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Research is the key to accuracy in all fields of study; it is particularly important in abnormal psychology because a wrong belief in this field can lead to great suffering. At the same time, clinical researchers, also called clinical scientists, face certain challenges that make their work very difficult. They must, for example, figure out how to measure such elusive concepts as private thoughts, mood changes, and human potential. They must consider the different cultural backgrounds, races, and genders of the people they choose to study. And they must always ensure that the rights of their research participants, both human and animal, are not violated. Let us examine the leading methods used by today’s researchers.

Clinical researchers try to discover broad laws, or principles, of abnormal psychological functioning. They search for a general, or nomothetic, understanding of the nature, causes, and treatments of abnormality. They do not typically assess, diagnose, or treat individual clients; that is the job of clinical practitioners. To gain a broad insight, clinical researchers, like scientists in other fields, use the scientific method—that is, they collect and evaluate information through careful observations. These observations in turn enable them to pinpoint and explain relationships between variables.

Simply stated, a variable is any characteristic or event that can vary, whether from time to time, from place to place, or from person to person. Age, sex, and race are human variables. So are eye color, occupation, and social status. Clinical researchers are interested in variables such as childhood upsets, present life experiences, moods, social functioning, and responses to treatment. They try to determine whether two or more such variables change together and whether a change in one variable causes a change in another. Will the death of a parent cause a child to become depressed? If so, will a given treatment reduce that depression?

Such questions cannot be answered by logic alone because scientists, like all human beings, frequently make errors in thinking. Thus, clinical researchers must depend mainly on three methods of investigation: the case study, which typically is focused on one individual, and the correlational method and experimental method, approaches that are usually used to gather information about many individuals. Each is best suited to certain kinds of circumstances and questions. As a group, these methods enable scientists to form and test hypotheses, or hunches, that certain variables are related in certain ways—and to draw broad conclusions as to why. More properly, a hypothesis is a tentative explanation offered to provide a basis for an investigation.

A case study is a detailed description of a person’s life and psychological problems. It describes the person’s history, present circumstances, and symptoms. It may also include speculation about why the problems developed, and it may describe the person’s treatment (Yin, 2013). As you will see in Chapter 5, one of the field’s best-known case studies, called The Three Faces of Eve, describes a woman with three alternating personalities, each having a distinct set of memories, preferences, and personal habits (Thigpen & Cleckley, 1957).

Most clinicians take notes and keep records in the course of treating their patients, and some further organize such notes into a formal case study to be shared with other professionals. The clues offered by a case study may help a clinician better understand or treat the person under discussion (Yin, 2013). In addition, case studies may play nomothetic roles that go far beyond the individual clinical case.

How Are Case Studies Helpful? Case studies can be a source of new ideas about behavior and “open the way for discoveries” (Bolgar, 1965). Freud’s theory of psychoanalysis was based mainly on the patients he saw in private practice. In addition, a case study may offer tentative support for a theory. Freud used case studies in this way as well, regarding them as evidence for the accuracy of his ideas. Conversely, case studies may serve to challenge a theory’s assumptions (Yin, 2013).

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Case studies may also show the value of new therapeutic techniques. And finally, case studies may offer opportunities to study unusual problems that do not occur often enough to permit a large number of observations (Goodwin & Goodwin, 2012). Investigators of problems such as personality disorder once relied entirely on case studies for information.

What Are the Limitations of Case Studies? Case studies also have limitations (Yin, 2013). First, they are reported by biased observers, that is, by therapists who have a personal stake in seeing their treatments succeed. These therapists must choose what to include in a case study, and their choices may at times be self-serving. Second, case studies rely on subjective evidence. Is a client’s problem really caused by the events that the therapist or client says are responsible? After all, those are only a fraction of the events that may be contributing to the situation. Finally, case studies provide little basis for generalization. Events or treatments that seem important in one case may be of no help at all in efforts to understand or treat others.

