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The present paper discusses criticisms of statistical significance testing from both historical and contemporary perspectives. Statistical significance testing is greatly influenced by sample size and often results in meaningless information being over-reported. Variance-accounted-for-effect sizes are presented as an alternative to statistical significance testing. A review of the "Journal of Clinical Psychology" (1993) reveals a continued reliance on statistical significance testing on the part of researchers. Finally, scatterplots and correlation coefficients are presented to illustrate the lack of linear relationship between sample size and effect size. Two figures are included. (Contains 24 references.) (Author)

First- and higher-order factor analyses are explained from a conceptual rather than a mathematical perspective. A case is made for performing higher-order factor analysis when factors are theoretically related. Actual scores of 301 children on 24 ability measures are used to demonstrate interpretation of second-order factors using the FORTRAN program SECONDOR. Higher-order factor analysis using interpretation aids such as the Schmid-Leiman (1957) solution allows the researcher to examine a complex world in a parsimonious manner. Seven tables illustrate the discussion. (Contains 11 references.) (Author/SLD)