Multivariate Regression Analysis for the Item Count Technique
项目计数法的多变量回归分析AI 解读 ↓
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项目计数法的多变量回归分析
- 研究问题
- 列表实验只观察项目计数而不直接观察敏感回答时，如何估计肯定回答概率随受访者特征变化的关系并提高效率？
- 方法与思路
- 作者提出非线性最小二乘和最大似然估计，开发两步程序与 EM 算法，并用种族与政治调查说明多变量分析。
- 材料陈述的贡献
- 摘要称，这些方法支持敏感回答概率的回归分析；小规模模拟提示最大似然估计可能更有效率，并提供开源实现。
- 阅读关注点
- 应核对列表实验的设计条件、计数与敏感回答之间的模型关系，以及两类估计量的识别要求和效率比较设置。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要，无法判断关键识别条件；小规模模拟不能支持普遍效率优势。
原文摘要
The item count technique is a survey methodology that is designed to elicit respondents’ truthful answers to sensitive questions such as racial prejudice and drug use. The method is also known as the list experiment or the unmatched count technique and is an alternative to the commonly used randomized response method. In this article, I propose new nonlinear least squares and maximum likelihood estimators for efficient multivariate regression analysis with the item count technique. The two-step estimation procedure and the Expectation Maximization algorithm are developed to facilitate the computation. Enabling multivariate regression analysis is essential because researchers are typically interested in knowing how the probability of answering the sensitive question affirmatively varies as a function of respondents’ characteristics. As an empirical illustration, the proposed methodology is applied to the 1991 National Race and Politics survey where the investigators used the item count technique to measure the degree of racial hatred in the United States. Small-scale simulation studies suggest that the maximum likelihood estimator can be substantially more efficient than alternative estimators. Statistical efficiency is an important concern for the item count technique because indirect questioning means loss of information. The open-source software is made available to implement the proposed methodology.