The purpose of this studywas to investigate the relationship between sample size and the quality of factor solutions obtained from exploratory factor analysis. This research expanded upon the range of conditions previously examined, employing a broad selection of criteria for the evaluation of the quality of sample factor solutions. Results showed that when communalities are high, sample size tended to have less influence on the quality of factor solutions than when communalities are low. Overdetermination of factors was also shown to improve the factor analysis solution. Finally, decisions about the quality of the factor solution depended upon which criteria were examined.

This research is an investigation of the effects of nonrandomly missing data in two-predictor regression analyses and the differences in the effectiveness of five common treatments of missing data on estimates of R2 and of each of the two standardized regression weights. Bootstrap samples of 50, 100, and 200 were drawn from three sets of actual field data. Nonrandomly missing data were created within each sample, and the parameter estimates were compared with those obtained from the same samples with no missing data. The results indicated that three imputation procedures (mean substitution, simple and multiple regression imputation) produced biased estimates of R2 and both regression weights. Two deletion procedures (listwise and pairwise) provided accurate parameter estimates with up to 30% of the data missing.

Empirical techniques to estimate the shrinkage of the sample R2 have been advocated as alternatives to analytical formulae. Although such techniques may be appropriate for estimating the coefficient of cross-validation, they do not provide accurate estimates of the population multiple correlation. The accuracy of four empirical techniques (simple cross-validation, multi-cross-validation, jackknife, and bootstrap) were investigated in a Monte Carlo study. Random samples of size 20 to 200 were drawn from a pseudopopulation of actual field data. Regression models were investigated with population coefficients of determination ranging from .04 to .50 and with numbers of regressors ranging from 2 to 10. Substantial statistical bias was evident when the shrunken R2 values were used to estimate the population squared multiple correlation. Researchers are advised to avoid the empirical techniques when the parameter of interest is the population coefficient of determination rather than the coefficient of cross-validation.

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Relationships between the clarity behaviors of teachers and the dual outcome measures of student achievement and satisfaction were examined. Relatively reliable measures of clarity (both of a low-inference and high-inference nature) on 32 preservice teachers who taught the same lesson within a small-group laboratory setting were generated by (a) trained observers, (b) participating students, and (c) the teachers themselves. The high and relatively lowinference measures of teacher clarity correlated highly, and both were significantly and positively related to postinstructional measures of student achievement and student satisfaction. A number of specific clarity behaviors have been identified that appear to be strongly and directly linked to desirable student outcomes.