The purposes of this study were: 1) to show the item difficulty hierarchy of walking/moving construct of the International Classification of Functioning, Disability and Health-Activity Measure (ICF-AM), 2) to evaluate the item-level psychometrics for model fit, 3) to describe the relevant physical activity defined by level of activity intensity expressed as Metabolic Equivalent of Tasks (MET), and 4) to explore what extent the empirical activity hierarchy of the ICF-AM is linked to the conceptual model based on the level of energy expenditure described as MET. One hundred and eight participants with lower extremity impairments were examined for the present study. A newly created activity measure, the ICF-AM using an item response theory (IRT) model and computer adaptive testing (CAT) method, has a construct on walking/moving construct. Based on the ICF category of walking and moving, the instrument comprised items corresponding t walking short distances, walking long distances, walking on different surfaces, walking around objects, climbing, and running. The item difficulty hierarchy was created using Winstep software for 20 items. The Rasch analyses (1-parameter IRT model) were performed on participants with lower extremity injuries who completed the paper and pencil version of walking/moving construct of the ICF-AM. The classification of physical activity can also be performed by the use of METs that is often preferred to determine the level of physical activity. The empirical item hierarchy of walking, climbing, running activities of the ICF-AM instrument was similar to the conceptual activity hierarchy based on the METs. The empirically derived item difficulty hierarchy of the ICF-AM may be useful in developing MET-based activity measure questionnaires. In addition to convenience of applying items to questionnaires, implications of the finding could lead to the use of CAT method without sacrificing the objectivity of physiologic measures.

The researchers’ experiential knowledge demonstrates that the task of predicting and controlling the difficulty level of the multiple-choice items of the College Scholastic Ability Test (CSAT) for English is substantially left to the subjective judgment of experienced item writers. The present study accordingly recognizes a need to identify item difficulty predictors and build an item difficulty prediction model to handle this pertinent issue. While taking separate interest in constructing a model for the multiplechoice reading subset of the CSAT, the study was conducted by identifying item difficulty predictor variables from previous research, and by validating the candidate predictors via questionnaires by highly experienced teacher-raters when asked to analyze reading items from the English reading subset of the preliminary CSAT (i.e., yun-hap-hak-ryuk-pyung-ga) administered in March 2009. Using multiple regression technique and maximum likelihood estimation, an item difficulty prediction model was generated. In order to check validity and applicability of the prediction model, the hypothetical model was finally tested on a subsequent version of the test administered in September 2009. This type of model building is expected to guide test developers design an item pool in accordance with special needs, such as to construct multiple test forms, which have similar mean difficulties.

The purpose of this study was to develop a statistical model of predicting item difficulty of the English reading test of College Scholastic Ability Test(CSAT). At the initial phase, the study investigated variables that were significantly correlated to item difficulty of English reading test. Using the correlated variables an instrument was designed to gather the data on item difficulty of each item of the English reading tests of 2002 and 2003 CSAT. Correlation analysis was done to get models which could predict item difficulty of the tests. To validate the results of the analysis, the models were applied to the September Tests of 2003 and 2004 CSAT. It was found that the 2003 linear regression equation model showed the acceptable level of stability and predictability. The study also revealed that statistically significant predictors were grammar, inference and options. It was also found that the proportion of variance accounted by the predictors was different. Some implications for CSAT design and further research were drawn from the findings.