Biomedical subjects

S Natasha Beretvas

Publications and source records attributed to S Natasha Beretvas.

5 recordsLinked to original sources

PubMed2005

Meta-analytic methods of pooling correlation matrices for structural equation modeling under different patterns of missing data.

Three methods of synthesizing correlations for meta-analytic structural equation modeling (SEM) under different degrees and mechanisms of missingness were compared for the estimation of correlation and SEM parameters and goodness-of-fit indices by using Monte Carlo simulation techniques. A revised generalized least squares (GLS) method for synthesizing correlations, weighted-covariance GLS (W-COV GLS), was compared with univariate weighting with untransformed correlations (univariate r) and univariate weighting with Fisher's z-transformed correlations (univariate z). These 3 methods were crossed with listwise and pairwise deletion. Univariate z and W-COV GLS performed similarly, with W-COV GLS providing slightly better estimation of parameters and more correct model rejection rates. Missing not at random data produced high levels of relative bias in correlation and model parameter estimates and higher incorrect SEM model rejection rates. Pairwise deletion resulted in inflated standard errors for all synthesis methods and higher incorrect rejection rates for the SEM model with univariate weighting procedures.

Humans↗

PubMed2005

The multilevel measurement model: introduction to the special issue.

An introduction to the special issue on the multilevel measurement model (MMM) is provided. The two- and three-level multilevel models for continuous outcomes are reviewed. The extension to the hierarchical generalized linear model and its use as a multilevel measurement model for dichotomous measurement indicators is demonstrated. The six articles in the special issue are described.

Humans↗

PubMed2005

The cross-classified multilevel measurement model: an explanation and demonstration.

The link between the hierarchical generalized linear model (HGLM) and the Rasch model's parameterization has already been demonstrated by several researchers. Extensions have been described that include higher clustering levels to model more appropriately the contextual effects that are frequently encountered in educational research. However, pure hierarchies are relatively rare and instead cross-classified data structures are more frequently encountered. Cross-classified random effect modeling (CCREM) is still not commonly used. Use of CCREM in combination with the multilevel measurement model (MMM) has been recently introduced and is described further in the current study. Specifically, the link between the MMM and the CCREM MMM (termed "CCMMM" model) is provided. A dataset was simulated to demonstrate interpretation of the CCMMM model's parameters and to compare results under a CCMMM versus HGLM analysis. An Appendix is provided to demonstrate SAS GLIMMIX code used to estimate HGLM and CCMMM models' parameters.

Data Interpretation, Statistical↗

PubMed2002

Moving the cut score on Rasch scored tests.

Empirically based item selection guidelines are presented for moving the cut score on equated tests consisting of n dichotomous items calibrated assuming the Rasch model. The cut score on a test form B, c(B), may be made higher than test form A's cut score, c(A), in the following ways: (1) select items for test form B such that the variance of test form B's item difficulties, sigma(2)(B), will be equal to test form A's sigma(2)(A), but test form B's mean item difficulty, mu(B), will be less that of test form A, mu(A); (2) given c(A) > n/2, select items for test form B such that mu(B) s(2)(A). To make c(B) lower than c(A), the direction of the changes listed above for the two tests item difficulties sigma(2) and mu should be reversed. Derivations of lemmas that underlie the guidelines are provided as well as a simulated example.

Educational Measurement↗

PubMedDate not supplied

Beginning literacy: links among teacher knowledge, teacher practice, and student learning.

Although the importance of phonological awareness has been discussed widely in the research literature, the concept is not well understood by many classroom teachers. In the study described here, we worked with groups of kindergarten and first-grade teachers (the experimental group) during a 2-week summer institute and throughout the school year. We shared with them research about learning disabilities and effective instruction, stressing the importance of explicit instruction in phonological and orthographic awareness. We followed the experimental group and a control group into their classrooms for a year, assessing teachers' classroom practices and their students' (n = 779) learning. The study yielded three major findings: We can deepen teachers' own knowledge of the role of phonological and orthographic information in literacy instruction; teachers can use that knowledge to change classroom practice; and changes in teacher knowledge and classroom practice can improve student learning.

Adult↗