EXPERIMENTAL GROUPS AND CONTROL GROUPS

EXPERIMENTAL GROUPS AND CONTROL GROUPS:

Experimental research requires, then, that the responses of at least two groups be compared. One group will receive some special  Treatment —the manipulation implemented by the experimenter—and another group will receive either no treatment or a different treatment. Any group that receives a treatment is called an Experimental group ; A group that receives no treatment is called a Control group. (In some experiments there are multiple experimental and control groups, each of which is compared with another group.)  By employing both experimental and control groups in an experiment, researchers are able to rule out the possibility that something other than the experimental manipulation produced the results observed in the experiment. Without a control group, we couldn’t be sure that some other variable, such as the temperature at the time we were running the experiment, the color of the experimenter’s hair, or even the mere passage of time, wasn’t causing the changes observed. For example, consider a medical researcher who thinks he has invented a medicine that cures the common cold. To test his claim, he gives the medicine one day to a group of 20 people who have colds and finds that 10 days later all of them are cured.  Eureka? Not so fast. An observer viewing this fl awed study might reasonably argue that the people would have gotten better even without the medicine. What the researcher obviously needed was a control group consisting of people with colds who  don’t get the medicine and whose health is also checked 10 days later. Only if there is a significant difference between experimental and control groups can the effectiveness of the medicine be assessed. Through the use of control groups, then, researchers can isolate specific causes for their findings—and draw cause-and-effect inferences. Returning to Latané and Darley’s experiment, we see that the researchers needed to translate their hypothesis into something testable. To do this, they decided to create a false emergency situation that would appear to require the aid of a bystander. As their experimental manipulation, they decided to vary the number of bystanders present. They could have had just one experimental group with, say, two people present, and a control group for comparison purposes with just one person present. Instead, they settled on a more complex procedure involving the creation of groups of three sizes—consisting of two, three, and six people—that could be compared with one another. 

Description About Experimental Research

Experimental Research:

The only way psychologists can establish cause-and-effect relationships through research is by carrying out an experiment. In a formal Experiment, the researcher investigates the relationship between two (or more) variables by deliberately changing one variable in a controlled situation and observing the effects of that change on other aspects of the situation. In an experiment, then, the conditions are created and controlled by the researcher, who deliberately makes a change in those conditions in order to observe the effects of that change.

The change that the researcher deliberately makes in an experiment is called the Experimental manipulation. Experimental manipulations are used to detect relationships between different variables (Staub, 2011).

Several steps are involved in carrying out an experiment, but the process typically begins with the development of one or more hypotheses for the experiment to test. For example, Latané and Darley, in testing their theory of the diffusion of responsibility in bystander behavior, developed this hypothesis: The higher the number of people who witness an emergency situation is, the less likely it is that any of them will help the victim. They then designed an experiment to test this hypothesis.

Their first step was to formulate an operational definition of the hypothesis by conceptualizing it in a way that could be tested. Latané and Darley had to take into account the fundamental principle of experimental research mentioned earlier: Experimenters must manipulate at least one variable in order to observe the effects of the manipulation on another variable while keeping other factors in the situation constant. However, the manipulation cannot be viewed by itself, in isolation; if a cause-and-effect relationship is to be established, the effects of the manipulation must be compared with the effects of no manipulation or a different kind of manipulation.

Description Of Correlational Research

CORRELATIONAL RESEARCH:

In using the descriptive research methods we have discussed, researchers often wish to determine the relationship between two variables. Variables are behaviors, events, or other characteristics that can change, or vary, in some way. For example, in a study to determine whether the amount of studying makes a difference in test scores, the variables would be study time and test scores.

In correlational research, two sets of variables are examined to determine whether they are associated, or “correlated.” The strength and direction of the relationship between the two variables are represented by a mathematical statistic known as  a correlation (or, more formally, a correlation coefficient), which can range from +1.0 to -1.0.

