Two-stage multilevel latent class analysis with covariates in the presence of direct effects

Multivariate analysis of psychological dat

SEM-based out-of-sample predictions

Multivariate analysis of psychological dat

Development, internal reliability and preliminary construct validity of the Dutch Dietary Intention Evaluation Tool for In-patients (DIETI)

Multivariate analysis of psychological dat

Determination of the size distribution of non-spherical nanoparticles by electric birefringence-based methods

The in situ determination of the size distribution of dispersed non-spherical nanoparticles is an essential characterization tool for the investigation and use of colloidal suspensions. In this work, we test a size characterization method based on the measurement of the transient behaviour of the birefringence induced in the dispersions by pulsed electric fields. The specific shape of such relaxations depends on the distribution of the rotational diffusion coefficient of the suspended particles. We analyse the measured transient birefringence with three approaches: the stretched-exponential, Watson-Jennings, and multiexponential methods. These are applied to six different types of rod-like and planar particles: PTFE rods, goethite needles, single- and double-walled carbon nanotubes, sodium montmorillonite particles and gibbsite platelets. The results are compared to electron microscopy and dynamic light scattering measurements. The methods here considered provide good or excellent results in all cases, proving that the analysis of the transient birefringence is a powerful tool to obtain complete size distributions of non-spherical particles in suspension.Financial support of this investigation by Junta de Andalucía, Spain (grant No. PE2012-FQM0694) and University of Granada (Program “Proyectos de investigación precompetitivos”) is gratefully acknowledged

Trajectories of Early Adolescent Loneliness: Implications for Physical Health and Sleep

The current study examines the relationship between prolonged loneliness, physical health, and sleep among young adolescents (10–13 years; N = 1214; 53% girls). Loneliness was measured at 10, 12 and 13 years of age along with parent-reported health and sleep outcomes. Using growth mixture modelling, 6 distinct trajectories were identified: ‘low increasing to high loneliness’ (n = 23, 2%), ‘high reducing loneliness’ (n = 28, 3%), ‘medium stable loneliness’ (n = 60, 5%), ‘medium reducing loneliness’ (n = 185, 15%), ‘low increasing to medium loneliness’ (n = 165, 14%), and ‘low stable loneliness’ (n = 743, 61%). Further analyses found non-significant differences between the loneliness trajectories and parent-report health and sleep outcomes including visits to health professionals, perceived general health, and sleep quality. The current study offers an important contribution to the literature on loneliness and health. Results show that the relationship may not be evident in early adolescence when parent reports of children’s health are used. The current study highlights the importance of informant choice when reporting health. The implications of the findings for future empirical work are discussed

Mostly Harmless Direct Effects: A Comparison of Different Latent Markov Modeling Approaches

Multivariate analysis of psychological dat

Robustness of stepwise latent class modeling with continuous distal outcomes

Recently, several bias-adjusted stepwise approaches to latent class modeling with continuous distal outcomes have been proposed in the literature and implemented in generally available software for latent class analysis. In this article, we investigate the robustness of these methods to violations of underlying model assumptions by means of a simulation study. Although each of the 4 investigated methods yields unbiased estimates of the class-specific means of distal outcomes when the underlying assumptions hold, 3 of the methods could fail to different degrees when assumptions are violated. Based on our study, we provide recommendations on which method to use under what circumstances. The differences between the various stepwise latent class approaches are illustrated by means of a real data application on outcomes related to recidivism for clusters of juvenile offenders. Keywords: latent class analysis, robustness, stepwise approache

Relating latent class membership to continuous distal outcomes: improving the LTB approach and a modified three-step implementation.

FSW – Publicaties zonder aanstelling Universiteit Leide

A Random-covariate Approach for Distal Outcome Prediction with Latent Class Analysis

While latent class (LC) models with distal outcomes are becoming popular in literature as a consequence of the increasing use of stepwise estimators, these models still suffer from severe shortcomings. Namely, using the currently available stepwise estimators the direct effects between the distal outcome and the indicators of the LC membership cannot be easily modeled. At the same time using the traditional Full Information Maximum Likelihood (FIML) approach the LC solution can become dominated by the distal outcome, especially when model misspecifications occur, and the relationship between the distal outcome and LC is strong. In this paper, we consider a more general formulation, typical in cluster-weighted models, which embeds both the latent class regression and the distal outcome models. This allows us to test simultaneously both whether the distribution of the distal outcome differs across classes, and whether there are significant direct effects of the distal outcome on the indicators, by including most of the information about the distal outcome - latent variable relationship. We propose a two-step estimator for these models that makes it possible to separate the estimation of the measurement and structural model, that is much desired for distal outcome models, while keeping the possibility of modeling direct effects open. We show the advantages of the proposed modeling approach through a simulation study and an empirical application on assets ownership of Italian households.Multivariate analysis of psychological dat

Relating latent class assignments to external variables:Standard errors for correct inference

Latent class analysis is used in the political science literature in both substantive applications and as a tool to estimate measurement error. Many studies in the social and political sciences relate estimated class assignments from a latent class model to external variables. Although common, such a “three-step” procedure effectively ignores classification error in the class assignments; Vermunt (2010, “Latent class modeling with covariates: Two improved three-step approaches,” Political Analysis 18:450–69) showed that this leads to inconsistent parameter estimates and proposed a correction. Although this correction for bias is now implemented in standard software, inconsistency is not the only consequence of classification error. We demonstrate that the correction method introduces an additional source of variance in the estimates, so that standard errors and confidence intervals are overly optimistic when not taking this into account. We derive the asymptotic variance of the third-step estimates of interest, as well as several candidate-corrected sample estimators of the standard errors. These corrected standard error estimators are evaluated using a Monte Carlo study, and we provide practical advice to researchers as to which should be used so that valid inferences can be obtained when relating estimated class membership to external variables