MULTIVARIATE ANALYSIS

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

Measurement scales  Metric and Non-metric scales Rahul Chandra

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

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

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

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

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

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

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

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

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

Bootstrapping Rahul Chandra

Dummy Variable Rahul Chandra

Effect size Rahul Chandra

Dependence Techniques Rahul Chandra

Interdependence Techniques Rahul Chandra

Power of a Test Rahul Chandra

Power Rahul Chandra

Classification of Multivariate Techniques Rahul Chandra

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

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

Factor Analysis Rahul Chandra

Multiple Regression Rahul Chandra

Discriminant Analysis Rahul Chandra

Canonical Correlation Rahul Chandra

MANOVA Rahul Chandra

Cluster Analysis Rahul Chandra

MDS Rahul Chandra

Correspondence Analysis Rahul Chandra

SEM Rahul Chandra