The use of alternative preference elicitation methods in complex discrete choice experiments.
1School of Economics, University of New South Wales, Australia; Durham University Business School, Durham University, United Kingdom.
Journal of Health Economics
|October 23, 2013
Summary
Respondents in a nursing job study valued financial benefits more in complex choices. This preference for pecuniary over non-pecuniary gains was not solely due to cognitive load.
Area of Science:
- Health Economics
- Behavioral Economics
- Nursing Workforce Studies
Background:
- Understanding factors influencing job choices is crucial for nursing workforce planning.
- Discrete choice experiments (DCEs) are valuable tools for analyzing stated preferences in healthcare settings.
- Comparing different DCE methodologies can reveal insights into respondent behavior and data quality.
Purpose of the Study:
- To compare two discrete choice experiment (DCE) methods for analyzing stated preferences in nursing job selection.
- To investigate how different experimental designs affect the valuation of job attributes, particularly pecuniary versus non-pecuniary factors.
- To identify potential reasons for discrepancies in valuation between different DCE approaches.
Main Methods:
- Analysis of stated preference data from two distinct discrete choice experiments (DCEs).
- A multi-profile case best-worst scaling (BWS) experiment involving selection of best and worst jobs.
- A profile case BWS experiment focusing on the selection of best and worst job attributes.
Main Results:
- Respondents placed a higher value on pecuniary (financial) gains compared to non-pecuniary gains in the multi-profile BWS scenario.
- There was minimal evidence suggesting that increased cognitive burden from processing multiple profiles simultaneously explained this valuation discrepancy.
- The profile case BWS allowed for the identification of additional utility parameters and was perceived as cognitively less demanding.
Conclusions:
- The design of discrete choice experiments can influence how individuals weigh job attributes, particularly financial versus non-financial aspects.
- Cognitive load may not be the primary driver for the observed differences in valuing job attributes between multi-profile and profile case BWS.
- Further research is needed to explore alternative mechanisms influencing stated preferences in job choice studies.
Keywords:
Best-worst scalingC23C25C81Discrete choice experimentJ44Latent class logitMaximum-difference modelPreference elicitationRank-ordered dataMore Related Videos
20:24Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Published on: January 31, 2014
16.3K
13:04Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
11.6K
Related Concept Videos
01:16Experimental Designs
11.3K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
11.3K
02:01Factorial Design
13.2K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.2K
01:15Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
422
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
422
01:14Decision Making: Traditional Method
4.4K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.4K
01:09Decision Making: P-value Method
5.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.8K
01:16Crossover Experiments
3.4K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
3.4K