Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Author "Koch, William R."

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Computerized adaptive testing with polytomous items
    (1995) Dodd, Barbara G.; De Ayala, R. J.; Koch, William R.
    Polytomous item response theory models and the research that has been conducted to investigate a variety of possible operational procedures for polytomous model-based computerized adaptive testing (CAT) are reviewed. Studies that compared polytomous CAT systems based on competing item response theory models that are appropriate for the same measurement objective, as well as applications of polytomous CAT in marketing and educational psychology, also are reviewed. Directions for future research using polytomous model-based CAT are suggested. Index terms: computerized adaptive testing, polytomous item response theory, polytomous scoring.
  • Thumbnail Image
    Item
    Effects of variations in item step values on item and test information in the partial credit model
    (1987) Dodd, Barbara G.; Koch, William R.
    Simulated data were used to investigate systematically the impact of various orderings of step difficulties on the distribution of item information for the partial credit model. It was found that the distribution of information for an item was a function of (1) the range of the step difficulty values, (2) the number of step difficulties that were out of sequential order, and (3) the distance between the step values that were out of order. Also, by using relative efficiency comparisons, the relationship between the step estimates and the distribution of item information was used to demonstrate the effects of various test revisions (through the addition and/or deletion of items with specific step characteristics) on the resulting test’s precision of measurement. The usefulness of item and test information functions for specific measurement applications of the partial credit model is also discussed.
  • Thumbnail Image
    Item
    Likert scaling using the graded response latent trait model
    (1983) Koch, William R.
    The two-parameter graded response latent trait model was applied to data collected from a conventionally constructed Likert-type attitude scale. Comparisons were made of both the person latent trait estimates and the item parameter estimates with their counterparts from the conventional scaling method. Also studied were the goodness of fit of the graded response model and the information function feature of the model indicating the precision of measurement at each level of the attitude trait continuum. The results demonstrated that the graded response model could be successfully used to perform attitude measurement for Likert scales.
  • Thumbnail Image
    Item
    Operational characteristics of adaptive testing procedures using the graded response mode
    (1989) Dodd, Barbara G.; Koch, William R.; De Ayala, Ralph J.
    The purpose of the present research was to develop general guidelines to assist practitioners in setting up operational computerized adaptive testing (CAT) systems based on the graded response model. Simulated data were used to investigate the effects of systematic manipulation of various aspects of the CAT procedures for the model. The effects of three major variables were examined: item pool size, the stepsize used along the trait continuum until maximum likelihood estimation could be calculated, and the stopping rule employed. The findings suggest three guidelines for graded response CAT procedures: (1) item pools with as few as 30 items may be adequate for CAT; (2) the variable-stepsize method is more useful than the fixed-stepsize methods; and (3) the minimum-standard-error stopping rule will yield fewer cases of nonconvergence, administer fewer items, and produce higher correlations of CAT θ estimates with full-scale estimates and the known θs than the minimum-information stopping rule. The implications of these findings for psychological assessment are discussed. Index terms: computerized adaptive testing, graded response model, item response theory, polychotomous scoring.