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Ferrari, Pier Alda; Barbiero, Alessandro – Multivariate Behavioral Research, 2012
The increasing use of ordinal variables in different fields has led to the introduction of new statistical methods for their analysis. The performance of these methods needs to be investigated under a number of experimental conditions. Procedures to simulate from ordinal variables are then required. In this article, we deal with simulation from…
Descriptors: Data, Statistical Analysis, Sampling, Simulation
Ruscio, John; Mullen, Tara – Multivariate Behavioral Research, 2012
It is good scientific practice to the report an appropriate estimate of effect size and a confidence interval (CI) to indicate the precision with which a population effect was estimated. For comparisons of 2 independent groups, a probability-based effect size estimator (A) that is equal to the area under a receiver operating characteristic curve…
Descriptors: Computation, Statistical Analysis, Probability, Effect Size
Sterba, Sonya K.; MacCallum, Robert C. – Multivariate Behavioral Research, 2010
Different random or purposive allocations of items to parcels within a single sample are thought not to alter structural parameter estimates as long as items are unidimensional and congeneric. If, additionally, numbers of items per parcel and parcels per factor are held fixed across allocations, different allocations of items to parcels within a…
Descriptors: Sampling, Computation, Statistical Analysis, Computer Software
Fritz, Matthew S.; Taylor, Aaron B.; MacKinnon, David P. – Multivariate Behavioral Research, 2012
Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special…
Descriptors: Statistical Analysis, Error of Measurement, Statistical Bias, Sampling
Cafri, Guy; Kromrey, Jeffrey D.; Brannick, Michael T. – Multivariate Behavioral Research, 2010
This article uses meta-analyses published in "Psychological Bulletin" from 1995 to 2005 to describe meta-analyses in psychology, including examination of statistical power, Type I errors resulting from multiple comparisons, and model choice. Retrospective power estimates indicated that univariate categorical and continuous moderators, individual…
Descriptors: Periodicals, Effect Size, Sampling, Psychology

Shirkey, Edwin C.; Dziuban, Charles D. – Multivariate Behavioral Research, 1976
Distributional characteristics of the measure of sampling adequacy (MSA) were investigated in sample correlation matrices generated from multivariate normal populations with covariance matrix equal to the identity. Systematic variation of sample size and number of variables resulted in minimal fluctuation of the overall MSA from .50. (Author/RC)
Descriptors: Factor Analysis, Matrices, Sampling, Statistical Analysis

Joe, George W.; Woodward, J. Arthur – Multivariate Behavioral Research, 1975
Descriptors: Correlation, Matrices, Sampling, Statistical Analysis

Lambert, Zarrel V.; And Others – Multivariate Behavioral Research, 1989
Bootstrap methodology is presented that yields approximations of the sampling variation of redundancy estimates while assuming little a priori knowledge about the distributions of these statistics. Results of numerical demonstrations suggest that bootstrap confidence intervals may offer substantial assistance in interpreting the results of…
Descriptors: Estimation (Mathematics), Predictor Variables, Sampling, Statistical Analysis

Humphreys, Lloyd G.; Montanelli, Richard G. – Multivariate Behavioral Research, 1975
Descriptors: Correlation, Factor Analysis, Matrices, Sampling

Barchard, Kimberly A.; Hakstian, A. Ralph – Multivariate Behavioral Research, 1997
Two studies, both using Type 12 sampling, are presented in which the effects of violating the assumption of essential parallelism in setting confidence intervals are studied. Results indicate that as long as data manifest properties of essential parallelism, the two methods studied maintain precise Type I error control. (SLD)
Descriptors: Error of Measurement, Robustness (Statistics), Sampling, Statistical Analysis

Hall, Charles E. – Multivariate Behavioral Research, 1974
Descriptors: Analysis of Variance, Comparative Analysis, Correlation, History

Buss, Allan R. – Multivariate Behavioral Research, 1975
The procedures involve a planned data gathering strategy consisting of at least two different groups, each receiving two different test batteries. A combination of Tucker's interbattery technique and congruence measures was the recommended strategy. Limitations of the concept of factor invariance are briefly discussed. (Author/BJG)
Descriptors: Comparative Analysis, Data Collection, Factor Analysis, Measurement Techniques

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

Hummel, Thomas J.; Feltovich, Paul J. – Multivariate Behavioral Research, 1975
Monte Carlo methods were used to investigate the robustness of techniques used in judging the magnitude of a sample correlation coefficient when observations are correlated. Empirical distributions of r, t, and Fisher's z were generated. A technique for controlling error rates in certain situations is suggested. (Author/BJG)
Descriptors: Computer Science, Correlation, Error Patterns, Monte Carlo Methods

Dudzinski, M. L.; And Others – Multivariate Behavioral Research, 1975
Descriptors: Comparative Analysis, Correlation, Factor Analysis, Homogeneous Grouping
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