The Effect of Propensity Scores on DIF Analysis: Inference on the Potential Cause of DIF

Differential item functioning (DIF) analysis is important in terms of test fairness. While DIF analyses have mainly been conducted with manifest grouping variables, such as gender or race/ethnicity, it has been recently claimed that not only the grouping variables but also contextual variables pertaining to examinees should be considered in DIF analyses. This study adopted propensity scores to incorporate the contextual variables into the gender DIF analysis. In this study, propensity scores were used to control for the contextual variables that potentially affect the gender DIF. Subsequent DIF analyses with the Mantel-Haenszel (MH) procedure and the Logistic Regression (LR) model were run with the propensity score applied reference (males) and focal groups (females) through propensity score matching. The propensity score embedded MH model and LR model detected fewer number of gender DIF than the conventional MH and LR models. The propensity score embedded models, as a confirmatory approach in DIF analysis, could contribute to hypothesizing an inference on the potential cause of DIF. Also, salient advantages of propensity score embedded DIF analysis models are discussed.     

Drawing Causal Inferences Using Propensity Scores: A Practical Guide for Community Psychologists

Confounding present in observational data impede community psychologists’ ability to draw causal inferences. This paper describes propensity score methods as a conceptually straightforward approach to drawing causal inferences from observational data. A step-by-step demonstration of three propensity score methods—weighting, matching, and subclassification—is presented in the context of an empirical examination of the causal effect of preschool experiences (Head Start vs. parental care) on reading development in kindergarten. Although the unadjusted population estimate indicated that children with parental care had substantially higher reading scores than children who attended Head Start, all propensity score adjustments reduce the size of this overall causal effect by more than half. The causal effect was also defined and estimated among children who attended Head Start. Results provide no evidence for improved reading if those children had instead received parental care. We carefully define different causal effects and discuss their respective policy implications, summarize advantages and limitations of each propensity score method, and provide SAS and R syntax so that community psychologists may conduct causal inference in their own research.     

The Advantages of Using Standardized Scores in Causal Analysis

This article compares the usefulness of standard score results (such as correlations and standardized regression coefficients) to that of raw scores results (such as covariances and raw score regression coefficients). The 2 main advantages of presenting standardized results is then examined. First, reporting standardized coefficients allows recognition of causal relationships that replicate across studies, facilitating meta‐analysis. Second, an analysis with standardized coefficients allows the researcher to determine which of the predictor variables has the largest relative impact on the criterion variable. The implications of these 2 standardized score advantages are demonstrated with examples.     

社交网站使用对青少年孤独感的影响:链式中介效应分析

为探讨社交网站使用、线上积极反馈、友谊质量和青少年孤独感的关系,采用社交网站使用强度问卷、线上积极反馈问卷、友谊质量量表和孤独感量表对538名中学生进行调查,结果发现:(1)社交网站使用与线上积极反馈和友谊质量都呈显著正相关,而与孤独感显著负相关,线上积极反馈与友谊质量显著正相关,而且两者都与孤独感显著负相关;(2)社交网站使用对孤独感没有直接的预测作用,但能通过两条路径的间接作用影响青少年孤独感:通过线上积极反馈的中介作用;通过线上积极反馈和友谊质量的链式中介作用。研究结果揭示了社交网站使用对青少年孤独感的影响及其作用机制,能为网络时代的青少年降低孤独感提供有益启示。     

An Evaluation of Weighting Methods Based on Propensity Scores to Reduce Selection Bias in Multilevel Observational Studies

Observational studies of multilevel data to estimate treatment effects must consider both the nonrandom treatment assignment mechanism and the clustered structure of the data. We present an approach for implementation of four propensity score (PS) methods with multilevel data involving creation of weights and three types of weight scaling (normalized, cluster-normalized and effective), followed by estimation of multilevel models with the multilevel pseudo-maximum likelihood estimation method. Using a Monte Carlo simulation study, we found that the multilevel model provided unbiased estimates of the Average Treatment Effect on the Treated (ATT) and its standard error across manipulated conditions and combinations of PS model, PS method, and type of weight scaling. Estimates of between-cluster variances of the ATT were biased, but improved as cluster sizes increased. We provide a step-by-step demonstration of how to combine PS methods and multilevel modeling to estimate treatment effects using multilevel data from the Early Childhood Longitudinal Study–Kindergarten Cohort (ECLS-K).     

Current Directions in Mediation Analysis

ABSTRACT— Mediating variables continue to play an important role in psychological theory and research. A mediating variable transmits the effect of an antecedent variable on to a dependent variable, thereby providing more detailed understanding of relations among variables. Methods to assess mediation have been an active area of research for the last two decades. This paper describes the current state of methods to investigate mediating variables.     

