20 A major consideration in multitrait analysis is which traits should be jointly analyzed. As a 21 common strategy, multitrait analysis is performed on either pairs of traits or on all of traits. To 22 fully exploit the power of multitrait analysis, we propose variable selection to choose a subset of 23 informative traits for multitrait quantitative trait locus (QTL) mapping. The proposed meth...

In order to adjust personality measurements to children's developmental level, we constructed the Pictorial Personality Traits Questionnaire for Children (PPTQ-C). To validate the measure, we conducted a study with a total group of 1028 children aged between 7 and 13 years old. Structural validity was established through Exploratory Structural Equation Model (ESEM). Criterion validity was confi...

The Multitrait-Multimethod Matrix is used to evaluate construct validity; Sawilowsky (2002) created the I test to analyze the matrix. This article examined the robustness and power of the Sawilowsky I test. Ad hoc critical values were determined to improve the statistical power of the technique for analyzing the Multitrait-Multimethod Matrix.

MOTIVATION Current methodology and software for quantitative trait loci (QTL) analyses do not use all available information and are inadequate to deal with the huge amount of QTL analyses to be needed in forecoming genetical genomics' studies. RESULTS We show that a mixed model statistical framework provides a very flexible tool for QTL modeling in a variety of populations, be it a cross betw...

Maximum likelihood (ML) estimation of categorical multitrait-multimethod (MTMM) data is challenging because the likelihood involves high-dimensional integrals over the crossed method and trait factors, with no known closed-form solution. The purpose of the study is to introduce three newly developed ML methods that are eligible for estimating MTMM models with categorical responses: Variational ...

The classical multitrait-multimethod (MTMM) matrix can be viewed as a two-dimensional cross-classification of traits and methods. Beside commonly used analysis methods such as structural equation modeling and generalizability theory, multilevel analysis offers attractive possibilities. If the focus is only on analyzing classical MTMM data, the multilevel approach has no surplus value, because t...

Administrative data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audi...