The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects

In the multilevel modelling literature, methodologists widely acknowledge that a level-1 variable can have distinct within-cluster and between-cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level-1 variables in a model is not problematic if substantive interest lies only in effects of level-2 predictors. Researchers commonly follow this advice and use methods that do not disaggregate effects of level-1 control variables (e.g., grand mean centering) when examining effects of level-2 predictors. The primary purpose of this paper is to show that this is a dangerous practice. When level-specific effects of level-1 variables differ, failing to disaggregate them can severely bias estimation of level-2 predictor slopes. We show mathematically why this is the case and highlight factors that can exacerbate such bias. We corroborate these findings with simulations and present an empirical example, showing how such distortions can severely alter substantive conclusions. We ultimately recommend that simply including the cluster mean of the level-1 variable as a control will alleviate the problem.     

Belief in Hell and Parenting Priorities Concerning Child Independence and Obedience: Does Economic Context Matter?

This study examines the ways that belief in hell is associated with parental goals concerning child independence and obedience. Further, it assesses how this association is contingent upon economic circumstances of a nation, reflected by a country's Gross Domestic Product (GDP) per capita. Multilevel analyses with data from the sixth wave of the World Values Survey (2010-2014) show that belief in hell is associated with a greater likelihood of opposing independence and endorsing obedience. Yet, these observed associations differ across national economic context. Specifically, the negative association between belief in hell and parental endorsement of independence is greater in countries that are more economically developed. Similarly, the positive association between belief in hell and parental endorsement of obedience is greater in countries that are more economically developed. The theoretical implications of these findings are discussed regarding the interplay of religion, parental values about child socialization, and social context.     

Inherited and Environmental Moderators of Mother-Child Behavioral Contingency and Contingent Negativity at 27 Months

Previous research has shown that as depressive symptomology increases, mothers tend to show withdrawn or harsh interaction patterns with their children, and the quality of these interaction patterns have subsequently been linked to child behavior problems. However, little research has examined bidirectional influences between mothers and their children, and how these moment-to-moment contingencies differ based on heritable and environmental characteristics. We used data from the Early Growth and Development Study a prospective adoption study to examine how adoptive mothers’ depressive symptoms and children’s heritable tendencies for negative affectivity interact to predict the quality of mother-child interactions at child age 27-months. Results detected two distinct dyadic interaction patterns. The first was a withdrawn interaction style and was observed in children with a high heritable tendency for negative affect. In that style, mother and child interactions were not contingent upon each other, suggesting a lack of joint engagement. The second was a volatile interaction style, observed in children with a low heritable tendency for negative affect. In these cases, mother and child interactions were highly contingent but negative. Our findings demonstrate essential differences in how dyadic interaction patterns vary according to level of depressive symptomology and heritable tendency for negative affect.     

多层线性模型在纵向研究中的运用

纵向研究中传统统计技术主要是重复测量的方差分析和多元回归分析，但是这两种技术存在一些局限性，不能合理而充分地解释纵向研究资料。近年来出现的多层线性模型能够更有效地利用纵向数据。为促进多层线性模型在纵向研究中的运用，简要论述了传统统计技术的局限，介绍了多层线性模型的原理及其在纵向研究中的作用，以一组模拟数据为例对多层线性模型中各种参数在纵向研究中的含义进行了详细讨论，对于纵向研究中运用多层线性模型时须注意的问题提出了建议。     

Psychometric Modeling of response speed and accuracy with mixed and conditional regression

Human performance in cognitive testing and experimental psychology is expressed in terms of response speed and accuracy. Data analysis is often limited to either speed or accuracy, and/or to crude summary measures like mean response time (RT) or the percentage correct responses. This paper proposes the use of mixed regression for the psychometric modeling of response speed and accuracy in testing and experiments. Mixed logistic regression of response accuracy extends logistic item response theory modeling to multidimensional models with covariates and interactions. Mixed linear regression of response time extends mixed ANOVA to unbalanced designs with covariates and heterogeneity of variance. Related to mixed regression is conditional regression, which requires no normality assumption, but is limited to unidimensional models. Mixed and conditional methods are both applied to an experimental study of mental rotation. Univariate and bivariate analyzes show how within-subject correlation between response and RT can be distinguished from between-subject correlation, and how latent traits can be detected, given careful item design or content analysis. It is concluded that both response and RT must be recorded in cognitive testing, and that mixed regression is a versatile method for analyzing test data.I am grateful to Rogier Donders for putting his data at my disposal.     

