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Recovering preipsative information from additive ipsatized data - A factor score approach

机译:从附加的ipsatized数据中恢复提示信息-一种因子评分方法

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摘要

Response bias has long been recognized as an issue in the behavioral and social sciences, especially in cross-cultural research. Transforming raw data into ipsatized data, individual scores subject to a constant sum constraint, is proposed to be an effective measure to minimize response bias. One major problem of applying ipsatized data is that scores are incomparable across individuals. By assuming that the ipsatized data are transformed from a preipsative confirmatory factor analytic model, factor scores based on the ipsatized data are proposed to serve as proxy variables for the preipsative information in this study. The ipsative factor scores can be used for further data analysis. Simulation results reveal that the proposed method works reasonably well. A real example is used to illustrate how this method can be applied to real data sets.
机译:长期以来,响应偏差一直被认为是行为和社会科学中的一个问题,尤其是在跨文化研究中。提议将原始数据转换为ipsatized数据,即受恒定总和约束的单个分数,是一种将响应偏差最小化的有效措施。应用ipsatized数据的一个主要问题是分数在个人之间是无法比拟的。通过假设归纳化数据是从假设性验证性因子分析模型转换而来的,提出了基于归化化数据的因子分数作为本研究中代表性信息的代理变量。趋同因子得分可用于进一步的数据分析。仿真结果表明,该方法具有较好的效果。一个真实的例子用来说明如何将该方法应用于真实数据集。

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