A positive correlation indicates that as the value of one variable increases, we can predict that the value of the other variable will also increase. For example, if we predict that the more time students spend studying for a test, the higher their grades on the test will be, and that the less they study, the lower their test scores will be, we are expecting to find a positive correlation. (Higher values of the variable “amount of study time” would be associated with higher values of the variable “test score,” and lower values of “amount of study time” would be associated with lower values of “test score.”) The correlation, then, would be indicated by a positive number, and the stronger the association was between studying and test scores, the closer the number would be to + 1.0. For example, we might find a correlation of +.85 between test scores and amount of study time, indicating a strong positive association.

In contrast, a negative correlation tells us that as the value of one variable increases, the value of the other decreases. For instance, we might predict that as the number of hours spent studying increases, the number of hours spent partying decreases. Here we are expecting a negative correlation, ranging between 0 and - 1.0. More studying is associated with less partying, and less studying is associated with more partying. The stronger the association between studying and partying is, the closer the correlation will be to -1.0. For instance, a correlation of -.85 would indicate a strong negative association between partying and studying. 

Of course, it’s quite possible that little or no relationship exists between two variables. For instance, we would probably not expect to find a relationship between number of study hours and height. Lack of a relationship would be indicated by a correlation close to 0. For example, if we found a correlation of - .02 or +.03, it would indicate that there is virtually no association between the two variables; knowing how much someone studies does not tell us anything about how tall he or she is. 

When two variables are strongly correlated with each other, we are tempted to assume that one variable causes the other. For example, if we find that more study time is associated with higher grades, we might guess that more studying  causes higher grades. Although this is not a bad guess, it remains just a guess—because finding that two variables are correlated does not mean that there is a causal relationship between them. The strong correlation suggests that knowing how much a person studies can help us predict how that person will do on a test, but it does not mean that the studying causes the test performance. Instead, for instance, people who are more interested in the subject matter might study more than do those who are less interested, and so the amount of interest, not the number of hours spent studying, would predict test performance. The mere fact that two variables occur together does not mean that one causes the other. 

Similarly, suppose you learned that the number of houses of worship in a large sample of cities was positively correlated with the number of people arrested, meaning that the more houses of worship, the more arrests there were in a city. Does this mean that the presence of more houses of worship caused the greater number of arrests? Almost surely not, of course. In this case, the underlying cause is probably the size of the city: In bigger cities, there are both more houses of worship and more arrests.

One more example illustrates the critical point that correlations tell us nothing about cause and effect but merely provide a measure of the strength of a relationship between two variables. We might find that children who watch a lot of television programs featuring high levels of aggression are likely to demonstrate a relatively high degree of aggressive behavior and that those who watch few television shows that portray aggression are apt to exhibit a relatively low degree of such behavior. But we cannot say that the aggression is caused by the TV viewing, because many other explanations are possible. 

For instance, it could be that children who have an unusually high level of energy seek out programs with aggressive content and are more aggressive. The children’s energy level, then, could be the true cause of the children’s higher incidence of aggression. Also, people who are already highly aggressive might choose to watch shows with a high aggressive content because they are aggressive. Clearly, then, any number of causal sequences are possible—none of which can be ruled out by correlational research (Feshbach & Tangney, 2008; Grimes & Bergen, 2008). 

The inability of correlational research to demonstrate cause-and-effect relationships is a crucial drawback to its use. There is, however, an alternative technique that does establish causality: the experiment. 

Description Of Case Study

THE CASE STUDY :

When they read of a suicide bomber in the Middle East, many people wonder what it is about the terrorist’s personality or background that leads to such behavior. To answer this question, psychologists might conduct a case study. In contrast to a survey, in which many people are studied, a Case Study is an in-depth, intensive investigation of a single individual or a small group. Case studies often include psychological testing, a procedure in which a carefully designed set of questions is used to gain some insight into the personality of the individual or group (Gass et al., 2000;Addus, Chen, & Khan, 2007).

When case studies are used as a research technique, the goal is often not only to learn about the few individuals being examined but also to use the insights gained from the study to improve our understanding of people in general. Sigmund Freud developed his theories through case studies of individual patients. Similarly, case studies of terrorists might help identify others who are prone to violence. The drawback to case studies? If the individuals examined are unique in certain ways, it is impossible to make valid generalizations to a larger population. Still, they sometimes lead the way to new theories and treatments for psychological disorders.

