Complex experimental designs in research
Complex experimental designs in research, particularly in psychology, are sophisticated methodologies used to explore how multiple variables interact to influence behavior. These designs go beyond simple and intermediate research approaches, which tend to focus on single behaviors or relationships between a single predictor and an outcome. Instead, complex designs, such as factorial or mixed designs, allow researchers to examine the effects of two or more variables simultaneously, including both manipulated variables (like treatments or stimuli) and subject variables (like age or gender).
For instance, a study might investigate how different messaging about recycling influences behavior across various age and gender groups, revealing important interaction effects that indicate the effectiveness of messages can vary based on individual characteristics. Statistical techniques like analysis of variance (ANOVA) are commonly employed to analyze the data from these complex designs, providing insights into main effects and interactions among variables.
Understanding these interactions is crucial for both theoretical advancements in psychology and practical applications, such as tailoring effective communications for specific audiences. Ultimately, complex experimental designs are essential for capturing the multifaceted nature of human and animal behavior, acknowledging that various factors can influence actions in interconnected ways.
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Complex experimental designs in research
- TYPE OF PSYCHOLOGY: Psychological methodologies
- Complex experimental designs in psychology research investigate the effects of two or more variables on an individual’s behavior; when the effects of these variables combine to predict the behavior, rather than acting independently, this is called an interaction. Knowledge of interactions contributes to an ability to understand research observations.
Introduction
Psychology seeks to predict and understand the behavior of individuals, whether humans or other animals. To produce general rules about individual behavior, its research methods usually involve making large numbers of observations of behavior of different individuals or of the same individual at different times.
At the simple level of design, psychological research measures a single behavior repeatedly and summarizes these observations—for example, describing the mean amount of practice needed to learn a task, the percentage of people who express a particular attitude, or the mean reaction time to answer a question. Such research can show what typically happens, but it does not predict when this behavior will occur, suggest how it can be altered, or explain what causes it.
At an intermediate level of design, research in psychology is concerned with how one measured characteristic or experience of an individual human or animal relates to some behavior of that same individual that the researcher is trying to predict or understand. In a post facto intermediate research design, the predictor variable is some quality or characteristic of the individual that already exists, called a subject variable. In an experimental intermediate research design, the predictor variable is some recent experience or current , called a manipulated variable, that the psychologist performing the research has selected and administered.
Behavior is called a variable because it can differ (or vary) among individuals or in the same individual at different times. Subject variables, such as gender, self-consciousness, age, and birth order, also differ among individuals and, in some cases, within the same individual over time. Manipulated variables include , persuasive arguments, sensory isolation, and psychotherapy. They vary in the sense that the researcher exposes different individuals, or the same individual at different times, to different levels or amounts of that treatment.
Factorial Research Designs
Complex (or factorial) research designs investigate how two or more predictor variables are related to the individual’s behavior. An example would be studying how a number of subject variables, such as age, ethnicity, religion, gender, and intelligence test scores, all combine to predict political attitudes. In this case, one has a post facto complex research design. If the predictor variables are all manipulated by the experimenter—the amount of an administered drug varied, differing anxiety-inducing instructions given, and tasks of contrasting difficulties presented—then this is an experimental complex research design. The most common approach combines the measurement of subject variables with the manipulation of variables for the purpose of learning whether the effects of the manipulation have the same effects on all kinds of individuals. This is the mixed complex research design. An example of the latter design would be the measurement of gender and age followed by the manipulation of the kind of message used to persuade individuals about the importance of recycling waste; later, the individuals’ recycling behavior would be observed.
In this last example, the three factors being measured to see if they are related to recycling behavior may yield between zero and seven statistically significant results from an analysis of variance of the data. An analysis of variance is defined as a statistical technique, commonly abbreviated as ANOVA, used in inferential statistics to determine which behaviors that are measured are related to differences in other variables. Of the significant results, three may be , meaning that the differences in observed behavior occur for any of the variables when averaged across all levels of the other variables. Any effects that are not main effects are interactions, meaning that the effect on behavior of one or more variables may be affected by a change in another variable.
