives of the study.

The hypothesis predicts what your study is attempting to find – the result of the study itself. It’s a statement. There are two different types of hypotheses in research.

• The null hypothesis states that the two variables that you are studying do not have a relationship. That means that one of them does not affect the other in any way, shape or form.
• Alternative hypothesis states the variables you are studying have a relationship and do affect one another. They do so in a significant fashion.

Experimental methods for every experiment include a set of variables that will help to formulate that hypothesis. The two sets of variables include an independent and a dependent variable. Independent variable is the one that the scientist manipulates.  The dependent variable is the one that the scientist is trying to find out. The researcher needs to also control for confounding or extraneous variables. These variables can compromise the results of their study.

Experimental methods: Hypothesis example

Let’s try to create a hypothesis ourselves. We can have a theory that video game players have better reflexes compared to non-video game players. That is an alternative hypothesis that tries to predict whether video games have an effect on reflexes in human participants. The independent variable, in this case, would be the video games themselves. The dependent variable is the speed of the reflexes.

Now, we need to take into an account the confounding and extraneous variables. For example, there are many video games in this world. Is there a difference, let’s say, between strategy and action games? We further define our hypothesis saying that action video game players will have better reflexes compared to non-video game players.

Extraneous variables

Other extraneous variables come to mind. If we do find an effect, is there a subsequent gender difference or an age difference? That could potentially lead to a new series of experiments. In the first experiment, we test whether video game players have better reflexes than non-players. In the second experiment, we compare male and female video game players. We try to eliminate the possibility for gender to be a confounding variable. In the third experiment, we could compare adults to teenagers.

We can see how a simple question can turn into a lengthy study with many different participants and variables. This is by no means a well-thought out hypothesis. If we were to spend some more time on it, we’d probably find more variables that we need to control for. We’d find ourselves in front of at least four or five different experiments with the use of many experimental methods.

The experiments themselves can be classified into different categories.

Experimental methods categories

Experimental methods: Experiments

Experimental methods: field experiments.

These types of experiments happen in participant’s real life situation. The scientist here will manage the independent variable but in a setting outside of the laboratory environment.

There are certain advantages to field experiments. Due to the setting being realistic the participants will behave in a more normal and ordinary way than they would in a laboratory. This brings higher ecological validity to the experiment compared to a lab experiment.

• Ecological validity: how can the outcome of your study apply to real life?

Due to the fact that the participants usually do not know that they are being studied, they will not change their behavior subconsciously or consciously. Participants tend to do that when they think they know the purpose of the study that the researcher is conducting. That can seriously compromise the results of any experiment due to them not being genuine and create outliers in the further statistical analysis.

Experimental methods: Drawbacks of field experiments

We can see some challenges this set of experiments can produce due to the inability of the experimenter to control the confounding and extraneous variables that will for sure appear on the horizon during the study. Due to the inability to control for extraneous variables, there is a lesser likelihood to replicate the study which could be quite damaging for the project overall.

Experimental methods: Quasi/Natural experiments

The main difference between ‘Natural experiments’ and ‘Field experiments’ is that in the first one the scientist has no control over any variables that occur during the experiment. Because the experiment occurs in even more real-life settings than the field experiment, the participants are a lot more likely to act genuinely and naturally. This is one of the main strengths of these type of experiments. The ecological validity of them is quite high when compared to laboratory experiments and, even, field experiments. Because the participants do not know about the study or they might not suspect they are being studied, they will not try to subconsciously or consciously sabotage the results of the study which is a huge advantage in itself.

Experimental methods: Drawbacks of quasi/natural experiments

The scientist can go virtually anywhere and observe any situation. A big and obvious limitation of these types of experiments is the serious lack of control over any variables so it is either very difficult or sometimes virtually impossible for a future researcher to conduct a study in a similar way.

Experimental methods: Laboratory experiments

Laboratory experiments are the most controlled ones out of the three and they can use a variety of experimental methods for data collection due to the fact that they are the most objective ones. They allow the scientist to measure things and control for everything that he can possibly control for due to the fact that the researcher himself decides the place of the experiment, the time, the participants and the circumstances.

Participants: Random assignment

Usually, for the best outcome, the researchers try to get a random assignment of the participants to avoid a bias of only picking females over the age of 35 of higher-middle class. That would be quite a significant confounding variable that would surely compromise the results of any study. Sometimes, however, a certain population needs to be used, for example, if the scientists are testing a new drug for schizophrenia. Clearly, the participants of that study would have to have been diagnosed with schizophrenia beforehand.

Drawbacks of laboratory experiments

Laboratory experiments are easily replicable due to the fact that everything that happens in the experiment needs to follow a concise step-by-step procedure that the researcher will then share in his methods section. Avoidance of the extraneous variables becomes easier due to this controlled environment. A big limitation of laboratory controlled experiments in the field of psychology is the control itself.Due to the fact that many studies in psychology deal with human participants, the artificial environment may encourage the participants to act in a way that is not genuine or normal for them. On top of that, the participants usually know that they are a part of the experiment and, therefore, might subconsciously or consciously sabotage the results.

Experimental methods: Data collection

There are various different experimental methods of collecting data and two different types of data can be distinguished.

Experimental methods: Two types of data

Qualitative data is mainly a part of an exploratory research. It is less objective than quantitative data. Some common types of qualitative analysis include interviews, focus groups, participation/observation and case studies. In case studies, in particular, researchers are able to look into one specific problem or one particular participant that is of their interest.

Quantitative data usually generates data that is numerical and is able to be statistically analyzed. Quantitative data is the type of data that is mainly used in laboratory research because it allows structure and standardization. Scientists use quantitative research to study behaviors, opinions and other variables that are clearly defined. Usually, researchers will use a small sample and then attempt to generalize the results to a larger population.

Quantitative data can come in the form of surveys, interviews and questionnaires and longitudinal studies. During the experiments, other various scientific and experimental methods are used in the form of eye-tracking and neuroimaging methods.

Experimental methods: Patience, hard work, and dedication

This is by far not all that there is to know about the experimental methods in the field of psychology. People become statisticians for the sole purpose of analyzing data and clinical lab technicians that help with the experiments themselves. Despite that, it gives an insight of what it is like to be a researcher in the modern society and how many different variables need to be taken account of. Just to think that completely different ethical guidelines are used for animal and human research and different countries have distinct guidelines for both! It is important not to despair because only through research we are able to find out about ourselves. Only through research, we can look for prevention, diagnosis, and treatment of various diseases. It’s important to follow the guidelines and the rules for the society’s benefit .

