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SINGLE CASE, QUASI-EXPERIMENTAL, and DEVELOPMENTAL RESEARCH
LEARNING OBJECTIVES
· 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
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.
FIGURE 11.1
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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FIGURE 11.2
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
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.
In any control group, the participants in the experimental condition and the control condition must be equivalent. If participants in the two groups Page 230differ before the manipulation, they will probably differ after the manipulation as well. The next design illustrates this problem.
Nonequivalent Control Group Design
The nonequivalent control group design employs a separate control group, but the participants in the two conditions—the experimental group and the control group—are not equivalent. In other words, the two groups are not the result of random assignment. The differences become a confounding variable that provides an alternative explanation for the results. This problem, called selection differences or selection bias, usually occurs when participants who form the two groups in the experiment are chosen from existing natural groups. If the relaxation training program is studied with the nonequivalent control group design, the design will look like this:
The participants in the first group are given the smoking frequency measure after completing the relaxation training. The people in the second group do not participate in any program. In this design, the researcher does not have any control over which participants are in each group. Suppose, for example, that the study is conducted in a division of a large company. All of the employees who smoke are identified and recruited to participate in the training program. The people who volunteer for the program are in the experimental group, and the people in the control group are simply the smokers who did not sign up for the training. The problem of selection differences arises because smokers who choose to participate may differ in some important way from those who do not. For instance, they may already be light smokers compared with the others and more confident that a program can help them. If so, any difference between the groups on the smoking measure would reflect preexisting differences rather than the effect of the relaxation training. Such a preexisting difference is what we have previously described as a confound (see Chapter 4 ).
It is important to note that the problem of selection differences arises in this design even when the researcher apparently has successfully manipulated the independent variable using two similar groups. For example, a researcher might have all smokers in the engineering division of a company participate in the relaxation training program and smokers who work in the marketing division serve as a control group. The problem here, of course, is that the smokers in the two divisions may have differed in smoking patterns prior to the relaxation program.
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Nonequivalent Control Group Pretest-Posttest Design
The nonequivalent control group posttest-only design can be greatly improved if a pretest is given. When this is done, we have a nonequivalent control group pretest-posttest design, one of the most useful quasi-experimental designs. It can be diagrammed as follows:
This design is similar to the pretest-posttest design described in Chapter 8 . However, this is not a true experimental design because assignment to groups is not random; the two groups may not be equivalent. We have the advantage, however, of knowing the pretest scores. Thus, we can see whether the groups were the same on the pretest. Even if the groups are not equivalent, we can look at changes in scores from the pretest to the posttest. If the independent variable has an effect, the experimental group should show a greater change than the control group (see Kenny, 1979).
An evaluation of National Alcohol Screening Day (NASD) provides an example of the use of a nonequivalent control group pretest-posttest design (Aseltine, Schilling, James, Murray, & Jacobs, 2008). NASD is a community-based program that provides free access to alcohol screening, a private meeting with a health professional to review the results, educational materials, and referral information if necessary. For the evaluation, NASD attendees at five community locations completed a baseline (pretest) measure of their recent alcohol consumption. This measure was administered as a posttest 3 months later. A control group was formed 1 week following NASD at the same locations using displays that invited people to take part in a health survey. These individuals completed the same pretest measure and were contacted in 3 months for the posttest. The data analysis focused on participants identified as at-risk drinkers; the NASD participants showed a significant decrease in alcohol consumption from pretest to posttest when compared with similar individuals in the control group.
Propensity Score Matching of Nonequivalent Treatment and Control Groups
The nonequivalent control group designs lack random assignment to conditions and so the groups may in fact differ in important ways. For example, people who decide to attend an alcohol screening event may differ from those who Page 232are interested in a health screening. Perhaps the people at the health screening are in fact healthier than the alcohol screening participants.
One approach to making the groups equivalent on a variable such as health is to match participants in the conditions on a measure of health (this is similar to matched pairs designs, covered in Chapter 8 ). The health measure can be administered to everyone in the treatment condition and all individuals who are included in the control condition. Now, each person in the treatment condition would be matched with a control individual who possesses an identical or highly similar health score. Once this has been done, the analysis of the dependent measure can take place. This procedure is most effective when the measure used for the matching is highly reliable and the individuals in the two conditions are known to be very similar. Nonetheless, it is still possible that the two groups are different on other variables that were not measured.
Advances in statistical methods have made it possible to simultaneously match individuals on multiple variables. Instead of matching on just one variable such as health, the researcher can obtain measures of other variables thought to be important when comparing the groups. The scores on these variables are combined to produce what is called a propensity score (the statistical procedure is beyond the scope of the book). Individuals in the treatment and control groups can then be matched on propensity scores—this process is called propensity score matching (Guo & Fraser, 2010; Shadish, Cook, & Campbell, 2002).
Interrupted Time Series Design and Control Series Design
Campbell (1969) discusses at length the evaluation of one specific legal reform: the 1955 crackdown on speeding in Connecticut. Although seemingly an event in the distant past, the example is still a good illustration of an important methodological issue. The crackdown was instituted after a record high number of traffic fatalities occurred in 1955. The easiest way to evaluate this reform is to compare the number of traffic fatalities in 1955 (before the crackdown) with the number of fatalities in 1956 (after the crackdown). Indeed, the number of traffic deaths fell from 324 in 1955 to 284 in 1956. This single comparison is really a one-group pretest-posttest design with all of that design’s problems of internal validity; there are many other reasons that traffic deaths might have declined. One alternative is to use an interrupted time series design that would examine the traffic fatality rates over an extended period of time, both before and after the reform was instituted. Figure 11.3 shows this information for the years 1951–1959. Campbell (1969) argues that the drop from 1955 to 1956 does not look particularly impressive, given the great fluctuations in previous years, but there is a steady downward trend in fatalities after the crackdown. Even here, however, Campbell sees a problem in interpretation. The drop could be due to statistical regression: Because 1955 was a record high year, the probability is that there would have been a drop anyway. Still, the data for the years extending before and after the crackdown allow for a less ambiguous interpretation than would be possible with data for only 1955 and 1956.
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FIGURE 11.3
Connecticut traffic fatalities, 1951–1959
One way to improve the interrupted time series design is to find some kind of control group—a control series design. In the Connecticut speeding crackdown, this was possible because other states had not instituted the reform. Figure 11.4 shows the same data on traffic fatalities in Connecticut plus the fatality figures of four comparable states during the same years. The fact that the fatality rates in the control states remained relatively constant while those in Connecticut consistently declined led Campbell to conclude that the crackdown did indeed have some effect.
