Self-Control Motivation and Capacity Scale: A New Measure of Multiple Facets of Self-Control

abstract: Self-control has been shown to predict both health risk and health protective outcomes. Although top-down or “good” self-control is typically examined as a unidimensional construct, research on “poor” self-control suggests that multiple dimensions may be necessary to capture aspects of self-control. The current study sought to create a new brief survey measure of top-down self-control that differentiates between self-control capacity, internal motivation, and external motivation. Items were adapted from the Brief Self-Control Scale (BSCS; Tangney, Baumeister, & Boone, 2004) and were administered through two online surveys to 347 undergraduate students enrolled in introductory psychology courses at Arizona State University. The Self-Control Motivation and Capacity Survey (SCMCS) showed strong evidence of validity and reliability. Exploratory and confirmatory factor analyses supported a 3-factor structure of the scale consistent with the underlying theoretical model. The final 15-item measure demonstrated excellent model fit, chi-square = 89.722 p=.077, CFI = .989, RMSEA = .032, SRMR = .045. Despite several limitations including the cross-sectional nature of most analyses, self-control capacity, internal motivation, and external motivation uniquely related to various self-reported behavioral outcomes, and accounted for additional variance beyond that accounted for by the BSCS. Future studies are needed to establish the stability of multiple dimensions of self-control, and to develop state-like and domain-specific measures of self-control. While more research in this area is needed, the current study demonstrates the importance of studying multiple aspects of top-down self-control, and may ultimately facilitate the tailoring of interventions to the needs of individuals based on unique profiles of self-control capacity and motivation.Dissertation/ThesisMasters Thesis Psychology 201

Unlocking the potential of big data to support tactical performance analysis in professional soccer: A systematic review

In professional soccer, increasing amounts of data are collected that harness great potential when it comes to analysing tactical behaviour. Unlocking this potential is difficult as big data challenges the data management and analytics methods commonly employed in sports. By joining forces with computer science, solutions to these challenges could be achieved, helping sports science to find new insights, as is happening in other scientific domains. We aim to bring multiple domains together in the context of analysing tactical behaviour in soccer using position tracking data. A systematic literature search for studies employing position tracking data to study tactical behaviour in soccer was conducted in seven electronic databases, resulting in 2338 identified studies and finally the inclusion of 73 papers. Each domain clearly contributes to the analysis of tactical behaviour, albeit in - sometimes radically - different ways. Accordingly, we present a multidisciplinary framework where each domain's contributions to feature construction, modelling and interpretation can be situated. We discuss a set of key challenges concerning the data analytics process, specifically feature construction, spatial and temporal aggregation. Moreover, we discuss how these challenges could be resolved through multidisciplinary collaboration, which is pivotal in unlocking the potential of position tracking data in sports analytics.Algorithms and the Foundations of Software technolog