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.

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.

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).

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.

Smoothed Quantiles for 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.