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Muth, Chelsea; Bales, Karen L.; Hinde, Katie; Maninger, Nicole; Mendoza, Sally P.; Ferrer, Emilio – Educational and Psychological Measurement, 2016

Unavoidable sample size issues beset psychological research that involves scarce populations or costly laboratory procedures. When incorporating longitudinal designs these samples are further reduced by traditional modeling techniques, which perform listwise deletion for any instance of missing data. Moreover, these techniques are limited in their…

Descriptors: Sample Size, Psychological Studies, Models, Statistical Analysis

Peer reviewed

Hinkle, Dennis E.; Oliver, J. Dale – Educational and Psychological Measurement, 1983

In this paper, tables for the appropriate sample sizes are presented and discussed in the context that the determination of the effect size must precede the determination of the sample size. (Author/PN)

Descriptors: Effect Size, Research Methodology, Research Needs, Research Problems

Peer reviewed

Aiken, Lewis R. – Educational and Psychological Measurement, 1985

Three numerical coefficients for analyzing the validity and reliability of ratings are described. Each coefficient is computed as the ratio of an obtained to a maximum sum of differences in ratings. The coefficients are also applicable to the item analysis, agreement analysis, and cluster or factor analysis of rating-scale data. (Author/BW)

Descriptors: Computer Software, Data Analysis, Factor Analysis, Item Analysis

Peer reviewed

Terrell, Colin D. – Educational and Psychological Measurement, 1982

Tables are presented giving the critical values of the biserial and the point biserial correlation coefficients (when the null hypothesis assumes a value of zero for the coefficient) at the 0.05 and the 0.01 levels of significance. (Author)

Descriptors: Correlation, Mathematical Formulas, Probability, Research Tools

Peer reviewed

Terrell, Colin D. – Educational and Psychological Measurement, 1982

A table is presented that directly converts any known point biserial coefficient to the biserial coefficient, providing the largest proportion of the dichotomous variable is also known. (Author)

Descriptors: Computation, Correlation, Data Analysis, Mathematical Formulas

Peer reviewed

Krus, David J.; Liang, Kun-Hsia T. – Educational and Psychological Measurement, 1984

An algorithm for estimation of means and standard deviations or raw scores underlying table values of standard test scores is presented. An application of a computer program, operationalizing the suggested algorithm, is discussed within the framework of a comparison of estimated and known table values for sample Minnesota Multiphasic Personality…

Descriptors: Algorithms, Estimation (Mathematics), Scores, Standardized Tests

Peer reviewed

Cziko, Gary A. – Educational and Psychological Measurement, 1984

Some problems associated with the criteria of reproducibility and scalability as they are used in Guttman scalogram analysis to evaluate cumulative, nonparametric scales of dichotomous items are discussed. A computer program is presented which analyzes response patterns elicited by dichotomous scales designed to be cumulative. (Author/DWH)

Descriptors: Scaling, Statistical Analysis, Test Construction, Test Items

Peer reviewed

Wilcox, Rand R. – Educational and Psychological Measurement, 1980

When analyzing a squared multiple correlation coefficient, an investigator may be interested in determining whether it is above or below a known constant, rather than testing the null hypothesis. This paper gives the sample sizes required for answering this question when indifference zone formulation of the problem is used. (Author/BW)

Descriptors: Correlation, Hypothesis Testing, Sampling

Peer reviewed

Vegelius, Jan – Educational and Psychological Measurement, 1981

The G index is a measure of the similarity between individuals over dichotomous items. Some tests for the G-index are described. For each case an example is included. (Author/GK)

Descriptors: Hypothesis Testing, Mathematical Formulas, Mathematical Models, Nonparametric Statistics

Tables for Determining the Minimum Incremental Significance of the Multiple Correlation Coefficient.

Peer reviewed

Dutoit, Eugene F.; Penfield, Douglas A. – Educational and Psychological Measurement, 1979

Assuming a multiple linear regression model with q independent variables, a procedure is developed for determining the minimum statistically significant increase in the multiple correlation coefficient when an additional independent variable is considered for regression. The procedure is presented analytically and in table form. Examples are…

Descriptors: Correlation, Multiple Regression Analysis, Predictor Variables, Tables (Data)

Peer reviewed

Zalinski, James; And Others – Educational and Psychological Measurement, 1979

A set of tables for determining tetrachoric correlations is presented. The coefficients have been determined to three-decimal accuracy. (JKS)

Descriptors: Correlation, Expectancy Tables, Nonparametric Statistics, Tables (Data)

Peer reviewed

Roth, Gary L.; Daniel, Wayne W. – Educational and Psychological Measurement, 1978

A table of critical values is presented for use with Chacko's ordered alternatives test for the analysis of variance. (Author/JKS)

Descriptors: Analysis of Variance, Hypothesis Testing, Research Design, Statistical Data