FIGURE 11.4
Control series design comparing Connecticut traffic fatality rate (solid color line) with the fatality rate of four comparable states (dotted black line)
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DEVELOPMENTAL RESEARCH DESIGNS
Developmental psychologists often study the ways that individuals change as a function of age. A researcher might test a theory concerning changes in reasoning ability as children grow older, the age at which self-awareness develops in young children, or the global values people have as they move from adolescence through old age. In all cases, the major variable is age. Developmental researchers face an interesting choice in designing their studies because there are two general methods for studying individuals of different ages: the cross-sectional method and the longitudinal method. You will see that the cross-sectional method shares similarities with the independent groups design whereas the longitudinal method is similar to the repeated measures design. We will also examine a hybrid approach called the sequential method. The three approaches are illustrated in Figure 11.5 .
Cross-Sectional Method
In a study using the cross-sectional method, persons of different ages are studied at only one point in time. Suppose you are interested in examining how the ability to learn a computer application changes as people grow older. Using the cross-sectional method, you might study people who are currently 20, 30, 40, and 50 years of age. The participants in your study would be given the same computer learning task, and you would compare the groups on their performance.
FIGURE 11.5
Three designs for developmental research
Page 235In a recent study by Tymula, Belmaker, Ruderman, and Levy (2013), subjects in four age groups (12–17; 21–25; 30–50; 65–90) completed the same financial decision-making task. The task involved choosing among options with varying levels of risk and reward that led to an expected financial outcome for each subject. Individuals in the oldest age group made the poorest financial decisions with more inconsistent decisions and lower financial outcomes.
Longitudinal Method
In the longitudinal method, the same group of people is observed at different points in time as they grow older. Perhaps the most famous longitudinal study is the Terman Life Cycle Study that was begun by Stanford psychologist Lewis Terman in 1921. Terman studied 1,528 California schoolchildren who had intelligence test scores of at least 135. The participants, who called themselves “Termites,” were initially measured on numerous aspects of their cognitive and social development in 1921 and 1922. Terman and his colleagues continued studying the Termites during their childhood and adolescence and throughout their adult lives (cf. Terman, 1925; Terman & Oden, 1947, 1959).
Terman’s successors at Stanford continue to track the Termites until each one dies. The study has provided a rich description of the lives of highly intelligent individuals and disconfirmed many negative stereotypes of high intelligence—for example, the Termites were very well adjusted both socially and emotionally. The data have now been archived for use by other researchers such as Friedman and Martin (2011), who used the Terman data to study whether personality and other factors are related to health and longevity. To complete their investigations, Friedman and Martin obtained death certificates of Terman participants to have precise data on both how long they lived and the causes of death. One strong pattern that emerged was that the personality dimension of “conscientiousness” (being self-disciplined, organized) that was measured in childhood was related to longevity. Of interest is that changes in personality qualities also affected longevity. Participants who had become less conscientious as adults had a reduction in longevity; those who became more conscientious as adults experienced longer lives. Another interesting finding concerned interacting with pets. Questions about animals were asked when participants were in their sixties; contrary to common beliefs, having or playing with pets was not related to longevity.
A unique longitudinal study on aging and Alzheimer’s disease called the Nun Study illustrates a different approach (Snowden, 1997). In 1991, all members of a particular religious order born prior to 1917 were asked to participate by providing access to their archived records as well as various annual medical and psychological measures taken over the course of the study. The Page 236sample consisted of 678 women with a mean age of 83. One fascinating finding from this study was based on autobiographies that all sisters wrote in 1930 (Danner, Snowden, & Friesen, 2001). The researchers devised a coding system to measure positive emotional content in the autobiographies. Greater positive emotions were strongly related to actual survival rate during the course of the study. Other longitudinal studies may study individuals over only a few years. For example, a 9-year study of U.S. children found a variety of impacts—positive and negative—of early non-maternal child care (NICHD Early Child Care Research Network, 2005).
Comparison of Longitudinal and Cross-Sectional Methods
The cross-sectional method is much more common than the longitudinal method primarily because it is less expensive and immediately yields results. Note that, with a longitudinal design, it would take 30 years to study the same group of individuals from age 20 to 50, but with a cross-sectional design, comparisons of different age groups can be obtained relatively quickly.
There are, however, some disadvantages to cross-sectional designs. Most important, the researcher must infer that differences among age groups are due to the developmental variable of age. The developmental change is not observed directly among the same group of people, but rather is based on comparisons among different cohorts of individuals. You can think of a cohort as a group of people born at about the same time, exposed to the same events in a society, and influenced by the same demographic trends such as divorce rates and family size. If you think about the hairstyles of people you know who are in their 30s, 40s, 50s, and 60s, you will immediately recognize the importance of cohort effects! More crucially, differences among cohorts reflect different economic and political conditions in society, different music and arts, different educational systems, and different child-rearing practices. In a cross-sectional study, a difference among groups of different ages may reflect developmental age changes; however, the differences may result from cohort effects (Schaie, 1986).
To illustrate this issue, let’s return to our hypothetical study on learning to use computers. Suppose you found that age is associated with a decrease in ability such that the people in the 50-year-old group score lower on the learning measure than the 40-year-olds, and so on. Should you conclude that the ability to learn to use a computer application decreases with age? That may be an accurate conclusion; alternatively, the differences could be due to a cohort effect: The older people had less experience with computers while growing up. The key point here is that the cross-sectional method confounds age and cohort effects. (Review the discussion of confounding and internal validity at the beginning of Chapter 8 .) Finally, you should note that cohort effects are most likely to be a problem when the researcher is examining age effects across a wide range of ages (e.g., adolescents through older adults).
The only way to conclusively study changes that occur as people grow older is to use a longitudinal design. Also, longitudinal research is the best way Page 237to study how scores on a variable at one age are related to another variable at a later age. For example, researchers at the National Children’s Study ( http://www.nationalchildrensstudy.gov ) began collecting data in 2009 at 105 study locations across the United States. In each of those study sites, participants (new parents) are being recruited to participate in the study that will run from the birth of their child until the child is 21 years of age. The goal of the study is to better understand the interactions of the environment and genetics and their effects on child health and well-being. The alternative in this case would be to study samples of children of various ages and ask them or their parents about the earlier home environment; this retrospective approach has its own problems when one considers the difficulty of remembering events in the distant past.
