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- Hao Wu, Michael C. Neale
- Psychometrika
- 2013

The ACE and ADE models have been heavily exploited in twin studies to identify the genetic and environmental components in phenotypes. However, the validity of the likelihood ratio test (LRT) of the existence of a variance component, a key step in the use of such models, has been doubted because the true values of the parameters lie on the boundary of theâ€¦ (More)

- Jolynn C. X. Pek, Hao Wu
- Psychometrika
- 2015

Structural equation models (SEM) are widely used for modeling complex multivariate relationships among measured and latent variables. Although several analytical approaches to interval estimation in SEM have been developed, there lacks a comprehensive review of these methods. We review the popular Wald-type and lesser known likelihood-based methods inâ€¦ (More)

- Kuo-Yang Kao, Christiane SpitzmÃ¼ller, Konstantin P. Cigularov, Hao Wu
- Journal of occupational health psychology
- 2016

This study investigated why and how insomnia can relate to workplace injuries, which continue to have high human and economic costs. Utilizing the self-regulatory resource theory, we argue that insomnia decreases workers' safety behaviors, resulting in increased workplace injuries. Moreover, in order to ultimately derive organizational interventions toâ€¦ (More)

- Yael Arbel, Hao Wu
- Neuropsychologia
- 2016

The efficiency with which one processes external feedback contributes to the speed and quality of one's learning. Previous findings that the feedback related negativity (FRN) event related potential (ERP) is modulated by learning outcomes suggested that this ERP reflects the extent to which feedback is used by the learner to improve performance. To furtherâ€¦ (More)

- Hao Wu
- Psychometrika
- 2016

In this note, we prove that the 3 parameter logistic model with fixed-effect abilities is identified only up to a linear transformation of the ability scale under mild regularity conditions, contrary to the claims in Theorem 2 of San MartÃn et al. (Psychometrika, 80(2):450-467, 2015a).

- Hao Wu, Michael W. Browne
- Psychometrika
- 2015

We present an approach to quantifying errors in covariance structures in which adventitious error, identified as the process underlying the discrepancy between the population and the structured model, is explicitly modeled as a random effect with a distribution, and the dispersion parameter of this distribution to be estimated gives a measure ofâ€¦ (More)

- Hao Wu, Michael W. Browne
- Psychometrika
- 2015

In this rejoinder we discuss the following aspects of our approach to model discrepancy: the interpretations of the two populations and adventitious error, the choice of inverse Wishart distribution, the perceived danger of justifying a model with bad fit, the relationship among our new approach, Chen's (J R Stat Soc Ser B, 41:235-248, 1979) approach andâ€¦ (More)

The specifications of state space model for some principal component-related models are described, including the independent-group common principal component (CPC) model, the dependent-group CPC model, and principal component-based multivariate analysis of variance. Some derivations are provided to show the equivalence of the state space approach and theâ€¦ (More)

- Hao Wu, Michael C. Neale
- Behavior genetics
- 2012

It is well known that the regular likelihood ratio test of a bounded parameter is not valid if the boundary value is being tested. This is the case for testing the null value of a scalar variance component. Although an adjusted test of variance component has been suggested to account for the effect of its lower bound of zero, no adjustment of its intervalâ€¦ (More)

- Hao Wu, Ryne Estabrook
- Psychometrika
- 2016

This article considers the identification conditions of confirmatory factor analysis (CFA) models for ordered categorical outcomes with invariance of different types of parameters across groups. The current practice of invariance testing is to first identify a model with only configural invariance and then test the invariance of parameters based on thisâ€¦ (More)