Accuracy and precision of responses to visual analog scales: Inter- and intra-individual variability

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García Pérez, Miguel A. and Alcalá Quintana, Rocío (2022) Accuracy and precision of responses to visual analog scales: Inter- and intra-individual variability. Behavior Research Methods . ISSN 1554-3528

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Official URL: https://doi.org/10.3758/s13428-022-02021-0




Abstract

Visual analog scales (VASs) are gaining popularity for collecting responses in computer administration of psychometric tests and surveys. The VAS format consists of a line marked at its endpoints with the minimum and maximum positions that it covers for respondents to place a mark at their selected location. Creating the line with intermediate marks along its length was discouraged, but no empirical evidence has ever been produced to show that their absence does any good. We report a study that asked respondents to place marks at pre-selected locations on a 100-unit VAS line, first when it only had numerical labels (0 and 100) at its endpoints and then when intermediate locations (from 0 to 100 in steps of 20) were also labeled. The results show that settings are more accurate and more precise when the VAS line has intermediate tick marks: The average absolute error decreased from 3.02 units without intermediate marks to 0.82 units with them. Provision of intermediate tick marks also reduced substantially inter- and intra-individual variability in accuracy and precision: The standard deviation of absolute error decreased from 0.87 units without tick marks to 0.25 units with them and the standard deviation of signed distance to target decreased from 1.16 units without tick marks to 0.24 units with them. These results prompt the recommendation that the design of VASs includes intermediate tick marks along the length of the line.


Item Type:Article
Additional Information:

CRUE-CSIC (Acuerdos Transformativos 2022)

Uncontrolled Keywords:Likert scales, Visual analog scales, Pseudoneglect, Accuracy, Precision
Subjects:Medical sciences > Psychology > Psychometrics
ID Code:75716
Deposited On:24 Nov 2022 13:43
Last Modified:25 Nov 2022 08:50

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