Alternative Multiple Imputation Inference for Categorical Structural Equation Modeling

The use of item responses from questionnaire data is ubiquitous in social science research. One side effect of using such data is that researchers must often account for item level missingness. Multiple imputation is one of the most widely used missing data handling techniques. The traditional multiple imputation approach in structural equation modeling has a number of limitations. Motivated by Lee and Cai’s approach, we propose an alternative method for conducting statistical inference from multiple imputation in categorical structural equation modeling. We examine the performance of our proposed method via a simulation study and illustrate it with one empirical data set.     

积极心理资本的结构、功能及干预研究述评*

积极心理资本自提出以来,便备受关注,它超越了人力资本和社会资本,是一种能够被有效开发和管理,并能够对个体的绩效产生重要影响的核心积极心理要素。在以往研究中,研究者提出了积极心理资本的四维结构说、三维结构说和多维结构说,并据此开发了相应的测量工具。在后果变量方面,主要探讨的是积极心理资本与工作态度、工作行为、工作绩效、幸福感等方面的关系。积极心理资本自身也是一个重要的中介与调节变量,而PCI干预模型可有效提高个体的心理资本水平。今后研究的重点包括研究对象与内容的拓宽、研究方法的改进与完善以及本土化和跨文化研究的加强等。     

Predicting Chinese international students’ acculturation strategies from socio‐demographic variables and social ties

Different acculturation strategies (i.e. integration, assimilation, separation and marginalization) tend to contribute to different adaptation outcomes for international students. The current study examines Chinese international students’ acculturation strategies under the influence of sociodemographic variables and social ties they developed in the host country. A group of Chinese international students in Belgium (N = 183) participated in an online survey. The results indicate that integration was the most commonly adopted strategy among Chinese international students in Belgium, followed by separation, marginalization and assimilation. The results from three separate multiple regressions show that English proficiency, local language proficiency, prior adaptation experience and female were significant predictors of Chinese students’ social ties (i.e. host‐national ties, international ties and co‐national ties). The discriminant analysis identified host‐national ties, international ties, co‐national ties, local language proficiency and prior adaptation experience as important variables that can distinguish Chinese students’ acculturation strategies.     

Addressing confounding errors when using non-experimental, observational data to make causal claims

In their recent book, Is Inequality Bad for Our Health?, Daniels, Kennedy, and Kawachi claim that to “act justly in health policy, we must have knowledge about the causal pathways through which socioeconomic (and other) inequalities work to produce differential health outcomes.” One of the central problems with this approach is its dependency on “knowledge about the causal pathways.” A widely held belief is that the randomized clinical trial (RCT) is, and ought to be the “gold standard” of evaluating the causal efficacy of interventions. However, often the only data available are non-experimental, observational data. For such data, the necessary randomization is missing. Because the randomization is missing, it seems to follow that it is not possible to make epistemically warranted claims about the causal pathways. Although we are not sanguine about the difficulty in using observational data to make warranted causal claims, we are not as pessimistic as those who believe that the only warranted causal claims are claims based on data from (idealized) RCTs. We argue that careful, thoughtful study design, informed by expert knowledge, that incorporates propensity score matching methods in conjunction with instrumental variable analyses, provides the possibility of warranted causal claims using observational data.     

Re-conceptualizing the past: Historical data in vocational interest research

Noteworthy progress has been made in the development of statistical models for evaluating the structure of vocational interests over the past three decades. It is proposed that historically significant interest datasets, when combined with modern structural methods of data analysis, provide an opportunity to re-examine the underlying assumptions of J.L. Holland’s [Holland, J. L. (1959). A theory of vocational choice. Journal of Counseling Psychology, 6, 35–45; Holland, J. L. (1997). Making vocational choices (3rd ed.). Odessa, FL: Psychological Assessment Resources] RIASEC model. To illustrate this potential, data obtained from J. P. Guilford’s study of interest structure were re-analyzed using modern circumplex and hierarchical clustering techniques to evaluate Holland’s and I. Gati’s [Gati, I. (1979). A hierarchical model for the structure of interests. Journal of Vocational Behavior, 15, 90–106; Gati, I. (1991). The structure of vocational interests. Psychological Bulletin, 109, 309–324] interest structures. Obtained results indicate that a circumplex model can be used to effectively represent the structure underlying Guilford’s interest measures. However, hierarchical clustering results suggest that Holland’s RIASEC types may not be the most effective categories for grouping specific interest measures into broader interest areas. The current findings provide support for the continued investigation of alternatives to Holland’s interest categories using modern measures of basic interests.     

