Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming
بواسطة
Barbara M. Byrne
لا توجد تقييمات بعد
Business & Economics
Health & Wellness
Psychology
تنسيق
غلاف ورقي
صفحات
432
لغة
الإنجليزية
منشور
Jul 20, 2011
الناشر
Routledge
الطبعة
1
رقم ISBN-10
1848728395
رقم ISBN-13
9781848728394
الوصف
Barbara M. Byrne offers readers an accessible entry into the world of structural equation modeling through a focus on practical applications and programming using Mplus. This approach allows those new to the field to grasp fundamental concepts without being overwhelmed by complex mathematical formulas. The book serves as a bridge for social scientists, educators, and researchers who seek to enhance their analytical skills while working with multivariate data.
Throughout the narrative, Byrne emphasizes the use of Mplus as a powerful tool for exploring relationships within data. Readers are guided through various real-world scenarios, demonstrating how Mplus can effectively address research questions. This illustration of applications solidifies understanding and showcases the software's versatility across different fields.
With clear explanations and user-friendly programming insights, the text empowers readers to implement their own structural equation modeling projects confidently. By demystifying the process, this guide fosters a deeper appreciation for the intricacies of data analysis and the insights that can be derived from it.
Throughout the narrative, Byrne emphasizes the use of Mplus as a powerful tool for exploring relationships within data. Readers are guided through various real-world scenarios, demonstrating how Mplus can effectively address research questions. This illustration of applications solidifies understanding and showcases the software's versatility across different fields.
With clear explanations and user-friendly programming insights, the text empowers readers to implement their own structural equation modeling projects confidently. By demystifying the process, this guide fosters a deeper appreciation for the intricacies of data analysis and the insights that can be derived from it.