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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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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 . https://esa.un.org/unpd/wpp/ . 47

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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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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

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Salomé Aubert, Joel D. Barnes, Nicolas Aguilar-Farias, Greet Cardon, Chen-Kang Chang, Christine Delisle Nyström, Yolanda Demetriou, Lowri Edwards, Arunas Emeljanovas, Aleš Gába, Wendy Y. Huang, Izzeldin A.E. Ibrahim, Jaak Jürimäe, Peter T. Katzmarzyk, Agata Korcz, Yeon Soo Kim, Eun-Young Lee, Marie Löf, Tom Loney, Shawnda A. Morrison, Jorge Mota, John J. Reilly, Blanca Roman-Viñas, Natasha Schranz, John Scriven, Jan Seghers, Thomas Skovgaard, Melody Smith, Martyn Standage, Gregor Starc, Gareth Stratton, Tim Takken, Tuija Tammelin, Chiaki Tanaka, David Thivel, Richard Tyler, Alun Williams, Stephen H.S. Wong, Paweł Zembura and Mark S. Tremblay

( 18 ): 2938 – 2940 . PubMed ID: 28645171 doi:10.1093/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. The World Bank . GINI index

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Koren L. Fisher, Bruce A. Reeder, Elizabeth L. Harrison, Brenda G. Bruner, Nigel L. Ashworth, Punam Pahwa, Nazmi Sari, M. Suzanne Sheppard, Christopher A. Shields and Karen E. Chad

imputation of the intention-to-treat approach ( Hedecker & Gibbons, 1997 ; Kivitz et al., 2008 ; Pauler, McCoy, & Moinpour, 2003 ). The results of both approaches were compared, and minimal differences were found between the two. Results A total of 172 participants (46 men and 126 women) were randomized at

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Timothy C. Howle, James A. Dimmock, Nikos Ntoumanis, Nikos L.D. Chatzisarantis, Cassandra Sparks and Ben Jackson

.34 for skewness and 1.62 for kurtosis. As such, data imputation and variable transformation were not necessary. The sample of 150 participants ( n male  = 71 and n female  = 79) who were retained for analyses (see Responses to Manipulations subsection) were split across the four conditions as follows

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Angela Papadimitriou and Mark Perry

imputation. Some of the potential methodological bias was due to unavoidable issues, such as the inability to blind participants and health care providers to the intervention. However, there were also some avoidable risks of bias. One such risk of bias arose from the use of exercise in the intervention group

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Barbara E. Bechter, James A. Dimmock, Joshua L. Howard, Peter R. Whipp and Ben Jackson

, NY). Missing data (which comprised 0.5% of all cases) were missing completely at random; a Little chi-square test was nonsignificant, χ 2 (1,246) = 1,320.35, p  = .07, and missing data were subsequently replaced using the program’s single imputation function. Subsequently, standard confirmatory