Examining Local Item Dependence Effects in a Large-Scale Science Assessment by a Rasch Partial Credit Model.

Context-dependent items are traditionally analyzed independently, creating a situation in which the potential local item dependence effects among these items may cause a biased estimation of examinees' abilities. This study investigated the local item dependence effects on testlets in the tryout version of a statewide science assessment by a Rasch partial credit model. Cluster sampling combined with stratified sampling was used. Data were analyzed in five different configurations to study the relationships between context-dependent items at the individual item level and at the testlet level. It is shown that local dependence effects may be controlled and a better fit for testlet calibration can be obtained by employing the Rasch partial credit model for some, but not all testlets. (Contains 2 figures, 11 tables, and 35 references.) (Author/SLD)