多层多项Logit模型：原理与应用

常用的多层线性模型要求因变量是服从正态分布的连续变量,却无法处理因变量为离散变量的嵌套数据.多层多项Logit模型能够处理因变量是无序多分类的多层嵌套数据,但这一模型在我国心理学研究中却鲜有介绍和应用.研究简要介绍了多层多项Logit模型的原理、参数估计和假设检验,然后分别用该模型和传统Logistic回归模型探讨个体因素和区域因素对35721名学生进入不同高校就读机会的影响并对结果进行了解释,结果表明多层多项Logit模型比传统Logistic回归模型更拟合数据.     

基于多层结构方程模型的情境效应分析—— 兼与多层线性模型比较

多层(嵌套)数据的变量关系研究, 必须借助多层模型来实现。两层模型中, 层一自变量Xij按组均值中心化, 并将组均值 置于层2截距方程式中, 可将Xij对因变量Yij的效应分解为组间和组内部分, 二者之差被称为情境效应, 称为情境变量。多层结构方程模型(MSEM)将多层线性模型(MLM)和结构方程模型(SEM)相结合, 通过设置潜变量和多指标的方法校正了MLM在情境效应分析中出现的抽样误差和测量误差, 同时解决了数据的多层(嵌套)结构和潜变量的估计问题。除了分析原理的说明, 还以班级平均竞争氛围对学生竞争表现的情境效应为例进行分析方法的示范, 并比较MSEM和MLM的异同, 随后展望了MSEM情境效应模型、情境效应无偏估计方法和情境变量研究的拓展方向。     

班级环境与学生适应性的多层线性模型

此研究探索中国中小学体制下的班级社会心理环境对于学生的适应性的关系。以江光荣和林盂平所编制的《我的班级》问卷测量班级环境．选择学生的学校适应（由Teacher-Child Rating Scale（T-CRS）测量）、主观幸福感（以Student’s Life Satisfaction Scale（SLSS）测量）和焦虑（用State-Trait Anxiety Inventory for Children（STAlC）测量）作为适应指标．以多层线性模型（HLM）方法进行分析,结果显示：学生个体所知觉到的班级环境,对其适应水平有相当肯定的鳃释力,而一个班级学生整体适应水平的高低,与这个班的班级环境有极大关联。此结果表明．中国学校体制下的班级社会心理环境对于学生的发展和适应状况,具有举足轻重的作用。     

虚拟团队共享心理模型与团队效能的关系

通过对62个虚拟团队的调研,检验了虚拟团队认同式共享心理模型与分布式共享心理模型构思,并运用多层线性建模技术,对团队特征变量、共享心理模型与虚拟团队效能间的关系进行了分析。结果表明：两类共享心理模型与团队效能间存在显著正相关;团队时间会影响共享心理模型与虚拟团队任务效能间的关系,团队时间会削弱认同式共享心理模型对任务效能间的正向关系,但能加强分布式共享心理模型对任务效能的正面影响;团队规模则主要影响共享心理模型对合作效能的效应,团队规模增加会加强认同式共享心理模型与合作效能间的相关,却会削弱分布式共享心理模型与合作效能间的关系     

二分数据的多层线性模型:原理与应用

分类数据的多层线性模型在我国的心理学研究中鲜有使用。本研究旨在将这种模型引入到我国心理学研究之中。论文首先介绍了二分数据的多层线性模型的原理和假设条件、参数估计和假设检验,然后以6187名小学生为被试,采用二分变量的多层线性模型,说明了个体因素和学校因素对儿童攻击行为的影响,并对分析结果进行了解释。     

组织创新文化、组织文化强度与员工创新行为：多层线性模型的分析

本研究通过对某大型电网公司156家供电局的10856名员工的问卷调查,检验了组织创新文化、组织文化强度（即员工价值观一致性）与员工创新行为的关系。多层线性分析（HLM）结果表明：（1）组织创新文化与员工创新行为有正向关联;（2）组织文化强度与员工创新行为有负向关联;但是（3）组织文化强度却可以增强创新文化与员工创新行为的正向关系。研究提出,引入组织文化的内容属性和强度属性的交互作用,可以更精确地理解组织文化与个体创新行为的关系。     

生涯适应力的作用：个体与组织层的跨层面分析*

生涯适应力(career adaptability)是生涯建构理论提出的自我职业生涯管理的核心概念,但国外理论和实践一直认为生涯适应力是一把双刃剑(生涯适应力高的员工工作绩效高,但其离职意向也高),本研究将对此进行检验;同时,生涯适应力作为个体职业生涯开发的核心变量,它在组织职业生涯管理跨层面作用中的价值也未曾研究。通过员工在两个时间点自评和管理者他评问卷,获得54家单位的485份有效调查问卷。结构方程模型分析的结果表明,生涯适应力不仅与工作绩效有显著正相关,而且也与离职意向有显著负相关。层次回归分析结果表明,工龄是生涯适应力与离职意向、工作绩效关系的调节变量;也就是说,工龄短员工的生涯适应力与工作绩效呈显著正相关,与离职意向呈显著负相关,但工龄长员工的生涯适应力与二者的关系都不显著。基于跨层面研究设计,多层线性模型(HLM)分析的结果表明,生涯适应力是组织职业生涯管理与个体工作绩效之间的完全中介变量,但在组织职业生涯管理与离职意向之间的中介作用不显著。这不但回答了生涯适应力对组织价值的管理困境问题,也解决了组织职业生涯管理与个体工作绩效的连接纽带问题。     

