Analytic strategy

Stata/SE 16 was used to analyze data from the online survey. First, a descriptive analysis of sociodemographic variables was conducted and is presented in Table 1. Next, I examined the response distribution for each item on the scales, as well as bivariate analyses on the links between mental health symptoms and key independent variables, which can be found in the Supplementary materials. Finally, hierarchical multiple regression models were estimated to examine the relationships between acculturative stress, everyday racism, and mental health among South Asian respondents and presented in Tables 2, ​,3.3. This technique is useful for comparing different statistical models and can demonstrate if certain predictors explain a significant amount of variance in the outcome(s) of interest after accounting for all other variables (42). One strength of this approach is that the researcher can select the order in which the variables are entered, based on a theoretical rationale and/or their research questions (43). Hierarchical multiple regression models have been used in other studies that examine acculturation, discrimination, and/or mental health (25, 44, 45)—and in the context of this investigation—can reveal if everyday racism explains variance in depressive or anxiety-related symptoms, above and beyond the effect of acculturative stress.

Descriptive statistics.

Hierarchical multiple regression models predicting anxiety-related symptoms (n = 200).

* p < 0.05, **p < 0.01.

Hierarchical multiple regression models predicting depressive symptoms (n = 200).

*p < 0.05, **p < 0.01, ***p < 0.001.