• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Structural equation modeling >A Comparison of Approaches for the Analysis of Interaction Effects Between Latent Variables Using Partial Least Squares Path Modeling
【24h】

A Comparison of Approaches for the Analysis of Interaction Effects Between Latent Variables Using Partial Least Squares Path Modeling

机译：偏最小二乘路径模型分析潜在变量之间相互作用的方法的比较

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In social and business sciences, the importance of the analysis of interaction effects between manifest as well as latent variables steadily increases. Researchers using partial least squares (PLS) to analyze interaction effects between latent variables need an overview of the available approaches as well as their suitability. This article presents 4 PLS-based approaches: a product indicator approach (Chin, Marcolin, & Newsted, 2003), a 2-stage approach (Chin et al., 2003; Henseler & Fassott, in press), a hybrid approach (Wold, 1982), and an orthogonalizing approach (Little, Bovaird, & Widaman, 2006), and contrasts them using data related to a technology acceptance model. By means of a more extensive Monte Carlo experiment, the different approaches are compared in terms of their point estimate accuracy, their statistical power, and their prediction accuracy. Based on the results of the experiment, the use of the orthogonalizing approach is recommendable under most circumstances. Only if the orthogonalizing approach does not find a significant interaction effect, the 2-stage approach should be additionally used for significance test, because it has a higher statistical power. For prediction accuracy, the orthogonalizing and the product indicator approach provide a significantly and substantially more accurate prediction than the other two approaches. Among these two, the orthogonalizing approach should be used in case of small sample size and few indicators per construct. If the sample size or the number of indicators per construct is medium to large, the product indicator approach should be used.
机译：在社会科学和商业科学中，分析清单变量和潜在变量之间的交互作用的重要性不断提高。研究人员使用偏最小二乘（PLS）分析潜在变量之间的相互作用影响时，需要对可用方法及其适用性进行概述。本文介绍了4种基于PLS的方法：产品指标方法（Chin，Marcolin和Newsted，2003年），两阶段方法（Chin等人，2003年； Henseler＆Fassott，印刷中），混合方法（Wold） （1982年）和正交化方法（Little，Bovaird和Widaman，2006年），并使用与技术接受模型相关的数据对它们进行了对比。通过更广泛的蒙特卡洛实验，比较了不同方法的点估计精度，统计能力和预测精度。根据实验结果，在大多数情况下建议使用正交化方法。仅当正交化方法未发现显着的交互作用时，才应另外使用2级方法进行显着性检验，因为该方法具有较高的统计功效。为了预测准确性，与其他两种方法相比，正交化和乘积指示符方法提供了明显得多且实质上更准确的预测。在这两种方法中，如果样本量较小且每个结构的指标较少，则应使用正交化方法。如果样本大小或每个构建体的指标数量中等到较大，则应使用产品指标方法。

著录项

  • 来源
    《Structural equation modeling》 |2010年第1期|82-109|共28页
  • 作者

    Joerg Henseler; Wynne W. Chin;

  • 作者单位

    Institute for Management Research, Nijmegen School of Management, Radboud University Nijmegen, Thomas van Aquinostraat 1, 6525 GD Nijmegen, The Netherlands;

    Department of Decision and Information Sciences University of Houston;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号