Comment on fitting MA time series by structural equation models

In a recent paper by van Buuren (1997) it is concluded that parameter estimates in pure moving-average (MA) models, obtained by software for fitting structural equation models (SEMs), are biased and inefficient. In this comment it is shown that this negative finding may be due to a particular feature of van Buuren's simulation experiment. A modified procedure for fitting MA models by means of SEM software is proposed, and some of its implications are discussed.     

On the estimation of polychoric correlations and their asymptotic covariance matrix

A general theory for parametric inference in contingency tables is outlined. Estimation of polychoric correlations is seen as a special case of this theory. The asymptotic covariance matrix of the estimated polychoric correlations is derived for the case when the thresholds are estimated from the univariate marginals and the polychoric correlations are estimated from the bivariate marginals for given thresholds. Computational aspects are also discussed.The research was supported by the Swedish Council for Research in the Humanities and Social Sciences (HSFR) under the programMultivariate Statistical Analysis. The author thanks a reviewer for pointing out an error in the original version of the paper.     

A new proof for monotone likelihood ratio for the sum of independent bernoulli random variables

By use of an inequality of Marcus and Lopes for elementary symmetric functions, a new proof is presented for the following result by Ghurye and Wallace: Given that the independent random variablesX _j are Bernoulli with success probabilityp _j () strictly between 0 and 1 and nondecreasing in, the sum X _j has monotone likelihood ratio.     

On the relation between demographic variables and neuropsychological test performance

This paper reports a study of the relation between demographic variables and neuropsychological test performance in a sample of 141 normal subjects. A preliminary analysis demonstrated the importance of age, educational level, sex, and Verbal IQ for the prediction of neuropsychological test performance. The main study is a detailed analysis of the relation between these four variables and neuropsychological test performance. The results are presented in the form of standardized regression coefficients and are discussed in relation to the problem of predicting expected premorbid performance in neuropsychological tests.     

A Comparison of Methods for Creating Multiple Imputations of Nominal Variables

Many variables that are analyzed by social scientists are nominal in nature. When missing data occur on these variables, optimal recovery of the analysis model's parameters is a challenging endeavor. One of the most popular methods to deal with missing nominal data is multiple imputation (MI). This study evaluated the capabilities of five MI methods that can be used to treat incomplete nominal variables: multiple imputation with chained equations (MICE) using polytomous regression as the elementary imputation method; MICE based on classification and regression trees (CART); MICE based on nested logistic regressions; the ranking procedure described by Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]); and a joint modeling approach based on the general location model. We first motivate our inquiry with an applied example and then present the results of a Monte Carlo simulation study that compared the performance of the five imputation methods under conditions of varying sample size, percentage of missing data, and number of nominal response categories. We found that MICE with polytomous regression was the strongest performer while the Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]) ranking procedure and MICE with CART performed poorly in most conditions.     

Empirically Corrected Rescaled Statistics for SEM with Small N and Large p

Survey data often contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. With typical nonnormally distributed data in practice, a rescaled statistic T_rml proposed by Satorra and Bentler was recommended in the literature of SEM. However, T_rml has been shown to be problematic when the sample size N is small and/or the number of variables p is large. There does not exist a reliable test statistic for SEM with small N or large p, especially with nonnormally distributed data. Following the principle of Bartlett correction, this article develops empirical corrections to T_rml so that the mean of the empirically corrected statistics approximately equals the degrees of freedom of the nominal chi-square distribution. Results show that empirically corrected statistics control type I errors reasonably well even when N is smaller than 2p, where T_rml may reject the correct model 100% even for normally distributed data. The application of the empirically corrected statistics is illustrated via a real data example.     

组织文化、组织文化吻合度与员工的组织公民行为

以中国普通员工为被试,考察了组织文化、组织文化吻合度与员工组织公民行为(OCB)之间的关系.结果发现: (1)不同年龄、不同类型企业员工的OCB存在显著差异,但在性别、教育水平方面没有差异; (2)不同取向的组织文化对员工不同层面的OCB具有不同的预测作用.具体而言.组织文化对社会层面OCB的预测作用较强;就单一取向的组织文化来看,人本与创新取向的组织文化对OCB的预测作用最强; (3)在组织文化影响员工组织公民行为时,组织文化吻合度具有一定的调节作用,这种调节作用在人本与创新取向以及规范与安定取向的组织文化影响OCB时表现更加明显.     

小学儿童CVS策略的发展及其对教育的启示

CVS策略是国外不少研究者正深入研究的一种学习策略,关于它的研究得出了许多有价值的结论。本文首先分析了CVS的概念、研究方法和研究意义,然后对当前的研究结论进行了梳理,从五大方面详细分析了小学儿童CVS策略的发展特点,最后又从四个发面提出了这些研究对当前教育的启示。     

Alcohol use among adults in Kenya: Results from the National Non-Communicable Diseases Risk Factor survey, 2015

The aim of this study was to assess the prevalence and correlates of alcohol use among Kenyan adults. We analysed data from the Kenya cross-sectional national Non-Communicable Diseases Risk Factor survey, 2015. The survey sampled 4 469 adults (median age 38.0 years, interquartile range = 23, age range of 18–69 years). Results indicate that 6.7% were hazardous or harmful alcohol users and 12.8% past month binge-drinkers. In adjusted logistic regression analysis, being male, middle aged, belonging to the Luhya or Kalenjin ethic group, tobacco use, and having hypertension increased the odds for hazardous or harmful alcohol use. Socio-demographic and health factors appear to influence risk for hazardous or harmful alcohol use among adults in Kenya.     

Model Implied Instrumental Variables (MIIVs): An Alternative Orientation to Structural Equation Modeling

Few dispute that our models are approximations to reality. Yet when it comes to structural equation models (SEMs), we use estimators that assume true models (e.g. maximum likelihood) and that can create biased estimates when the model is inexact. This article presents an overview of the Model Implied Instrumental Variable (MIIV) approach to SEMs from Bollen (1996). The MIIV estimator using Two Stage Least Squares (2SLS), MIIV-2SLS, has greater robustness to structural misspecifications than system wide estimators. In addition, the MIIV-2SLS estimator is asymptotically distribution free. Furthermore, MIIV-2SLS has equation-based overidentification tests that can help pinpoint misspecifications. Beyond these features, the MIIV approach has other desirable qualities. MIIV methods apply to higher order factor analyses, categorical measures, growth curve models, dynamic factor analysis, and nonlinear latent variables. Finally, MIIV-2SLS permits researchers to estimate and test only the latent variable model or any other subset of equations. In addition, other MIIV estimators beyond 2SLS are available. Despite these promising features, research is needed to better understand its performance under a variety of conditions that represent empirical applications. Empirical and simulation examples in the article illustrate the MIIV orientation to SEMs and highlight an R package MIIVsem that implements MIIV-2SLS.