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.     

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。     

Bias in cross-sectional analyses of longitudinal mediation

Most empirical tests of mediation utilize cross-sectional data despite the fact that mediation consists of causal processes that unfold over time. The authors considered the possibility that longitudinal mediation might occur under either of two different models of change: (a) an autoregressive model or (b) a random effects model. For both models, the authors demonstrated that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters even under the ideal conditions when mediation is complete. In longitudinal models where variable M completely mediates the effect of X on Y, cross-sectional estimates of the direct effect of X on Y, the indirect effect of X on Y through M, and the proportion of the total effect mediated by M are often highly misleading.     

Spelling HUMBLE with U and ME: The role of perceived humility in intimate partner relationships

Humility predicts relationship satisfaction, partially mediated by commitment, in college students. The present study tested this mediation in a non-college sample of participants who have been in exclusive relationships for at least three months (N = 349). We supported a partial mediation model in predicting relationship satisfaction (Hypothesis 1). After controlling for commitment, accurate view of self was the most important factor in predicting relational satisfaction (Hypothesis 2). A simultaneous meditational analysis revealed that perceived humility predicted relationship satisfaction, mediated by gratitude in relationships (Hypothesis 3). A multiple regression analysis revealed that after controlling for personal virtues, perceiving humility in one’s partner predicted additional variance in relational satisfaction (Hypothesis 4). Our study provided support for a mediational model, but do not allow causal inferences because of cross-sectional design. Thus, we recommend that future studies include longitudinal studies to investigate the meditational models we observed.     

Experimental Power for Indirect Effects in Group-randomized Studies with Group-level Mediators

Mediation analyses have provided a critical platform to assess the validity of theories of action across a wide range of disciplines. Despite widespread interest and development in these analyses, literature guiding the design of mediation studies has been largely unavailable. Like studies focused on the detection of a total or main effect, an important design consideration is the statistical power to detect indirect effects if they exist. Understanding the sensitivity to detect indirect effects is exceptionally important because it directly influences the scale of data collection and ultimately governs the types of evidence group-randomized studies can bring to bear on theories of action. However, unlike studies concerned with the detection of total effects, literature has not established power formulas for detecting multilevel indirect effects in group-randomized designs. In this study, we develop closed-form expressions to estimate the variance of and the power to detect indirect effects in group-randomized studies with a group-level mediator using two-level linear models (i.e., 2-2-1 mediation). The results suggest that when carefully planned, group-randomized designs may frequently be well positioned to detect mediation effects with typical sample sizes. The resulting power formulas are implemented in the R package PowerUpR and the PowerUp!-Mediator software (causalevaluation.org).     

基于两水平被试内设计的中介效应分析方法

在心理学和其他社科研究领域,通常的中介效应分析都基于被试间设计,研究者对于如何分析基于被试内设计的中介效应往往并不清楚。本文阐述了两水平被试内设计的中介效应分析方法(依次检验法和路径分析法),综合各方法优缺点给出一个分析流程,并用应用研究实例演示如何分析两水平被试内中介效应,最后展望了基于被试内设计的中介效应分析研究的拓展方向。     

Sequential mediating effects of provided and received social support on trait emotional intelligence and subjective happiness: A longitudinal examination in Hong Kong Chinese university students

Past research has often focused on the effects of emotional intelligence and received social support on subjective well‐being yet paid limited attention to the effects of provided social support. This study adopted a longitudinal design to examine the sequential mediating effects of provided and received social support on the relationship between trait emotional intelligence and subjective happiness. A total of 214 Hong Kong Chinese undergraduates were asked to complete two assessments with a 6‐month interval in between. The results of the sequential mediation analysis indicated that the trait emotional intelligence measured in Time 1 indirectly influenced the level of subjective happiness in Time 2 through a sequential pathway of social support provided for others in Time 1 and social support received from others in Time 2. These findings highlight the importance of trait emotional intelligence and the reciprocal exchanges of social support in the subjective well‐being of university students.     

