Describe cross-sectional, longitudinal, and sequential research designs, including the advantages and disadvantages of each design.

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Respond to the following in a minimum of 175 words:

A researcher is interested in studying the effects of a music therapy program on depression in nursing home residents. The IV is the music therapy program (2 sessions per week for 6 weeks) and the DV is scores on three different measures of depression. If you were conducting this research, which of the following designs would you choose? Explain.

one-group post-test only design
one-group pretest-posttest design
non-equivalent control group design
non-equivalent pretest-posttest design
interrupted time series design
control series design

Week Five Homework Exercise IS ATTACHED ABOVE

PART3-Prepare a 2-page outline of your Research Proposal. The outline should provide an overview of the main elements of your proposal. It should include:

An introduction
A brief statement of purpose
The rationale for conducting the study
Your hypotheses and research questions
Methods (participants, procedures, materials, instruments), and appropriate statistical test(s) for data analysis
List of at least three relevant articles for the proposal
Submit your assignment.

The purpose of this outline is to obtain feedback from your instructor on your progress and on the feasibility of your topic and design.




Describe single-case experimental designs and discuss reasons to use this design.
Describe the one-group posttest-only design.
Describe the one-group pretest-posttest design and the associated threats to internal validity that may occur: history, maturation, testing, instrument decay, and regression toward the mean.
Describe the nonequivalent control group design and nonequivalent control group pretest-posttest design, and discuss the advantages of having a control group.
Distinguish between the interrupted time series design and control series design.
Describe cross-sectional, longitudinal, and sequential research designs, including the advantages and disadvantages of each design.
Define cohort effect.
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IN THE CLASSIC EXPERIMENTAL DESIGN DESCRIBED IN CHAPTER 8, PARTICIPANTS ARE RANDOMLY ASSIGNED TO THE INDEPENDENT VARIABLE CONDITIONS, AND A DEPENDENT VARIABLE IS MEASURED. The responses on the dependent measure are then compared to determine whether the independent variable had an effect. Because all other variables are held constant, differences on the dependent variable must be due to the effect of the independent variable. This design has high internal validity—we are very confident that the independent variable caused the observed responses on the dependent variable. You will frequently encounter this experimental design when you explore research in the behavioral sciences. However, other research designs have been devised to address special research problems.

This chapter focuses on three types of special research situations. The first is the instance in which the effect of an independent variable must be inferred from an experiment with only one participant—single-case experimental designs. Second, we will describe pre-experimental and quasi-experimental designs that may be considered if it is not possible to use one of the true experimental designs described in Chapter 8. Third, we consider research designs for studying changes that occur with age.

Single-case experimental designs have traditionally been called single-subject designs; an equivalent term you may see is small N designs. Much of the early interest in single-case designs in psychology came from research on operant conditioning pioneered by B. F. Skinner (e.g., Skinner, 1953). Today, research using single-case designs is often seen in applied behavior analysis in which operant conditioning techniques are used in clinical, counseling, educational, medical, and other applied settings (Kazdin, 2011, 2013).

Single-case experiments were developed from a need to determine whether an experimental manipulation had an effect on a single research participant. In a single-case design, the subject’s behavior is measured over time during a baseline control period. The manipulation is then introduced during a treatment period, and the subject’s behavior continues to be observed. A change in the subject’s behavior from baseline to treatment periods is evidence for the effectiveness of the manipulation. The problem, however, is that there could be many explanations for the change other than the experimental treatment (i.e., alternative explanations). For example, some other event may have coincided with the introduction of the treatment. The single-case designs described in the following sections address this problem.

Reversal Designs
As noted, the basic issue in single-case experiments is how to determine that the manipulation of the independent variable had an effect. One method is Page 222to demonstrate the reversibility of the manipulation. A simple reversal design takes the following form:


This basic reversal design is called an ABA design; it requires observation of behavior during the baseline control (A) period, again during the treatment (B) period, and also during a second baseline (A) period after the experimental treatment has been removed. (Sometimes this is called a withdrawal design, in recognition of the fact that the treatment is removed or withdrawn.) For example, the effect of a reinforcement procedure on a child’s academic performance could be assessed with an ABA design. The number of correct homework problems could be measured each day during the baseline. A reinforcement treatment procedure would then be introduced in which the child received stars for correct problems; the stars could be accumulated and exchanged for toys or candies. Later, this treatment would be discontinued during the second baseline (A) period. Hypothetical data from such an experiment are shown in Figure 11.1. The fact that behavior changed when the treatment was introduced and reversed when the treatment was withdrawn is evidence for its effectiveness.

Figure 11.1 depicts a treatment that had a relatively dramatic impact on behavior. Some treatments do produce an immediate change in behavior, but many other variables may require a longer time to show an impact.

