Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.

Panel Models in Sociological Research: Theory into Practice

Abstract: A selection of panel studies appearing in the American Sociological Review and the American Journal of Sociology between 1990 and 2003 shows that sociologists have been slow to capitalize on the advantages of panel data for controlling unobservables that threaten causal inference in observational studies. This review emphasizes regression methods that capitalize on the strengths of panel data for consistently estimating causal parameters in models for metric outcomes when measured explanatory variables are correlated with unit-specific unobservables. Both static and dynamic models are treated. Among the major subjects are fixed versus random effects methods, Hausman tests, Hausman-Taylor models, and instrumental variables methods, including Arrelano-Bond and Anderson-Hsaio estimation for models with lagged endogenous variables.

Linear models for the prediction of animal breeding values

Abstract: Part 1 Genetic evaluation with different sources of records: the basic model breeding value prediction from animal own performance breeding value prediction from progeny records breeding value prediction from pedigree breeding value prediction for one trait from another selection index. Part 2 Genetic relationship between relatives: the numerator relationship matrix decomposing the relationship matrix computing inverse of the relationship matrix inverse of the relationship matrix for sizes and maternal grandsires. Part 3 Best linear unbiased prediction of breeding value - univariate models with one random effect: brief theoretical background a model for an animal evaluation (animal model) a sire model reduced animal model animal model with groups. Part 4 Best linear unbiased prediction of breeding value - models with environmental effects: repeatability model models with common environmental effects. Part 5 Best linear unbiased prediction of breeding value - multivariate models: equal design matrices and no missing records canonical transformation equal design matrices with missing records Cholesky transformation unequal design matrices different traits measured on relatives. Part 6 Maternal trait models - animal and reduced animal models: animal model for a maternal trait reduced animal model with maternal effects multivariate maternal animal model. Part 7 Non-additive animal models: dominance relationship matrix animal model with dominance effects method for rapid inversion of the dominance matrix epistatis. Part 8 Solving linear equations: direct inversion iterating on the mixed model equations iterating on the data.

Generalized Linear Mixed Models

Abstract: This article provides an overview of generalized linear mixed models (GLMMs), how they are fit to data, and the inferences possible when using them. GLMMs are a class of statistical models that handle a wide variety of distributions for the outcome, accommodate nonlinear models, and model correlated data. As regression methods, they are not only capable of estimation and testing of covariate effects but also can be used to draw inferences about correlation structures in the data and are able to calculate predicted values that take into account not only covariates but also observed outcomes. We briefly describe software available for fitting GLMMs.

What Forty Years of Research Says About the Impact of Technology on Learning: A Second-Order Meta-Analysis and Validation Study

Abstract: This research study employs a second-order meta-analysis procedure to summarize 40 years of research activity addressing the question, does computer technology use affect student achievement in formal face-to-face classrooms as compared to classrooms that do not use technology? A study-level meta-analytic validation was also conducted for purposes of comparison. An extensive literature search and a systematic review process resulted in the inclusion of 25 meta-analyses with minimal overlap in primary literature, encompassing 1,055 primary studies. The random effects mean effect size of 0.35 was significantly different from zero. The distribution was heterogeneous under the fixed effects model. To validate the second-order meta-analysis, 574 individual independent effect sizes were extracted from 13 out of the 25 meta-analyses. The mean effect size was 0.33 under the random effects model, and the distribution was heterogeneous. Insights about the state of the field, implications for technology use, and pro...