On the Joys of Missing Data

Little, Todd D.; Jorgensen, Terrence D.; Lang, Kyle M.; Moore, E. Whitney G.

doi:10.1093/jpepsy/jst048

Cited by 538 publications

(363 citation statements)

References 17 publications

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“…By diminishing the PMCEQ's items, one or more different scales could be included within the original length of the PMCEQ. A short form could decrease the total survey length, which could decrease participant fatigue and increase data quality (Graham, Olchowski, & Gilreath, 2007;Little et al, 2014). In addition, the repetitiousness of some items could be removed, which may also decrease participant fatigue.…”

Section: Psychometric Properties Of the Abbreviated Perceived Motivatmentioning

confidence: 99%

Psychometric Properties of the Abbreviated Perceived Motivational Climate in Exercise Questionnaire

Moore

Brown

Fry

2015

Measurement in Physical Education and Exercise Science

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The purpose of this study was to develop an abbreviated version of the Perceived Motivational Climate in Exercise Questionnaire (PMCEQ-A) to provide a more practical instrument for use in applied exercise settings. In the calibration step, 2 shortened versions' measurement and latent model values were compared to each other and the original PMCEQ using a 3-group CFA invariance testing approach with previously collected exercise setting data (N = 5,427). Based on the model fit and reliability values, the 12-item version performed better than the 17-item version. The resultant 12-item PMCEQ-A's CFA model estimates were then compared to the PMCEQ's model values for 2 different, previously conducted studies of exercise settings (N = 414 and 770). The more parsimonious 12-item PMCEQ-A can be used by exercise psychology researchers to gain insight into members' perspectives on the motivational climate and may lead to developing effective strategies to enhance members' experiences and commitment.

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Section: Psychometric Properties Of the Abbreviated Perceived Motivatmentioning

confidence: 99%

Psychometric Properties of the Abbreviated Perceived Motivational Climate in Exercise Questionnaire

Moore

Brown

Fry

2015

Measurement in Physical Education and Exercise Science

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“…Asimismo, existen técnicas de sustitución de datos de más larga data tales como sustitución por la media o regresión, que requerirían el cumplimiento del supuesto de MCAR, lo cual es una condición difícil de cumplir al menos que los datos perdidos hayan sido incluidos como parte del diseño (tema abordado más adelante). Si se procede con un enfoque clásico sin tener datos perdidos con un patrón MCAR, estimaciones tales como correlaciones, diferencias de medias, etc., serán muy grandes o se verían atenuados, asimismo los errores estándar de las pruebas de significancia serían menores al introducir valores artificiales tales como la media de la variable o la media del grupo (Enders 2010;Graham, 2009Graham, , 2012Little et al, 2014;Little, 2013).…”

Section: Tratamiento De Datos Perdidosunclassified

“…Investigadores en el tratamiento de datos perdidos han argumentado que este tipo de diseños pueden también mejorar la validez de un instrumento en muchas circunstancias donde factores como la fatiga, dificultad o reactividad frente al test pueden ser una amenaza a la validez de una medida (Enders 2010;Graham, 2009Graham, , 2012Little et al, 2014;van Buuren, 2012).…”

Section: Diseño De Datos Perdidos Para La Implementación De Instrumenunclassified

“…Como se mencionó anteriormente, cuando los datos perdidos obedecen al mecanismo de MCAR, no se introduce sesgo en la estimación de los parámetros del modelo, no obstante el poder estadístico puede ciertamente, verse impactado. De esta forma, al sustituir los datos perdidos, las estimaciones no son diferentes de los valores que hubiera sido estimados si no existiera valores perdidos (Graham, 2009;Little et al, 2014).…”

Section: Diseño De Datos Perdidos Para La Implementación De Instrumenunclassified

“…Debido a esto, el principal problema ya no es la presencia de valores perdidos, el verdadero problema es como lidiamos con los datos perdidos (Little, Jorgensen, Lang, & Moore, 2014).…”

Section: Introductionunclassified

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No le tema a los datos perdidos: enfoques modernos para el manejo de datos perdidos

