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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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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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Zachary Y. Kerr, Andrew E. Lincoln, Shane V. Caswell, David A. Klossner, Nina Walker and Thomas P. Dompier

of each category AE count do not equal total AE count due to rounding error (due to the use of mean imputation values based on all other valid AE data from the same year, division, and event type for missing data). Table 2 Injury Frequencies and Rates With 95% Confidence Intervals (CI) by Season and

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Nicole D. Bolter, Lindsay Kipp and Tyler Johnson

(i.e., less than 5%). Since there was no discernable pattern of the missing values, missing data were deemed missing completely at random (MCAR) and mean imputation was used in favor of listwise deletion ( Tabachnick & Fidell, 2007 ). All values of skewness (<|3.0|) and kurtosis (<|8.0|) were

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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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Guohua Zheng, Xin Zheng, Junzhe Li, Tingjin Duan, Kun Ling, Jing Tao and Lidian Chen

fixed effect and participant as a random variable. The baseline data and comparison group were used as the reference values. A fully unstructured variance–covariance matrix was assumed for the error terms. Missing data were imputed using a multiple imputation method based on a fully conditional

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Eric MacIntosh, Keita Kinoshita and Popi Sotiriadou

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

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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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Dylan O. Blain, Thomas Curran and Martyn Standage

pattern of physical literacy scores, for the participants only missing the pedometer step count score, a fractional imputation method was used. Here, the overall physical literacy score (out of 100) was determined by dividing the total score recorded from the completed protocols by the maximum possible

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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