A Markov chain Monte Carlo algorithm for multiple imputation in large surveys

Schunk, Daniel

doi:10.1007/s10182-008-0053-6

Cited by 111 publications

(85 citation statements)

References 25 publications

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“…been implemented in the Spanish Survey of Household Finances (EFF) (Barceló, 2006;Bover, 2004) and the German SAVE survey (Schunk, 2007(Schunk, , 2008. …”

Section: Why Multiple Imputation?mentioning

confidence: 99%

“…Figure 2: Overview of the multiple imputation method for SAVE Source: own figure based on Schunk (2008).…”

Section: The Imputation Algorithm For Savementioning

confidence: 99%

“…Ziegelmeyer (2009a) Schunk (2007Schunk ( , 2008) with low missing rates (mainly socio-demographic, psychometric, expectations, and health variables) are stochastically imputed first. Then core variables (income, saving, asset, and credit variables) 10 are imputed making use of the additional information of the already imputed noncore variables.…”

Section: The Imputation Algorithm For Savementioning

confidence: 99%

“…In the SAVE study, a German survey focused on households' saving behavior, the missing values are filled with appropriate substitutes using a "Markov Chain Monte Carlo multiple imputation procedure" (Schunk, 2007(Schunk, , 2008. This paper contributes to the methodological literature on the imputation of large scale micro datasets by evaluating different techniques for the imputation of monetary variables.…”

Section: Introductionmentioning

confidence: 99%

“…Section 3 describes the German SAVE Survey and the multiple imputation method for SAVE (MIMS) implemented by Schunk (2007Schunk ( , 2008. Section 4 deals with the evaluation of different algorithms for the imputation of monetary variables.…”

Section: Introductionmentioning

confidence: 99%

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Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey

Ziegelmeyer

2012

AStA Adv Stat Anal

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Questions about monetary variables (such as income, wealth or savings) Key words: Imputation methods, Monte-Carlo simulation, imputation evaluation, itemnonresponse, missing data, imputation, retransformation, sample surveys, SAVE JEL classification: C01, C81, C49 1 I would like to thank Michela Coppola (MEA) for very helpful support, as well as Axel Börsch-Supan (MEA), Daniel Schunk (Johannes Gutenberg University Mainz), Armin Rick (University of Chicago), Mathias Sommer, Hendrik Jürges (Universität Wuppertal), Uli Schlieper (MEA), Cornelius Schmidt (Université de Lausanne) for their helpful comments. Alexander Aldinger, Helen Grupp, Dörte Heger, Corinna Kartzke, Julius Nick, Michael Schardt, Stefanie Schulz, and Marius Wolter provided excellent research assistance. I am grateful to the seminar participants at MEA for many useful comments. MEA is a research institute which is funded by two thirds through public third-party means for which I am very grateful. Furthermore, I would like to thank the state of Baden-Württemberg and the German Insurance Association for financing the basic funding of MEA. I am particularly grateful to the German Research Foundation (Deutsche Forschungsgemeinschaft) for financing the SAVE survey.

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“…been implemented in the Spanish Survey of Household Finances (EFF) (Barceló, 2006;Bover, 2004) and the German SAVE survey (Schunk, 2007(Schunk, , 2008. …”

Section: Why Multiple Imputation?mentioning

confidence: 99%

“…Figure 2: Overview of the multiple imputation method for SAVE Source: own figure based on Schunk (2008).…”

Section: The Imputation Algorithm For Savementioning

confidence: 99%

Section: The Imputation Algorithm For Savementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey

Ziegelmeyer

2012

AStA Adv Stat Anal

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Gender Diversity and Brain Morphology Among Adolescents

