Survey data are unavoidably contaminated with measurement error. We focus on formatting error, the increase in con dential intervals of survey estimates, making it more difficult to detect existing dierences. Formatting error happens when respondents do not fi nd an option on a rating scale that perfectly reflects their true value. The dierence between the true value and the chosen response option is formatting error.

From a theoretical point of view, continuous visual analogue scales (VASs) have on the individual as well as on the aggregated level an expected formatting error of zero, because there is a perfectly tting option for every graduation of the true value. An empirical determination of formatting error with VASs is pending and it is unclear, if populations with a low formal education are able to use VASs in a meaningful way.

Formatting error with categorical scale is dierent for single individual variables and for aggregate data. In the first case, it depends on the number of categories only. In the second case it is additionally in uenced by the actual distribution of values in the sample. We simulated differently distributed data (e.g. from uniform distributions, narrow and wide normal distributions, chi-square and exponential distributions) to determine the expected formatting error with categorical scales consisting of 3 to 21 categories. Overall (N = 1909), we found a very low mean empirical formatting error of M = -1.24 percentage points (SD = 3.05). Ratings on plain VASs without any marker (n = 167) were worse (M = -2.48, SD = 2.64) and formal education (below college: M = -2.87, SD = 2.93; at least college: M = -1.71, SD = 1.61) made a statistically signi cant dierence: F(1, 166) = 7.56, p < .01, eta2 = .04. VASs with ten markers (n = 181) lead to the smallest formatting error (M = -0.41, SD = 1.55) for respondents with a low education (M = -0.47, SD = 1.63) and respondents with a high formal education (M = -0.30, SD = 1.39), F(1, 180) < 1. For individual variables, the empirical formatting error for VASs with ten markers is even with respondents with a low formal education lower than the expected formatting error for categorical scales up to 50 options. Overall, the authors strongly recommend considering VASs in computer-based self-administered questionnaires for the sake of more con dent survey estimates.