Description Of Survey Research

SURVEY RESEARCH:

There is no more straightforward way of finding out what people think, feel, and do than asking them directly. For this reason, surveys are an important research method. In Survey Research, a sample of people chosen to represent a larger group of interest (a population) is asked a series of questions about their behavior, thoughts, or attitudes. Survey methods have become so sophisticated that even with a very small sample researchers are able to infer with great accuracy how a larger group would respond. For instance, a sample of just a few thousand voters is sufficient to predict within one or two percentage points who will win a presidential election—if the representative sample is chosen with care (Sommer & Sommer, 2001; Groves et al., 2004; Igo, 2006). 

Researchers investigating helping behavior might conduct a survey by asking people to complete a questionnaire in which they indicate their reluctance for giving aid to someone. Similarly, researchers interested in learning about sexual practices have carried out surveys to learn which practices are common and which are not and to chart changing notions of sexual morality over the last several decades (Reece et al.,2009; Santelli et al., 2009).

However, survey research has several potential pitfalls. For one thing, if the sample of people who are surveyed is not representative of the broader population of interest, the results of the survey will have little meaning. For instance, if a sample of voters in a town includes only Republicans, it would hardly be useful for predicting the results of an election in which both Republicans and Democrats are voting. Consequently, researchers using surveys strive to obtain a random sample of the population in question, in which every voter in the town has an equal chance of being included in the sample receiving the survey (Dale, 2006; Vitak et al., 2011; Davern, 2013).

In addition, survey respondents may not want to admit to holding socially undesirable attitudes. (Most racists know they are racists and might not want to admit it.) 

Furthermore, people may not want to admit they engage in behaviors that they feel are somehow abnormal—a problem that plagues surveys of sexual behavior because people are often reluctant to admit what they really do in private. Finally, in some cases, people may not even be consciously aware of what their true attitudes are or why they hold them. 

Description Of Naturalistic Observation

NATURALISTIC OBSERVATION:

In naturalistic observation, the investigator observes some naturally occurring behavior and does not make a change in the situation. For example, a researcher investigating helping behavior might observe the kind of help given to victims in a high-crime area of a city. The important point to remember about naturalistic observation is that the researcher simply records what occurs, making no modification in the situation that is being observed (Moore, 2002; Rustin, 2006; Kennison & Bowers, 2011).

Although the advantage of naturalistic observation is obvious—we get a sample of what people do in their “natural habitat”—there is also an important drawback: the inability to control any of the factors of interest. For example, we might find so few naturally occurring instances of helping behavior that we would be unable to draw any conclusions. Because naturalistic observation prevents researchers from making changes in a situation, they must wait until the appropriate conditions occur. Furthermore, if people know they are being watched, they may alter their reactions and produce behavior that is not truly representative..

Description of Archival research..

ARCHIVAL RESEARCH:

Suppose that, like the psychologists Latané and Darley (1970), you were interested in finding out more about emergency situations in which bystanders did not provide help. One of the first places you might turn to would be historical accounts. By searching newspaper records, for example, you might find support for the notion that a decrease in helping behavior historically has accompanied an increase in the number of bystanders. 

Using newspaper articles is an example of archival research. In archival research existing data, such as census documents, college records, online databases, and newspaper,clippings, are examined to test a hypothesis. For example, college transcripts may be used to determine if gender differences exist in academic performance (Sullivan, Riccio, & Reynolds, 2008; Fisher & Barnes-Farrell, 2013).

Archival research is a relatively inexpensive means of testing a hypothesis because someone else has already collected the basic data. Of course, the use of existing data has several drawbacks. For one thing, the data may not be in a form that allows the researcher to test a hypothesis fully. The information could be incomplete, or it could have been collected haphazardly (Simonton, 2000; Riniolo et al., 2003; Vega, 2006). Most attempts at archival research are hampered by the simple fact that records with the necessary information often do not exist. In these instances, researchers often turn to another research method: naturalistic observation...