The ABA design can be greatly improved by extending it to an ABAB design, in which the experimental treatment is introduced a second time, or even to an ABABAB design that allows the effect of the treatment to be tested a third time. This is done to address two problems with the ABA reversal design. First, a single reversal is not extremely powerful evidence for the effectiveness of the treatment. The observed reversal might have been due to a random fluctuation in the child’s behavior; perhaps the treatment happened to coincide with some other event, such as the child’s upcoming birthday, that caused the change (and the post-birthday reversal). These possibilities are much less likely if the treatment has been shown to have an effect two or more times; random or coincidental events are unlikely to be responsible for both reversals. The second problem is ethical. As Barlow, Nock, and Hersen (2009) point out, it does not seem right to end the design with the withdrawal of a treatment that may be very beneficial for the participant. Using an ABAB design provides the opportunity to observe a second reversal when the treatment is introduced again. The sequence ends with the treatment rather than the withdrawal of the treatment.



Hypothetical data from ABA reversal design

Page 223The logic of the reversal design can also be applied to behaviors observed in a single setting. For example, Kazbour and Bailey (2010) examined the effectiveness of a procedure designed to increase use of designated drivers in a bar. The percentage of bar patrons either serving as or being with a designated driver was recorded over a baseline period of 2 weeks. A procedure to increase the use of designated drivers was then implemented during the treatment phase. Designated drivers received a $5 gas card, and the driver and passengers received free pizza on their way out of the bar. The pizza and gas incentive was discontinued during the final phase of the study. The percentage of bar patrons engaged in designated driver arrangements increased substantially during the treatment phase but returned to baseline levels when the incentive was withdrawn.

Multiple Baseline Designs
It may have occurred to you that a reversal of some behaviors may be impossible or unethical. For example, it would be unethical to reverse treatment that reduces dangerous or illegal behaviors, such as indecent exposure or alcoholism, even if the possibility exists that a second introduction of the treatment might be effective. Other treatments might produce a long-lasting change in behavior that is not reversible. In such cases, multiple measures over time can be made before and after the manipulation. If the manipulation is effective, a change in behavior will be immediately observed, and the change will continue to be reflected in further measures of the behavior. In a multiple baseline design, the effectiveness of the treatment is demonstrated when a behavior changes only after the manipulation is introduced. To demonstrate the effectiveness of the treatment, such a change must be observed under multiple circumstances to rule out the possibility that other events were responsible.

There are several variations of the multiple baseline design (Barlow et al., 2009). In the multiple baseline across subjects, the behavior of several subjects is measured over time; for each subject, though, the manipulation is introduced at a different point in time. Figure 11.2 shows data from a hypothetical smoking reduction experiment with three subjects. Note that introduction of the manipulation was followed by a change in behavior for each subject. However, because this change occurred across all individuals and the manipulation was introduced at a different time for each subject, we can rule out explanations based on chance, historical events, and so on.

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Hypothetical data from multiple baseline design across three subjects (S1, S2, and S3)

In a multiple baseline across behaviors, several different behaviors of a single subject are measured over time. At different times, the same manipulation is applied to each of the behaviors. For example, a reward system could be instituted to increase the socializing, grooming, and reading behaviors of a psychiatric patient. The reward system would be applied to each of these behaviors at different times. Demonstrating that each behavior increased when the reward system was applied would be evidence for the effectiveness of the manipulation.

The third variation is the multiple baseline across situations, in which the same behavior is measured in different settings, such as at home and at work. Again, a manipulation is introduced at a different time in each setting, with the expectation that a change in the behavior in each situation will occur only after the manipulation.

Replications in Single-Case Designs
The procedures for use with a single subject can, of course, be replicated with other subjects, greatly enhancing the generalizability of the results. Usually, reports of research that employs single-case experimental procedures do present Page 225the results from several subjects (and often in several settings). The tradition in single-case research has been to present the results from each subject individually rather than as group data with overall means. Sidman (1960), a leading spokesperson for this tradition, has pointed out that grouping the data from a number of subjects by using group means can sometimes give a misleading picture of individual responses to the manipulation. For example, the manipulation may be effective in changing the behavior of some subjects but not others. This was true in a study conducted by Ryan and Hemmes (2005) that investigated the impact of rewarding college students with course grade points for submitting homework. For half of the 10 chapters, students received points for submitting homework; however, there were no points given if they submitted homework for the other chapters (to control for chapter topic, some students had points for odd-numbered chapters only and others received points for the even-numbered chapters). Ryan and Hemmes found that on average students submitted more homework assignments and performed better on chapter-based quizzes that were directly associated with point rewards. However, some individual participants performed about the same regardless of condition. Because the emphasis of the study was on the individual subject, this pattern of results was quickly revealed.

