贝叶斯结构方程模型及其研究现状

在心理学研究中结构方程模型(Structural Equation Modeling, SEM)被广泛用于检验潜变量间的因果效应, 其估计方法有频率学方法(如, 极大似然估计)和贝叶斯方法两类。近年来由于贝叶斯统计的流行及其在结构方程建模中易于处理小样本、缺失数据及复杂模型等方面的优势, 贝叶斯结构方程模型发展迅速, 但其在国内心理学领域的应用不足。主要介绍了贝叶斯结构方程模型的方法基础和优良特性, 及几类常用的贝叶斯结构方程模型及其应用现状, 旨在为应用研究者介绍新的研究工具。     

Hopes and cautions in implementing Bayesian structural equation modeling

Muthén and Asparouhov (2012) have proposed and demonstrated an approach to model specification and estimation in structural equation modeling (SEM) using Bayesian methods. Their contribution builds on previous work in this area by (a) focusing on the translation of conventional SEM models into a Bayesian framework wherein parameters fixed at zero in a conventional model can be respecified using small-variance priors and (b) implementing their approach in software that is widely accessible. We recognize potential benefits for applied researchers as discussed by Muthén and Asparouhov, and we also see a tradeoff in that effective use of the proposed approach introduces increased demands in terms of expertise of users to navigate new complexities in model specification, parameter estimation, and evaluation of results. We also raise cautions regarding the issues of model modification and model fit. Although we see significant potential value in the use of Bayesian SEM, we also believe that effective use will require an awareness of these complexities. (PsycINFO Database Record (c) 2012 APA, all rights reserved).     

Generalized multilevel structural equation modeling

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.gllamm can be downloaded from http://www.gllamm.org. The paper was written while Sophia Rabe-Hesketh was employed at and Anders Skrondal was visiting the Department of Biostatistics and Computing, Institute of Psychiatry, King's College London.     

Meta-analytic structural equation modeling: a two-stage approach

To synthesize studies that use structural equation modeling (SEM), researchers usually use Pearson correlations (univariate r), Fisher z scores (univariate z), or generalized least squares (GLS) to combine the correlation matrices. The pooled correlation matrix is then analyzed by the use of SEM. Questionable inferences may occur for these ad hoc procedures. A 2-stage structural equation modeling (TSSEM) method is proposed to incorporate meta-analytic techniques and SEM into a unified framework. Simulation results reveal that the univariate-r, univariate-z, and TSSEM methods perform well in testing the homogeneity of correlation matrices and estimating the pooled correlation matrix. When fitting SEM, only TSSEM works well. The GLS method performed poorly in small to medium samples.     

Bayesian estimation and testing of structural equation models

The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. If the prior distribution over the parameters is uninformative, the posterior is proportional to the likelihood, and asymptotically the inferences based on the Gibbs sample are the same as those based on the maximum likelihood solution, for example, output from LISREL or EQS. In small samples, however, the likelihood surface is not Gaussian and in some cases contains local maxima. Nevertheless, the Gibbs sample comes from the correct posterior distribution over the parameters regardless of the sample size and the shape of the likelihood surface. With an informative prior distribution over the parameters, the posterior can be used to make inferences about the parameters underidentified models, as we illustrate on a simple errors-in-variables model.We thank David Spiegelhalter for suggesting applying the Gibbs sampler to structural equation models to the first author at a 1994 workshop in Wiesbaden. We thank Ulf Böckenholt, Chris Meek, Marijtje van Duijn, Clark Glymour, Ivo Molenaar, Steve Klepper, Thomas Richardson, Teddy Seidenfeld, and Tom Snijders for helpful discussions, mathematical advice, and critiques of earlier drafts of this paper.     

Networks of meaning and the bicultural mind: A structural equation modeling approach

The paper examines cultural frame-switching among bicultural participants at the level of associated meanings. Bicultural participants of Greek descent who live in the Netherlands were randomly primed with either Greek or Dutch cultural symbols and language. A monocultural Dutch group in the Netherlands and a monocultural Greek group in Greece, were involved as control groups. Structural equation modeling indicated that the Greek participants in Greece and the bicultural participants primed with Greek culture had a similar pattern of associations. This pattern differed from that of the Dutch participants and the participants primed with Dutch culture. Thus, the two different monocultural association patterns were replicated among bicultural individuals. The findings show that meanings are affected by cultural frame-switching, and provide further support for the dynamic constructivist approach.     

Missing data techniques for structural equation modeling

As with other statistical methods, missing data often create major problems for the estimation of structural equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, structural equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling missing data in an optimal fashion. In addition to maximum likelihood, this article also discusses multiple imputation. This method has statistical properties that are almost as good as those for maximum likelihood and can be applied to a much wider array of models and estimation methods.     

