1. Too many zeroes: understanding and modeling zero-inflated continuous outcomes in experience sampling data using two-part mixed effect models
- Author
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Lafit, Ginette, Pan, Sangqi, and Kirtley, Olivia
- Subjects
FOS: Psychology ,Intensive longitudinal data ,Clinical Psychology ,Sports Studies ,Semicontinuous data ,Personality and Social Contexts ,Psychology ,Ambulatory Assessment ,Quantitative Psychology ,Social and Behavioral Sciences ,Experience sampling research ,Two-part mixed effect models - Abstract
Data collected with the Experience Sampling Method (ESM) have been increasingly used to investigate individuals’ behaviours, emotions, and thoughts in daily life. Typically, linear mixed-effect models are used to analyze these data. This model is widely used in ESM research because it allows partitioning of the variability in the data into variance at the individual level and at the measurement level. However, the implementation of linear mixed-effect models presents challenges when analyzing statistically rare phenomena. For example, suicidal thoughts and behaviors within non-clinical populations, psychopathology symptoms (e.g., insomnia), or negative emotions (e.g., anger). In ESM studies it has been observed that certain items (e.g., suicidal thoughts and behaviors, negative affect items) include a large number of zeros, representing moments in which participants have not experienced them. Meanwhile, the non-zero participants’ responses to these behaviors or emotions are positive skewed continuous variables. The two-part semi-continuous mixed-effect regression has been proposed to model a variable in which a portion of the responses are equal to a single value, that represents whether an individual engaged in the target behaviour or they experience a certain emotion, and a continuous and skewed distribution for the remaining values. In this paper, we present an application of the two-part semi-continuous mixed-effect model to analyze semi-continuous ESM outcomes with a large number of zeros. Specifically, we use pre-existing data from four ESM studies to investigate the consequences of model misspecification (i.e., when we fit a linear mixed-effect model but responses follow a two-part semicontinuous distribution). We also compare the predictive accuracy of linear and two-part mixed-effect models as a function of the sample size. The paper includes a tutorial that researchers can use to evaluate estimation and predictive accuracy, and calculate statistical power for semi-continuous outcomes in ESM data.
- Published
- 2022
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