Infant Predictors of Toddler Effortful Control: A Multi‐method Developmentally Sensitive Approach

Effortful control (EC) is a form of self‐regulation characterized by inhibition of dominant responses in favour of more adaptive subdominant responses. In the present study, temperamental antecedents in infancy were examined in relationship to toddler EC. Parents reported on infant temperament at 6, 8, 10, and 12 months, using the regulatory capacity/orienting (RCO) factor of the Infant Behavior Questionnaire‐Revised. Laboratory indicators of this temperamental construct were also collected at each time point via the Temperament Laboratory Assessment. Relations between changes in temperament across the first year of life and toddler EC (mean age = 22 months) were examined using multilevel modelling. Parent‐reported RCO and the component subscale of cuddliness more specifically were the only temperamental variables that prospectively predicted toddler EC. Copyright © 2016 John Wiley & Sons, Ltd.     

等级反应多水平侧面模型及其在主观题评分中的应用

探讨了康春花,孙小坚和曾平飞(2016)提出的等级反应多水平侧面模型(GR-MLFM)在包含被试及评分者层面预测变量(完整模型)下的返真性和适用性。结果表明:(1)GR-MLFM完整模型具有逻辑上和数理上的合理性,可用于主观题的评分情境,能较好地检测出评分者效应、影响因素及其影响程度;(2)在数学问题解决的评分实践中,评分员存在两种类型的评分倾向(宽松和严格效应),但绝大多数评分员的宽严度不明显;评分者的责任心可正向预测其严格程度,自信心可正向预测其宽松程度,而情绪稳定性和评分经验的预测作用不显著。     

Predicting Romantic Interest at Zero Acquaintance: Evidence of Sex Differences in Trait Perception but Not in Predictors of Interest

We evaluated five competing hypotheses about what predicts romantic interest. Through a half‐block quasi‐experimental design, a large sample of young adults (i.e. responders; n = 335) viewed videos of opposite‐sex persons (i.e. targets) talking about themselves, and responders rated the targets' traits and their romantic interest in the target. We tested whether similarity, dissimilarity or overall trait levels on mate value, physical attractiveness, life history strategy and the Big Five personality factors predicted romantic interest at zero acquaintance and whether sex acted as a moderator. We tested the responders' individual perception of the targets' traits, in addition to the targets' own self‐reported trait levels and a consensus rating of the targets made by the responders. We used polynomial regression with response surface analysis within multilevel modelling to test support for each of the hypotheses. Results suggest a large sex difference in trait perception; when women rated men, they agreed in their perception more often than when men rated women. However, as a predictor of romantic interest, there were no sex differences. Only the responders' perception of the targets' physical attractiveness predicted romantic interest; specifically, responders' who rated the targets' physical attractiveness as higher than themselves reported more romantic interest. Copyright © 2017 European Association of Personality Psychology     

The role of formal information sharing in key account team effectiveness: does informal control matter and when

Team selling is a useful approach for retaining strategically important accounts in business-to-business markets. For key account sales teams, ensuring adequate access to information about customer needs offers sustainable competitive advantages. However, the internal alignment of market information remains a recurring managerial issue in key account team selling. This study develops a moderated mediation model to identify management strategies that encourage team members' formal information sharing, which in turn improves team effectiveness. Analysis of a multilevel data set comprising 37 fluid key account teams reveals that customer orientation and task interdependence increase formal information sharing among team members. Managers' exercise of professional control strengthens the positive impact that formal information sharing has on team satisfaction. However, the practice has a dysfunctional influence on the positive effect of task interdependence on formal information sharing. Firms with key account management (KAM) programs must decide whether the marginal benefits attained by advocating such professional control outweigh the possible drawbacks.     

A multilevel multitrait‐multimethod analysis of self‐ and peer‐reported daily affective experiences

We examined the psychometric properties of an experience‐sampling measure of affect (PANAS) using data from self‐ and peer reports. A multivariate multilevel model was used to assess the reliability of the latent PANAS scales at the within‐ and between‐person level. Findings suggest satisfying internal consistencies for self‐ and peer reports of affective experiences at both levels of analysis. Convergent and discriminant validity of the two affect scales were examined by means of a multilevel multitrait‐multimethod approach (MLM‐MTMM) indicating distinct findings at the within‐ and between‐person level. These findings provide further insights into the structural relations between the two PANAS scales: Whereas positive and negative affect were unrelated at the between‐person level; they were negatively correlated at the within‐person level. Copyright © 2010 John Wiley & Sons, Ltd.     

