Hierarchical Linear Modeling in Organizational Research: Longitudinal Data Outside the Context of Growth Modeling

Organizational researchers, including those carrying out occupational stress research, often conduct longitudinal studies. Hierarchical linear modeling (HLM; also known as multilevel modeling and random regression) can efficiently organize analyses of longitudinal data by including within- and between-person levels of analysis. A great deal of longitudinal research has been conducted in the context of growth studies in which change in the dependent variable is examined in relation to the passage of time. HLM can treat longitudinal data, including data outside the context of the growth study, as nested data, reducing the problem of censoring. Within-person equation coefficients can represent the impact of Time t − 1 working conditions on Time t outcomes using all appropriate pairs of data points. Time itself need not be an independent variable of interest

How trace plots help interpret meta-analysis results

The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of tau, the between-study standard deviation, and the shrunken estimates of the study effects as a function of tau. With a small or moderate number of studies, tau is not estimated with much precision, and parameter estimates and shrunken study effect estimates can vary widely depending on the correct value of tau. The trace plot allows visualization of the sensitivity to tau along with a plot that shows which values of tau are plausible and which are implausible. A comparable frequentist or empirical Bayes version provides similar results. The concepts are illustrated using examples in meta-analysis and meta-regression; implementaton in R is facilitated in a Bayesian or frequentist framework using the bayesmeta and metafor packages, respectively.Comment: 13 pages, 18 Figure

Validación de un documento de Word bajo la norma NTC1486 a partir de la metadata

Investigación TecnológicaEn el presente documento se encuentra contemplado el fundamento teórico de los tipos de datos y de los modelos de datos que se manejan en la actualidad, así como la web semántica y aspectos de ingeniería de software requeridos para la construcción de un prototipo de software que automatice el proceso de extracción de metadatos en un documento de Word, validando que la información contenida en este cumpla con la NTC 1486 en su totalidad, siendo una herramienta que facilite el proceso de calificación y caracterización de trabajos de grado y documentos de tipo investigativo. Esta investigación tiene un impacto dentro de la comunidad investigativa en la medida en que genera un prototipo de software que facilita el proceso de revisión de documentos investigativos en construcción.Glosario RESUMEN INTRODUCCIÓN 1.JUSTIFICACIÓN 2. PLANTEAMIENTO DEL PROBLEMA 3. OBJETIVO GENERAL 4. MARCO TEÓRICO 5. MARCO CONCEPTUAL 6. ESTADO DEL ARTE 7. METODOLOGÍA 8. RESULTADOS 9. CONCLUSIONES 10. TRABAJOS FUTUROS 11. BIBLIOGRAFÍAPregradoIngeniero de Sistema

Quality-of-life assessment in dementia: the use of DEMQOL and DEMQOL-Proxy total scores

Purpose There is a need to determine whether health-related quality-of-life (HRQL) assessments in dementia capture what is important, to form a coherent basis for guiding research and clinical and policy decisions. This study investigated structural validity of HRQL assessments made using the DEMQOL system, with particular interest in studying domains that might be central to HRQL, and the external validity of these HRQL measurements. Methods HRQL of people with dementia was evaluated by 868 self-reports (DEMQOL) and 909 proxy reports (DEMQOL-Proxy) at a community memory service. Exploratory and confirmatory factor analyses (EFA and CFA) were conducted using bifactor models to investigate domains that might be central to general HRQL. Reliability of the general and specific factors measured by the bifactor models was examined using omega (?) and omega hierarchical (? h) coefficients. Multiple-indicators multiple-causes models were used to explore the external validity of these HRQL measurements in terms of their associations with other clinical assessments. Results Bifactor models showed adequate goodness of fit, supporting HRQL in dementia as a general construct that underlies a diverse range of health indicators. At the same time, additional factors were necessary to explain residual covariation of items within specific health domains identified from the literature. Based on these models, DEMQOL and DEMQOL-Proxy overall total scores showed excellent reliability (? h > 0.8). After accounting for common variance due to a general factor, subscale scores were less reliable (? h < 0.7) for informing on individual differences in specific HRQL domains. Depression was more strongly associated with general HRQL based on DEMQOL than on DEMQOL-Proxy (?0.55 vs ?0.22). Cognitive impairment had no reliable association with general HRQL based on DEMQOL or DEMQOL-Proxy. Conclusions The tenability of a bifactor model of HRQL in dementia suggests that it is possible to retain theoretical focus on the assessment of a general phenomenon, while exploring variation in specific HRQL domains for insights on what may lie at the ‘heart’ of HRQL for people with dementia. These data suggest that DEMQOL and DEMQOL-Proxy total scores are likely to be accurate measures of individual differences in HRQL, but that subscale scores should not be used. No specific domain was solely responsible for general HRQL at dementia diagnosis. Better HRQL was moderately associated with less depressive symptoms, but this was less apparent based on informant reports. HRQL was not associated with severity of cognitive impairment

David Rindskopf - Extensions and special cases in latent class analysis

Content: Overview of Issues; Latent Class Analysis; Post Traumata Stress Disorder; Plot of Parameters; Logistic Regression with Floor and Ceiling Effect

Parameterizing inequality constraints on unique variances in linear structural models

structural models, inequality constraints,

A general framework for using latent class analysis to test hierarchical and nonhierarchical learning models

latent class analysis, latent structure analysis, Guttman models,