Buku Practical Guide of the Integrated Structural Equation Modeling (SEM) with LISREL and AMOS for Marketing & Social Sciences Thesis

Sinopsis Buku Practical Guide of the Integrated Structural Equation Modeling (SEM) with LISREL and AMOS for Marketing & Social Sciences Thesis

Buku ini terdiri dari beberapa bab. Bab pertama membahas tentang what is structural equation modeling, bab dua tentang why is structural equation modeling (SEM), bab tiga tentang basic concept of structural equation modeling (SEM), dan bab empat tentang SEM procedure.

Adapun bab lima membahas tentang tutorial, bab enam tentang SEM tutorial with LISREL, bab tujuh tentang SEM tutorial with AMOS, dan bab yang terakhir bab delapan tentang statistical results comparation LISREL VS AMOS

Let’s discuss one by one. It is very clear now that univariate is only involving a single variable. For instance, we just want to investigate the brand image in a hospital. We would study and  explore the brand image only in that hospital. Then, we have to use univariate technique. We would receive the data as the results, and we name it as univariate data. Univariate data does not deal with causes or relationship. We would not see any relationship in the univariate data. It is because the major purpose of univariate analysis is only taking data, summarizing, and describing data. On the other hand, bivariate is only involving two factors or variables. It deals with causes or relationship but only between the two variables. The major purpose of bivariate analysis is explaining and examining two variables simultaneously.

Many often, researchers are having a set of interrelated questions in their study. Yet none of the other of multivariate technique enable researchers to investigate the questions with one integrated technique (Hair et al. 2007).  Multiple regression, factor analysis, multivariate analysis of variance (MANOVA), discriminant analysis, and other techniques can only examine the relationship in a single relationship at a time. Even the techniques allowing for multiple dependent variables, such as MANOVA and canonical analysis, they are still examining only a single relationship between the dependent and independent variables.

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