1. MULTIVARIATE ANALYSIS

2. Multivariate analysis  It refers to all statistical techniques that simultaneously analyze multiple measurements on objects under investigation. In other words any simultaneous analysis of more than two variables can be considered as multivariate analysis. Rahul Chandra

3. Measurement scales  Metric and Non-metric scales Rahul Chandra

4. Metric scales  Interval and Ratio scales falls in this category. Rahul Chandra

5. Non-metric scales  Nominal and Ordinal scale falls in this category. Analyst can not perform operations like sum, averages, multiplication & division on non-metric data. Some multivariate techniques are specifically designed for such data. Rahul Chandra

6. Measurement errors  Degree to which observed value differs from actual value. Rahul Chandra

7. Validity  The ability of a scale to measure what was intended to be measured. Rahul Chandra

8. Internal validity  Also called causality, examines whether the observed change in a dependent variable is indeed caused by a corresponding change in hypothesized independent variable, and not by variables extraneous to the research context. Rahul Chandra

9. External validity  It is also referred as generalizability and refers to whether the observed associations can be  generalized from the sample to the population (population validity), or to other people,  organizations, contexts, or time (ecological validity). Rahul Chandra

10. Construct validity  Examines how well a given measurement scale is measuring the theoretical construct that it is expected to measure. Many constructs used in social science research such as empathy, resistance to change, and organizational learning are difficult to define, much less measure. Rahul Chandra

11. Reliability  The degree to which measures are free from random error and therefore yield consistent results. Rahul Chandra

12. Statistical Significance  It requires the researcher to specify an acceptable levels of statistical error due to using a sample. Significance level (alpha) is the probability of rejecting a true hypothesis (type 1 error). Type II error (beta) is the probability of failing to reject a false hypothesis. Rahul Chandra

13. Bootstrapping Rahul Chandra

14. Dummy Variable Rahul Chandra

15. Effect size Rahul Chandra

16. Dependence Techniques Rahul Chandra

17. Interdependence Techniques Rahul Chandra

18. Power of a Test Rahul Chandra

19. Power Rahul Chandra

20. Classification of Multivariate Techniques Rahul Chandra

21. Dependence Techniques  ANOVA  MANOVA  Multiple Regression  Canonical Regression  Logistic Regression  Discriminant Analysis  SEM Rahul Chandra

22. Interdependence Techniques  Factor Analysis  Cluster Analysis  Multi Dimensional Scaling  Correspondence Analysis Rahul Chandra

23. Factor Analysis Rahul Chandra

24. Multiple Regression Rahul Chandra

25. Discriminant Analysis Rahul Chandra

26. Canonical Correlation Rahul Chandra

27. MANOVA Rahul Chandra

28. Cluster Analysis Rahul Chandra

29. MDS Rahul Chandra

30. Correspondence Analysis Rahul Chandra

31. SEM Rahul Chandra