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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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• Experimental Methods

Experimental methods are research designs in which the researcher explicitly and intentionally induces exogenous variation in the intervention assignment to facilitate causal inference. Experimental methods typically include directly randomized variation of programs or interventions. This page outlines common types of experimental methods and explains how they avoid biased results.

• 1 Read First
• 2 Common Types of Experimental Methods
• 3 Experimental Methods as a Solution for Bias
• 4 Additional Resources
• Experimental methods introduce exogeneity, allowing researchers to draw conclusions about the effects of an event or a program. (See study design for key principles of designing an evaluation.)
• Without experimental methods, results may be biased by confounding variables or reverse causality.
• During data collection and analysis , make sure to consider and account for differential take-up, compliance, and attrition between randomized groups.

Common Types of Experimental Methods

Experimental methods typically include directly randomized variation of programs or interventions offered to study populations. This variation is usually broadly summarized as " Randomized Control Trials ,” but can include cross-unit variation with one or more periods (cross-sectional designs); within-participant variation (panel studies); or treatment randomization at a clustered level with further variation within clusters (multi-level), for example.

Researchers can also achieve exogenous variation on the research side through randomized variation in the survey methodology. For example, public health surveys may use mystery patients to identify the quality of medical advice given to people in primary care settings. By comparing the outcomes with other health care providers given medical vignettes instead of mystery patients, by changing the information given from the patient to the provider, or by changing the setting in which the interaction is conducted, researchers can use the data collected to estimate causal differences in outcomes. Other designs like endorsement experiments and list experiments randomly vary the contents of the survey itself.

Experimental Methods as a Solution for Bias

Experimental variation imposes known variation on the study population, guaranteeing an un-confounded intervention effect. Without exogenous variation, however, the treatment effect may be biased by an external variable. Consider the following examples of non-experimental research in which bias confounds results:

• The estimate of the intervention on the outcome may mask an effect produced by another, correlated variable. For example, schooling may improve the quality of job offers via network exposure, but the actual education adds no value. In this case the result would remain "correct" in the sense that those who got more schooling did, in fact, receive higher earnings, but "incorrect" in the sense that the estimate does not represent marginal value of education. Through randomization and exogeneity, experimental methods ensure that the analysis is not biased by confounding variables like that highlighted above.
• The direction of causality may be reversed or simultaneous. For example, individuals who are highly motivated may choose to complete more years of schooling as well as being more competent at work in general; or those who are highly motivated by financial returns in the workplace may choose more schooling because of that motivation. Again, through randomization and exogeneity, experimental methods avoid reverse causality or endogeneity like that highlighted above.

Additional Resources

• See JPAL’s Impact Evaluation Methods Chart .
• Running Randomized Evaluations includes all content from the book Running Randomized Evaluations, supplemental materials like case studies, and a blog.
• The World Bank and The IDB’s Impact Evaluation in Practice contains plentiful information on how to implement and analyze via experimental methods
• See Impact Evaluation Design Principles from OECD.
• The Behavioral Evidence Hub (B-Hub) is a continually updated collection of strategies drawn from insights about human behavior that are proven to solve real world problems. All results published on the B-Hub are evaluated with randomized controlled trials.
• For information on quasi-experimental methods, see Quasi-Experimental Methods .
• Research Design

Controlled Experiment

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

This is when a hypothesis is scientifically tested.

In a controlled experiment, an independent variable (the cause) is systematically manipulated, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

The researcher can operationalize (i.e., define) the studied variables so they can be objectively measured. The quantitative data can be analyzed to see if there is a difference between the experimental and control groups.

What is the control group?

In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference – experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Randomly allocating participants to independent variable groups means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

What are extraneous variables?

The researcher wants to ensure that the manipulation of the independent variable has changed the changes in the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

In practice, it would be difficult to control all the variables in a child’s educational achievement. For example, it would be difficult to control variables that have happened in the past.

A researcher can only control the current environment of participants, such as time of day and noise levels.

Why conduct controlled experiments?

Scientists use controlled experiments because they allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.

Controlled experiments also follow a standardized step-by-step procedure. This makes it easy for another researcher to replicate the study.

Key Terminology

Experimental group.

The group being treated or otherwise manipulated for the sake of the experiment.

Control Group

They receive no treatment and are used as a comparison group.

Ecological validity

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) – is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables that are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

What is the control in an experiment?

In an experiment , the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental group, which does receive the treatment or manipulation.

The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to the experimental treatment.

Establishing a cause-and-effect relationship between the manipulated variable (independent variable) and the outcome (dependent variable) is critical in establishing a cause-and-effect relationship between the manipulated variable.

What is the purpose of controlling the environment when testing a hypothesis?

Controlling the environment when testing a hypothesis aims to eliminate or minimize the influence of extraneous variables. These variables other than the independent variable might affect the dependent variable, potentially confounding the results.

By controlling the environment, researchers can ensure that any observed changes in the dependent variable are likely due to the manipulation of the independent variable, not other factors.

This enhances the experiment’s validity, allowing for more accurate conclusions about cause-and-effect relationships.

It also improves the experiment’s replicability, meaning other researchers can repeat the experiment under the same conditions to verify the results.

Why are hypotheses important to controlled experiments?

Hypotheses are crucial to controlled experiments because they provide a clear focus and direction for the research. A hypothesis is a testable prediction about the relationship between variables.

It guides the design of the experiment, including what variables to manipulate (independent variables) and what outcomes to measure (dependent variables).

The experiment is then conducted to test the validity of the hypothesis. If the results align with the hypothesis, they provide evidence supporting it.

The hypothesis may be revised or rejected if the results do not align. Thus, hypotheses are central to the scientific method, driving the iterative inquiry, experimentation, and knowledge advancement process.

What is the experimental method?

The experimental method is a systematic approach in scientific research where an independent variable is manipulated to observe its effect on a dependent variable, under controlled conditions.

