SPSS is a widely used program for statistical analysis in social science. It is also used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations, data miners, and others.
- First, tips and hints for the best WORKFLOW showing how to use commands step-by-step.
- Manage, edit and structure large databases ready for data analysis, up to 2 billion observations*.
- Generate descriptive statistics, summary tables, cross tabulations, frequencies and much more.
- Create powerful publication-quality graphs showcasing hidden info insights, edit and combine them in order to send clear messages.
- Do your own statistical models in order to predict and/or forecast your key interest variables or events.
- Complete guidance and orientation, the course is made into sections that show a natural progression order.
- Quizzes after each section, so you have a way to keep practicing on your own.
- The course is made in sections with lectures that grow into several Data Analysis/Estimations Projects considering all lectures and workflow taught in order for full grasp of STATA capabilities.
- Last but not least, be sure to look at the previews.
- According to STATA official capabilities (http://www.stata.com/new-in-stata/huge-datasets/)
- STATA (IC, SE or MP) version 12 or higher.
- Microsoft Office (2003 or higher) or OpenOffice
- Desire to master Data Analysis and STATA !
Who is the target audience ?
• Undergraduate and graduate students needing to do quantitative analysis for your own requirements
• Some of the careers that nowadays need quantitative analysis are : Biostatistics, Business Administration, Economics, Education, Epidemiology, Finance, Marketing Research, Medical Research, Political Science, Public Health, Public Policy, Sociology and many others.
STATA Course outlines :
1. Creating a working dataset from raw data
- Stata environment, help files, and third-party packages
- Using do-files : commenting
- Importing data : excel, CSV, public data source
- Data structure : wide vs long
- Cleaning the dataset : missing values, recode, renaming, and labeling
- Utilizing loops
- Describing the data : summarize
- Using log files
2. Data manipulation
- Generating new variables : gen, egen, xtiles
- Using the by command
- Logical expressions
- Using preserve and restore
- Keeping or dropping variables
- Creating dummy variables
- Combining datasets
- Using the collapse command
3. Basic statistical routines
- Creating a summary statistics
- Student t-test
- Cross tabulation and Chi-squared test
- Scatter plot
- Line plot
- Bar graph
- Pie chart
- Fitted regression line
- Overlaying two graphs : twoway
- Combining two graphs : graph combine
5. Regression Analysis
- Model specification
- Using the reg command
- Analytical weights
- Estimation of standard errors : robust, cluster, bootstrap
- Interaction terms
- Analysis of marginal effects : margins and marginsplot
- Using esttab to export regression results to .docx or LaTex