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Wiens, Stefan; Nilsson, Mats E. – Educational and Psychological Measurement, 2017
Because of the continuing debates about statistics, many researchers may feel confused about how to analyze and interpret data. Current guidelines in psychology advocate the use of effect sizes and confidence intervals (CIs). However, researchers may be unsure about how to extract effect sizes from factorial designs. Contrast analysis is helpful…
Descriptors: Data Analysis, Effect Size, Computation, Statistical Analysis
Grice, James W.; Yepez, Maria; Wilson, Nicole L.; Shoda, Yuichi – Educational and Psychological Measurement, 2017
An alternative to null hypothesis significance testing is presented and discussed. This approach, referred to as observation-oriented modeling, is centered on model building in an effort to explicate the structures and processes believed to generate a set of observations. In terms of analysis, this novel approach complements traditional methods…
Descriptors: Hypothesis Testing, Models, Observation, Statistical Inference
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

Wilcox, Rand R. – Educational and Psychological Measurement, 1983
When comparing k normal populations an investigator might want to know the probability that the population with the largest population mean will have the largest sample mean. This paper describes and illustrates methods of approximating this probability when the variances are unknown and possibly unequal. (Author/BW)
Descriptors: Data Analysis, Hypothesis Testing, Mathematical Formulas, Probability

Huck, Schuyler W.; Malgady, Robert G. – Educational and Psychological Measurement, 1978
The computation of F ratios from cell means and standard deviations is shown for a two-way analysis of variance. (JKS)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Mathematical Models

Hopkins, Kenneth D. – Educational and Psychological Measurement, 1983
A general analysis strategy is proposed such that the universe of inference is increased incrementally. The strategy prevents logically incongruent findings that occasionally result when the conventional analysis strategy is employed. (Author)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Research Design

Karpman, Mitchell B. – Educational and Psychological Measurement, 1983
When homogeneity of slopes is not present, the Johnson-Neyman technique has been considered as an alternative to analysis of covariance. This paper describes how to apply the Johnson-Neyman technique for one or two covariates using the Statistical Package for the Social Sciences (SPSS) or BMDP (Biomedical Computer Programs). (Author/BW)
Descriptors: Analysis of Covariance, Computer Programs, Data Analysis, Hypothesis Testing

Elshout, Jan; And Others – Educational and Psychological Measurement, 1979
It has been shown that the degree of restriction of range taken into account in testing the hypothesis that rho equals zero, entails risks of incorrect inferences. It is argued that an alternative is to disregard the restriction of range and to use the common t-statistics proposed by regression theory. (Author/JKS)
Descriptors: Correlation, Data Analysis, Hypothesis Testing, Multiple Regression Analysis

Rotton, James; Schonemann, Peter H. – Educational and Psychological Measurement, 1978
In numerous applications of the analysis of variance, it is necessary to compute the power of F tests having numerically high alpha (significance) levels. This article tabulates the power of F tests for a variety of degrees of freedom. (Author/JKS)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Power (Statistics)

Huberty, Carl J.; Holmes, Susan E. – Educational and Psychological Measurement, 1983
An alternative analysis of the two-group single response variable design is proposed. It involves the classification of experimental units to populations represented by the two groups. Three real data sets are provided to illustrate the utility of the classification analysis. A table of sample sizes required for the analysis is presented.…
Descriptors: Classification, Data Analysis, Hypothesis Testing, Research Design

Renner, Barbara Rochen; Ball, Donald W. – Educational and Psychological Measurement, 1983
To determine the effect of violating the assumption of homogeneity of covariance for the Tukey Wholly Significant Difference (WSD) test, Monte Carlo simulations varied the number of treatment groups, sample size, and degree of covariance heterogeneity. As covariance heterogeneity was increased, the empirical significance levels increased beyond…
Descriptors: Data Analysis, Hypothesis Testing, Monte Carlo Methods, Research Methodology

Skinner, Harvey A. – Educational and Psychological Measurement, 1977
EXPLORE is a flexible computer program for analyzing multiple data sets. The investigator has the option of focusing on the original variables, or of selecting a reduced rank solution where original variables are summarized by a principal components analysis. (Author/JKS)
Descriptors: Computer Programs, Correlation, Data Analysis, Factor Analysis

Whitney, Douglas R.; Feldt, Leonard S. – Educational and Psychological Measurement, 1973
Descriptors: Data Analysis, Hypothesis Testing, Mathematical Applications, Measurement Techniques

Meyer, Lennart – Educational and Psychological Measurement, 1979
The PM statistical index, which indicates the probability that a person will belong to a particular clinical class, is described. The coefficient is similar to the G index but is easier to compute. An empirical example is presented. (JKS)
Descriptors: Adults, Clinical Diagnosis, Data Analysis, Hypothesis Testing

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
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