TOPICS COVERAGE

Lectures

1. Study designs.
2. Sampling Techniques, Sample size determination
3. Data structure:
   1. Understanding variable
   2. Measurement scale of variables
   3. Types of variables
   4. Method of data entry in softwares
4. Basic idea of probability and probability distributions.
   

Lectures supported by MS-Excel and SPSS:

5. Univariate data summary
   1. Graphical descriptors: Construction and use
      1. Different kind of Bar Graphs
      2. Graphs for quantitative data
      3. Box and whisker plot.
   2. Numerical descriptor: calculation and interpretation.
      1. Proportion, odds, odds ratio
      2. Measuring central value
      3. Measurement of dispersion, spreadness, peakness.
6. Bivariate data summary
   1. Graphical understanding of data ( scatter diagram)
   2. Measurement of strength of relationship between variables.
      1. Correlation
      2. Correlation matrix
      3. Association,
      4. Basics of Regression
7. Estimation & Hypothesis testing
   1. Construction of confidence interval with selected confidence
   2. Parametric test (t- test, Chi square test, F test, ANOVA)
   3. Non Parametric test (Mann-Whitney test, Wilcoxon test, Kruskal -Wallis test, Friedman’s Rank test)
      
8. Exposure to Analysis of Multivariate data
   1. Regression analysis (simple and multiple)
   2. Logistic regression analysis
   3. Analysis of covariates(ANCOVA)
   4. Dimension reduction technique: Factor analysis
      1. Exploratory Factor Analysis(EFA)
      2. Confirmatory Factor Analysis(CFA)
9. Introduction to structural Equation Modeling Using AMOS
   1. Choosing between CB-SEM and PLS-SEM
   2. Types of constructs (Reflective and Formative)
   3. Structural Model Assessment, Path analysis, Interaction effect
   4. Bootstrapping technique
10. Mediation and Moderation Process Macro based.
11. Multi-Group Analysis and Bayesian Modelling.