Purpose: Teachers’ satisfaction with their jobs has reached the lowest point in 25 years. One contributing factor is when teachers experience information-poor hiring processes and do not obtain an accurate preview of their positions, their person–organization (P-O) fit, and person–job (P-J) fit. Sparked by a renewed focus on the variables that can influence teacher satisfaction, the purpose of this study was to examine the relationships among accurate job preview, P-O and P-J fit, and job satisfaction among teachers. Research Approach: Drawing on existing literature, a mediation model was hypothesized. Using existing data collected by the Center for Research, Evaluation, and Advancement of Teacher Education, a structural equation model was tested with a sample of 729 newly hired teachers. Specifically addressed was the extent to which P-O and P-J fit mediated the relationship between accurate job preview and satisfaction. Findings: Accurate job preview predicted future P-O and P-J fit. Higher levels of P-O and P-J fit were linked to higher teacher satisfaction rates. Accurate job previews worked through P-J fit and P-O fit to result in increased teacher satisfaction. Additionally, 53.3% of the variance in satisfaction with the campus was explained by the model. Implications for Research and Practice: Providing newly hired teachers with accurate job previews was related to higher satisfaction rates, so school and district leaders should consider ways to increase candidates’ knowledge during the hiring process about specific school settings and students’ needs.
The purpose of the present study is to provide a historical account and metasynthesis of which statistical techniques are most frequently used in the fields of education and psychology. Six articles reviewing the American Educational Research Journal from 1969 to 1997 and five articles reviewing the psychological literature from 1948 to 2001 resulted in a total number of 17,698 techniques recorded from the 12,012 articles reviewed. No prior study of analytic practices has considered this broad scope of time and articles. Trends are discussed for the education and psychology literature both individually and collectively.
Previous simulation research has focused on evaluating the impact of analytic assumption violations on statistics related to the F test and associated p CALCULATED values. The present article evaluated the bias of classical estimates of practical significance (i.e., effect size sample estimators [Formula: see text], [Formula: see text], and [Formula: see text]) in a one-way between-subjects univariate ANOVA when assumptions are violated. The simulation conditions modeled were selected on the basis of prior empirical research. Estimated (1) sampling error bias and (2) precision computed for each of the three effect size estimates for the 5,000 samples drawn for each of the 270 (5 parameter Cohen's d values × 3 group size ratios × 3 population distribution shapes × 3 variance ratios × 2 total ns) conditions were modeled for each of the k = 2, 3, and 4 group analyses. Our results corroborate the limited previous related research and suggest that [Formula: see text] should not be used as an ANOVA effect size estimator, even though [Formula: see text] is the only available choice in the menus in most commonly available software.
The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.
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