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Raykov, Tenko; Marcoulides, George A.; Akaeze, Hope O. – Educational and Psychological Measurement, 2017

This note is concerned with examining the relationship between within-group and between-group variances in two-level nested designs. A latent variable modeling approach is outlined that permits point and interval estimation of their ratio and allows their comparison in a multilevel study. The procedure can also be used to test various hypotheses…

Descriptors: Comparative Analysis, Models, Statistical Analysis, Hierarchical Linear Modeling

Green, Samuel B.; Redell, Nickalus; Thompson, Marilyn S.; Levy, Roy – Educational and Psychological Measurement, 2016

Parallel analysis (PA) is a useful empirical tool for assessing the number of factors in exploratory factor analysis. On conceptual and empirical grounds, we argue for a revision to PA that makes it more consistent with hypothesis testing. Using Monte Carlo methods, we evaluated the relative accuracy of the revised PA (R-PA) and traditional PA…

Descriptors: Accuracy, Factor Analysis, Hypothesis Testing, Monte Carlo Methods

Shear, Benjamin R.; Zumbo, Bruno D. – Educational and Psychological Measurement, 2013

Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

Descriptors: Error of Measurement, Multiple Regression Analysis, Data Analysis, Computer Simulation

Schmitt, Thomas A.; Sass, Daniel A. – Educational and Psychological Measurement, 2011

Exploratory factor analysis (EFA) has long been used in the social sciences to depict the relationships between variables/items and latent traits. Researchers face many choices when using EFA, including the choice of rotation criterion, which can be difficult given that few research articles have discussed and/or demonstrated their differences.…

Descriptors: Hypothesis Testing, Factor Analysis, Correlation, Criteria

Sass, Daniel A. – Educational and Psychological Measurement, 2010

Exploratory factor analysis (EFA) is commonly employed to evaluate the factor structure of measures with dichotomously scored items. Generally, only the estimated factor loadings are provided with no reference to significance tests, confidence intervals, and/or estimated factor loading standard errors. This simulation study assessed factor loading…

Descriptors: Intervals, Simulation, Factor Structure, Hypothesis Testing

Algina, James; Keselman, H. J. – Educational and Psychological Measurement, 2008

Applications of distribution theory for the squared multiple correlation coefficient and the squared cross-validation coefficient are reviewed, and computer programs for these applications are made available. The applications include confidence intervals, hypothesis testing, and sample size selection. (Contains 2 tables.)

Descriptors: Intervals, Sample Size, Validity, Hypothesis Testing

Hirschfeld, Robert R.; Thomas, Christopher H.; McNatt, D. Brian – Educational and Psychological Measurement, 2008

The authors explored implications of individuals' self-deception (a trait) for their self-reported intrinsic and extrinsic motivational dispositions and their actual learning performance. In doing so, a higher order structural model was developed and tested in which intrinsic and extrinsic motivational dispositions were underlying factors that…

Descriptors: Deception, Predictor Variables, Motivation, Incentives

Peer reviewed

Wilson, Gale A.; Martin, Samuel A. – Educational and Psychological Measurement, 1983

Either Bartlett's chi-square test of sphericity or Steiger's chi-square test can be used to test the significance of a correlation matrix to determine the appropriateness of factor analysis. They were evaluated using computer-generated correlation matrices. Steiger's test is recommended due to its increased power and computational simplicity.…

Descriptors: Comparative Analysis, Correlation, Factor Analysis, Hypothesis Testing

Peer reviewed

Haller, Otto; Edgington, Eugene S. – Educational and Psychological Measurement, 1983

A general method for identifying the separate components of the Rod-and-Frame Test consists of correlating theoretical patterns of scores with obtained test scores of single subjects. The correlation test calculates probability values from the test data. In this way, fit can be determined between theoretical pattern and test scores. (Author/BW)

Descriptors: Cognitive Style, Correlation, Goodness of Fit, Hypothesis Testing

Peer reviewed

Huberty, Carl J. – Educational and Psychological Measurement, 1983

The basic notion of variability is generalized from a univariate context to a multivariate context using two matrix functions, a determinant, and a trace, yielding a number of alternative multivariate indices of shared variation. Some problems in the interpretation of tests of multivariate hypotheses are reviewed. (Author/BW)

Descriptors: Analysis of Variance, Correlation, Data Analysis, Hypothesis Testing

Peer reviewed

Strahan, Robert F. – Educational and Psychological Measurement, 1982

While Spearman's rho and Kendall's tau are equally powerful rank-order correlation coefficients under conditions of normality, they have different metrics. Applied to the same data, tau is smaller in absolute value, often no more than two-thirds of the size of rho. This difference in correlational metric appears to need emphasis. (Author/CM)

Descriptors: Correlation, Evaluation Methods, Experimental Psychology, Hypothesis Testing

Peer reviewed

James, Lawrence R.; And Others – Educational and Psychological Measurement, 1982

An analytic procedure is presented which casts sequential moderator analysis in the role of a multivariate test of parallelism of regressions. The procedure addresses a test for comparing predictor-criterion relationships for one set of measurements or multiple predictors and repeated measurements on a criterion. (Author/PN)

Descriptors: Correlation, Hypothesis Testing, Measurement Techniques, Multivariate Analysis

Peer reviewed

Hancock, Gregory R. – Educational and Psychological Measurement, 1997

Methods are offered for conducting hypothesis testing associated with disattenuated validity coefficients to overcome limitations of some other suggested approaches. Through using classical test theory's notion of reliability in the form of structured path models, such hypothesis testing may be done with hierarchically related structural equation…

Descriptors: Correlation, Hypothesis Testing, Reliability, Scores

Peer reviewed

Silver, N. Clayton; Dunlap, William P. – Educational and Psychological Measurement, 1989

A Monte Carlo simulation examined the Type I error rates and power of four tests of the null hypothesis that a correlation matrix equals the identity matrix. The procedure of C. J. Brien and others (1984) was found to be the most powerful test maintaining stable empirical alpha values. (SLD)

Descriptors: Correlation, Hypothesis Testing, Monte Carlo Methods, Power (Statistics)

Peer reviewed

Willson, Victor L. – Educational and Psychological Measurement, 1980

Guilford's average interrater correlation coefficient is shown to be related to the Friedman Rank Sum statistic. Under the null hypothesis of zero correlation, the resultant distribution is known and the hypothesis can be tested. Large sample and tied score cases are also considered. An example from Guilford (1954) is presented. (Author)

Descriptors: Correlation, Hypothesis Testing, Mathematical Formulas, Reliability

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