Millimeter-Wave Radio SLAM: End-to-End Processing Methods and Experimental Validation

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• Published: 13 September 2024

A method for compressing AIS trajectory based on the adaptive core threshold difference Douglas–Peucker algorithm

• Ting Zhang 1 , 2   na1 ,
• Zhiming Wang 1   na1 &
• Peiliang Wang 1 , 3  

Scientific Reports volume  14 , Article number:  21408 ( 2024 ) Cite this article

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• Computer science
• Mathematics and computing
• Ocean sciences
• Scientific data

Traditional trajectory compression algorithms, such as the siliding window (SW) algorithm and the Douglas–Peucker (DP) algorithm, typically use static thresholds based on fixed parameters like ship dimensions or predetermined distances, which limits their adaptive capabilities. In this paper, the adaptive core threshold difference-DP (ACTD-DP) algorithm is proposed based on traditional DP algorithm. Firstly, according to the course value of automatic identification system (AIS) data, the original trajectory data is preprocessed and some redundant points are discarded. Then the number of compressed trajectory points corresponding to different thresholds is quantified. The function relationship between them is established by curve fitting method. The characteristics of the function curve are analyzed, and the core threshold and core threshold difference are solved. Finally, the compression factor is introduced to determine the optimal core threshold difference, which is the key parameter to control the accuracy and efficiency of the algorithm. Five different algorithms are used to compress the all ship trajectories in the experimental water area. The average compression ratio (ACR) of the ACTD-DP algorithm is 87.53%, the average length loss ratio (ALLR) is 23.20%, the AMSED (mean synchronous Euclidean distance of all trajectories) is 68.9747 mx, and the TIME is 25.6869 s. Compared with the other four algorithms, the ACTD-DP algorithm shows that the algorithm can not only achieve high compression ratio, but also maintain the integrity of trajectory shape. At the same time, the compression results of four different trajectories show that ACTD-DP algorithm has good robustness and applicability. Therefore, ACTD-DP algorithm has the best compression effect.

Introduction

With the deepening of global economic integration, the maritime industry’s role within the global economic system has become increasingly pivotal. The density of ship traffic in coastal ports, estuarial jetties, and other water areas continues to rise. The complex navigational environment presents novel challenges to maritime traffic supervision authorities and staff 1 , 2 . As an important carrier of ship motion information, AIS facilitates ship supervision and management. In accordance with the requirements of the SOLAS (International Convention for the Safety of Life at Sea), an increasing number of ships are mandated to be equipped with AIS devices to mitigate the risk of maritime collisions 3 .

The massive AIS data information provides significant support for research across various domains, including studies on ship behavior patterns 4 , 5 , maritime route planning 6 , 7 , and navigation safety 8 , 9 , 10 . However, the raw AIS data contains a plethora of redundant information, which can adversely affect the processing time of ship trajectories 11 .Therefore, studies on the characteristic direction of ship trajectory all need to compress AIS data, such as ship navigation prediction 12 and ship abnormal behavior detection 13 , 14 .

Compression algorithms commonly used for AIS data can be categorized into offline and online approaches. Offline compression algorithms include the Douglas–Peucker (DP) algorithm 15 and the TD-TR (top–down time ratio)algorithm 16 etc., while online compression algorithms encompass the Sliding Window (SW) algorithm 17 and the opening window time ratio (OPW-TR) algorithm 18 etc. Among these, the selection of the threshold value is most critical when utilizing algorithms such as DP and SW for the compression of AIS data. The literature 19 synthesized the ship’s course deviation, position deviation and the spatiotemporal characteristics of AIS data to set the threshold value of SW algorithm. This approach facilitates the extraction of key feature points from the ship trajectory, thereby ship trajectory data is compressed. But the choice of distance and angle thresholds needs to be analyzed according to the experimental waters and the type of vessel, the distance threshold in paper was set as [0.731, 1.274] times the ship width, and the Angle threshold was set as [ \(\hbox {3.8}^{\circ }\) , \(\hbox {5.0}^{\circ }\) ]. The literature 20 enhanced the traditional SW algorithm by combining speed values varied significantly. The threshold was dynamically adjusted to reduce the scale deviation caused by the speed difference. Consequently, it more effectively preserved the shape characteristics of the ship trajectory. However, this method is more suitable for trajectories with obvious velocity changes, and the improvement effect may not be as good as expected for trajectories with little velocity changes or simple motion modes. The literature 21 initialized and dynamically adjusted the threshold of DP algorithm to proportion value of the ship’s length, based on the characteristics of ship trajectory.The literature statistically analyzed the relative azimuth differences between trajectory points to identify and order the features points, thereby yielded the final compressed trajectory. The literature uses the parameter self-selection method for the Silhouette Coefficient scores, but the determination of the optimal parameter combination will require extensive experiments and adjustments.Based on the traditional DP algorithm, the literature 22 used the minimum ship domain evaluation method to optimize the threshold setting method, and combined the ship parameters and maneuverability to set the threshold to 0.8 times the ship length. However, the selection of threshold value is based on the empirical method, and the generalization ability of the method is insufficient. Based on the traditional DP algorithm and SW algorithm, literature 23 combined with the space and motion characteristics of the trajectory, applied statistical theory to determine the algorithm threshold for compression. The paper recommended that the distance threshold value for DP algorithm was 0.8 times the ship length and the threshold coefficient of SW algorithm was 1.6.The threshold value in the literature is a fixed value and cannot provide an adaptive adjustment mechanism. The author 24 compressed ship trajectory using DP algorithm, and a novel metric known as ACS(Average Compression Score) was introduced as an evaluative criterion. But the choice of the most threshold is still based on experience, especially the determination of the optimal ACS value. The distance threshold of DP algorithm was set 0.87 nautical miles, the ACS reaches the optimal value of 0.1472, and the compression rate can reach 92.59%. The literature 25 , aiming to facilitate the rapid display of AIS trajectories on ECDIS (Electronic Chart Display and Information System), has utilized DP algorithm for compressing ship trajectory on nautical charts of varying scales. However, the threshold value is obtained based on the test of the research water area, and the generalization ability is weak. The results show that the recommended threshold range was [10,20] meters and the compression ratio was [94%,97%] for charts (scale 1:100,000 to 1:2990,000), while the recommended threshold was 20 meters and the compression ratio was 97% for charts (scale 1:3,000,000). The literature 26 introduced the ADPS (Adaptive Douglas–Peucker with Speed) algorithm, which combined trajectory characteristics, the rate of change of ship speed, and the distances between feature points to automatically calculate and set thresholds suitable for each trajectory. This approach ensured the retention of key features and pertinent information while the trajectory was compressed. However, the performance of the algorithm still depends on the initial setting of some parameters, such as the method of determining the baseline. The literature 27 used DP algorithm with distinct distance thresholds to compress the upbound and down-bound trajectories respectively. The DTW (Dynamic Time Warping) algorithm was used to evaluate the compression effect and solved the optimal compression threshold. However, the threshold determined by this method depends on the set of threshold values, so it is easy to fall into the local optimal value. The literature 28 proposed the MPDP (Multi-Objective Peak Douglas-Peucker) algorithm, which adopted a peak sampling strategy. The three optimization objectives of the trajectory, spatial characteristics, course, and speed, were considered. Additionally, the obstacle detection mechanism was added, aiming to achieve a compression algorithm more suited for curved trajectories. However, the threshold of this method is still a fixed value and lacks adaptability. The literature 29 proposed the PDP(Partition Douglas-Peucker) algorithm, which partitioned ship trajectory shapes and employs dynamic threshold settings to enhance the efficiency and accuracy of trajectory compression. This approach has successfully reduced the time required for compression and minimized data loss. However, the dynamic threshold determination within the PDP algorithm is still anchored to the ship’s length, which limits its adaptiveness. The literature 30 proposed the ADP(Adaptive-threshold Douglas-Peucker) algorithm, which makes the threshold setting more flexible and accurate, and improves the computational efficiency of the algorithm by using matrix operations. However, when the cyclic ship trajectory was compressed by the ADP algorithm, the critical point information was lost more. At the same time, the complexity of ADP algorithm was higher to increase the calculation time and storage cost.

