Development and Validation of the Perceived Linguistic Microaggression Scale

Linguistic microaggressions, subtle and often unintentional verbal slights rooted in language bias, remain understudied in multilingual contexts because existing measures capture only overt and generalized forms of language-based discrimination. This study developed and validated the Perceived Linguistic Microaggression Scale (PLMS), a context-sensitive measure capturing subtle, interactional exclusions related to language use. In Stage 1, in-depth semistructured interviews with Mandarin and regional dialect speakers in China generated an initial pool of 15 items grounded in three categories: microassaults (e.g., accent mockery), microinsults (e.g., competence judgments), and microinvalidations (e.g., exclusion from conversations). A panel of experts then refined this pool for redundancy and clarity, resulting in a 10-item instrument. Stage 2 employed exploratory factor analysis ( N = 264), revealing a unidimensional structure, indicating that these diverse expressions of linguistic bias cohere as a single underlying construct. Stage 3 used confirmatory factor analysis ( N = 267) that supported this single-factor model (CFI = 0.968; TLI = 0.960; RMSEA = 0.07; SRMR = 0.03). The PLMS demonstrated strong internal consistency (α = .78) and measurement invariance across gender, region, and dialect groups. Construct validity was established through significant correlations with psychological outcomes: higher PLMS scores were associated with elevated depression ( r = .46, p < .001) and anxiety ( r = .26, p < .001), and with lower self-efficacy ( r = −.22, p < .001), resilience ( r = −.19, p < .001), and life satisfaction ( r = −.15, p < .001). These results confirm the PLMS as a reliable, valid tool for assessing everyday linguistic microaggressions. The PLMS equips researchers and practitioners to identify, quantify, and ultimately mitigate the subtle mechanisms that reinforce social hierarchies and undermine inclusion in linguistically diverse communities.