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Rice University

Measuring and predicting extreme response style: A latent class approach

Abstract

dc:description.abstract

The purpose of this study was to explore various ways to predict and measure extreme response style, or overuse of endpoint categories in rating scales. Data was collected from a total of 913 regular participants and 240 peer participants, who completed an online battery of self-report and peer report questionnaires respectively. In addition to verifying the stability and generality of extreme responding, extreme response style was related to two personality predictors: intolerance of ambiguity and decisiveness. Both main effects and interactive effects with speed of survey completion were uncovered. Extreme response style was measured with several simple proportional methods, which were all shown to tap a latent factor of response extremity, and a latent class method, which did not achieve significant relationships with the personality predictors.

Degree

thesis:*
Name thesis:degree_name
Master of Arts
Level thesis:degree_level
Masters
Discipline thesis:degree_discipline
Social Sciences
Grantor
Rice University
Year dc:date.issued
2006

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Naemi, Bobby Darius
Advisor dc:contributor.advisor
  • Beal, Daniel

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1911/17901
OAI identifier oai:identifier
oai:repository.rice.edu:1911/17901

Chain of custody

source
Harvested from
Rice University
Base URL
repository.rice.edu/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Naemi, Bobby Darius. Measuring and predicting extreme response style: A latent class approach. Masters thesis, Rice University, 2006. https://hdl.handle.net/1911/17901