Clarifying the role of impulsivity in dietary restraint: A structural equation modeling approach

This study was designed to clarify the relationship between the four dimensions of impulsivity in Whiteside and Lynam’s (2001) model and the two aspects of dietary restraint (Concern for Dieting and Weight Fluctuation) in a non-clinical sample. Data were collected from a volunteer community sample (N = 216) of women who responded to two self-report instruments related to impulsivity (UPPS Impulsive Behavior Scale, Whiteside & Lynam, 2001) and dietary restraint (Restraint Scale, Polivy, Herman, & Warsh, 1978). A structural equation model was tested. The model provided a good fit to the data (χ²/df = 1.64, p < .0001, RMSEA = 0.054, 90% CI = 0.050–0.058, p-value for test of close fit (RMSEA > 0.05) = .035) and revealed that Concern for Dieting was positively related to Urgency (standardized β = 0.25, p < .01). Weight Fluctuation was related to lack of Perseverance (standardized β = 0.25, p < .01) and tended to relate to Sensation Seeking (standardized β = 0.17, p = .056). Urgency and lack of Perseverance were found to play a significant role in predicting Concern for Dieting and Weight Fluctuation.     

Using self-determination theory to understand motivation for walking: Instrument development and model testing using Bayesian structural equation modelling

ObjectiveThe motivational processes underpinning walking behaviour are not well understood. This study aimed to develop walking-specific motivation measures drawn from self-determination theory (SDT), assess the psychometric properties of the measures, incorporating Baysesian structural equation modelling (BSEM), and examine how these variables relate to walking behaviour.MethodParticipants (n = 298; mean age = 41.69; S.D. = 11.06; male = 57) completed the Behavioural Regulations in Walking Questionnaire (BRWQ), Psychological Needs Satisfaction for Walking Scale (PNSWS) and the IPAQ-long form, from which measures of workplace, transport and leisure walking were extracted. BSEM was used to test the hypothesized factor structures of the BRWQ and PNSWS. Internal reliabilities were assessed using the composite reliability coefficient. Convergent and discriminant validity were assessed by examining the relationships between the variables in relation to established theory.ResultsBSEM showed excellent fit for the BRWQ and PNSWS measurement models. The scales demonstrated good internal consistency. The associations within and between the BRWQ and PNSWS subscales were generally as expected. The relationship between the BRWQ subscales and walking for transport and leisure were also generally as expected, but there were no significant relationships for walking at work. Two PNSWS subscales were significantly related to walking for leisure, but no significant relationships were evident for walking for transport and at work.ConclusionsThere is preliminary evidence for the acceptable psychometric properties of instruments to measure SDT constructs in walking, and the findings highlight the advantages of BSEM. The findings also suggest that the motivational processes underpinning walking may vary by type of walking.     

结构方程模型统计检验力分析：原理与方法

结构方程模型是心理学、管理学、社会学等学科中重要的统计工具之一。然而, 大量使用结构方程模型的研究忽视了对该方法的统计检验力进行必要的分析和报告, 在一定程度上降低了这些研究的结果的证明效力。结构方程模型的统计检验力分析方法主要有Satorra-Saris法、MacCallum法与Monte Carlo法三类。其中Satorra-Saris法适用于备择模型清晰、检验对象相对简单、检验方法基于χ²分布的情形; MacCallum法适用于基于χ²分布的模型拟合检验且备择模型不明的情形; Monte Carlo法适用于检验对象相对复杂、采用模拟或重抽样方法进行检验的情形。在实际应用中, 研究者应当首先判断检验的目的、方法以及是否有明确的备择模型, 并根据这些信息选择具体的分析方法。     

Thought-action fusion: a comprehensive analysis using structural equation modeling

Thought-action fusion (TAF), the phenomenon whereby one has difficulty separating cognitions from corresponding behaviors, has implications in a wide variety of disturbances, including eating disorders, obsessive-compulsive disorder, generalized anxiety disorder, and panic disorder. Numerous constructs believed to contribute to the etiology or maintenance of TAF have been identified in the literature, but to date, no study has empirically integrated these findings into a comprehensive model. In this study, we examined simultaneously an array of variables thought to be related to TAF, and subsequently developed a model that elucidates the role of those variables that seem most involved in this phenomenon using a structural equation modeling approach. Results indicated that religiosity, as predicted by ethnic identity, was a significant predictor of TAF. Additionally, the relation between ethnic identity and TAF was partially mediated by an inflated sense of responsibility. Both TAF and obsessive-compulsive symptoms were found to be significant predictors of engagement in neutralization activities. Clinical and theoretical implications are discussed.     

