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。     

Interval estimation of gamma for an<Emphasis Type="Italic">R</Emphasis>×<Emphasis Type="Italic">S</Emphasis> table

When the underlying responses are on an ordinal scale, gamma is one of the most frequently used indices to measure the strength of association between two ordered variables. However, except for a brief mention on the use of the traditional interval estimator based on Wald's statistic, discussion of interval estimation of the gamma is limited. Because it is well known that an interval estimator using Wald's statistic is generally not likely to perform well especially when the sample size is small, the goal of this paper is to find ways to improve the finite-sample performance of this estimator. This paper develops five asymptotic interval estimators of the gamma by employing various methods that are commonly used to improve the normal approximation of the maximum likelihood estimator (MLE). Using Monte Carlo simulation, this paper notes that the coverage probability of the interval estimator using Wald's statistic can be much less than the desired confidence level, especially when the underlying gamma is large. Further, except for the extreme case, in which the underlying gamma is large and the sample size is small, the interval estimator using a logarithmic transformation together with a monotonic function proposed here not only performs well with respect to the coverage probability, but is also more efficient than all the other estimators considered here. Finally, this paper notes that applying an ad hoc adjustment procedure—whenever any observed frequency equals 0, we add 0.5 to all cells in calculation of the cell proportions—can substantially improve the traditional interval estimator. This paper includes two examples to illustrate the practical use of interval estimators considered here.The authors wish to thank the Associate Editor and the two referees for many valuable comments and suggestions to improve the contents and clarity of this paper. The authors also want to thank Dr. C. D. Lin for his graphic assistance.     

The order-restricted association model: Two estimation algorithms and issues in testing

This paper presents a row-column (RC) association model in which the estimated row and column scores are forced to be in agreement with an a priori specified ordering. Two efficient algorithms for finding the order-restricted maximum likelihood (ML) estimates are proposed and their reliability under different degrees of association is investigated by a simulation study. We propose testing order-restricted RC models using a parametric bootstrap procedure, which turns out to yield reliablep values, except for situations in which the association between the two variables is very weak. The use of order-restricted RC models is illustrated by means of an empirical example. Francisca Galindo performed this research as a part of her PhD. dissertation project at Tilburg University.     

Manipulation of visual information does not change the accuracy of distance estimation during a blindfolded walking task

While humans rely on vision during navigation, they are also competent at navigating non-visually. However, non-visual navigation over large distances is not very accurate and can accumulate error. Currently, it is unclear whether this accumulation of error is due to the visual estimate of the distance or to the locomotor production of the distance. In a series of experiments, using a blindfolded walking test, we examine whether enhancing the visual estimate of the distance to a previously seen target, through environmental enrichment, visual imagery, or repeated exposure would improve the accuracy of blindfold navigation across different distances. We also attempt to decrease the visual estimate in order to see if the opposite effect would occur. Our results would indicate that manipulation of the static visual distance estimate did not change the navigation accuracy to any great extent. The only condition that improved accuracy was repeated exposure to the environment through practice. These results suggest that error observed during blindfold navigation may be due to the locomotor production of the distance, rather than the visual process.     

Does time fly when you're counting down? The effect of counting direction on subjective time judgment

We show that counting downward while performing a task shortens the perceived duration of the task compared to counting upward. People perceive that less time has elapsed when they were counting downward versus upward while using a product (Studies 1 and 3) or watching geometrical shapes (Study 2). The counting direction effect is obtained using both prospective and retrospective time judgments (Study 3), but only when the count range begins with the number “1” (Study 2). Furthermore, the counting direction affects peoples' attitude toward the product, their likelihood of using it again, and their purchase intentions. We test several plausible accounts for the counting direction effect, including task difficulty, numerical anchoring, and arousal. We find preliminary evidence that downward counting feels shorter because it is more arousing than upward counting.     

