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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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Yuri Alberto Freire, Geovani de Araújo Dantas de Macêdo, Rodrigo Alberto Vieira Browne, Luiz Fernando Farias-Junior, Ágnes Denise de Lima Bezerra, Ana Paula Trussardi Fayh, José Cazuza de Farias Júnior, Kevin F. Boreskie, Todd A. Duhamel and Eduardo Caldas Costa

trapezoidal method using the GraphPad Prism (version 6) for Windows (GraphPad Software ® , San Diego, CA). Intention-to-treat principle was used for analyses; that is, data from participants who completed only 1 or 2 experimental sessions were included in the final analysis without data 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

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Victoria McGee and J.D. DeFreese

as a cut-off criterion for acceptability. Descriptive statistics and scale reliabilities were calculated for all study variables at every time point. Missing data from a completed assessment wave were replaced via mean imputation. Multilevel linear modeling (MLM; Singer & Willett, 2003 ) using

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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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José-Antonio Cecchini, Antonio Méndez-Giménez and Beatriz Sánchez-Martínez

imputation process, and its estimates are the least biased ( Schafer & Graham, 2002 ). Measurements and Instruments Types of motivation The Perceived Locus of Causality Questionnaire ( Goudas, Biddle, & Fox, 1994 ) was used; we selected the version adapted and validated in Spanish by Moreno, González

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Wonjae Choi and Seungwon Lee

analysis and were substituted by multiple imputation methods. The missing data were imputed by using Fully Conditional Specification algorithm which is an iterative Markov chain Monte Carlo method. Five imputed datasets were generated and then pooled outcomes were used for the intention-to-treat analysis

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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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Taru Manyanga, Joel D. Barnes, Chalchisa Abdeta, Ade F. Adeniyi, Jasmin Bhawra, Catherine E. Draper, Tarun R. Katapally, Asaduzzaman Khan, Estelle Lambert, Daga Makaza, Vida K. Nyawornota, Reginald Ocansey, Narayan Subedi, Riaz Uddin, Dawn Tladi and Mark S. Tremblay

.1093/bioinformatics/btx364 10.1093/bioinformatics/btx364 45. Kowarik A , Templ M . Imputation with the R Package VIM . J Stat Softw . 2016 ; 74 ( 7 ): 1 – 16 . doi:10.18637/jss.v074.i07 10.18637/jss.v074.i07 46. United Nations . World population prospects 2017 . 2017 . . 47

Open access

Silvia A. González, Joel D. Barnes, Patrick Abi Nader, Dolores Susana Andrade Tenesaca, Javier Brazo-Sayavera, Karla I. Galaviz, Marianella Herrera-Cuenca, Piyawat Katewongsa, Juan López-Taylor, Yang Liu, Bilyana Mileva, Angélica María Ochoa Avilés, Diego Augusto Santos Silva, Pairoj Saonuam and Mark S. Tremblay

. UpSetR: an R package for the visualization of intersecting sets and their properties . Bioinformatics . 2017 ; 33 ( 18 ): 2938 – 2940 . PubMed ID: 28645171 doi:10.1093/bioinformatics/btx364 10.1093/bioinformatics/btx364 41. Kowarik A , Templ M . Imputation with the R Package VIM . J Stat Softw