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Sarahjane Belton, Gavin Breslin, Stephen Shannon, Wesley O’Brien, Ben Fitzpatrick, Tandy Haughey, Fiona Chambers, Danielle Powell, Darryl McCullagh and Deirdre Brennan

authors are confident that the valid sample remained representative, given there were no significant differences in age or BMI between the original and valid sample. Further research may consider conducting imputation methods for improving sample size, such as multiple imputation or expectation

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Rebecca A. Schlaff, Meghan Baruth, Faith C. LaFramboise and Samantha J. Deere

scales, the average CESD-10 item score for questions 1 through 9 was calculated for each participant and mean imputation was used for item 10. 30 , 31 Statistical Analyses Means and SDs as well as frequencies were calculated for all variables of interest. Linear regression was used to assess the

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Janet Robertson, Eric Emerson, Susannah Baines and Chris Hatton

for the outcome “frequent participation in sport/exercise,” the strength of association between sociodemographic factors, and participation separately for participants with and without intellectual disability. Missing data among sociodemographic variables were imputed using multiple imputation

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Florian Herbolsheimer, Stephanie Mosler, Richard Peter and the ActiFE Ulm Study Group

accelerometer. The maximum distance between a weather station and a participant’s residence was 40 km. Furthermore, sex and age (in years) were reported. Missing values Complete data were provided by 77.2% of all participants. An imputation procedure was applied for the depression scale if one of seven items

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James Annesi

participants who initiated treatment and deidentified prior to analyses. Multiple imputation 34 was used to replace missing cases within the longitudinal analyses. Because of consistencies in both the directionality of changes in self-regulation for physical activity, negative mood, exercise-related self

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James J. Annesi

provided monthly to reinforce self-regulatory skills usage. All assessments were completed by study staff in a private area. Data Analyses After determining that the 11% of missing cases were missing at random, 15 the expectation–maximization algorithm 16 was applied for imputation. Based on an effect

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Jakob Tarp, Anna Bugge, Niels Christian Møller, Heidi Klakk, Christina Trifonov Rexen, Anders Grøntved and Niels Wedderkopp

missing data (n = 697). A flowchart of participants is available in Supplementary Figure S1 and multiple imputation analysis including all 1209 consenting participants at baseline is available in Supplementary Table S4 (available online). Results from imputed data yielded similar conclusions as

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Jocelyn Kernot, Lucy Lewis, Tim Olds and Carol Maher

the trial remained in the sample for analysis. 43 Multiple imputation (fully conditional specification—as testing showed that missing data were not random) was implemented to account for missing data. Data were combined using Rubin’s rule. 44 To determine the effectiveness of the MSIU program, random

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Jonathan M. Miller, Mark A. Pereira, Julian Wolfson, Melissa N. Laska, Toben F. Nelson and Dianne Neumark-Sztainer

Institute, Cary, NC) Proc MI and MIANALYZE. Twenty imputed data sets were created using a Markov Chain Monte Carlo algorithm and all correlates and demographics. Multiple imputation methods for EAT-2010 have been previously described in greater detail. 22 Statistical Analyses All statistical analyses were

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Yara Fidelix, Mara C. Lofrano-Prado, Leonardo S. Fortes, James O. Hill, Ann E. Caldwell, João P. Botero and Wagner L. do Prado

for gender and baseline BMI. The procedures were performed following an intention-to-treat principle without data imputation. In addition, the effect size (ES) for each intervention was calculated as proposed by Cohen ( d ). The significance level was set at P  < .05. Statistical procedures were