A Monte Carlo simulation study investigated the effect of scoring format, item parameterization, threshold configuration, and prior ability distribution on the accuracy of ability estimation given various IRT models. Item response data on 30 items from 1,000 examinees was simulated using known item parameters and ability estimates. The item response data sets were submitted to seven dichotomous or polytomous IRT models with different item parameterization to estimate examinee ability. The accuracy of the ability estimation for a given IRT model was assessed by the recovery rate and the root mean square errors. The results indicated that polytomous models produced more accurate ability estimates than the dichotomous models, under all combinations of research conditions, as indicated by higher recovery rates and lower root mean square errors. For the item parameterization models, the one-parameter model out-performed the two-parameter and three-parameter models under all research conditions. Among the polytomous models, the partial credit model had more accurate ability estimation than the other three polytomous models. The nominal categories model performed better than the general partial credit model and the multiple-choice model with the multiple-choice model the least accurate. The results further indicated that certain prior ability distributions had an effect on the accuracy …

How to identify effective teachers who improve student achievement despite diverse student populations and school contexts is an ongoing discussion in public education. The need to show communities and parents how well teachers and schools improve student learning has led districts and states to seek a fair, equitable and valid measure of student growth using student achievement. This study investigated a two stage hierarchical model for estimating teacher effect on student achievement. This measure was entitled a Classroom Effectiveness Index (CEI). Consistency of this model over time, outlier influences in individual CEIs, variance among CEIs across four years, and correlations of second stage student residuals with first stage student residuals were analyzed. The statistical analysis used four years of student residual data from a state-mandated mathematics assessment (n=7086) and a state-mandated reading assessment (n=7572) aggregated by teacher. The study identified the following results. Four years of district grand slopes and grand intercepts were analyzed to show consistent results over time. Repeated measures analyses of grand slopes and intercepts in mathematics were statistically significant at the .01 level. Repeated measures analyses of grand slopes and intercepts in reading were not statistically significant. The analyses indicated consistent results over time for reading …

The supply of non-physician clinicians (NPCs), such as physician assistant (PAs), could significantly influence demand requirements in medical workforce projections. This study predicts supply of and demand for PAs from 2006 to 2020. The PA supply model utilized the number of certified PAs, the educational capacity (at 10% and 25% expansion) with assumed attrition rates, and retirement assumptions. Gross domestic product (GDP) chained in 2000 dollar and US population were utilized in a transfer function trend analyses with the number of PAs as the dependent variable for the PA demand model. Historical analyses revealed strong correlations between GDP and US population with the number of PAs. The number of currently certified PAs represents approximately 75% of the projected demand. At 10% growth, the supply and demand equilibrium for PAs will be reached in 2012. A 25% increase in new entrants causes equilibrium to be met one year earlier. Robust application trends in PA education enrollment (2.2 applicants per seat for PAs is the same as for allopathic medical school applicants) support predicted increases. However, other implications for the PA educational institutions include recruitment and retention of qualified faculty, clinical site maintenance and diversity of matriculates. Further research on factors affecting …

The distributional properties of improvement-over-chance, I, effect sizes derived from linear and quadratic predictive discriminant analysis (PDA) and from logistic regression analysis (LRA) for the two-group univariate classification were examined. Data were generated under varying levels of four data conditions: population separation, variance pattern, sample size, and prior probabilities. None of the indices provided acceptable estimates of effect for all the conditions examined. There were only a small number of conditions under which both accuracy and precision were acceptable. The results indicate that the decision of which method to choose is primarily determined by variance pattern and prior probabilities. Under variance homogeneity, any of the methods may be recommended. However, LRA is recommended when priors are equal or extreme and linear PDA is recommended when priors are moderate. Under variance heterogeneity, selecting a recommended method is more complex. In many cases, more than one method could be used appropriately.