{Reference Type}: Journal Article
{Title}: F-type testlets and the effects of feedback and case-specificity.
{Author}: Baldwin P;Baldwin SG;Haist SA;
{Journal}: Acad Med
{Volume}: 86
{Issue}: 10
{Year}: Oct 2011
{Factor}: 7.84
{DOI}: 10.1097/ACM.0b013e31822a6aa2
{Abstract}: BACKGROUND: A novel type of item sets, "f-type" testlets, was recently introduced on the United States Medical Licensing Examination. These testlets contain two or more questions associated with a common clinical scenario. In some cases, as the scenario unfolds, examinees are indirectly provided with feedback about their response to a testlet question. The effects of this format and of the provision of feedback to examinees about their performance are investigated.
METHODS: Examinee behavior is predicted using an item response model, and observed examinee responses are compared with model expectations for f-type testlets. Mean model-data discrepancies among specific examinee groups are compared to study the dependencies across within-testlet items (i.e., case-specificity) and the impact of providing feedback.
RESULTS: Findings showed that case-specificity effects were present (on average) for all examinee subgroups except examinees who both responded unsuccessfully to the initial item within an f-type testlet and received feedback. Case-specificity effects were negative for examinees who responded unsuccessfully to the initial testlet item but did not receive feedback. For those who responded successfully to the initial testlet items, case-specificity effects were positive.
CONCLUSIONS: Results suggest that responses to test questions within an f-type testlet are not independent-even after accounting for examinee proficiency and item characteristics. Case-specificity effects (i.e., dependencies) were observed on average for all examinees except those who both responded unsuccessfully to the initial item within an f-type testlet and received feedback. Research into modeling these effects through the use of more general item response models is recommended.