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。     

“Sherry’s Secret”

The case and commentaries below were developed as part of a project, Graduate Research Ethics Education, undertaken by the Association for Practical and Professional Ethics with funding from the National Science Foundation (NSF Grant No. SBR 9421897 and NSF Grant No. 9817880). The project aims at training graduate students in research ethics and building a community of scientists and engineers who are interested in and capable of teaching research ethics. As part of the project, each graduate student participant develops a case for use in teaching and writes a commentary to go with the case, and then a staff member is asked to write additional commentary on the case. The case below was written in the second year of the project and was published in Research Ethics: Cases and Commentaries edited by B. Schrag, Association for Practical and Professional Ethics, Bloomington, Indiana, Vol. II (1998). Publication of these cases and commentaries will be a recurring feature of Science and Engineering Ethics.     

项目反应理论中潜在心理特质“填补”的参数估计方法及其演变

在心理与教育测量中, 项目反应理论(Item Response Theory, IRT)模型的参数估计方法是理论研究与实践应用的基本工具。最近, 由于IRT模型的不断扩展与EM (expectation-maximization)算法自身的固有问题, 参数估计方法的改进与发展显得尤为重要。这里介绍了IRT模型中边际极大似然估计的发展, 提出了它的阶段性特征, 即联合极大似然估计阶段、确定性潜在心理特质“填补”阶段、随机潜在心理特质“填补”阶段, 重点阐述了它的潜在心理特质“填补” (data augmentation)思想。EM算法与Metropolis-Hastings Robbins-Monro (MH-RM)算法作为不同的潜在心理特质“填补”方法, 都是边际极大似然估计的思想跨越。目前, 潜在心理特质“填补”的参数估计方法仍在不断发展与完善。     

定序变量回归模型在心理数据分析中的应用

定序变量在心理现象和心理数据中随处可见, 采用综合的定序变量回归分析模型可以对“镜像模式”和“漏斗模型”的心理现象做出合理的解释和预测。首先通过非参数检验对影响因素进行初步降维, 其次用Probit定序回归对降维后的影响因素贡献率进行判别, 从而进一步筛选具有显著性判断水平的有效指标, 最后用Logistic回归模型对某种特定的心理现象发生与否进行信息量足够大的解释和预测。大学毕业生工作生活质量满意度的预测对这种综合定序变量回归分析模型的实例拟合, 证实了综合定序变量回归分析模型在心理现象和心理数据分析中的应用价值。     

Full Information Maximum Likelihood Estimation for Latent Variable Interactions With Incomplete Indicators

Researchers have developed missing data handling techniques for estimating interaction effects in multiple regression. Extending to latent variable interactions, we investigated full information maximum likelihood (FIML) estimation to handle incompletely observed indicators for product indicator (PI) and latent moderated structural equations (LMS) methods. Drawing on the analytic work on missing data handling techniques in multiple regression with interaction effects, we compared the performance of FIML for PI and LMS analytically. We performed a simulation study to compare FIML for PI and LMS. We recommend using FIML for LMS when the indicators are missing completely at random (MCAR) or missing at random (MAR) and when they are normally distributed. FIML for LMS produces unbiased parameter estimates with small variances, correct Type I error rates, and high statistical power of interaction effects. We illustrated the use of these methods by analyzing the interaction effect between advanced cancer patients’ depression and change of inner peace well-being on future hopelessness levels.     

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.     

Evaluation of Multi-parameter Test Statistics for Multiple Imputation

In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences. However, little is known about the performance of these tests, and virtually no research study has compared the Type 1 error rates and statistical power of these tests in scenarios that are typical of behavioral science data (e.g., small to moderate samples, etc.). This paper uses Monte Carlo simulation techniques to examine the performance of these multi-parameter test statistics for multiple imputation under a variety of realistic conditions. We provide a number of practical recommendations for substantive researchers based on the simulation results, and illustrate the calculation of these test statistics with an empirical example.     

Behavioral landscapes and earth mover’s distance: A new approach for studying individual differences in density distributions

Contemporary views of personality highlight intraindividual variability. We forward a general method for quantifying individual differences in behavioral tendencies based on Earth Mover’s Distance. Using data from 150 individuals who reported on their and others’ interpersonal behavior in 64,112 social interactions, we illustrate how this new approach can advance notions of personality as density distributions. Results provide independent confirmation and establish validity of existing representations of individual differences in interpersonal behavior, and identify new dimensions and profiles of personality and well-being. Benefits of the EMD method include freedom from assumptions about the shape and form of density distributions, generality of application to n-dimensional behavior captured in experience sampling studies, and natural integration of personality structure and dynamics.     

Introductory remarks by the issue editors: The multiple dimensions of the parent–baby story

Abstract

In their introductory remarks, the guest editors, themselves presenters at the conference “Parent–Infant Disturbance: Theory and Therapy,” introduce the philosophy and methodology underlying this interdisciplinary psychoanalytic conference and outline the multiple dimensions of the issue at hand, as they are addressed by the individual contributions to this volume. The paper concludes with a remembrance of and dedication to the victims of the Paris terrorist attack of November 13, 2015.     

互联网数据中的文化差异与文化变迁

互联网时代,人们在网络上留下了各种反映其心理过程与文化特征的信息。这些庞大多样的互联网数据为文化心理学研究提供了新视角。首先,当前文化心理学存在文化差异和文化变迁两种研究取向,互联网数据相较传统来源数据在这两种研究上均存在若干优势;其次,针对4种类型的互联网数据,文化心理学家利用文本分析、多媒体分析、社会网络分析和互联网使用行为分析的方法进行研究;再次,基于互联网数据及其分析方法,文化差异与变迁研究取得了丰硕的成果;最后,当前基于互联网数据的文化心理学研究存在效度、技术与理论局限,未来研究需通过合理抽样、检验新指标有效性、准因果分析、充分运用新技术、数据驱动等方法来提升方法效度、结果深度及理论多样性。