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There is a heightened focus within teacher education to centralize clinical experiences and develop strong partnerships between schools and universities. University field supervisors fulfill a critical role within clinical experiences because they are uniquely situated in spaces where they can help preservice teachers and school-based partners integrate theory and practice. However, historically, field supervision has been devalued within teacher education programs. The purpose of this research was to gain insight into how various teacher education programs are actualizing field experiences and specifically field supervision within this time of reform in teacher education. This survey explored the question, “What is the state of preservice teacher field supervision within elementary teacher education programs?” Findings from this study suggest teacher education programs surveyed are positioned to respond to reforms in the areas of enacting multiple field experiences as well as conceptualizing fiel...

This study analyzed the reporting of multilevel modeling applications of a sample of 99 articles from 13 peer-reviewed journals in education and the social sciences. A checklist, derived from the methodological literature on multilevel modeling and focusing on the issues of model development and specification, data considerations, estimation, and inference, was used to analyze the articles. The most common applications were two-level models where individuals were nested within contexts. Most studies were non-experimental and used nonprobability samples. The amount of data at each level varied widely across studies, as did the number of models examined. Analyses of reporting practices indicated some clear problems, with many articles not reporting enough information for a reader to critique the reported analyses. For example, in many articles, one could not determine how many models were estimated, what covariance structure was assumed, what type of centering if any was used, whether the data were consistent with assumptions, whether outliers were present, or how the models were estimated. Guidelines for researchers reporting multilevel analyses are provided.

Monte Carlo methods were used to examine techniques for constructing confidence intervals around multivariate effect sizes. Using interval inversion and bootstrapping methods, confidence intervals were constructed around the standard estimate of Mahalanobis distance ( D2), two bias-adjusted estimates of D2, and Huberty’s I. Interval coverage and width were examined across conditions by adjusting sample size, number of variables, population effect size, population distribution shape, and the covariance structure. The accuracy and precision of the intervals varied considerably across methods and conditions; however, the interval inversion approach appears to be promising for D2, whereas the percentile bootstrap approach is recommended for the other effect size measures. The results imply that it is possible to obtain fairly accurate coverage estimates for multivariate effect sizes. However, interval width estimates tended to be large and uninformative, suggesting that future efforts might focus on investigating design factors that facilitate more precise estimates of multivariate effect sizes.

The purpose of this study was to investigate and compare the performance of a stepwise variable selection algorithm to traditional exploratory factor analysis. The Monte Carlo study included six factors in the design; the number of common factors; the number of variables explained by the common factors; the magnitude of factor loadings; the number of variables not explained by the common factors; the type of anomaly evidenced by the poorly explained variables; and sample size. The performance of the methods was evaluated in terms of selection and pattern accuracy, and bias and root mean squared error of the structure coefficients. Results indicate that the stepwise algorithm was generally ineffective at excluding anomalous variables from the factor model. The poor selection accuracy of the stepwise approach suggests that it should be avoided.

The relationship between the numbers of computers in schools and student conduct was investigated using school-level data reported to the Department of Education by all Florida school districts for the 1993–1994, 1994–1995, and 1995–1996 school years. Computer use was defined as the total number of computers used for instruction, and student conduct was defined as the number of conduct violations and number of disciplinary actions taken. In addition, school attendance and staff turnover rates were analyzed. Results from the research among Florida schools reporting consistent increased use of computers in instruction revealed: (a) Elementary schools witnessed fewer conduct violations (effect sizes ranged from −0.67 to 0.04) and disciplinary actions (effect sizes ranged from −0.13 to −0.10), (b) middle/junior high schools experienced fewer conduct violations (effect sizes ranged from −0.35 to −0.14) and disciplinary actions (−0.21 to −0.18), and (c) high schools experienced fewer crimes against prop...

The delivery of post-secondary coursework via the Internet continues to gain momentum. As a result, investigations into effective and appropriate methods of evaluating the effectiveness of these courses are required. In an effort to meet this challenge, this study describes the development and implementation of an evaluation system applied to new online programs at a major research university. A systematic approach to evaluation provided formative feedback on the processes and products of course development using diverse data sources including course documents, interviews and web-based surveys. Results of both quantitative and qualitative analyses support the integrity of the evaluation system and provide preliminary indications of course effectiveness based on student satisfaction.

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.