scholarly journals Modeling and treating internalizing psychopathology in a clinical trial: a latent variable structural equation modeling approach

2013 ◽  
Vol 43 (8) ◽  
pp. 1611-1623 ◽  
Author(s):  
M. G. Kushner ◽  
R. F. Krueger ◽  
M. M. Wall ◽  
E. W. Maurer ◽  
J. S. Menk ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Treatment Condition ◽  
Specific Treatment ◽  
Internalizing Disorders ◽  
Equation Modeling ◽  
Internalizing Psychopathology ◽  
Co Morbidity

BackgroundClinical trials are typically designed to test the effect of a specific treatment on a single diagnostic entity. However, because common internalizing disorders are highly correlated (‘co-morbid’), we sought to establish a practical and parsimonious method to characterize and quantify changes in a broad spectrum of internalizing psychopathology targeted for treatment in a clinical trial contrasting two transdiagnostic psychosocial interventions.MethodAlcohol dependence treatment patients who had any of several common internalizing disorders were randomized to a six-session cognitive-behavioral therapy (CBT) experimental treatment condition or a progressive muscle relaxation training (PMRT) comparison treatment condition. Internalizing psychopathology was characterized at baseline and 4 months following treatment in terms of the latent structure of six distinct internalizing symptom domain surveys.ResultsExploratory structural equation modeling (ESEM) identified a two-factor solution at both baseline and the 4-month follow-up: Distress (measures of depression, trait anxiety and worry) and Fear (measures of panic anxiety, social anxiety and agoraphobia). Although confirmatory factor analysis (CFA) demonstrated measurement invariance between the time-points, structural models showed that the latent means of Fear and Distress decreased substantially from baseline to follow-up for both groups, with a small but statistically significant advantage for the CBT group in terms of Distress (but not Fear) reduction.ConclusionsThe approach demonstrated in this study provides a practical solution to modeling co-morbidity in a clinical trial and is consistent with converging evidence pointing to the dimensional structure of internalizing psychopathology.

scholarly journals Transdiagnostic and disorder-specific models of intergenerational transmission of internalizing pathology

2013 ◽  
Vol 44 (1) ◽  
pp. 161-172 ◽  
Author(s):  
L. R. Starr ◽  
C. C. Conway ◽  
C. L. Hammen ◽  
P. A. Brennan
Keyword(s):  
Anxiety Disorders ◽  
Maternal Depression ◽  
Intergenerational Transmission ◽  
Latent Variable ◽  
Structural Equation ◽  
Depressive Disorders ◽  
Internalizing Disorders ◽  
Equation Modeling ◽  
Co Morbidity ◽  
Child Psychiatric

BackgroundNumerous studies have supported an association between maternal depression and child psychiatric outcomes, but few have controlled for the confounding effects of both maternal and offspring co-morbidity. Thus, it remains unclear whether the correspondence between maternal and offspring depressive and anxiety disorders is better explained by associations between shared features of maternal and offspring internalizing disorders or by specific effects exerted by unique aspects of individual disorders.MethodPairs of mothers and offspring overselected for maternal depression (n = 815) were assessed at offspring age 15 years for anxiety and depressive disorders; 705 completed a follow-up at offspring age 20 years. For both mothers and offspring, structural equation modeling was used to distinguish transdiagnostic internalizing pathology – representing the overlap among all depressive and anxiety disorders – from diagnosis-specific forms of pathology. To discriminate between general versus specific pathways of intergenerational transmission of psychopathology, we examined (a) the general association between the maternal and offspring internalizing factors and (b) the correlations between maternal and offspring diagnosis-specific pathology for each disorder.ResultsFor mothers and offspring, a unidimensional latent variable model provided the best fit to the correlations among depressive and anxiety disorders. The maternal transdiagnostic internalizing factor strongly predicted the corresponding factor among offspring. In addition, the unique component of post-traumatic stress disorder among offspring was significantly related to the analogous unique component among mothers, but specific components of other maternal disorders, including depression, did not predict corresponding offspring pathology.ConclusionsResults suggest that intergenerational transmission of internalizing disorders is largely non-specific.

scholarly journals Longitudinal stability and interrelations between health behavior and subjective well-being in a follow-up of nine years

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259280
Author(s):  
Säde Stenlund ◽  
Niina Junttila ◽  
Heli Koivumaa-Honkanen ◽  
Lauri Sillanmäki ◽  
David Stenlund ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Health Behavior ◽  
Health Behaviors ◽  
Structural Equation ◽  
Well Being ◽  
Equation Modeling ◽  
Subjective Well Being ◽  
Cross Sectional ◽  
The Cross

