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Shelby Waldron, J.D. DeFreese, Brian Pietrosimone, Johna Register-Mihalik and Nikki Barczak

were missing for 12 variables, however, missing cases did not exceed 5% for any one variable so mean imputation was utilized. Data were collected from 249 participants, however, six cases were removed from analysis due to conflicting responses on specialization grouping variables ( n  = 5) and

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Jennifer Brunet, Eva Guérin and Nicolas Speranzini

violation of assumptions using IBM SPSS Statistics software (version 23; IBM Corporation, Armonk, NY) following procedures outlined by Tabachnick and Fidell ( 2007 ). Multiple imputation was performed to impute missing sociodemographic data given the low degree of missingness (< 5%). Four univariate

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Kirsi E. Keskinen, Merja Rantakokko, Kimmo Suomi, Taina Rantanen and Erja Portegijs

of education, data were missing for eight participants, and no imputation was conducted. The last change of address was missing for 30 participants, and these values were imputed as the sample average of the time lived in the current home. The intraclass correlation coefficient of PA values between

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Mindy Patterson, Wanyi Wang and Alexis Ortiz

II. Thus, the data was considered as Missing at Random. Therefore, multiple imputation methods were used to replace incomplete data. Specifically, each missing value was imputed (i.e., filled-in) using linear regression model 5 times to create 5 pseudo-complete datasets. Each dataset was analyzed to

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Heather K. Larson, Bradley W. Young, Tara-Leigh F. McHugh and Wendy M. Rodgers

duration was given for ages 6–10 years but not for age 11 years, we could not be sure of the number of years they had swam for 8 months or more, nor could we compute the sum of yearly training volume. Missing data were handled on an analysis-by-analysis basis. For correlations, we used multiple imputation

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Byron J. Kemp, Anne-Maree Parrish, Marijka Batterham and Dylan P. Cliff

Potential confounding variables were season of measurement and whether the child attended school on the day of TUD completion (yes/no). The season was derived from the date of interview. School attendance was included as a variable in the LSAC data sets. Where data were missing for this variable, imputation

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Eric MacIntosh, Keita Kinoshita and Popi Sotiriadou

). Multiple imputation: A primer . Statistical Methods in Medical Research, 8 ( 1 ), 3 – 15 . PubMed ID: 10347857 doi: 10.1177/096228029900800102 Simpson , N.S. , Gibbs , E.L. , & Matheson , G.O. ( 2017 ). Optimizing sleep to maximize performance: Implications and recommendations for elite

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Samantha M. Gray, Joan Wharf Higgins and Ryan E. Rhodes

qualitative findings according to the sequential explanatory mixed methods design. Quantitative analyses Prior to running any quantitative analyses, the data were evaluated for missingness. As Little’s test was nonsignificant (χ 2  = 0.000, df  = 44, p  = 1.00), the data could undergo multiple imputations

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Erika D. Van Dyke, Judy L. Van Raalte, Elizabeth M. Mullin and Britton W. Brewer

particular need for mental focus on the beam ( Bannon, 2016 ). Statistical Analyses Once exclusion criteria were applied, it was found that 16 participants omitted a combined total of 20 items from the self-talk measures. The maximum number of participants missing any single item was two. Mean imputation

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J.D. DeFreese, Travis E. Dorsch and Travis A. Flitton

Cronbach’s α values) were calculated for all study variables. Missing data from participants completing the entire survey were replaced for focal variables with values calculated via mean imputation ( Tabachnick & Fidell, 2013 ). Confirmatory factor analyses were conducted for the adapted measures of sport