Latent Curve Models: A Structural Equation Perspective (Hardcover)

Kenneth A. Bollen, Patrick J. Curran

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商品描述

An effective technique for data analysis in the social sciences

The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories.

The authors synthesize a vast amount of research and findings and, at the same time, provide original results. The book analyzes LCMs from the perspective of structural equation models (SEMs) with latent variables. While the authors discuss simple regression-based procedures that are useful in the early stages of LCMs, most of the presentation uses SEMs as a driving tool. This cutting-edge work includes some of the authors' recent work on the autoregressive latent trajectory model, suggests new models for method factors in multiple indicators, discusses repeated latent variable models, and establishes the identification of a variety of LCMs.

This text has been thoroughly class-tested and makes extensive use of pedagogical tools to aid readers in mastering and applying LCMs quickly and easily to their own data sets. Key features include:

  • Chapter introductions and summaries that provide a quick overview of highlights
  • Empirical examples provided throughout that allow readers to test their newly found knowledge and discover practical applications
  • Conclusions at the end of each chapter that stress the essential points that readers need to understand for advancement to more sophisticated topics
  • Extensive footnoting that points the way to the primary literature for more information on particular topics

With its emphasis on modeling and the use of numerous examples, this is an excellent book for graduate courses in latent trajectory models as well as a supplemental text for courses in structural modeling. This book is an excellent aid and reference for researchers in quantitative social and behavioral sciences who need to analyze longitudinal data.

商品描述(中文翻譯)

社會科學中的數據分析的有效技術

社會科學中的長期數據的爆炸式增長凸顯了這本及時出版物的必要性。《潛在曲線模型:結構方程觀點》提供了一種分析潛在曲線模型(LCMs)的有效技術。這種類型的數據具有允許樣本中的每個案例在時間上有不同軌跡的隨機截距和斜率。此外,研究人員可以包括變量來預測控制這些軌跡的參數。

作者綜合了大量的研究和發現,同時提供了原創的結果。該書從潛在變量的結構方程模型(SEMs)的角度分析了LCMs。雖然作者討論了在LCMs的早期階段有用的簡單的基於回歸的程序,但大部分的演示使用SEMs作為驅動工具。這項尖端工作包括了作者們最近在自回歸潛在軌跡模型上的工作,提出了多指標中方法因子的新模型,討論了重複潛在變量模型,並確定了各種LCMs的識別。

這本教材經過了徹底的課堂測試,並廣泛使用教學工具,以幫助讀者快速且輕鬆地掌握和應用LCMs到自己的數據集中。主要特點包括:

- 提供章節介紹和摘要,快速概述重點
- 整本書都提供實證例子,讓讀者測試他們新學到的知識並發現實際應用
- 每章結束時的結論強調讀者需要理解的關鍵點,以便進一步研究更複雜的主題
- 大量的腳註指向特定主題的主要文獻,以獲取更多信息

由於其強調建模和使用眾多例子,這本書非常適合潛在軌跡模型的研究生課程,也是結構建模課程的補充教材。對於需要分析長期數據的定量社會和行為科學研究人員來說,這本書是一個很好的輔助工具和參考資料。