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Jesse C. Christensen, Caitlin J. Miller, Ryan D. Burns and Hugh S. West

patient for KOS-ADL (final score−admission score) and NPRS (admission score−final score) outcomes. All patients in the analysis completed the patient-reported outcomes at both time points; therefore, there was no concern for response bias or potential estimate errors based on statistical imputation for

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Thiago R.S. Tenório, P. Babu Balagopal, Lars B. Andersen, Raphael M. Ritti-Dias, James O. Hill, Mara C. Lofrano-Prado and Wagner L. Prado

time interaction. The procedures were developed considering the principle of intention-to-treat without data imputation. In addition, the Cohen’s d effect size (ES) was used to analyze the statistical differences of each group from a practical point of view. To verify the relationships between the

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Sarah G. Sanders, Elizabeth Yakes Jimenez, Natalie H. Cole, Alena Kuhlemeier, Grace L. McCauley, M. Lee Van Horn and Alberta S. Kong

. Accelerometer files were read into R and summarized with package GGIR (version 1.5; open source software maintained by Vincent van Hees: ), using the Euclidean Norm Minus One metric and with no imputation. 15 – 17 The auto-calibration and detection of

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Katherine Reta Devonshire-Gill and Kevin Ian Norton

imputation for missing data. The following self-reported PA measures were derived for each respondent: (1) total PA minutes (sum of minutes walking, moderate LTPA, and vigorous LTPA weighted by a factor of 2) and (2) total sessions (sum of walking, moderate LTPA, and vigorous LTPA sessions). Based on these

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Mark A. Tully, Ilona I. McMullan, Nicole E. Blackburn, Jason J. Wilson, Laura Coll-Planas, Manuela Deidda, Paolo Caserotti, Dietrich Rothenbacher and on behalf of the SITLESS group

level of missing data of 5–6% in the variables of LSNS-6, DGLS-6, MVPA, SB, and LPA (Table  1 ), multi-imputation was applied using an expectation maximization approach in SPSS (version 25; IBM Corp., Armonk, NY). A multilevel linear regression analysis was carried out which addressed clustering by

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Leticia Oseguera, Dan Merson, C. Keith Harrison and Sue Rankin

representative as possible of the total sample, resulting in a weighted dataset of 8,018. The dataset was imputed to maintain sample size using the maximum likelihood estimation-based Expectation-Maximization (EM) data imputation method ( Allison, 2003 ; Graham, 2009 ; Musil, Warner, Yobas, & Jones, 2002 ) in

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Terese Wilhelmsen, Marit Sørensen and Ørnulf N. Seippel

platforms for the analyses. To handle missing values in the data, we used the R package “Multivariate Imputation by Chained Equations (MICE)” ( Van Buuren & Groothuis-Oudshoorn, 2011 ). To perform the two-step fsQCA analyses ( Schneider & Wagemann, 2006 ), we used the R package “QCAQUI” ( Dusa, 2007

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Samantha M. Ross, Ellen Smit, Joonkoo Yun, Kathleen Bogart, Bridget Hatfield and Samuel W. Logan

/are there: (1) a park or playground, or (2) a recreation center, community center, or boys’ and girls’ club.” Missing data for child’s sex, race, and Hispanic ethnicity were treated using a hot-deck imputation method, while missingness for highest education of the primary adult respondent for household and

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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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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