The limitations of the case study are largely addressed by two other methods of investigation: the correlational method and the experimental method. These methods do not offer the rich details that make case studies so interesting, but they do help investigators draw broad conclusions about abnormality in the population at large. Thus they are now the preferred methods of clinical investigation.

Three features of the correlational and experimental methods enable clinical investigators to gain general insights: (1) The researchers typically observe many individuals (see MindTech below). (2) The researchers apply procedures uniformly and can thus repeat, or replicate, their investigations. And (3) the researchers use statistical tests to analyze the results of a study.

Correlation is the degree to which events or characteristics vary with each other. The correlational method is a research procedure used to determine this “co-relationship” between variables. This method can be used, for example, to answer the question, “Is there a correlation between the amount of stress in people’s lives and the degree of depression they experience?” That is, as people keep experiencing stressful events, are they increasingly likely to become depressed?

To test this question, researchers have collected life stress scores (for example, the number of threatening events experienced during a certain period of time) and depression scores (for example, scores on a depression survey) from individuals and have correlated these scores. The people who are chosen for a study are its subjects, or participants, the term preferred by today’s investigators. Typically, investigators have found that life stress and depression variables do indeed increase or decrease together (Monroe et al., 2014). That is, the greater someone’s life stress score, the higher his or her score on the depression scale. When variables change the same way, their correlation is said to have a positive direction and is referred to as a positive correlation. Alternatively, correlations can have a negative rather than a positive direction. In a negative correlation, the value of one variable increases as the value of the other variable decreases. Researchers have found, for example, a negative correlation between depression and activity level. The greater one’s depression, the lower the number of one’s activities.

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There is yet a third possible outcome for a correlational study. The variables under study may be unrelated, meaning that there is no consistent relationship between them. As the measures of one variable increase, those of the other variable sometimes increase and sometimes decrease. Studies have found that depression and intelligence are unrelated, for example.

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In addition to knowing the direction of a correlation, researchers need to know its magnitude, or strength. That is, how closely do the two variables correspond? Does one always vary along with the other, or is their relationship less exact? When two variables are found to vary together very closely in person after person, the correlation is said to be high, or strong.

The direction and magnitude of a correlation are often calculated numerically and expressed by a statistical term called the correlation coefficient. The correlation coefficient can vary from +1.00, which indicates a perfect positive correlation between two variables, down to –1.00, which represents a perfect negative correlation. The sign of the coefficient (+ or –) signifies the direction of the correlation; the number represents its magnitude. The closer the correlation is to .00, the weaker, or lower in magnitude, it is. Thus correlations of +.75 and –.75 are of equal magnitude and equally strong, whereas a correlation of +.25 is weaker than either.

Everyone’s behavior is changeable, and many human responses can be measured only approximately. Most correlations found in psychological research, therefore, fall short of perfect positive or negative correlation. For example, one early study of life stress and depression, with a sample of 68 adults, found a correlation of +.53 (Miller, Ingham, & Davidson, 1976). Although hardly perfect, a correlation of this magnitude is considered large in psychological research.

When Can Correlations Be Trusted? Scientists must decide whether the correlation they find in a given sample of participants accurately reflects a real correlation in the general population. Could the observed correlation have occurred by mere chance? They can test their conclusions with a statistical analysis of their data, using principles of probability. In essence, they ask how likely it is that the study’s particular findings have occurred by chance. If the statistical analysis indicates that chance is unlikely to account for the correlation they found, researchers may conclude that their findings reflect a real correlation in the general population.

What Are the Merits of the Correlational Method? The correlational method has certain advantages over the case study (see Table 1.3). Because researchers measure their variables, observe many participants, and apply statistical analyses, they are in a better position to generalize their correlations to people beyond the ones they have studied. Furthermore, researchers can easily repeat correlational studies using new samples of participants to check the results of earlier studies.