计算机动态测验中问题解决过程策略的分析：多水平混合IRT模型的拓展与应用

学生在完成计算机动态测验过程中,会产生大量带有时间标记的过程性数据。本研究基于5个国家(地区)3196名学生在PISA2012一道交通问题解决任务上的139990条数据,将多水平混合IRT(MMix IRT)模型进行拓展,用于探索问题解决过程策略的类别特点。结果表明,该模型不仅可以基于行为序列对不同国家(地区)学生在解决问题时策略使用情况的典型特征进行分析,还可以提供个体水平的能力估计值。拓展的MMixIRT模型可用于分析过程性数据的特征。     

Centering Predictor Variables in Three-Level Contextual Models

Hierarchical data are becoming increasingly complex, often involving more than two levels. Centering decisions in multilevel models are closely tied to substantive hypotheses and require researchers to be clear and cautious about their choices. This study investigated the implications of group mean centering (i.e., centering within context; CWC) and grand mean centering (CGM) of predictor variables in three-level contextual models. The goals were to (a) determine equivalencies in the means and variances across the centering options and (b) use the algebraic relationships between the centering choices to clarify the interpretation of the estimated parameters. We provide recommendations to assist the researcher in making centering decisions for analysis of three-level contextual models     

Effect Partitioning in Cross-Sectionally Clustered Data Without Multilevel Models

Abstract

Effect partitioning is almost exclusively performed with multilevel models (MLMs) – so much so that some have considered the two to be synonymous. MLMs are able to provide estimates with desirable statistical properties when data come from a hierarchical structure; but the random effects included in MLMs are not always integral to the analysis. As a result, other methods with relaxed assumptions are viable options in many cases. Through empirical examples and simulations, we show how generalized estimating equations (GEEs) can be used to effectively partition effects without random effects. We show that more onerous steps of MLMs such as determining the number of random effects and the structure for their covariance can be bypassed with GEEs while still obtaining identical or near-identical results. Additionally, violations of distributional assumptions adversely affect estimates with MLMs but have no effect on GEEs because no such assumptions are made. This makes GEEs a flexible alternative to MLMs with minimal assumptions that may warrant consideration. Limitations of GEEs for partitioning effects are also discussed.     

Estimation Methods for Mixed Logistic Models with Few Clusters

For mixed models generally, it is well known that modeling data with few clusters will result in biased estimates, particularly of the variance components and fixed effect standard errors. In linear mixed models, small sample bias is typically addressed through restricted maximum likelihood estimation (REML) and a Kenward-Roger correction. Yet with binary outcomes, there is no direct analog of either procedure. With a larger number of clusters, estimation methods for binary outcomes that approximate the likelihood to circumvent the lack of a closed form solution such as adaptive Gaussian quadrature and the Laplace approximation have been shown to yield less-biased estimates than linearization estimation methods that instead linearly approximate the model. However, adaptive Gaussian quadrature and the Laplace approximation are approximating the full likelihood rather than the restricted likelihood; the full likelihood is known to yield biased estimates with few clusters. On the other hand, linearization methods linearly approximate the model, which allows for restricted maximum likelihood and the Kenward-Roger correction to be applied. Thus, the following question arises: Which is preferable, a better approximation of a biased function or a worse approximation of an unbiased function? We address this question with a simulation and an illustrative empirical analysis.     

The Development of Early Attachment Behaviours

The normative development of attachment behaviours and the changing relationship between the level of activation of the attachment system and the intensity of subsequent attachment behaviours were studied longitudinally from 3 to 12 months of age. The study was based on Bowlby's notion about increasing goal directedness of the attachment system. The sample consisted of two groups of children, 33 children born with cleft lip and palate and 34 children without this congenital anomaly. To circumvent the age-limited applicability of the Strange Situation procedure, a new observational instrument was developed, the so-called Induced Stress at Home procedure. It appeared that the ISH procedure is a valid alternative to induce and measure the attachment behaviours proximity seeking, contact maintenance and avoidance. Using the multilevel model for longitudinal data, it was shown that attachment behaviours are not very stable across age. An elaborated model revealed that the relationship between activation of the attachment system and subsequent contact maintenance and resistant behaviour become less strong with age. No differences between children with and without cleft lip and palate were found.     

