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Peer reviewedBrown, Bruce L.; Harshbarger, Thad R. – Educational and Psychological Measurement, 1976
A test is developed for hypotheses about the grand mean in the analysis of variance, using the known relationship between the t distribution and the F distribution with 1 df (degree of freedom) for the numerator. (Author/RC)
Descriptors: Analysis of Variance, Hypothesis Testing, Statistical Significance
Peer reviewedLevy, Kenneth J.; And Others – Educational and Psychological Measurement, 1978
It is suggested that Levy's procedure for testing predicted trends in independent correlations should not produce grossly incorrect inferences when one is dealing with the sorts of correlations which are ordinarily encountered in empirical research. (Author/JKS)
Descriptors: Correlation, Hypothesis Testing, Research Design, Trend Analysis
Peer reviewedShine, Lester C., II – Educational and Psychological Measurement, 1978
Some recent developments for the Shine-Bower single-subject analysis of variance (ANOVA) and the Shine Combined ANOVA are integrated in order to remove the restriction of an even number of trials for the Shine Combined ANOVA. (Author/JKS)
Descriptors: Analysis of Variance, Hypothesis Testing, Nonparametric Statistics
Peer reviewedWarner, Lyle G.; Gray, Louis – Educational and Psychological Measurement, 1978
The Koppa coefficient is a measure of association between two variables which have been measured dichotomously. Significance tests for comparing Koppa coefficients from multiple samples are presented. (JKS)
Descriptors: Correlation, Hypothesis Testing, Nonparametric Statistics, Statistical Significance
Peer reviewedJennrich, Robert I. – Psychometrika, 1978
Under mild assumptions, when appropriate elements of a factor loading matrix are specified to be zero, all orthogonally equivalent matrices differ at most by column sign changes. A variety of results are given here for the more complex case in which the specified values are not necessarily zero. (Author/JKS)
Descriptors: Factor Analysis, Hypothesis Testing, Matrices, Orthogonal Rotation
Peer reviewedCoons, David F. – Educational and Psychological Measurement, 1978
A concise method for computing areas under the normal curve using only functions typically found on desk and hand calculators is given. One version of this method will give three-decimal-pace accuracy, and a second, simpler version gives accuracy to two places. (Author/JKS)
Descriptors: Computation, Hypothesis Testing, Statistical Data, Tables (Data)
Peer reviewedPohl, Norval F.; Tsai, San-Yun W. – Educational and Psychological Measurement, 1978
The nature of the approximate chi-square test for hypotheses concerning multinomial probabilities is reviewed. Also, a BASIC computer program for calculating the sample size necessary to control for both Type I and Type II errors in chi-square tests for hypotheses concerning multinomial probabilities is described.
Descriptors: Computer Programs, Hypothesis Testing, Research Design, Sampling
Peer reviewedFleiss, Joseph L.; Cicchetti, Domenic V. – Applied Psychological Measurement, 1978
The accuracy of the large sample standard error of weighted kappa appropriate to the non-null case was studied by computer simulation for the hypothesis that two independently derived estimates of weighted kappa are equal, and for setting confidence limits around a single value of weighted kappa. (Author/CTM)
Descriptors: Correlation, Hypothesis Testing, Nonparametric Statistics, Reliability
Peer reviewedHuynh, Huynh – Psychometrika, 1978
Four approximate statistical tests are considered for repeated measurement designs in which observations are multivariate normal with arbitrary variance-covariance matrices. (Author/JKS)
Descriptors: Analysis of Variance, Hypothesis Testing, Research Design
Peer reviewedBerry, Kenneth J.; And Others – Educational and Psychological Measurement, 1977
A FORTRAN program, GAMMA, computes Goodman and Kruskal's coefficient of ordinal association, gamma, and Somer's coefficient. The program also provides associated standard errors, standard scores, and probability values. (Author/JKS)
Descriptors: Computer Programs, Correlation, Hypothesis Testing, Statistical Analysis
A Program for Calculating the Exact Probability Along with Explorations of M by N Contingency Tables
Peer reviewedFleishman, Allen I. – Educational and Psychological Measurement, 1977
A computer program for testing the association of two nominal variables using Fisher's exact probability test is described. Theoretical corrections according to recent literature are made. Various methods of discovering the source of association at the cellwise level are given. (Author/JKS)
Descriptors: Computer Programs, Hypothesis Testing, Probability, Tables (Data)
Peer reviewedHsu, Louis – Educational and Psychological Measurement, 1977
A statistic for testing monotonic trend that has been presented in the literature is shown not to be the binomial random variable it is contended to be, but rather it is linearly related to Kendall's tau statistic. (JKS)
Descriptors: Correlation, Hypothesis Testing, Nonparametric Statistics, Trend Analysis
Peer reviewedThomas, Hoben – Psychometrika, 1977
Individuals are classified in a cross-classification table where two behavioral observations on each individual determine the classification. The problem is to test certain structural models assumed to underlie the cross-classified observations. A minimum chi-square test procedure is proposed. (Author)
Descriptors: Classification, Cognitive Development, Growth Patterns, Hypothesis Testing
Peer reviewedRamsay, J. O. – Psychometrika, 1977
A variety of distributional assumptions for dissimilarity judgments in multidimensional scaling are considered, with the lognormal distribution being favored for most situations. Procedures for maximum likelihood estimation in this setting are described and examples are presented. (Author/JKS)
Descriptors: Hypothesis Testing, Maximum Likelihood Statistics, Multidimensional Scaling
Peer reviewedHubert, Lawrence J.; Baker, Frank B. – Journal of Educational Statistics, 1977
A statistical technique is proposed for comparing an empirically obtained matrix of the perceived similarity of paired stimuli against a set of distinctive features that supposedly characterize the stimuli on which the matrix is based. The statistical development of the technique and an example are presented. (Author/JKS)
Descriptors: Cues, Hypothesis Testing, Matrices, Nonparametric Statistics


