Google Research

Response Option Order Effects in Cross-Cultural Context. An experimental investigation

2019 Conference of the European Association for Survey Research (ESRA), Zagreb (2019) (to appear)

Abstract

Response option order effect occurs when different orders of rating scale response options lead to different distribution or functioning of survey questions. Theoretical interpretations, notably satisficing, memory bias (Krosnick & Alwin, 1987) and anchor-and-adjustment (Yan & Keusch, 2015) have been used to explain such effects. Visual interpretive heuristics (esp. “left-and-top-mean-first” and “up-means-good”) may also provide insights on how positioning of response options may affect answers (Tourangeau, Couper, & Conrad, 2004, 2013). Most existing studies that investigated the response option order effect were conducted in mono-cultural settings. However, the presence and extent of response option order effect may be affected by “cultural” factors in a few ways. First, interpretive heuristics, such as “left-means-first” may work differently due to varying reading conventions (e.g., left-to-right vs. right-to-left). Furthermore, people within cultures where there are multiple primary languages and multiple reading conventions might possess different positioning heuristics. Finally, respondents from different countries may have varying degree of exposure and familiarity to a specific type of visual design. In this experimental study, we investigate rating scale response option order effect across three countries with different reading conventions and industry norms for answer scale designs -- US, Israel, Japan. The between-subject factor of the experiment consists of four combinations of scale orientation (vertical and horizontal) and the positioning of the positive end of the scale. The within-subject factors are question topic area and the number of scale points. The effects of device (smartphone vs. desktop computer/tablet), age, gender, education, and the degree of exposure to left-to-right contents will also be evaluated. We incorporate a range of analytical approaches: distributional comparisons, analysis of response latency and paradata, and latent structure modeling. We will discuss implications on choosing response option orders for mobile surveys and on comparing data obtained from different response option orders.

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