Thus, the longitudinal approach, despite being expensive and difficult, has definite advantages. However, there is one major problem: Over the course of a longitudinal study, people may move, die, or lose interest in the study. Researchers who conduct longitudinal studies become adept at convincing people to continue, often travel anywhere to collect more data, and compare test scores of people who drop out with those who stay to provide better analyses of their results. In sum, a researcher should not embark on a longitudinal study without considerable resources and a great deal of patience and energy!
Sequential Method
A compromise between the longitudinal and cross-sectional methods is to use the sequential method. This method, along with the cross-sectional and longitudinal methods, is illustrated in Figure 11.5 . In the figure, the goal of the study is to minimally compare 55- and 65-year-olds. The first phase of the sequential method begins with the cross-sectional method; for example, you could study groups of 55- and 65-year-olds. These individuals are then studied using the longitudinal method with each individual tested at least one more time.
Orth, Trzesniewski, and Robins (2010) studied the development of self-esteem over time using just such a sequential method. Using data from the Americans’ Changing Lives study, Orth and his colleagues identified six different age cohorts (25–34, 35–44, 45–54, 55–64, 65–74, 75+) and examined their self-esteem ratings from 1986, 1989, 1994, and 2002. Thus, they were interested in changes in self-esteem for participants at various ages, over time. Their findings provide an interesting picture of how self-esteem changes over time: They found that self-esteem gradually increases from age 25 to around age 60 and then declines in later years. If this were conducted as a full longitudinal study, it would require 100 years to complete!
Clearly, this method takes fewer years and less effort to complete than a longitudinal study, and the researcher reaps immediate rewards because data on the different age groups are available in the first year of the study. On the other hand, the participants are not followed over the entire time span as they would be in a full longitudinal investigation; that is, no one in the Orth study was followed from age 25 to 100.
Page 238We have now described most of the major approaches to designing research. In the next two chapters, we consider methods of analyzing research data.
ILLUSTRATIVE ARTICLE: A QUASI-EXPERIMENT
Sexual violence on college and university campuses has been and continues to be a widespread problem. Programs designed to prevent sexual violence on campuses have shown mixed results: Some evidence suggests that they can be effective, but other evidence shows that they are not.
Banyard, Moynihan, and Crossman (2009) implemented a prevention program that utilized specific sub groups of campus communities to “raise awareness about the problem of sexual violence and build skill that individuals can use to end it.” They exposed dormitory resident advisors to a program called “Bringing in the Bystander” and assessed change in attitudes as well as a set of six outcome measures (e.g., willingness to help).
First, acquire and read the article:
Banyard, V. L., Moynihan, M. M., & Crossman, M. T. (2009). Reducing sexual violence on campus: The role of student leaders as empowered bystanders. Journal of College Student Development, 50, 446–457. doi:10.1353/csd.0.0083
Then, after reading the article, consider the following:
1. This study was a quasi-experiment. What is the specific design?
2. What are the potential weaknesses of the design?
3. The discussion of this article begins with this statement: “The results of this study are promising.” Do you agree or disagree? Support your position.
4. How would you determine if there is a need to address the problem of sexual violence on your campus? If you discover that there is a need, would the program described here be appropriate? Why or why not?
Study Terms
Baseline ( p. 221 )
Cohort ( p. 236 )
Cohort effects ( p. 236 )
Control series design ( p. 233 )
Cross-sectional method ( p. 234 )
History effects ( p. 227 )
Instrument decay ( p. 228 )
Interrupted time series design ( p. 232 )
Longitudinal method ( p. 235 )
Maturation effects ( p. 227 )
Page 239Multiple baseline design ( p. 223 )
Nonequivalent control group design ( p. 230 )
Nonequivalent control group pretest-posttest design ( p. 231 )
One-group posttest-only design ( p. 226 )
One-group pretest-posttest design ( p. 227 )
Propensity score matching ( p. 232 )
Quasi-experimental design ( p. 225 )
Regression toward the mean (Statistical regression) ( p. 228 )
Reversal design ( p. 222 )
Selection differences ( p. 230 )
Sequential method ( p. 237 )
Single-case experimental design ( p. 221 )
Testing effects ( p. 227 )
Review Questions
1. What is a reversal design? Why is an ABAB design superior to an ABA design?
2. What is meant by baseline in a single-case design?
3. What is a multiple baseline design? Why is it used? Distinguish between multiple baseline designs across subjects, across behaviors, and across situations.
4. Why might a researcher use a quasi-experimental design rather than a true experimental design?
5. Why does having a control group eliminate the problems associated with the one-group pretest-posttest design?
6. Describe the threats to internal validity discussed in the text: history, maturation, testing, instrument decay, regression toward the mean, and selection differences.
7. Describe the nonequivalent control group pretest-posttest design. Why is this a quasi-experimental design rather than a true experiment?
8. Describe the interrupted time series and the control series designs. What are the strengths of the control series design as compared with the interrupted time series design?
9. Distinguish between longitudinal, cross-sectional, and sequential methods.
10. What is a cohort effect?
Activities
1. Your dog gets lonely while you are at work and consequently engages in destructive activities such as pulling down curtains or strewing Page 240wastebasket contents all over the floor. You decide that playing a radio while you are gone might help. How might you determine whether this “treatment” is effective?
2. Your best friend frequently suffers from severe headaches. You have noticed that your friend consumes a great deal of diet cola, and so you consider the hypothesis that the artificial sweetener in the cola is responsible for the headaches. Devise a way to test your hypothesis using a single-case design. What do you expect to find if your hypothesis is correct? If you obtain the expected results, what do you conclude about the effect of the artificial sweetener on headaches?
3. Dr. Smith learned that one sorority on campus had purchased several MacBooks and another sorority had purchased several Windows-based computers. Dr. Smith was interested in whether the type of computer affects the quality of students’ papers, so he went to each of the sorority houses to collect samples of papers from the members. Two graduate students in the English department then rated the quality of the papers. Dr. Smith found that the quality of the papers was higher in one sorority than in the other. What are the independent and dependent variables in this study? Identify the type of design that Dr. Smith used. What variables are confounded with the independent variable? Design a true experiment that would address Dr. Smith’s original question.
4. Gilovich (1991) described an incident that he read about during a visit to Israel. A very large number of deaths had occurred during a brief time period in one region of the country. A group of rabbis attributed the deaths to a recent change in religious practice that allowed women to attend funerals. Women were immediately forbidden to attend funerals in that region, and the number of deaths subsequently decreased. How would you explain this phenomenon?
