Does Controlling for Scale Heterogeneity Better Explain Respondents’ Preference Segmentation in Discrete Choice Experiments? A Case Study of US Health Insurance Demand

Abstract

Analyses of preference evidence frequently confuse heterogeneity in the effects of attribute parameters (i.e., taste coefficients) and the scale parameter (i.e., variance). Standard latent class models often produce unreasonable classes with high variance and disordered coefficients because of confounding estimates of effect and scale heterogeneity. In this study, we estimated a scale-adjusted latent class model in which scale classes (heteroskedasticity) were identified using respondents’ randomness in choice behavior on the internet panel (e.g., time to completion and time of day). Hence, the model distinctly explained the taste/preference variation among classes associated with individual socioeconomic characters, in which scales are adjusted. Using data from a discrete-choice experiment on US health insurance demand among single employees, the results demonstrated how incorporating behavioral data enhances the interpretation of heterogeneous effects. Once scale heterogeneity was controlled, we found substantial heterogeneity with 4 taste classes. Two of the taste classes were highly premium sensitive (economy class), coming mostly from the low-income group, and the class associated with better educational backgrounds preferred to have a better quality of coverage of health insurance plans. The third class was a highly quality-sensitive class, with a higher SES background and lower self-stated health condition. The last class was identified as stayers, who were not premium or quality sensitive. This case study demonstrates that one size does not fit all in the analysis of preference heterogeneity. The novel use of behavioral data in the latent class analysis is generalizable to a wide range of health preference studies.

Exploring the importance of controlling heteroskedasticity and heterogeneity in health valuation: a case study on Dutch EQ-5D-5L

“…Also, due to the design with a constant comparator in PC tasks and relatively smaller sample size, our capability to explore heterogeneity in larger dimensions was beyond the scope. Lastly, important variables such as income and time to complete the tasks were missing in the dataset, which would have been good indicators for class membership, as shown in previous studies [ 18 ]. Given this, this study is the first attempt to explore heteroskedasticity and heterogeneity in a health valuation study and should aid others considering similar approaches.…”

“…As an extension of the HCL [ 30 , 31 ], the standard SALC model [ 20 ] identifies differences in scale by latent groups (i.e., scale classes), but scale remains constant within each scale class. [ 18 , 32 ]. A SALC model can allow heteroskedasticity by letting the scale factor vary by observable factors within each scale class (i.e., heteroskedastic SALC).…”

“…Apart from heteroskedasticity, we also examine two types of preference heterogeneity [ 18 ]. First, groups of respondents like or dislike different alternatives in a systematic way that reflects the relative importance of the attributes (i.e., taste classes).…”

Preference heterogeneity in health valuation: a latent class analysis of the Peru EQ-5D-5L values

“…Preference heterogeneity from latent sources may be modeled using individual-specific or class-specific parameters (e.g., mixed or latent class logit). The scale-adjusted latent class (SALC) analysis used in this study examined class-specific heterogeneity (scale and taste) in the presence of heteroskedasticity [ 8 , 13 , 17 ]. The SALC model is an advanced econometric approach where in addition to the standard decomposition of the population into distinct taste classes, the model allows for heterogeneity in scale with distinct scale classes (each with relatively different error variances).…”

“…Individuals with similar scales can also be grouped into a scale class. Scale class differences may be related to differences in utility caused by differences in the randomness of individual behavior that creates scale variation across persons [ 8 ]. Previously, some health valuation papers observed preference heterogeneity using the standard latent class model where no scale issues were controlled for [ 9 , 10 ].…”