Analysis of Incomplete Multivariate Data

Schafer, Joseph L.

doi:10.1201/9781439821862

Cited by 5,147 publications

(4,085 citation statements)

References 0 publications

Supporting

Mentioning

3,996

Contrasting

Unclassified

Order By: Relevance

“…Five imputations were used, with missing data estimated from all other EF data available. All subsequent analyses were performed on each of the imputed data sets, and results for each were then averaged (Schafer, 1997). Table 1 presents the descriptive statistics for the key study variables.…”

Section: Preliminary Analysesmentioning

confidence: 99%

A secure base from which to regulate: Attachment security in toddlerhood as a predictor of executive functioning at school entry.

Bernier

Beauchamp

Carlson

et al. 2015

Developmental Psychology

102

View full text Add to dashboard Cite

In light of emerging evidence suggesting that the affective quality of parent-child relationships may relate to individual differences in young children's executive functioning (EF) skills, the aim of this study was to investigate the prospective associations between attachment security in toddlerhood and children's EF skills in kindergarten. Mother-child dyads (N = 105) participated in two toddlerhood visits in their homes, when children were 15 months and 2 years of age.Mother-child attachment security was assessed with the Attachment Q-Sort during both these visits. When children were in kindergarten (ages 5-6), they were administered a battery of EF tasks, and their teachers completed the Behavior Rating Inventory of Executive Function to assess children's EF problems. The results indicated that kindergarteners who were more securely attached to their mothers in toddlerhood showed better performance on all EF tasks, and were considered by their teachers to present fewer EF problems in everyday school situations. These results held above family SES and child age, sex, and general cognitive functioning. The fact that early attachment security uniquely predicted both teacher reports and children's objective EF task performance suggests that parent-child attachment may be a promising factor to consider in the continuing search for the social antecedents of young children's EF.

show abstract

Section: Preliminary Analysesmentioning

confidence: 99%

A secure base from which to regulate: Attachment security in toddlerhood as a predictor of executive functioning at school entry.

Bernier

Beauchamp

Carlson

et al. 2015

Developmental Psychology

102

View full text Add to dashboard Cite

show abstract

“…To retain as many children as possible who met our eligibility criteria for inclusion in our analyses (i.e., at least age 7 by the time of the 2000 assessment), we used MI (Baer, Kivlahan, Blume, McKnight, & Marlatt, 2001;Rubin, 1987;Schafer, 1997). MI replaces missing values with predictions based on all the other information observed in the study.…”

Section: Missing Data and Multiple Imputationmentioning

confidence: 99%

Maternal Employment and Child Development: A Fresh Look Using Newer Methods.

Hill¹,

Waldfogel²,

Brooks‐Gunn³

et al. 2005

Developmental Psychology

175

174

View full text Add to dashboard Cite

The employment rate for mothers with young children has increased dramatically over the past 25 years. Estimating the effects of maternal employment on children's development is challenged by selection bias and the missing data endemic to most policy research. To address these issues, this study uses propensity score matching and multiple imputation. The authors compare outcomes across 4 maternal employment patterns: no work in first 3 years postbirth, work only after 1st year, part-time work in 1st year, and full-time work in 1st year. Our results demonstrate small but significant negative effects of maternal employment on children's cognitive outcomes for full-time employment in the 1st year postbirth as compared with employment postponed until after the 1st year. Multiple imputation yields noticeably different estimates as compared with a complete case approach for many measures. Differences between results from propensity score approaches and regression modeling are often minimal.

show abstract

“…Two acceptable methods of handling missing data for ITT analyses are the full information maximum likelihood method (FIML); (Little and Rubin, 1987), and multiple imputation (Rubin, 1987(Rubin, , 1996Schafer, 1997;Schafer and Graham, 2002). FIML estimates are computed by maximizing the likelihood based on the variables observed for each case, assuming that the data are missing at random (Rubin, 1976), sometimes averaging over covariates that predict missingness (Baker et al, 2006).…”

Section: Handling Missing Data In Itt Analyses In Multilevel Rfts-mismentioning

confidence: 99%

“…Multiple imputation forms a set of complete datasets based on an imputation model, then uses an analytic model to assess intervention effects on each of the completed datasets. The imputation model used to replace the missing data should always be at least as complex as the analytic model used to examine intervention impact (Collins et al, 2001;Graham et al, 2006Graham et al, , 2007Schafer, 1997Schafer, , 1999Schafer and Graham, 2002).…”

Section: Handling Missing Data In Itt Analyses In Multilevel Rfts-mismentioning

confidence: 99%

“…These complete datasets are then analyzed using standard statistical methods, and inferences on such statistics as the odds ratio for GBG versus internal GBG control DISC diagnoses, are made by accounting for two sources of variation: the average standard errors of the odds ratios (within variation) and the variation in these odds ratios across the multiple imputations standard errors (between variation). Confidence intervals can also be formed according to Rubin (1987Rubin ( , 1996 and Schafer (1997Schafer ( , 1999.MI has some advantages over FIML since it can use this additional information to impute values from a large number of observed extra variables that never appear in the final analysis. FIML can also be used with a modest number of extra variables, collapsing over those not used in the final model as we did in Table 6.…”

Section: Handling Missing Data In Itt Analyses In Multilevel Rfts-mismentioning

confidence: 99%

See 1 more Smart Citation

Methods for testing theory and evaluating impact in randomized field trials: Intent-to-treat analyses for integrating the perspectives of person, place, and time

Brown¹,

Wang²,

Kellam³

et al. 2008

Drug and Alcohol Dependence

150

139

View full text Add to dashboard Cite

Randomized field trials provide unique opportunities to examine the effectiveness of an intervention in real world settings and to test and extend both theory of etiology and theory of intervention. These trials are designed not only to test for overall intervention impact but also to examine how impact varies as a function of individual level characteristics, context, and across time. Examination of such variation in impact requires analytical methods that take into account the trial's multiple nested structure and the evolving changes in outcomes over time. The models that we describe here merge multilevel modeling with growth modeling, allowing for variation in impact to be represented through discrete mixtures-growth mixture models-and nonparametric smooth functions-generalized additive mixed models. These methods are part of an emerging class of multilevel growth mixture models, and we illustrate these with models that examine overall impact and variation in impact. In this paper, we define intent-to-treat analyses in group-randomized multilevel field trials and discuss appropriate ways to identify, examine, and test for variation in impact without inflating the Type I error rate. We describe how to make causal inferences more robust to misspecification of covariates * Corresponding author. Tel.: +1 813 974 6672. E-mail address: hbrown@health.usf.edu (C.H. Brown). Conflict of InterestAuthor Muthén is a co-developer of Mplus, which is discussed in this paper. There are no conflicts of interest. in such analyses and how to summarize and present these interactive intervention effects clearly. Practical strategies for reducing model complexity, checking model fit, and handling missing data are discussed using six randomized field trials to show how these methods may be used across trials randomized at different levels. NIH Public Access

show abstract

Analysis of Incomplete Multivariate Data

Cited by 5,147 publications

References 0 publications

A secure base from which to regulate: Attachment security in toddlerhood as a predictor of executive functioning at school entry.

A secure base from which to regulate: Attachment security in toddlerhood as a predictor of executive functioning at school entry.

Maternal Employment and Child Development: A Fresh Look Using Newer Methods.

Methods for testing theory and evaluating impact in randomized field trials: Intent-to-treat analyses for integrating the perspectives of person, place, and time

Contact Info

Product

Resources

About