Psychometric analysis of performance on categories of client needs and nursing process with the NLN Diagnostic Readiness Test.
This article provides psychometric analysis of the performance of nursing students on categories of client needs (CN) and nursing process (NP) measured by the NLN Diagnostic Readiness Test (NLN-DRT) for RN licensure. While analyses of items and number-right score performance with NLN tests are well documented, the analysis of proficiency on categories that organize items at the conceptual level is limited to reporting basic classical statistics (e.g., proportion of correct scores and percentiles). The psychometric analysis of proficiency on categories of CN and NP in this article is based on item response theory and takes into account that the binary scores on these 2 categories (1 = mastered, 0 = nonmastered) are obtained through summative standardized scoring and not through direct responses of examinees. NLN-DRT data for a local population of 646 students enrolled in an NLN accredited associate degree program was obtained 3 weeks prior to graduation. This article illustrates the application of IRT using the Rasch Model and the 2-parameter logistic model in a method of psychometric analysis that deals with proficiency on conceptual categories and provides measurement feedback to nursing educators for curriculum (or instruction) intervention in a specific educational context. Among the components of such feedback provided in this article are: (a) difficulty, discrimination, and characteristic curves of CN and NP categories, (b) performance patterns by level of success on each category, and (c) domain scores by ability levels for the population of nursing students. The Rasch Model, which was calculated using RASCAL, did not fit the data with the categories of CN or NP at the .05 level of statistical significance. However, the 2-parameter logistic model fit the data with both CN and NP categories while using the XCALIBRE computer program. The IRT approach used in this article demonstrated some measurement perspectives on linking an instrument's conceptual base to theory in the context of nursing education, and provide valuable measurement feedback for improving the quality of curriculum and teaching in institutions that use the NLN-DRT in their assessment practice.
Dimitrov
Dimiter M
DM
Graduate School of Education, George Mason University, Fairfax, VA 22030-4444, USA. ddimitro@gmu.edu
Shelestak
Debra
D
D004507:Education, Nursing, Associate; D004521:Educational Measurement; D006801:Humans; D016015:Logistic Models; D020380:Needs Assessment; D009736:Nursing Process; D011594:Psychometrics; D014481:United States