Americans’ attitudes toward foreign accents: evaluative hierarchies and underlying processes

ABSTRACT

This study examined Americans’ attitudes toward standard American English (SAE) and nine, non-Anglo foreign accents: Arabic, Farsi, French, German, Hindi, Hispanic, Mandarin, Russian, and Vietnamese. Compared to SAE speakers, all foreign-accented speakers were rated as harder to understand, more likely to be categorised as foreign (rather than American), and attributed less status and solidarity. However, not all foreign accents were equally denigrated on status and solidarity traits. Instead, an evaluative hierarchy emerged, with speakers of some varieties (e.g. French, German) consistently rated more favourably than speakers of others (e.g. Arabic, Farsi, Vietnamese). This variation in language attitudes was associated with variation in social categorisation – i.e. the higher the percentage of nonstigmatized foreign categorizations (i.e. Anglosphere, Western Europe) for a given foreign variety, the more favourably speakers of that variety were rated – and listeners’ processing fluency – i.e. the easier speakers of a given foreign variety were to understand, the more favourably they were rated.

KEYWORDS:

• Language attitudes
• foreign accent
• social categorisation
• stereotypes
• intergroup
• processing fluency
• metacognition

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 For all repeated measures ANOVAs throughout this manuscript, Greenhouse-Geisser estimates were used when the assumption of sphericity was violated. In all instances, this had no bearing on the significance of results. Consequently, original degrees of freedom are reported throughout the manuscript for clarity and consistency.

2 Thirty-four participants categorised all speakers as stigmatised or all speakers as nonstigmatized and were excluded from these analyses.

3 With this method, Pearson correlation coefficients are first converted into Fisher’s z-scores (Fisher 1921), which are used to calculate the average and confidence intervals, and then these values are converted back into Pearson correlation coefficients.

Additional information

Funding

This work was supported by a Research and Creative Activities Program (RCAP) grant from the College of Communication and Information at the University of Kentucky.