5. The captain of each precinct of a metropolitan police department selected two officers to participate in a program designed to reduce prejudice by increasing sensitivity to racial and ethnic group differences and community issues. The training program took place every Friday morning for 3 months. At the first and last meetings, the officers completed a measure of prejudice. To assess the effectiveness of the program, the average prejudice score at the first meeting was compared with the average score at the last meeting; it was found that the average score was in fact lower following the training program. What type of design is this? What specific problems arise if you try to conclude that the training program was responsible for the reduction in prejudice?
6. Many elementary schools have implemented a daily “sustained silent reading” period during which students, faculty, and staff spend 15–20 minutes silently reading a book of their choice. Advocates of this policy claim that the activity encourages pleasure reading outside the required Page 241silent reading time. Design a nonequivalent control group pretest-posttest quasi-experiment to test this claim. Include a well-reasoned dependent measure as well.
7. For the preceding situation, discuss the advantages and disadvantages of using a quasi-experimental design in contrast to conducting a true experiment.
8. Dr. Cardenas studied political attitudes among different groups of 20-, 40-, and 60-year-olds. Political attitudes were found to be most conservative in the age-60 group and least conservative in the age-20 group.
a. What type of method was used in this study?
b. Can you conclude that people become more politically conservative as they get older? Why or why not?
c. Propose alternative ways of studying this topic.
1. Answer the following below using chapter 11 attachment.
From the research designs list below, select one to discuss here. Collect an example from research wherein the research design you selected is applied. It might be an example from research that you already have for the final paper, or another that you like that pertains to psychology. Briefly describe (a) how the research data collected answered the overall research objective, (b) the limitations that the researcher(s) detail when interpreting the results, and (c) how closely your description of limitations from the research example resemble limitations of that same design given in Chapter 11 of our text. Briefly describe with a maximum of 250 words (excluding citations).
1. One group posttest design
2. One group pretest only design
3. Longitudinal design
4. Cross sectional design
5. Sequential design
GIVE FEEDBACK TO PARAGRAPHS BELOW (150-200 WORD REPONSE)
1. GIVE FEEDBACK
According to a study by CJ Mann, cross-sectional studies are primarily used to determine prevalence and infer causation (2003). These types of studies have advantages because the subjects aren’t exposed, treated, or not treated, which cuts back on the amount of ethical difficulties that are present. The data that is collected makes it easier to answer the research question because only one group is used, data is collected once, and multiple outcomes can be studied, which is why it is called cross- sectional (Mann 2003). The data helps answer the research question through questionnaires. One limitation that researchers face is the possibility of a low response rate. Any study with a low response rate can be criticized because it can miss significant differences in both responders and non-responders. The only difference in limitations as described by Cozby is the presence of cohort effects, in which not every person is developmentally the same (Cozby 2015).
Cozby, P. C., & Bates, S. C. (2015). Methods in Behavioral Research. New York, NY: McGraw-Hill.
Mann, C.J. (2003). Observational research methods. Research design II: cohort, cross sectional, and case-control studies. Emerg Med J; 20: 54-60. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1726024/pdf/v020p00054.pdf
2. GIVE FEEDBACK
The Longitudinal design is used in substance abuse recovery research. Longitudinal studies are needed to asses certain changes in any type of study for a variety of purposes (Cozby, 2015). When coming to drug use research, the data gets collected, would be performed via repeated measures in regards to the past use, current use and over a long period of time. According to Brecht, Herbeck, & n.d (2010), in order to complete the assessments Treatment Utilization & Effectiveness Longitudinal Study, Natural history interview and Addiction Severity Index, were actually administered as three annual follow ups for the purpose of assessing to address the frequency of drug use including, marijuana and methamphetamine uses. The drug uses for marijuana and methamphetamines were found to be comparable to alcohol, cocaine and heroin use. The differences in terminology amongst the instruments, were part of the discrepancies found. The examination of self-reporting drug use, resulted similar across the board, including all illicit drugs and alcohol abuse as well.
References:
Cozby, P. C., & Bates, S. C. (2015). Methods in Behavioral Research. New York, NY: McGraw-Hill.
Brecht M., Herbeck, M.A., Hser, Yih-lng, H., Huang, & , Murphy D. (210) Self-report of Longitudinal Substance Use: A Comparison of the UCLA Natural History Interview and the Addiction Severity Index 40(2) 495-516 Retrieved from:https;//www.ncbi.nih.gov
3. GIVE FEEDBACK
Cross-sectional studies have advantages which include no exposures and or treatments (Cozby, 2015).
Cross-sectional descriptive studies are used for investigative purposes when coming to Alcohol related disorder and substance related issues. The correlation between disorders and alcohol/illicit drug abuse also relate to crime and violence. Regression models, were used within this study in order to externalize and internalize problems related to crime due to symptoms the person is undergoing. The problems were in relation to gender, age, violence and crime. The factual was resulting directly to crime and violence behaviors as most of the individuals whom suffer from addiction reside in a vulnerable social environment Bourdreaux, De Almeida Lopes Fernandes, & n.d 2015)
References:
Cozby, P. C., & Bates, S. C. (2015). Methods in Behavioral Research. New York, NY: McGraw-Hill.
Bourdreaux J.,De Almeida Lopes Fernandes, I., Ferreira de Oliveira M., Garcia Claro, H., Hayasi, P., & Ribeiro Tarifa, R., (2015) Drug use, mental health and problems related to crime and violence: cross-sectional study Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4664019/
4. GIVE FEEDBACK
Designing or crafting a research project takes quite some time, skills as well as awareness. To begin with is sampling, this kind of mistake befalls when a probability sampling technique is employed in selecting a sample. However, the preceding example is not demonstrative concerning people concern. Inappropriately, some sampling errors are inevitable. The other limitation is surveyed questions and analyses (Bell, 2014). Methods of study vary in kind of data each process gathers as well as frequent questions that might be asked. Arguably, self-administered inquiries are more multifaceted and long than interrogator-administered questions. At one fell swoop, interviewer-administered survey forms are more complex compared to self-administered surveys since the interviewer controls or regulate the navigations. The other limitation is cost. Study expenses for all approaches increase over few years (Thomas, Silverman, & Nelson, 2015). Due to an increase in technology, there is a decrease in expenditures. However, the need of using extra resources with the aim of compensating for declines will raise expenses or costs. Expenditures for in-person reviews are increasing speedily; therefore, this type of survey is used less frequently. On the other hand, costs for telephone survey mainly RDD surveys are cumulative too. The other limitation is Non-responsive. Nonresponse inaccuracy or mistake occurs when an acquired sample varies from a unique selected sample. For instance, in the phone survey, some respondents are inaccessible since they are not present in their household for initial calls. Last not least is measurement. Analysis process causes this error, and it also characterizes or embodies difference among the information created as well as the info required by researchers.
