Introduction to Stata

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Note: Topics are subject to change.  Participants must sign up for the entire series.

This workshop introduces participants to the use of Stata for statistical analysis and data management. After an introduction to the fundamentals of the Stata environment, the workshop introduces importing and entering data, managing data sets, performing statistical analyses (including descriptive analysis, hypothesis testing, regression analysis, and analysis of survey data), and graphing tools within Stata.  The workshop will be taught using Stata for Windows.


This portion introduces Stata for Windows, including the menus, help systems, search systems, and main windows within the Stata Environment. Entering data into Stata and defining variable attributes is introduced, in addition to the various methods of importing external data files into Stata. Using the menus to set up commands and procedures is contrasted with entering Stata commands interactively.

Data Management

This section introduces working with date and time variables, generating new variables and replacing values in existing variables, sorting data files, merging data files, computing dummy variables, and keeping and dropping variables and cases. Methods for describing and comparing data sets are also introduced.

Statistical Analysis

This section introduces basic descriptive and summary analyses in Stata, including standard numerical summaries of continuous variables, frequency and cross-tabulation analysis, and hypothesis testing for means and proportions. Commands for common regression analysis procedures (including linear and logistic regression) are also introduced, along with methods for analyzing data from complex sample surveys. Methods for analyzing subsets of data and performing analyses stratified by a categorical variable are also covered.

Additional Topics

The workshop will also provide participants with an introduction to commonly used graphing tools in Stata. And a basic introduction to Stata .do files.

Statistics: A Review

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A one-day, intensive review of common statistical methods of design, measurement analysis and presentation of scientific investigations.  The workshop is designed for any scholar engaged in quantitative research.

Statistics: A Review discusses answers to the following questions:

  • What should we measure?
  • What are the main design types; what are the comparative advantages of each?
  • How are the sample sizes determined?
  • What are the appropriate inference procedures?
  • What do standard error, p-value and confidence level mean?
  • What are some dangers we need to avoid?
  • How should we display our results?
  • What are the statistical software options?

Introduction to SPSS

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Note: Topic order is subject to change.  Participants must sign up for the entire series.


This portion introduces SPSS for Windows, the menu and the help systems, the three main types of files used, and printing from within SPSS.  It then addresses defining variables, attaching labels, defining missing values, and various ways to enter data into SPSS.  Finally, it covers a brief introduction to obtaining frequency distributions, descriptive statistics, and cross tabulations of variables.

Within-Case Transformations

This portion introduces data management capabilities, including recoding variables (manual and automatic), computing new variables using formulas, and counting occurrences of values within subjects.  Attention then turns to temporary transformations, conditional processing of transformations, and repetitive transformations.  SPSS syntax is also introduced.

Data Management with Multiple Files

This portion begins with a discussion of subsetting data files by drawing samples, selecting groups and excluding groups from analysis.  Then, the two main methods of merging SPSS data files are covered: adding additional variables and adding additional cases.  Next, creating aggregated data sets and applying aggregated data to individuals is covered.  Lastly, importing and exporting data between SPSS and other statistical programs (Excel, dBase, SAS) is demonstrated.

Basic Statistics and Graphics

This portion covers basic exploratory procedures, including obtaining percentiles, frequencies, descriptive statistics, and cross tabulations. Basic comparative procedures including two-sample t-tests, paired t-tests, and one-way analysis of variance are also covered.  Then, simple bivariate correlation analysis is introduced.  Participants are given a basic introduction to commonly used graphical procedures for displaying data, including scatter plots, bar graphs, histograms, and boxplots.

Applied Survival Analysis

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This workshop covers basic concepts and common analytical approaches for time-to-event data, known variously as survival analysis (in biological and medical sciences), event history analysis (in social sciences), or reliability analysis (in engineering).  The workshop will be held in a computer lab and methods will be illustrated with hands-on exercises in SAS, R, SPSS, and/or Stata, as needed.  Topics include Kaplan-Meier estimation, two-sample comparisons, Cox proportional hazards regression, and discrete time models.

