Abnormal Psychology. William J. Ray

assigned to the treatment group or the control group for the independent variable being studied

      match subjects design: a design type in psychopathology research in which the closer a scientist can match individuals in the experimental and control groups, the stronger the logic of the design

      Much of the early research involving clinical disorders was based on observation and interviews. With the advent of psychological testing and more advanced neuroscience techniques, greater insight has been gained in the structures and mechanisms involved in mental health and illness. Since mental illness cannot be studied in a traditional experimental manner, new methodologies and statistical techniques for comparing genetic, physiological, cognitive, emotional, gender, and stress factors are being developed.

      One important design type in psychopathology research is a match subjects design. The closer a scientist can match individuals in the experimental and control groups, the stronger the logic of the design. A researcher cannot randomly assign individuals to control and experimental groups if the experimental group is composed of individuals with a specific disorder and the control group is composed of individuals without that disorder. Therefore, an alternative method other than random assignment must be used to ensure that the two groups are similar on characteristics not under study. Some important variables include education level, socioeconomic status (SES), general health, types of medications used, age, gender, and others related to the specific question being asked in the research. Thus, the general task is to ensure that the participants in the study are like one another except for having a mental illness. For example, when Tom Borkovec and his colleagues (Borkovec & Ruscio, 2001) sought to determine if cognitive behavioral therapy was effective in treating generalized anxiety disorder (GAD) as compared with other treatments, it was important that those with anxiety were matched in terms of age, gender, income, education level, and other such variables in terms of the experimental conditions.

      The strongest version of matching is to use identical twins. The situation in which one twin has a particular disorder and the other does not helps to focus on the critical variables. One classic study involving twins was performed by E. Fuller Torrey (1994) and his colleagues in the 1990s. They studied individuals who developed schizophrenia or bipolar disorder (manic depression). Twins who lived together were matched in terms of such factors as genetics, family history, SES, and a number of others. However, twins are not always available, and thus researchers need to match those with a disorder and those without. I’ll discuss the use of twins in psychological research at greater length in the Behavioral Genetics section later in this chapter.

      Designing and Structuring the Experimental Study

      Science is a way of asking questions about the world. The quality of the answers we receive is influenced by several factors, one of the most important being the experimental design that we use. Somewhat like a blueprint, the experimental design directs the procedures and gives form to the experiment. In essence, an experimental design is a plan for how a study is to be structured.

      In an outline form, a design tells us what will be done to whom and when. To be evaluated favorably, a design must perform two related functions. First, it must provide a logical structure that enables us to pinpoint the effects of the IV on the DV and thus answer our research questions. Second, it must help us rule out confounds as an alternative explanation for our findings.

      Imagine a study in which a clinical psychologist was interested in determining if teaching children with autism to look at a person’s face would increase their interaction with others. Thus, the question asked would be whether looking at a face (IV) results in the participant interacting more with others (DV). After the subject was taught to look at a face, he or she could be placed in situations in which other individuals were present. The researcher could measure the number of interactions in which the child engaged.

      If we were to diagram the design of this study, it would be as follows:

      Select the group → Teach facial focus → Measure number of interactions

      If we performed the experiment with just a single group, what could we conclude? We could determine if our participants had a certain number of interactions. However, this would not help us determine if this was related to the facial focus procedure. Such a design would not be much help in pinpointing the effect of the IV on the DV, nor would it rule out confounds.

      A stronger design would use a control group. This design would appear as follows:

      Experimental group → Teach facial focus → Measure number of interactions

      Control group → No treatment → Measure number of interactions

      Since the control group had not received the treatment, we would have stronger evidence that the treatment was related to differences in number of interactions.

      null hypothesis: a statistical hypothesis that is tested to determine if there are differences between the experimental and control groups; the null hypothesis states that there is no difference

      confound hypothesis: a conceptual question that asks if results of an experiment could have been influenced by a factor other than the independent variable (IV)

      research hypothesis: the formal statement of the manner in which the dependent variable (DV) is related to the independent variable (IV)

      Is the Dependent Variable Related to the Independent Variable?

      Once we have collected our data, we want to know how to interpret the experimental results. We do this by considering three separate hypotheses:

      1 Null hypothesis—This is a statistical hypothesis that is tested to determine if there are differences between the experimental and control groups. Part of this statistical procedure is to ask the question of whether our results could have happened by chance.

      2 Confound hypothesis—This is a conceptual question that asks if our results could be the result of a factor other than the IV.

      3 Research hypothesis—This asks the question of whether our results are related to the IV.

      Null Hypothesis and Inferential Statistics

      We usually perform research with a limited number of individuals so that we can infer the behavior of all individuals related to the group we studied. For example, we might study how a group of individuals with depression responded to mindfulness meditation in terms of measures of distress.

      What are the odds that the individuals with depression in our group are like all those with depression everywhere? To determine this probability, we use inferential statistics. Technically, we refer to all individuals with depression as the population and the particular participants in our study as the sample. Inferential statistics concerns the relationship between the statistical characteristics of the population and those of the sample.

      One way of viewing the inferential process conceptually is to assume that the same experiment was run an infinite number of times, each time with a different sample of individuals chosen from the entire population. If we were to plot the statistics from each experiment, the population of estimates would then represent all the possible outcomes of the experiment.

      In more technical language, inferential statistics is used to infer, from a given sample of scores on some measure, the parameters for the set of all possible scores. All possible scores would be that of the population from which our particular sample was drawn. Implicit in this statement is the assumption that the sample we are discussing is the result of random sampling or some systematic form of sampling. That is, each person in the population of all people is equally likely to be included in the sample, with some known probability.

      The important thing to remember is that inferential statistics constitutes a set of tools for inferring from a particular sample to larger populations. One way of viewing this conceptually is to ask how the statistics of our sample (that is, the mean and