279 citations · 581 across the 11 of their papers we have counts for
12 papers
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…
Identifying good forecasters via adaptive cognitive tests
Edgar C. Merkle, Nikolay Petrov, Sophie Ma Zhu +3
Assessing forecasting performance is a time intensive activity, often requiring months or years before we know whether or not the reported forecasts were accurate. Cognitive tests…
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…
Efficient Bayesian Structural Equation Modeling in Stan
Edgar C. Merkle, Ellen Fitzsimmons, James Uanhoro +1
Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is…
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…
Model Selection of Nested and Non-Nested Item Response Models using Vuong Tests
Lennart Schneider, R. Philip Chalmers, Rudolf Debelak +1
In this paper, we apply Vuong's (1989) general approach of model selection to the comparison of nested and non-nested unidimensional and multidimensional item response theory (IRT)…
Bayesian comparison of latent variable models: Conditional vs marginal likelihoods
E. C. Merkle, D. Furr, S. Rabe-Hesketh
Typical Bayesian methods for models with latent variables (or random effects) involve directly sampling the latent variables along with the model parameters. In high-level software…
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…
blavaan: Bayesian structural equation models via parameter expansion
Edgar C. Merkle, Yves Rosseel
This article describes blavaan, an R package for estimating Bayesian structural equation models (SEMs) via JAGS and for summarizing the results. It also describes a novel parameter…
Testing non-nested structural equation models
Edgar C. Merkle, Dongjun You, Kristopher J. Preacher
In this paper, we apply Vuong's (1989) likelihood ratio tests of non-nested models to the comparison of non-nested structural equation models. Similar tests have been previously ap…