Longitudinal data are often collected in different research areas such as medicine, biology, education, and psychology. We can build a transitional model using longitudinal binary data, which aims...Show moreLongitudinal data are often collected in different research areas such as medicine, biology, education, and psychology. We can build a transitional model using longitudinal binary data, which aims to model the probability of transition between the response categories. In this type of data is common to find missing values due to dropouts, costs, and organizational problems. The missing-indicator model is often used as a method to handle missing values in this type of data. This method consists in creating a new category for the missing values. Therefore, the binary logistic model changes to a baseline-category logit model. This study aims to evaluate the bias of the estimated coefficients when the missing-indicator method is used in the response of a binary transitional model. Based on an empirical example, a Monte Carlo simulation with three factors is carried out: (1) type of missingness, (2) sample size, and (3) proportion of missing data. The coefficients bias from the baseline-category logit model is evaluated using boxplots and a three-way MANOVA analysis. The results suggest that sample size, the proportion of missing data, type of missingness and the interaction between sample size and proportion affect the bias of the estimated coefficients; nonetheless, the effect size is small. When each dependent variable is analysed separately using ANOVA, the effects of the proportion of missing and the interaction between sample size and proportion were statistically significant for only one coefficient. However, the effect size is still small. Therefore, the conclusion is that the estimated coefficients' bias for all the missingness types is low.Show less
