Understanding Conjoint Analysis Applications in Health

A Checklist for Conjoint Analysis Applications in Health: Report of the Ispor Conjoint Analysis Good Research Practices Task Force

Conjoint analysis applications in health--a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force.

BACKGROUND The application of conjoint analysis (including discrete-choice experiments and other multiattribute stated-preference methods) in health has increased rapidly over the past decade. A wider acceptance of these methods is limited by an absence of consensus-based methodological standards. OBJECTIVE The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Res...

Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force.

Stated-preference methods are a class of evaluation techniques for studying the preferences of patients and other stakeholders. While these methods span a variety of techniques, conjoint-analysis methods-and particularly discrete-choice experiments (DCEs)-have become the most frequently applied approach in health care in recent years. Experimental design is an important stage in the development...

Understanding Student Attitudes of Mobile Phone Applications and Tools: A study using conjoint, cluster and SEM analyses

Mobile phone use has grown dramatically throughout the world. While researchers have explored various facets of use and perceptions across age groups, genders and nationalities, segmentation by utility of mobile phone attributes has received little attention in academic research. Further, understanding how antecedent of attitudes differ by utility-based consumer segments has been underexplored....

Understanding student attitudes of mobile phone features: Rethinking adoption through conjoint, cluster and SEM analyses

Young adults have been labelled as one of the most important segments for mobile phones, however there is little empirical evidence to indicate how these young adults value the feature richness of their devices. This research presents a richer view of mobile phone user preferences and perceptions by applying methodologies from the marketing and information systems domains. Conjoint analysis pro...

Causal Inference in Conjoint Analysis : Understanding Multidimensional Choices via

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show how conjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and ...