Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation

Often when participants have missing scores on one or more of the items comprising a scale, researchers compute prorated scale scores by averaging the available items. Methodologists have cautioned that proration may make strict assumptions about the mean and covariance structures of the items comprising the scale (Schafer &; Graham, 2002 Schafer, J.L., &; Graham, J.W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147–177.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; Graham, 2009 Graham, J.W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549–576.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]; Enders, 2010 Enders, C.K. (2010). Applied missing data analysis. New York, NY: Guilford Press. [Google Scholar]). We investigated proration empirically and found that it resulted in bias even under a missing completely at random (MCAR) mechanism. To encourage researchers to forgo proration, we describe a full information maximum likelihood (FIML) approach to item-level missing data handling that mitigates the loss in power due to missing scale scores and utilizes the available item-level data without altering the substantive analysis. Specifically, we propose treating the scale score as missing whenever one or more of the items are missing and incorporating items as auxiliary variables. Our simulations suggest that item-level missing data handling drastically increases power relative to scale-level missing data handling. These results have important practical implications, especially when recruiting more participants is prohibitively difficult or expensive. Finally, we illustrate the proposed method with data from an online chronic pain management program.     

Accounting for sensation seeking in route choice behavior with travel time information

The purpose of this paper is to demonstrate that latent variables, with the focus on sensation seeking concepts, incorporated in new technique of route choice modeling, improve our analyzing of route choice behavior with pre-trip travel time information. The application of a hybrid discrete choice model framework integrates a latent variable model and a route choice model by combining their measurement and structural equations. The model is estimated based on data from a laboratory experiment and a field study of a simple network. The results show that certain sensation seeking domains (e.g., thrill and adventure seeking) alongside traditional variables (e.g., travel time information) enrich our understanding and provide more insight into route choice behavior. Furthermore, observed personal variables, such as gender and marital status, may serve as causal indicators to sensation seeking variables.     

On the explaining‐away phenomenon in multivariate latent variable models

Many probabilistic models for psychological and educational measurements contain latent variables. Well‐known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the ‘explaining‐away’ phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well‐known latent variable models by using both theoretical and real data examples.     

The role of self‐image as a predictor of psychotherapy outcome

The present study examined the relationship between self‐image and outcome in psychotherapy. Patients (n = 170) received treatment‐as‐usual at a university clinic, and met diagnostic criteria for mostly anxiety and depression related disorders. Self‐image was measured with the Structural Analysis of Social Behavior (SASB‐I) introject pre and post‐treatment. Using multiple regression analyses, higher levels of Self‐ignore and Self‐blame pre‐treatment predicted a poorer treatment outcome in terms of symptoms (SCL‐90‐R) and interpersonal problems (IIP‐64), respectively. Increase in Self‐love and decrease in Self‐blame (pre to post) predicted reduced symptoms at post‐treatment, whereas decrease in Self‐attack and Self‐control, as well as increase in Self‐affirm, predicted reduced interpersonal problems. The results suggest that self‐image improvement may be important in order to achieve a good outcome in psychotherapy.     

Los efectos placebo en la psicología experimental y clínica

RESUMEN

Este trabajo comienza planteando que el término “placebo” se ha convertido en un cajón de sastre en el que los autores engloban diferentes, y a menudo opuestas y dispares, concepciones. Después de destacar la atención creciente a los efectos placebo en la psicología actual y la tendencia a la convergencia entre distintos enfoques en psicoterapia, la autora aborda las divergencias y dificultades para definir placebo aludiendo a concepciones de diferentes autores y centrándose en el planteamiento de Grünbaum (1985). Avia coincide con Brody (1985) en que el problema de nuestro desconocimiento sobre qué sea placebo afecta, recíprocamente, a nuestra falta de conocimiento sobre lo que debe considerarse terapia, ofreciendo algunas reflexiones en torno a posibles explicaciones de este fenómeno y destacando la débil frontera que separa éstas de las explicaciones sobre ciertas variables cognitivas. La autora concluye la presentación de este dossier afirmando que será a partir de la elaboración teórica y el trabajo de investigación como podremos profundizar en el conocimiento del fenómeno placebo.     

