Aptitude-treatment interaction (ATI) studies have been used with some frequency, yet many researchers do not understand fully what interaction effects are. Because the means for interactions involve fewer persons per mean, power to detect interaction effects is typically smallest for the highest-order interaction in a given design. This phenomenon…

Intended for use on Conversational Monitor System (CMS), the Tukey FORTRAN program facilitates pairwise comparisons among means following a significant Fratio in an analysis of variance. Tukey's statistic can be obtained by entering information regarding the design and analysis of variance results. Two variations are also available. (Author/BS)

Details are given for the use of the mixed effects multivariate analysis of variance table provided by the BMD12V computer program to compute raw generalized variances and hence the U and F statistics for the mixed effects model. (Author/JKS)

This paper explains the underlying assumptions of the sampling distribution and its role in significance testing. To compute statistical significance, estimates of population parameters must be obtained so that only one sampling distribution is defined. A sampling distribution is the underlying distribution of a statistic. Sampling distributions…

Focuses on Binomial Effect Size Display (a concept helpful in interpreting size of an experimental effect). Suggests that both statistical significance and explained variance be reported in experimental research and that values for the latter concepts can be viewed with a more accurate perspective by using BESD. (Author/JN)

Explains statistical significance so reference librarians can better understand users' information needs who ask statistics-related reference questions. Highlights include the t-test; bell curve, or Central Limit Theorem; reference interview theory; and analysis of variance. (LRW)

As an extension of B. Thompson's 1998 invited address to the American Educational Research Association, this paper cites two additional common faux pas in research methodology and explores some research issues for the future. These two errors in methodology are the use of univariate analyses in the presence of multiple outcome variables (with the…

The various costs of Type I and Type II errors of inference from data are discussed. Six methods for minimizing each error type are presented, which may be employed even after data collection for Type I and which minimizes Type II errors by a study design and analytical means combination. (Author/CM)

Constructs a model that explains a large fraction of the variance in political science departmental rankings. Divides the objective predictors into two sets: one reflecting faculty quality ratings of department members, the other the effects of circumstances beyond a department's control. This model works well with most social science disciplines.…

Asserts that variance between a university's reputation as an institution and its commitment to research have a greater impact on political science department rankings than any internal factors within the department. Includes several tables showing statistical variables of department and university rankings. (MJP)

Analyzes the National Research Council's rating of political science departments and discovers the ratings reflect two general sets of characteristics, the size and productivity of the faculty. Reveals that the quality and impact of faculty research is more important than the overall output. Includes tables of statistical data. (MJP)