Multidisciplinary Research Lab

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Structural Equation Modelling Made Easy – Part 1

Structural Equation Modelling (SEM) is a multivariate data analysis technique that combines factor analysis and multiple regression analysis in testing networks of causal relationships between variables. The course is aimed at social science researchers and research students which have some working knowledge of regression and factor analysis techniques and would like to extend their knowledge into analyzing more complex models. The session will be very practical with hands-on training using SPSS and Amos.

Topics to be covered include:

  1. Introduction to Structural Equation Modelling (SEM)
  2. Confirmatory Factor Analysis
  3. Model Fit and Model Modification
  4. Validating the Measurement Model
  5. Specifying the Structural Model
  6. Building Nested Models

 

Each participant received a copy of the SEM book authored by Dr. Sheena Lovia Boateng, titled “Structural Equation Modelling (SEM) Made Easy for Business and Social Science Research Using SPSS and AMOS’.

Buy SEM Made Easy Book at Amazon – http://a.co/d/hgHu6zE

Click to Read the comments of participants who attended a quantitative data analysis training workshop based on the book.

Simple Linear, Multiple and Hierarchical Regression

This workshop will teach participants techniques in how to test the significance of relationships between dependent and independent variables. They will obtain skills in testing causal relationships, as well as how to find support for research hypotheses formulated for specific studies.

Learning Outcomes

By the end of the workshop, participants will know:

  • The components of the regression equation/ model.
  • How to construct a regression equation/ model.
  • How to run simple, multiple and hierarchical regression analysis.
  • How to interpret and present the SPSS output.

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Descriptive Statistics and Exploratory Factor Analysis (with Pearson Correlation)

This workshop will teach participants techniques in how to summarise categorical and continuous data, understand the structure of a large set of variables and how to reduce them to more a manageable size, as well as to determine the strength and direction of the relationships between these variables.

Learning Outcomes

By the end of the workshop, participants will know:

  • When to use Descriptive statistics, EFA and Pearson correlation
  • How to run Descriptive statistics, EFA and Pearson correlation in SPSS
  • How to interpret and present the SPSS output

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