Montenegro-Montenegro

Oh²,

Chesnut

2015

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No le tema a los datos perdidos: enfoques modernos para el manejo de datos perdidosResumen. La mayoría de los datos en ciencias sociales y educación presentan valores perdidos debido al abandono del estudio o la ausencia de respuesta. Los métodos para el manejo de datos perdidos han mejorado dramáticamente en los últimos años, y los programas computacionales ofrecen en la actualidad una variedad de opciones sofisticadas. A pesar de la amplia disponibilidad de métodos considerablemente justificados, muchos investigadores e investigadoras siguen confiando en técnicas viejas de imputación que pueden crear análisis sesgados. Este artículo presenta una introducción conceptual a los patrones de datos perdidos. Seguidamente, se introduce el manejo de datos perdidos y el análisis de los mismos con base en los mecanismos modernos del método de máxima verosimilitud con información completa (FIML, siglas en inglés) y la imputación múltiple (IM). Asimismo, se incluye una introducción a los diseños de datos perdidos así como nuevas herramientas computacionales tales como la función Quark y el paquete semTools. Se espera que este artículo incentive el uso de métodos modernos para el análisis de los datos perdidos.Palabras clave. datos perdidos, máxima verosimilitud con información completa, imputación múltiple, diseños de datos perdidos, psicometría.Abstract. Most of the social and educational data have missing observations due to either attrition or nonresponse. Missing data methodology has improved dramatically in recent years, and popular computer programs as well as software now offer a variety of sophisticated options. Despite the widespread availability of theoretically justified methods, many researchers still rely on old imputation techniques that can create biased analysis. This article provides conceptual introductions to the patterns of missing data. In line with that, this article introduces how to handle and analyze the missing information based on modern mechanisms of full-information maximum likelihood (FIML) and multiple imputation (MI). An introduction about planned missing designs is also included and new computational tools like Quark function, and semTools package are also mentioned. The authors hope that this paper encourages researchers to implement modern methods for analyzing missing data.

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Assessment of Psychosocial and Neonatal Risk Factors for Trajectories of Behavioral Dysregulation Among Young Children From 18 to 72 Months of Age

et al. 2023

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ImportanceEmotional and behavioral dysregulation during early childhood are associated with severe psychiatric, behavioral, and cognitive disorders through adulthood. Identifying the earliest antecedents of persisting emotional and behavioral dysregulation can inform risk detection practices and targeted interventions to promote adaptive developmental trajectories among at-risk children.ObjectiveTo characterize children’s emotional and behavioral regulation trajectories and examine risk factors associated with persisting dysregulation across early childhood.Design, Setting, and ParticipantsThis cohort study examined data from 20 United States cohorts participating in Environmental influences on Child Health Outcomes, which included 3934 mother-child pairs (singleton births) from 1990 to 2019. Statistical analysis was performed from January to August 2022.ExposuresStandardized self-reports and medical data ascertained maternal, child, and environmental characteristics, including prenatal substance exposures, preterm birth, and multiple psychosocial adversities.Main Outcomes and MeasuresChild Behavior Checklist caregiver reports at 18 to 72 months of age, with Dysregulation Profile (CBCL-DP = sum of anxiety/depression, attention, and aggression).ResultsThe sample included 3934 mother-child pairs studied at 18 to 72 months. Among the mothers, 718 (18.7%) were Hispanic, 275 (7.2%) were non-Hispanic Asian, 1220 (31.8%) were non-Hispanic Black, 1412 (36.9%) were non-Hispanic White; 3501 (89.7%) were at least 21 years of age at delivery. Among the children, 2093 (53.2%) were male, 1178 of 2143 with Psychosocial Adversity Index [PAI] data (55.0%) experienced multiple psychosocial adversities, 1148 (29.2%) were exposed prenatally to at least 1 psychoactive substance, and 3066 (80.2%) were term-born (≥37 weeks’ gestation). Growth mixture modeling characterized a 3-class CBCL-DP trajectory model: high and increasing (2.3% [n = 89]), borderline and stable (12.3% [n = 479]), and low and decreasing (85.6% [n = 3366]). Children in high and borderline dysregulation trajectories had more prevalent maternal psychological challenges (29.4%-50.0%). Multinomial logistic regression analyses indicated that children born preterm were more likely to be in the high dysregulation trajectory (adjusted odds ratio [aOR], 2.76; 95% CI, 2.08-3.65; P &lt; .001) or borderline dysregulation trajectory (aOR, 1.36; 95% CI, 1.06-1.76; P = .02) vs low dysregulation trajectory. High vs low dysregulation trajectories were less prevalent for girls compared with boys (aOR, 0.60; 95% CI, 0.36-1.01; P = .05) and children with lower PAI (aOR, 1.94; 95% CI, 1.51-2.49; P &lt; .001). Combined increases in PAI and prenatal substance exposures were associated with increased odds of high vs borderline dysregulation (aOR, 1.28; 95% CI, 1.08-1.53; P = .006) and decreased odds of low vs high dysregulation (aOR, 0.77; 95% CI, 0.64-0.92; P = .005).Conclusions and RelevanceIn this cohort study of behavioral dysregulation trajectories, associations were found with early risk factors. These findings may inform screening and diagnostic practices for addressing observed precursors of persisting dysregulation as they emerge among at-risk children.

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On the Joys of Missing Data

Cited by 538 publications

References 17 publications

Psychometric Properties of the Abbreviated Perceived Motivational Climate in Exercise Questionnaire

Psychometric Properties of the Abbreviated Perceived Motivational Climate in Exercise Questionnaire

No le tema a los datos perdidos: enfoques modernos para el manejo de datos perdidos

Assessment of Psychosocial and Neonatal Risk Factors for Trajectories of Behavioral Dysregulation Among Young Children From 18 to 72 Months of Age

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