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Although correlations allow researchers to describe the relationship between two variables, they do not explain the relationship (Jackson, 2012). When we look at the positive correlation found in many life stress studies, we may be tempted to conclude that increases in recent life stress cause people to feel more depressed. In fact, however, the two variables may be correlated for any one of three reasons: (1) Life stress may cause depression. (2) Depression may cause people to experience more life stress (for example, a depressive approach to life may cause people to perform poorly at work or may interfere with social relationships). (3) Depression and life stress may each be caused by a third variable, such as financial problems (Gutman & Nemeroff, 2011).

Although correlations say nothing about causation, they can still be of great use to clinicians. Clinicians know, for example, that suicide attempts increase as people become more depressed. Thus, when they work with severely depressed clients, they stay on the lookout for signs of suicidal thinking. Perhaps depression directly causes suicidal behavior, or perhaps a third variable, such as a sense of hopelessness, causes both depression and suicidal thoughts. Whatever the cause, just knowing that there is a correlation may enable clinicians to take measures (such as hospitalization) to help save lives.

Special Forms of Correlational Research Epidemiological studies and longitudinal studies are two kinds of correlational research used widely by clinical investigators. Epidemiological studies reveal the incidence and prevalence of a disorder in a particular population. Incidence is the number of new cases that emerge during a given period of time. Prevalence is the total number of cases in the population during a given period; prevalence includes both existing and new cases.

Over the past 40 years, clinical researchers throughout the United States have worked on one of the largest epidemiological studies of mental disorders ever conducted, called the Epidemiologic Catchment Area Study (Ramsey et al., 2013). They have interviewed more than 20,000 people in five cities to determine the prevalence of many psychological disorders and the treatment programs used. Two other large-scale epidemiological studies in the United States, the National Comorbidity Survey and the National Comorbidity Survey Replication, have questioned more than 9,000 individuals (Martin, Neighbors, & Griffith, 2013). All these studies have been further compared with epidemiological studies of specific groups, such as Hispanic and Asian American populations, or with epidemiological studies conducted in other countries, to see how rates of mental disorders and treatment programs vary from group to group and from country to country (Jimenez et al., 2010).

Such epidemiological studies have helped researchers identify groups at risk for particular disorders. Women, it turns out, have a higher rate of anxiety disorders and depression than men, while men have a higher rate of alcoholism than women. Elderly people have a higher rate of suicide than young people. Hispanic Americans experience posttraumatic stress disorder more than other racial and ethnic groups in the United States. And persons in some countries have higher rates of certain mental disorders than those in other countries. Eating disorders such as anorexia nervosa, for example, appear to be more common in Western countries than in non-Western ones.

In longitudinal studies, correlational studies of another kind, researchers observe the same individuals on many occasions over a long period of time. In several such studies, investigators have observed the progress over the years of normally functioning children whose mothers or fathers suffered from schizophrenia (Rasic et al., 2014; Mednick, 1971). The researchers have found, among other things, that the children of the parents with the most severe cases of schizophrenia were particularly likely to develop a psychological disorder and to commit crimes at later points in their development.

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An experiment is a research procedure in which a variable is manipulated and the manipulation’s effect on another variable is observed. The manipulated variable is called the independent variable and the variable being observed is called the dependent variable.

To examine the experimental method more fully, let’s consider a question that is often asked by clinicians (Toth et al., 2014): “Does a particular therapy relieve the symptoms of a particular disorder?” Because this question is about a causal relationship, it can be answered only by an experiment (see Table 1.4). That is, experimenters must give the therapy in question to people who are suffering from a disorder and then observe whether they improve. Here the therapy is the independent variable, and psychological improvement is the dependent variable.

As with correlational studies, investigators who conduct experiments must do a statistical analysis on their data and find out how likely it is that the observed improvement is due to chance. Again, if that likelihood is very low, the improvement is considered to be statistically significant, and the experimenter may conclude with some confidence that it is due to the independent variable.

