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STATISTICAL METHODS FOR RESEARCH: AN INTRODUCTION

Level of measurement of variable Diagrammatic representation of data Descriptive Statistics Measures of central tendency and dispersion Sampling distribution Central Limit Theorem Z distribution t-distribution Errors in hypothesis testing Power Meaning of p-value Factors impacting power of a test Confidence level and confidence interval Cross tabulation and Chi-square goodness of fit Comparing means for two groups (t-test) Comparing means between more than two groups (Analysis of Variance) Post-hoc comparison Pearson product moment correlation Linear multiple regression Concept of ordinary least square method Testing for significance of single predictor Testing for overall model significance Coefficient of determination Standardized regression coefficient.

Prerequisites for this FDP: There are no specific prerequisites for this FDP.

MULTIVARIATE STATISTICAL TECHNIQUES FOR RESEARCH

Factor analysis Assumptions of factor analysis Deriving factors and assessing overall fit Interpreting factors Eigenvalue Scree plot Factor rotation Factor loading Multiple regression Assessing multicollinearity Tolerance and Variance Inflation factor Identifying influential observations Analysis of residuals Parsimony of model R-square and Adjusted R-square Standardized vs. Unstandardized regression coefficient Interpretation of regression coefficient for categorical predictors Logistic regression Interpretation of regression coefficient using odds ratio Model checking Logit models for nominal responses Cumulative logit models for ordinal responses Dsicriminant analysis Estimation of discriminant model Assessing overall model fit Interpretation of results Multiple analysis of variance (MANOVA) Analysis of Covariance (ANCOVA) Factorial designs (two or more treatments) Conjoint analysis Specifying the model Additive model and Interaction effects Model assumptions Estimation of model and assessment of model fit Interpretation of results Cluster analysis Determining number of clusters Similarity measures Assumptions and assessment of model fit Interpretation of results Multidimensional Scaling Decompositional vs. Compositional approach Understanding a perceptual map Interpretation of results.

Prerequisites for this FDP: Participants should be familiar and comfortable with the basic concepts of statistics (listed under the first FDP "Statistical Methods for Research: An Introduction"). These basic concepts will not be discussed in this programme, owing to time limitations.