An Algorithm for Converting Ordinal Scale Measurement Data to Interval/Ratio Scale

The extensive use of survey instruments in the social sciences has long created debate and concern about validity of outcomes, especially among instruments that gather ordinal-level data. Ordinal-level survey measurement of concepts that could be measured at the interval or ratio level produce errors because respondents are forced to truncate or round off their responses to fit a given ordinal scale. This article presents a Markov chain Monte Carlo modeling technique that converts ordinal measurements to interval/ ratio. Simulated data demonstrate the robustness of this technique, and implications of this technique are discussed.