If the true cause of changes in the dependent variable cannot be separated from other possible causes, then an experiment gives very little information. Thus, experimenters must try to eliminate all confounds from their studies—variables other than the independent variable that may also be affecting the dependent variable. When there are confounds in an experiment, they, rather than the independent variable, may be causing the observed change.

For example, situational variables, such as the location of the therapy office (say, a quiet country setting) or soothing background music in the office, may have a therapeutic effect on participants in a therapy study. Or perhaps the participants are unusually motivated or have high expectations that the therapy will work, factors that thus account for their improvement. To guard against confounds, researchers should include three important features in their experiments—a control group, random assignment, and a blind design (see MediaSpeak below).

The Control Group A control group is a group of research participants who are not exposed to the independent variable under investigation but whose experience is similar to that of the experimental group, the participants who are exposed to the independent variable. By comparing the two groups, an experimenter can better determine the effect of the independent variable.

To study the effectiveness of a particular therapy, for example, experimenters typically divide participants into two groups. The experimental group may come into an office and receive the therapy for an hour, while the control group may simply come into the office for an hour. If the experimenters find later that the people in the experimental group improve more than the people in the control group, they may conclude that the therapy was effective, above and beyond the effects of time, the office setting, and any other confounds. To guard against confounds, experimenters try to provide all participants, both control and experimental, with experiences that are identical in every way—except for the independent variable.

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Random Assignment Researchers must also watch out for differences in the makeup of the experimental and control groups since those differences may also confound a study’s results. In a therapy study, for example, the experimenter may unintentionally put wealthier participants in the experimental group and poorer ones in the control group. This difference, rather than their therapy, may be the cause of the greater improvement later found among the experimental participants. To reduce the effects of preexisting differences, experimenters typically use random assignment. This is the general term for any selection procedure that ensures that every participant in the experiment is as likely to be placed in one group as the other. Researchers might, for example, assign people to groups by flipping a coin or picking names out of a hat.

Blind Design A final confound problem is bias. Participants may bias an experiment’s results by trying to please or help the experimenter. In a therapy experiment, for example, if those participants who receive the treatment know the purpose of the study and which group they are in, they might actually work harder to feel better or fulfill the experimenter’s expectations. If so, subject, or participant, bias rather than therapy could be causing their improvement.

To avoid this bias, experimenters can prevent participants from finding out which group they are in. This experimental strategy is called a blind design because the individuals are blind as to their assigned group. In a therapy study, for example, control participants could be given a placebo (Latin for “I shall please”), something that looks or tastes like real therapy but has none of its key ingredients. This “imitation” therapy is called placebo therapy. If the experimental (true therapy) participants improve more than the control (placebo therapy) participants, experimenters have more confidence that the true therapy has caused their improvement.

An experiment may also be confounded by experimenter bias—that is, experimenters may have expectations that they unintentionally transmit to the participants in their studies. In a drug therapy study, for example, the experimenter might smile and act confident while providing real medications to the experimental participants but frown and appear hesitant while offering placebo drugs to the control participants. This kind of bias is sometimes referred to as the Rosenthal effect, after the psychologist who first identified it (Rosenthal, 1966). Experimenters can eliminate their own bias by arranging to be blind themselves. In a drug therapy study, for example, an aide could make sure that the real medication and the placebo drug look identical. The experimenter could then administer treatment without knowing which participants were receiving true medications and which were receiving false medications. While either the participants or the experimenter may be kept blind in an experiment, it is best that both be blind—a research strategy called a double-blind design. In fact, most medication experiments now use double-blind designs to test promising drugs (Pratley, Fleck, & Wilson, 2014).

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Alternative Experimental Designs Clinical researchers must often settle for experimental designs that are less than ideal (Manton et al., 2014). The most common such variations are the quasi-experimental design, the natural experiment, the analogue experiment, and the single-subject experiment.