In other words, if one message was found to be more effective than the other for boys but not for girls, an interaction would be said to exist. The effect of the message on recycling behavior would not be the same for all people, but rather would depend on the gender of the listener. When such an interaction occurs, it can be said that the effect of one variable on the measured behavior depends on the level of the other variable.
In this three-factor example, there are four possible interactions. One of them is the three-way interaction, since there is a possibility that the behavior for each combination of the factors cannot be predicted merely by adding the independent main effects for each factor. This means that the three factors contribute to behavior in some manner such that they interact, so that they are not independent of one another. An example would be if one of the messages is less appealing to older girls than it is to younger girls, or to boys of any age. The other three possible interactions in this experiment are all possible pairings of the variables: gender and age, gender and message, and age and message.
Varying Research Factors
Imagine the example given above concerning recycling first as a simple research design. In this case, measurement (in some carefully defined and described manner) of behavior alone might find that 15 percent of all people practice recycling. To learn more about the causes of recycling, one might shift to an experimental intermediate research design.
By measuring the effects of varying the message about recycling, one might find the results shown in the left panel of the figure that accompanies this essay. This shows that message B produced more recycling (24 percent) than did message A (17 percent).
One should be wary of such a conclusion, however, whether it is found in published research or in one’s own investigations. The mixed complex research design can make further measurements on subject variables to see whether these findings are the same for all individuals, or whether there is some personal characteristic that predicts the effect of the manipulated variable.
The right panel of the figure shows what might be found if gender were measured as well. Because equal numbers of boys and girls were assigned to each group, the mean recycling for each message remains the same (24 percent for message B, and 17 percent for message A), but it can be seen that the effect of each message was very different, depending on the gender of the individual. Here an interaction, which was not evident in the simple or intermediate research designs, can be seen. It is an important interaction because it shows that the effect of the messages on recycling behavior is different, depending on whether the listener is a boy or a girl.
Now imagine that another subject variable, age, were to be measured. The children were grouped into two categories, ages six to ten and ages eleven to fourteen. Thus, there were four groups, differentiated by gender and age: younger girls, older girls, younger boys, and older boys. Keeping the numbers in each group equal, it might be found that there are no effects of age on which message influences recycling behavior except in the girls who heard message B. In this group, young girls showed 23 percent recycling and older girls 13 percent. Thus, there would be a three-way interaction, in which both gender and age were dependent on each other to determine the effect of the message on behavior.
Practical and Theoretical Applications
Psychological researchers want to know about such interaction effects for both practical and theoretical purposes. An example of a practical application might be environmentalists reading this research to learn how best to improve recycling by children. Whether through television commercials broadcast during programs with a known audience, or through messages in the schools, environmentalists can tailor the message that will be the most effective for a given audience.
Basic researchers are trying to develop theories to understand more general behavior, such as attitude change or motivational processes. These theories may become sophisticated enough to make predictions for practical purposes such as the recycling program mentioned here, or changing people’s behavior in therapy. In the present example, the interactions observed may cause the researcher to look at the content of message B to attempt to understand why it was more effective for all boys and for younger girls but was rejected by older girls. If the researcher noted that the message used a popular cartoon action figure as a role model for recycling, then possibly it is only boys and younger girls who identify with that action figure.
From further reading in the field of developmental psychology, the researcher may hypothesize that older girls would identify more with romantic fictional characters, and so design a recycling message that would have more appeal to them. The test of this new message would be an example of how complex research designs work to build cumulatively on previous research to produce more precise practical applications and also to improve theories about the sources of individual behavior.
Analyzing Complex Behavior
Complex experimental designs were developed to answer detailed questions about individual behavior. Simple and intermediate research designs provide some information, but the experience of psychological researchers is that behavior is not simple. Behavior has multiple causes that do not always act independently of one another. Thus, there is the necessity for complex research designs, advanced statistical techniques, and sophisticated theories.
