Dimensional Reduction of Word-Frequency Data as a Substitute for Intersubjective Content Analysis

IF 5.4 2区 社会学 Q1 POLITICAL SCIENCE Political Analysis Pub Date : 2004-02-01 DOI:10.1093/pan/mph004
Adam F. Simon, Michael A. Xenos
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引用次数: 43

Abstract

This paper presents a method for using dimensional reduction in the analysis of political content. We draw inspiration from latent semantic analysis (LSA) theory, which posits that factor analysis can successfully model human language. We suggest that the factor analysis of word frequencies generated from any political text—for example, open-ended survey responses—provides adequate content analysis categories and can substitute for more commonly practiced techniques. The method proceeds in three steps: data preparation, exploratory factor analyses, and hypothesis testing. This method may produce other benefits by allowing the data to speak more clearly in the development of coding dictionaries while avoiding the problems of inferential circularity common in other data-driven approaches. We demonstrate the method using responses collected in the execution of an experimental design dealing with the topic of partial-birth abortion and assess the demonstration by presenting a human coding of the same material.
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替换主体间性内容分析的词频数据降维
本文提出了一种在政治内容分析中使用降维的方法。我们从潜在语义分析(LSA)理论中得到启发,该理论认为因素分析可以成功地模拟人类语言。我们建议对任何政治文本(例如,开放式调查回复)生成的词频进行因子分析,提供足够的内容分析类别,并可以替代更常用的技术。该方法分三步进行:数据准备、探索性因素分析和假设检验。这种方法还可以产生其他好处,它允许数据在编码字典的开发中更清楚地表达,同时避免了其他数据驱动方法中常见的循环推理问题。我们使用在处理部分分娩流产主题的实验设计执行中收集的响应来演示该方法,并通过呈现相同材料的人类编码来评估演示。
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来源期刊
Political Analysis
Political Analysis POLITICAL SCIENCE-
CiteScore
8.80
自引率
3.70%
发文量
30
期刊介绍: Political Analysis chronicles these exciting developments by publishing the most sophisticated scholarship in the field. It is the place to learn new methods, to find some of the best empirical scholarship, and to publish your best research.
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