Intermediate Topics in SPSS: Data Management and Macros

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Note: This is a companion workshop of “Intermediate Topics in SPSS: Advanced Statistical Models,” and participants may register for one or both days.

This two-half-days workshop is designed to provide experienced SPSS users (see prerequisites below) with hands-on exposure to more advanced data management topics in SPSS, using SPSS for Windows. The workshop will cover the following topics at a moderate pace: Restructuring Data Sets, Updating Data Sets, Using Frequency Weights in SPSS Base (and why the Complex Samples Module is needed for sampling weights), Advanced Syntax, Macros, What’s new in the latest versions of SPSS, and Additional SPSS Modules. There will also be time for an open discussion of managing data in SPSS.

Introduction to SAS®

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Note: Topic order is subject to change. Participants must sign up for the all sessions.

Fundamentals: This portion introduces SAS for Windows environment, creating and submitting command files, printing output and simple trouble shooting techniques. Basics of how to read in raw data from different types of files are covered. Simple methods for data checking also are demonstrated.

Transformations and Recodes: This portion introduces the use of SAS to create new variables using formulas, recoding continuous variables into categories, creating dummy variables, the use of dates in SAS and defining missing values.

Data Management: This portion covers how to create and read permanent SAS datasets, basics of how to combine SAS data sets, both to add cases and to add variables.

Importing Data: This portion introduces the basics of importing data from other programs, such as Excel, Access and SPSS into SAS. Guidelines for preparing data for use with other programs are covered.

Introduction to Programming in Stata

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Note: Topic order is subject to change. Participants must sign up for all the sessions. Please note the course prerequisites below.

This workshop introduces participants to programming in Stata. The workshop will cover many of Stata’s commands designed to make repetitive tasks easier. Topics such as indexing, looping, using calculated results, macros, and the creation of programs will be covered and these concepts will be taught through many hands-on exercises.  Familiarity with Stata is suggested. The workshop will be conducted using Stata for Windows.

Topics include:

  • Interactive and mechanical programming modes
  • Understanding the difference between Stata .do files, programs and .ado files
  • Indexing
  • Looping and branching
  • Local macros
  • Debugging tools
  • Using Stata’s calculated results stored in r() and e()
  • Getting started with programs and .ado files

Intermediate Topics in SPSS: Advanced Statistical Models

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This two-half-day workshop (see prerequisites below) with hands-on exposure to more advanced statistical analysis techniques in SPSS, using SPSS for Windows.

The workshop will cover the following topics at a moderate pace: General Linear Models, Repeated Measures Analysis of Variance (ANOVA), Linear Mixed Models / Hierarchical Linear Models, Nonparametric Analysis, and Reliability Analysis. There will also be time for an open discussion of participant issues with analyzing data in SPSS.

Regression Analysis

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This workshop will provide participants with an overview of commonly used methods in simple linear regression and multiple linear regressions. There will be both lecture and hands-on computer work, using SPSS. Topics will include: the basic regression model, model assumptions, interpretation of coefficients, significance testing, interactions between variables and the use and interpretation of dummy variables. Model checking methods, including residual plots, collinearity diagnostics, and influence plots will also be covered. Several methods for model selection, including all possible regressions and stepwise selection will be included.

Statistical Analysis with R

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This workshop will introduce participants to R. R is a free and open source environment for data analysis and statistical computing.  While R contains many built-in statistical procedures, the most unique feature of R is the facility for users to extend these procedures to suit their own needs.  Excellent graphics are another reason R is gaining wide popularity.

  • How to Obtain R
  • Help Tools
  • Importing / Exporting Data
  • Data Management
  • Descriptive Statistics
  • Multivariate Statistical Analyses (Regression Modeling, ANOVA, etc.)
  • Graphics
  • Creating Functions