Regression Mixture Models: Does Modeling the Covariance Between Independent Variables and Latent Classes Improve the Results?

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we tested the effects of violating an implicit assumption often made in these models; that is, independent variables in the model are not directly related to latent classes. Results indicate that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. In addition, we tested whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a reanalysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted.     

Dealing with Multiple Solutions in Structural Vector Autoregressive Models

Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.     

A Note on Recurring Misconceptions When Fitting Nonlinear Mixed Models

Nonlinear mixed-effects (NLME) models are used when analyzing continuous repeated measures data taken on each of a number of individuals where the focus is on characteristics of complex, nonlinear individual change. Challenges with fitting NLME models and interpreting analytic results have been well documented in the statistical literature. However, parameter estimates as well as fitted functions from NLME analyses in recent articles have been misinterpreted, suggesting the need for clarification of these issues before these misconceptions become fact. These misconceptions arise from the choice of popular estimation algorithms, namely, the first-order linearization method (FO) and Gaussian-Hermite quadrature (GHQ) methods, and how these choices necessarily lead to population-average (PA) or subject-specific (SS) interpretations of model parameters, respectively. These estimation approaches also affect the fitted function for the typical individual, the lack-of-fit of individuals’ predicted trajectories, and vice versa.     

Expertise in soccer teams: A thematic inquiry into the role of Shared Mental Models within team chemistry

AimsThe purpose of the current study was to establish a conceptual framework of team chemistry components in sport with an emphasis on Shared Mental Models (SMM).MethodElite soccer coaches (n = 6) and players (n = 3) were interviewed using a semi-structured interview guide. An inductive thematic analysis was employed to analyze the data.ResultsFour themes related to team chemistry components were identified: (1) members' characteristics (i.e., demographic data, on-field characteristics, and member's ego), (2) coach–players interactions (i.e., professional interaction and emotional intelligence of coaches), (3) interactions among the players (i.e., professional understanding, efficacy beliefs, team cohesion, players' emotional intelligence, team roles, and goals), and (4) interactions with environmental factors (i.e., owners, management, fans, and media).ConclusionsThe cognitive components of the team chemistry model were clustered to establish a conceptual framework of SMM. Furthermore, the cognitive-affective-social-behavioral linkage of team chemistry and athletic performance is examined in light of the emerged model and conceptual framework. Implications and future directions are also discussed.     

Children's and Mothers' Contribution to Joint Reminiscing in Different Sociocultural Contexts: Who Speaks and What is Said

The study compares mothers' conversation with their 4‐year‐old children about two past events in two autonomy‐oriented (35 German and 42 Swedish families), one relatedness‐oriented (22 Cameroonian Nso families) and one autonomy‐relatedness oriented (38 Estonian families) contexts. German mothers were rather similar to Swedish mothers in talking a lot, providing a lot of information and engaging children into conversation, but they differed from Swedish mothers by talking more about social content. Swedish children were more independent conversational partners to their mothers than other children, including German children. Estonian mothers' contribution to conversation was similar to Cameroonian Nso mothers, except that they asked a lot of open‐ended questions to engage children in conversations. Estonian children did not differ from Swedish and German children in their contribution to conversations. Compared to Swedish mothers, past event talk of Estonian mothers was characterized by a bigger proportion of talk devoted to social content, but also to the child, mental states and non‐social content. It was characteristic of Cameroonian Nso mothers that they focused more on other people and actions, and their conversational dominance was larger. Differences in reminiscing were consistent with different cultural models of self and the type of autonomy – psychological or action – promoted. Copyright © 2015 John Wiley & Sons, Ltd.     