References
Bell, J. (2014). Doing Your Research Project: A guide for first-time researchers. McGraw-Hill Education (UK).
Thomas, J. R., Silverman, S., & Nelson, J. (2015). Research methods in physical activity, 7E. Human kinetics.
The Hawthorne Effect
Use the table below to answer the following. Be sure to write in complete sentences.
· Investigate the history of the Hawthorne Effect and discuss why it is important for researchers to know about this phenomenon.
· Brainstorm ways that researchers can eliminate this confound.
| History and definition of Hawthorne Effect | |
| Example of Hawthorne Effect | |
| Why is it important for researchers to know about this? | |
| Strategies to avoid the Hawthorne Effect (at least two) |
Week Five Homework Exercise
Answer the following questions, covering material from Ch. 11 of the Methods in Behavioral Research text:
1. What are single-case designs and when are they most useful?
2. How may a researcher enhance the generalizability of the results of a single case design?
3. What is the relationship between quasi-experiments and confounding variables? Provide an example
4. Provide examples of: one-group posttest designs and one-group pretest and posttest designs. What are the limitations of each?
5. Provide examples of non-equivalent control group designs. What are the advantages of having a control group?
6. What is a quasi-experimental research design? Why would a researcher use a quasi-experimental design rather than a true experimental design?
7. What is the difference between a cross-sectional and a longitudinal study? What is a sequential study? Which of these designs is most vulnerable to cohort effects? Which design is most vulnerable to the effects of attrition?
8. What are the differences between: needs assessment, program assessment, process evaluation, outcome evaluation, and efficacy assessment? Why is program evaluation important to the field?
9. A researcher wants to investigate patriotic behavior across the lifespan. She samples people in the following age groups: 18–28, 29–39, 40–50, 51–60, and 61 and above. All participants are interviewed and asked to complete questionnaires and rating scales about patriotic behavior. This type of developmental research design is called ________________. What is the primary disadvantage of this type of design? Explain.
Running head: EFFECTIVE TREATMENT FOR CHILDHOOD SEXUAL ABUSE 1
EFFECTIVE TREATMENT FOR CHILDHOOD SEXUAL ABUSE 18
Assignment Part 1 (Completed)
Create a topic (EFFECTIVE TREATMENT FOR CHILDHOOD SEXUAL ABUSE) of interest within psychology, then create a
Research Proposal based on that topic
Develop an original research study proposal and describe it in detail in a 10-12 page (APA style) paper. Include at least 10 scholarly references in your proposal. Use the following outline as a guide when writing your paper. Be sure to include detailed information on all of the topics listed below and use headings to organize your thoughts.
1. Statement of the problem: Introduce the reader to the problem to be studied. Provide sufficient background information such that the reader has a grasp of the situation and its importance.
2. Review of the literature: Provide the reader with a review of most relevant literature, beginning with general information, and narrowing the focus to the specific issues under consideration in the study.
3. Purpose of the study: Identify why the study that you are proposing is needed.
4. Hypotheses or research questions: List them as simple statements. Make sure they are measurable.
5. Definition of terms: Operationally define terms the average reader may not know, or that have a specific meaning in your study.
6. Assumptions: Identify issues you assume to be true in order for your study to be valid.
7. Research methods and procedures
a. Population: Describe the population sample to be studied
b. Procedure: Discuss how the study will be carried out.
c. Instruments: Describe the specific measurements (instruments) to be used to test each hypothesis (research question).
d. Data Analysis: Describe the procedures you intend to use to analyze the data produced from your instruments, and how that would answer the hypotheses (research questions).
e. Discussion: Since you are only proposing (not conducting) a research study, you will not have results; however, you can discuss potential outcomes. Review your hypothesis and discuss how this study will address it. For example, if the results allow you to reject the null hypothesis, what are the implications? What would happen if you fail to reject the null hypothesis? Discuss the implications of your proposed study, the limitations of your study, and future research ideas and directions.
8. Implications: Provide a brief summary of your proposal and a powerful statement as to how your study would advance the field.
9. References: Include at least 10 scholarly sources in your Reference section. Be sure to use APA style throughout your paper.
Assignment Part 1 (Continued)(Completed)
Effective Treatment for Childhood Sexual Abuse
Statement of problem
Child sexual abuse (CSA) has become a prevalent problem in today’s society. Another term for child sexual abuse is child molestation. This is a form of child abuse where older adolescent or an adult uses a child for sexual stimulation. There are different forms of child sexual abuse. These forms include using a child to make child pornography, child grooming, indecent exposure (female nipples or genitals), taking part in sexual activities with a child (whether by pressuring, asking, or using other means) (Finkelhor, Shattuck, Turner, & Hamby, 2014).
This problem affects many children, especially those with the age of between 2 and 17. This is irrespective of race, ethnicity, or socio-economic status. There are many cases of child sexual abuse that go unreported. However, the incidence of this problem is large compared to cases that are reported to the authorities (Andrews, Corry, Slade, Issakidis, & Swanston, 2004). According to a report that was compiled by the U.S. Department of Health and Human Services’ Children’s Bureau in the year 2010, 9.2 percent of children who were victimized were sexually assaulted. Out of every 5 girls and 20 boys, one of them falls victim to child sexual abuse. As per self-report studies, 5-10 percent of males and 20 percent of females recall a childhood sexual abuse or sexual assault incident. Children who are more vulnerable to incidences of child sexual abuses are those aged 2 and 13.
Out of every 4 adolescents that were sexually abused, 3 were abused by victims who they knew well. This is according to a report that was compiled by National Institute of Justice in 2003. Children who are sexually abused suffer negative effects.
Review of literature
According to Finkelhor and colleagues, there are many children who have been sexually assaulted. However, what should be recognized is that many cases go unreported. This makes it difficult to accurately tell the cases of child sexual abuse.
