Within the last year
Within the past 3 years
1 - 8 of 8 articles
In this study of polytomous items, the number of items and categories per item were varied to explore the effects on estimation of item parameters in the nominal response model. De Ayala and Sava-Bolesta's (1999) work suggested that the ratio of the sample size to the total number of item...
Courses in introductory educational measurement are often hampered by the lack of computer programs by which to analyze test data. To be sure, computer software exists (e.g., SPSS, SAS, Iteman) that performs these analyses; however, these programs come at a high cost and are not designed for...
A method based on 0-1 linear programming (LP) is presented to stratify an item pool optimally for use in ॅ-stratified adaptive testing. Because the 0-1 LP model belongs to the subclass of models with a network flow structure, efficient solutions are possible. The method is applied to a previous...
This study presents new findings on the utility of S - X 2 as an item fit index for dichotomous item response theory models. Results are based on a simulation study in which item responses were generated and calibrated for 100 tests under each of 27 conditions. The item fit indices S - X 2 and Q...
To increase the number of items available for adaptive testing and reduce the cost of item writing, the use of techniques of item cloning has been proposed. An important consequence of item cloning is possible variability between the item parameters. To deal with this variability, a multilevel...
Read and print from thousands of top scholarly journals.
Sign up with Facebook
Sign up with Google
Already have an account? Log in
Save this article to read later. You can see your Read Later on your DeepDyve homepage.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Sign Up Log In
To subscribe to email alerts, please log in first, or sign up for a DeepDyve account if you don’t already have one.
To get new article updates from a journal on your personalized homepage, please log in first, or sign up for a DeepDyve account if you don’t already have one.