Course Contents

1: i. Power of a Test ii. Factors affecting the power of a test iii. Effect Size iv. Power consideration in terms of sample size 2: i. Testing Hypothesis ii. Analysis of variance: the one way design: the underlying logic of the ANOVA, Fixed versus Random Models iii. Analysis of variance: Factorial design 3: i. Squares, Interactions: simple effects, multiple comparisons, higher-order Factorial Design ii. Analysis of variance: repeated measures designs: The structural model, the covariance matrix, a priori comparison iii. Post-Hoc comparisons: Tukey’s test, the Scheffe test, Bonferoni test. 4: i. Multivariate Analyses ii. Multivariate analysis I: the analysis of covariance and correlations (principal components and exploratory factor analysis) iii. Multivariate analysis II: confirmatory factor analysis and covariance structure models iv. Multivariate analysis III: cluster analysis, discriminant analysis and multidimensional scaling 5: i. Regression and Model Testing ii. The covariance iii. Assumptions underlying Regression and Correlation 6: i. Multiple regression ii. Mediating and Moderating Relationships, Path analysis 7: iii. Non-parametric statistics 8: i. How to select a test ii. The Chi-Square iii. Likelihood Ratio Tests iv. The Wilcoxon test v. Kruskal-Wallis Test vi. Loglinear model vii. Logistic Regression 9: i. Introduction to SPSS ii. What is SPSS and how is it useful in research analysis. iii. Basic skills needed for using SPSS- statistical knowledge and computer skills iv. Resources for SPSS- how to obtain on-line tutorial and help in SPSS 10: i. Research Planning ii. Designing and planning a study iii. Choosing appropriate measures iv. Variable names, coding responses, coding open ended responses. v. Working with data files, SPSS menus, SPSS windows and dialogue boxes 11: i. Preparing the Data File ii. Defining the variables iii. Creating the data file and entering data iv. Recoding & Computing v. Importing files from other software vi. Checking for errors and correcting errors 12: i. Preliminary Analyses ii. Overview of statistical techniques iii. Exploring variables iv. Descriptive statistics v. Use of graphs vi. Data manipulation and transformation of variables 13: i. Statistical Techniques to Explore Relationship among variables ii. Correlation iii. Partial correlation iv. Multiple regression analysis v. Factor analysis vi. Revision of the basics 14: i. Discourse Analysis ii. Procedure and application 15: i. Statistical techniques to compare groups ii. Type I and Type II error iii. Post hoc analysis, effect size iv. One way ANOVA v. Two way ANOVA vi. Non parametric tests 16: i. Making sense of the SPSS output ii. Interpreting SPSS output iii. Writing up results

Course Synopsis

By the end of the semester, Students will be able to: • Make quantitative statements about a data set in terms of statistics. • Develop statistical models from data with parametric and non-parametric techniques • Quantify spatially distributed data in terms of spatial statistics, estimate and model summary • Able to conduct factor analysis on a certain tool

Course Learning Outcomes

The students will be capable of dealing with statistical data and advanced computerized analysis not only in their final research thesis but in variety of other projects.