This course on data analysis for management is focused on the use of SPSS and SmartPLS. Starting with SPSS, the course offers an introduction to data analysis techniques through the lens of IBM SPSS Statistics software. Beginning with an introduction to SPSS, participants will delve into understanding the structure of data using Variable and Data views, followed by basic descriptive statistics and data transformation methods. The course then progresses to statistical tests for comparing means, correlation, and regression. Following SPSS, the course will offer a thorough introduction to Structural Equation Modelling (SEM) and its application using SmartPLS4. Participants will learn to assess the measurement model, covering constructs reliability, validity, and solutions to related issues. Structural model analysis will delve into direct relationships, mediation, moderation, and the evaluation of explanatory and predictive power, including higher-order construct analysis.
The objectives of this course on Data analysis for management encompass a comprehensive understanding of data analysis in business and management using SPSS and SmartPLS. At the end of the course, the participants will be able to:
- Delve into basic descriptive statistics, enabling them to summarize and interpret data effectively using SPSS.
- Understand statistical techniques such as comparative tests for means, correlation, and regression analysis using SPSS.
- Understand the fundamental concepts of Structural Equation Modeling (SEM) and gain proficiency in using SmartPLS for model estimation and analysis using SmartPLS4.
- Conduct Measurement Model Analysis, including assessing construct reliability and validity, addressing common issues, and estimating higher-order constructs of both reflective-reflective and reflective-formative using SmartPLS4.
- Perform Structural Model Analysis, covering techniques such as mediation, moderation, and assessing explanatory and predictive power, as well as higher-order construct analysis using SmartPLS4.
- Explore advanced topics including Necessary Condition Analysis and Importance-Performance Map Analysis, facilitating a holistic comprehension of PLS-SEM methodology and its applicability in empirical research.