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。     

Understanding Power Anomalies in Mediation Analysis

Previous studies have found some puzzling power anomalies related to testing the indirect effect of a mediator. The power for the indirect effect stagnates and even declines as the size of the indirect effect increases. Furthermore, the power for the indirect effect can be much higher than the power for the total effect in a model where there is no direct effect and therefore the indirect effect is of the same magnitude as the total effect. In the presence of direct effect, the power for the indirect effect is often much higher than the power for the direct effect even when these two effects are of the same magnitude. In this study, the limiting distributions of related statistics and their non-centralities are derived. Computer simulations are conducted to demonstrate their validity. These theoretical results are used to explain the observed anomalies.     

类别变量的中介效应分析

在心理学和其他社科研究领域,研究者能熟练地进行连续变量的中介效应分析,但面对自变量、中介变量或(和)因变量为类别变量的中介效应分析,研究者往往束手无策。在阐述类别自变量中介分析方法的基础上,我们建议使用整体和相对中介相结合的类别自变量中介分析方法,并给出了分析流程。以二分因变量为例,讨论了中介变量或(和)因变量为类别变量的中介分析方法的发展过程(即尺度统一的过程),建议通过检验Za×Zb的显著性来判断中介效应的显著性。用二个实际例子演示如何进行类别变量的中介效应分析。最后展望了类别变量的中介效应分析研究的拓展方向。     

The Mediation Myth

The mediation myth is the false belief that mediation is actually estimated in the typical mediation analysis. This myth is based on a trifecta of shortcomings: (1) the typical mediation study relies on an inadequate design; (2) the researcher uses a flawed analysis strategy; and (3) there is scant attention to assumptions that are required when estimating mediation. These problems stem from overgeneralizing the classical product method for estimating mediation and overreliance on statistical significance testing as a decision criterion in mediation analysis. The goals of this article are to (1) raise awareness of these difficulties among researchers and (2) provide a roadmap about design and analysis options for a more rigorous and scientifically valid approach to mediation analysis.     

Flexible Mediation Analysis in the Presence of Nonlinear Relations: Beyond the Mediation Formula

In the social sciences, mediation analysis has typically been formulated in the context of linear models using the Baron &; Kenny (1986) Baron, R. M. and Kenny, D. A. 1986. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51: 1173–1182. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar] approach. Extensions to nonlinear models have been considered but lack formal justification. By placing mediation analysis within the counterfactual framework of causal inference one can define causal mediation effects in a way that is not tied to a specific statistical model and identify them under certain no unmeasured confounding assumptions. Corresponding estimation procedures using parametric or nonparametric models, based on the so-called mediation formula, have recently been proposed in the psychological literature and made accessible through the R-package mediation. A number of limitations of the latter approach are discussed and a more flexible approach using natural effects models is proposed as an alternative. The latter builds on the same counterfactual framework but enables interpretable and parsimonious modeling of direct and mediated effects and facilitates tests of hypotheses that would otherwise be difficult or impossible to test. We illustrate the approach in a study of individuals who ended a romantic relationship and explore whether the effect of attachment anxiety during the relationship on unwanted pursuit behavior after the breakup is mediated by negative affect during the breakup.     

Bayesian Model Averaging for Propensity Score Analysis

This article considers Bayesian model averaging as a means of addressing uncertainty in the selection of variables in the propensity score equation. We investigate an approximate Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the R package BMA but that ignores uncertainty in the propensity score. We also provide a fully Bayesian model averaging approach via Markov chain Monte Carlo sampling (MCMC) to account for uncertainty in both parameters and models. A detailed study of our approach examines the differences in the causal estimate when incorporating noninformative versus informative priors in the model averaging stage. We examine these approaches under common methods of propensity score implementation. In addition, we evaluate the impact of changing the size of Occam’s window used to narrow down the range of possible models. We also assess the predictive performance of both Bayesian model averaging propensity score approaches and compare it with the case without Bayesian model averaging. Overall, results show that both Bayesian model averaging propensity score approaches recover the treatment effect estimates well and generally provide larger uncertainty estimates, as expected. Both Bayesian model averaging approaches offer slightly better prediction of the propensity score compared with the Bayesian approach with a single propensity score equation. Covariate balance checks for the case study show that both Bayesian model averaging approaches offer good balance. The fully Bayesian model averaging approach also provides posterior probability intervals of the balance indices.     

留守对农村儿童青少年社会适应的影响:倾向值匹配的比较分析

以河南和陕西两省3812名4~9年级农村学生为研究样本, 考察其在抑郁、自尊、问题行为、幸福感、未来压力感知及人际关系方面的社会适应状况, 并运用倾向值匹配方法探讨了父母外出务工对其产生的影响。结果发现, 在倾向值匹配之前, 双亲外出务工的留守儿童在未来压力感知、抑郁和幸福感3方面的适应状况均比非留守儿童差, 在师生关系上得分高于非留守儿童;单亲外出务工的留守儿童感知到的未来压力及抑郁水平也显著高于非留守儿童。经过倾向值匹配处理后, 双亲外出留守儿童的幸福感仍显著低于非留守儿童, 单亲外出留守儿童的未来压力感知也高于非留守儿童, 但其他方面的差异不再显著。研究结果提示对于留守与非留守儿童存在的社会适应差异不能完全归咎于父母的外出务工状态。     

