2.4. Data Analysis
eating behaviors
REGRESSION
The data were analyzed in several phases, following the purposes of the present study. First, missing values for the 14 items of SEREB-C ranged from 0.1% to 1.0% (M = 0.55%) and were imputed using regression imputation. Second, to examine the SEREB-C’s factor structure, participants were randomly split into two groups (i.e., Group 1, With Group 1, we conducted a principal component analysis (PCA) using SPSS, version 28.0, with direct oblimin rotation (delta = 0). The appropriate number of factors for retention was determined by several criteria: the scree plots, eigenvalue > 1.0, and conceptual meaningfulness of items on each factor.With Group 2, we conducted a confirmatory factor analysis (CFA) using AMOS, version 28.0. Aiming to examine whether self-efficacy to regulate eating behaviors in activation and temptation situations and self-efficacy to regulate eating behaviors in negative emotional situations were empirically distinguishable, we compared the difference in goodness-of-fit between (a) a one-factor model (i.e., factorially indistinct) and (b) a two-factor model (i.e., factorially distinct). Moreover, the models were evaluated through multiple goodness-of-fit indicators, including CFI ≥ 0.95 [The SEREB-C’s reliability (i.e., convergent validity) was assessed using average variance extracted, composite reliability, and alpha and omega coefficients. According to Hair et al. [