Skip to main content
Kent Academic Repository

Ordinal Factor Analysis of Graded-Preference Questionnaire Data

Brown, Anna, Maydeu-Olivares, Alberto (2018) Ordinal Factor Analysis of Graded-Preference Questionnaire Data. Structural Equation Modeling: A Multidisciplinary Journal, 25 (4). pp. 516-529. ISSN 1532-8007. (doi:10.1080/10705511.2017.1392247) (KAR id:63990)


We introduce a new comparative response format, suitable for assessing personality and similar constructs. In this “graded-block” format, items measuring different constructs are first organized in blocks of 2 or more; then, pairs are formed from items within blocks. The pairs are presented one at a time, to enable respondents expressing the extent of preference for one item or the other using several graded categories. We model such data using confirmatory factor analysis (CFA) for ordinal outcomes. We derive Fisher information matrices for the graded pairs, and supply R code to enable computation of standard errors of trait scores. An empirical example illustrates the approach in low-stakes personality assessments and shows that similar results are obtained when using graded blocks of size 3 and a standard Likert format. However, graded-block designs may be superior when insufficient differentiation between items is expected (due to acquiescence, halo or social desirability).

Item Type: Article
DOI/Identification number: 10.1080/10705511.2017.1392247
Uncontrolled keywords: Thurstonian IRT model, ipsative data, graded preferences, graded response model
Subjects: B Philosophy. Psychology. Religion > BF Psychology
H Social Sciences > HA Statistics
Divisions: Divisions > Division of Human and Social Sciences > School of Psychology
Depositing User: Anna Brown
Date Deposited: 19 Oct 2017 16:14 UTC
Last Modified: 04 Mar 2024 15:44 UTC
Resource URI: (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.