Getting the Big Picture in Community Science: Methods That Capture Context

Community science has a rich tradition of using theories and research designs that are consistent with its core value of contextualism. However, a survey of empirical articles published in the American Journal of Community Psychology shows that community scientists utilize a narrow range of statistical tools that are not well suited to assess contextual data. Multilevel modeling, geographic information systems (GIS), social network analysis, and cluster analysis are recommended as useful tools to address contextual questions in community science. An argument for increased methodological consilience is presented, where community scientists are encouraged to adopt statistical methodology that is capable of modeling a greater proportion of the data than is typical with traditional methods.     

Multilevel Reliability Measures of Latent Scores Within an Item Response Theory Framework

Abstract

This paper evaluated multilevel reliability measures in two-level nested designs (e.g., students nested within teachers) within an item response theory framework. A simulation study was implemented to investigate the behavior of the multilevel reliability measures and the uncertainty associated with the measures in various multilevel designs regarding the number of clusters, cluster sizes, and intraclass correlations (ICCs), and in different test lengths, for two parameterizations of multilevel item response models with separate item discriminations or the same item discrimination over levels. Marginal maximum likelihood estimation (MMLE)-multiple imputation and Bayesian analysis were employed to evaluate the accuracy of the multilevel reliability measures and the empirical coverage rates of Monte Carlo (MC) confidence or credible intervals. Considering the accuracy of the multilevel reliability measures and the empirical coverage rate of the intervals, the results lead us to generally recommend MMLE-multiple imputation. In the model with separate item discriminations over levels, marginally acceptable accuracy of the multilevel reliability measures and empirical coverage rate of the MC confidence intervals were found in a limited condition, 200 clusters, 30 cluster size, .2 ICC, and 40 items, in MMLE-multiple imputation. In the model with the same item discrimination over levels, the accuracy of the multilevel reliability measures and the empirical coverage rate of the MC confidence intervals were acceptable in all multilevel designs we considered with 40 items under MMLE-multiple imputation. We discuss these findings and provide guidelines for reporting multilevel reliability measures.     

国内调节效应的方法学研究

在心理学和其他社科研究领域, 大量实证文章建立调节效应模型, 以分析自变量对因变量的影响是如何随着调节变量的变化而改变。过去10多年, 调节效应分析成了方法学研究的一个热点。从显变量的调节效应、潜变量的调节效应、多层数据的调节效应、基于两层回归模型的单层调节分析、纵向数据的调节效应、调节和中介的整合模型六个主题系统地总结了国内调节效应分析的方法学研究的发展历程。最后对调节效应的未来研究方向做了讨论和拓展。     

Levels of analysis and sources of data in sales research: a multilevel-multisource review

Sales researchers are increasingly adopting a multilevel-multisource (MLMS) approach to answer many key questions involving sales managers, salespeople and customers. MLMS research involves the acquisition and analysis of data collected from two or more sources pertaining to multiple hierarchical levels and presents a number of opportunities and challenges for sales researchers to consider. The authors highlight the benefits and the drawbacks of MLMS research and describe various approaches for implementing an MLMS collection and analysis. Additionally, a review of the MLMS studies conducted in the sales domain is provided which summarizes and delineates relationships tested in the extant literature. Based on this review, the authors advance a number of underdeveloped areas of research where MLMS approaches can be applied to further the understanding of the dynamic conditions that typify sales research.     

When do employees cross boundaries? Individual and contextual determinants of career mobility

This study investigates the joint effects of individual characteristics and the labour market on career mobility. We propose that level of education, openness to experience, and a favourable labour market relate positively to employees crossing organizational, industrial, and occupational boundaries. Management programme alumni (N = 503) provided information through an online survey about their career histories, their level of education, and their openness to experience. Additionally, we used the unemployment rate as an indicator for yearly changes in the labour market. The results of our cross-classified multilevel analysis indicate that both individual characteristics and the labour market are determinants of career mobility. Level of education had a positive effect on organizational and industrial boundary crossing, and changes in the labour market related to organizational boundary crossing. Against our assumptions, openness to experience had no effect on career mobility, and none of the predictors were related to occupational boundary crossing. Our results demonstrate the importance of investigating career mobility from a boundary perspective combined with a focus on both individual and contextual characteristics. The dominance of education compared to personality and the difficulty of explaining occupational mobility open new research avenues and yield practical implications for employees, career counsellors, and organizations.