Cross-Sectional Analysis of Longitudinal Mediation Processes

Statistical mediation analysis can help to identify and explain the mechanisms behind psychological processes. Examining a set of variables for mediation effects is a ubiquitous process in the social sciences literature; however, despite evidence suggesting that cross-sectional data can misrepresent the mediation of longitudinal processes, cross-sectional analyses continue to be used in this manner. Alternative longitudinal mediation models, including those rooted in a structural equation modeling framework (cross-lagged panel, latent growth curve, and latent difference score models) are currently available and may provide a better representation of mediation processes for longitudinal data. The purpose of this paper is twofold: first, we provide a comparison of cross-sectional and longitudinal mediation models; second, we advocate using models to evaluate mediation effects that capture the temporal sequence of the process under study. Two separate empirical examples are presented to illustrate differences in the conclusions drawn from cross-sectional and longitudinal mediation analyses. Findings from these examples yielded substantial differences in interpretations between the cross-sectional and longitudinal mediation models considered here. Based on these observations, researchers should use caution when attempting to use cross-sectional data in place of longitudinal data for mediation analyses.     

学校氛围和青少年病理性网络游戏使用的关系:有调节的中介模型

采用问卷法对3056名青少年进行调查研究,考察了学校氛围对青少年病理性网络游戏使用(Pathological online game use, POGU)的影响,同时考察了学校参与在其中的中介作用,以及该过程是否受到父子关系或母子关系的调节.结果发现:(1)在控制了性别、年龄、父母受教育水平、亲子关系、家庭收入和冲动性后,学校氛围对POGU仍具有显著的负向预测作用;(2)学校氛围不仅可以直接负向预测POGU,还可以通过影响学校参与进而间接预测青少年POGU;(3)母子关系显著调节学校氛围对学校参与的影响,而父子关系显著调节学校参与对青少年POGU的作用.总之,在学校氛围影响青少年POGU的过程中,既存在中介效应,又存在调节效应.实际干预时,可以通过提高学校参与降低青少年POGU,父子关系和母子关系可以进一步调节该过程.     

Ten Ways to Improve the Use of Statistical Mediation Analysis in the Practice of Child and Adolescent Treatment Research

Despite guidelines and repeated calls from the literature, statistical mediation analysis in youth treatment outcome research is rare. Even more concerning is that many studies that have reported mediation analyses do not fulfill basic requirements for mediation analysis, providing inconclusive data and clinical implications. As a result, after more than five decades of research, it is still largely unknown through which processes youth treatment works and what the effective treatment components are. In this article, we present ten ways in which the use of statistical mediation analysis in youth treatment outcome research may be improved. These ten ways are related both to conceptual and methodological issues. In discussing how youth clinical researchers may optimally implement these directions, we argue that studies should employ the strongest research designs possible. In so doing, we describe different levels of a mediation evidence ladder. Studies on each step of the ladder contribute to an understanding of mediation processes, but the strongest evidence for mediation is provided by studies that can be classified at the highest level. With the help of the ladder of mediation evidence, results from youth mediation treatment outcome research can be evaluated on their scientific as well as clinical impact.     

对罗夏测验中某些认知发展质量记分及相应部位记分的解释

1　前言　　罗夏测验是临床心理学界用得最广的心理测验之一 ,但由于其心理计量学特征不易确定 ,因而多受批评和怀疑。批判和怀疑态度的产生是因为不同的罗夏系统 (指Ｂｅｃｋ、Ｋｉｏｐｆｅｒ、Ｐｉｏｔｒｏｗｓｋｉ、Ｈｅｒｔｚ和Ｒａｐａｐｏｒｔ的系统 )在施测、记分时没有统一的标准 ,多数人视之为投射测验 ,解释时使用了精神分析理论。Ｅｘｎｅｒ(1978,1986)提出了综合罗夏系统 ,统一了施测和记分的标准 ,在实证研究的基础上建立了解释系统 ,肯定了其信度和效度。综合罗夏系统能鉴别精神分裂症、抑郁、强迫等异常心理 ,是临床诊断的有…     

社交网站使用对青少年幸福感的影响:一个有调节的中介模型

在社会比较的视角下探讨社交网站使用对青少年幸福感的影响,建构了一个有调节的中介模型考察消极社会比较的中介作用和社交焦虑的调节作用。采用社交网站使用强度问卷、消极社会比较问卷、社交焦虑量表和主观幸福感问卷对798名中学生进行问卷调查。结果表明:(1)社交网站使用通过消极社会比较的中介作用间接影响青少年的幸福感;(2)该间接效应受到社交焦虑的调节,相对于低社交焦虑的青少年,高社交焦虑青少年的社交网站使用诱发更多的消极社会比较。