Using a Few Snapshots to Distinguish Mountains from Waves: Weak Factorial Invariance in the Context of Trait-State Research

In this article, we show that the underlying dimensions obtained when factor analyzing cross-sectional data actually form a mix of within-person state dimensions and between-person trait dimensions. We propose a factor analytical model that distinguishes between four independent sources of variance: common trait, unique trait, common state, and unique state. We show that by testing whether there is weak factorial invariance across the trait and state factor structures, we can tackle the fundamental question first raised by Cattell; that is, are within-person state dimensions qualitatively the same as between-person trait dimensions? Furthermore, we discuss how this model is related to other trait-state factor models, and we illustrate its use with two empirical data sets. We end by discussing the implications for cross-sectional factor analysis and suggest potential future developments.     

Friendship Dissolution Within Social Networks Modeled Through Multilevel Event History Analysis

A social network perspective can bring important insight into the processes that shape human behavior. Longitudinal social network data, measuring relations between individuals over time, has become increasingly common—as have the methods available to analyze such data. A friendship duration model utilizing discrete-time multilevel survival analysis with a multiple membership random effect structure is developed and applied here to study the processes leading to undirected friendship dissolution within a larger social network. While the modeling framework is introduced in terms of understanding friendship dissolution, it can be used to understand microlevel dynamics of a social network more generally. These models can be fit with standard generalized linear mixed-model software, after transforming the data to a pair-period data set. An empirical example highlights how the model can be applied to understand the processes leading to friendship dissolution between high school students, and a simulation study is used to test the use of the modeling framework under representative conditions that would be found in social network data. Advantages of the modeling framework are highlighted, and potential limitations and future directions are discussed.     

A Comparison of Population-Averaged and Cluster-Specific Approaches in the Context of Unequal Probabilities of Selection

Sampling designs of large-scale survey studies are typically complex, involving multiple design features such as clustering and unequal probabilities of selection. Single-level (i.e., population-averaged) methods that use adjusted variance estimators and multilevel (i.e., cluster-specific) methods provide two alternatives for modeling clustered data. Although the literature comparing these methods is vast, comparisons have been limited to the context in which all sampling units are selected with equal probabilities (thus circumventing the need for sampling weights). The goal of this study was to determine under what conditions single-level and multilevel estimators outperform one another in the context of a two-stage sampling design with unequal probabilities of selection. Monte Carlo simulation methods were used to evaluate the impact of several factors, including population model, informativeness of the design, distribution of the outcome variable, intraclass correlation coefficient, cluster size, and estimation method. Results indicated that the unweighted estimators performed similarly across conditions, whereas the weighted single-level estimators tended to outperform the weighted multilevel estimators, particularly under nonideal sample conditions. Multilevel weight approximation methods did not perform well when the design was informative. An empirical example is provided to demonstrate how researchers might investigate the implications of the simulation results in practice.     

Person‐specific versus multilevel autoregressive models: Accuracy in parameter estimates at the population and individual levels

This paper compares the multilevel modelling (MLM) approach and the person‐specific (PS) modelling approach in examining autoregressive (AR) relations with intensive longitudinal data. Two simulation studies are conducted to examine the influences of sample heterogeneity, time series length, sample size, and distribution of individual level AR coefficients on the accuracy of AR estimates, both at the population level and at the individual level. It is found that MLM generally outperforms the PS approach under two conditions: when the sample has a homogeneous AR pattern, namely, when all individuals in the sample are characterized by AR processes with the same order; and when the sample has heterogeneous AR patterns, but a multilevel model with a sufficiently high order (i.e., an order equal to or higher than the maximum order of individual AR patterns in the sample) is fitted and successfully converges. If a lower‐order multilevel model is chosen for heterogeneous samples, the higher‐order lagged effects are misrepresented, resulting in bias at the population level and larger prediction errors at the individual level. In these cases, the PS approach is preferable, given sufficient measurement occasions (T ≥ 50). In addition, sample size and distribution of individual level AR coefficients do not have a large impact on the results. Implications of these findings on model selection and research design are discussed.     

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.