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had viewed the violent video game gave significantly longer noise blasts than did the students who had played the
nonviolent game.
Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed
them to observe differences in the white noise levels between the two groups after the experimental manipulation,
leading to the conclusion that it was the independent variable (and not some other variable) that caused these
differences. The idea is that the only thing that was different between the students in the two groups was the video
game they had played.
Despite the advantage of determining causation, experiments do have limitations. One is that they are often
conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether
results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is
that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study
the influence of the size of a mob on the destructiveness of its behavior, or to compare the personality characteristics
of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed
using correlational designs, because it is simply not possible to experimentally manipulate these variables.
KEY TAKEAWAYS
- Descriptive, correlational, and experimental research designs are used to collect and analyze data.
- Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a
picture of the current thoughts, feelings, or behaviors in a given group of people. Descriptive research is summarized
using descriptive statistics. - Correlational research designs measure two or more relevant variables and assess a relationship between or among
them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation
Coefficient (r) is a measure of the strength of linear relationship between two variables. - Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a
spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from
correlational research designs. - Experimental research involves the manipulation of an independent variable and the measurement of a dependent
variable. Random assignment to conditions is normally used to create initial equivalence between the groups,
allowing researchers to draw causal conclusions.