In quasi-experiments, or mixed designs, investigators do not randomly assign participants to control and experimental groups but instead make use of groups that already exist in the world at large (Girden & Kabacoff, 2011). Consider, for example, research into the effects of child abuse. Because it would be unethical for investigators of this issue to actually abuse a randomly chosen group of children, they must instead compare children who already have a history of abuse with children who do not. To make this comparison as valid as possible, they may further use matched control participants. That is, they match the experimental participants with control participants who are similar in age, sex, race, number of children in the family, socioeconomic status, type of neighborhood, or other characteristics. When the data from studies of this kind show that abused children are typically sadder and have lower self-esteem than matched control participants who have not been abused, the investigators can conclude with some confidence that abuse is causing the differences (Lindert et al., 2013).

In natural experiments, nature itself manipulates the independent variable, and the experimenter observes the effects. Natural experiments must be used for studying the psychological effects of unusual and unpredictable events, such as floods, earthquakes, plane crashes, and fires. Because the participants in these studies are selected by an accident of fate rather than by the investigators’ design, natural experiments are actually a kind of quasi-experiment.

On December 26, 2004, an earthquake occurred beneath the Indian Ocean off the coast of Sumatra, Indonesia. The earthquake triggered a series of massive tsunamis that flooded the ocean’s coastal communities, killed more than 225,000 people, and injured and left millions of survivors homeless, particularly in Indonesia, Sri Lanka, India, and Thailand. Within months of this disaster, researchers conducted natural experiments in which they collected data from hundreds of survivors and from control groups of people who lived in areas not directly affected by the tsunamis. The disaster survivors scored significantly higher on anxiety and depression measures (dependent variables) than the controls did. The survivors also experienced more sleep problems, feelings of detachment, arousal, difficulties concentrating, startle responses, and guilt feelings than the controls did (Musa et al., 2014; Heir et al., 2010). Over the past several years, other natural experiments have focused on survivors of the 2010 Haitian earthquake, the massive earthquake and tsunami in Japan in 2011, the Northeast’s Superstorm Sandy in 2012, and the unprecedented Oklahoma tornados in 2013. These studies have also revealed lingering psychological symptoms among survivors of those disasters (Iwadare et al., 2013).

Experimenters often run analogue experiments. Here they induce laboratory participants to behave in ways that seem to resemble real-life abnormal behavior and then conduct experiments on the participants in the hope of shedding light on the real-life abnormality. For example, as you’ll see in Chapter 6, investigator Martin Seligman, in a classic body of work, has produced depression-like symptoms in laboratory participants—both animals and humans—by repeatedly exposing them to negative events (shocks, loud noises, task failures) over which they have no control. In these “learned helplessness” analogue studies, the participants seem to give up, lose their initiative, and become sad—suggesting to some clinicians that human depression itself may indeed be caused by loss of control over the events in one’s life.

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Finally, scientists sometimes do not have the luxury of experimenting on many participants. They may, for example, be investigating a disorder so rare that few participants are available. Experimentation is still possible, however, with a single-subject experimental design. Here a single participant is observed both before and after the manipulation of an independent variable (Richards, Taylor, & Ramasamy, 2014).

For example, using a particular kind of single-subject design, called an ABAB, or reversal, design, one researcher sought to determine whether the systematic use of rewards would reduce a teenage boy’s habit of disrupting his special education class with loud talk (Deitz, 1977). He rewarded the boy, who suffered from intellectual disability (previously called mental retardation), with extra teacher time whenever he went 55 minutes without interrupting the class more than three times. In condition A, the student was observed prior to receiving any reward, and he was found to disrupt the class frequently with loud talk. In condition B, the boy was given a series of teacher reward sessions (introduction of the independent variable); as expected, his loud talk decreased dramatically. Next, the rewards from the teacher were stopped (condition A again), and the student’s loud talk increased once again. Apparently the independent variable had indeed been the cause of the improvement. To be still more confident about this conclusion, the researcher had the teacher apply reward sessions yet again (condition B again). Once again the student’s behavior improved.

