Do teachers differ by certification route? novice teachers' sense of self-efficacy, commitment to teaching, and preparedness to teach

Alternative teacher certification (ATC) programs are one method created to help alleviate teacher shortages (Cox, Matthews, & Assoc, 2001; Hallinan & Khmelkov, 2001). While much debate has arisen over ATC programs, very few have empirically examined their impact on the teaching pool (Darling-Hammond, Berry, & Thoreson, 2001; Darling-Hammond, Chung, & Frelow, 2002; Goldhaber, 2000; Ingersoll, 1999; Shen, 1997, 1999). The present study was designed to explore differences by certification type and program characteristics based on novice teachers' demographics, educational attainment, sense of self-efficacy, and sense of preparedness to enter the classroom. Results from the present study suggest ATC programs are somewhat diversifying the teaching population by bringing in more minorities and science majors, but do not appear to be bringing in more experienced scientists and mathematicians nor do they appear to be alleviating the teacher shortage. In this sample, traditionally certified teachers felt better prepared than ATC teachers with the biggest differences on Promoting Student Learning. Regardless of certification route, prior classroom experience was a strong predictor of Overall Preparedness and a teacher's perception of his or her ability to be an effective teacher. For ATC teachers, a positive mentoring experience was a strong predictor of Overall Preparedness. The discussion of whether or not ATC programs should exist should now be replaced with a discussion of how to ensure that these programs produce better teachers and improve student learning. The underlying theme from the present study was that, in order to feel prepared and have high self-efficacy, novice teachers needed instruction in the majority of the components identified by research and by the National Commission on Teaching and America's Future (1996), including positive mentoring experiences, field based experiences, and curriculum based on child development, learning theory, cognition, motivation, and subject matter pedagogy. Results from the present study support the assertion that teacher preparation programs, program components, mentoring experiences, and field-based experiences do impact teacher effectiveness in the classroom

The Assumption of a Reliable Instrument and Other Pitfalls to Avoid When Considering the Reliability of Data

The purpose of this article is to help researchers avoid common pitfalls associated with reliability including incorrectly assuming that (a) measurement error always attenuates observed score correlations, (b) different sources of measurement error originate from the same source, and (c) reliability is a function of instrumentation. To accomplish our purpose, we first describe what reliability is and why researchers should care about it with focus on its impact on effect sizes. Second, we review how reliability is assessed with comment on the consequences of cumulative measurement error. Third, we consider how researchers can use reliability generalization as a prescriptive method when designing their research studies to form hypotheses about whether or not reliability estimates will be acceptable given their sample and testing conditions. Finally, we discuss options that researchers may consider when faced with analyzing unreliable data

Investigating bias in squared structure coefficients

The importance of structure coefficients and analogs of regression weights for analysis within the general linear model (GLM) has been well-documented. The purpose of this study was to investigate bias in squared structure coefficients in the context of multiple regression and to determine if a formula that had been shown to correct for bias in squared Pearson correlation coefficients and coefficients of determination could be used to correct for bias in squared regression structure coefficients. Using data from a Monte Carlo simulation, this study found that squared regression structure coefficients corrected with Pratt’s formula produced less biased estimates and might be more accurate and stable estimates of population squared regression structure coefficients than estimates with no such corrections. While our findings are in line with prior literature that identified multicollinearity as a predictor of bias in squared regression structure coefficients but not coefficients of determination, the findings from this study are unique in that the level of predictive power, number of predictors, and sample size were also observed to contribute bias in squared regression structure coefficient

All–possible–subsets for MANOVA and factorial MANOVAs: Alternative to the weekend project

Multivariate techniques are increasingly popular as researchers attempt to accurately model a complex world. MANOVA is a multivariate technique used to investigate the dimensions along which groups differ, and how these dimensions may be used to predict group membership. A concern in a MANOVA analysis is to determine if a smaller subset of variables may be used in the classification functions without any loss of explanatory power when precision of parameter estimates or parsimony needs to be addressed (cf. Huberty, 1984; Huberty & Olejnik, 2006). One way to address these concerns is through the use of all possible subsets. However, not all common statistical packages easily facilitate this analysis, and the analysis can be a weekend project (Huberty & Olejnik, 2006). As such, the purpose of the current paper is to examine and demonstrate R and SPSS solutions to conduct an allpossible-subsets MANOVA, including all-possible-subsets factorial MANOVA

Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses

Empirical Reporting Practices in Community College Journal of Research and Practice and Journal of Developmental Education From 2002 to 2011: A Systematic Review

The empirical reporting practices of developmental education and community college researchers who published in the Community College Journal of Research and Practice (CCJRP) and the Journal of Developmental Education (JDE) from 2002 to 2011 were investigated. Of the 1,165 articles available, 181 articles met the inclusion criteria and were subjected to full review. Authors identified the following components in the published research studies: problem formulation, theoretical framework, sources of evidence, measurement, statistical analyses, and figures and correlation matrices. Though more than half of the reviewed article authors provided research questions, more than half of those phrased at least one research question in a dichotomous response format. A theoretical framework was reported only 36.5% of the time. The setting for the majority of the reviewed articles was a two-year college; however, this finding varied by journal type. At least 20% of the reviewed article authors did not report the sampling method. The majority of the reviewed article authors did not use a proprietary instrument. Polynomial trend lines were used to describe the fit of the observed frequency of reported statistical techniques in each of the journals. The values of the r2 were greatest for the t test (CCJRP r2quadratic = 89.8%), ANOVA (CCJRP r2cubic = 75.0%), and regression (CCJRP r2quadratic = 72.6%). The most frequently used analysis across both journals was ANOVA (24.5%). Figures (18.2%) and correlation matrices (8.3%) frequently were not reported. Recommendations for improved empirical reporting practices in developmental education research are presented