Poisson Multilevel Models with Small Samples

Recent methodological studies have investigated the properties of multilevel models with small samples. Previous work has primarily focused on continuous outcomes and little attention has been paid to count outcomes. The estimation of count outcome models can be difficult because the likelihood has no closed-form solution, meaning that approximation methods are required. Although adaptive Gaussian quadrature (AGQ) is generally seen as the gold standard, its comparative performance has been investigated with larger samples. AGQ approximates the full likelihood, a function that is known to produce biased estimates with small samples with continuous outcomes. Conversely, penalized quasi-likelihood (PQL) is considered to be a less desirable approximation; however, it can approximate the restricted likelihood function, a function that is known to perform well with smaller samples with continuous outcomes. The goal of this paper is to compare the small sample bias of full likelihood methods to the linearization bias of PQL with restricted likelihood. Simulation results indicate that the linearization bias of PQL is preferable to the finite sample bias of AGQ with smaller samples.     

Simple Structure Detection Through Bayesian Exploratory Multidimensional IRT Models

In modern validity theory, a major concern is the construct validity of a test, which is commonly assessed through confirmatory or exploratory factor analysis. In the framework of Bayesian exploratory Multidimensional Item Response Theory (MIRT) models, we discuss two methods aimed at investigating the underlying structure of a test, in order to verify if the latent model adheres to a chosen simple factorial structure. This purpose is achieved without imposing hard constraints on the discrimination parameter matrix to address the rotational indeterminacy. The first approach prescribes a 2-step procedure. The parameter estimates are obtained through an unconstrained MCMC sampler. The simple structure is, then, inspected with a post-processing step based on the Consensus Simple Target Rotation technique. In the second approach, both rotational invariance and simple structure retrieval are addressed within the MCMC sampling scheme, by introducing a sparsity-inducing prior on the discrimination parameters. Through simulation as well as real-world studies, we demonstrate that the proposed methods are able to correctly infer the underlying sparse structure and to retrieve interpretable solutions.     

Omitted Variables in Multilevel Models

Statistical methodology for handling omitted variables is presented in a multilevel modeling framework. In many nonexperimental studies, the analyst may not have access to all requisite variables, and this omission may lead to biased estimates of model parameters. By exploiting the hierarchical nature of multilevel data, a battery of statistical tools are developed to test various forms of model misspecification as well as to obtain estimators that are robust to the presence of omitted variables. The methodology allows for tests of omitted effects at single and multiple levels. The paper also introduces intermediate-level tests; these are tests for omitted effects at a single level, regardless of the presence of omitted effects at a higher level. A simulation study shows, not surprisingly, that the omission of variables yields bias in both regression coefficients and variance components; it also suggests that omitted effects at lower levels may cause more severe bias than at higher levels. Important factors resulting in bias were found to be the level of an omitted variable, its effect size, and sample size. A real data study illustrates that an omitted variable at one level may yield biased estimators at any level and, in this study, one cannot obtain reliable estimates for school-level variables when omitted child effects exist. However, robust estimators may provide unbiased estimates for effects of interest even when the efficient estimators fail, and the one-degree-of-freedom test helps one to understand where the problem is located. It is argued that multilevel data typically contain rich information to deal with omitted variables, offering yet another appealing reason for the use of multilevel models in the social sciences. This research was supported by the National Academy of Education/Spencer Foundation and the National Science Foundation, Grant Number SES-0436274.     

A Comparison of Inverse-Wishart Prior Specifications for Covariance Matrices in Multilevel Autoregressive Models

Multilevel autoregressive models are especially suited for modeling between-person differences in within-person processes. Fitting these models with Bayesian techniques requires the specification of prior distributions for all parameters. Often it is desirable to specify prior distributions that have negligible effects on the resulting parameter estimates. However, the conjugate prior distribution for covariance matrices—the Inverse-Wishart distribution—tends to be informative when variances are close to zero. This is problematic for multilevel autoregressive models, because autoregressive parameters are usually small for each individual, so that the variance of these parameters will be small. We performed a simulation study to compare the performance of three Inverse-Wishart prior specifications suggested in the literature, when one or more variances for the random effects in the multilevel autoregressive model are small. Our results show that the prior specification that uses plug-in ML estimates of the variances performs best. We advise to always include a sensitivity analysis for the prior specification for covariance matrices of random parameters, especially in autoregressive models, and to include a data-based prior specification in this analysis. We illustrate such an analysis by means of an empirical application on repeated measures data on worrying and positive affect.     

我国近十年来心理学研究中HLM方法的应用述评

以2002-2011年中国期刊网收录的50例应用多层线性模型(HLM)的心理学期刊论文为研究对象,从样本描述、模型发展与规范、数据准备、估计方法与假设检验4个角度进行文献计量和内容分析,对我国心理学研究中HLM方法的使用现状进行评估。结果表明,HLM方法主要用于管理、发展和教育心理学,绝大多数应用都是两层模型且层2样本量较大。HLM方法在广泛应用的同时仍存在忽略前提假设检验、分析过程中的重要信息和结果报告不完整等问题,随后提供了4条建议。