It is a common practice to treat refusals as a missing value and exclude them from data analysis. To avoid biased results obtained from complete cases, imputation and reclassification of refusals into other response categories are frequently used. The appropriateness and effectiveness of different methods, however, remain unclear. This study attempts to compare results among different imputation methods using refusals in a Guttman-type scale as an example. The results indicate that formula for estimating accuracy of single imputation can be derived from the observed frequency of the response patterns that correspond to Guttman-scale types. In addition, refusal rates did not have much impact on the accuracy of simple imputation due to the fixed refusal patterns simulated from the gold standard. On the other hand, the nearest-neighbor method achieves the highest accuracy among the imputation methods examined. Discussions on the imputation results and imputation for further research are provided.