Ethnic Identity and Well-Being of Andean Indigenous People: The Effect of Individualistic and Collectivist Value Orientations.

The aim of this research was to evaluate the mediating effect of the value orientations of collectivism and individualism on the relationship between ethnic identity and well-being, the latter conceived from the worldview of Andean natives. For this purpose, under an observational and cross-sectional design, 395 Lickan-Antay adults (57% women) living in areas of indigenous development and in two cities in northern Chile were surveyed. We used the Lickan-Antay BLA32 well-being scale, a short version of the Portrait 21 Values Questionnaire to measure individualistic and collectivistic values, and an adapted version of the Ethnic Identity Scale. The results show that ethnic identity had a direct positive effect on all three dimensions of well-being (harmony with community life, ethnic harmony and harmony with nature), and total indirect effects on all five dimensions of well-being, one of them originating mainly from collectivist orientations. Individualistic orientations also showed a positive, though less intense, mediating effect on well-being. We conclude that collectivist and individualistic motivational patterns coexist in Lickan-Antay natives and explain an important part of the relationship between ethnic identity and well-being. Finally, we discuss our results and suggest replication of this study in other ethnic contexts to assess the generalizability of these findings to other native peoples of the Andean region of South America.

Specification issues in nonlinear sem: the moderation that wasn't / Problemas de especificación en SEM no lineal: la moderación que no lo era

BACKGROUND: Analysis of interaction or moderation effects between latent variables is a common requirement in the social sciences. However, when predictors are correlated, interaction and quadratic effects become more alike, making them difficult to distinguish. As a result, when data are drawn from a quadratic population model and the analysis model specifies interactions only, misleading results may be obtained. METHOD: This article addresses the consequences of different types of specification error in nonlinear structural equation models using a Monte Carlo study. RESULTS: Results show that fitting a model with interactions when quadratic effects are present in the population will almost certainly lead to erroneous detection of moderation effects, and that the same is true in the opposite scenario. Simultaneous estimation of interactions and quadratic effects yields correct results. CONCLUSIONS: Simultaneous estimation of interaction and quadratic effects prevents detection of spurious or misleading nonlinear effects. Results are discussed and recommendations are offered to applied researchers

ANTECEDENTES: el análisis de efectos de interacción o moderación entre variables latentes es común en ciencias sociales. Sin embargo, cuando los predictores están correlacionados, los efectos de interacción y cuadráticos se vuelven parecidos, haciendo difícil su distinción. Así, cuando los datos provienen de un modelo de cuadrático a nivel poblacional y el modelo de análisis solo especifica interacciones, se pueden obtener resultados engañosos. MÉTODO: este artículo aborda las consecuencias de diferentes tipos de errores de especificación en modelos de ecuaciones estructurales no lineales utilizando un estudio de Monte Carlo. RESULTADOS: los resultados muestran que estimar un modelo con interacciones cuando en la población hay efectos cuadráticos conducirá a una detección equivocada de efectos de moderación con casi plena seguridad, y lo mismo ocurrirá en el escenario opuesto. La estimación simultánea de interacciones y efectos cuadráticos en el modelo conduce a resultados correctos. CONCLUSIONES: la estimación simultánea de efectos de interacción y cuadráticos permite evitar detectar efectos no lineales espurios o engañosos. Los resultados se discuten para ofrecer recomendaciones a los investigadores aplicados

Specification issues in nonlinear SEM: The moderation that wasn't.

BACKGROUND: Analysis of interaction or moderation effects between latent variables is a common requirement in the social sciences. However, when predictors are correlated, interaction and quadratic effects become more alike, making them difficult to distinguish. As a result, when data are drawn from a quadratic population model and the analysis model specifies interactions only, misleading results may be obtained. METHOD: This article addresses the consequences of different types of specification error in nonlinear structural equation models using a Monte Carlo study. RESULTS: Results show that fitting a model with interactions when quadratic effects are present in the population will almost certainly lead to erroneous detection of moderation effects, and that the same is true in the opposite scenario. Simultaneous estimation of interactions and quadratic effects yields correct results. CONCLUSIONS: Simultaneous estimation of interaction and quadratic effects prevents detection of spurious or misleading nonlinear effects. Results are discussed and recommendations are offered to applied researchers.