Summarizing the aforementioned research, the selection methods for threshold values in compressing ship trajectories based on DP and SW algorithms are primarily based on expert experience or trial-and-error approaches. The subjective of expert experience method is high, necessitating multiple iterations and experiments to ascertain the threshold values, resulting in low work efficiency. The thresholds set by the trial-and-error method are mostly based on the ship field or the ship static information, such as length and width, which are limited by the sailing waters and ship types. Furthermore, erroneous information within massive AIS datasets can also impact the setting of thresholds, thereby the compression effect can be diminished.

To overcome the deficiencies and limitations of DP algorithm in threshold setting, which is often based on expert experience or trial-and-error approaches, this paper proposes the ACTD-DP (Adaptive Threshold Difference -DP) algorithm. The ACTD-DP algorithm effectively reduces the reliance on experience or static ship information for threshold determination, demonstrating good applicability. Moreover, the ACTD-DP algorithm is suitable for compressing trajectory data of different types of water area and ship types, exhibiting strong robustness.

The remainder of this paper is organized as follows. In “ Methods ”, the traditional DP algorithm , relevant definitions and evaluation metrics are introduced, while the logic and flow of ACTD-DP algorithm are introduced. In “ Experiment and analysis ”, the experimental results of different algorithms are compared and analyzed in the selected research area. In “ Discussion and conclusion ”, this paper is discussed and summarized , and the directions of future research are outlined.

Traditional DP algorithm

The DP algorithm, initially introduced by Douglas and Peucker in 1973 31 , is predicated on the concept of “straightening the curve” by approximating a curve with a series of points and reducing the number of points, which is commonly utilized for simplifying the motion trajectories of objects.

Assuming a curve is constituted by a set of points { \(P_{1},P_{2},P_{3},P_{4},P_{5},P_{6}\) } as shown in Fig. 1 a, with a given threshold . The compression process of the DP algorithm is proceeds as follows:

(1) Connect the beginning and end of the curve \(P_{1}\) - \(P_{6}\) as the baseline line (dashed line in Fig. 1 a), calculate the vertical distance between the remaining points and the reference line, and obtain the maximum distance value \( d_{max1} \) and corresponding points \(P_{4}\) .

(2) If \(d_{max1}< \varepsilon \) , all the intermediate points are removed, and the curve point set is compressed as \(\left\{ P_{1}, P_{6} \right\} \) ; otherwise, save \(P_{4} \) as the key point, and divide the curve into two sub-curve point sets: \(\left\{ P_{1}, P_{2}, P_{3}, P_{4} \right\} \) and \(\left\{ P_{4}, P_{5}, P_{6} \right\} \) , as shown in Fig. 1 b. The reference lines are \(P_{1}\) - \(P_{4}\) and \(P_{4}\) - \(P_{6}\) respectively.

(3) Repeat steps (1) and (2) for the above two sub-curves respectively, as shown in Fig. 1 b, c. Finally, the compression curve is \(\left\{ P_{1}, P_{2}, P_{4}, P_{6} \right\} \) , as shown in Fig. 1 d.

DP algorithm.

Relevant definition of trajectory compression

Ship trajectory is similar the curve shown in Fig. 1 , thus by setting an appropriate threshold, the DP algorithm can detect and retain the critical points, discard non-critical points within the ship’s trajectory. During compressing the curve by DP algorithm, distances and thresholds are calculated based on the Cartesian coordinate system. However, ship trajectory point data are typically based on the geographic coordinate system, where the calculation of spherical distances is more complex, particularly when determining the distance between a trajectory point and a line.

Consequently, prior to the compression of ship trajectories by DP algorithm, a transformation from the geographic coordinate system to the Mercator projection coordinate system is required to facilitate the calculation of distance values, which is called projection.

\(\left( \lambda ,\varphi \right) \) denote the longitude and latitude value of the trajectory point in the geographical coordinate system, and \(\left( x,y \right) \) represents the coordinate value projected in the Mercator coordinate system. The conversion formula is as follows:

where, R represents the parallel circle radius of standard latitude; \(\delta \) represents the long radius of the earth’s ellipsoid; \(\varphi _{0} \) represents the standard latitude in the Mercator projection; e represents the first eccentricity of the earth ellipsoid; q represents isometric latitude.

Algorithm performance evaluation metrics

As shown in Fig. 2 , let \(Tra_{org}=\left\{ P_{1},P_{2},\cdots ,P_{n} \right\} \) be the original ship trajectory, \(Tra_{cmp}=\left\{ P_{1},P_{n} \right\} \) , and n be the compressed trajectory and n be the number of trajectory points. Each trajectory point encompasses two fundamental attributes: coordinate values and a timestamp, that is, \(P_{i}=\left\{ x_{i},y_{i},t_{i} \right\} \) .

Trajectory compression diagram.

