= The Low Response Score (LRS): A Metric to Locate, Predict, and Manage Hard-to-Survey Populations =

'''The Low Response Score (LRS): A Metric to Locate, Predict, and Manage Hard-to-Survey Populations''' (DOI: https://doi.org/10.1093/poq/nfw040) was written by Chandra Erdman and Nancy Bates in 2017. It was published in ''Public Opinion Quarterly'' (vol. 81, no. 1).

This paper introduces the [[UnitedStates/CensusBureau/PlanningDatabase|LRS]], a model-based score that is predictive of difficulty to survey. This is an improvement upon the HTC (hard-to-count) score created in 2001.

The LRS comes from a crowdsourced challenge administered by Kaggle.com. Model submissions are evaluated with respect to population-weighted MSE and ranked continuously.
 * 244 participants
 * top 3 used machine learning methods, specifically [[Statistics/GradientBoosting|gradient boosting]] or [[Statistics/RandomForest|random forests]]
 * were able to use non-PDB covariates, but the most predictive ones are available for the [[UnitedStates/CensusBureau|Census Bureau]] to use
   * "the single most influential predictor in the winning model was the percentage of renter households in a block group"

The most influential applicable covariates identified through the competition were then used as predictors in a [[Statistics/OrdinaryLeastSquares|regression model]]. The LRS is the model-based probability of nonresponse at the [[UnitedStates/CensusBureau#Geographies|block group and tract level]].

The authors then examine the LRS in light of three comparable [[UnitedStates/WashingtonDC|D.C.]] block groups: Columbia Heights, Trinidad, and Anacostia.



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