To pool or not to pool: Accounting for task non-attendance in subgroup analysis

Juan Marcos Gonzalez, F. Reed Johnson and Eric Finkelstein

Journal of choice modelling, 2024, vol. 51, issue C

Abstract: Pooling data from different subgroups offers advantages of shrinking standard errors and simplifying characterization of the data structure. The ability to pool data also facilitates meta-analysis to evaluate consensus among multiple studies and to inform benefit transfer to new choice settings. Testing for poolability requires accounting for differences in response variance or scale among subgroups. This is commonly done by assuming a single scale factor within each subgroup of interest. This assumption may not hold for many subgroups, especially when task non-attendance is present. We use data from a prior DCE study to show that task non-attendance, and by extension the assumption of a single scale factor across subgroups, can lead to inaccurate conclusions when determining poolability. To address this concern, we propose a latent-class/random-parameters Logit (LCRP) model specification that accommodates task non-attendance or other causes of scale differences within subgroups and directly tests for poolability.

Keywords: Discrete-choice experiment; Task non-attendance; Poolability (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1755534524000198
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:51:y:2024:i:c:s1755534524000198

DOI: 10.1016/j.jocm.2024.100487

Access Statistics for this article

Journal of choice modelling is currently edited by S. Hess and J.M. Rose

More articles in Journal of choice modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().