SHORT COURSE

Short Course in Structural Equation Modeling (with AMOS)

Course Description

If you are looking to test a complex structural model, then you already know the importance of AMOS. It is a powerful and one of the most popular tools for doing Structural Equation Modeling.

Learn Structural Equation Modeling, Path Analysis Confirmatory Analysis using IBM SPSS AMOS from scratch. The AMOS software lets you build and test complex models more accurately and efficiently than standard multivariate statistics techniques.

In this course you will learn how to do SEM from scratch using AMOS. AMOS is a powerful tool for confirmatory validation and often used by researchers for research and high impact publishing. It enables you to specify, estimate, assess and present models to show hypothesized relationships among variables. The AMOS software lets you build and test complex models more accurately and efficiently than standard multivariate statistics techniques.

𝑬𝒎𝒂𝒊𝒍: info@stanford.lk
𝑯𝒐𝒕𝒍𝒊𝒏𝒆: 0777 81 45 81 
                  071 18 18 597
To Whom: University Undergraduates/Postgraduate Students/Master’s Students/Researchers/Ph.D. Candidates/Academic Staff of Universities & Educational Institutes
Lecturer: Professor Nalaka Wickramasinghe
Study Method: 100% Online (zoom)
Medium: English & Sinhala (Mix)
Duration: 04 Days
Certificate: E – Certificate (A printed certificate will be provided on request) (Conditions Applied)
Course Fee: 5000/- LKR  (Group Registrations Available)

Course Content

➤ Introduction to SPSS (Data Management and Data Handling)

➤ Descriptive Statistics, Normality Tests, Crosstabs

➤ Hypothesis Testing (Parametric and Non-Parametric Tests)

➤ Correlation, Regression – Simple/Multiple/Logistic

Learning Outcomes

Upon successful completion of the course unit, the student should be able to perform the following tasks.

➤ Do confirmatory analysis using AMOS.

➤ Establish reliability and validity of a scale using AMOS.

➤ Do Structural Equation Modelling using AMOS.

➤ Analyse complex path models and derive insight from multivariate data.

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