Only true experiments offer definitive evidence for causal inferences, but not all educational interventions are readily amenable to experiments. Correlational evidence can at least tentatively inform evidence-based practice when sophisticated causal modeling or exclusion methods are employed. Correlational evidence is most informative when…

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…

Use of the bootstrap method in a canonical correlation analysis to evaluate the replicability of a study's results is illustrated. More confidence may be vested in research results that replicate. (SLD)

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…

It is argued that analysis of variance (ANOVA) and related methods should be taught using a general linear model (GLM) approach, rather than a classical ordinary sums of squares approach. The GLM approach emphasizes the linkages among conventional parametric methods, emphasizing that all classical parametric methods are least squares procedures…

Effect sizes have been increasingly emphasized in research as more researchers have recognized that: (1) all parametric analyses (t-tests, analyses of variance, etc.) are correlational; (2) effect sizes have played an important role in meta-analytic work; and (3) statistical significance testing is limited in its capacity to inform scientific…

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…