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R J De Ayala

Publications and source records attributed to R J De Ayala.

2 recordsLinked to original sources

Estimating person locations from partial credit data containing missing responses.

Certain assessment situations produce partial credit data. For instance, performance assessment items may utilize a rubric that assigns partial credit for some not completely correct responses. In some cases examinees may choose to not answer each question. This study investigated the effect of various strategies for handling these missing responses for estimating a respondent's location. These methods included ignoring the omitted response, selecting the "midpoint" category score, treating the omitted response as incorrect, hotdecking, and a likelihood-based approach. A simulation study was performed to examine the efficacy of these methods with the partial credit and generalized partial credit models. Expected a posteriori (EAP) ability estimation was used. Results showed that the Midpoint and Likelihood procedures performed the best of methods examined. In contrast, omitted responses should not be treated as incorrect nor ignored when estimating an examinee's proficiency using EAP. Implications for practitioners are discussed.

Data Collection↗

The effect of missing data on estimating a respondent's location using ratings data.

In social science research there are a number of instruments that utilize a rating scale such as a Likert response scale. For a number of reasons a respondent's response vector may not contain responses to each item. This study investigated the effect on a respondent's location estimate when a respondent is presented an item, has ample time to answer the item, but decides to not respond to the item. For these situations different strategies have been developed for handling missing data. In this study, four different approaches for handling missing data were investigated for their capability to mitigate against the effect of omitted responses on person location estimation. These methods included ignoring the omitted response, selecting the "midpoint" response category, hot-decking, and a Likelihood-based approach. A Monte Carlo study was performed and the effect of different levels of omissions on the simulees' location estimates was determined. Results showed that the hot-decking procedure performed the best of methods examined. Implications for practitioners were discussed.

Humans↗