These methods are used in a great many areas of research in psychology. Therefore, the individual who wishes to know, at a professional level, about the behavior of humans and other animals must be able to understand the reports of the psychologists who do this research. Others can learn about psychology at a more general level by relying on secondary accounts written for a broader audience. Much of this research depends on the statistical technique of analysis of variance. This is often performed by using a computer software package designed for this purpose, including PsyToolKit, Statistical Package for the Social Sciences (SPSS), JMP Statistical Discovery, and Stata. PsyTooKit is a free program that can be used to store and analyze data from cognitive psychology experiments. Though the cost is appealing, other programs can conduct more complex statistical analysis, such as SPSS, which is beginner-friendly. When analyzing complex experimental designs with multiple constructs, JMP may be most appropriate, and Stata is commonly used for regression analysis. These statistical software packages are great labor savers; at the same time, however, they can mislead researchers into incorrect conclusions about behavior if they are not familiar with experimental methodology.
Subject Versus Manipulated Variables
It is especially important when drawing conclusions to discriminate between subject variables and manipulated variables. The reason is causality. Subject variables are measured after the fact (post facto) and consist of characteristics that the individual already possesses. Logically, subject variables cannot be assured to be part of a cause-and-effect connection with the behavior of interest. They may possibly be the cause—for example, a measured trait of anxiety may affect reactions to stress—but characteristics such as age and gender may instead be contributors to , experiences, hormonal changes, or peer pressure. Other subject variables such as education or social class may be a result of behavior rather than its cause. When subject variables are found in research to be related to behavior, they may be a cause of that behavior, but the research does not provide evidence to justify that conclusion. Only manipulated variables—in a careful, controlled experiment—may be assumed to cause the behavior that they precede.
A Range of Behavioral Influences
It is also important that the researcher remember that the variables selected represent only a few of the possible influences on behavior. In some cases, the subject variables are only related to more powerful variables yet to be discovered. For the example used here, it was suggested that gender and age were predictors of the most effective persuasive message. Perhaps what was most important was that these were approximate predictors of which individuals prefer action figures over romantic figures. An analysis of how the persuasive messages are working may find that the answer to a question such as “Which figures do you prefer for playing?” would be a much more accurate predictor of the behavior in this situation than the mere assumption that boys and girls separate along lines of gender in every preference and psychological process. Awareness of these and similar research refinements comes with experience and training.
Bibliography
Edwards, Allen Louis. Experimental Design in Psychological Research. 5th ed., Harper, 1985.
Howitt, Dennis. Introduction to Qualitative Research Methods in Psychology: Putting Theory into Practice. 4th ed., Pearson Education, 2019.
Kara, Helen, et al. Creative Research Methods: A Practical Guide. 2nd ed., Policy Press, 2020.
Kazdin, Alan E. Research Design in Clinical Psychology. 6th ed., Cambridge UP, 2024.
Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences. Sage, 2013.
Martin, David W. Doing Psychology Experiments. 7th ed., Wadsworth, 2008.
McLeod, Saul. "Experimental Design: Types, Examples & Methods." Simply Psychology, 31 July 2023, www.simplypsychology.org/experimental-designs.html. Accessed 20 Dec. 2024.
Myers, Jerome L. Fundamentals of Experimental Design. 3rd ed., Allyn, 1979.
Ryan, Thomas P. Modern Experimental Design. Wiley-Interscience, 2007.
Schneider, Sandra L. Experimental Design in the Behavioral and Social Sciences. Sage, 2013.
Sprinthall, Richard C. Basic Statistical Analysis. 8th ed., Allyn, 2007.
Vercruyssen, M., and Hal W. Hendrick. Behavioral Research and Analysis: An Introduction to Statistics within the Context of Experimental Design. CRC, 2012.
Winer, B. J. Statistical Principles in Experimental Design. 3rd ed., McGraw, 1991.