Identifying Variables Responsible for Data not Missing at Random

When data are not missing at random (NMAR), maximum likelihood (ML) procedure will not generate consistent parameter estimates unless the missing data mechanism is correctly modeled. Understanding NMAR mechanism in a data set would allow one to better use the ML methodology. A survey or questionnaire may contain many items; certain items may be responsible for NMAR values in other items. The paper develops statistical procedures to identify the responsible items. By comparing ML estimates (MLE), statistics are developed to test whether the MLEs are changed when excluding items. The items that cause a significant change of the MLEs are responsible for the NMAR mechanism. Normal distribution is used for obtaining the MLEs; a sandwich-type covariance matrix is used to account for distribution violations. The class of nonnormal distributions within which the procedure is valid is provided. Both saturated and structural models are considered. Effect sizes are also defined and studied. The results indicate that more missing data in a sample does not necessarily imply more significant test statistics due to smaller effect sizes. Knowing the true population means and covariances or the parameter values in structural equation models may not make things easier either. The research was supported by NSF grant DMS04-37167, the James McKeen Cattell Fund.     

初中生家庭功能类型与其价值观形成的关系探讨

采用亲密度与适应性量表中文版（FACESⅡ-CV）和价值观自测问卷对259名绍兴市区初中生进行了调查,结果发现：（1）亲密度高、情感联系强的家庭更容易形成符合社会规范的价值观,而情感联系弱的家庭容易形成自我中心的价值观;（2）适应性强、应变功能高的无规律型家庭有利于自律、内控的自我导向价值观的形成,并对从众型和冷漠型价值观的形成具有抑制作用;（3）家庭亲密度与适应性功能水平和踏实型价值观类型存在显著的正相关,适应性功能水平与功利型和冷漠型价值观类型存在显著的负相关。     

A Deep Inference System for the Modal Logic S5

We present a cut-admissible system for the modal logic S5 in a formalism that makes explicit and intensive use of deep inference. Deep inference is induced by the methods applied so far in conceptually pure systems for this logic. The system enjoys systematicity and modularity, two important properties that should be satisfied by modal systems. Furthermore, it enjoys a simple and direct design: the rules are few and the modal rules are in exact correspondence to the modal axioms. Presented by Heinrich Wansing     

Putting the system back into systems change: a framework for understanding and changing organizational and community systems

Systems change has emerged as a dominant frame through which local, state, and national funders and practitioners across a wide array of fields approach their work. In most of these efforts, change agents and scholars strive to shift human services and community systems to create better and more just outcomes and improve the status quo. Despite this, there is a dearth of frameworks that scholars, practitioners, and funders can draw upon to aid them in understanding, designing, and assessing this process from a systemic perspective. This paper provides one framework--grounded in systems thinking and change literatures--for understanding and identifying the fundamental system parts and interdependencies that can help to explain system functioning and leverage systems change. The proposed framework highlights the importance of attending to both the deep and apparent structures within a system as well as the interactions and interdependencies among these system parts. This includes attending to the dominant normative, resource, regulative, and operational characteristics that dictate the behavior and lived experiences of system members. The value of engaging critical stakeholders in problem definition, boundary construction, and systems analysis are also discussed. The implications of this framework for systems change researchers and practitioners are discussed.     

个人目标取向、课堂目标结构及文化因素与学业求助策略的关系研究

本研究以982名初中学生为被试,采用验证性因素分析和多元回归方法对个人目标取向、课堂目标结构及文化因素与学业求助策略的关系进行研究。结果表明:(1)个人目标取向可以区分出任务取向、趋向型自我取向和逃避型自我取向,它们对学业求助的影响是不同的,只有逃避型自我取向的学生才倾向于作不利于学习的执行性求助。(2)课堂目标结构对学业求助有影响 ,在任务取向的课堂里 ,学生倾向于作工具性求助。(3)文化因素对学业求助有影响。认同中国求助格言的学生愿意作工具性求助,在意他人评价的学生倾向于回避求助或作执行性求助。(4)个人目标取向、课堂目标结构和文化因素对不同年级学生学业求助的影响模式不同。     

An alternative two stage least squares (2SLS) estimator for latent variable equations

The Maximum-likelihood estimator dominates the estimation of general structural equation models. Noniterative, equation-by-equation estimators for factor analysis have received some attention, but little has been done on such estimators for latent variable equations. I propose an alternative 2SLS estimator of the parameters in LISREL type models and contrast it with the existing ones. The new 2SLS estimator allows observed and latent variables to originate from nonnormal distributions, is consistent, has a known asymptotic covariance matrix, and is estimable with standard statistical software. Diagnostics for evaluating instrumental variables are described. An empirical example illustrates the estimator. I gratefully acknowledge support for this research from the Sociology Program of the National Science Foundation (SES-9121564) and the Center for Advanced Study in the Behavioral Sciences, Stanford, California. This paper was presented at the Interdisciplinary Consortium for Statistical Applications at Indiana University at Bloomington (March 2, 1994) and at the RMD Conference on Causal Modeling at Purdue University, West Lafayette, Indiana (March 3-5, 1994).     

A note on distributional properties of the Jöreskog-Sörbom fit indices

In introducing the LISREL model for systems of linear structural equations, Jöreskog and Sörbom proposed two goodness-of-fit indices, GFI and AGFI. Their asymptotic distributions and some statistical properties are discussed.The authors would like to acknowledge the helpful comments and suggestions from the Associate Editor and two anonymous reviewers.     

Effect analysis and causation in linear structural equation models

This paper considers total and direct effects in linear structural equation models. Adopting a causal perspective that is implicit in much of the literature on the subject, the paper concludes that in many instances the effects do not admit the interpretations imparted in the literature. Drawing a distinction between concomitants and factors, the paper concludes that a concomitant has neither total nor direct effects on other variables. When a variable is a factor and one or more intervening variables are concomitants, the notion of a direct effect is not causally meaningful. Even when the notion of a direct effect is meaningful, the usual estimate of this quantity may be inappropriate. The total effect is usually interpreted as an equilibrium multiplier. In the case where there are simultaneity relations among the dependent variables in tghe model, the results in the literature for the total effects of dependent variables on other dependent variables are not equilibrium multipliers, and thus, the usual interpretation is incorrect. To remedy some of these deficiencies, a new effect, the total effect of a factorX on an outcomeY, holding a set of variablesF constant, is defined. When defined, the total and direct effects are a special case of this new effect, and the total effect of a dependent variable on a dependent variable is an equilibrium multiplier.For helpful comments, I am grateful to G. Arminger, K. Bollen, W. Faris, R. m. Hauser, T. Petersen, three anonymous Psychometrikas reviewers, and the Editor. For computational assistance, I am grateful to B. D. Kim.