Literature DB >> 18480508

Effects of varying magnitude and patterns of response dependence in the unidimensional Rasch model.

Ida Marais1, David Andrich.   

Abstract

By adding items with responses identical to a selected item, Smith (2005) investigated the effect of the response dependence on person and item parameter estimates in the dichotomous Rasch model. By varying the magnitude of response dependence among selected items, rather than their having perfect dependence, this paper provides additional insights into the effects of response dependence on the same estimates in the same model. Two sets of simulations are reported. In the first set, responses to all items except the first were dependent on either the first item or on the immediately preceding item; in the second set, subsets of items were formed first, and then within each of these subsets, responses to all items in a subset except the first were dependent on either the first item or on the immediately preceding item. The effects of dependence were noticeable in all of the statistics reported. In particular, the fit statistics and the parameter estimates showed increasing discrepancies from their theoretical values as a function of the magnitude of the dependence. In some cases, however, two related statistics gave the impression of improvement as a function of increased dependency; first the standard deviation of person estimates showed an increase, and second the index analogous to traditional reliability showed relative increase. In addition to the estimates and depending on the structure and magnitude of the dependence, the person distribution was affected systematically, ranging from becoming skewed to becoming bimodal. The effects on the distribution help explain some of the effects on the statistics reported. In the case of the second set of simulations in which the dependence is within subsets of items, it is possible to take account of the response dependence. This is done by summing the responses of the items within each subset to form a polytomous item and then analyzing the data in terms of a smaller number of polytomous items. This way of accounting for dependence, in which the maximum score for the test as a whole remains the same, gives a more accurate value of the reliability and a more realistic distribution of the person estimates than when the dependence within subsets of items is not taken into account.

Entities:  

Mesh:

Year:  2008        PMID: 18480508

Source DB:  PubMed          Journal:  J Appl Meas        ISSN: 1529-7713


  22 in total

1.  Assessing the invariance of a culturally competent multi-lingual unmet needs survey for immigrant and Australian-born cancer patients: a Rasch analysis.

Authors:  J A McGrane; P N Butow; M Sze; M Eisenbruch; D Goldstein; M T King
Journal:  Qual Life Res       Date:  2014-05-24       Impact factor: 4.147

2.  Critical Values for Yen's Q3: Identification of Local Dependence in the Rasch Model Using Residual Correlations.

Authors:  Karl Bang Christensen; Guido Makransky; Mike Horton
Journal:  Appl Psychol Meas       Date:  2016-11-16

3.  Validation of the WHOQOL-BREF quality of life questionnaire for general use in New Zealand: confirmatory factor analysis and Rasch analysis.

Authors:  Christian U Krägeloh; Paula Kersten; D Rex Billington; Patricia Hsien-Chuan Hsu; Daniel Shepherd; Jason Landon; Xuan Joanna Feng
Journal:  Qual Life Res       Date:  2012-09-15       Impact factor: 4.147

4.  Rasch analysis on OSCE data : An illustrative example.

Authors:  E Tor; C Steketee
Journal:  Australas Med J       Date:  2011-06-30

5.  Polytomous Testlet Response Models for Technology-Enhanced Innovative Items: Implications on Model Fit and Trait Inference.

Authors:  Hyeon-Ah Kang; Suhwa Han; Doyoung Kim; Shu-Chuan Kao
Journal:  Educ Psychol Meas       Date:  2021-08-02       Impact factor: 3.088

6.  Uncovering indicators of the international classification of functioning, disability, and health from the 39-item Parkinson's disease questionnaire.

Authors:  Maria H Nilsson; Albert Westergren; Gunilla Carlsson; Peter Hagell
Journal:  Parkinsons Dis       Date:  2010-07-12

7.  Is the pain visual analogue scale linear and responsive to change? An exploration using Rasch analysis.

Authors:  Paula Kersten; Peter J White; Alan Tennant
Journal:  PLoS One       Date:  2014-06-12       Impact factor: 3.240

8.  Internal construct validity of the stress-energy questionnaire in a working population, a cohort study.

Authors:  Emina Hadzibajramovic; Gunnar Ahlborg; Anna Grimby-Ekman; Åsa Lundgren-Nilsson
Journal:  BMC Public Health       Date:  2015-02-25       Impact factor: 3.295

9.  Measuring the bright side of being blue: a new tool for assessing analytical rumination in depression.

Authors:  Skye P Barbic; Zachary Durisko; Paul W Andrews
Journal:  PLoS One       Date:  2014-11-14       Impact factor: 3.240

10.  Measuring Neurobehavioral Functioning in People With Traumatic Brain Injury: Rasch Analysis of Neurobehavioral Functioning Inventory.

Authors:  Karol J Czuba; Paula Kersten; Nicola M Kayes; Greta A Smith; Suzanne Barker-Collo; William J Taylor; Kathryn M McPherson
Journal:  J Head Trauma Rehabil       Date:  2016 Jul-Aug       Impact factor: 2.710

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.