Definition 1

SED (synchronous euclidean distance). SED is generally used to evaluate the effectiveness of compression. SED denotes the Euclidian distance between the point P1 in the original trajectory \(Tra_{org}\) and the corresponding position \(P_{syn} \) in the compressed trajectory \(Tra_{cmp}\) , calculated in proportion to time. \(P_{ped} \) is the foot of \(P_{i} \) on the compression trajectory, \(P_{i}P_{ped} \) length is the vertical Euclidean distance corresponding to \(P_{i} \) , and \(P_{i}P_{syn} \) length is the corresponding to \(P_{i} \) . \(P_{syn} \left( x_{syn},y_{syn} \right) \) coordinates of trajectory points and corresponding SED formulas are as follows:

The mean SED of a single trajectory is denoted as MSED and the mean SED of all trajectories is expressed as AMSED. The formula is as follows:

where, N represents the number of all trajectories.

MSED represents the location discrepancy between the original trajectory and the compressed trajectory, and the smaller the value, the smaller the discrepancy, the smaller the trajectory distortion, and the higher the integrity of the trajectory shape.

Definition 2

CR (compression ratio). CR is the ratio of the number of points discarded during the compression process to the total number of points in the original trajectory. When only the CR is considered, the higher the CR, the better the compression effect of the compression algorithm. The formula is as follows:

ACR (average compression ratio) denotes the average compression ratio of all trajectories. The formula is as follows:

The CR indicates the compression degree of the algorithm on the trajectory. The larger the value, the higher the compression degree of the trajectory, and the simpler the compression trajectory obtained.

LLR (length loss ratio). LLR is the ratio of the reduced length of the compressed trajectory to the length of the original track. The formula is as follows:

where, \(Len_{org}\) represents the length of the original trajectory; \(Len_{cmp}\) represents the length of the compressed trajectory.

ALLR (average length loss ratio) denotes to the mean LLR of all trajectories. The formula is as follows:

The LLR denotes the degree of loss on the track length. The larger the value, the more length information is lost during the compression process, and the greater the probability of deviation of some track features.

Define 4 TIME

TIME is the total time taken by the algorithm to compress all trajectories in the current research area.

• ACTD-DP algorithm

The purpose of trajectory compression is to reduce the number of trajectory points while maintaining the integrity of the trajectory’s shape, thereby enhancing the velocity of trajectory display and processing. The goal is to achieve an optimal balance between the quantity and the quality of the trajectory points. The compression quality of DP algorithm is predominantly determined by the threshold value. The larger the threshold value, the higher the compression rate and the larger the trajectory distortion; the smaller the threshold value, the lower the compression rate and the smaller the trajectory distortion.

Traditional DP algorithms and the aforementioned studies in references 22 , 23 , 24 , 25 set threshold values based on static information such as ship length, ship width, and fixed distance values, which diminishes the algorithm’s adaptive capacity. The ACTD-DP algorithm is proposed, as depicted in Fig. 6 , drawing on the conceptual frameworks of the PDP algorithm 29 and the APD algorithm 30 . The ACTD-DP algorithm employs an adaptive threshold difference approach, aiming to diminish reliance on static information. Initially, the original trajectory data is preprocessed using the course attribute from AIS data, the certain trajectory points are discarded. Subsequently, a curve fitting method is used to establish a functional relationship between the threshold and the number of trajectory points. The characteristics of the function curve are then analyzed to determine the core threshold and the core threshold difference. Finally, the compression factor is introduced to ascertain the optimal threshold difference, which serves as the key parameter to control the accuracy and efficiency of the algorithm. In comparison with the PDP and ADP algorithms, the ACTD-DP algorithm is capable of achieving a higher compression rate while maintaining the integrity of the trajectory shape. Additionally, the ACTD-DP algorithm demonstrates adaptability across various maritime environments and ship types.

Preprocessing trajectory

Compared with the traditional DP algorithm, ACTD-DP algorithm needs to solve the optimal core threshold difference, which may increase the algorithm’s execution time. To reduce the time consuming of the ACTD-DP algorithm, this paper processes the data and eliminate the noise data, drift data and other outliers in the data. After that, following the approach in reference 17 , this paper preprocesses the data based on the course differences between original trajectory points to reduce the number of trajectory points. The trajectory preprocessing method is as follows:

Let \(Tra_{org}\) be the original trajectory (Fig. 2 ) and \(\theta _{th} \) be the course change threshold . Then \(\left\{ P_{1},P_{n} \right\} \) is saved in the preprocessing trajectory.

Iteratively calculate the course difference between adjacent trajectory points as \(\Delta \theta \) . If \(\Delta \theta > \theta _{th} \) , the point is saved to middle \(Tra_{pre}\) ; Otherwise, the point is discarded.

To ensure data integrity, if the number of points discarded between adjacent two points in \(Tra_{pre}\) is higher than that of \(\Delta n\) (the general value is \(\left[ 10,30\right] \) ), trajectory points are selected from \(Tra_{org}\) in equal proportion with equal proportional interpolation and saved to \(Tra_{pre}\) .

The DR (discard ratio) is the ratio of the number of points discarded during the preprocessing of the trajectory to the total number of points in the original trajectory. Based on the experimental AIS data (as described in “Description of experimental data”), the relationship between DR and AMSED values with respect to threshold \(\theta _{th} \) is depicted in Fig. 3 . When threshold \(\theta _{th} < \hbox {10}^{\circ } \) is set, both DR and AMSED exhibit more pronounced changes. At threshold \(\theta _{th} = \hbox {1}^{\circ } \) , DR is 42.89% and AMSED is 8.5372 m. When threshold \(\theta _{th} = \hbox {10}^{\circ } \) is used, DR increases to 72.30% and AMSED to 17.30 m. At threshold \(\theta _{th} > \hbox {10}^{\circ } \) , the variation in DR is relatively minor. Compared to the data in Table 5 , the AMSED values presented in Fig. 3 are consistently lower, indicating minimal distortion of the trajectory and a minor impact on the trajectory’s shape features due to the discarded points. Consequently, the threshold \(\theta _{th} = \hbox {10}^{\circ } \) and the number of points \(\Delta n=10\) are selected for further analysis.

DR and AMSED values with respect to threshold \(\theta _{th} \) .

Fitting thresholds-points function

The threshold of trajectory compression algorithms based on DP algorithm is directly correlated with the number of points in the compressed trajectory, which subsequently affects the quality of the compressed trajectory. To quantify this relationship between the threshold values and the number of trajectory points for the ACTD-DP algorithm, this paper , based on the experimental AIS data(as detailed in “Description of experimental data”), presents a statistical analysis of the total number of compression trajectory points across a threshold range of [0.01, 10] times the ship’s length, as depicted in Fig. 4 a. Statistical analysis demonstrates a nonlinear negative correlation between threshold values and the count of trajectory points. Initially, when the threshold is smaller, there is a significant and rapid decrease in the number of trajectory points. Subsequently, with an increase in the threshold, the rate of reduction in trajectory points attenuates, and in certain regions, there may be observed slight oscillations or a tendency towards rebound.

