Psychometrics & post-data analysis : a software implementation for binary logistic regression in jamovi
department or school: Education, Faculty of
department or school: Educational and Counselling Psychology, and Special Education (ECPS), Department of
data provider: DSpace
degree campus: UBCV
creator: Friesen, Lucas
issued: 2019-08-15T20:49:05Z
issued: 2019
related degree: Master of Arts - MA
degree grantor: University of British Columbia
description: Differential item functioning (DIF) analysis is an invaluable tool for providing evidence that a test or measure is functioning as intended, and has been used extensively to show evidence that the items of tests are (not) favouring one group of test takers over another. Beyond evidence (for) against differential performance of items, DIF analysis provides extremely useful validation evidence in psychosocial measures because its application need not be limited to simple cases like, for example, detecting DIF based on a binary gender classification. However, the technical knowledge requirements of DIF analysis software can be prohibitively high in some research settings. The jamovi suite “Psychometric Post-Data Analysis” has been developed with a module for performing DIF analysis on dichotomously scored items using a generalized linear model framework (GLIM) that incorporates the GLIM family of methods for detecting DIF in a user- friendly interface. In addition, the idea of Type-M errors (Gelman & Carlin, 2014) was adapted and expanded to the DIF context, and then incorporated into the module. This tool is especially useful in the context of low-powered research settings. The goal of developing this module is to encourage the use of the GLIM framework for DIF analysis in the broader psychosocial measure validation praxis by lowering the technical barrier to assessing DIF and increasing the interpretability of DIF analysis results via post-data analytic techniques.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/71304
note: Psychometrics & Post-Data Analysis: A Software Implementation for Binary Logistic Regression in Jamovi by Lucas Friesen B.A., Simon Fraser University, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in The Faculty of Graduate and Postdoctoral Studies (Measurement, Evaluation, and Research Methodology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2019 © Lucas Friesen, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Psychometrics & Post-Data Analysis: A Software Implementation for Binary Logistic Regression in Jamovi submitted by Lucas Friesen in partial fulfillment of the requirements for the degree of Master of Arts in Measurement, Evaluation, and Research Methodology Examining Committee: Dr. Bruno D. Zumbo Supervisor Dr. Edward Kroc Supervisory Committee Member Dr. Richard Sawatzky Additional Examiner iii Abstract Differential item functioning (DIF) analysis is an invaluable tool for providing evidence that a test or measure is functioning as intended, and has been used extensively to show evidence that the items of tests are (not) favouring one group of test takers over another. Beyond evidence (for) against differential performance of items, DIF analysis provides extremely useful validation evidence in psychosocial measures because its application need not be limited to simple cases like, for example, detecting DIF based on a binary gender classification. However, the technical knowledge requirements of DIF analysis software can be prohibitively high in some research settings. The jamovi suite “Psychometric Post-Data Analysis” has been developed with a module for performing DIF analysis on dichotomously scored items using a generalized linear model framework (GLIM) that incorporates the GLIM family of methods for detecting DIF in a user-friendly interface. In addition, the module contains …
has type: Thesis/Dissertation
date issued: 2019-09
is shown at: 10.14288/1.0380439
language: eng
degree discipline: Measurement, Evaluation and Research Methodology
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/71304