scholarly journals Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood

2021 ◽  
Vol 12 ◽  
Author(s):  
Lihan Chen ◽  
Victoria Savalei
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Parameter Estimates ◽  
Full Information ◽  
Finite Sample ◽  
Missing Information ◽  
Full Information Maximum Likelihood ◽  
Missing Data Analysis ◽  
Statistical Programming ◽  
The Impact

In missing data analysis, the reporting of missing rates is insufficient for the readers to determine the impact of missing data on the efficiency of parameter estimates. A more diagnostic measure, the fraction of missing information (FMI), shows how the standard errors of parameter estimates increase from the information loss due to ignorable missing data. FMI is well-known in the multiple imputation literature (Rubin, 1987), but it has only been more recently developed for full information maximum likelihood (Savalei and Rhemtulla, 2012). Sample FMI estimates using this approach have since then been made accessible as part of the lavaan package (Rosseel, 2012) in the R statistical programming language. However, the properties of FMI estimates at finite sample sizes have not been the subject of comprehensive investigation. In this paper, we present a simulation study on the properties of three sample FMI estimates from FIML in two common models in psychology, regression and two-factor analysis. We summarize the performance of these FMI estimates and make recommendations on their application.

scholarly journals Recursive partitioning imputation versus full information maximum likelihood in finite mixture modeling

2021 ◽  
Author(s):  
Marcus Richard Waldman ◽  
Katherine E. Masyn
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Multiple Imputation ◽  
Latent Profile Analysis ◽  
Recursive Partitioning ◽  
External Information ◽  
Parameter Estimates ◽  
Full Information ◽  
Single Class ◽  
Full Information Maximum Likelihood

It is well established that omitting important variables that are related to the propensity for missingness can lead to biased parameter estimates and invalid inference. Nevertheless, researchers conducting a person-centered analysis ubiquitously adopt a full information maximum likelihood (FIML) approach to treat missing data in a manner that assumes the missingness is only related to the observed indicators and is not related to any external variables. Such an assumption is generally considered overly restrictive in the behavioral sciences where the data are observational in nature. At the same time, previous research has discouraged the adoption of multiple imputation to treat missing data in person-centered analyses because traditional imputation models make a single-class assumption and do not reflect the multiple group structure of data with latent subpopulations (Enders & Gottschall, 2011). However, more modern imputation models that rely on recursive partitioning do not impose a single-class structure to the data. Focusing on latent profile analysis, we demonstrate in simulations that in samples of N = 1,200 or greater, recursive partitioning imputation algorithms can effectively incorporate external information from auxiliary variables to attenuate nonresponse bias better than FIML and multivariate normal imputation. Moreover, we find that recursive imputation models lead to confidence intervals with adequate coverage and they better recover posterior class probabilities than alternative missing data strategies. Taken together, our findings point to the promise and potential of multiple imputation in person-centered analyses once remaining methodological gaps around pooling and class enumeration are filled.

scholarly journals Best (but oft-forgotten) practices: missing data methods in randomized controlled nutrition trials

2019 ◽  
Vol 109 (3) ◽  
pp. 504-508 ◽  
Author(s):  
Peng Li ◽  
Elizabeth A Stuart
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Causal Inference ◽  
Randomized Controlled Trials ◽  
Multiple Imputation ◽  
Controlled Trials ◽  
Full Information ◽  
Complete Case ◽  
Full Information Maximum Likelihood ◽  
Randomized Controlled

ABSTRACT Missing data ubiquitously occur in randomized controlled trials and may compromise the causal inference if inappropriately handled. Some problematic missing data methods such as complete case (CC) analysis and last-observation-carried-forward (LOCF) are unfortunately still common in nutrition trials. This situation is partially caused by investigator confusion on missing data assumptions for different methods. In this statistical guidance, we provide a brief introduction of missing data mechanisms and the unreasonable assumptions that underlie CC and LOCF and recommend 2 appropriate missing data methods: multiple imputation and full information maximum likelihood.

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