Sample Size Requirements for Traditional and Regression-Based Norms.
Test norms enable determining the position of an individual test taker in the group. The most frequently used approach to obtain test norms is traditional norming. Regression-based norming may be more efficient than traditional norming and is rapidly growing in popularity, but little is known about its technical properties. A simulation study was conducted to compare the sample size requirements for traditional and regression-based norming by examining the 95% interpercentile ranges for percentile estimates as a function of sample size, norming method, size of covariate effects on the test score, test length, and number of answer categories in an item. Provided the assumptions of the linear regression model hold in the data, for a subdivision of the total group into eight equal-size subgroups, we found that regression-based norming requires samples 2.5 to 5.5 times smaller than traditional norming. Sample size requirements are presented for each norming method, test length, and number of answer categories. We emphasize that additional research is needed to establish sample size requirements when the assumptions of the linear regression model are violated.
Tilburg University, Tilburg, Netherlands h.e.m.oosterhuis@tilburguniversity.edu.
Hannah E M
HE
Oosterhuis
University of Amsterdam, Amsterdam, Netherlands.
L Andries
LA
van der Ark
Tilburg University, Tilburg, Netherlands.
Klaas
K
Sijtsma
D000328:Adult; D005260:Female; D006760:Hospitalization; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000175:diagnosis / Q000523:psychology; D008875:Middle Aged; D010552:Personality Assessment / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000706:statistics & numerical data*; D012016:Reference Values; D012044:Regression Analysis; D015203:Reproducibility of Results; D018401:Sample Size
minimum sample size requirements for norms; norm distribution of test scores; precise test norms; regression-based norming; traditional norming