According to the curve (Fig. 4 a) analysis, the functional relationship between the thresholds and the number of trajectory points may conform to the characteristics of power function and exponential function. Therefore, the fitting function equations can be assumed to be:

where, \(\omega _{i}\left( i=1,2\ldots 6 \right) \) represents the equation parameters.

Then, The equations are solved according to the statistical data, and the parameter values are obtained as shown in Table 1 . The fitting function curves were shown in Fig. 4 b.

Thresholds-points function fitting. ( a ) Thresholds-points statistics and ( b ) the fitting function curves.

Finally, the determination coefficient is selected as the Goodness of Fit for the two functions. The determination coefficient, as \(R^2\) , is the description of the degree of variation of the function independent variable and dependent variable, and it is an essential metric for assessing the fit of a regression equation. The formula for its calculation is as follows:

where, \(S_{r}\) represents residual sum of squares; \(S_{t}\) represents total sum of squares; \(y_{i}\) represents the actual value; \(\widehat{y_{i} }\) represents the fitting curve value; \(\overline{y}\) represents the average value.

The determination coefficients for Eq. ( 8 ), \(R^2=0.9872\) , and for Eq. ( 9 ), \(R^2=0.9589\) , indicate that the values derived from Eq. ( 8 ) are closely aligned with the actual data, signifying a higher degree of fit for the curve equation and greater applicability. Consequently, the power function, the thresholds-points function, is selected to represent the functional relationship between the threshold and the number of trajectory points.

Optimal core threshold difference calculating

After the thresholds-points function has been established, the further analysis of the function curve characteristics is necessitated. This is essential for evaluating the compression effectiveness during the trajectory compression process. It is imperative to establish an intrinsic link between the curve and the ACTD-DP algorithm. Additionally, the role of thresholds or differences therein within the algorithm must be explicitly defined.

When the ACTD-DP algorithm compresses the trajectory, the maximum distance of all trajectory points to the baseline (similar to that depicted in Fig. 1 ) within each compressed trajectory segment is calculated. This maximum distance value is hypothesized as the core threshold for that trajectory segment, denoted as .The difference in core thresholds obtained from two consecutive compressions is denoted as the core threshold difference. The formula is as follows:

where, \(\Delta \varepsilon _{k}\) represents the core threshold difference at the second compression; \(\varepsilon _{core}^{k}\) represents the core threshold obtained during the second compression; k represents the compression order.

During the trajectory compression process, the overall trend of the core threshold difference is a gradual decrease (as shown in Fig. 5 a). When \(\Delta \varepsilon _{k} < \Delta \varepsilon _{o} \le \Delta \varepsilon _{k-i} \left( i=1,2\ldots ,k-1 \right) \) is true, it indicates that the change trend of core threshold difference tends to be stable, the trajectory shape changes little, and the trajectory compression ratio also tends to be stable. At this point, \(\Delta \varepsilon _{o} \) is identified as the optimal core threshold difference, achieving the best balance betweenbetween the quantity and the quality of the trajectory points, signifying the completion of trajectory compression. Therefore, the process of balancing between the quantity and the quality of the trajectory points is the process of searching the optimal core threshold difference.

Combined with the curve (Fig. 4 a) analysis of Equation ( 8 ), the core threshold difference corresponds to the derivative of the fitted curve (Fig. 5 a). Consequently, the optimal core threshold difference can be calculated based on the angle difference between two points on the fitting curve (as illustrated in Fig. 5 b), where the core threshold difference at the position of the maximum angle difference of the fitting curve is identified as the optimal core threshold difference. The formulas for calculating the derivative of the fitting curve and the angle difference are as follows:

The analysis (Fig. 5 b) shows that when \(x=0.4\) , the angle difference reaches the maximum, and the corresponding optimal core threshold difference is \(\Delta \varepsilon _{o} =3.097\) .

The fitted curve analysis diagram. ( a ) The derivative of the fitted curve and ( b ) the angle difference of the fitted curve.

Compression factor

Different purposes of trajectory research require different compression effects. When studying the traffic flow state of water area, the higher compression ratio is preferred; whereas when examining the ship navigation state, the higher quality of compression is more desirable. To meet the diverse research objectives, this paper introduces a compression factor, denoted as \(\rho \) , based on the optimal core threshold difference, with a default value of 0.5.

In summary, after the optimal core threshold difference is obtained, the trajectory compression can be carried out. The ACTD-DP Algorithm flow is shown in Fig. 6 , and the algorithm code is shown in Algorithm 1.

ATD-DP (Part 1).

Algorithm flow.

ATD-DP (Part 2).

Experiment and analysis

Description of experimental data.

In order to verify the scientificity of the proposed algorithm, the AIS data of Zhoushan waters in China is selected as the experimental water area shown in Table 2 , and the time range is from 00:00 to 24:00 on May 1st, 2021. According to the ship MMSI statistical analysis, the experimental waters shared 515 ships and 404,646 AIS data. The statistics of ship types are shown in Table 3 , and the statistics of ship dimensions are shown in Table 4 .

The experimental water area and the original ship trajectories are depicted in Fig. 7 a. The raw data contains a significant amount of noise and redundant information. Preprocessing is conducted according to the method described in Section 2.3.1, and the results are presented in Fig. 7 b. The experiment was implemented on the computer(Window64 Intel(R) Xeon(R) Gold 5218R CPU @ 2.10 GHz 2.10 GHz and 32.0 GB of RAM) using MATLAB(version R2024a 32 ).

The experimental water area and AIS data (The color curves are ship trajectories. The yellow polygon area is the land part of the experimental area. The white background indicates the experimental water area. The figure is drawn using MATLAB, which version is R2024a 32 .). ( a ) The original trajectories and ( b ) the preprocessing trajectories.

