Chapter 14.docx

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12 Dec 2012
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Chapter 14
Generalizing to other populations of research participants
- College Students
- Volunteers
- Gender considerations
- Locale participants in one locale may differ from participants in another locale.
Generalization as a Statistical Interaction
- The porblem of generalization can be thought of as an interaction in a factorial design.
- As interaction occurs when a relationship between variables exists under one condition but not
another or when the nature of the relationship is different in one condition than in another.
- Researchers can address generalization issues that stem from the different populations by
including subject type as a variable in the study. Ex: gender, ethnic group, age, etc.
Generalizing to other experiments
- The person who actually conducts the experiment is the source of another generalization
problem. In most experiments, only one experiment is used and rarely is much attention paid to
the personal characteristics of the experimenter. One solution is to use more than one
experimenters.
- Kintz and colleagues included personality of the experimenter which says they should be warm
and friendly. Also consider their gender since the participants would seem to perform better
when tested by an experimenter of the opposite sex.
Pretests and Generalization
- Researchers are often faced with the decision wheter to give a pretest.
- An important reason for using pretest is that it enables the reseracher to assess mortality effects
when it is likely that some participants will withdraw from an experiment. If you give a pretest,
you can determine whether the people who withdrew are different from those who completed
the study.
- Solomon four-group design can be used in situations in which a pretest is desirable but there is
concern over the possible impact of taking the pretest. In the Solomon four group design, half
the participants are given the pretest , the other half is given the posttest only.
Generalizing from Laboratory Setting
- Research conducted in a laboratory setting has the advantage of allowing the experimenter to
study the impact of independent variables under highly controlled conditions.
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