No magic bullet: estimating anti-immigrant sentiment and social desirability bias with the item-count technique

S Rinken,M Rueda,B Cobo,S Pasadas-Del-Amo

doi:10.1007/s11135-021-01098-7

S Rinken, M Rueda + Show 2 more

Open Access

https://doi.org/10.1007/s11135-021-01098-7

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Abstract

Extant scholarship on attitudes toward immigration and immigrants relies mostly on direct survey items. Thus, little is known about the scope of social desirability bias, and even less about its covariates. In this paper, we use probability-based mixed-modes panel data collected in the Southern Spanish region of Andalusia to estimate anti-immigrant sentiment with both the item-count technique, also known as list experiment, and a direct question. Based on these measures, we gauge the size of social desirability bias, compute predictor models for both estimators of anti-immigrant sentiment, and pinpoint covariates of bias. For most respondent profiles, the item-count technique produces higher estimates of anti-immigrant sentiment than the direct question, suggesting that self-presentational concerns are far more ubiquitous than previously assumed. However, we also find evidence that among people keen to position themselves as all-out xenophiles, social desirability pressures persist in the list-experiment: the full scope of anti-immigrant sentiment remains elusive even in non-obtrusive measurement.

Highlights

Known technically as social desirability bias (SDB), the divergence of stated from true scores affects any survey on behaviors or attitudes that some interviewees are wary to disclose
This study focuses on anti-immigrant sentiment (AIS), the affective core of xenophobia, as dependent and compares the estimates produced using a direct question with those obtained by an unobtrusive question format, namely, via item-count technique (ICT)
On account of the “more is better” approach, we deduce that ICT reduces SDB substantially: in relative terms, about 40% of prevalent AIS, as detected by ICT, goes unobserved in obtrusive measurement

Summary

Introduction

Known technically as social desirability bias (SDB), the divergence of stated from true scores affects any survey on behaviors or attitudes that some interviewees are wary to disclose. Unless SDB is eluded by specific survey techniques, unfavorable attitudes are prone to be underestimated by unknown margins. Given this methodological challenge, one would expect ATII scholars to pioneer research on bias-reducing procedures. Much extant scholarship relies on expansive notions of prejudice as an alleged antidote against unrealistically low animosity estimates: any unfavorable opinion on international migration is commonly accepted as telltale of anti-foreigner prejudice. Such dilution of the focal construct does not reliably detect hostile views. The combination of “false negatives” and “false positives” suggests that expansive notions of prejudice are methodologically flawed

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