Analysis of meteorological conditions in Spain by means of clustering techniques

A comprehensive analysis of clustering techniques is presented in this paper through their application to data on meteorological conditions. Six partitional and hierarchical clustering techniques (k-means, k-medoids, SOM k-means, Agglomerative Hierarchical Clustering, and Clustering based on Gaussian Mixture Models) with different distance criteria, together with some clustering evaluation measures (Calinski–Harabasz, Davies–Bouldin, Gap and Silhouette criterion clustering evaluation object), present various analyses of the main climatic zones in Spain. Real-life data sets, recorded by AEMET (Spanish Meteorological Agency) at four of its weather stations, are analyzed in order to characterize the actual weather conditions at each location. The clustering techniques process the data on some of the main daily meteorological variables collected at these stations over six years between 2004 and 2010.     

范畴变量对虚假记忆的影响

以中文词为材料,考察范畴样例重复,样例熟悉性、典型性和范畴大小对虚假再认的影响。实验1表明,随着范畴样例增加,虚假再认也增加。实验2表明,对高熟悉的样例虚假再认多。实验3表明,对高典型样例更易虚假再认。实验4表明,和大范畴样例比,对小范畴样例有更多的虚假再认。整个结果可以用激活扩散理论统一地加以解释     

Rethinking the Control of Nuisance Variables in Theory Testing

In testing hypotheses, researchers frequently have used multiple regression analysis to control for nuisance variables (i.e., potential confounding variables that are correlated with hypothesized causal variables). In this paper, we highlight limitations of this control strategy, and we discuss fundamental issues that should be considered in deciding whether to use it. Ultimately, we suggest the use of multiple regression analysis sometimes may not improve causal understanding and may actually limit the generalizability of results. Instead of the common practice of controlling for nuisance variables, we suggest that looking at shared variance frequently is a more appropriate test of a theoretical hypothesis.     

The conjunctive model of hierarchical classes

This paper describes the conjunctive counterpart of De Boeck and Rosenberg's hierarchical classes model. Both the original model and its conjunctive counterpart represent the set-theoretical structure of a two-way two-mode binary matrix. However, unlike the original model, the new model represents the row-column association as a conjunctive function of a set of hypothetical binary variables. The conjunctive nature of the new model further implies that it may represent some conjunctive higher order dependencies among rows and columns. The substantive significance of the conjunctive model is illustrated with empirical applications. Finally, it is shown how conjunctive and disjunctive hierarchical classes models relate to Galois lattices, and how hierarchical classes analysis can be useful to construct lattice models of empirical data.The research reported in this paper was supported by NATO (Grant CRG.921321 to Iven Van Mechelen and Seymour Rosenberg) and by the Research Fund of Katholieke Universiteit Leuven (Grants PDM92/19 and POR93/3 to Iven Van Mechelen; Grants OT89/9 and F91/56 to Paul De Boeck).     

The effects of bilingual language proficiency on recall accuracy and semantic clustering in free recall output: evidence for shared semantic associations across languages

Two experiments investigated how well bilinguals utilise long-standing semantic associations to encode and retrieve semantic clusters in verbal episodic memory. In Experiment 1, Spanish-English bilinguals (N?=?128) studied and recalled word and picture sets. Word recall was equivalent in L1 and L2, picture recall was better in L1 than in L2, and the picture superiority effect was stronger in L1 than in L2. Semantic clustering in word and picture recall was equivalent in L1 and L2. In Experiment 2, Spanish-English bilinguals (N?=?128) and English-speaking monolinguals (N?=?128) studied and recalled word sequences that contained semantically related pairs. Data were analyzed using a multinomial processing tree approach, the pair-clustering model. Cluster formation was more likely for semantically organised than for randomly ordered word sequences. Probabilities of cluster formation, cluster retrieval, and retrieval of unclustered items did not differ across languages or language groups. Language proficiency has little if any impact on the utilisation of long-standing semantic associations, which are language-general.