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Hwang, Heungsun; Dillon, William R. – Multivariate Behavioral Research, 2010
A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which "k"-means is…
Descriptors: Data Analysis, Multivariate Analysis, Classification, Monte Carlo Methods
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Steyn, H. S., Jr.; Ellis, S. M. – Multivariate Behavioral Research, 2009
When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…
Descriptors: Effect Size, Multivariate Analysis, Computation, Monte Carlo Methods
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Ruscio, John; Kaczetow, Walter – Multivariate Behavioral Research, 2008
Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate…
Descriptors: Monte Carlo Methods, Correlation, Sampling, Multivariate Analysis
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Stadnytska, Tetiana; Braun, Simone; Werner, Joachim – Multivariate Behavioral Research, 2008
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…
Descriptors: Models, Identification, Multivariate Analysis, Correlation
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Barcikowski, Robert S.; Stevens, James P. – Multivariate Behavioral Research, 1975
Results showed that the canonical correlations are very stable upon replication. The results also indicated that there is no solid evidence for concluding that components are superior to the coefficients, at least not in terms of being more reliable. (Author/BJG)
Descriptors: Correlation, Factor Analysis, Matrices, Monte Carlo Methods
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Mendoza, Jorge L.; And Others – Multivariate Behavioral Research, 1978
Four testing procedures for establishing the number of non-zero population roots in canonical analysis are investigated. Results of a Monte Carlo study indicate that three well-established procedures were effective, and a new procedure designed to correct a supposed flaw in the other procedures was ineffective. (JKS)
Descriptors: Correlation, Hypothesis Testing, Monte Carlo Methods, Multivariate Analysis
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Thorndike, Robert M. – Multivariate Behavioral Research, 1976
In their Monte Carlo study of canonical analysis, Barcikowski and Stevens evaluated the relative stability of canonical weights and loadings. This paper identifies some weaknesses in their study, suggests directions for future research in this area, and discusses interpretation of canonical analysis both in development and in cross-validation. For…
Descriptors: Correlation, Measurement Techniques, Monte Carlo Methods, Multivariate Analysis
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Barcikowski, Robert S.; Stevens, James P. – Multivariate Behavioral Research, 1976
This article is a rejoinder to TM 502 249. Each of Thorndike's comments are examined. A possible solution to the large number of subjects necessary for stable weights and variate-variable correlations using ridge regression procedures is suggested. (RC)
Descriptors: Correlation, Measurement Techniques, Monte Carlo Methods, Multivariate Analysis
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Vallejo, Guillermo; Ato, Manuel – Multivariate Behavioral Research, 2006
The standard univariate and multivariate methods are conventionally used to analyze continuous data from groups by trials repeated measures designs, in spite of being extremely sensitive to departures from the multisample sphericity assumption when group sizes are unequal. However, in the last 10 years several authors have offered alternative…
Descriptors: Interaction, Multivariate Analysis, Monte Carlo Methods, Least Squares Statistics
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Pavur, Robert; Nath, Ravinder – Multivariate Behavioral Research, 1989
A Monte Carlo simulation study compared the power and Type I errors of the Wilks lambda statistic and the statistic of M. L. Puri and P. K. Sen (1971) on transformed data in a one-way multivariate analysis of variance. Preferred test procedures, based on robustness and power, are discussed. (SLD)
Descriptors: Comparative Analysis, Mathematical Models, Monte Carlo Methods, Multivariate Analysis
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Cohen, Jacob; Nee, John C. M. – Multivariate Behavioral Research, 1990
The analysis of contingency tables via set correlation allows the assessment of subhypotheses involving contrast functions of the categories of the nominal scales. The robustness of such methods with regard to Type I error and statistical power was studied via a Monte Carlo experiment. (TJH)
Descriptors: Computer Simulation, Monte Carlo Methods, Multivariate Analysis, Power (Statistics)
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Everitt, B. S. – Multivariate Behavioral Research, 1981
Results show that the proposed sampling distribution of the test appears to be appropriate only for sample sizes above 50, and for data where the sample size is 10 times the number of variables. For such cases the power of the test is found to be fairly low. (Author/RL)
Descriptors: Mathematical Formulas, Maximum Likelihood Statistics, Monte Carlo Methods, Multivariate Analysis
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Maraun, Michael D.; Slaney, Kathleen – Multivariate Behavioral Research, 2005
MAXCOV-HITMAX was invented by Paul Meehl as a tool for the detection of latent taxonic structures (i.e., structures in which the latent variable, u, is not continuously, but rather Bernoulli, distributed). It involves the examination of the shape of a certain conditional covariance function and is based on Meehl's claims that (R1) Taxonic…
Descriptors: Multivariate Analysis, Hypothesis Testing, Monte Carlo Methods, Behavioral Science Research
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Spiegel, Douglas K. – Multivariate Behavioral Research, 1986
Tau, Lambda, and Kappa are measures developed for the analysis of discrete multivariate data of the type represented by stimulus response confusion matrices. The accuracy with which they may be estimated from small sample confusion matrices is investigated by Monte Carlo methods. (Author/LMO)
Descriptors: Mathematical Models, Matrices, Monte Carlo Methods, Multivariate Analysis
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Blair, R. Clifford; And Others – Multivariate Behavioral Research, 1994
Multivariate permutation tests are described, and some are suggested as substitutions for Hotelling's one-sample T2 test in common situations in behavioral science research. A Monte Carlo study shows advantages of these tests when the T2 test fails or is suspect. (SLD)
Descriptors: Behavioral Science Research, Correlation, Graphs, Hypothesis Testing
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