There are significant negative psychological effects that are associated with child sexual abuse. Thus it should be known that not all victims of child sexual abuse show the symptoms of psychological effects. 40 percent of victims of child sexual abuse show the symptoms (GoodyearBrown et al., 2012).
For most of individuals who are sexually assaulted, they experience long-term and short-term social, physical, and emotional consequences together with increased risk of suicide attempts, academic underachievement, sexual perpetration, sexual promiscuity, depression, substance abuse, post-traumatic stress disorder (PTSD), sleep disorder, eating disorder, depressive disorder, and anxiety disorder. Out of all these conditions, the one that is most evident in most victims is post-traumatic stress disorder (PTSD). However, 36 percent of survivors of child sexual abuse meet the criteria for diagnosis Andrews et al., 2004). There are many victims of child sexual abuse that do not get a psychiatric diagnosis even with the significant symptomatology. Comparing possible treatment methods is has been a bit hard due to difficulties in establishing a diagnosis method.
Due to the difficulties that have experienced, there are attempts that have been carried out to help describe the whole developmental impacts of making use of a proposed diagnosis of Developmental Trauma Disorder that entirely captures the consequences of chronic, early, interfamilial, and extreme exposure to trauma (Van der Kolk, 2005). Some of the diagnostic issues have been addressed due to changes that have taken place in the Diagnostic and Statistical Manual, 5th edition (DSM-V). This has made sure that sexual assault is specifically mentioned.
Even with the lack of proper consensus with regards to diagnosis, there are many different treatment methods that have been brought forward as being important when it comes to treating individuals who have suffered child sexual abuse. Out of the many treatment methods that have been proposed, the one that is studied so much with many researchers and scholars is cognitive behavioral therapy (CBT). According to Macdonald and colleagues, cognitive behavioral therapy has a positive effect when it comes to the sequelae of child sexual abuse. However, there are other results that are not statistically significant (Cohen, Mannarino, Berliner, & Deblinger, 2000).
According to Bisson et al., (2007), the best interventions with regard to using cognitive behavioral therapy are while treating post traumatic stress disorder in children. In cases where cognitive behavioral therapy is used to deal with issues of child sexual abuse, it can be focused directly on the symptoms. In this case, it is referred to as Trauma-Focused Cognitive Behavioral Therapy (TF-CBT). There are other treatments that have also been proposed to be effective in treating child sexual abuse, such as eye movement desensitization and reprocessing (EMDR) and pet therapy among others. Apart from individual therapy, group therapy has also been found to be effective in treating child sexual abuse.
Purpose of the study
This study is aimed at determining the effective treatment for child sexual abuse. This is because there are very many methods that are available and no particular method or methods have been specified to be effective in solving this issue.
There is the need to come up with effective treatment since child sexual abuse produces significant negative effects on its victims. Among some of the effects of child sexual abuse are psychological effects and physical effects. Child sexual abuse has long-term and short-term harm, as well as, psychopathology in later stages of life. Some of the psychological effects are depression, eating disorder, anxiety, poor self-esteem, sleep disturbances, somatization, post traumatic stress disorder, and dissociative disorder. There are other children who show regressive behaviors, such as bedwetting or thumb sucking. The strongest sign of sexual abuse in some individuals has been inappropriate sexual interest and knowledge and sexual acting out. There are higher chances that a victim may withdraw from social and school activities. There are others who show learning problems, behavioral problems, such as cruelty to animals, oppositional defiant disorder (ODD), conduct disorder, and attention deficit/hyperactivity disorder (ADHD). During adolescent, there may be cases of risky sexual behaviors and teenage pregnancy. Additionally, there have been many cases of self-inflicted harm in individuals how has suffered from child sexual abuse.
The first physical effect of child sexual abuse is an injury. Child sexual abuse may bring about bleeding and internal laceration depending on the degree of force applied, size, and age of a child. There are severe cases where it has resulted in damage to internal organs, and the outcome of this may be death. Another physical effect of child sexual abuse is an infection. Child sexual abuse may lead to sexually transmitted diseases and other infections. Chances of infection may be increased as a result of lack of virginal fluid. This may depend on the size and age of a child. The third physical effect is neurological damage. It has been established that change in brain development and functioning are caused by traumatic stress due to child sexual abuse. According to Bisson et al., (2007), the deleterious effect on brain development may result from severe child sexual abuse. This is because many subjects who have been sexually abused will have greater left hemisphere coherence and reversed hemispheric asymmetry. Also, there are other studies that have shown that child sexual abuse can cause overexcitation of the limbic system, which is not properly developed.
Hypotheses or research questions
There are many treatment methods that have been proposed when it comes to child sexual abuse. However, the goal of this study is to determine the specific methods that are effective in treating symptoms of child sexual abuse. Therefore, the research questions are:
1. What treatment methods are effective in child sexual abuse?
2. What shows that the methods are effective?
Definition of terms
· Child grooming – exposing one’s body contrary to local moral as well as other standards of appropriate behavior
· Indecent exposure – it is befriending and creating an emotional connection with a child. It is lowering inhibition of a child with the goal of sexual abuse.
· Self-inflicted harm – also termed as self-injury. It is directing injury to the body tissues intentionally without suicidal intention.
· Limbic system – is brain structure that is found on both sides of the thalamus, just beneath the cerebrum.
Assumptions
There are some issues that are going to be assumed to be true for this study to be valid. Four assumptions will be made in this study. The first assumption is that child sexual abuse causes a significant negative effect on a child. This means that the action is harmful to children. The second assumption is that there are certain forms of child sexual abuse that are more harmful compared to others. Therefore, this means that there is the element of non-equivalence. Thirdly, there are certain consequences that can be associated with different forms of sexual abuse. The final assumption is that the effect of child sexual abuse does not vary even with regards to race and ethnicity.
Research methods and procedures
Population
The population sample to be studied is teenagers and young adults aged between 12 and 24 years. These are individuals who were victims of child sexual abuse. Both genders will be included in the research. Also, individuals from a different race, ethnic groups, the socio-economic status will be included.
Procedure
The records of the nearby hospitals will be used to find the population, especially those who were under a particular treatment method. Random sampling will be used to determine 200 participants for the study. With random sampling, it will be possible to select sample group that is representative of the whole population of child sexual abuse victims. The use of a large number of participants is important since it is going to increase accuracy and precision of the experiment.
