Depressive Symptoms, Alcohol Beliefs and Heavy Episodic Drinking in Adolescents

Adolescents who engage in heavy episodic drinking (HED—i.e., 5+ drinks on a single occasion) increase risks for psychopathology, alcohol dependence, and similar negative consequences in adulthood. We explored associations among depressive symptoms, positive alcohol beliefs, and progression of heavy episodic drinking (HED) in 3021 German adolescents (M(SD) age at baseline = 12.4 (1.0)) followed for 30 months in 4 waves, using a conditional parallel process linear growth model, with full information maximum likelihood estimation. By wave 4, 40.3% of participants had engaged in HED more than once; 16.4% had done so ≥5 times. Depressive symptoms were indirectly related to baseline values of HED (through positive beliefs and wave 1 drinking frequency and quantity) and to the rate of growth in HED (through positive beliefs and wave 1 quantity). Adolescents with higher levels of depressive symptoms and positive alcohol beliefs drink more frequently and at greater quantities, which is associated with initiating HED at a higher level and escalating HED more rapidly than peers with similar depressive symptoms who lack those beliefs. This suggests that, to the extent that positive alcohol beliefs can be tempered through public health campaigns, education and/or counseling, HED among depressed adolescents might be reduced.

Much Ado About Missingness: A Demonstration of Full Information Maximum Likelihood Estimation to Address Missingness in Functional Magnetic Resonance Imaging Data

The current paper leveraged a large multi-study functional magnetic resonance imaging (fMRI) dataset (N = 363) and a generated missingness paradigm to demonstrate different approaches for handling missing fMRI data under a variety of conditions. The performance of full information maximum likelihood (FIML) estimation, both with and without auxiliary variables, and listwise deletion were compared under different conditions of generated missing data volumes (i.e., 20, 35, and 50%). FIML generally performed better than listwise deletion in replicating results from the full dataset, but differences were small in the absence of auxiliary variables that correlated strongly with fMRI task data. However, when an auxiliary variable created to correlate r = 0.5 with fMRI task data was included, the performance of the FIML model improved, suggesting the potential value of FIML-based approaches for missing fMRI data when a strong auxiliary variable is available. In addition to primary methodological insights, the current study also makes an important contribution to the literature on neural vulnerability factors for obesity. Specifically, results from the full data model show that greater activation in regions implicated in reward processing (caudate and putamen) in response to tastes of milkshake significantly predicted weight gain over the following year. Implications of both methodological and substantive findings are discussed.

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

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.

Confronting Missing Ecological Data in the Age of Pandemic Lockdown

The COVID-19 pandemic profoundly affected research in ecology and evolution, with lockdowns resulting in the suspension of most research programs and creating gaps in many ecological datasets. Likewise, monitoring efforts directed either at tracking trends in natural systems or documenting the environmental impacts of anthropogenic activities were largely curtailed. In addition, lockdowns have affected human activity in natural environments in ways that impact the systems under investigation, rendering many widely used approaches for handling missing data (e.g., available case analysis, mean substitution) inadequate. Failure to properly address missing data will lead to bias and weak inference. Researchers and environmental monitors must ensure that lost data are handled robustly by diagnosing patterns and mechanisms of missingness and applying appropriate tools like multiple imputation, full-information maximum likelihood, or Bayesian approaches. The pandemic has altered many aspects of society and it is timely that we critically reassess how we treat missing data in ecological research and environmental monitoring, and plan future data collection to ensure robust inference when faced with missing data. These efforts will help ensure the integrity of inference derived from datasets spanning the COVID-19 lockdown and beyond.

Planned Missingness: How to and How Much?

Planned missingness (PM) can be implemented for survey studies to reduce study length and respondent fatigue. Based on a large sample of Big Five personality data, the present study simulates how factors including PM design (three-form and random percentage [RP]), amount of missingness, and sample size affect the ability of full-information maximum likelihood (FIML) estimation to treat missing data. Results show that although the effectiveness of FIML for treating missing data decreases as sample size decreases and amount of missing data increases, estimates only deviate substantially from truth in extreme conditions. Furthermore, the specific PM design, whether it be a three-form or RP design, makes little difference although the RP design should be easier to implement for computer-based surveys. The examination of specific boundary conditions for the application of PM as paired with FIML techniques has important implications for both the research methods literature and practitioners regularly conducting survey research

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

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.

Evaluating FIML And Multiple Imputation In Joint Ordinal-Continuous Measurements Models With Missing Data

Missing data is a common occurrence in confirmatory factor analysis (CFA). Much work had evaluated the performance of different techniques when all observed variables were either continuous or ordinal. However, few have investigated these techniques when observed variables are a mix of continuous and ordinal variables. This study investigated the performance of four approaches to handling missing data in these models, a joint ordinal-continuous full information maximum likelihood (JOC-FIML) approach and three multiple imputation approaches (fully conditional specification, fully conditional specification with latent variable formulation, and expectation-maximization with bootstrapping) combined with the weighted least squares with mean and variance adjustment (WLSMV) estimator. In a Monte-Carlo simulation, the JOC-FIML approach produced unbiased estimations of factor loadings and standard errors in almost all conditions. Fully conditional specification combined with WLSMV was second best, producing accurate estimates if the sample size was large. We recommend JOC-FIML across most conditions, except when certain ordinal categories have extremely low frequencies as it was less likely to converge. If the sample is large, fully conditional specification combined with weighted-least-squares is recommended when the FIML approach is not feasible (e.g., non-convergence, variables that predict missingness are not of interest to the analysis).