et al. 2023

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ImportanceGender-diverse youths have higher rates of mental health problems compared with the general population, as shown in both clinical and nonclinical populations. Brain correlates of gender diversity, however, have been reported only among youths with gender dysphoria or in transgender individuals.ObjectiveTo examine brain morphologic correlates of gender diversity among adolescents from a general pediatric population who were assigned male or female at birth, separately.Design, Setting, and ParticipantsThis cross-sectional study was embedded in Generation R, a multiethnic population-based study conducted in Rotterdam, the Netherlands. Adolescents who were born between April 1, 2002, and January 31, 2006, and had information on self-reported or parent-reported gender diversity and structural neuroimaging at ages 13 to 15 years were included. Data analysis was performed from April 1 to July 31, 2022.ExposuresGender-diverse experiences among adolescents were measured with selected items from the Achenbach System of Empirically Based Assessment forms and the Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults, as reported by adolescents and/or their parents.Main Outcomes and MeasuresHigh-resolution structural neuroimaging data were collected using a 3-T magnetic resonance imaging scanner (at a single site). We used linear regression models to examine differences in global brain volumetric measures between adolescents who reported gender diversity and those who did not.ResultsThis study included 2165 participants, with a mean (SD) age of 13.8 (0.6) years at scanning. A total of 1159 participants (53.5%) were assigned female at birth and 1006 (46.5%) were assigned male at birth. With regard to maternal country of origin, 1217 mothers (57.6%) were from the Netherlands and 896 (42.4%) were from outside the Netherlands. Adolescents who reported gender diversity did not differ in global brain volumetric measures from adolescents who did not report gender diversity. In whole-brain, vertexwise analyses among adolescents assigned male at birth, thicker cortices in the left inferior temporal gyrus were observed among youths who reported gender diversity compared with those who did not. No associations were observed between gender diversity and surface area in vertexwise analyses.Conclusions and RelevanceThe findings of this cross-sectional study suggest that global brain volumetric measures did not differ between adolescents who reported gender diversity and those who did not. However, these findings further suggest that gender diversity in the general population correlates with specific brain morphologic features in the inferior temporal gyrus among youths who are assigned male at birth. Replication of these findings is necessary to elucidate the potential neurobiological basis of gender diversity in the general population. Future longitudinal studies should also investigate the directionality of these associations.

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Association of Poor Family Functioning From Pregnancy Onward With Preadolescent Behavior and Subcortical Brain Development

et al. 2021

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IMPORTANCEThe association of poor family functioning, a potent stressor, with child behavior is potentially long term and relevant for a person's well-being later in life. Whether changes in brain development underlie the associations with preadolescent behavior and help identify periods of vulnerability is unclear.OBJECTIVE To assess the associations of poor family functioning from pregnancy onward with cortical, white matter, and subcortical volumes, and to examine the extent to which, in particular, hippocampal volume mediates the association of prenatal parental environmental exposures with child problem behavior in preadolescence. DESIGN, SETTING, AND PARTICIPANTSThis population-based cohort study, conducted from April 2002 to January 2006, was embedded in Generation R, a multiethnic population-based cohort from fetal life onward. All pregnant women living in Rotterdam, the Netherlands, with an expected delivery date between April 2002 and January 2006 were invited to participate. Of the 8879 pregnant women enrolled during pregnancy, 1266 mothers with no partner data and 490 with missing family functioning data were excluded, as well as 1 sibling of 32 twin pairs. After excluding an additional 657 children with poor imaging data quality or incidental findings, the final sample consisted of 2583 mother-child pairs. Data analysis was performed from March 1, 2019, to June 28, 2019.EXPOSURES Mother-and father-rated poor family functioning was repeatedly measured by the General Functioning subscale of the Family Assessment Device.MAIN OUTCOMES AND MEASURES Our primary hypothesis, formulated after data collection but before analysis, was that poor prenatal family functioning would be associated with smaller hippocampal and amygdala volumes in late childhood. High-resolution structural neuroimaging data of children aged 10 years were collected with a single 3-T magnetic resonance imaging system. Child emotional and behavioral problems were assessed with the Child Behavior Checklist. RESULTS Data were available for 2583 children (mean [SD] age, 10.1 [0.6] years; 1315 girls [50.9%]). Data for parents included 2583 mothers (mean [SD] age, 31.1 [4.7] years; 1617 Dutch race/ethnicity [62.6%]) and 1788 fathers (mean [SD] age, 33.5 [5.3] years; 1239 Dutch race/ethnicity [69.3%]). Children exposed to prenatal maternal-reported poor family functioning had smaller hippocampal (B = −0.08; 95% CI, −0.13 to −0.02) and occipital lobe (B = −0.70; 95% CI, −1.19 to −0.21) volumes in preadolescence. There was no evidence for an association of exposure to poor family functioning at mid-or late childhood with brain morphology. Hippocampal volumes partially mediated the association of prenatal maternal-reported poor family functioning with preadolescent problem behavior (B = 0.08; 95% CI, 0.03-0.13), even after adjusting for prior child problems at age 1.5 years. Analyses of combined maternal and paternal family functioning ratings showed similar results, but associations were largely driven by maternal family functioning reports. CONC...

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A Markov chain Monte Carlo algorithm for multiple imputation in large surveys

Abstract: JEL-CODES: C15, C421 I gratefully acknowledge many astute comments by

Cited by 111 publications

References 25 publications

Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey

Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey

Gender Diversity and Brain Morphology Among Adolescents

Association of Poor Family Functioning From Pregnancy Onward With Preadolescent Behavior and Subcortical Brain Development

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