TreeBUGS: An R package for hierarchical multinomial-processing-tree modeling

Multinomial processing tree (MPT) models are a class of measurement models that account for categorical data by assuming a finite number of underlying cognitive processes. Traditionally, data are aggregated across participants and analyzed under the assumption of independently and identically distributed observations. Hierarchical Bayesian extensions of MPT models explicitly account for participant heterogeneity by assuming that the individual parameters follow a continuous hierarchical distribution. We provide an accessible introduction to hierarchical MPT modeling and present the user-friendly and comprehensive R package TreeBUGS, which implements the two most important hierarchical MPT approaches for participant heterogeneity—the beta-MPT approach (Smith & Batchelder, Journal of Mathematical Psychology 54:167-183, 2010) and the latent-trait MPT approach (Klauer, Psychometrika 75:70-98, 2010). TreeBUGS reads standard MPT model files and obtains Markov-chain Monte Carlo samples that approximate the posterior distribution. The functionality and output are tailored to the specific needs of MPT modelers and provide tests for the homogeneity of items and participants, individual and group parameter estimates, fit statistics, and within- and between-subjects comparisons, as well as goodness-of-fit and summary plots. We also propose and implement novel statistical extensions to include continuous and discrete predictors (as either fixed or random effects) in the latent-trait MPT model.     

“There's No Place to Hide”: Exploring the Stressors Encountered by Elite Cricket Captains

This study aimed to enhance understanding of stressors that elite sporting captains face in their role. The autobiographies of 12 international cricket captains were sampled. Stressors relating to the captaincy role were identified, and following thematic analysis, 7 general dimensions of stressors were recognized: multiple roles, team stressors, interactions with players, selection, interactions with other personnel, the media, and extreme situations. It appears that stressors are heightened due to a combination of playing and leadership responsibilities that captains experience. Findings are considered, including how they might be used to inform practitioners and coaches who work with captains.     

Disentangling Child-Specific and Family-Wide Processes Underlying Negative Mother-Child Transactions

The current study examines the transactional processes between maternal negativity and child disruptive behavior during early childhood, using a sibling comparison design. This design allows for a more sophisticated test of the behavioral training hypothesis than is possible when only one child per family is included in the analysis as it excludes two confounders (passive gene-environment correlation and family-wide environmental influence). Three hundred and ninety-seven families were visited on three occasions when the target child was 1.5, 3 and 4.5 years old (920 children, 51.5% female). The target child and up to three older siblings per family were included in data collection and analysis. Mothers and fathers reported on children’s disruptive behavior and mothers reported on their negativity to children. Within-family cross-lag pathways, as well as a significant indirect effect through which children increased their own disruptive behavior via maternal negativity confirmed a behavioral training effect. Family level maternal negativity and sibling disruptive behavior showed high levels of stability over three to four years and no context effect for maternal negativity or sibling disruptive behavior was seen. The theoretical and clinical implications of these findings are discussed.     

The consequences of suggesting false childhood food events

We combined data across eight published experiments (N = 1369) to examine the formation and consequences of false autobiographical beliefs and memories. Our path models revealed that the formation of false autobiographical belief fully mediated the pathway between suggesting to people that they had experienced a positive or negative food-related event in the past and current preference for that food. Suggestion indirectly affected intention to eat the food via change in autobiographical belief. The development of belief with and without memory produced similar changes in food preferences and behavior intention, indicating that belief in the event drives changes in suggestion-related attitudes. Finally, positive suggestions (e.g., “you loved asparagus the first time you tried it”) yielded stronger effects than negative suggestions (e.g., “you got sick eating egg salad”). These findings show that false autobiographical suggestions lead to the development of autobiographical beliefs, which in turn, have consequences for one's attitudes and behaviors.     

Fitting direct covariance structures by the MSTRUCT modeling language of the CALIS procedure

This paper demonstrates the usefulness and flexibility of the general structural equation modelling (SEM) approach to fitting direct covariance patterns or structures (as opposed to fitting implied covariance structures from functional relationships among variables). In particular, the MSTRUCT modelling language (or syntax) of the CALIS procedure (SAS/STAT version 9.22 or later: SAS Institute, 2010) is used to illustrate the SEM approach. The MSTRUCT modelling language supports a direct covariance pattern specification of each covariance element. It also supports the input of additional independent and dependent parameters. Model tests, fit statistics, estimates, and their standard errors are then produced under the general SEM framework. By using numerical and computational examples, the following tests of basic covariance patterns are illustrated: sphericity, compound symmetry, and multiple‐group covariance patterns. Specification and testing of two complex correlation structures, the circumplex pattern and the composite direct product models with or without composite errors and scales, are also illustrated by the MSTRUCT syntax. It is concluded that the SEM approach offers a general and flexible modelling of direct covariance and correlation patterns. In conjunction with the use of SAS macros, the MSTRUCT syntax provides an easy‐to‐use interface for specifying and fitting complex covariance and correlation structures, even when the number of variables or parameters becomes large.