Single-case designs are useful for studying many research problems and should be considered a powerful alternative to more traditional research designs. They can be especially valuable for someone who is applying some change technique in a natural environment—for example, a teacher who is trying a new technique in the classroom. In addition, complex statistical analyses are not required for single-case designs.

Quasi-experimental designs address the need to study the effect of an independent variable in settings in which the control features of true experimental designs cannot be achieved. Thus, a quasi-experimental design allows us to examine the impact of an independent variable on a dependent variable, but causal inference is much more difficult because quasi-experiments lack important features of true experiments such as random assignment to conditions. In this chapter, we will examine several quasi-experimental designs that might be used in situations in which a true experiment is not possible. This is most likely to occur in applied settings when an independent variable is manipulated in a natural setting such as a school, business, hospital, or an entire city or state.

There are many types of quasi-experimental designs—see Campbell (1968, 1969), Campbell and Stanley (1966), Cook and Campbell (1979), Shadish, Cook, and Campbell (2002). Only six designs will be described. As you read about each design, compare the design features and problems with the randomized true experimental designs described in Chapter 8. We start out with the simplest and most problematic of the designs. In fact, the first three designs Page 226we describe are sometimes called “pre-experimental” to distinguish them from other quasi-experimental designs. This is because of the problems associated with these designs. Nevertheless, all may be used in different circumstances, and it is important to recognize the internal validity issues raised by each design.

One-Group Posttest-Only Design
Suppose you want to investigate whether sitting close to a stranger will cause the stranger to move away. You might try sitting next to a number of strangers and measure the number of seconds that elapse before they leave. Your design would look like this:


Now suppose that the average amount of time before the people leave is 9.6 seconds. Unfortunately, this finding is not interpretable. You do not know whether they would have stayed longer if you had not sat down or whether they would have stayed for 9.6 seconds anyway. It is even possible that they would have left sooner if you had not sat down—perhaps they liked you!

This one-group posttest-only design—called a “one-shot case study” by Campbell and Stanley (1966)—lacks a crucial element of a true experiment: a control or comparison group. There must be some sort of comparison condition to enable you to interpret your results. The one-group posttest-only design with its missing comparison group has serious deficiencies in the context of designing an internally valid experiment that will allow us to draw causal inferences about the effect of an independent variable on a dependent variable.

You might wonder whether this design is ever used. In fact, you may see this type of design used as evidence for the effectiveness of a program. For example, employees in a company might participate in a 4-hour information session on emergency procedures. At the conclusion of the program, they complete a knowledge test on which their average score is 90%. This result is then used to conclude that the program is successfully educating employees. Such studies lack internal validity—our ability to conclude that the independent variable had an effect on the dependent variable. With this design, we do not even know if the score on the dependent variable would have been equal, lower, or even higher without the program. The reason why results such as these are sometimes accepted is because we may have an implicit idea of how a control group would perform. Unfortunately, we need that comparison data.

One-Group Pretest-Posttest Design
One way to obtain a comparison is to measure participants before the manipulation (a pretest) and again afterward (a posttest). An index of change from Page 227the pretest to the posttest could then be computed. Although this one-group pretest-posttest design sounds fine, there are some major problems with it.

To illustrate, suppose you wanted to test the hypothesis that a relaxation training program will result in a reduction in cigarette smoking. Using the one-group pretest-posttest design, you would select a group of people who smoke, administer a measure of smoking, have them go through relaxation training, and then re-administer the smoking measure. Your design would look like this:


If you did find a reduction in smoking, you could not assume that the result was due to the relaxation training program. This design has failed to take into account several alternative explanations. These alternative explanations are threats to the internal validity of studies using this design and include history, maturation, testing, instrument decay, and regression toward the mean.

History History refers to any event that occurs between the first and second measurements but is not part of the manipulation. Any such event is confounded with the manipulation. For example, suppose that a famous person dies of lung cancer during the time between the first and second measures. This event, and not the relaxation training, could be responsible for a reduction in smoking. Admittedly, the celebrity death example is dramatic and perhaps unlikely. However, history effects can be caused by virtually any confounding event that occurs at the same time as the experimental manipulation.

Maturation People change over time. In a brief period they become bored, fatigued, perhaps wiser, and certainly hungrier; over a longer period, children become more coordinated and analytical. Any changes that occur systematically over time are called maturation effects. Maturation could be a problem in the smoking reduction example if people generally become more concerned about health as they get older. Any such time-related factor might result in a change from the pretest to the posttest. If this happens, you might mistakenly attribute the change to the treatment rather than to maturation.

