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Mahar, Matthew T.; Welk, Gregory J.; Rowe, David A. – Measurement in Physical Education and Exercise Science, 2018
Purpose: To develop models to estimate aerobic fitness (VO[subscript 2]max) from PACER performance in 10- to 18-year-old youth, with and without body mass index (BMI) as a predictor. Method: Youth (N = 280) completed the PACER and a maximal treadmill test to assess VO[subscript 2]max. Validation and cross-validation groups were randomly formed to…
Descriptors: Exercise, Physical Fitness, Preadolescents, Adolescents
Webb, Carrie; Vehrs, Pat R.; George, James D.; Hager, Ronald – Measurement in Physical Education and Exercise Science, 2014
The purpose of this study was to develop a step test with a personalized step rate and step height to predict cardiorespiratory fitness in 80 college-aged males and females using the self-reported perceived functional ability scale and data collected during the step test. Multiple linear regression analysis yielded a model (R = 0.90, SEE = 3.43…
Descriptors: Tests, Physical Fitness, College Students, Multiple Regression Analysis
Myers, Nicholas D.; Brincks, Ahnalee M.; Beauchamp, Mark R. – Measurement in Physical Education and Exercise Science, 2010
The primary purpose of this tutorial is to succinctly review some options for, and consequences of, centering Level 1 predictors in commonly applied cross-sectional two-level models. It is geared toward both practitioners and researchers. A general understanding of multilevel modeling is necessary prior to understanding the subtleties of centering…
Descriptors: Models, Statistical Analysis, Predictor Variables, Athletes
Multi-Level Modeling of Dyadic Data in Sport Sciences: Conceptual, Statistical, and Practical Issues
Gaudreau, Patrick; Fecteau, Marie-Claude; Perreault, Stephane – Measurement in Physical Education and Exercise Science, 2010
The goal of this article is to present a series of conceptual, statistical, and practical issues in the modeling of multi-level dyadic data. Distinctions are made between distinguishable and undistinguishable dyads and several types of independent variables modeled at the dyadic level of analysis. Multi-level modeling equations are explained in a…
Descriptors: Data, Models, Predictor Variables, Equations (Mathematics)
Myers, Nicholas D.; Paiement, Craig A.; Feltz, Deborah L. – Measurement in Physical Education and Exercise Science, 2007
The purpose of this study was to determine to what degree collective efficacy judgments based on summative team performance capabilities exhibited different levels of prediction for three additive intervals of team performance in women's ice hockey. Collective efficacy beliefs of 12 teams were assessed prior to Friday's game and Saturday's game…
Descriptors: Proximity, Team Sports, Intervals, Teamwork