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.     

Context matters: increasing understanding with interactive Clicker Case studies

Although interactive technology is presumed to increase student understanding in large classes, no previous research studies have empirically explored the effects of Clicker Cases on students?? performance. A Clicker Case is a story (e.g., a problem someone is facing) that uses clickers (student response systems) to engage students in understanding the meaning of the science contained within the story. Using an experimental randomized Solomon design across 11 institutions, we found that Clicker Cases increased student understanding more than PowerPoint lectures in large introductory biology classrooms, although there was variation across institutions and topics. By examining student performance in conjunction with faculty experience, we found that strong Clicker Cases created dissonance, captured attention and involved students in interpreting data or making decisions. This study provides a model for collaborative research across multiple institutions and demonstrates the need for using multiple institutions and topics in research on education.     

Construct validity of the childbirth trauma index for adolescents

The potentially traumatic nature of childbirth for adult mothers has been confirmed in research; however, adolescent childbirth trauma is unexplored. This article presents research on the construct validity of the Childbirth Trauma Index by providing a conceptual analysis of psychological childbirth trauma, factor validity of the Childbirth Trauma Index, and discussion of testing the Childbirth Trauma Index via contrasted-groups approach. Childbirth trauma can result in an acute stress reaction or actual posttraumatic stress disorder. Using subjective reports, the Impact of Event Scale, and the Childbirth Trauma Index, an appraisal of birth trauma, trauma impact, and indicators associated with childbirth trauma were revealed among 112 adolescents. Clinical implications and research recommendations are offered.     

Development of an Evaluation Tool for Online Food Safety Training Programs

Abstract: The objective of this study was to provide the person in charge and food safety instructors an assessment tool to help characterize, identify strengths and weaknesses, determine the completeness of the knowledge gained by the employee, and evaluate the level of content presentation and usability of current retail food safety training platforms. An existing evaluation tool was modified to assess online food safety training programs that contained 5 areas of emphasis: content and instruction, learners, job transfer, design and packaging, and operation. Participation included watching 3 food safety training modules and afterwards evaluate what degree the module content met objectives. More than 85% of the participants responded positively to the scale‐based items of the course evaluation. Items addressing the content and instruction received positive responses. The assessment tool helped panelists to objectively evaluate an online food safety training program that provided numeric data for a valid comparison.     

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.     

Identity‐trajectory: Reframing early career academic experience

Our longitudinal qualitative research program examining doctoral student, post‐PhD researcher and new lecturer experience is situated in an international literature documenting how early career academics learn through experience. In common with others, our work is framed within an identity perspective. What makes our view of identity distinct is a biographical focus: emphasizing individual agency; situating academic work within the personal arena; and encompassing transitions across roles. This paper demonstrates how the construct of ‘identity‐trajectory' which links a narrative approach with identity construction contributes to understanding how early career academics learn through experience and navigate their journeys.