加速追踪设计的方法和应用

加速追踪设计(ALD)是一种选择相邻多个群组同时进行短期追踪研究, 获得在测量上有重叠的多个群组追踪数据, 对多个群组数据进行合并建构一条在时间跨度上较长的发展趋势或增长曲线的方法。ALD结合真追踪和横断设计的特征, 既保持真追踪设计的大部分优点, 克服真追踪研究中由于重测效应和被试缺失导致的问题, 又尝试分离年龄、群组和历史时间效应, 在发展心理研究有重要应用。已有研究探讨ALD的数据分析方法、ALD的有效性及设计特征。未来研究应关注拓展设计条件下ALD的适应性, 探索非线性假设或群组效应显著时的数据分析方法和ALD中缺失数据处理问题。     

追踪研究方法在国内心理研究中的应用述评

以1982~2012年中国期刊网收录的88例追踪研究为对象,从应用现状、设计特征、数据处理三方面分析和评估追踪研究方法在国内心理研究的应用情况及存在的问题。结果显示,2005年之前追踪研究方法应用增长缓慢,2005年开始呈显著增长趋势,研究对象以未成年及成年早期群体为主。主要采用固定样本追踪设计,大部分研究测量2-3次、样本量在10~300之间、持续时间在3年内。61例有缺失的研究中,38例用删除法处理缺失;主要运用 HLM、方差分析、t检验和SEM分析追踪数据。相当部分研究存在测量次数少、样本量较小、持续时间短、被试缺失严重及数据处理方法相对陈旧问题。追踪研究方法的应用应注意,根据理论模型和研究有效性要求确定设计类型和设计特征,根据数据特征选择缺失处理方法和追踪数据分析方法。     

计划缺失设计——通过有意缺失让研究更高效

缺失值是社会科学研究中非常普遍的现象。全息极大似然估计和多重插补是目前处理缺失值最有效的方法。计划缺失设计利用特殊的实验设计有意产生缺失值, 再用现代的缺失值处理方法来完成统计分析, 获得无偏的统计结果。计划缺失设计可用于横断面调查减少(或增加)问卷长度和纵向调查减少测量次数, 也可用于提高测量有效性。常用的计划缺失设计有三式设计和两种方法测量。     

元代疫灾述论

元代疫灾严重程度难以想象,为害甚重。据史实考察可以发现：元代疫灾虽是由自然界的破坏力及人与人之间社会关系失调共同作用而引发的,但作用主体是有变化的,且时段划分比较明显。疫灾的时间分布密集,空间分布也较为平衡。元政府在防疫方面也有许多积极举措。     

The Journal of the Experimental Analysis of Behavior at fifty

The Journal of the Experimental Analysis of Behavior was founded in 1958 by a group of male psychologists, mainly from the northeastern USA and connected with either Harvard or Columbia. Fifty years later about 20% of both editors and authors reside outside this country and almost the same proportion is made up of women. Other changes in the journal include having its own website for more than a decade and now publishing online as well as on paper. A recent connection with PubMed Central of the National Library of Medicine has made possible the completely free electronic presentation of the entire archive of about 3,800 articles.     

Treatment of Missing Data: Beyond Ends and Means

The ethical decision making process behind the treatment of missing data has yet to be examined in the research literature in any discipline. The purpose of the current paper is to begin to discuss this decision-making process in view of a Foucauldian framework. The paper suggests how the ethical treatment of missing data should be considered from the adoption of this theoretical framework.     

Limited information estimation and testing of discretized multivariate normal structural models

Discretized multivariate normal structural models are often estimated using multistage estimation procedures. The asymptotic properties of parameter estimates, standard errors, and tests of structural restrictions on thresholds and polychoric correlations are well known. It was not clear how to assess the overall discrepancy between the contingency table and the model for these estimators. It is shown that the overall discrepancy can be decomposed into a distributional discrepancy and a structural discrepancy. A test of the overall model specification is proposed, as well as a test of the distributional specification (i.e., discretized multivariate normality). Also, the small sample performance of overall, distributional, and structural tests, as well as of parameter estimates and standard errors is investigated under conditions of correct model specification and also under mild structural and/or distributional misspecification. It is found that relatively small samples are needed for parameter estimates, standard errors, and structural tests. Larger samples are needed for the distributional and overall tests. Furthermore, parameter estimates, standard errors, and structural tests are surprisingly robust to distributional misspecification. This research was supported by the Department of Universities, Research and Information Society (DURSI) of the Catalan Government, and by grants BSO2000-0661 and BSO2003-08507 of the Spanish Ministry of Science and Technology.     

Component Models for Fuzzy Data

The fuzzy perspective in statistical analysis is first illustrated with reference to the “Informational Paradigm” allowing us to deal with different types of uncertainties related to the various informational ingredients (data, model, assumptions). The fuzzy empirical data are then introduced, referring to J LR fuzzy variables as observed on I observation units. Each observation is characterized by its center and its left and right spreads (LR₁ fuzzy number) or by its left and right “centers” and its left and right spreads (LR₂ fuzzy number). Two types of component models for LR₁ and LR₂ fuzzy data are proposed. The estimation of the parameters of these models is based on a Least Squares approach, exploiting an appropriately introduced distance measure for fuzzy data. A simulation study is carried out in order to assess the efficacy of the suggested models as compared with traditional Principal Component Analysis on the centers and with existing methods for fuzzy and interval valued data. An application to real fuzzy data is finally performed. We would like to express our gratitude to the Editor, the Associate Editor, and the Referees whose comments and suggestions improved significantly the quality of the paper.     

Gender bias in estimating political expertise

The present study reveals that there is a gender bias in estimates of spouses' political expertise. Data were collected from married couples in one city in north-eastern Japan and the results showed that when all respondents were included, the estimation of spouses' political expertise was moderately accurate. However, while husbands' estimates of the political expertise of their wives were lowered and less accurate, wives' estimates of the political expertise of their husbands were more accurate. The implication of these findings for the political equality of males and females is discussed.