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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
Yeatts, Paul E.; Barton, Mitch; Henson, Robin K.; Martin, Scott B. – Measurement in Physical Education and Exercise Science, 2017
A common practice in general linear model (GLM) analyses is to interpret regression coefficients (e.g., standardized ß weights) as indicators of variable importance. However, focusing solely on standardized beta weights may provide limited or erroneous information. For example, ß weights become increasingly unreliable when predictor variables are…
Descriptors: Predictor Variables, Correlation, Multiple Regression Analysis, Physical Fitness
Kern, Ben D.; Graber, Kim C. – Measurement in Physical Education and Exercise Science, 2017
Program satisfaction, self-efficacy to change, and willingness to change, are dispositions that influence physical education teacher change. The study purpose was to validate an instrument measuring program satisfaction, self-efficacy to change, and willingness to change relative to teachers' likelihood to change. A 15-item Teacher Change…
Descriptors: Physical Education Teachers, Test Validity, Questionnaires, Self Efficacy
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)