Causal Models for Mediation Analysis: An Introduction to Structural Mean Models [0.03%]
因果模型在中介分析中的应用：结构均值模型导论
Cheng Zheng,David C Atkins,Xiao-Hua Zhou et al.
Cheng Zheng et al.
Mediation analyses are critical to understanding why behavioral interventions work. To yield a causal interpretation, common mediation approaches must make an assumption of "sequential ignorability." The current article describes an alterna...
Constructing Confidence Intervals for Effect Size Measures of an Indirect Effect [0.03%]
效应量间接效果的置信区间构建方法研究
Sunbok Lee,Man Kit Lei,Gene H Brody
Sunbok Lee
Confidence intervals for an effect size can provide the information about the magnitude of an effect and its precision as well as the binary decision about the existence of an effect. In this study, the performances of five different method...
Reciprocal Markov Modeling of Feedback Mechanisms Between Emotion and Dietary Choice Using Experience-Sampling Data [0.03%]
基于体验抽样数据的反馈机制的情感与饮食选择互惠马尔可夫模型分析
Ji Lu,Junhao Pan,Qiang Zhang et al.
Ji Lu et al.
With intensively collected longitudinal data, recent advances in the experience-sampling method (ESM) benefit social science empirical research, but also pose important methodological challenges. As traditional statistical models are not ge...
Evaluating Structural Equation Models for Categorical Outcomes: A New Test Statistic and a Practical Challenge of Interpretation [0.03%]
关于类别结果结构方程模型的评价：一个新的检验统计量和实际解释难题
Scott Monroe,Li Cai
Scott Monroe
This research is concerned with two topics in assessing model fit for categorical data analysis. The first topic involves the application of a limited-information overall test, introduced in the item response theory literature, to structura...
Extending the Debate Between Spearman and Wilson 1929: When do Single Variables Optimally Reproduce the Common Part of the Observed Covariances? [0.03%]
西尔弗曼和威尔逊1929年辩论的延续：单变量最优再现观测协方差的共同部分的情况？
André Beauducel,Norbert Hilger
André Beauducel
The covariances of observed variables reproduced from conventional factor score predictors are generally not the same as the covariances reproduced from the common factors. We sought to find a factor score predictor that optimally reproduce...
Models for the Detection of Deviations from the Expected Processing Strategy in Completing the Items of Cognitive Measures [0.03%]
用于发现认知测评项目完成过程中与预期加工策略偏离的模型研究
Karl Schweizer,Michael Altmeyer,Xuezhu Ren et al.
Karl Schweizer et al.
This paper presents confirmatory factor models with fixed factor loadings that enable the identification of deviations from the expected processing strategy. The instructions usually define the expected processing strategy to a considerable...
Correcting Too Much or Too Little? The Performance of Three Chi-Square Corrections [0.03%]
纠正过多还是过少？三个卡方校正的性能分析
Njål Foldnes,Ulf Henning Olsson
Njål Foldnes
This simulation study investigates the performance of three test statistics, T1, T2, and T3, used to evaluate structural equation model fit under non normal data conditions. T1 is the well-known mean-adjusted statistic of Satorra and Bentle...
How Do Propensity Score Methods Measure Up in the Presence of Measurement Error? A Monte Carlo Study [0.03%]
关于测量误差存在时倾向得分方法表现如何的蒙特卡洛研究
Patricia Rodríguez De Gil,Aarti P Bellara,Rheta E Lanehart et al.
Patricia Rodríguez De Gil et al.
Considering that the absence of measurement error in research is a rare phenomenon and its effects can be dramatic, we examine the impact of measurement error on propensity score (PS) analysis used to minimize selection bias in behavioral a...
Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation [0.03%]
处理项目级缺失数据：比例法和全信息最大似然估计的比较
Gina L Mazza,Craig K Enders,Linda S Ruehlman
Gina L Mazza
Often when participants have missing scores on one or more of the items comprising a scale, researchers compute prorated scale scores by averaging the available items. Methodologists have cautioned that proration may make strict assumptions...
Comparative Study
Multivariate behavioral research. 2015;50(5):504-19. DOI:10.1080/00273171.2015.1068157 2015
A Comparison of Imputation Strategies for Ordinal Missing Data on Likert Scale Variables [0.03%]
李克特量表变量下序数型缺失数据填补策略的比较研究
Wei Wu,Fan Jia,Craig Enders
Wei Wu
This article compares a variety of imputation strategies for ordinal missing data on Likert scale variables (number of categories = 2, 3, 5, or 7) in recovering reliability coefficients, mean scale scores, and regression coefficients of pre...
Comparative Study
Multivariate behavioral research. 2015;50(5):484-503. DOI:10.1080/00273171.2015.1022644 2015