Methods of Psychology

Psychologists use a variety of research methods like descriptive, correlational and experimental methods.

Descriptive methods, including case studies, surveys and naturalistic observations, provide a good starting place for a new research question. Personal observations and “common sense” ideas are especially vulnerable to bias, but descriptive methods allow a researcher to make more careful, systematic, real-world observations. Armed with these scientific observations, the researcher will be in a strong position to generate hypotheses.

Case Studies

A case study provides an in-depth analysis of the behavior of one person or a small number of people. Many fields, including medicine, law, and business, use the case study method. Psychologists often use case studies in situations where large numbers of participants are not available or when a particular participant possesses unique characteristics, as in the case described in this section. Interviews, background records, observation, personality tests, cognitive tests, and brain imaging provide information necessary to evaluate the case.


Surveys allow you to ask large numbers of people questions about attitudes and behavior. Surveys provide a great deal of useful information very quickly at relatively little expense. One of the primary requirements for a good survey is the use of an appropriate sample, or subset of a population being studied. Good results require large samples that are typical, or representative, of the population you wish to describe. Surveys use self-report, so results can be influenced by people’s natural tendency to want to appear socially appropriate. In some cases where people believe that their true attitudes and behaviors will not be viewed favorably by others, they are likely to lie, even when their answers are confidential and anonymous.

Naturalistic Observations

Naturalistic observation is an in-depth study of a phenomenon in its natural setting, usually in large groups. Some naturalistic observations are conducted when people know they are being observed, while others are not. Both situations are challenging. The use of naturalistic observation illustrates the importance of choosing a method that is well suited to goals of the research to your research goals. This method can be very helpful for developing hypotheses, but other methods must be used to test them.

Correlational Methods

Correlational methods help psychologists see how two variables of interest, like the marriage rate and economic factors described earlier, relate to one another. Psychologists use experiments to test their hypotheses and to determine the causes of behavior. Correlations measure the direction and strength of the relationship between two variables, or factors that have values that can “vary,” like a person’s height and weight. We begin our analysis of correlations by measuring our variables. A measure answers the simple question of “how much” of a variable you have observed. After we obtain measures of each variable, we compare the values of one variable to those of the other and conduct a statistical analysis of the results. Three possible outcomes from the comparison between our two variables can occur: positive, negative, or zero correlations.

In a positive correlation, high levels of one variable are associated with high levels of the other variable. Two variables can also show a negative correlation, in which high values of one variable are associated with low values of another. For example, high levels of alcohol consumption among college students are usually associated with low grade point averages. The third possible outcome is a zero correlation, in which the two variables do not have any systematic relationship with each other at all. When variables have a zero correlation, knowing the value of one variable does not tell you anything about the value of the other variable. Correlational research results are frequently misunderstood. Correlations permit us to discuss the relationships between two variables but tell us nothing about whether one variable causes changes in the other.

Experimental Methods

Experimental methods start off with a researcher designing an experiment with a hypothesis, which is a smart guess based on systematic observations, a review of previous research, or a scientific theory. A hypothesis takes this form: “If I do this, that will happen.” To test the hypothesis, the researcher manipulates or modifies one or more variables and observes changes in others. The variable controlled and manipulated by an experimenter (“If I do this . . . .) is known as the independent variable. We need some way to evaluate the effects of this manipulation. We use a dependent variable, defined as the observed result of the manipulation of the independent variable, to tell us “that will happen” as a result of the independent variable.

In most experiments, we want to know how simply going through the procedures of being in an experiment influences our dependent variable. To evaluate these effects, we assign some of our participants to a control group, or a group that experiences all experimental procedures with the exception of exposure to the independent variable. The experience of the control group should be as similar as possible to that of the experimental groups, who do experience the independent variable. We want to ensure that our dependent variables reflect the outcomes of our independent variables, instead of individual differences among the participants’ personalities, abilities, motivations, and other similar factors. To prevent these individual differences from masking or distorting the effects of our independent variable, we randomly assign participants to experimental or control groups.

Random Assignment

Random assignment means that each participant has an equal chance of being assigned to any group in an experiment. With random assignment, any differences we see between the behavior of one group and that of another is unlikely to be the result of the individual differences among the participants, which tend to cancel each other out. Individual differences among participants are an example of confounding variables, or variables that are irrelevant to the hypothesis being tested that can alter conclusions.