Background The bidirectional relationship between health behavior and subjective well-being has previously been studied sparsely, and mainly for individual health behaviors and regression models. In the present study, we deepen this knowledge focusing on the four principal health behaviors and using structural equation modeling with selected covariates. Methods The follow-up data (n = 11,804) was derived from a population-based random sample of working-age Finns from two waves (2003 and 2012) of the Health and Social Support (HeSSup) postal survey. Structural equation modeling was used to study the cross-sectional, cross-lagged, and longitudinal relationships between the four principal health behaviors and subjective well-being at baseline and after the nine-year follow-up adjusted for age, gender, education, and self-reported diseases. The included health behaviors were physical activity, dietary habits, alcohol consumption, and smoking status. Subjective well-being was measured through four items comprising happiness, interest, and ease in life, and perceived loneliness. Results Bidirectionally, only health behavior in 2003 predicted subjective well-being in 2012, whereas subjective well-being in 2003 did not predict health behavior in 2012. In addition, the cross-sectional interactions in 2003 and in 2012 between health behavior and subjective well-being were statistically significant. The baseline levels predicted their respective follow-up levels, the effect being stronger in health behavior than in subjective well-being. Conclusion The four principal health behaviors together predict subsequent subjective well-being after an extensive follow-up. Although not particularly strong, the results could still be used for motivation for health behavior change, because of the beneficial effects of health behavior on subjective well-being.

Using WarpPLS in E-Collaboration Studies

2012 ◽  
pp. 62-78
Author(s):  
Ned Kock
Keyword(s):  
Structural Equation Modeling ◽  
Virtual Teams ◽  
Structural Equation ◽  
Research Literature ◽  
Previous Article ◽  
Equation Modeling ◽  
Modeling Analysis ◽  
Modeling Software ◽  
Nonlinear Structural Equation

This is a follow-up on a previous article (Kock, 2010b) discussing the five main steps through which a nonlinear structural equation modeling analysis could be conducted with the software WarpPLS (warppls.com). Both this and the previous article use data from the same E-collaboration study as a basis for the discussion of important WarpPLS features. The focus of this article is on specific features related to saving and analyzing grouped descriptive statistics, viewing and changing analysis algorithm and resampling settings, and viewing and saving the various minor and major results of the analysis. Even though its focus is on an E-collaboration study, this article contributes to the broad literature on multivariate analysis methods, in addition to the more specific research literature on E-collaboration. The vast majority of relationships between variables, in investigations of both natural and behavioral phenomena, are nonlinear; usually taking the form of U and S curves. Structural equation modeling software tools, whether variancE- or covariancE-based, typically do not estimate coefficients of association based on nonlinear analysis algorithms. WarpPLS is an exception in this respect. Without taking nonlinearity into consideration, the results can be misleading; especially in complex and multi-factorial situations such as those stemming from E-collaboration in virtual teams.

scholarly journals Individual Characteristics as Prognostic Factors of the Evolution of Hospitalized COVID-19 Romanian Patients: A Comparative Observational Study between the First and Second Waves Based on Gaussian Graphical Models and Structural Equation Modeling

2021 ◽  
Vol 10 (9) ◽  
pp. 1958
Author(s):  
Alexandra Mocanu ◽  
Gratiela Georgiana Noja ◽  
Alin Viorel Istodor ◽  
Georgiana Moise ◽  
Marius Leretter ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Observational Study ◽  
Graphical Models ◽  
Structural Equation ◽  
Specific Treatment ◽  
Individual Characteristics ◽  
Equation Modeling ◽  
Gaussian Graphical Models ◽  
Treatment Methods ◽  
Second Wave

This study examines the role played by individual characteristics and specific treatment methods in the evolution of hospitalized patients with coronavirus disease 2019 (COVID-19), through the lens of an observational study performed in a comparative approach between the first and second waves of coronavirus pandemic in Romania. The research endeavor is configured on a two-fold approach, including a detailed observation of the evolution of 274 hospitalized patients with COVID-19 (145 in the first wave and 129 in the second wave of infection) according to specific treatment methods applied and patients’ individual features, as well as an econometric (quantitative) analysis through structural equation modeling and Gaussian graphical models designed to acknowledge the correlations and causal relationship between all considered coordinates. The main results highlight that the specific treatment methods applied had a positive influence on the evolution of COVID-19 patients, particularly in the second wave of coronavirus pandemic. In case of the first wave of COVID-19 infection, GGM results entail that there is a strong positive correlation between the evolution of the patients and the COVID-19 disease form, which is further positively correlated with the treatment scheme. The evolution of the patients is strongly and inversely correlated with the symptomatology and the ICU hospitalization. Moreover, the disease form is strongly and inversely correlated with oxygen saturation and the residence of patients (urban/rural). The symptomatology at first appearance also strongly depends on the age of the patients (positive correlation) and of the fact that the patient is a smoker or non-smoker and has other comorbidities. Age and gender are also important credentials that shape the disease degree and patient evolution in responding to treatment as well, our study attesting strong interconnections between these coordinates, the form of disease, symptomatology and overall evolution of the patients.