Compression results of all trajectories

To compare the compression effects of different algorithms, the SW algorithm from the reference 19 (with the distance threshold of 0.8 times the ship width), the DP algorithm from the reference 22 (with a distance threshold of 0.8 times the ship length), and the PDP (Peak–Douglas–Peucker) algorithm from the reference 31 (with the distance threshold of 0.5 times the ship length and an angular threshold of \(\hbox {10}^{\circ }\) ) and the ADP algorithm from the reference 30 (with the optimal threshold change rate of 1.36) are selected as the comparative algorithms. All trajectories are compressed by different algorithms, and the evaluation metrics values obtained are shown in Table 5 and Fig. 8 .

Evaluation metric values comparison chart.

By analyzing Table 5 and Fig. 8 , it is obvious that the evaluation metric values corresponding to the five types of algorithms are different. The TIME value of the SW, DP, and PDP algorithms are obviously smaller than that of the ADP and ACTD-DP algorithms, indicating that the former trio exhibits lower complexity and superior computational efficiency, while the latter two algorithms, due to their trajectory division requirements, exhibit higher complexity and thus comparatively reduced computational efficiency. ADP algorithm has the lowest computational efficiency.

The SW algorithm exhibits the lowest values in terms of ACR and ALLR, yet it presents a notably high AMSED. This observation indicates that the SW algorithm, during the trajectory compression process, retains a relatively large number of non-critical points, leading to minimal discrepancies in length between the compressed and original trajectories. However, the location discrepancy is significantly pronounced, thereby yielding the least effective trajectory compression outcome among the evaluated methods.

The DP algorithm is characterized by the highest values in ACR,ALLR, and AMSED. These metrics suggest that the DP algorithm achieves a significant reduction in trajectory data, indicative of its pronounced compression efficacy. However, the substantial elimination of critical points results in considerable discrepancies in both length and location discrepancy between the compressed and original trajectories. Consequently, the DP algorithm’s trajectory compression performance is deemed to be of moderate quality.

The PDP algorithm exhibits a high ACR and the lowest ALLR among the evaluated methods, while its AMSED is relatively elevated yet notably lower than that of the DP algorithm. These observations indicate that the PDP algorithm exhibits a marked optimization over the traditional DP algorithm, yielding a more favorable trajectory compression outcome.

The ADP algorithm is distinguished by its higher ACR and ALLR, both of which surpass the corresponding values of the PDP algorithm. Additionally, the ADP algorithm exhibits a lower AMSED, signifying a reduced location discrepancy between the compressed and original trajectories. Collectively, these metric values underscore the ADP algorithm’s notable effectiveness in trajectory compression.

The ACTD-DP algorithm is notable for its minimal AMSED, which is significantly lower than the values observed in other algorithms. This algorithm also presents a higher ACR and a lower ALLR. These metric values indicate that the ACTD-DP algorithm excels in retaining critical points during the trajectory compression process, leading to a relatively higher length discrepancy but a markedly reduced location discrepancy when compared to the original trajectories. Consequently, the ACTD-DP algorithm is recognized for its superior compression performance.

Compression results of different types trajectories

To further demonstrate the robustness and applicability of the ACTD-DP algorithm, four ship trajectories were randomly selected for compression. The information for the chosen ships and their trajectories is presented in Table 6 .

Different algorithms compress the above trajectories respectively, and statistical evaluation metric values are shown in Table 7 . The TIME values corresponding to the compression of different ship trajectories by various algorithms align with the trends observed in Section 3.2, demonstrating that the TIME values for the ADP and ACTD-DP algorithms are significantly higher than those for the other algorithms.

Ship1’s course changes are small, the trajectory shape is simple, and the voyage is almost straight. MSED value of SW algorithm is the largest (62.7271 m, approximately1.1 times the ship length), and CR is suboptimal. The SW algorithm demonstrates the weaker capability in detecting and retaining critical points, particularly in areas where the trajectory’s curvature is low (region A in the Fig. 9 , where only one point is retained), leading to significant distortion in the trajectory. The DP algorithm exhibits the highest values for both CR and LLR, with the suboptimal MSED value. The DP algorithm performs the poorest in detecting and retaining critical points( region A in the Fig. 9 ,where no criticalpoints are retained), leading to the least effective compression effect. The PDP algorithm demonstrates the lowest values for LLR, with the MSED and CR being both smaller. The PDP algorithm demonstrates the stronger capability in detecting and retaining critical points, particularly handling the junctions between straight lines and curves more effectively than the DP algorithm(region A in the Fig. 9 , where three points are retained). The ADP algorithm exhibits the lowest value for CR, and LLR and MSED value are both smaller. APD algorithm has the strongest ability to retain critical points, resulting in an excessive number of trajectory points at the junctions between straight lines and curves (region A in the Fig. 9 , where ten points are retained).The ACTD-DP algorithm exhibits the lowest MSED value (42.907 m, approximately 0.8 times the ship length), CR and LLR are better. The ACTD-DP algorithm exhibits the strongest capability in detecting and retaining critical points, handling the transitions between straight lines and curves effectively ( region A in the Fig. 9 , where three points are retained too), resulting in minimal trajectory distortion and the best compression effect.

Ship2 is the fishing vessel with the lowest number of track points. However, the course changes are large and frequent, there are large angle turning and U-turn phenomena, and the navigation trajectory is the most complicated. Compared with the other three trajectories, the five algorithms yield lower CR and LLR values for this trajectory (with the best CR being 77.58% from the DP algorithm and the best LLR being 4.44% from the ADP algorithm), and the performance of MSED values is poor. The SW algorithm exhibits the lower MSED value (66.0019 m, approximately 1.8 times the ship length), while the DP algorithm exhibits the highest MSED value (155.6476 m, about 4.3 times the ship length). CR and MSED of the ACTD-DP algorithm are suboptimal. Consequently, the ACTD-DP algorithm maintains a good compression effect with the high CR and the low MSED. However, from the evaluation metrics analysis of the four trajectories compressed by ACTD-DP algorithm, the compression effect of this trajectory is the worst.