Testing Testing becomes a problem if simply taking the pretest changes the participant’s behavior—the problem of testing effects. For example, the smoking measure might require people to keep a diary in which they note every cigarette smoked during the day. Simply keeping track of smoking might be sufficient to cause a reduction in the number of cigarettes a person smokes. Thus, the reduction found on the posttest could be the result of taking the Page 228pretest rather than of the program itself. In other contexts, taking a pretest may sensitize people to the purpose of the experiment or make them more adept at a skill being tested. Again, the experiment would not have internal validity.

Instrument decay Sometimes, the basic characteristics of the measuring instrument change over time; this is called instrument decay. Consider sources of instrument decay when human observers are used to measure behavior: Over time, an observer may gain skill, become fatigued, or change the standards on which observations are based. In our example on smoking, participants might be highly motivated to record all cigarettes smoked during the pretest when the task is new and interesting, but by the time the posttest is given they may be tired of the task and sometimes forget to record a cigarette. Such instrument decay would lead to an apparent reduction in cigarette smoking.

Regression toward the mean Sometimes called statistical regression, regression toward the mean is likely to occur whenever participants are selected because they score extremely high or low on some variable. When they are tested again, their scores tend to change in the direction of the mean. Extremely high scores are likely to become lower (closer to the mean), and extremely low scores are likely to become higher (again, closer to the mean).

Regression toward the mean would be a problem in the smoking experiment if participants were selected because they were initially found to be extremely heavy smokers. By choosing people for the program who scored highest on the pretest, the researcher may have selected many participants who were, for whatever reason, smoking much more than usual at the particular time the measure was administered. Those people who were smoking much more than usual will likely be smoking less when their smoking is measured again. If we then compare the overall amount of smoking before and after the program, it will appear that people are smoking less. The alternative explanation is that the smoking reduction is due to statistical regression rather than the effect of the program.

Regression toward the mean will occur whenever you gather a set of extreme scores taken at one time and compare them with scores taken at another point in time. The problem is actually rooted in the reliability of the measure. Recall from Chapter 5 that any given measure reflects a true score plus measurement error. If there is perfect reliability, the two measures will be the same (if nothing happens to lower or raise the scores). If the measure of smoking is perfectly reliable, a person who reports smoking 20 cigarettes today will report smoking 20 cigarettes 2 weeks from now. However, if the two measures are not perfectly reliable and there is measurement error, most scores will be close to the true score but some will be higher and some will be lower. Thus, one smoker with a true score of 20 cigarettes per day might sometimes smoke 5 and sometimes 35; however, most of the time, the number is closer to 20 than the extremes. Another smoker might have a true score of 35 but on occasion smokes as few as 20 and as many as 50; again, most of the time, the number is Page 229closer to the true score than to the extremes. Now suppose that you select two people who said they smoked 35 cigarettes on the previous day, and that both of these people are included in the group—you picked the first person on a very unusual day and the second person on a very ordinary day. When you measure these people 2 weeks later, the first person is probably going to report smoking close to 20 cigarettes and the second person close to 35. If you average the two, it will appear that there is an overall reduction in smoking.

What if the measure were perfectly reliable? In this case, the person with a true score of 20 cigarettes would always report this amount and therefore would not be included in the heavy smoker (35+) group at all. Only people with true scores of 35 or more would be in the group, and any reduction in smoking would be due to the treatment program. The point here is that regression toward the mean is a problem if there is measurement error.

Statistical regression occurs when we try to explain events in the “real world” as well. Sports columnists often refer to the hex that awaits an athlete who appears on the cover of Sports Illustrated. The performances of a number of athletes have dropped considerably after they were the subjects of Sports Illustrated cover stories. Although these cover stories might cause the lower performance (perhaps the notoriety results in nervousness and reduced concentration), statistical regression is also a likely explanation. An athlete is selected for the cover of the magazine because he or she is performing at an exceptionally high level; the principle of regression toward the mean states that very high performance is likely to deteriorate. We would know this for sure if Sports Illustrated also did cover stories on athletes who were in a slump and this became a good omen for them!

All these problems can be eliminated by the use of an appropriate control group. A group that does not receive the experimental treatment provides an adequate control for the effects of history, statistical regression, and so on. For example, outside historical events would have the same effect on both the experimental and the control groups. If the experimental group differs from the control group on the dependent measure administered after the manipulation, the difference between the two groups can be attributed to the effect of the experimental manipulation.

Given these problems, is the one-group pretest-posttest design ever used? This design may in fact be used in many applied settings. Recall the example of the evaluation of a program to teach emergency procedures to employees. With a one group pretest-posttest design, the knowledge test would be given before and after the training session. The ability to observe a change from the pretest to the posttest does represent an improvement over the posttest-only design, even with the threats to internal validity that we identified. In addition, the ability to use data from this design can be enhanced if the study is replicated at other times with other participants. However, formation of a control group is always the best way to strengthen this design.C

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