Factores psicosociales y cáncer: una revisión crítica

Resumen

Se presenta en este artículo una introducción al campo de estudio de las relaciones entre factores psicosociales y neoplasia, enmarcándolo dentro de la medicina conductual y desde un enfoque interdisciplinar. A continuación se discute el papel que pueden desempeñar las variables psicosociales (estrés) en la vulnerabilidad del ser humano a la enfermedad en general a través de su acción sobre el sistema inmune. En el apartado III se realiza una revisión crítica en la investigación en la que se ha intentado delimitar la influencia de factores psicosociales en la aparición y pronóstico del cáncer, en animales (cáncer experimental) y en el ser humano. Se discute por último el papel de los mediadores biológicos propuestos como más importantes hasta el momento en la literatura del área (sistemas neuroendocrino e inmune). De esta revisión se desprende que los resultados de la investigación, aunque su- gerentes no son definitivos, ni tampoco están claros los mecanismos biológicos a través de los cuales se puede producir la influencia. Después de analizar algunas de las deficiencias metodológicas más importantes de los trabajos realizados, creemos que en enfoque más adecuado para poner a prueba la hipótesis de que los factores psicosociales afectan al desarrollo del cáncer debería ser longitudinal, utilizando grandes muestras y estudiando variables psicosociales y biomédicas conjuntamente.     

组织承诺研究综述

本文通过对组织承诺的文献分析,讨论了组织承诺概念的演变和其结构的发展,并对组织承诺的相关因素进行了评述。分析了组织承诺形成的影响变量和其效标变量,对今后的发展方向进行了展望。     

Notes on selective influence, probabilistic causality, and probabilistic dimensionality

The paper provides conceptual clarifications for the issues related to the dependence of jointly distributed systems of random entities on external factors. This includes the theory of selective influence as proposed in Dzhafarov [(2003a). Selective influence through conditional independence. Psychometrika, 68, 7-26] and generalized versions of the notions of probabilistic causality [Suppes, P., & Zanotti, M. (1981). When are probabilistic explanations possible? Synthese, 48, 191-199] and dimensionality in the latent variable models [Levine, M. V. (2003). Dimension in latent variable models. Journal of Mathematical Psychology, 47, 450-466]. One of the basic observations is that any system of random entities whose joint distribution depends on a factor set can be represented by functions of two arguments: a single factor-independent source of randomness and the factor set itself. In the case of random variables (i.e., real-valued random entities endowed with Borel sigma-algebras) the single source of randomness can be chosen to be any random variable with a continuous distribution (e.g., uniformly distributed between 0 and 1).     

Alternative Multiple Imputation Inference for Categorical Structural Equation Modeling

The use of item responses from questionnaire data is ubiquitous in social science research. One side effect of using such data is that researchers must often account for item level missingness. Multiple imputation is one of the most widely used missing data handling techniques. The traditional multiple imputation approach in structural equation modeling has a number of limitations. Motivated by Lee and Cai’s approach, we propose an alternative method for conducting statistical inference from multiple imputation in categorical structural equation modeling. We examine the performance of our proposed method via a simulation study and illustrate it with one empirical data set.     

Fault Lines in the Great EST Debate

Three fault lines traverse inquiries into empirically supported therapies, along which pro and contra positions can be taken. First, failure to distinguish scientific from artistic properties of therapy has lead to neglect of pervasive personological and cultural variables that are implicated in this field of inquiry. Second, scientific anomalies result from focusing more on what ESTs are than on how they effect change. Psyche is portrayed as a humanistic Cartesian place-holder for an encroaching neuro-endocrinological and systemic understanding of human experiencing. Third, the EST movement is seen as a socio-political and business model, confounded with and eroding scientific objectives.