Instruments
The research instrument that will be used is questionnaires. The questionnaire is a research instrument that has got a series of questions. It is designed to gather information from respondents. Therefore, the questionnaire will help to collect information from the selected participants. The participants will be required to answer a series of questions that will be on the questionnaires. The following are questions that will be on the questionnaires:
· When did you experience child sexual abuse?
· Was the perpetrator a person you knew or a stranger?
· Were there negative effects that child sexual abuse produced?
· What are some of these effects?
· What treatment method was used to help solve the problem?
· How long was the treatment method administered?
· After the treatment method, did you experience the negative effects of child sexual abuse?
· If yes, is there a different method that was used to help subdue the problem?
· From your perspective, was the treatment method effective?
Data analysis
This is a qualitative research. This meant that it is an exploratory type of research. The answers that are gotten from the questionnaires are going to be compiled and interpreted. The interpretation of the collected information will give insight and profound understanding regarding the effective treatment methods for child sexual abuse.
Discussion
The findings from the research of found that most of the victims of child sexual abuse were abused by someone they knew. All the participants suffered or experienced symptoms of child sexual abuse. The negative effects experienced were psychological or physical. There were other participants who experienced both the effects. The treatment methods that helped these participants were cognitive behavioral approaches, eye movement desensitization and reprocessing, play therapy and related treatment, animal therapy, and individual and group therapy.
When it comes to cognitive behavioral approaches, trauma-focused cognitive behavioral therapy (TF-CBT) has been proposed to be the most effective in treating child sexual abuse, especially when it comes to the short aftermath of child sexual abuse (Scheeringa, Weems, Cohen, Amaya‐Jackson, & Guthrie, 2011). What makes the method to be effective is that the central to the treatment process is the traumatic experiences. Problems associated with childhood trauma of CSA are reduced by TF-CBT. It directs attention on trauma narrative as well as anxiety and fear that arise from trauma history. From this study and other different studies that have been carried out, it has been found that TF-CBT improves different types of symptoms that are caused by child sexual abuse.
Cognitive behavioral therapy (CBT), which also falls under cognitive behavioral approaches, has been found to be effective in reducing symptoms caused by child sexual abuse. It reduces behavioral problems, depression, sexualized behavior, symptoms of post traumatic stress disorder, a level of fear, and anxiety. It increases skills of safety and coping, the ability to easily shift feelings of challenge beliefs and guilt, perception regarding sexual abuse, and credibility and trust. The treatment for cognitive behavioral approaches can be designed to focus on the standardized outcome, timing, sex, and even gender. There is now game-based cognitive behavioral therapy (GB-CBT) group program that is designed for young children.
Another method that has been found to be effective in treating child sexual abuse is eye movement desensitization and reprocessing (EMDR). The method has positive impact when it comes to reducing the symptoms of post traumatic stress disorder. It also has the benefit of improving behavioural problems. The evidence of the method can also be proven by a research that was carried out on Iranian girls who were sexually abused
(Andrews et al., 2004). There are self-reports that have favoured the use of eye movement desensitization are reprocessing compared to cognitive behavioral therapy. A rapid recovery is brought about by the use of eye movement desensitization are reprocessing. This means that the method can be used on traumatized individuals who had experienced child sexual abuse.
Play therapy is another effective treatment method for young children who have gone through child sexual abuse. The method is good since it gives room children who are traumatized to work through their problems (Barth, Bermetz, Heim, Trelle, & Tonia, 2013). This is because the method does not depend on verbal communication. It makes it possible to address problems in ways that talk therapy is not capable. Disclosure of child sexual abuse is important when it comes to therapeutic and healing process. During play, it becomes easier for a one to disclose child sexual abuse. This is because it gives appropriate intellectual and emotional capabilities. Due to the use of play therapy, there is a significant reduction in internalizing problems, such as trauma, anxiety, reframing feelings of self blame, embarrassment, shame, and reduction in nightmares. Also, it makes children become more verbal about their experience. It increases intellectual understanding of abuse and develops skills for avoiding future abuse.
Animal therapy, another effective method, entails using animal as motivator for therapy. Having an animal present leads to a safe, friendly, and positive perception of a situation. The perception makes it easy for a child to disclose child sexual abuse. The animal acts as a bridge. This will bring about a better connection between a therapist and a child. There are a number of things that animal therapy reduces in victims of child sexual abuse (Finkelhor et al., 2014). The presence of an animal buffers psychological and physiological responses to anxiety and stress. This lowers heart rate and blood pressure. There are some researchers who are arguing that this method is very effective when combined with other therapeutic methods. It is most effective when it is combined with other different accepted forms of therapy.
One good thing with group therapy that makes it effective is that one gets the opportunity tom interact with other victims of child sexual abuse. When both individual and group therapy are compared with regards to depression, anxiety, and self-concept, group therapy turns out to be effective in improving self-concept (Devries, Mak, Child, Falder, Bacchus, Astbury, & Watts, 2014). The two therapies show the same results when it comes to improving depressive and anxiety symptoms. Most people argue in support of group therapy since it helps build trust among members. Due to this, exploration and disclosure of feelings becomes easier. Also, it is argued that group therapy provides effective and safe place for individuals to practice and develop social skills that are appropriate.
Implications
Cases relating to child sexual abuse are on the rise. Most victims of child sexual abuse suffer psychological and physical effects. Victims of child sexual abuse experience long-term and short-term social, physical, and emotional consequences together with increased risk of suicide attempts, academic underachievement, sexual perpetration, sexual promiscuity, depression, substance abuse, post-traumatic stress disorder (PTSD), sleep disorder, eating disorder, depressive disorder, and anxiety disorder. Out of all these conditions, the one that is most evident in most victims is post-traumatic stress disorder (PTSD). The problems experienced by these victims are very many. Due to these problems there has been the need to determine effective treatment methods. The effective treatment methods for symptoms of child sexual abuse according to this research are cognitive behavioral approaches (trauma-focused cognitive behavioral therapy and cognitive behavioral therapy), eye movement desensitization and reprocessing, play therapy and related treatment, animal therapy, and individual and group therapy.
Since this research has looked at effective treatment method for child sexual abuse symptoms, there is the need to find ways of curbing the problem since it produces many negative effects on victims. Doing this will help advance this field of
References
Andrews, G., Corry, J., Slade, T., Issakidis, C., & Swanston, H. (2004). Child sexual abuse.
Comparative quantification of health risks: global and regional burden of disease attributable to selected major risk factors, 2, 1851-940.
