Latent interpretation classes as a representation of differential interpretation of vague quantifiers¶

Theory · All theories
Evidence · no claims cited

Description¶

This framework represents individual differences in how respondents interpret vague quantifiers of behavioral frequency as variability between latent interpretation classes, rather than as continuous individual differences. In the article, the framework is applied to data from an experiment embedded in the 2006 National Survey of Student Engagement, where the study "finds that differential interpretation may be best represented as variability between latent interpretation classes." The framework treats interpretation differences as a class-level latent structure that can be separated from substantive measurement.

Design Implications¶

Context¶

Requirements¶

• Requires data from a survey or experiment in which respondents interpret vague quantifiers, suitable for latent variable modeling.

Constraints¶

• The article states the representation is what the analysis finds "may be best represented as," for the 2006 NSSE experiment data.

Target Learners¶

• Survey respondents reporting behavioral frequencies in large-scale student surveys

Target Learning Objectives¶

• Accurate measurement of self-reported behavioral frequency in educational survey research

Claims¶

Related Theories¶

Examples¶

• Use latent variable models to extract differential interpretation as a methodological artifact from measurement models
• Experiment embedded in the 2006 National Survey of Student Engagement
• Experiment embedded in the 2006 National Survey of Student Engagement comparing numerically and vaguely quantified frequency reports

Key Sources¶

• Jamie Griffin. (2013). On the Use of Latent Variable Models to Detect Differences in the Interpretation of Vague Quantifiers. Public Opinion Quarterly, vol. 77, issue S1. https://academic.oup.com/poq