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 article describes the development and initial validation of scores from a survey designed to measure teachers’ reported use of technology in their classrooms. Based on data obtained from a sample of approximately 2,000 practicing teachers, factor analytic and correlational methods were used to obtain evidence of the validity of scores derived from responses to the instrument. In addition, analyses of Web and paper versions of the survey suggest relatively minor differences in responses, although the response rates for the paper version were substantially higher. The results were interpreted in terms of the utility of the instrument for measuring the confluence of factors that are critical for inquiry related to technology use in classrooms.

No abstract

Stepwise variable selection, exploratory factor analysis, goodness-of-fit, varimax rotation, statistical bias, selection accuracy, pattern accuracy,

In contrast to prospective power analysis, retrospective power analysis provides an estimate of the statistical power of a hypothesis test after an investigation has been conducted rather than before. In this article, three approaches to obtaining point estimates of power and an interval estimation algorithm are delineated. Previous research on the bias and sampling error of these estimates is briefly reviewed. Finally, an SAS macro that calculates the point and interval estimates is described. The macro was developed to estimate the power of an F test (obtained from analysis of variance, multiple regression analysis, or any of several multivariate analyses), but it may be easily adapted for use with other statistics, such as chi-square tests or t tests.