Cross-sectional,longitudinal and composite longitudinal data on the Career Maturity Inventory Attitude Scale

Longitudinal data collection designs are frequently used in the study of developmental variables but typically yield patterns of results that are different from those obtained using cross-sectional designs. Recent research has focused on ways to clarify and reconcile the methodological distinctions between these two designs by utilizing alternative designs that incorporate features of both. This investigation compared the results obtained by the two traditional designs on the Career Maturity Inventory-Attitude Scale with the effects of an alternative design: constructing a composite longitudinal gradient. The two traditional methodologies yielded different results from each other for males, but not for females. However, the alternative design failed to eliminate the obtained differences for males, and introduced differences for females, casting doubt on the efficacy of the “solution.” The implications of these results for developmental research and career maturity were discussed.     

Advancing knowledge about delinquency and crime: The need for a coordinated program of longitudinal research

Past research on delinquency and crime has provided convincing data about correlates but not about causes, because it has been largely cross-sectional in design. Whereas cross-sectional research focuses on variations between subjects, longitudinal research can provide information about changes within subjects, which are more important for causes, prevention, and treatment. Many of the most important results in criminology have been obtained in longitudinal or experimental studies, and the major studies of these types are reviewed here. It is now desirable to combine the two methods in a new coordinated program of longitudinal studies including experimental interventions or quasi-experimental analyses. Such projects could study several overlapping cohorts, extend from birth to adulthood, have frequent data collection, and obtain data from a variety of sources about many different types of antisocial behavior.     

A Comparison of Multilevel Mediation Modeling Methods: Recommendations for Applied Researchers

Multilevel structural equation modeling (MSEM) has been proposed as a valuable tool for estimating mediation in multilevel data and has known advantages over traditional multilevel modeling, including conflated and unconflated techniques (CMM & UMM). Recent methodological research has focused on comparing the three methods for 2-1-1 designs, but in regards to 1-1-1 mediation designs, there are significant gaps in the published literature that prevent applied researchers from making educated decisions regarding which model to employ in their own specific research design. A Monte Carlo study was performed to compare MSEM, UMM, and CMM on relative bias, confidence interval coverage, Type I Error, and power in a 1-1-1 model with random slopes under varying data conditions. Recommendations for applied researchers are discussed and an empirical example provides context for the three methods.     

Socialization tactics and newcomer adjustment: A meta-analytic review and test of a model

One of the most popular and often studied topics in the organizational socialization literature is Van Maanen and Schein’s [Van Maanen, J., & Schein, E. H. (1979). Toward a theory of organizational socialization. In B. M. Staw (Ed.), Research in organizational behavior (Vol. 1), pp. 209-264. Greenwich, CT: JAI Press.] theory of organizational socialization tactics. Over 30 studies on socialization tactics have been conducted in the past 20 years. In this meta-analysis, we examine the relationships between six socialization tactics and various indicators of newcomer adjustment as well as the moderating effects of study design (cross-sectional vs. longitudinal), measurement scale (use of complete vs. modified tactics scale), and type of newcomer (recent graduates vs. other newcomers). Our results indicate that institutionalized socialization tactics were negatively related to role ambiguity, role conflict, and intentions to quit, and positively related to fit perceptions, job satisfaction, organizational commitment, job performance, and a custodial role orientation. We also found that the social tactics (serial and investiture) were the strongest predictors of adjustment outcomes. The results also indicated that the relationships between the tactics and outcomes were stronger for recent graduates compared to other newcomers; cross-sectional designs compared to longitudinal designs; and when Jones’ [Jones, G. R. (1986). Socialization tactics, self-efficacy, and newcomers’ adjustments to organizations. Academy of Management Journal, 29, 262-279.] complete scales were used compared to modified versions. Support was also found for a mediation model of newcomer adjustment in which role conflict, role ambiguity, and fit perceptions partially mediate some of the relationships between the socialization tactics and distal outcomes of adjustment. The implications of these results for research and practice are discussed.     