We began this section by noting that clinical scientists look for general laws that will help them understand, treat, and prevent psychological disorders. As we have seen, however, circumstances can interfere with their progress.

Each method of investigation that we have observed addresses some of the problems involved in studying human behavior, but no one approach overcomes them all. Thus it is best to view each research method as part of a team of approaches that together may shed light on abnormal human functioning. When more than one method has been used to investigate a disorder, it is important to ask whether all the results seem to point in the same direction. If they do, clinical scientists are probably making progress toward understanding and treating that disorder. Conversely, if the various methods seem to produce conflicting results, the scientists must admit that knowledge in that particular area is still limited.

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Human research participants have needs and rights that must be respected. In fact, researchers’ primary obligation is to avoid harming the human participants in their studies—physically or psychologically.

The vast majority of researchers are conscientious about fulfilling this obligation. They try to conduct studies that test their hypotheses and further scientific knowledge in a safe and respectful way. But there have been some notable exceptions to this over the years, particularly several infamous studies conducted in the mid-1900s. Partly because of such exceptions, the government and the institutions in which research is conducted now take careful measures to ensure that the safety and rights of human research participants are properly protected.

Who, beyond researchers themselves, might directly watch over the rights and safety of human participants? For the past few decades, that responsibility has been given to Institutional Review Boards, or IRBs. Each research facility has an IRB—a committee of five or more members who review and monitor every study conducted at that institution, starting when the studies are first proposed. The institution may be a university, medical school, psychiatric or medical hospital, private research facility, mental health center, or the like. If research is conducted there, the institution must have an IRB, and that IRB has the responsibility and power to require changes in a proposed study as a condition of approval. If acceptable changes are not made by the researcher, then the IRB can disapprove the study altogether. Similarly, if, over the course of the study, the safety or rights of the participants are placed in jeopardy, the IRB must intervene and can even stop the study if necessary. These powers are granted to IRBs (or similar ethics committees) by nations around the world. In the United States, for example, IRBs are empowered by two agencies of the federal government—the Office for Human Research Protections and the Food and Drug Administration.

It turns out that protecting the rights and safety of human research participants is a complex undertaking. Thus, IRBs often are forced to conduct a kind of risk-benefit analysis in their reviews. They may, for example, approve a study that poses minimal or slight risks to participants if that “acceptable” level of risk is offset by the study’s potential benefits to society. In general, IRBs try to ensure that each study grants the following rights to its participants (NIJ, 2010):

Unfortunately, even with IRBs on the job, these rights can be in jeopardy. Consider, for example, the right of informed consent. To help ensure that participants understand what they are getting into when they enlist for a study, IRBs typically require that the individuals read and sign an “informed consent form” that spells out everything they need to know. But how clear are such forms? Not very, according to some investigations (Albala, Doyle, & Appelbaum, 2010; Mathew & McGrath, 2002).

It turns out that most such forms—the very forms deemed acceptable by IRBs—are written at an advanced college level, making them incomprehensible to a large percentage of participants. In fact, fewer than half of all participants may fully understand the informed consent forms they are signing. Still other investigations indicate that only around 10 percent of human participants carefully read the informed consent forms before signing them, and only 30 percent ask questions of the researchers during the informed consent phase of their studies (CISCRP, 2013).

In short, the IRB system is flawed, much like the research undertakings it oversees. There are various reasons for this. One is that ethical principles are subtle and elusive notions that do not always translate into simple regulations and guidelines. Another reason is that ethical decisions—whether by IRB members or by researchers—are subject to differences in perspective, interpretation, decision-making style, and the like. Despite such problems and limitations, most observers agree that the creation and work of IRBs have helped improve the rights and safety of human research participants over the years. The boards may reflect an imperfect system, but they play a necessary and important role in monitoring the quality and appropriateness of research undertakings.