279 citations · 581 across the 11 of their papers we have counts for
6 papers · 1 filter
Identification and Scaling of Latent Variables in Ordinal Factor Analysis
Edgar C. Merkle, Sonja D. Winter, Ellen Fitzsimmons
Social science researchers are generally accustomed to treating ordinal variables as though they are continuous. In this paper, we consider how identification constraints in ordina…
Self-normalized score-based tests to detect parameter heterogeneity for mixed models
Ting Wang, Edgar Merkle
Score-based tests have been used to study parameter heterogeneity across many types of statistical models. This chapter describes a new self-normalization approach for score-based…
Opaque prior distributions in Bayesian latent variable models
Edgar C. Merkle, Oludare Ariyo, Sonja D. Winter +1
We review common situations in Bayesian latent variable models where the prior distribution that a researcher specifies differs from the prior distribution used during estimation.…
Computation and application of generalized linear mixed model derivatives using lme4
Ting Wang, Benjamin Graves, Yves Rosseel +1
Maximum likelihood estimation of generalized linear mixed models(GLMMs) is difficult due to marginalization of the random effects. Computing derivatives of a fitted GLMM's likeliho…
Score-based Tests for Explaining Upper-Level Heterogeneity in Linear Mixed Models
Ting Wang, Edgar C. Merkle, Joaquin A. Anguera +1
Cross-level interactions among fixed effects in linear mixed models (also known as multilevel models) are often complicated by the variances stemming from random effects and residu…
Derivative Computations and Robust Standard Errors for Linear Mixed Effects Models in lme4
Ting Wang, Edgar C. Merkle
While robust standard errors and related facilities are available in R for many types of statistical models, the facilities are notably lacking for models estimated via lme4. This…