Continuous norming of psychometric tests: A simulation study of parametric and semi-parametric approaches


Autoři: Alexandra Lenhard aff001;  Wolfgang Lenhard aff002;  Sebastian Gary aff002
Působiště autorů: Test Development Center, Psychometrica, Dettelbach, Bavaria, Germany aff001;  Institute of Psychology, University of Wuerzburg, Bavaria, Germany aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pone.0222279

Souhrn

Continuous norming methods have seldom been subjected to scientific review. In this simulation study, we compared parametric with semi-parametric continuous norming methods in psychometric tests by constructing a fictitious population model within which a latent ability increases with age across seven age groups. We drew samples of different sizes (n = 50, 75, 100, 150, 250, 500 and 1,000 per age group) and simulated the results of an easy, medium, and difficult test scale based on Item Response Theory (IRT). We subjected the resulting data to different continuous norming methods and compared the data fit under the different test conditions with a representative cross-validation dataset of n = 10,000 per age group. The most significant differences were found in suboptimal (i.e., too easy or too difficult) test scales and in ability levels that were far from the population mean. We discuss the results with regard to the selection of the appropriate modeling techniques in psychometric test construction, the required sample sizes, and the requirement to report appropriate quantitative and qualitative test quality criteria for continuous norming methods in test manuals.

Klíčová slova:

Research and analysis methods – Simulation and modeling – Biology and life sciences – Psychology – Psychometrics – Social sciences – People and places – Population groupings – Age groups – Physical sciences – Mathematics – Probability theory – Probability distribution – Skewness – Normal distribution – Statistical distributions – Algebra – Polynomials – Statistics – Statistical models


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PLOS One


2019 Číslo 9

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