When disgust leads to dysphoria: A three-wave longitudinal study assessing the temporal relationship between self-disgust and depressive symptoms

Research has shown that feelings of self-disgust may have a functional role in the genesis of depression by partially mediating the cross-sectional relationship between dysfunctional thoughts and depressive symptoms. However, there are many outstanding issues regarding these hypothesised associations. First, it is not yet clear whether self-disgust is a temporal antecedent, concomitant, or consequence of depressive experience. Second, it is not known whether the hypothesised mediation sequence is valid over time. Third, the relative contribution of disgust towards different aspects of the self has not yet been examined. In the present longitudinal study, participants completed measures of dysfunctional cognitions, self-disgust and depressive symptoms at baseline, and at six and 12-month follow-ups. Analysis showed that self-disgust is best considered as antecedent to depressive symptoms; the hypothesised mediation model was partially supported, but is too simplistic; and disgust towards physical aspects of the self, rather than behaviour, was more important as a temporal predictor of depressive symptoms. The current results help elucidate the role of self-disgust as an antecedent of depressive experience.     

Testing mediational models with longitudinal data: questions and tips in the use of structural equation modeling

R. M. Baron and D. A. Kenny (1986; see record 1987-13085-001) provided clarion conceptual and methodological guidelines for testing mediational models with cross-sectional data. Graduating from cross-sectional to longitudinal designs enables researchers to make more rigorous inferences about the causal relations implied by such models. In this transition, misconceptions and erroneous assumptions are the norm. First, we describe some of the questions that arise (and misconceptions that sometimes emerge) in longitudinal tests of mediational models. We also provide a collection of tips for structural equation modeling (SEM) of mediational processes. Finally, we suggest a series of 5 steps when using SEM to test mediational processes in longitudinal designs: testing the measurement model, testing for added components, testing for omitted paths, testing the stationarity assumption, and estimating the mediational effects.     

Cross-sectional and longitudinal comparisons of adolescent self-esteem.

The purpose of this study was to examine the self-esteem of adolescents over three years of high school. The study was designed to allow for cross-sectional and longitudinal comparisons of adolescent self-esteem, including the effects of gender and socioeconomic status. Two hundred seventy ninth graders completed the Coopersmith Self-Esteem Inventory for each of three years. Grade level was found to be significantly related to self-esteem in the longitudinal but not the cross-sectional design. The mediational effects of gender and socioeconomic status were found to be minimal. Theoretical implications of these results are discussed, and implications for future research are indicated.     

Examining Spirituality Over Time: Latent Growth Curve and Individual Growth Curve Analyses

SUMMARY

Research on spirituality and religiousness has gained growing attention in recent years; however, most studies have used cross-sectional designs. As research on this topic evolves, there has been increasing recognition of the need to examine these constructs and their effects through the use of longitudinal designs. Beyond repeated-measures ANOVA and OLS regression models, what tools are available to examine these constructs over time? The purpose of this paper is to provide an overview of two cutting-edge statistical techniques that will facilitate longitudinal investigations of spirituality and religiousness: latent growth curve analysis using structural equation modeling (SEM) and individual growth curve models. The SEM growth curve approach examines change at the group level, with change over time expressed as a single latent growth factor. In contrast, individual growth curve models consider longitudinal change at the level of the person. While similar results may be obtained using either method, researchers may opt for one over the other due to the strengths and weaknesses associated with these methods. Examples of applications of both approaches to longitudinal studies of spirituality and religiousness are presented and discussed, along with design and data considerations when employing these modeling techniques.     

你能看见我的努力吗：社会地位感知对消费者繁简偏好的影响

文章通过5个实验(包括1个预注册实验)探讨了个体感知到的相对社会地位如何影响消费者对产品繁简设计的偏好。实验1和2发现处于相对低社会地位的个体会偏好设计繁复的产品。实验3和4探究了该效应的中介机制, 即繁复设计的产品可以传递出努力线索, 而相对低社会地位个体因为重视努力进而偏好繁复设计产品。实验5通过调节变量的方式进一步验证上述机制, 发现社会地位对繁简偏好的影响只存在于那些重视努力价值的个体身上。文章推进了消费者审美偏好, 主观社会地位和消费者努力等方向的研究进展。     

Bias in Cross-Sectional Analyses of Longitudinal Mediation: Partial and Complete Mediation Under an Autoregressive Model