A Computationally More Efficient and More Accurate Stepwise Approach for Correcting for Sampling Error and Measurement Error

Over the last decade or two, multilevel structural equation modeling (ML-SEM) has become a prominent modeling approach in the social sciences because it allows researchers to correct for sampling and measurement errors and thus to estimate the effects of Level 2 (L2) constructs without bias. Because the latent variable modeling software Mplus uses maximum likelihood (ML) by default, many researchers in the social sciences have applied ML to obtain estimates of L2 regression coefficients. However, one drawback of ML is that covariance matrices of the predictor variables at L2 tend to be degenerate, and thus, estimates of L2 regression coefficients tend to be rather inaccurate when sample sizes are small. In this article, I show how an approach for stabilizing covariance matrices at L2 can be used to obtain more accurate estimates of L2 regression coefficients. A simulation study is conducted to compare the proposed approach with ML, and I illustrate its application with an example from organizational research.     

Estimating Optimal Weights for Compound Scores: A Multidimensional IRT Approach

A method is proposed for constructing indices as linear functions of variables such that the reliability of the compound score is maximized. Reliability is defined in the framework of latent variable modeling [i.e., item response theory (IRT)] and optimal weights of the components of the index are found by maximizing the posterior variance relative to the total latent variable variance. Three methods for estimating the weights are proposed. The first is a likelihood-based approach, that is, marginal maximum likelihood (MML). The other two are Bayesian approaches based on Markov chain Monte Carlo (MCMC) computational methods. One is based on an augmented Gibbs sampler specifically targeted at IRT, and the other is based on a general purpose Gibbs sampler such as implemented in OpenBugs and Jags. Simulation studies are presented to demonstrate the procedure and to compare the three methods. Results are very similar, so practitioners may be suggested the use of the easily accessible latter method. A real-data set pertaining to the 28-joint Disease Activity Score is used to show how the methods can be applied in a complex measurement situation with multiple time points and mixed data formats.     

Ipsilateral Loudness Adaptation Over Multiple Intensity Levels

Three mirror tracing experiments were conducted to investigate the connection between perception and motor behavior. In the first experiment, some subjects traced a hex-maze, other subjects traced a hex-maze after observing a model trace, others traced a hex-maze after reading instructions on mirror images, and others traced a hex-maze after having observed a model and heard the instructions. There were no significant differences between the groups' error scores, but their time scores differed significantly, although not always in the predicted direction. In Experiment 2, the subjects were to trace selected letters of the alphabet. Error scores for the second experiment did not differ much from those for the first experiment. In Experiment 3, the experimenter gave each subject commands for the correct directions of movement, using the subject's body as a frame of reference. There was little improvement in motor performance. These results suggest that the visual information presented in the mirror captured the subjects' attention and blocked their motor tracing program.     

Multiple timescales of body schema reorganization due to plastic surgery

Plastic surgery modifies the distribution of mass centers of a person’s body segments, changing his or her posture. The functional reorganization processes that lead subjects to re-integrate these body changes into a new stable body (posture) schema is poorly understood but current theories suggest the possible contribution of two components: a feedback mechanism that strongly depends on sensory input and an internal model that is relatively less dependent on sensory input and improves posture control, for example by compensating for delayed feedback. To assess the relative contributions of these two mechanisms during the functional reorganization of a posture scheme, we have conducted a longitudinal postural study in a population of healthy adults who were subject to breast plastic surgery to reduce or augment body weight. We measured participants’ orthostatic posture and ground reaction force immediately after, after 4 months, and 1 year after the surgery. To investigate the role of visual sensory information in the reorganization process we tested the participants with eyes open and closed. Our results indicate that participants find a new dynamical equilibrium within a few days. However, posture maintenance remains sub-optimal long after the center of masses and the resultant of ground reaction force stop changing; in some cases, for more than 4 months. Furthermore, the re-adaptation process is faster and more efficient in the eyes-open than in the eyes-closed condition. These results suggest that the reorganization involves different subsystems (responsible for the biomechanical changes, the re-calibration of feedback mechanisms, and the re-adaptation of internal models), which act at different timescales.