Resilience and coping in trauma spectrum symptoms prediction: A structural equation modeling approach

Resilience is increasingly recognized as a relevant factor in shaping psychological response to natural disasters. Aim of the study is to examine in the context of a natural disaster the potential effects of resilience on the relation between coping and trauma spectrum symptoms, using structural equation modeling.A sample of 371 students who survived the earthquake in L’Aquila (Italy) were cross-sectionally evaluated using Resilience Scale for Adolescents, Brief Cope and Trauma and Loss Spectrum scale.The model shows a direct path of positive and emotional coping styles on resilience. Emotional coping shows also a direct impact on the outcome; positive and emotional coping results to be positively correlated as well as emotional and disengagement coping styles. Resilience directly affects the PTSD symptoms, partially mediating the impact of the coping styles. The model explains 30% of the variance in the outcome, i.e. the Post-Traumatic Stress Disorder (PTSD) symptoms, with very good fit indexes.Resilience operates as a protective factor from stress symptom development. It is likely that emotional and disengagement coping skills are rapidly involved after a traumatic exposure but when problem focused coping intervenes, resilience allows it to buffer the stressors or even guides toward a more successful outcome.     

Constrained versus unconstrained estimation in structural equation modeling

Recently, R. D. Stoel, F. G. Garre, C. Dolan, and G. van den Wittenboer (2006) reviewed approaches for obtaining reference mixture distributions for difference tests when a parameter is on the boundary. The authors of the present study argue that this methodology is incomplete without a discussion of when the mixtures are needed and show that they only become relevant when constrained difference tests are conducted. Because constrained difference tests can hide important model misspecification, a reliable way to assess global model fit under constrained estimation would be needed. Examination of the options for assessing model fit under constrained estimation reveals that no perfect solutions exist, although the conditional approach of releasing a degree of freedom for each active constraint appears to be the most methodologically sound one. The authors discuss pros and cons of constrained and unconstrained estimation and their implementation in 5 popular structural equation modeling packages and argue that unconstrained estimation is a simpler method that is also more informative about sources of misfit. In practice, researchers will have trouble conducting constrained difference tests appropriately, as this requires a commitment to ignore Heywood cases. Consequently, mixture distributions for difference tests are rarely appropriate.     

Predictors of champion behaviors in an evidence-based parenting program: A structural equation modeling approach

The importance of champions in the implementation of evidence-based public health programs has been well established. Champions perform a range of behaviors which can have significant influence on the adoption and sustainability of interventions. This study investigates champion behaviors in parenting practitioners with the aim to provide insight into the range and extent that practitioners engage in champion behaviors and to examine predictors of practitioners' champion behaviors using structural equation modeling. Data were collected during a large international implementation survey of 1606 practitioners trained to deliver an evidence-based parenting program, the triple P—Positive Parenting Program. We developed a 13-item Champion Behaviors Scale which was administered alongside other measures of predictors of implementation outcomes. Perceived usefulness of the program was the most important facilitator of both public and personal champion behaviors. Certain desirable features of the program tended to not only be positively associated with the perceived usefulness of the program, but also had a unique impact on practitioners' personal champion behaviors. Higher positions within organizations were linked with more public champion behaviors. Although organizational support was found to be unrelated to champion behaviors in the structural model, it facilitated other predictors of champion behaviors.     

Perceptions of ambiguously unpleasant interracial interactions: a structural equation modeling approach

Despite a general consensus in the United States that overtly racist acts are unacceptable, many ambiguous situations in everyday life raise questions of whether racism has influenced a person's behavior in an interracial encounter. The authors of the present study sought to (a) examine simultaneously an array of variables thought to be related to perceived racism and (b) investigate how the contribution of these variables may differ with respect to the asymmetry hypothesis, which suggests that acts of discrimination from a dominant person toward a subordinate person will be viewed as more biased than if the situation were reversed. The authors used a dual structural equation modeling approach. Results indicated that ethnic identity significantly predicted perceived racism. In addition, the extent to which cognitive interpretation style significantly predicted perceived racism depended on the ethnicity of participants involved in the interaction.     

Departure from normal assumptions: A promise for future psychometrics with substantive mathematical modeling

Normal assumptions have been used in many psychometric methods, to the extent that most researchers do not even question their adequacy. With the rapid advancement of computer technologies in recent years, psychometrics has extended its territory to include intensive cognitive diagnosis, etcetera, and substantive mathematical modeling ha become essential. As a natural consequence, it is time to consider departure from normal assumptions seriously. As examples of models which are not based on normality or its approximation, the logistic positive exponent family of models is discussed. These models include the item task complexity as the third parameter, which determines the single principle of ordering individuals on the ability scale.