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Ruscio, John; Ruscio, Ayelet Meron; Meron, Mati – Multivariate Behavioral Research, 2007
Meehl's taxometric method was developed to distinguish categorical and continuous constructs. However, taxometric output can be difficult to interpret because expected results for realistic data conditions and differing procedural implementations have not been derived analytically or studied through rigorous simulations. By applying bootstrap…
Descriptors: Sampling, Equated Scores, Data Interpretation, Inferences

MacCallum, Robert C.; And Others – Multivariate Behavioral Research, 1994
Alternative strategies for two-sample cross-validation of covariance structure models are described and investigated. Results of an empirical sampling study show that for tighter strategies simpler models are preferred in smaller samples, but when cross-validation is employed, a more complex model is supported even for small samples. (SLD)
Descriptors: Comparative Analysis, Evaluation Methods, Models, Research Methodology

Lunneborg, Clifford E.; Tousignant, James P. – Multivariate Behavioral Research, 1985
This paper illustrates an application of Efron's bootstrap to the repeated measures design. While this approach does not require parametric assumptions, it does utilize distributional information in the sample. By appropriately resampling from study data, the bootstrap may determine accurate sampling distributions for estimators, effects, or…
Descriptors: Hypothesis Testing, Research Design, Research Methodology, Sampling

Lambert, Zarrel V.; And Others – Multivariate Behavioral Research, 1991
A method is presented that eliminates some interpretational limitations arising from assumptions implicit in the use of arbitrary rules of thumb to interpret exploratory factor analytic results. The bootstrap method is presented as a way of approximating sampling distributions of estimated factor loadings. Simulated datasets illustrate the…
Descriptors: Behavioral Science Research, Computer Simulation, Estimation (Mathematics), Factor Structure

Buja, Andreas; Eyuboglu, Nermin – Multivariate Behavioral Research, 1992
Use of parallel analysis (PA), a selection rule for the number-of-factors problem, is investigated from the viewpoint of permutation assessment through a Monte Carlo simulation. Results reveal advantages and limitations of PA. Tables of sample eigenvalues are included. (SLD)
Descriptors: Computer Simulation, Correlation, Factor Structure, Mathematical Models