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Sample size, statistical power and discrete choice experiments: How much is enough

Rose, J (U of Technology, Sydney)
Wednesday 17 August 2011, 11:00-11:45

Seminar Room 1, Newton Institute


Discrete choice experiments (DCE) represent an important method for capturing data on the preferences held by both patients and health care practitioners for various health care policies and/or products. Identifying methods for reducing the number of respondents required for SC experiments is important for many studies given increases in survey costs. Such reductions, however, must not come at the cost of a lessening in the reliability of the parameter estimates obtained from models of discrete choice.

The usual method of reducing the number of sampled respondents in DCE experiments conducted in health studies appears to be using orthogonal fractional factorial experimental designs with respondents assigned to choice situations via either a blocking variable or via random assignment. Through the use of larger block sizes (i.e., each block has a larger number of choice situations) or by the use of a greater number of choice situations being randomly assigned per respondent, analysts may decrease the number of respondents whilst retaining a fixed number of choice observations collected. It should be noted, however, that whilst such strategies reduce the number of respondents required for DCE experiments, they also reduce the variability observed in other covariates collected over the sample.

Yet despite practical reasons to reduce survey costs, particularly through reductions in the sample sizes employed in DCE studies, questions persist as to the minimum number of choice observations, both in terms of the number respondents as well as the number of questions asked of each respondent, that are required to obtain reliable parameter estimates for discrete choice models estimated from DCE data. In this talk, we address both issues in the context of the main methods of generating experimental designs for DCEs in health care studies. We demonstrate a method for calculating the minimum sample size required for a DCE that does not require rules of thumb.


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