Advantages of the use of multivariate commonality analysis are discussed and a small data set is used to illustrate the analysis and as a model to enable readers to conduct such an analysis. A noteworthy advantage of commonality analysis is that commonality honors the relationships among variables by determining the degree to which predictors in a…

Bargmann's test for examining the statistical significance of simple structure in factor analysis, an extension of the original significance tables, and the application of the significance test to several noted factor analytic findings are described. Using Bargmann's test, only 2 of 25 factor analytic studies reviewed are considered acceptable.…

A procedure was developed within hypothesis-testing logic that allows researchers to support a hypothesis that has traditionally been the statistical or null hypothesis. Four activities involved in attainment of this goal were discussed: (1) development of statistical logic needed to define the sampling distribution associated with the hypothesis…

The Chi-Square Test for Hierarchical Dependency (THD) is presented. The THD tests the hypothesis that all members of a population who possess a certain skill are a subset of the members who possess another skill. This hypothesis is basic to the writings of several prominent theorists, such as Gagne and Piaget. The THD is designed as an improvement…

Faced with a nonstandard, complicated practical problem in statistical inference, the applied statistician sometimes must use asymptotic approximations in order to compute standard errors and confidence intervals and to test hypotheses. This usually requires that he derive formulas for one or more asymptotic sampling variances (and covariances)…

Three procedures are examined that educational researchers can use to analyze quantitatively the extent to which modifications of specific teacher behaviors lead to changes in student achievement. The task requires decent descriptions of the behaviors, appropriate statistical tools and measures of student outcomes that are worth while examining.…

Although it makes a great deal of sense to attempt to design reading research so as to be able to generalize beyond the particular reading passages used in an experiment, the suggestion that tests of statistical significance are a necessary part of making valid generalizations is nonsense. (HOD)

Indicates that the explanation of what it means to obtain a significant F-ratio in discriminant function analysis, presented by K. E. Anderson in an earlier volume of Science Education, is incorrect. (JR)

Remarkability is introduced as a quantifiable attribute of given data and as a basis upon which one may rationally judge its scientific value. Applications of remarkability theory to various research and statistical problems and procedures are discussed. (DG)

Practical formulas for several analysis of variance (ANOVA) designs and models are presented which make it possible for readers to compute strength of association measures without the use of complete ANOVA tables. (Author/PN)

Points out that 108 studies in the first 25 volumes of AV Communication Review used univariate analysis of variance to test experimental hypotheses, and suggests that researchers include omega-squared values in their reports to permit estimation of the relationship between x and y variables, and correlation data for comparing studies. (MER)

Three nonparametric alternatives to the parametric Bartlett test are presented for handling the K-sample equality of variance problem. The two-sample Siegel-Tukey test, Mood test, and Klotz test are extended to the multisample situation by Puri's methods. These K-sample scale tests are illustrated and compared. (Author/GDC)

Hawkin's procedure for testing a sequence of observations for a shift in location could have applicability for assessing change within a single subject. Monte Carlo results suggest that Hawkins' procedure is robust with respect to moderate violations of its underlying assumptions of homogeneity of variance and normality. (Author/GDC)

Errors of misclassification and their effects on categorical data analysis are discussed. The chi-square test for equality of two proportions is examined in the context of errorful categorical data. The effects of such errors are illustrated. A correction procedure is developed and discussed. (Author/MH)