Estimating Better Left-Right Positions Through Statistical Scaling of Manual Content Analysis

@inproceedings{Dubler2017EstimatingBL,
  title={Estimating Better Left-Right Positions Through Statistical Scaling of Manual Content Analysis},
  author={Thomas D{\"a}ubler and Kenneth Benoit},
  year={2017}
}
Borrowing from automated “text as data” approaches, we show how statistical scaling models can be applied to hand-coded content analysis to improve estimates of political parties’ leftright policy positions. We apply a Bayesian item-response theory (IRT) model to category counts from coded party manifestos, treating the categories as “items” and policy positions as a latent variable. This approach also produces direct estimates of how each policy category relates to left-right ideology, without… CONTINUE READING
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