Maxwell and Cole (2007) Maxwell, S. E. and Cole, D. A. 2007. Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12: 23–44. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar] showed that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters in the special case of complete mediation. However, their results did not apply to the more typical case of partial mediation. We extend their previous work by showing that substantial bias can also occur with partial mediation. In particular, cross-sectional analyses can imply the existence of a substantial indirect effect even when the true longitudinal indirect effect is zero. Thus, a variable that is found to be a strong mediator in a cross-sectional analysis may not be a mediator at all in a longitudinal analysis. In addition, we show that very different combinations of longitudinal parameter values can lead to essentially identical cross-sectional correlations, raising serious questions about the interpretability of cross-sectional mediation data. More generally, researchers are encouraged to consider a wide variety of possible mediation models beyond simple cross-sectional models, including but not restricted to autoregressive models of change.     

Potential Mediators in Parenting and Family Intervention: Quality of Mediation Analyses

Parenting and family interventions have repeatedly shown effectiveness in preventing and treating a range of youth outcomes. Accordingly, investigators in this area have conducted a number of studies using statistical mediation to examine some of the potential mechanisms of action by which these interventions work. This review examined from a methodological perspective in what ways and how well the family-based intervention studies tested statistical mediation. A systematic search identified 73 published outcome studies that tested mediation for family-based interventions across a wide range of child and adolescent outcomes (i.e., externalizing, internalizing, and substance-abuse problems; high-risk sexual activity; and academic achievement), for putative mediators pertaining to positive and negative parenting, family functioning, youth beliefs and coping skills, and peer relationships. Taken as a whole, the studies used designs that adequately addressed temporal precedence. The majority of studies used the product of coefficients approach to mediation, which is preferred, and less limiting than the causal steps approach. Statistical significance testing did not always make use of the most recently developed approaches, which would better accommodate small sample sizes and more complex functions. Specific recommendations are offered for future mediation studies in this area with respect to full longitudinal design, mediation approach, significance testing method, documentation and reporting of statistics, testing of multiple mediators, and control for Type I error.     

KOHLBERG'S THEORY OF MORAL DEVELOPMENT: CRITICAL ANALYSIS OF VALIDATION STUDIES WITH THE DEFINING ISSUES TEST

This paper evaluates studies that have used the Defining Issues Test for validating Kohlberg's theory of moral stage development. Although this test was introduced to overcome inadequacies with Kohlberg's procedure, it too encounters serious methodological and conceptual difficulties. Its limitations include truncation of the stages and age range assessed. The validation studies, both cross-sectional and longitudinal, have used a correlational design and confounded age with other variables. Threats to internal and external validity obviate support claimed for Kohlberg; moral reasoning thus measured seems a function of education, IQ, direct moral training, and cultural values rather than maturation. Nor is the stage model upheld by longitudinal data, which fail to reveal sequential stepwise change and belie occasional regression. Attempted redefinition of the stage concept to salvage these data has not rescued Kohlberg's proposal, which remains to be validated empirically.     

Bayesian analysis of longitudinal multitrait–multimethod data with ordinal response variables

A new multilevel latent state graded response model for longitudinal multitrait–multimethod (MTMM) measurement designs combining structurally different and interchangeable methods is proposed. The model allows researchers to examine construct validity over time and to study the change and stability of constructs and method effects based on ordinal response variables. We show how Bayesian estimation techniques can address a number of important issues that typically arise in longitudinal multilevel MTMM studies and facilitates the estimation of the model presented. Estimation accuracy and the impact of between‐ and within‐level sample sizes as well as different prior specifications on parameter recovery were investigated in a Monte Carlo simulation study. Findings indicate that the parameters of the model presented can be accurately estimated with Bayesian estimation methods in the case of low convergent validity with as few as 250 clusters and more than two observations within each cluster. The model was applied to well‐being data from a longitudinal MTMM study, assessing the change and stability of life satisfaction and subjective happiness in young adults after high‐school graduation. Guidelines for empirical applications are provided and advantages and limitations of a Bayesian approach to estimating longitudinal multilevel MTMM models are discussed.