How Do Profiles of Need-Supportive and Controlling Coaching Relate to Team Athletes' Motivational Outcomes? A Person-Centered Approach

Building on recent self-determination theory research differentiating controlling coaching into a demanding and domineering approach, this study examined the role of both approaches in athletes' motivational outcomes when accompanied by autonomy support or structure. Within team-sport athletes (N = 317; mean age = 17.67), four sets of k-means cluster analyses systematically pointed toward a four-cluster solution (e.g., high-high, high-low, low-high, and low-low), regardless of the pair of coaching dimensions used. One of the identified coaching profiles involved coaches who are perceived to combine need-supportive and controlling behaviors (i.e., high-high). Whereas combining need-supportive and domineering behaviors (i.e., high-high) yields lower autonomous motivation and engagement compared with a high need-support profile (i.e., high-low), this is less the case for the combination of need-supportive and demanding behaviors (i.e., high-high). This person-centered approach provides deeper insights into how coaches combine different styles and how some forms of controlling coaching yield a greater cost than others.

Modeling Heterogeneous Peer Assortment Effects Using Finite Mixture Exponential Random Graph Models

This article develops a class of models called sender/receiver finite mixture exponential random graph models (SRFM-ERGMs). This class of models extends the existing exponential random graph modeling framework to allow analysts to model unobserved heterogeneity in the effects of nodal covariates and network features without a block structure. An empirical example regarding substance use among adolescents is presented. Simulations across a variety of conditions are used to evaluate the performance of this technique. We conclude that unobserved heterogeneity in effects of nodal covariates can be a major cause of misfit in network models, and the SRFM-ERGM approach can alleviate this misfit. Implications for the analysis of social networks in psychological science are discussed.

Identifying subtypes among children with developmental coordination disorder and mathematical learning disabilities, using model-based clustering

A relationship between motor and mathematical skills has been shown by previous research. However, the question of whether subtypes can be differentiated within developmental coordination disorder (DCD) and/or mathematical learning disability (MLD) remains unresolved. In a sample of children with and without DCD and/or MLD, a data-driven model-based clustering was used to identify subgroups of individuals with relatively homogeneous profiles on measures associated with motor and mathematical skills. One subgroup of children with motor problems was found based on motor variables. Based on mathematical variables, two clinical clusters were found: a subtype with number fact retrieval problems and a subtype with procedural calculation problems. Clustering with motor and mathematical skills revealed two clinical clusters: a cluster with number fact retrieval as well as procedural calculation problems and below average motor and visual-motor integration skills. A second cluster of children had only procedural calculation and visual-motor problems. Our results raise questions about the usefulness of placing children who have below average mathematical skills into a single diagnostic category. Furthermore, we inform ongoing debates about the overlap between DCD and MLD, as below average motor skills were found in both MLD subgroups, although a different motor profile is linked to a different mathematical profile.

Critical hopefulness: a person-centered analysis of the intersection of cognitive and emotional empowerment

Leaders in struggles for social justice agree on the importance and the difficulty of maintaining hopefulness while developing critical awareness of social issues. Research has indicated that the analogous components of psychological empowerment (emotional and cognitive) often do not co-vary across populations. This study used a person-centered analytic approach, latent class analysis, to identify subpopulations of participants (n = 1,322) according to the cognitive and emotional components of psychological empowerment. Four distinct sub-groups emerged: those who were relatively (1) critical but alienated, (2) uncritical but hopeful, (3) uncritical and alienated, or (4) critical and hopeful. These clusters were then examined for demographic differences and relationships with a set of conceptually relevant variables including social capital, psychological sense of community, openness, organizational participation and mental wellbeing. Results shed light on the complexity of empowerment processes and yield implications for ongoing community research and action.

Principal Cluster Axes: A Projection Pursuit Index for the Preservation of Cluster Structures in the Presence of Data Reduction

A measure of "clusterability" serves as the basis of a new methodology designed to preserve cluster structure in a reduced dimensional space. Similar to principal component analysis, which finds the direction of maximal variance in multivariate space, principal cluster axes find the direction of maximum clusterability in multivariate space. Furthermore, the principal clustering approach falls into the class of projection pursuit techniques. Comparisons are made with existing methodologies both in a simulation study and analysis of real-world data sets. Furthermore, a demonstration of how to interpret the results of the principal cluster axes is provided on the analysis of Supreme Court voting data and similarities between the interpretation of competing procedures (e.g., factor analysis and principal component analysis) are provided. In addition to the Supreme Court analysis, we analyze several data sets often used to test cluster analysis procedures, including Fisher's Iris data, Agresti's Crab data, and a data set on glass fragments. Finally, discussion is provided to help determine when the proposed procedure will be the most beneficial to the researcher.

A Tabu-Search Heuristic for Deterministic Two-Mode Blockmodeling of Binary Network Matrices

Two-mode binary data matrices arise in a variety of social network contexts, such as the attendance or non-attendance of individuals at events, the participation or lack of participation of groups in projects, and the votes of judges on cases. A popular method for analyzing such data is two-mode blockmodeling based on structural equivalence, where the goal is to identify partitions for the row and column objects such that the clusters of the row and column objects form blocks that are either complete (all 1s) or null (all 0s) to the greatest extent possible. Multiple restarts of an object relocation heuristic that seeks to minimize the number of inconsistencies (i.e., 1s in null blocks and 0s in complete blocks) with ideal block structure is the predominant approach for tackling this problem. As an alternative, we propose a fast and effective implementation of tabu search. Computational comparisons across a set of 48 large network matrices revealed that the new tabu-search heuristic always provided objective function values that were better than those of the relocation heuristic when the two methods were constrained to the same amount of computation time.