Measuring Income (In)equality: Comparing Survey Questions With Unipolar and Bipolar Scales in a Probability-Based Online Panel

2022-02 | journal article; research paper. A publication of Göttingen

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​Measuring Income (In)equality: Comparing Survey Questions With Unipolar and Bipolar Scales in a Probability-Based Online Panel​
Höhne, J. K.; Krebs, D. & Kühnel, S.-M. ​ (2022) 
Social science computer review40(1) pp. 108​-123​.​ DOI: https://doi.org/10.1177/0894439320902461 

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Authors
Höhne, Jan Karem; Krebs, Dagmar; Kühnel, Steffen-M. 
Abstract
In social science research, unipolar and bipolar scales are commonly used methods in measuring respondents’ attitudes and opinions. Compared to other rating scale characteristics, scale polarity (unipolar and bipolar) and its effects on response behavior have rarely been addressed in previous research. To fill this gap in the literature, we investigate whether and to what extent fully verbalized unipolar and bipolar scales influence response behavior by analyzing observed and latent response distributions and latent thresholds of response categories. For this purpose, we conducted a survey experiment in a probability-based online panel and randomly assigned respondents to a unipolar or bipolar scale condition. The results reveal substantial differences between the two rating scales. They show significantly different response distributions and measurement non-invariance. In addition, response categories (and latent thresholds) of unipolar and bipolar scales are not equally distributed. The findings show that responses to unipolar and bipolar scales differ not only on the observational level but also on the latent level. Both rating scales vary with respect to their measurement properties, so that the responses obtained using each scale are not easily comparable. We recommend not considering unipolar and bipolar scales as interchangeable.
Issue Date
February-2022
Journal
Social science computer review 
Organization
Sozialwissenschaftliche Fakultät ; Methodenzentrum Sozialwissenschaften ; Abteilung Quantitative Methoden und Statistik 
ISSN
0894-4393
eISSN
1552-8286
ISSN
0894-4393
eISSN
1552-8286
Language
English

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