Barth, J., Bermetz, L., Heim, E., Trelle, S., & Tonia, T. (2013). The current prevalence of
child sexual abuse worldwide: a systematic review and meta-analysis. International journal of public health, 58(3), 469-483.
Bisson, J. I., Ehlers, A., Matthews, R., Pilling, S., Richards, D., & Turner, S. (2007).
Psychological treatments for chronic post-traumatic stress disorder. The British journal of psychiatry, 190(2), 97-104.
Cohen, J. A., Mannarino, A. P., Berliner, L., & Deblinger, E. (2000). Trauma-focused
cognitive behavioral therapy for children and adolescents: An empirical update. Journal of Interpersonal Violence, 15(11), 1202-1223.
Devries, K. M., Mak, J. Y., Child, J. C., Falder, G., Bacchus, L. J., Astbury, J., & Watts, C.
H. (2014). Childhood sexual abuse and suicidal behavior: a meta-analysis. Pediatrics, peds-2013.
Finkelhor, D., Shattuck, A., Turner, H. A., & Hamby, S. L. (2014). The lifetime prevalence
of child sexual abuse and sexual assault assessed in late adolescence. Journal of Adolescent Health, 55(3), 329-333.
Gaskill, R. L., & Perry, B. D. (2012). Child sexual abuse, traumatic experiences, and their
impact on the developing brain. Handbook of child sexual abuse: Identification, assessment, and treatment, 29-47.
Paolucci, E. O., Genuis, M. L., & Violato, C. (2001). A meta-analysis of the published
research on the effects of child sexual abuse. The Journal of psychology, 135(1), 17-36.
Scheeringa, M. S., Weems, C. F., Cohen, J. A., Amaya‐Jackson, L., & Guthrie, D. (2011).
Trauma‐focused cognitive‐behavioral therapy for posttraumatic stress disorder in three‐through six year‐old children: A randomized clinical trial. Journal of Child Psychology and Psychiatry, 52(8), 853-860.
Van der Kolk, B. A. (2005). Editorial introduction: Child abuse & victimization. Psychiatric
Annals, 2005, 374-378.
Assignment topic (EFFECTIVE TREATMENT FOR CHILDHOOD SEXUAL ABUSE) Part 2 (Needs To Be Completed)
Research Evaluation Worksheet
Title:
Full Article Reference (APA style):
Abstract
Introduction
a. Is the need for the study clearly stated in the introduction? Explain by using information presented in the literature review.
b. What is the research hypothesis or question?
c. What are the variables of interest (independent and dependent variables)?
d. How are the variables operationally defined?
Method
a. Sample Size (Total): ________________ Size Per Group/Cell: _______________
b. Were the methods and procedures described so that the study could be replicated without further information? What information, if any, would you need to replicate or reproduce this study?
Participants
a. How were participants selected and recruited? b. Were subjects randomly selected? c. Were there any biases in sampling? Explain
d. Were the samples appropriate for the population to which the researcher wished to generalize?
e. What are the characteristics of the sample populations?
Research Design (check which design applies)
_______ Single group, time series study
_______ Multiple baseline (sequential) design: ______________
_______ Single group, no measurement
_______ Single group with measurement: Pre ______ During _____ Post _____
_______ Two groups classic experimental versus control group, randomly assigned
_______ (quasi-experimental) two groups experimental versus control group,
not randomly assigned
_______ Correlation research, not manipulated, degree of relationship
_______ Descriptive research (qualitative study)
_______ Natural observation
_______ Analytical research
_______ Interview research
_______ Historical study
_______ Survey research
_______ Legal study
_______ Ethnography research
_______ Policy analysis
_______ Fieldwork research
_______ Evaluation study
_______ Phenomenology
_______ Grounded theory
_______ Protocol analysis (collection and analysis of verbatim reports)
_______ Case study, no measurement
_______ Case study, with measurement: Pre _________ During _______ Post _________
_______ Developmental research
_______ Longitudinal (same group of subjects over period of time)
_______ Cross-sectional (subjects from different age groups compared)
_______ Cross-sequential (subjects from different age groups, shorter period of time)
_______ Correlation, more than two groups: control, treatment, and other treatment comparisons
_______ Factorial design, two or more groups: other treatment differences, no untreated controls
_______ Two or more dependent variables (MANOVA)
_______ Other design: __________________________________________________________
Consider the Following Questions:
a. Was a control group used? Yes ______ No ______ If yes, complete b, c, and d below. b. Was the “control” method for the study appropriate?
c. What variable was being controlled for?
d. In the case of an experimental study, were subject randomly assigned to groups?
Measures
a. Describe the Dependent Measure(s)/Instruments used:
b. Describe the Measurement/Instrument Validity Information:
c. Describe the Measurement/Instrument Reliability Information:
Consider the Following Questions:
a. For all measures (measures to classify subjects, dependent variables, etc..) was evidence of reliability and validity provided, either through summarizing the data, or by referring the reader to an available source for that information?
b. Do the reliability and validity data justify the use of the measure?
c. Are the measures appropriate (if not, why not)?
d. Are multiple measures used, particularly those that sample the same domains, or constructs but with different methods (e.g., self-report, rating scales, self-monitoring, or direct observation)?
f. If human observers, judges, or raters were involved, was inter-observer or inter-rater agreement (reliability) assessed? Was it obtained for a representative sample of the data? Did the two raters do their ratings independently? Was their reliability satisfactory?
Independent and Dependent Variables
a. What is/are the Independent Variable(s):
b. What is/are the Dependent Variable(s):
Data Analysis
Scales of Measurement (check those that apply):
Nominal _______ Ordinal _______ Interval _______ Ratio _______
a. What type of statistical techniques are used?
b. What type of tables and graphs are used?
Consider the Following Questions:
a. Were tests of significance used and reported appropriately (e.g., with sufficient detail to understand what analysis was being conducted)?
b. Do the researchers report means and standard deviations (if relevant) so that the reader can examine whether statistically significant differences are large enough to me meaningful? c. Other comments on the reported statistical analyses?
Discussion
Evaluate the Summary and Conclusions of the study (Usefulness):
Describe the Strength(s) and Limitation(s) of the Study:
Describe what you learned from the study:
List any remaining questions you have about the study:
*Adapted from form created by Dr. Randy Buckner, University of Phoenix Instructor
Assignment topic (EFFECTIVE TREATMENT FOR CHILDHOOD SEXUAL ABUSE) Part 3 (Needs To Be Completed)
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
