高中生化学问题解决思维过程口语报告编码分析

将高中生化学问题解决思维过程口语报告分为相应的10个行为指标进行人工编码和定量分析。被试是56名高中生,材料为6个中等难度的化学问题,通过“出声思考”实验,记录口语报告,并对口语报告进行编码和数据统计分析,结果发现:(1)高中优生的重复、解释、推理、归纳、总结等行为指标上明显优于中等生;(2)在解决不同类型的化学问题时,重复、设问、推理、归纳和反思等指标存在明显差异;(3)学生能力层次与问题类别在重复、推理、归纳和试误等行为指标上存在明显交互作用。     

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.     

医学专业精神及医疗保密：美国历史及现状中的冲突

讨论卫生部门宣传和加强强制汇报的法律的努力、强制性汇报的规定与医学专业精神及个人自主性之间的冲突、私人医生避免遵守这些规则的策略、病人和倡议者设法避免强制性登记和检测的策略,以及医生/病人和公共卫生官员达成的妥协。最后对公共卫生政策提出建议,认识到强制性汇报存在的问题和卫生政策透明度的重要性。     

Pooling data versus averaging model fits for some prototypical multinomial processing tree models

For a study with multinomial data where there are n_g individuals and with each person having n_r test trials, the question arises as to how to fit the parameters of a multinomial processing tree (MPT) model. Should each parameter be estimated for each individual and then averaged to obtain a group estimate, or should the frequencies in the multinomial categories be pooled so that the model is fit once for the entire group? This basic question is explored with a series of Monte Carlo simulations for some prototypical MPT models. There is a general finding of a pooling advantage for the case where there is a single experimental condition. Also when there are different experimental conditions, there is reduced bias for detecting condition differences for a method based on the pooled data. Although the focus of the paper is on multinomial models, a general theorem is advanced that establishes a basic condition that determines whether there is or is not a difference between the averaging of individual estimates and the estimate based on the pooled data.     

职业评定量表(VIESA)的信度和效度研究

根据D.E.Super的职业发展理论,职业选择的成熟与发展一般分为五个阶段,中学生正处在从探索到成熟的重要转变期。本研究以美国大学测验中心的职业分类系统为基础,以职业评定量表VIESA为工具和对象,对北京市第六十五中学的179名高中三年级学生和北京师范大学心理学系的55名二年级学生进行了施测,检验了量表的信度和效度。量表的信度指标基本在.70以上,构想效度良好,以专业满意感为效标的效标效度达到显著水平。     

Exploratory analysis of contingency tables by loglinear formulation and generalizations of correspondence analysis

Goodman's (1979, 1981, 1985) loglinear formulation for bi-way contingency tables is extended to tables with or without missing cells and is used for exploratory purposes. A similar formulation is done for three-way tables and generalizations of correspondence analysis are deduced. A generalized version of Goodman's algorithm, based on Newton's elementary unidimensional method is used to estimate the scores in all cases.This research was partially supported by National Science and Engineering Research Council of Canada, Grant No. A8724. The author is grateful to the reviewers and the editor for helpful comments.     

Quality home visits: Activities to promote meaningful interactions

Examination of and support for specific practices that promote high-quality home visiting are essential as family support programs continue to expand across the country. The current study used direct observation of 91 home visits across 41 home visitors to examine relations among interaction partners, content of the interactions, the home-visitors’ activities, and quality of home-visitors’ practices and family-members’ engagement within programs funded by the Maternal, Infant, and Early Childhood Home Visiting program. More time spent in triadic interactions focused on child-related content, as measured by the Home Visit Rating Scale-Revised, was related to higher quality of family engagement in home visits, as measured with the Home Visit Observation Rating Scales. Time spent in adult-focused interactions and administrative tasks, however, was related to lower quality of home-visiting practices and family engagement. Implications for research and practice are discussed.     

纵向数据的中介效应分析

目前中介效应检验主要是基于截面数据,但许多时候截面数据的中介分析不适合进行因果推断,因而需要收集历时性的纵向数据,进行纵向数据的中介分析。评介了基于交叉滞后面板模型、多层线性模型和潜变量增长模型的纵向数据的中介分析方法及其四个发展。第一,中介效应随时间变化,如连续时间模型、多层时变系数模型。第二,中介效应随个体变化,如随机效应的交叉滞后面板模型和多层自回归模型。第三,中介模型的整合,如交叉滞后面板模型与多层线性模型整合为多层自回归模型。第四,中介检验方法的发展,建议使用Monte Carlo、Bootstrap和贝叶斯法进行纵向数据的中介分析。总结出一个纵向数据的中介分析流程并给出相应的Mplus程序。随后展望了纵向数据的中介分析的拓展方向。     

Opportunities and Issues in Modeling Intensive Longitudinal Data: Learning from the COGITO Project

Abstract

Technological developments increasingly permit the collection of longitudinal data sets in which the data structure contains a large number of participants N and a large number of measurement occasions T. Promising new dynamical systems approaches to the analysis of large N, large T data sets have been proposed that utilize both between-subjects and within-subjects information. The COGITO project, begun over a decade ago, is an early large N?=?204, large T?=?100 study that collected high quality cognitive and psychosocial data. In this introduction, I describe the COGITO project and conceptual and statistical issues that arise in the analysis of large N, large T data sets. I provide a brief overview of the five papers in the special section which include conceptual pieces, a didactic presentation of a dynamic structural equation approach, and papers reporting new statistical analyses of the COGITO data set to answer substantive questions. Although many challenges remain, these new approaches offer the promise of improving scientific inquiry in the behavioral sciences.     

An entropy approach to the scaling of ordinal categorical data

This paper studies the problem of scaling ordinal categorical data observed over two or more sets of categories measuring a single characteristic. Scaling is obtained by solving a constrained entropy model which finds the most probable values of the scales given the data. A Kullback-Leibler statistic is generated which operationalizes a measure for the strength of consistency among the sets of categories. A variety of data of two and three sets of categories are analyzed using the entropy approach.This research was partially supported by the Air Force Office of Scientific Research under grant AFOSR 83-0234. The support by the Air Force through grant AFOSR-83-0234 is gratefully acknowledged. The comments of the editor and referees have been most helpful in improving the paper, and in bringing several additional references to our attention.