scholarly journals The Use of Mixed Models for the Analysis of Mediated Data with Time-Dependent Predictors

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Emily A. Blood ◽  
Debbie M. Cheng
Keyword(s):  
Longitudinal Data ◽  
Mixed Models ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Type I Error ◽  
Time Dependent ◽  
Type I ◽  
Delayed Effects ◽  
Causal Pathways ◽  
Observational Cohort

Linear mixed models (LMMs) are frequently used to analyze longitudinal data. Although these models can be used to evaluate mediation, they do not directly model causal pathways. Structural equation models (SEMs) are an alternative technique that allows explicit modeling of mediation. The goal of this paper is to evaluate the performance of LMMs relative to SEMs in the analysis of mediated longitudinal data with time-dependent predictors and mediators. We simulated mediated longitudinal data from an SEM and specified delayed effects of the predictor. A variety of model specifications were assessed, and the LMMs and SEMs were evaluated with respect to bias, coverage probability, power, and Type I error. Models evaluated in the simulation were also applied to data from an observational cohort of HIV-infected individuals. We found that when carefully constructed, the LMM adequately models mediated exposure effects that change over time in the presence of mediation, even when the data arise from an SEM.

The Robustness of the Likelihood Ratio Chi-Square Test for Structural Equation Models: A Meta-Analysis

2001 ◽  
Vol 26 (1) ◽  
pp. 105-132 ◽  
Author(s):  
Douglas A. Powell ◽  
William D. Schafer
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Type I Error ◽  
Meta Analysis ◽  
Generalized Least Squares ◽  
Error Rates ◽  
Type I ◽  
Chi Square ◽  
Distribution Free ◽  
Projection Techniques

The robustness literature for the structural equation model was synthesized following the method of Harwell which employs meta-analysis as developed by Hedges and Vevea. The study focused on the explanation of empirical Type I error rates for six principal classes of estimators: two that assume multivariate normality (maximum likelihood and generalized least squares), elliptical estimators, two distribution-free estimators (asymptotic and others), and latent projection. Generally, the chi-square tests for overall model fit were found to be sensitive to non-normality and the size of the model for all estimators (with the possible exception of the elliptical estimators with respect to model size and the latent projection techniques with respect to non-normality). The asymptotic distribution-free (ADF) and latent projection techniques were also found to be sensitive to sample sizes. Distribution-free methods other than ADF showed, in general, much less sensitivity to all factors considered.

One Size Fits All?

Methodology ◽  
2012 ◽  
Vol 8 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Patrick E. McKnight
Keyword(s):  
Repeated Measures ◽  
Structural Equation ◽  
Type I Error ◽  
Equation Modeling ◽  
Type I ◽  
Finite Sample ◽  
Traditional Methods ◽  
Repeated Measures Anova ◽  
Special Cases ◽  
Latent Curve

The use of latent curve models (LCMs) has increased almost exponentially during the last decade. Oftentimes, researchers regard LCM as a “new” method to analyze change with little attention paid to the fact that the technique was originally introduced as an “alternative to standard repeated measures ANOVA and first-order auto-regressive methods” (Meredith & Tisak, 1990, p. 107). In the first part of the paper, this close relationship is reviewed, and it is demonstrated how “traditional” methods, such as the repeated measures ANOVA, and MANOVA, can be formulated as LCMs. Given that latent curve modeling is essentially a large-sample technique, compared to “traditional” finite-sample approaches, the second part of the paper addresses the question to what degree the more flexible LCMs can actually replace some of the older tests by means of a Monte-Carlo simulation. In addition, a structural equation modeling alternative to Mauchly’s (1940) test of sphericity is explored. Although “traditional” methods may be expressed as special cases of more general LCMs, we found the equivalence holds only asymptotically. For practical purposes, however, no approach always outperformed the other alternatives in terms of power and type I error, so the best method to be used depends on the situation. We provide detailed recommendations of when to use which method.