组织中的人际冲突:类型、模型与表达

组织中的人际冲突是组织成员在分歧、负面情绪与干扰的相互作用下,通过意义建构导致互动双方在认知、情感与行为方面产生的一系列反应过程。现有研究将组织中的人际冲突分为单特征型和多特征型,集中探讨作用模型,并基于表达层面解析冲突的维度和作用模式。本文尝试将冲突类型、表达方式以及作用模型进行嵌入式整合,提出组织中人际冲突的具体应对策略。未来研究可将认知差异嵌入过程框架,探求冲突显现的临界点,验证应对方式的有效性,探究组织权力的影响,开展基于多元亚文化下组织中人际冲突的本土化研究。     

Walking through doorways causes forgetting: Event structure or updating disruption?

According to event cognition theory, people segment experience into separate event models. One consequence of this segmentation is that when people transport objects from one location to another, memory is worse than if people move across a large location. In two experiments participants navigated through a virtual environment, and recognition memory was tested in either the presence or the absence of a location shift for objects that were recently interacted with (i.e., just picked up or set down). Of particular concern here is whether this location updating effect is due to (a) differences in retention intervals as a result of the navigation process, (b) a temporary disruption in cognitive processing that may occur as a result of the updating processes, or (c) a need to manage multiple event models, as has been suggested in prior research. Experiment 1 explored whether retention interval is driving this effect by recording travel times from the acquisition of an object and the probe time. The results revealed that travel times were similar, thereby rejecting a retention interval explanation. Experiment 2 explored whether a temporary disruption in processing is producing the effect by introducing a 3-second delay prior to the presentation of a memory probe. The pattern of results was not affected by adding a delay, thereby rejecting a temporary disruption account. These results are interpreted in the context of the event horizon model, which suggests that when there are multiple event models that contain common elements there is interference at retrieval, which compromises performance.     

Considering too few alternatives: The mental model theory of extensional reasoning

When solving a simple probabilistic problem, people tend to build an incomplete mental representation. We observe this pattern in responses to probabilistic problems over a set of premises using the conjunction, disjunction, and conditional propositional connectives. The mental model theory of extensional reasoning explains this bias towards underestimating the number of possibilities: In reckoning with different interpretations of the premises (logical rules, mental model theoretical, and, specific to conditional premises, conjunction and biconditional interpretation) the mental model theory accounts for the majority of observations. Different interpretations of a premise result in a build-up of mental models that are often incomplete. These mental models are processed using either an extensional strategy relying on proportions amongst models, or a conflict monitoring strategy. The consequence of considering too few possibilities is an erroneous probability estimate akin to that faced by decision makers who fail to generate and consider all alternatives, a characteristic of bounded rationality. We compare our results to the results published by Johnson-Laird, Legrenzi, Girotto, Legrenzi, and Caverni [Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999 Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106, 62–88. doi:10.1037/0033-295X.106.1.62[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106 Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106, 62–88. doi:10.1037/0033-295X.106.1.62[Crossref], [PubMed], [Web of Science ®] , [Google Scholar], 62 Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106, 62–88. doi:10.1037/0033-295X.106.1.62[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]–88 Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106, 62–88. doi:10.1037/0033-295X.106.1.62[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]. doi:10 Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106, 62–88. doi:10.1037/0033-295X.106.1.62[Crossref], [PubMed], [Web of Science ®] , [Google Scholar].1037 Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106, 62–88. doi:10.1037/0033-295X.106.1.62[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]/0033 Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106, 62–88. doi:10.1037/0033-295X.106.1.62[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]-295X Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106, 62–88. doi:10.1037/0033-295X.106.1.62[Crossref], [PubMed], [Web of Science ®] , [Google Scholar].106 Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106, 62–88. doi:10.1037/0033-295X.106.1.62[Crossref], [PubMed], [Web of Science ®] , [Google Scholar].1 Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106, 62–88. doi:10.1037/0033-295X.106.1.62[Crossref], [PubMed], [Web of Science ®] , [Google Scholar].62 Johnson-Laird, P., Legrenzi, P., Girotto, V., Legrenzi, M., &; Caverni, J. (1999). Naive probability: A mental model theory of extensional reasoning. Psychological Review, 106, 62–88. doi:10.1037/0033-295X.106.1.62[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]], and we observe lower performance levels than those in the original article.