Latent variables should remain as such: Evidence from a Monte Carlo study.

Use of subject scores as manifest variables to assess the relationship between latent variables produces attenuated estimates. This has been demonstrated for raw scores from classical test theory (CTT) and factor scores derived from factor analysis. Conclusions on scores have not been sufficiently extended to item response theory (IRT) theta estimates, which are still recommended for estimation of relationships between latent variables. This is because IRT estimates appear to have preferable properties compared to CTT, while structural equation modeling (SEM) is often advised as an alternative to scores for estimation of the relationship between latent variables. The present research evaluates the consequences of using subject scores as manifest variables in regression models to test the relationship between latent variables. Raw scores and three methods for obtaining theta estimates were used and compared to latent variable SEM modeling. A Monte Carlo study was designed by manipulating sample size, number of items, type of test, and magnitude of the correlation between latent variables. Results show that, despite the advantage of IRT models in other areas, estimates of the relationship between latent variables are always more accurate when SEM models are used. Recommendations are offered for applied researchers.

[Clinical skills of Chilean general practitioners for the management of depressive disorders]. / Habilidades clínicas para el manejo de trastornos depresivos en médicos generales en Santiago de Chile.

BACKGROUND: Diverse and sustained efforts have been developed to improve the management of depression by general practitioners (GPs), but they have not improved treatment coverage and quality of services. AIM: To explore the level of knowledge and clinical skills to diagnose and treat depression by GPs in Primary Health Care (PHC) in the Metropolitan Area of Santiago de Chile (RM). MATERIAL AND METHODS: Theoretical knowledge (TK), diagnostic skills (DS) and treatment skills (TS) were evaluated in 56 GPs of the RM with a battery of specially designed instruments. RESULTS: In TK there were significant differences between GPs aged 31 years or less and their older counterparts and between Chilean and foreign doctors. Five percent of observed differences in TK were explained by age and nationality, respectively. Chilean GPs achieved higher scores in recognition of symptoms (RS), one of the dimensions of DS. No significant differences by age or nationality were observed for DS and TS. CONCLUSIONS: Achievement of GPs on tests measuring TK, DS, and TS was generally below 50%. This deficiency should be improved.

Habilidades clínicas para el manejo de trastornos depresivos en médicos generales en Santiago de Chile / Clinical skills of Chilean general practitioners for the management of depressive disorders

Background: Diverse and sustained efforts have been developed to improve the management of depression by general practitioners (GPs), but they have not improved treatment coverage and quality of services. Aim: To explore the level of knowledge and clinical skills to diagnose and treat depression by GPs in Primary Health Care (PHC) in the Metropolitan Area of Santiago de Chile (RM). Material and Methods: Theoretical knowledge (TK), diagnostic skills (DS) and treatment skills (TS) were evaluated in 56 GPs of the RM with a battery of specially designed instruments. Results: In TK there were significant differences between GPs aged 31 years or less and their older counterparts and between Chilean and foreign doctors. Five percent of observed differences in TK were explained by age and nationality, respectively. Chilean GPs achieved higher scores in recognition of symptoms (RS), one of the dimensions of DS. No significant differences by age or nationality were observed for DS and TS. Conclusions: Achievement of GPs on tests measuring TK, DS, and TS was generally below 50%. This deficiency should be improved.

Reexamining Nonlinear Structural Equation Modeling Procedures: The Effect of Parallel and Congeneric Measures.

The current study examines the performance of the extended unconstrained approach (EXUC) and the latent moderated structural equation modeling procedure (LMS) in situations where quadratic and interaction terms are tested simultaneously and investigates their limitations with regard to the employment of parallel and congeneric measures, relatively low indicator reliabilities, and relatively large numbers of indicators. By means of a Monte Carlo study, we found LMS to be the best option for testing multiple nonlinear effects given sufficient sample size (n ≥ 500) and normally distributed exogenous variables. Its advantages became more prominent when indicator reliabilities were heterogeneous and small. The EXUC was a viable option for estimating the model when indicators were parallel and exhibited large indicator reliabilities. An empirical example of the results is provided, and the relevance of measurement model characteristics to assess nonlinear relationships is discussed.