This study investigated the performance of five small-sample statistics (Lord’s, Kristof’s, McNemar’s,
Forsyth and Feldt’s, and Braden’s) that test whether
two variables measure the same trait except for measurement
error. The conservative Type I error rates of
the Lord and Kristof procedures and the liberal error
rates of the McNemar, Forsyth and Feldt, and Braden
procedures were corrected by determining appropriate
critical values. Power comparisons were then made at
the fixed α levels. In general, the McNemar statistic
was shown to be the most powerful. Finally, the effects
of non-normality were investigated, and it was
demonstrated that the Braden technique became very
liberal, whereas the other statistics tended to be somewhat
liberal at the .01 significance level and reasonably
robust at the .05 level. Index terms: Disattenuated
measures, Measurement error, Monte carlo
simulation, Non-normality, Small-sample statistics,
Type I error and power rates.
Rasmussen, Jeffrey L. (1988). Evaluation of small-sample statistics that test whether variables measure the same trait. Applied Psychological Measurement, 12, 177-187. doi:10.1177/014662168801200206
Rasmussen, Jeffrey L...
Evaluation of small-sample statistics that test whether variables measure the same trait.
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