Dissecting the depressed mood criterion in adult depression: The heterogeneity of mood disturbances in major depressive episodes.

BACKGROUND: Mood disturbances have historically remained a core criterion in diagnosing major depressive episode. DSMs have illustrated this criterion with depressed, hopeless, discouraged, cheerless, and irritable mood, suggesting interchangeability. Extant research has examined individual forms of mood disturbance to depression severity. Less examined is the heterogeneity in mood disturbances and its implication to their association to depression presentations and outcomes. METHOD: The current study used a nationally representative sample of U.S. adults with unipolar major depressive disorder to study the association between specific forms of mood disturbances to depression severity, chronicity, or symptoms, above and beyond other forms, as well as their relations to functional impairment, suicidal outcomes, and psychiatric comorbidity via generalized linear models. RESULTS: Cheerless and hopeless mood were associated with depression severity. Hopeless and irritable mood were associated with depression chronicity. Different forms of mood disturbance showed differential relations to depressive symptoms. Cheerless, hopeless, and irritable mood were associated with depression-specific functional interference, incremental to depression severity. Cheerless, hopeless, and discouraged mood were associated with passive suicidal ideation. Hopeless mood was associated with active suicidal ideation. Hopeless and irritable mood were associated with both suicide plan and suicide attempt. Different forms of mood disturbance demonstrated differential associations to comorbid psychiatric conditions. DISCUSSION: The relations between different forms of mood disturbances and various aspects of depression are nuanced. Theoretically, these relations highlight the potential utility in acknowledging the complexity and heterogeneity in mood disturbances. Clinically, our results suggest potential utility in routinely monitoring mood disturbances.

Rotating Factors to Simplify Their Structural Paths.

Applications of structural equation modeling (SEM) may encounter issues like inadmissible parameter estimates, nonconvergence, or unsatisfactory model fit. We propose a new factor rotation method that reparameterizes the factor correlation matrix in exploratory factor analysis (EFA) such that factors can be either exogenous or endogenous. The proposed method is an oblique rotation method for EFA, but it allows directional structural paths among factors. We thus referred it to as FSP (factor structural paths) rotation. In particular, we can use FSP rotation to "translate" an SEM model to incorporate theoretical expectations on both factor loadings and structural parameters. We illustrate FSP rotation with an empirical example and explore its statistical properties with simulated data. The results include that (1) EFA with FSP rotation tends to fit data better and encounters fewer Heywood cases than SEM does when there are cross-loadings and many small nonzero loadings, (2) FSP rotated parameter estimates are satisfactory for small models, and (3) FSP rotated parameter estimates are more satisfactory for large models when the structural parameter matrices are sparse.

Quantifying Model Error in P-technique Factor Analysis.

P-technique factor analysis is an exploratory factor model for multivariate time series data. Assessing model fit of P-technique factor models is non-trivial because time series data are correlated at nearby time points. We present a test statistic that is appropriate for P-technique factor analysis. In addition, the test statistic allows researchers to quantify the amount of model error. We explore the statistical properties of the test statistic with simulated data and we illustrate its use with an empirical study of personality states. Results of the simulation study include (1) the empirical distributions of the test statistic approximately followed their respective theoretical chi-square distributions, (2) the empirical Type I error rates of the test of perfect fit are close to the nominal level and the empirical Type I error rates of the test of close fit are slightly lower than the nominal level, and (3) the empirical power rates of the test of perfect fit are satisfactory but the empirical power rates of the test of close fit are only satisfactory for small models.

A Sandwich Standard Error Estimator for Exploratory Factor Analysis With Nonnormal Data and Imperfect Models.

This article is concerned with standard errors (SEs) and confidence intervals (CIs) for exploratory factor analysis (EFA) in different situations. The authors adapt a sandwich SE estimator for EFA parameters to accommodate nonnormal data and imperfect models, factor extraction with maximum likelihood and ordinary least squares, and factor rotation with CF-varimax, CF-quartimax, geomin, or target rotation. They illustrate the sandwich SEs and CIs using nonnormal continuous data and ordinal data. They also compare SE estimates and CIs of the conventional information method, the sandwich method, and the bootstrap method using simulated data. The sandwich method and the bootstrap method are more satisfactory than the information method for EFA with nonnormal data and model approximation error.

Target rotation with both factor loadings and factor correlations.

Factor rotation is conducted to aid interpretation in exploratory factor analysis (EFA). Target rotation allows researchers to directly examine the match between the rotated factor loading matrix and their expected factor loading pattern. In some EFA applications, however, researchers have expectations on both the factor loading pattern and the factor correlation pattern. We propose to extend target rotation such that target values can be specified for both factor loadings and factor correlations. We illustrate extended target rotation with a memory study and a personality study with the multitrait-multimethod design. We also explore the statistical properties of extended target rotation using simulated data. (PsycINFO Database Record (c) 2019 APA, all rights reserved).