Abstract

Likert-type scales require individuals to choose between a limited number of choices, and have been criticized in the scholarly literature for causing loss of information, allowing the researcher to affect responses by determining the range, and being ordinal in nature. The use of online surveys allows for the easy implementation of continuous rating scales, which have a long history in psychophysical measurement but are rarely used in IS surveys. This type of measurement requires survey participants to express their opinion in a visual form. That not only solves the problems of information loss, but also allows for applying advanced robust statistical analyses. We use a real-world sample and a simulation to illustrate how noise impacts our data set. A noise level of 10% has only a small effect on both classical and robust estimates, but when 20% of noise is added, the classical estimators become severely distorted.

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Benefits from Using Continuous Rating Scales in Online Survey Research

Likert-type scales require individuals to choose between a limited number of choices, and have been criticized in the scholarly literature for causing loss of information, allowing the researcher to affect responses by determining the range, and being ordinal in nature. The use of online surveys allows for the easy implementation of continuous rating scales, which have a long history in psychophysical measurement but are rarely used in IS surveys. This type of measurement requires survey participants to express their opinion in a visual form. That not only solves the problems of information loss, but also allows for applying advanced robust statistical analyses. We use a real-world sample and a simulation to illustrate how noise impacts our data set. A noise level of 10% has only a small effect on both classical and robust estimates, but when 20% of noise is added, the classical estimators become severely distorted.