Diagram of A Hypothetical Two-Factor Study Discussion
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Diagram of A Hypothetical Two-Factor Study Discussion
But experiments also have drawbacks. One drawback (as noted) is that it’s sometimes unclear which aspect of the manipulation was important. Another drawback is that experiments on people usually involve events of relatively short duration, in carefully controlled conditions. The correlational method, in contrast, lets you examine events that take place over long periods (even decades) and events that are much more elaborate.
Correlational studies also let you get information about events in which experimental manipulation would be unethical—for example, how being raised by divorced parents affects people’s personality.
Personality psychologists sometimes also criticize experiments on the grounds that the kinds of relationships they obtain often have little to do with the central issues of personality. Even experiments that seem to bear on important issues in personality may tell less than they seem to.
Box 2.1 Correlations in the News The fact that a correlation cannot establish causality is ignored to an amazing degree, pretty much every place you look. Here’s an example, straight from a recent report on the national evening news.
An article had just been published that day in a medical journal showing that people who retire earlier from their jobs showed greater cognitive decline (poorer mental function) compared with people who hadn’t retired. This was exciting news (so exciting that the news was announced in breath- less terms by the network’s medical correspondent, a physi- cian). The description of the result was followed by comments about how beneficial it is for people to keep working, that staying active in your job keeps you mentally fit, that we should all think twice about retiring.
There’s just one problem. The finding, despite being described in terms of groups, was in fact correlational in nature. The finding was that retiring earlier was associated with poorer cognitive function. That doesn’t mean that retir- ing caused poorer cognitive function. It is entirely possible that poorer cognitive function led people to retire earlier, a possibility that was never mentioned in the report.
Dependent Measure: Performance on a Second Task
Low Self-Esteem High Self-Esteem
Initial Success
Initial Failure
Figure 2.7 Diagram of a hypothetical two-factor study. Each square represents the combination of the value listed above it and the value listed to the left. In multifactor studies, all combinations of values of the pre- dictor variables are created in this fashion.
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effect of one variable (success vs. failure) differs across the two levels of the other variable (degree of self-esteem). That is the meaning of the term interaction. In the case in Figure 2.8, A, a failure has an effect at one level of the sec- ond variable (the low self-esteem group) but has no effect at the other level of the second variable (the high self- esteem group).
Two more points about interactions: First, to find an interaction, it’s absolutely necessary to study more than one factor at a time. It’s impossible to find an interaction unless both variables in it are studied at once. This is one reason researchers often use multifactor designs: They allow the possibility for interactions to emerge.
Sometimes the factors are all experimental manipula- tions. Sometimes they’re all personality variables. Often, though, experimental manipulations are crossed by indi- vidual-difference variables. The example shown in Fig- ure 2.7 is such a design. The self-esteem factor is the level of self-esteem the people had when they came to the study. This is a personality dimension (thus correlational). The success–failure factor is an experimental manipulation, which takes place during the session. In this particular experiment, the dependent measure is performance on a second task, which the participants attempt after the suc- cess–failure manipulation.
These designs allow researchers to examine how dif- ferent types of people respond to situations. They thus offer a glimpse into the underlying dynamics of the indi- vidual-difference variable. Because this type of study com- bines experimental procedures and individual differences, it’s often referred to as experimental personality research.
2.2.8: Reading Figures from Multifactor Research Because multifactor designs are more complex than single- factor studies, what they can tell you is also potentially more complex. Indeed, people who do experimental per- sonality research use these designs precisely for this rea- son.
You don’t always get a complex result from a multifac- tor study. Sometimes you find only the same outcomes you would have found if you had studied each predictor sepa- rately. When you find that a predictor variable is linked to the outcome in a systematic way, completely separate from the other predictor, the finding is called a main effect. For example, the study outlined in Figure 2.7 might find sim- ply that people of both initial self-esteem levels perform worse after a failure than after a success, but they don’t dif- fer in how much worse.
Complexity emerges when a study finds what’s termed an interaction. Figure 2.8 portrays two interac- tions, each a possible outcome of the hypothetical study of Figure 2.7. In each case, the vertical dimension portrays the dependent measure: performance on the second task. The two marks on the horizontal line represent the two values of the manipulated variable: initial success versus failure. The color of the line depicts the other predictor variable: the colored line represents people high in self-esteem, and the black line represents those low in self-esteem.
We emphasize that these graphs show hypothetical out- comes. They are intended only to give you a clearer under- standing of what an interaction means. Figure 2.8, A, portrays a finding that people who are low in self-esteem perform worse after an initial failure than after a success. Among people high in self-esteem, however, this doesn’t occur. Failure apparently has no effect on them.
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