The Impact of Misspecifying Class-Specific Residual Variances in Growth Mixture Models

The purpose of this study was to examine the impact of misspecifying a growth mixture model (GMM) by assuming that Level-1 residual variances are constant across classes, when they do, in fact, vary in each subpopulation. Misspecification produced bias in the within-class growth trajectories and variance components, and estimates were substantially less precise than those obtained from a correctly specified GMM. Bias and precision became worse as the ratio of the largest to smallest Level-1 residual variances increased, class proportions became more disparate, and the number of class-specific residual variances in the population increased. Although the Level-1 residuals are typically of little substantive interest, these results suggest that researchers should carefully estimate and report these parameters in published GMM applications.     

Multiple Imputation Strategies for Multiple Group Structural Equation Models

Although structural equation modeling software packages use maximum likelihood estimation by default, there are situations where one might prefer to use multiple imputation to handle missing data rather than maximum likelihood estimation (e.g., when incorporating auxiliary variables). The selection of variables is one of the nuances associated with implementing multiple imputation, because the imputer must take special care to preserve any associations or special features of the data that will be modeled in the subsequent analysis. For example, this article deals with multiple group models that are commonly used to examine moderation effects in psychology and the behavioral sciences. Special care must be exercised when using multiple imputation with multiple group models, as failing to preserve the interactive effects during the imputation phase can produce biased parameter estimates in the subsequent analysis phase, even when the data are missing completely at random or missing at random. This study investigates two imputation strategies that have been proposed in the literature, product term imputation and separate group imputation. A series of simulation studies shows that separate group imputation adequately preserves the multiple group data structure and produces accurate parameter estimates.     

The incorporation of health and welfare risks into technological forecasting

The application of the outputs of scientific and technological development to industry snd society brings with it a shift in the pattern and distribution of health and safety risks to worker, user and third party. A quatitative method is suggested of measuring and controlling this reduction in an economic and socially acceptable way os that both growth and quality of life may be preserved.     

A psychometric evaluation of the digital logic concept inventory

Concept inventories hold tremendous promise for promoting the rigorous evaluation of teaching methods that might remedy common student misconceptions and promote deep learning. The measurements from concept inventories can be trusted only if the concept inventories are evaluated both by expert feedback and statistical scrutiny (psychometric evaluation). Classical Test Theory and Item Response Theory provide two psychometric frameworks for evaluating the quality of assessment tools. We discuss how these theories can be applied to assessment tools generally and then apply them to the Digital Logic Concept Inventory (DLCI). We demonstrate that the DLCI is sufficiently reliable for research purposes when used in its entirety and as a post-course assessment of students’ conceptual understanding of digital logic. The DLCI can also discriminate between students across a wide range of ability levels, providing the most information about weaker students’ ability levels.     

An Assessment of the Nonparametric Approach for Evaluating the Fit of Item Response Models

As item response theory has been more widely applied, investigating the fit of a parametric model becomes an important part of the measurement process. There is a lack of promising solutions to the detection of model misfit in IRT. Douglas and Cohen introduced a general nonparametric approach, RISE (Root Integrated Squared Error), for detecting model misfit. The purposes of this study were to extend the use of RISE to more general and comprehensive applications by manipulating a variety of factors (e.g., test length, sample size, IRT models, ability distribution). The results from the simulation study demonstrated that RISE outperformed G² and S‐X² in that it controlled Type I error rates and provided adequate power under the studied conditions. In the empirical study, RISE detected reasonable numbers of misfitting items compared to G² and S‐X², and RISE gave a much clearer picture of the location and magnitude of misfit for each misfitting item. In addition, there was no practical consequence to classification before and after replacement of misfitting items detected by three fit statistics.     

The Examination of a Pullout STEM Program for Urban Upper Elementary Students

The purpose of this study is to determine whether a pullout STEM program (STARBASE) makes reading and math scores decrease and examine its impact on urban fourth, fifth, and sixth grade students’ attitudes and perceptions regarding STEM education and careers. We employed a mixed-methods, case study approach that involved two published and one indigenous instrument composed of Likert scales, semantic differential scales, and open-ended items, along with structured interviews. Results indicate that it is possible for reading, math, and social studies scores to be unaffected when implementing a STEM pullout program, as adjusted trends (for ethnicity and socioeconomic status) are not statistically different from those of non-STARBASE school divisions in the area. Additionally, the Black/White achievement gap, as measured by standardized test scores, was substantially closed over the last decade within STARBASE schools. Quantitative measures revealed few differences in student attitudes from pre- to posttest. Student qualitative responses were overwhelmingly positive. Students wanted more STARBASE time in the regular school year. Students, particularly Black students, also wanted an increase in career education opportunities. Implications regarding curricular changes aimed at STEM education and model adoption are addressed.