Ship3’s trajectory is relatively complex, with large course changes and sharp turns. However, due to the large proportion of straight line segments in the trajectory, the compression effect of each algorithm is similar to that of ship1. The CR Value of the SW algorithm is the highest, while its LLR and MSED values are the second highest among the evaluated methods. But the compression effect is poor for the location where the curvature of the trajectory curve changes frequently (region B in the Fig. 9 ). The DP algorithm demonstrates the highest values for CR, LLR and MSED(199.1665 m, approximately 1.3 times the ship length). This indicates a weaker capability in detecting critical points, resulting in the fewest number of critical points retained (region B in the Fig. 9 ). Consequently, the DP algorithm is associated with the poorest compression performance among the evaluated methods. The PDP algorithm exhibits the second lower CR, indicative of a relatively higher retention of trajectory points. Its LLR and MSED values are moderate, reflecting a balanced performance in terms of trajectory fidelity and compression efficiency.The PDP algorithm effectively captures critical points in the trajectory with significant changes in course, yet the fixed threshold discards many critical points (region B in the Fig. 9 ). Despite this, the overall compression performance of the PDP algorithm is commendably effective. The ADP algorithm exhibits the lowest values for CR, LLR and MSED(12.3542 meters, approximately 0.08 times the ship length). Compared to the PDP algorithm, the ADP algorithm demonstrates a heightened ability to detect critical points within the curved segments of the trajectory. However, it is noted that the ADP algorithm retains an excessive number of critical points(region B in the Fig. 9 ). This over-retention, while enhancing detection, may lead to a less efficient compression outcome.The ACTD-DP algorithm is distinguished by a high CR and is second only in terms of the lowest LLR and MSED, with the MSED (20.507 m, approximately 0.14 times the ship length). Comparatively, the ACTD-DP algorithm outperforms the ADP algorithm in the detection of critical points. During the compression of linear segments of trajectories, the algorithm retains a greater number of critical points. In the compression of curved trajectory segments, the number of retained critical points is moderate (region B in the Fig. 9 ). Therefore, the ACTD-DP algorithm is recognized for its superior compression efficacy.

Ship4’s trajectory is relatively simple with distinct boundaries between straight and curved segments. However, there is a high number of anchoring points, constituting 72% of the total number of points. As a result, the compression of the anchoring paths is significant, leading to the higher LLR values.The SW algorithm exhibits the lowest values for both CR(only 22.05%) and LLR, with the relatively large MSED. The ability of SW algorithm to detect the critical points of the line segments and the curve segments is quite different. And the SW algorithm fails to effectively process critical points in anchoring trajectories (region C in the Fig. 9 ), with the CR of less than 1% for such trajectories, thus resulting in the poorest compression performance. The DP algorithm demonstrates the highest values for CR, LLR and MSED(373.5619 meters, approximately 1.7 times the ship length). This indicates a significant reduction in trajectory detail, which adversely affects the detection capability for critical points, particularly in anchoring trajectories(region C in the Fig. 9 ). The DP algorithm’s approach compresses the entire anchoring trajectory into a single critical point, which fails to meet the research requirements for analyzing anchorage stay patterns and utilization rates. The PDP algorithm exhibits a lower CR and a suboptimal MSED, suggesting a more conservative compression strategy. While the PDP algorithm performs well in compressing linear and typical curved trajectories, its heightened sensitivity to transitional segments leads to a less effective compression of anchoring trajectories(region C in the Fig. 9 ).This overemphasis on detecting connections between trajectory segments may compromise the fidelity of the compressed trajectory in representing the original anchoring behavior. The ADP algorithm exhibits moderate values for CR, LLR and MSED. Compared to the PDP algorithm, the ADP algorithm demonstrates enhanced capabilities in detecting and retaining critical points, particularly at the junctions between linear and curved segments of trajectories. However, akin to the DP algorithm, the ADP algorithm struggles to effectively process anchoring trajectories (region C in the Fig. 9 )), thereby limiting its utility in accurately capturing the nuances of such trajectories.The ACTD-DP algorithm is distinguished by the second-highest values for CR and LLR and the lowest MSED (24.9138 m, approximately 0.11 times the ship’s length). This algorithm excels in the detection and retention of critical points, particularly with a uniform distribution of critical points in linear trajectory segments. In comparison to the ADP algorithm, the ACTD-DP algorithm also demonstrates efficacy in handling anchoring trajectories, capturing the typical positions and trajectories during the ship’s anchoring process(region C in the Fig. 9 )). Consequently, upon comprehensive analysis of the compression effects, the ACTD-DP algorithm is concluded to provide the optimal compression performance.

Trajectory compression comparison.

Discussion and conclusion

Building upon the traditional DP algorithm 31 and drawing inspiration from the methodology of the PDP algorithm 29 and the ADP algorithm 30 , the ACTD-DP algorithm is proposed and experimental validation is conducted. The threshold values of other comparison algorithms are based on static information (ship length, ship width, fixed distance value, etc.), and the trajectory compression effects are greatly affected by the external environment, and the algorithms have poor adaptive ability. In contrast, the ACTD-DP algorithm employs the optimal threshold difference method, reducing reliance on fixed thresholds and enhancing the robustness and applicability of the algorithm. From the overall analysis of compression effects (Table 5 and Fig. 8 ), compared to the other four algorithms, the ACTD-DP algorithm demonstrates the strongest capability in detecting and retaining key points. It maintains the smallest AMSED value while preserving the higher ACR value, resulting in the best compression performance. Analyzing the evaluation metrics for the four trajectories, the ACTD-DP algorithm exhibits the best compression performance for all trajectories except Ship2’s trajectory, demonstrating strong adaptability to different trajectories.

However, the ACTD-DP algorithm also has a notable drawback. The ACTD-DP algorithm requires curve fitting and the calculation of core thresholds and optimal threshold difference. Consequently, the computational complexity is relatively high, leading to increased algorithmic execution time. Concurrently, the ACTD-DP algorithm yields the lower CR for trajectories with a limited number of points and abrupt changes in course, such as Ship2. The compression performance for these types of trajectories could be further enhanced. These observations also provide directions for future research endeavors.

Data availability

The data that support the findings of this study are available from Shanghai Maritime Safety Administration but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. The datasets generated and analysed during the current study are not publicly available due data sensitivity but are available from the corresponding author on reasonable request.

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Acknowledgements

This study is supported by the research project “Science and Technology Commission of Shanghai Municipality” (Grant Nos. 22010502000 and 23010501900).

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These authors contributed equally: Ting Zhang, Zhiming Wang.

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Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China

Ting Zhang, Zhiming Wang & Peiliang Wang

Shandong Transport Vocational College, Weifang, 261206, Shandong, China

Shandong Vocation College of Science and Technology, Weifang, 261053, Shandong, China

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Zhang, T., Wang, Z. & Wang, P. A method for compressing AIS trajectory based on the adaptive core threshold difference Douglas–Peucker algorithm. Sci Rep 14 , 21408 (2024). https://doi.org/10.1038/s41598-024-71779-4

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DOI : https://doi.org/10.1038/s41598-024-71779-4

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