lclogit: A Stata Module for Estimating a Mixed Logit Model with Discrete Mixing Distribution Via the Expectation-Maximization Algorithm

Pacifico, Daniele; Yoo, Hong Il

doi:10.2139/ssrn.2205054

Cited by 15 publications

(13 citation statements)

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“…The RPL model was estimated in Stata 13.0 using mixlogit command [49]. For the LC model the lclogit and post-estimation command lclogitml were used [50]. The main objective was to estimate preference weights for attributes and attribute levels used in the experiment that are consistent with the observed pattern of choices by respondents.…”

Section: Discussionmentioning

confidence: 99%

Incentives for Blood Donation: A Discrete Choice Experiment to Analyze Extrinsic Motivation

Sadler

Shi

Bethge

et al. 2018

Transfus Med Hemother

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Background: Demographic trends affect size and age structure of populations. One of the consequences will be an increasing need for blood products to treat age-related diseases. Donation services rely on voluntariness and charitable motivation. It might be questioned whether there will be sufficient blood supply with voluntary donation. The present study focused on elicitation of preferences for incentives and aimed to contribute to the discussion on how to increase donation rates. Methods: A self-administered discrete choice experiment (DCE) was applied. Respondents were repeatedly asked to choose between hypothetical blood donation centers. In case of reluctance to receiving incentives a none-option was included. Random parameter logit (RPL) and latent class models (LCM) were used for analysis. Results: The study sample included 416 college students from the US and Germany. Choice decisions were significantly influenced by the characteristics of the donation center in the DCE. Incentives most preferred were monetary compensation, paid leave, and blood screening test. LCM identified subgroups with preference heterogeneity. Small subgroups indicated moderate to strong aversion to incentives. Conclusion: The majority of the sample positively responded to incentives and indicated a willingness to accept incentives. In face of future challenges, the judicious use and appropriate utilization of incentives might be an option to motivate potential donors and should be open to discussion.

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Section: Discussionmentioning

confidence: 99%

Incentives for Blood Donation: A Discrete Choice Experiment to Analyze Extrinsic Motivation

Sadler

Shi

Bethge

et al. 2018

Transfus Med Hemother

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“…While it has fewer built-in commands for estimating discrete choice models than Nlogit, there is a range of user-written commands freely available that can be used to implement the methods covered in Section 2 [40][41][42][43]. It has routines for generating predicted probabilities, and simulations can be performed and elasticities calculated by using the generated probabilities.…”

Section: Statamentioning

confidence: 99%

Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software

2017

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We provide a user guide on the analysis of data (including best-worst and best-best data) generated from discrete choice experiments (DCEs), comprising a theoretical review of the main choice models followed by practical advice on estimation and post estimation. We also provide a review of standard software. In providing this guide we endeavor not only to provide guidance on choice modeling, but to do so in a way that provides researchers to the practicalities of data analysis. We argue that choice of modeling approach depends on: the research questions; study design and constraints in terms of quality/quantity of data and that decisions made in relation to analysis of choice data are often interdependent rather than sequential. Given the core theory and estimation of choice models is common across settings, we expect the theoretical and practical content of this paper to be useful not only to researchers within but also beyond health economics. Compliance with Ethical StandardsNo funding was received for the preparation of this paper. Emily Lancsar is funded by an ARC Fellowship (DE1411260). Emily Lancsar, Denzil Fiebig and Arne Risa Hole have no conflicts of interest. 2 Key Points for Decision Makers We provide a user guide on the analysis of data, including best-worst and best-best data, generated from DCEs, addressing the questions of DCE  We provide a theoretical overview of the main choice models and review three standard statistical software packages: Stata; Nlogit; and Biogeme.  Choice of modeling approach depends on the research questions; study design and constraints in terms of quality/quantity of data and decisions made in relation to analysis of choice data are often interdependent rather than sequential.  A health based DCE example for which we provide the data and estimation code is used throughout to demonstrate the data set up, variable coding, various model estimation and post estimation approaches.3

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“…An alternative model for exploring heterogeneity in preferences and WTP across respondents is the latent class (LC) model (Pacifico and Yoo 2013, Colombo et al 2009, Scarpa and Thiene 2005, Boxall and Adamowicz 2002. This model estimates discrete sets of coefficients , which are indexed over classes .…”

Section: Identifying Determinants Of Choice Behaviour and Wtpmentioning

confidence: 99%

Motivations matter: Behavioural determinants of preferences for remote and unfamiliar environmental goods

Bӧrger

Hattam

2017

Ecological Economics

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lclogit: A Stata Module for Estimating a Mixed Logit Model with Discrete Mixing Distribution Via the Expectation-Maximization Algorithm

Cited by 15 publications

References 0 publications

Incentives for Blood Donation: A Discrete Choice Experiment to Analyze Extrinsic Motivation

Incentives for Blood Donation: A Discrete Choice Experiment to Analyze Extrinsic Motivation

Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software

Motivations matter: Behavioural determinants of preferences for remote and unfamiliar environmental goods

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