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Valérian Cece, Noémie Lienhart, Virginie Nicaise, Emma Guillet-Descas and Guillaume Martinent

, associations between YBRSQ scores and demographic variables (sex, competitive level, and type of sport) were assessed with multiple indicators multiple causes (MIMIC) models. With the MIMIC models, we examined uniform DIF as well as the influence of covariates on YBRSQ scores ( Morin, Marsh, & Nagengast, 2013

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Sebastian Uhrich and Martin Benkenstein

This article reports the findings of an investigation into the atmosphere in stadiums during live team sports. Experiencing this special atmosphere represents an essential part of the total service provided by the organizers of sport events. However, existing research into the concept of atmosphere focuses on the retail environment. Our first step was therefore to define sport stadium atmosphere as a theoretical construct, drawing on theories from environmental psychology. We then developed a mimic (multiple indicator-multiple cause) model to measure the construct. To specify the mimic model, we generated and selected formative measures by means of a delphi study (N = 20), qualitative expert interviews (N = 44), and an indicator sort task (N = 34). The results indicate that various physical and social aspects of the stadium environment are causal indicators of sport stadium atmosphere. Following this, we conducted phenomenological interviews with spectators at sport events (N = 5) to identify typical affective responses to stadium environment (representing the reflective indicators of the mimic model). These interviews revealed that fans’ experience of stadium environment is characterized by high levels of arousal and pleasure. In addition to our findings, the mimic model developed in this study represents a useful tool for future research into sport stadium atmosphere.

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Andrew J. Martin, David V. Tipler, Herbert W. Marsh, Garry E. Richards and Melinda R. Williams

This study presents a new, multidimensional approach to physical activity motivation that is operationalized through four primary factors: adaptive cognitive dimensions, adaptive behavioral dimensions, impeding cognitive dimensions, and maladaptive behavioral dimensions. Among 171 Australian high school students, the study assessed the structure of this four-factor framework (a within-network construct validity approach) and also examined the relationships between motivation and three key correlates: flow in physical activity, physical self-concept, and physical activity level (a between-network construct validity approach). The four-factor framework demonstrated within-network validity in the form of reliable subscales and a sound factor structure. In terms of between-network validity, relationships between the adaptive behavioral and cognitive aspects of motivation and physical self-concept, flow, and activity levels were found to be positive and significant, whereas significant inverse relationships were found between impeding and maladaptive motivation dimensions and flow and physical self-concept. Additional analysis utilizing multiple-indicator multiple-cause (MIMIC) modeling showed that during earlier adolescence girls are more motivated than boys to engage in physical activity, but by later adolescence boys are more motivated to do so. Results are interpreted in terms of future directions for possible physical activity interventions aimed at increasing both the uptake and continuation of activity.

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James Du, Heather Kennedy, Jeffrey D. James and Daniel C. Funk

answer these questions, we surveyed participants of two distance-running events held in the northeastern United States. A Multiple Indicator Multiple Causes (MIMIC) model was used to identify five theoretically supported benefits resulting from PSE training and participation: (a) euphoric, (b

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Collin A. Webster, Diana Mîndrilă, Chanta Moore, Gregory Stewart, Karie Orendorff and Sally Taunton

model with the entire sample, using the same estimation procedure (Model 3). After reaching an optimal factor structure, we included the implementation variable as a covariate on the identified factors. This is a multiple indicator multiple cause model (MIMIC model), which helps determine whether the