E-ISSN : 2733-4538
Reliable estimation of a psychological test norm requires a large number of samples that well represent characteristics of the population distribution. Samples used in many test norm development studies have been classified into several subgroups, according to age and years of education, for example, whose norms need to be estimated separately. However, in most of the studies, sample sizes of subgroups are not large enough for reliable estimation of norms of the psychological test under investigation. The current study explained why having a large enough sample size for each subgroup is important in test norm estimation and introduced a model-based estimation procedure that provides more reliable norm estimates than a typical norming procedure, especially when the sample size of each subgroup is less than desirable. Specifically, this procedure, under normality assumption of the test scores, uses a regression model that estimates the means and variances of all of the subgroups simultaneously. An example analysis was illustrated in order to demonstrate how to use the suggested procedure using a normative sample of Korean-version Boston Naming Test scores from 1067 normal elderlies. Finally, considerations in application of a model-based norming procedure were discussed.
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