Multivariate Analysis Module – IBSS DBA Program
To equip students with theoretical and practical knowledge in using and utilizing methods and techniques of multivariate data analize
Upon completion of this subject, students should be able to:
- Explain the rationale, discuss the usage and carry out basic computational exercise for each of the technique/ topics covered to analyze the data for research purposes.
- Explain the underlying assumptions and limitations in the use of each technique / tools discussed for different types of data collected.
- Adopt and use relevant technique, compute and analyze the data and evaluate and synthesis the results obtained to explain the phenomenon being studied or the issue being discussed.
Information gathering, data processing, interpretation, critical analizing of data and writing skills and ability to work independently.
This module aims to enhance students’ understanding of the advanced statistical tools for quantitative research. While there are many multivariate techniques of analyzing data, only the commonly used ones are discussed. Emphasis is more on an application-oriented approach to multivariate analyze addressing a conceptual understanding of various statistical procedures without having to delve into the mathematics of the tools. This is facilitated by familiarizing students with statistical packages. Each of the statistical procedures is examined is relation to the rationale for using them, the assumptions for using them, the process of using each tool and the interpretation
Combinations of class lectures, tutorials, group discussions of issues and case studies and assignments.
Understanding and Preparation for Multivariate Analizing
Cleaning and Transforming Data
- Profiling Data and Treatments of Missing Data and Outliers
- Testing the Assumptions
- Concepts, types and uses of Factor Analysis
- Assumptions Underlying
- The Steps
Analizing using Dependence Techniques
Multiple Regression Analysis
- Basic Concepts in Multiple Regression Analizing
- Linear Regression vs Multiple Regression
- Assumptions For Using Multiple Regression
- The Process Of Using Multiple Regression
- Concepts and uses of Canonical Correlations Analysis
- Objectives and Assumptions Underlying Canonical Correlations Analysis
- Deriving Canonical Functions, Interpret and Validate Canonical Variate
Multivariate Discriminant Analysis
- What Is Multivariate Discriminant Analysis?
- Assumptions For Using Multivariate Discriminant
- The Process Of Using Multivariate Discriminant
- Interpretation Of The Discriminant Function
Multivariate Analysis of Variance
- Univariate vs Multivariate Analysis Of Variance
- Decision To Use MANOVA
- Assumptions For Using MANOVA
- The DecisionProcess Of Using MANOVA
- Factorial Design For MANOVA
Analizing Using Interdependence Techniques – Cluster Analizing
- Concepts and Uses of Cluster Analysis and how it Works
- Differences Between Factor And Cluster
- Objectives, Design and Assumptions
- Deriving and Assessing Cluster Fit
- Interpretation, Validation and Profiling Of The Clusters
Structured Equation Modeling
- Concepts, Objectives, Assumptions and Uses of Structured Equation Modeling (SEM)
- Differences with other Multivariate Techniques
- Role Of Theory In SEM
- The Steps in SEM
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