A Realistic Evaluation of Methods for Handling Missing Data When There is a Mixture of MCAR, MAR, and MNAR Mechanisms in the Same Dataset.

The impact of missing data on statistical inference varies depending on several factors such as the proportion of missingness, missing-data mechanism, and method employed to handle missing values. While these topics have been extensively studied, most recommendations have been made assuming that all missing values are from the same missing-data mechanism. In reality, it is very likely that a mixture of missing-data mechanisms is responsible for missing values in a dataset and even within the same pattern of missingness. Although a mixture of missing-data mechanisms and causes within a dataset is a likely scenario, the performance of popular missing-data methods under these circumstances is unknown. This study provides a realistic evaluation of methods for handling missing data in this setting using Monte Carlo simulation in the context of regression. This study also seeks to identify acceptable proportions of missing values that violate the missing-data mechanism assumed by the method used to handle missing values. Results indicate that multiple imputation (MI) performs better than other principled or ad-hoc methods. Different missing-data methods are also compared via the analysis of a real dataset in which mixtures of missingness mechanisms are created. Recommendations are provided for the use of different methods in practice.

Smoothed Quantiles for χ2 Type Test Statistics with Applications.

Chi-square type test statistics are widely used in assessing the goodness-of-fit of a theoretical model. The exact distributions of such statistics can be quite different from the nominal chi-square distribution due to violation of conditions encountered with real data. In such instances, the bootstrap or Monte Carlo methodology might be used to approximate the distribution of the statistic. However, the sample quantile may be a poor estimate of the population counterpart when either the sample size is small or the number of different values of the replicated statistic is limited. Using statistical learning, this article develops a method that yields more accurate quantiles for chi-square type test statistics. Formulas for smoothing the quantiles of chi-square type statistics are obtained. Combined with the bootstrap methodology, the smoothed quantiles are further used to conduct equivalence testing in mean and covariance structure analysis. Two real data examples illustrate the applications of the developed formulas in quantifying the size of model misspecification under equivalence testing. The idea developed in the article can also be used to develop formulas for smoothing the quantiles of other types of test statistics or parameter estimates.

Subtypes of the missing not at random missing data mechanism.

issing values that are missing not at random (MNAR) can result from a variety of missingness processes. However, two fundamental subtypes of MNAR values can be obtained from the definition of the MNAR mechanism itself. The distinction between them deserves consideration because they have characteristic differences in how they distort relationships in the data. This has implications for the validity of statistical results and generalizability of methodological findings that are based on data (empirical or generated) with MNAR values. However, these MNAR subtypes have largely gone unnoticed by the literature. As few studies have considered both subtypes, their relevance to methodological and substantive research has been overlooked. This article systematically introduces the two MNAR subtypes and gives them descriptive names. A case study demonstrates they are mechanically distinct from each other and from other missing-data mechanisms. Applied examples are given to help researchers conceptually identify MNAR subtypes in real data. Methods are provided to generate missing values from both subtypes in simulation studies. Simulation studies for regression and growth curve modeling contexts show MNAR subtypes consistently differ in the severity of their impact on statistical inference. This behavior is examined in light of how relationships in the data become characteristically distorted. The contents of this article are intended to provide a foundation and tools for organized consideration of MNAR subtypes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Comorbidity, Functional Impairment, and Emotional Distress: A Coping Mediation Model for Persons With Cancer.

BACKGROUND: Comorbid disease in cancer patients can substantially impact medical care, emotional distress, and mortality. However, there is a paucity of research on how coping may affect the relationship between comorbidity and emotional distress. PURPOSE: The current study investigated whether the relations between comorbidity and emotional distress and between functional impairment and emotional distress were mediated by three types of coping: action planning (AP), support/advice seeking (SAS), and disengagement (DD). METHODS: Four hundred and eighty-three persons with cancer completed a measure of functional impairment (Sickness Impact Profile), the Checklist of Comorbid Conditions, the Brief COPE, the Hospital Anxiety and Depression Scale, the Quality of Life Assessment for Cancer Survivors (Negative Feelings Scale), and the Distress Screening Schedule (Emotional Distress Scale). The latter three measures were used to form a latent construct representing the outcome, emotional distress. RESULTS: Model comparison analysis indicated that the model with DD as a mediator had a better fit than models containing AP and SAS. DD mediated the relationship between functional impairment and emotional distress, so that engaging in DD was associated with greater distress. In addition, comorbidity and functional impairment were directly and positively related to emotional distress, but the relation between comorbidity and distress was not mediated by coping type. CONCLUSIONS: Both comorbidity and functional impairment may be associated with distress, but disengagement coping only mediated the relation involving functional impairment and was positively associated with distress. Future studies can investigate whether teaching active coping or adaptive coping (e.g., through mindfulness exercises) can decrease distress in cancer patients, despite functional impairments.

An overview of applied robust methods.

Data in social sciences are typically non-normally distributed and characterized by heavy tails. However, most widely used methods in social sciences are still based on the analyses of sample means and sample covariances. While these conventional methods continue to be used to address new substantive issues, conclusions reached can be inaccurate or misleading. Although there is no 'best method' in practice, robust methods that consider the distribution of the data can perform substantially better than the conventional methods. This article gives an overview of robust procedures, emphasizing a few that have been repeatedly shown to work well for models that are widely used in social and behavioural sciences. Real data examples show how to use the robust methods for latent variable models and for moderated mediation analysis when a regression model contains categorical covariates and product terms. Results and logical analyses indicate that robust methods yield more efficient parameter estimates, more reliable model evaluation, more reliable model/data diagnostics, and more trustworthy conclusions when conducting replication studies. R and SAS programs are provided for routine applications of the recommended robust method.

The social context of nonsuicidal self-injury: Links to severity, suicide risk, and social factors.

OBJECTIVES: Though nonsuicidal self-injury (NSSI) is generally considered to be a private act, 21%-52% of individuals who engage in NSSI do so around others. Those who engage in NSSI alone often demonstrate severe behavior. However, little is known about the distinction between those who only sometimes versus always engage in NSSI when they are around others. Three groups of individuals who engage in NSSI were examined: Always, sometimes, and never alone. METHOD: Participants (N = 861; 84.2% female; M age = 20.06) were undergraduates who answered online questionnaires. Severity of NSSI, suicide risk, and social risk factors were used to predict group membership. RESULTS: Engaging in NSSI around others aligned with less severe NSSI behavior, lower suicide risk, and fewer interpersonal difficulties versus those who engage in NSSI alone. CONCLUSIONS: NSSI's social context may indicate clinical severity. This information is useful for clinicians who work with individuals with a history of NSSI.