An IRT Model with a Parameter-Driven Process for Change

An IRT model with a parameter-driven process for change is proposed. Quantitative differences between persons are taken into account by a continuous latent variable, as in common IRT models. In addition, qualitative interindividual differences and autodependencies are accounted for by assuming within-subject variability with respect to the parameters of the IRT model. In particular, the parameters of the IRT model are governed by an unobserved or “hidden'” homogeneous Markov process. The model includes the mixture linear logistic test model (Mislevy & Verhelst, 1990), the mixture Rasch model (Rost, 1990), and the Saltus model (Wilson, 1989) as specific instances. The model is applied to a longitudinal experiment on discontinuity in conservation acquisition (van der Maas, 1993). Frank Rijmen was supported by the Fund for Scientific Research Flanders (FWO), the GOA/2000/02 granted by the Katholieke Universiteit Leuven to Paul De Boeck and Iven Van Mechelen, and the PDM/02/067 granted by the Katholieke Universiteit Leuven to Paul De Boeck.     

Hierarchical classes models for three-way three-mode binary data: interrelations and model selection

Several hierarchical classes models can be considered for the modeling of three-way three-mode binary data, including the INDCLAS model (Leenen, Van Mechelen, De Boeck, and Rosenberg, 1999), the Tucker3-HICLAS model (Ceulemans, Van Mechelen, and Leenen, 2003), the Tucker2-HICLAS model (Ceulemans and Van Mechelen, 2004), and the Tucker1-HICLAS model that is introduced in this paper. Two questions then may be raised: (1) how are these models interrelated, and (2) given a specific data set, which of these models should be selected, and in which rank? In the present paper, we deal with these questions by (1) showing that the distinct hierarchical classes models for three-way three-mode binary data can be organized into a partially ordered hierarchy, and (2) by presenting model selection strategies based on extensions of the well-known scree test and on the Akaike information criterion. The latter strategies are evaluated by means of an extensive simulation study and are illustrated with an application to interpersonal emotion data. Finally, the presented hierarchy and model selection strategies are related to corresponding work by Kiers (1991) for principal component models for three-way three-mode real-valued data.     

思想考古方法与中国哲学史研究

中国哲学史研究中长期运用的主要方法是经典的解读及理论的分析,但这种方法并不是惟一的、完全没有问题或缺陷的,所以,必须引入一些新的方法如思想考古的方法予以补充。所谓思想考古,即是借鉴历史学和人类文化学等学科的田野调查方法,去思想家生活、求学、家居、从业的地方进行详细的实地考察,以感受和体验研究对象的情感、思绪,把思想家的思想还原为在具体情境中的思想,找到其思想学说或行为的外在根据,并进而印证或质疑某些史书中对思想家的言行记载,对某些语焉不详的史料或有争议的问题做出分析与考辨。     

Dyadic longitudinal models: A critical review

In this review, we discuss the most commonly used models to analyze dyadic longitudinal data. We start the review with a definition of dyadic longitudinal data that allows relationship researchers to identify when these models might be appropriate. Then, we go on to describe the three major models commonly used when one has dyadic longitudinal data: the dyadic growth curve model (DGCM), the actor–partner interdependence model (APIM), and the common fate growth model (CFGM). We discuss when each model might be used and strengths and weaknesses of each model. We end with additional thoughts that focus on extensions to new methods being discussed in the literature, along with some of the challenges of collecting and analyzing dyadic longitudinal data that might be helpful for future dyadic researchers.     

Mother–child relationship quality from preschool to adolescence: Variation by maternal education

Using longitudinal data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (N = 1088), we examine changes in maternal perception of closeness and conflict in the mother–child relationship from the child's preschool to adolescent years, with attention to variation by maternal education. Analyses using individual growth models show that mother–child closeness increases, while mother–child conflict decreases, from preschool to first grade. From first grade to age 15, mother–child closeness decreases, while mother–child conflict increases, both gradually. The decrease in mother–child conflict from preschool to first grade and the increases in mother–child conflict from first to fifth grade, sixth grade, and age 15 are less steep for mothers with a college degree than for mothers without a college degree. These findings underscore the importance of examining changes in parent–child relationships using longitudinal data across children's developmental stages and their variations by parental social and economic status.     