Using WarpPLS in e-Collaboration Studies

2011 ◽  
Vol 7 (2) ◽  
pp. 1-18 ◽  
Author(s):  
Ned Kock
Keyword(s):  
Structural Equation Modeling ◽  
Virtual Teams ◽  
Structural Equation ◽  
Research Literature ◽  
Previous Article ◽  
Equation Modeling ◽  
Modeling Analysis ◽  
Modeling Software ◽  
Nonlinear Structural Equation

This is a follow-up on a previous article (Kock, 2010b) discussing the five main steps through which a nonlinear structural equation modeling analysis could be conducted with the software WarpPLS (warppls.com). Both this and the previous article use data from the same e-collaboration study as a basis for the discussion of important WarpPLS features. The focus of this article is on specific features related to saving and analyzing grouped descriptive statistics, viewing and changing analysis algorithm and resampling settings, and viewing and saving the various minor and major results of the analysis. Even though its focus is on an e-collaboration study, this article contributes to the broad literature on multivariate analysis methods, in addition to the more specific research literature on e-collaboration. The vast majority of relationships between variables, in investigations of both natural and behavioral phenomena, are nonlinear; usually taking the form of U and S curves. Structural equation modeling software tools, whether variance- or covariance-based, typically do not estimate coefficients of association based on nonlinear analysis algorithms. WarpPLS is an exception in this respect. Without taking nonlinearity into consideration, the results can be misleading; especially in complex and multi-factorial situations such as those stemming from e-collaboration in virtual teams.

scholarly journals Neuroticism as a common dimension in the internalizing disorders

2009 ◽  
Vol 40 (7) ◽  
pp. 1125-1136 ◽  
Author(s):  
J. W. Griffith ◽  
R. E. Zinbarg ◽  
M. G. Craske ◽  
S. Mineka ◽  
R. D. Rose ◽  
...  
Keyword(s):  
Anxiety Disorders ◽  
Structural Equation ◽  
Discriminant Validity ◽  
Self Report ◽  
Equation Modeling ◽  
Internalizing Psychopathology ◽  
Mood And Anxiety Disorders ◽  
Co Morbidity ◽  
Peer Report ◽  
The Relationship

BackgroundSeveral theories have posited a common internalizing factor to help account for the relationship between mood and anxiety disorders. These disorders are often co-morbid and strongly covary. Other theories and data suggest that personality traits may account, at least in part, for co-morbidity between depression and anxiety. The present study examined the relationship between neuroticism and an internalizing dimension common to mood and anxiety disorders.MethodA sample of ethnically diverse adolescents (n=621) completed self-report and peer-report measures of neuroticism. Participants also completed the Structured Clinical Interview for DSM-IV (SCID).ResultsStructural equation modeling showed that a single internalizing factor was common to lifetime diagnosis of mood and anxiety disorders, and this internalizing factor was strongly correlated with neuroticism. Neuroticism had a stronger correlation with an internalizing factor (r=0.98) than with a substance use factor (r=0.29). Therefore, neuroticism showed both convergent and discriminant validity.ConclusionsThese results provide further evidence that neuroticism is a necessary factor in structural theories of mood and anxiety disorders. In this study, the correlation between internalizing psychopathology and neuroticism approached 1.0, suggesting that neuroticism may be the core of internalizing psychopathology. Future studies are needed to examine this possibility in other populations, and to replicate our findings.

Testing the Assumption of Uncorrelated Errors for Short Scales by Means of Structural Equation Modeling

2014 ◽  
Vol 35 (4) ◽  
pp. 201-211 ◽  
Author(s):  
André Beauducel ◽  
Anja Leue
Keyword(s):  
Structural Equation Modeling ◽  
Negative Correlation ◽  
Structural Equation ◽  
Error Score ◽  
Classical Test Theory ◽  
Test Theory ◽  
Equation Modeling ◽  
Eysenck Personality Questionnaire ◽  
Personality Questionnaire ◽  
True Score

It is shown that a minimal assumption should be added to the assumptions of Classical Test Theory (CTT) in order to have positive inter-item correlations, which are regarded as a basis for the aggregation of items. Moreover, it is shown that the assumption of zero correlations between the error score estimates is substantially violated in the population of individuals when the number of items is small. Instead, a negative correlation between error score estimates occurs. The reason for the negative correlation is that the error score estimates for different items of a scale are based on insufficient true score estimates when the number of items is small. A test of the assumption of uncorrelated error score estimates by means of structural equation modeling (SEM) is proposed that takes this effect into account. The SEM-based procedure is demonstrated by means of empirical examples based on the Edinburgh Handedness Inventory and the Eysenck Personality Questionnaire-Revised.

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