A fourth reason to use correlational research is to study relationships that are naturally occurring. You might assess the relationship between student test scores and involvement in extracurricular activities, without wanting to manipulate either one of those variables. Relatedly, assessing naturally occurring relationships may complement experimental studies conducted in the lab that have been described as artificial.
The Language of Correlation and Causation
The words you use to describe your research have certain implications. When you use such words as impact, influence, determine, control, regulate, shape, alter, or modify, you suggest causality. When you use such words as association, relationship, link, or correspondence, you suggest correlation. In your writing, it is important to use the language that matches the kind of research you have done (see also Chapter 13 on writing up research).
Correlational Research Approaches: Correlational and Quasi-Experimental
Correlational research approaches can be divided into two general categories: correlational and quasi-experimental. What the two approaches have in common is that only correlational relationships are involved; that is, no causality can be inferred. What is typically called a correlational approach involves questions about a randomly selected sample as a whole in which two or more variables are measured (Category 1 from Figure 2.1); what is called a quasi-experimental approach typically involves questions about differences between preexisting groups (Categories 2 and 4 from Figure 2.1).
Although there are groups in quasi-experimental designs, they have not been randomly assigned. Quasi means “resembling.” Even though a quasi-experimental design mirrors a true experimental approach in some aspects (e.g., asking questions about group differences), it does not include the critical aspect of random assignment to condition and the groupings preexist (e.g., gender) or can be formed from preexisting situations (e.g., dividing students into those who own cars vs. those who do not based on responses to a questionnaire). Examples of such preexisting groups in quasi-experimental research are gender, class year, marital status, athlete status, coffee drinker or not, or almost anything where the characteristic in question preexisted or was naturally formed.
True experimental approach: Research approach in which one or more variables are manipulated and participants are randomly assigned to condition.
Random assignment: When participants are randomly assigned to the conditions of the study.
Hallmarks of True Experimental Approaches
In experimental approaches, participants are randomly assigned to conditions in which one or more variables of interest have been manipulated. There is an attempt to control extraneous variables and to measure as many potential third variables as necessary. The outcomes you measure test the effect of these manipulations. As an example, in Devlin et al. (2013), participants (students and adults from the community) viewed one of four photographs of the office of a psychotherapist that varied in the kind of art displayed (Western vs. Multicultural) and the number of art objects on view (1 vs. 6). Participants were randomly assigned to one of the four conditions generated by crossing the two art traditions (Western vs. Multicultural) with the two different numbers of art objects (1 vs. 6) (see Figure 3.1).
Figure 3.1 Four Types of Photographs Displayed in a Psychotherapist’s Office
Source: Copyright © 2013 by the American Psychological Association. Devlin, Borenstein, Finch, Hassan, Iannotti, and Koufopoulos, 2013. Multicultural art in the therapy office: Community and student perceptions of the therapist. Professional Psychology: Research & Practice, 44, 168–176. The use of this information does not imply endorsement by the publisher.
Between-subjects design: Research in which the conditions of an experiment are distributed across participants such that each participant is in only one condition.
Within-subjects design: Type of experimental design in which participants are exposed to all of the conditions.
Participants answered a series of questions about the characteristics of the therapist whose office they viewed; the office was created for the purposes of the research. The research question was whether the display of art that differed in (a) cultural tradition and (b) number of art objects would impact participants’ judgments of therapists, in particular, their openness to multiculturalism. This experimental approach is called a between-subjects design because the conditions are distributed between (across) participants. In the between subjects approach, each person participates in only one condition. This approach is often used because the researcher is concerned that participating in more than one condition would produce different results than participating in a single condition and that the impact of a given condition could not be isolated. Moreover, the between subjects approach reduces the likelihood that participants will guess the hypothesis of the research. In contrast, in the experimental approach called a within-subjects design, all participants would have seen all four photographs; that is, they would have been exposed to all of the conditions. Researchers often select a within-subjects design when (a) effects of participating in one condition on another are unlikely or (b) there are such effects that carry over and researchers want to study them. Between- and within-subjects approaches are covered in more depth in Chapters 9 and 10, respectively.
Try This Now 3.2
How does a between-subjects design help prevent participants from guessing the research question?
Differentiation of Independent and Dependent Variables
A course in research methods exposes you to specific terms that communicate important information. In this chapter, we have already seen important terms, like correlational and quasi-experimental designs. Two critical terms to understand are independent and dependent variable. Often these are referred to as the IV and DV, respectively. An independent variable is manipulated or varied (like our example of art in the previous section). You could think of this as the variable that is independent or “free to differ.” A dependent variable is the outcome of (depends on or is constrained by) exposure to the independent variable. Some researchers look at the independent variable as preceding an effect and, hence, as a cause; the dependent variable reflects the impact of the independent variable and is the outcome or effect.
Independent variable (IV): Variable that is manipulated in an experiment.
Dependent variable (DV): Variable that reflects the impact of the manipulated or independent variable in research.
Quasi-IV: Independent variable (IV) that is naturally occurring (e.g., race and gender) and as a consequence is not assigned at random.
We also need to identify what is called a quasi-IV. You remember that we talked about the difference between quasi-experiments and true experiments (where variables