This article focuses on steps in conducting empirical-analytic research and the problems of controlling for or estimating three sources of error: the amount of measurement error, research design error, and the amount of statistical or sampling error. (Author/CT)

Meta-analysis has become an important supplement to traditional methods of research reviewing, although many problems must be addressed by the reviewer who carries out a meta-analysis. These problems include identifying and obtaining appropriate studies, extracting estimates of effect size from the studies, coding or classifying studies, analyzing…

For the conditions investigated in the study, the parametric ANCOVA was typically the procedure of choice both as a test of equality of conditional means and as a test of equality of conditional distributions. (Author/LMO)

Statistics are an essential tool for making proper judgement decisions. It is concerned with probability distribution models, testing of hypotheses, significance tests and other means of determining the correctness of deductions and the most likely outcome of decisions. Measures of central tendency include the mean, median and mode. A second…

The wide availability of large amounts of inexpensive computing power has encouraged statisticians to explore many approaches to a basis for inference. This paper presents one such "computer-intensive" approach: the bootstrap of Bradley Efron. This methodology fits between the cases where it is assumed that the form of the distribution…

The nature and appropriate application of the technique of multivariate analysis are discussed. More specifically, the intent of the paper is to demystify and explain the use of multivariate analysis as well as provide guidelines for selection of the most effective statistics for use in specific situations. For the purpose of this paper, the term…

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)

Rules, procedures, and algorithms intended to aid researchers and practitioners in the application of generalizability theory to a broad range of measurement problems are presented. Two examples of measurement research are G studies, which examine the dependability of some general measurement procedure; and D studies, which provide the data for…

Addresses three issues concerning the assessment of the overrating and underrating of academic competence: (1) the impossibility of separating effects of overrating and underrating from effects of perceived and actual competence; (2) the questionable validity of Connell and Ilardi's method; and (3) the proposal of a new method and its implications…

Failure to consider errors of measurement when using partial correlation or analysis of covariance techniques can result in erroneous conclusions. Certain aspects of this problem are discussed and particular attention is given to issues raised in a recent article by Brewar, Campbell, and Crano. (Author)

A broad range of strategies in sampling, measurement, design, execution, implementation, and analysis are examined. Specific strategies are suggested that tend to be successful in the real world of psychiatric clinical research. (Author)

This study investigated seven methods for analyzing multivariate group differences. Bonferroni t statistics, multivariate analysis of variance (MANOVA) followed by analysis of variance (ANOVA), and five other methods were studied using Monte Carlo methods. Methods were compared with respect to (1) experimentwise error rate; (2) power; (3) number…

Thirty-four papers on educational statistics which were presented at the 1971 AERA Conference are summarized. Six major interest areas are covered: (a) general information; (b) non-parametric methods; (c) errors of measurement and correlation techniques; (d) regression theory; (e) univariate and multivariate analysis; (f) factor analysis. (MS)