Strong Preferences or Simplifying Heuristics? Using Internal Validity Tests and Latent Class Analysis to Better Understand Stated Preference Survey Results. A Case Example in Health Preferences Research.

Objectives: Internal-validity tests (IVTs) are used in discrete choice experiments (DCEs) to check decision heuristics, choice logic, response consistency, and tradeoffs. There is no standard for how many IVT failures classify respondents as having unacceptable data quality or how to account for failures in choice models. We assessed IVT failures and used latent class analysis to identify choice patterns consistent with statistically informative DCE data.

Methods: We conducted a DCE with 4 attributes (3 ordered), 12 experimental choice tasks, and 2 constructed IVT choice tasks. Respondents with IVT failures were asked questions about their choices. We evaluated preference heterogeneity controlling for attribute dominance using a 4-class latent class model with attribute-specific alternative-specific constants and compared with a 1-class model without attribute-specific alternative-specific constants.

Results: Of the 201 respondents, 34 had IVT failures of which 38% to 42% provided reasons other than nonattendance or simplifying heuristics. Comparing the 4-class latent class model no-dominance class with the 1-class model, the coefficients of 2 ordered attributes were significantly different, illustrating potential bias due to simplifying heuristics. Attribute-specific dominance class probability varied by number of choice tasks respondents exhibited attribute dominance on, ranging from 8 to 10 for a class-membership probability of 50%.

Conclusions: IVT "failures" should be interpreted as unexpected responses warranting further inquiry. Including understanding questions could yield insights about stated preferences; however, these increase respondent burden and may not explain simplifying heuristics. Single subjective "rules of thumb" for attribute dominance thresholds may not be adequate. Latent class models controlling for attribute dominance are a data-driven approach that should be considered to assess simplifying heuristics and attribute dominance thresholds.