Estimation of MIMIC Model Parameters with Multilevel Data

The purpose of this simulation study was to assess the performance of latent variable models that take into account the complex sampling mechanism that often underlies data used in educational, psychological, and other social science research. Analyses were conducted using the multiple indicator multiple cause (MIMIC) model, which is a flexible and effective tool for relating observed and latent variables. The data were simulated in a hierarchical framework (e.g., individuals nested in schools) so that a multilevel modeling approach would be appropriate. Analyses were conducted accounting for and not accounting for the nested data to determine the impact of ignoring such multilevel data structures in full structural equation models. Results highlight the differences in modeling results when the analytic strategy is congruent with the data structure and what occurs when this congruency is absent. Type I error rates and power for the standard and multilevel methods were similar for within-cluster variables and for the multilevel model with between-cluster variables. However, Type I error rates were inflated for the standard approach when modeling between-cluster variables.     

Multivariate Meta-Analysis as Structural Equation Models

Multivariate meta-analysis has become increasingly popular in the educational, social, and medical sciences. It is because the outcome measures in a meta-analysis can involve more than one effect size. This article proposes 2 mathematically equivalent models to implement multivariate meta-analysis in structural equation modeling (SEM). Specifically, this article shows how multivariate fixed-, random- and mixed-effects meta-analyses can be formulated as structural equation models. metaSEM (a free R package based on OpenMx) and Mplus are used to implement the proposed procedures. A real data set is used to illustrate the procedures. Formulating multivariate meta-analysis as structural equation models provides many new research opportunities for methodological development in both meta-analysis and SEM. Issues related to and extensions on the SEM-based meta-analysis are discussed.     

An Evaluation of Latent Growth Models for Propensity Score Matched Groups

Because random assignment is not possible in observational studies, estimates of treatment effects might be biased due to selection on observable and unobservable variables. To strengthen causal inference in longitudinal observational studies of multiple treatments, we present 4 latent growth models for propensity score matched groups, and evaluate their performance with a Monte Carlo simulation study. We found that the 4 models performed similarly with respect to model fit, bias of parameter estimates, Type I error, and power to test the treatment effect. To demonstrate a multigroup latent growth model with dummy treatment indicators, we estimated the effect of students changing schools during elementary school years on their reading and mathematics achievement, using data from the Early Childhood Longitudinal Study Kindergarten Cohort.     

Disentangling Shape from Level Effects in Person-Centered Analyses: An Illustration Based on University Teachers’ Multidimensional Profiles of Effectiveness

This study compares alternative ways of disentangling the effects of level (the tendency for a person to be high, medium, or low across all factors) and shape (the tendency for a person to have a distinct pattern of factors on which they are high, medium, or low) in profile analyses. This issue is particularly relevant to performance appraisals where it is often useful to identify specific strengths and weaknesses over and above a person global performance, but also to person-centered analyses more generally where the observation of qualitative (shape) differences between profiles is often used as justification for the added value of profiles. Substantively, this study illustrates these issues in the identification of profiles of teachers based on multidimensional students’ ratings of their effectiveness, using an archival data set of 31,951 class-average ratings based on the Students’ Evaluations of Educational Quality (SEEQ) instrument collected over a 13-year period. The results show the superiority of a factor mixture operationalization of teaching effectiveness in which a global effectiveness factor was used to control for unnecessary level effects in the profiles.     

Modeling Nonlinear Structural Equation Models: A Comparison of the Two-Stage Generalized Additive Models and the Finite Mixture Structural Equation Model

Researchers have devoted some time and effort to developing methods for fitting nonlinear relationships among latent variables. In particular, most of these have focused on correctly modeling interactions between 2 exogenous latent variables, and quadratic relationships between exogenous and endogenous variables. All of these approaches require prespecification of the nonlinearity by the researcher, and are limited to fairly simple nonlinear relationships. Other work has been done using mixture structural equation models (SEMM) in an attempt to fit more complex nonlinear relationships. This study expands on this earlier work by introducing the 2-stage generalized additive model (2SGAM) approach for fitting regression splines in the context of structural equation models. The model is first described and then investigated through the use of simulated data, in which it was compared with the SEMM approach. Results demonstrate that the 2SGAM is an effective tool for fitting a variety of nonlinear relationships between latent variables, and can be easily and accurately extended to models including multiple latent variables. Implications of these results are discussed.     

Technology User Groups and Early Childhood Education: A Preliminary Study

This article presents a preliminary examination of the potential of Technology User Groups as a professional development venue for early childhood education professionals in developing operational and functional competence in using hardware and software components of a Technology toolkit. Technology user groups are composed of varying numbers of participants having an interest in technology, and are led by one or more skilled facilitators who meet with participants across time to help them acquire and demonstrate new technology skill sets. A series of these groups were conducted with seven early education professionals serving young preschool children who were at risk or who had disabilities. The impact of these technology user groups was examined using self-reports subsequent to individual participation. Specific data were collected regarding the types of technologies that had been used, and the types of classroom instructional products that had been created and implemented in classrooms using the technologies. A discussion of the value of technology user groups is presented.     

The Growth of Phonological Awareness by Children With Reading Disabilities: A Result of Semantic Knowledge or Knowledge of Grapheme-Phoneme Correspondences?

According to the Lexical Restructuring Model (Metsala & Walley, 1998 Metsala, J. L. and Walley, A. C. 1998. “Spoken vocabulary growth and the segmental restructuring of lexical representation: Precursors to phonemic awareness and early reading ability”. In Word recognition in beginning literacy, Edited by: Metsala, J. L. and Ehri, L. C. 89–120. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.  [Google Scholar]), children move from holistic representations of words, to syllabic representations, and finally to phonemic representations through a restructuring process driven by their developing lexical base. In contrast, the psycholinguistic grain size theory put forth by Ziegler and Goswami (2005) Ziegler, J. C. and Goswami, U. 2005. Reading acquisition, developmental dyslexia, and skilled reading across languages: A psycholinguistic grain size theory.. Psychological Bulletin, 131: 3–29. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar] suggests that the awareness of individual phonemes is not possible without direct literacy instruction. The purpose of this study was to examine whether semantic knowledge and/or knowledge of grapheme/phoneme correspondences influenced the acquisition of word-blending skills by a sample of children with a reading disability. Participants were 211 second-grade and third-grade students from public elementary schools who were assigned to a reading intervention. Hierarchical Linear Modeling techniques were used to model individual growth curves of word-blending skills. Overall, findings support the psycholinguistic grain size theory of reading and suggest that instruction in the relationship between orthographic patterns and their corresponding sounds is necessary for the development of phonological awareness.     

Teachers' Emotional Support Consistency Predicts Children's Achievement Gains and Social Skills

Research Findings: It is widely acknowledged that consistent, high-quality teacher–student interactions promote optimal developmental outcomes for children. Previous research on the quality of teacher–student interactions provides empirical support for this premise. Little research has been conducted on the consistency of teacher–student interactions. This study examines whether consistency in teachers' emotional support is related to better academic and social outcomes for children. Multiple observations were conducted in 694 prekindergarten classrooms. Mean levels of emotional support and consistency of emotional support were used as predictors in multilevel models. Results indicated that when mean levels of emotional support were controlled, within-day consistency of emotional support predicted several academic outcomes in prekindergarten as well as social competence in kindergarten. Practice or Policy: Results indicate that teachers' consistency of emotional support is a salient aspect of children's classroom environment. Findings suggest that consistency should be considered when evaluating teachers' emotionally supportive interactions.