基于大数据的文化心理分析

随着大数据技术与文化心理学的融合发展, 计算文化心理学作为一门新兴交叉学科逐渐兴起, 大尺度、近乎全样本的文化心理分析真正得以实现。计算文化心理学关注的文化变量主要围绕个人主义/集体主义这一文化心理学使用最为广泛的维度展开, 分析方法包括特征词典、机器学习、社会网络分析、仿真模拟等, 分析思路包括时间维度上的文化变迁效应以及空间维度上的文化地理效应。当然, 计算文化心理学在为传统文化心理研究提供新方法、新范式的同时, 也存在解码失真、样本偏差、词语多义性、隐私风险等局限, 未来研究应重视变量理论解释、文化动态演化分析、学科深度整合、生态效度等问题。     

Disclosure of Tests and Raw Test Data to the Courts: A Need for Reform

Psychologists' current practices with respect to the disclosure of tests and raw test data to courts are damaging their reputations as scientists, undermining their credibility as honest professionals acting in good faith, and contrary to the best interests of consumers. The profession is conducting itself in a contradictory fashion that deserves reform. There is need for an interdisciplinary panel drawn from the American Psychological Association and American Bar Association to develop reasonable procedures for disclosure of tests and test data in legal proceedings. These procedures should be developed with input from experienced psychological expert witnesses, attorneys, and judges from a variety of state and federal jurisdictions, diverse geographical areas, and different types of legal proceedings. This expert panel should also address the issue of attorney coaching of clients in preparation for assessment by psychologists.     

A note on the use of rank-ordered logit models for ordered response categories

Models for rankings have been shown to produce more efficient estimators than comparable models for first/top choices. The discussions and applications of these models typically only consider unordered alternatives. But these models can be usefully adapted to the case where a respondent ranks a set of ordered alternatives that are ordered response categories. This paper proposes eliciting a rank order that is consistent with the ordering of the response categories, and then modelling the observed rankings using a variant of the rank ordered logit model where the distribution of rankings has been truncated to the set of admissible rankings. This results in lower standard errors in comparison to when only a single top category is selected by the respondents. And the restrictions on the set of admissible rankings reduces the number of decisions needed to be made by respondents in comparison to ranking a set of unordered alternatives. Simulation studies and application examples featuring models based on a stereotype regression model and a rating scale item response model are provided to demonstrate the utility of this approach.     

Cultural evolution over the last 40 years in China: Using the Google Ngram Viewer to study implications of social and political change for cultural values

Chinese people have held collectivistic values such as obligation, giving to other people, obedience and sacrifice of personal interests for thousands of years. In recent decades, China has undergone rapid economic development and urbanisation. This study investigates changing cultural values in China from 1970 to 2008 and the relationship of changing values to ecological shifts. The conceptual framework for the study was Greenfield's (2009) theory of social change and human development. Changing frequencies of contrasting Chinese words indexing individualistic or collectivistic values show that values shift along with ecological changes (urbanisation, economic development and enrollment in higher education), thereby adapting to current sociodemographic contexts. Words indexing adaptive individualistic values increased in frequency between 1970 and 2008. In contrast, words indexing less adaptive collectivistic values either decreased in frequency in this same period of time or else rose more slowly than words indexing contrasting individualistic values.     

BIG DATA: NEW SCIENCE,NEW CHALLENGES,NEW DIALOGICAL OPPORTUNITIES

The advent of extremely large data sets, known as “big data,” has been heralded as the instantiation of a new science, requiring a new kind of practitioner: the “data scientist.” This article explores the concept of big data, drawing attention to a number of new issues—not least ethical concerns, and questions surrounding interpretation—which big data sets present. It is observed that the skills required for data scientists are in some respects closer to those traditionally associated with the arts and humanities than to those associated with the natural sciences; and it is urged that big data presents new opportunities for dialogue, especially concerning hermeneutical issues, for theologians and data scientists.