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。     

A Monte Carlo evaluation of masked visual analysis in response‐guided versus fixed‐criteria multiple‐baseline designs

We developed masked visual analysis (MVA) as a structured complement to traditional visual analysis. The purpose of the present investigation was to compare the effects of computer‐simulated MVA of a four‐case multiple‐baseline (MB) design in which the phase lengths are determined by an ongoing visual analysis (i.e., response‐guided) versus those in which the phase lengths are established a priori (i.e., fixed criteria). We observed an acceptably low probability (less than .05) of false detection of treatment effects. The probability of correctly detecting a true effect frequently exceeded .80 and was higher when: (a) the masked visual analyst extended phases based on an ongoing visual analysis, (b) the effects were larger, (c) the effects were more immediate and abrupt, and (d) the effects of random and extraneous error factors were simpler. Our findings indicate that MVA is a valuable combined methodological and data‐analysis tool for single‐case intervention researchers.     

A unified approach to exploratory factor analysis with missing data,nonnormal data,and in the presence of outliers

Factor analysis is regularly used for analyzing survey data. Missing data, data with outliers and consequently nonnormal data are very common for data obtained through questionnaires. Based on covariance matrix estimates for such nonstandard samples, a unified approach for factor analysis is developed. By generalizing the approach of maximum likelihood under constraints, statistical properties of the estimates for factor loadings and error variances are obtained. A rescaled Bartlett-corrected statistic is proposed for evaluating the number of factors. Equivariance and invariance of parameter estimates and their standard errors for canonical, varimax, and normalized varimax rotations are discussed. Numerical results illustrate the sensitivity of classical methods and advantages of the proposed procedures.This project was supported by a University of North Texas Faculty Research Grant, Grant #R49/CCR610528 for Disease Control and Prevention from the National Center for Injury Prevention and Control, and Grant DA01070 from the National Institute on Drug Abuse. The results do not necessarily represent the official view of the funding agencies. The authors are grateful to three reviewers for suggestions that improved the presentation of this paper.     

时变效应模型及在密集追踪数据分析中的应用

密集追踪数据通常蕴含了心理过程的详细变化信息,反映了某些心理的复杂变化过程。时变效应模型用函数替代恒定的系数,可描述密集追踪数据中随时间推移心理的动态变化过程和时变效应,是分析复杂心理过程的有效方法。在介绍时变效应模型的原理后,通过模拟研究考察模型的表现,结果显示:(1)样本量增加可降低函数估计的误差;(2)惩罚样条法的节点数选择与函数的复杂度有关,函数越复杂,所需节点越多;(3)样本量与节点数对函数估计误差的交互效应不显著。进一步应探讨测量次数、数据分布形态、数据缺失等如何影响模型的表现。     

Multi‐set factor analysis by means of Parafac2

We consider multi‐set data consisting of observations, k = 1,…, K (e.g., subject scores), on J variables in K different samples. We introduce a factor model for the J × J covariance matrices , k = 1,…, K, where the common part is modelled by Parafac2 and the unique variances , k = 1,…, K, are diagonal. The Parafac2 model implies a common loadings matrix that is rescaled for each k, and a common factor correlation matrix. We estimate the unique variances by minimum rank factor analysis on for each k. The factors can be chosen orthogonal or oblique. We present a novel algorithm to estimate the Parafac2 part and demonstrate its performance in a simulation study. Also, we fit our model to a data set in the literature. Our model is easy to estimate and interpret. The unique variances, the factor correlation matrix and the communalities are guaranteed to be proper, and a percentage of explained common variance can be computed for each k. Also, the Parafac2 part is rotationally unique under mild conditions.     

Why and how we should care about the General Data Protection Regulation

Abstract

The General Data Protection Regulation (GDPR) is the new European Union-wide (EU) law on data protection, which is a great step towards more comprehensive and more far-reaching protection of individuals' personal data. In this editorial, we describe why and how we – as researchers within the field of health psychology – should care about the GDPR. In the first part, we explain when the GDPR is applicable, who is accountable for data protection, and what is covered by the notions of personal data and processing. In the second part, we explain aspects of the GDPR that are relevant for researchers within the field of health psychology (e.g., obtaining informed consent, data minimisation, and open science). We focus on questions that researchers may ask themselves in their daily practice. Compliance with the GDPR requires adopting research practices (e.g., data minimisation and anonymization procedures) that are not yet commonly used, but serve the fundamental right to protection of personal data of study participants.     

The choice of constraints in correspondence analysis

A discussion of alternative constraint systems has been lacking in the literature on correspondence analysis and related techniques. This paper reiterates earlier results that an explicit choice of constraints has to be made which can have important effects on the resulting scores. The paper also presents new results on dealing with missing data and probabilistic category assignment.I am most grateful to the following for their helpful comments. Arto Demirjian, Michael Greenacre, Michael Healy, Shizuhiko Nishisato, Roderick Mcdonald, and several anonymous referees.     

A reformulation of the general Euclidean model for the external analysis of preference data

This paper discusses least squares methods for fitting a reformulation of the general Euclidean model for the external analysis of preference data. The reformulated subject weights refer to a common set of reference vectors for all subjects and hence are comparable across subjects. If the rotation of the stimulus space is fixed, the subject weight estimates in the model are uniquely determined. Weight estimates can be guaranteed nonnegative. While the reformulation is a metric model for single stimulus data, the paper briefly discusses extensions to nonmetric, pairwise, and logistic models. The reformulated model is less general than Carroll's earlier formulation.The author is grateful to Christopher J. Nachtsheim for his helpful suggestions.     

Constrained maximum likelihood estimation of two-level covariance structure model via EM type algorithms

In this paper, the constrained maximum likelihood estimation of a two-level covariance structure model with unbalanced designs is considered. The two-level model is reformulated as a single-level model by treating the group level latent random vectors as hypothetical missing-data. Then, the popular EM algorithm is extended to obtain the constrained maximum likelihood estimates. For general nonlinear constraints, the multiplier method is used at theM-step to find the constrained minimum of the conditional expectation. An accelerated EM gradient procedure is derived to handle linear constraints. The empirical performance of the proposed EM type algorithms is illustrated by some artifical and real examples.This research was supported by a Hong Kong UCG Earmarked Grant, CUHK 4026/97H. We are greatly indebted to D.E. Morisky and J.A. Stein for the use of their AIDS data in our example. We also thank the Editor, two anonymous reviewers, W.Y. Poon and H.T. Zhu for constructive suggestions and comments in improving the paper. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.