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Linting, Marielle; van Os, Bart Jan; Meulman, Jacqueline J. – Psychometrika, 2011
In this paper, the statistical significance of the contribution of variables to the principal components in principal components analysis (PCA) is assessed nonparametrically by the use of permutation tests. We compare a new strategy to a strategy used in previous research consisting of permuting the columns (variables) of a data matrix…
Descriptors: Intervals, Simulation, Statistical Significance, Factor Analysis
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Doebler, Anna; Doebler, Philipp; Holling, Heinz – Psychometrika, 2013
The common way to calculate confidence intervals for item response theory models is to assume that the standardized maximum likelihood estimator for the person parameter [theta] is normally distributed. However, this approximation is often inadequate for short and medium test lengths. As a result, the coverage probabilities fall below the given…
Descriptors: Foreign Countries, Item Response Theory, Computation, Hypothesis Testing
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Lazraq, Aziz; Cleroux, Robert – Psychometrika, 2002
Studied the interrelationships between two sets of data measured on the same subjects via redundancy analysis in a simulation study. Under the hypothesis of multinormality, obtained tests of significance for each successive redundancy component so that only the significant factors are retained for prediction purposes. (SLD)
Descriptors: Simulation, Statistical Significance
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Kraemer, Helena Chmura – Psychometrika, 1979
It is demonstrated that tests of homogeneity of independent correlation coefficients based on the simple forms of the normal and t approximations to the distribution of the correlation coefficients are comparable in terms of robustness, size and power. (Author)
Descriptors: Correlation, Sampling, Simulation, Statistical Significance
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Ekbohm, Gunnar – Psychometrika, 1982
The problem of testing two correlated proportions with incomplete data is considered by means of Monte Carlo simulations studies. A test proposed in this paper, which can be regarded as a generalization of McNemar's test, is recommended in all cases with incomplete data and not too small samples. (Author)
Descriptors: Correlation, Hypothesis Testing, Nonparametric Statistics, Statistical Significance
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Kraemer, Helena Chmura – Psychometrika, 1975
A selection of statistical problems commonly encountered in psychological or psychiatric research concerning correlation coefficients are re-evaluated in the light of recently developed simplifications in the forms of the distribution theory of the intraclass correlation coefficient, of the product-moment correlation coefficient, and the Spearman…
Descriptors: Correlation, Hypothesis Testing, Nonparametric Statistics, Statistical Significance
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Marcus, Ruth – Psychometrika, 1978
A general method of devising stepwise multiple testing procedures with fixed experimentwise error is applied to the problem of non-parametric randomized block design with ordered alternatives. In addition, the method is applied to other models with ordered alternatives. (Author)
Descriptors: Hypothesis Testing, Nonparametric Statistics, Statistical Analysis, Statistical Significance
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Levy, Kenneth J. – Psychometrika, 1974
Descriptors: Analysis of Variance, Hypothesis Testing, Models, Sampling
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D'Agostino, Ralph B.; Rosman, Bernard – Psychometrika, 1971
Descriptors: Hypothesis Testing, Research Methodology, Statistical Analysis, Statistical Significance
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Thomas, Hoben – Psychometrika, 1981
Psychophysicists neglect to consider how error should be characterized in applications of the power law. Failures of the power law to agree with certain theoretical predictions are examined. A power law with lognormal product structure is proposed and approximately unbiased parameter estimates given for several common estimation situations.…
Descriptors: Mathematical Models, Power (Statistics), Psychophysiology, Statistical Bias
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Kearns, Jack; Meredith, William – Psychometrika, 1975
Examines the question of how large a sample must be in order to produce empirical Bayes estimates which are preferable to other commonly used estimates, such as proportion correct observed score. (Author/RC)
Descriptors: Bayesian Statistics, Item Analysis, Probability, Sampling
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Overall, John E.; Woodward J. Arthur – Psychometrika, 1974
A procedure for testing heterogeneity of variance is developed which generalizes readily to complex, multi-factor experimental designs. Monte Carlo studies indicate that the Z-variance test statistic presented here yields results equivalent to other familiar tests for heterogeneity of variance in simple one-way designs where comparisons are…
Descriptors: Analysis of Variance, Hypothesis Testing, Research Design, Sampling
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Steiger, James H.; Browne, Michael W. – Psychometrika, 1984
A general procedure is provided for comparing correlation coefficients between optimal linear composites. It allows computationally efficient significance tests on independent or dependent multiple correlations, partial correlations, and canonical correlations, with or without the assumption of multivariate normality. Evidence from Monte Carlo…
Descriptors: Correlation, Hypothesis Testing, Monte Carlo Methods, Statistical Distributions
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Timm, Neil H.; Carlson, James E. – Psychometrika, 1976
Extending the definitions of part and bipartial correlation to sets of variates, the notion of part and bipartial canonical correlation analysis are developed and illustrated. (Author)
Descriptors: Correlation, Hypothesis Testing, Matrices, Multivariate Analysis
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Feldt, Leonard S. – Psychometrika, 1980
Procedures are developed for testing the hypothesis that Cronbach's alpha reliability coefficient is equal for two tests given to the same subjects. (Author/JKS)
Descriptors: Error of Measurement, Hypothesis Testing, Measurement, Statistical Significance
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