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Development and evaluation of weighting methods for the 2024 national pharmacist workforce study
David A Mott1, Vibhuti Arya2, Brianne K Bakken3
1University of Wisconsin-Madison, School of Pharmacy, Madison, WI, USA.
Research in Social & Administrative Pharmacy : RSAP
|June 11, 2026
Summary
Post-stratification weighting using raking improved the representativeness of the 2024 National Pharmacist Workforce Study (NPWS) sample. This method refined estimates of pharmacist work and work-life characteristics by aligning sample data with population demographics.
Area of Science:
- Pharmacy Workforce Research
- Biostatistics
- Survey Methodology
Background:
- Post-stratification weighting is crucial for aligning sample characteristics with population data.
- Improving the accuracy of estimates for work and work-life characteristics in workforce studies is essential.
Purpose of the Study:
- To assess demographic discrepancies between 2024 National Pharmacist Workforce Study (NPWS) respondents and population data.
- To develop and evaluate five weighting methods, including proportional weights and raking, to enhance sample representativeness.
- To quantify the impact of weighting on estimates of pharmacist work and work-life characteristics.
Main Methods:
- Utilized American Community Survey data for population characteristics (region, gender, age, race).
- Evaluated weighting approaches: proportional weights alone and combined with raking.
- Assessed goodness-of-fit using a fit index and compared weighted vs. unweighted estimates for work and work-life factors.
Main Results:
- The unweighted NPWS sample underrepresented younger, male, and non-White pharmacists.
- Raking incorporating two-way gender x age, region, gender, and race achieved the best fit.
- Weighting adjusted work and work-life estimates, aligning with expected age and gender effects.
Conclusions:
- Post-stratification weights derived from raking significantly improved NPWS sample representativeness.
- Weighting refined estimates of pharmacist work characteristics, enhancing data accuracy.
- The NPWS data analysis can benefit from employing weighting techniques to improve estimate precision.
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