Explains basic logic of canonical analysis, illustrates that canonical analysis is general parametric analytic method subsuming other methods, and offers guidelines regarding use of this analytic approach. Concludes that canonical analysis is potent because it does not require researcher to discard variance of any variables and because it honors…

Conventional canonical methods distinguish between the two variable sets being analyzed, but the methods do not attempt to optimize the variance from a given variable set that will be contained in the final solution. In this respect canonical methods are said the be "symmetric." This paper proposes two non-symmetric, canonical-like…

Virtually all parametric statistical procedures have been shown to be special cases of canonical correlation analysis, which is a useful research methodology particularly when augmented by the calculation of canonical structure, index, and invariance coefficients. A logic for conducting stepwise canonical correlation analysis based upon evaluation…

This paper explains in user-friendly terms why multivariate statistics are so important in educational research. The basic logic of canonical correlation analysis is presented as a simple or bivariate Pearson "r" procedure. It is noted that all statistical tests implicitly involve the calculation of least squares weights, and that all…

The author contends that model misspecification can occur even after researchers have selected the generally most appropriate class of methods, or general linear model techniques. It is suggested specifically that canonical correlation analysis may provide more meaningful results, as compared with regression, particularly if analysis is augmented…

Virtually all parametric statistical procedures have been shown to be special cases of canonical correlation analysis. This article proposes a logic for conducting stepwise canonical correlation analyses, based upon evaluation of canonical communality coefficients. The procedure is a direct analogue of stepwise multiple regression. (Author/RD)

Conventional statistical significance tests do not inform the researcher regarding the likelihood that results will replicate. One strategy for evaluating result replication is to use a "bootstrap" resampling of a study's data so that the stability of results across numerous configurations of the subjects can be explored. This paper…

Selected methods for evaluating the stability of research results empirically are described, especially with regard to multivariate analysis. It is critically important to evaluate the influences of sampling error on obtained results; statistical significance testing does not inform judgment regarding the probable replicability or the sampling…

Dissertations are the cumulative, tangible "best evidence" of interests of doctoral faculty and students in serious and incisive scholarship. Thus, dissertations are thoroughly studied by the program review teams periodically hired by boards of higher education in most states. The present paper explores seven errors in quantitative…

The present paper suggests that multivariate methods ought to be used more frequently in behavioral research and explores the potential consequences of failing to use multivariate methods when these methods are appropriate. The paper explores in detail two reasons why multivariate methods are usually vital. The first is that they limit the…

Although methodological integrity is not the sole determinant of the value of a program evaluation, decision-makers do have a right, at a minimum, to be able to expect competent work from evaluators. This paper explores five areas where evaluators might improve methodological practices. First, evaluation reports should reflect the limited…

Dissertations are an important component of the effort to generate knowledge. Thus, dissertation quality may be seen by accreditation and coordinating-board reviewers as a noteworthy reflection on the quality of doctoral programs themselves. The present study reviews methodological errors within Ph.D. dissertations. The illustrative errors are…