Many core concepts in political science are latent and therefore can only be measured with error. Measurement error in a predictor attenuates slope coefficient estimates in regression, biasing them toward zero. We show that widely used strategies for correcting attenuation bias -- including instrumental variables and the method of composition -- are themselves biased when applied to latent regressors, sometimes even more than simple regression ignoring the measurement error altogether. We derive a correlation-based correction using split-sample measurement strategies. Rather than assuming a particular estimation strategy for the latent trait, our approach is modular and can be easily deployed with a wide variety of latent trait measurement strategies, including additive score, factor, or machine learning models, requiring no joint estimation while yielding consistent slopes under standard assumptions. Simulations and applications show stronger relationships after our correction, sometimes by as much as 50%. Open-source software implements the procedure. Results underscore that latent predictors demand tailored error correction; otherwise, conventional practice can exacerbate bias.

翻译：政治学中的许多核心概念是潜在的，因此只能通过带有误差的方式进行测量。预测变量中的测量误差会衰减回归中的斜率系数估计，使其偏向于零。我们证明，当应用于潜在回归变量时，广泛使用的校正衰减偏误的策略——包括工具变量法和组合法——本身也存在偏误，有时甚至比完全忽略测量误差的简单回归偏误更大。我们利用分样本测量策略推导出一种基于相关性的校正方法。与假设特定的潜在特质估计策略不同，我们的方法是模块化的，可以轻松地与多种潜在特质测量策略结合使用，包括加性评分、因子或机器学习模型，无需联合估计，且在标准假设下能产生一致的斜率。模拟和应用结果表明，经过我们的校正后关系强度有所增强，有时增幅高达50%。开源软件实现了该程序。结果强调，潜在预测变量需要量身定制的误差校正；否则，常规做法可能会加剧偏误。