### Data Analysis Training in STATA, SAS, SPSS, Eviews, R & Excel package

Training Content

• How to develop effective research tools
• Understanding the relationships between research tools and data analysis
• Skills of managing FGDs, KIIs and individual interviews
• Skill of data editing, cleaning and coding questionnaires
• Skill of developing a data entry templates
• Mastering data entry in a given template
• Univariate, Bivariate and Multivariate data analysis procedures
• Interpretation of analysis package outputs
• Fundamentals of research reports writing

TRAINING CURRICULUM FOR STATISTICAL ANALYSIS PACKAGES

A). ADVANCED  EXCEL

 Topic Content Mode of Delivery Understanding Data & its Concepts ·   What is statistics? ·   Types of data ·   Variables ·   Measurements of variable –Interactive Teaching Designing and managing Data Entry Screens ·   Brief introduction to Microsoft Excel ·   Designing entry screens for surveys ·   Using Data validation while designing entry screens ·   Data conditional formatting ·   Using advance EXCEL form Add-on ·   Practicing Data entry ·   Data entry using Excel forms -Interactive Teaching     -Take home assignment Database Management ·   Introduction to building databases in EXCEL ·   Conducting data manipulations and transformations using advanced formulae ·   Creating new variable using EXCEL functions such as IF functions ·   Merging datasets using the VLOOKUP Function, HLOOKUP Function ·   Using excel tables ·   Concatenating Functions ·   Importing and exporting EXCEL data –Interactive Teaching     -Take home assignment Group work Data analysis and graphing Conducting Univariate analysis ·   Analyzing data by use of frequency tables ·   Conducting Descriptive statistics ·   Graphing of quantitative, qualitative, ·   Trend analysis graphing –Interactive Teaching   -Take home assignment Data analysis and interpretation using PIVOT table and graph ·   Conducting Bivariate analysis ·   Cross tabulations ·   Pivot graphs ·   Formatting a PivotTable ·   Using Advanced Data Field Settings in Pivot table ·   Creating PivotChart Reports from Scratch ·   Adding Fields to a Pivot Chart -Interactive Teaching   – One on one guidance on personal computer Regression Analysis and Correlational analysis ·   Conducting correlation analysis ·   Simple linear regression ·   Multiple regressions ·   Testing for differences using T-Tests -Interactive Teaching Linking and Automating Data analysis tables and graphs ·Introduction to linking sheets in same or different worksheet ·Using Count, count if and count ifs function ·Using the Sum if and sum ifs ·Using the average if and average ifs ·Combining various formulae ·File Protection ·Cell Protection ·Data backups -Interactive Teaching   Group presentation

B) . ANALYSIS USING  STATA, R, SPSS,  SAS & EVIEWS

 Topic Content Mode of Delivery Understanding data and its concepts ·   Different types of data ·   Variables (Qualitative and quantitative) ·   Measurement scales ·   Basic statistical concepts -Interactive Teaching   -Take home assignment Use of specific analysis package Data Management ·   Software concepts ·   Designing data entry templates/screens ·   Data coding ·   Data entry ·   Data transformations (generating new variables from existing variables) ·   Merging datasets Interactive Teaching   – Take home assignment Univariate analysis ·   Descriptive statistical analysis ·   Frequency tables ·   Graphing data (pie charts, bar graphs, scatter plots, line graphs) -Interactive Teaching   -Group work Bivariate data analysis ·   Correlation analysis ·   Association tests (chi-square tests) ·   Analysis of quantitative and qualitative variables (E.g Comparing means by different categories) ·   Tests for differences (One sample T-test, paired sample t tests, independent sample t-tests, ANOVA tests) -Interactive Teaching   -Individual presentation  – One on one guidance on computer Multivariate Analysis ·   Data modeling concepts ·   Model specification and selection criteria ·   Diagnostic tests for model specification errors ·   Linear regression models (simple and multiple regressions) ·   Count dependent variable models (poison regression models) ·   Binary dependent models (Logit and Probit models) ·   Categorical outcome models (Multinomial logistics models, ordered logit models) ·   Step wise regression models ·   Dummy variable regression models –Interactive Teaching   -Individual presentation – One on one guidance on computer