Statistical Analysis
Statistical analysis was performed using IBM SPSS (28.0; IBM Corp, 2017), MATLAB (version R2020a, MathWorks), and JASP (Version 0.16; JASP Team, 2021).
To assess if the severity of misophonia predicts the prevalence of mimicry, we employed binomial logistic regression with mimicking (yes/no) as a response variable and severity of misophonia (range 0 to 15) as the explanatory variable. We also explored whether the tendency to mimic depends on any particular sound category. To assess this, we used a multivariable logistic regression with mimicking (yes/no) as a response variable and the distress experienced on 7 different sound categories (people eating, repetitive tapping, rustling, nasal sounds, throat sounds, consonant/vowels, environmental sounds) as explanatory variables.
Misophonia scores for different sound categories tend to be correlated: somebody triggered by eating sounds could also be triggered by nasal/throat sounds and other related sounds as well. A multivariate analysis using principal component analysis (PCA) can take into consideration the correlation among variables and aims to find common latent variable/s underlying the measured variables. We ran PCA with promax rotation on the data consisting of 8 variables (distress on 7 sound categories and mimicry) from 676 participants. Component loadings > 0.5 were considered meaningful and significant.