scholarly journals Balancing Type I error and power in linear mixed models

2017 ◽  
Vol 94 ◽  
pp. 305-315 ◽  
Author(s):  
Hannes Matuschek ◽  
Reinhold Kliegl ◽  
Shravan Vasishth ◽  
Harald Baayen ◽  
Douglas Bates
Keyword(s):  
Mixed Models ◽  
Type I Error ◽  
Linear Mixed Models ◽  
Type I

scholarly journals Chronic Disturbance in a Tropical Dry Forest: Disentangling Direct and Indirect Pathways Behind the Loss of Plant Richness

2021 ◽  
Vol 4 ◽  
Author(s):  
Andrea Jara-Guerrero ◽  
Diego González-Sánchez ◽  
Adrián Escudero ◽  
Carlos I. Espinosa
Keyword(s):  
Species Richness ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Woody Species ◽  
Biodiversity Loss ◽  
Dry Forest ◽  
Seasonally Dry ◽  
Causal Pathways ◽  
Large Trees ◽  
Chronic Disturbance

Chronic disturbance is widely recognized as one of main triggers of diversity loss in seasonally dry tropical forests (SDTFs). However, the pathways through which diffuse disturbance is acting on the forest are little understood. This information is especially demanded in the case of vanishing Neotropical seasonally dry forests such as the Tumbesian ones. We proposed a conceptual model to analyze the factors behind the loss of woody species richness along a forest disturbance gradient, explicitly considering the existence of direct and indirect causal pathways of biodiversity loss. We hypothesized that the chronic disturbance can act on the woody species richness directly, either by selective extraction of resources or by browsing of palatable species for livestock, or indirectly, by modifying characteristics of the forest structure and productivity. To test our model, we sampled forest remnants in a very extensive area submitted to long standing chronic pressure. Our forests cells (200 × 200 m) were characterized both in terms of woody species composition, structure, and human pressure. Our structural equation models (SEMs) showed that chronic disturbance is driving a loss of species richness. This was done mainly by indirect effects through the reduction of large trees density. We assume that changes in tree density modify the environmental conditions, thus increasing the stress and finally filtering some specific species. The analysis of both, direct and indirect, allows us to gain a better understanding of the processes behind plant species loss in this SDTF.

Reciprocal effects of stable and temporary components of neuroticism and affective disorders: results of a longitudinal cohort study

2013 ◽  
Vol 44 (2) ◽  
pp. 337-348 ◽  
Author(s):  
P. Spinhoven ◽  
E. Penelo ◽  
M. de Rooij ◽  
B. W. Penninx ◽  
J. Ormel
Keyword(s):  
Anxiety Disorder ◽  
Affective Disorders ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Prior History ◽  
Delayed Effect ◽  
Cross Sectional ◽  
Delayed Effects ◽  
Downward Spiral ◽  
History Of

BackgroundCross-sectional studies show that neuroticism is strongly associated with affective disorders. We investigated whether neuroticism and affective disorders mutually reinforce each other over time, setting off a potential downward spiral.MethodA total of 2981 adults aged 18–65 years, consisting of healthy controls, persons with a prior history of affective disorders and persons with a current affective disorder were assessed at baseline (T1) and 2 (T2) and 4 years (T3) later. At each wave, affective disorders according to DSM-IV criteria were assessed with the Composite Interview Diagnostic Instrument (CIDI) version 2.1 and neuroticism with the Neuroticism–Extraversion–Openness Five Factor Inventory (NEO-FFI).ResultsUsing structural equation models the association of distress disorders (i.e. dysthymia, depressive disorder, generalized anxiety disorder) and fear disorders (i.e. social anxiety disorder, panic disorder, agoraphobia without panic) with neuroticism could be attributed to three components: (a) a strong correlation of the stable components of distress and fear disorders with the stable trait component of neuroticism; (b) a modest contemporaneous association of change in distress and fear disorders with change in neuroticism; (c) a small to modest delayed effect of change in distress and fear disorders on change in neuroticism. Moreover, neuroticism scores in participants newly affected at T2 but remitted at T3 did not differ from their pre-morbid scores at T1.ConclusionsOur results do not support a positive feedback cycle of changes in psychopathology and changes in neuroticism. In the context of a relative stability of neuroticism and affective disorders, only modest contemporaneous and small to modest delayed effects of psychopathology on neuroticism were observed.

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