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.

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.

Determining Sufficient Sample Size

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This workshop outlines how to calculate an appropriate sample size (n) to address the objectives of a research project. Participants will be led through essential steps for the design of a study: specifying the outcome variable, outlining hypothesis tests, estimating the variance or other “nuisance parameters,” determining power to detect particular differences, and balancing these considerations against cost to arrive at a final sample size.

Participants have hands-on instruction in a computer classroom, using different sample size software such as nQuery Advisor (available via virtual sites and presented mostly by the instructor) or built-in sample size applications within Stata or SAS (hands-on experience during workshop) to compute sample sizes and plot power curves.  (This workshop does not cover survey research designs.)

Sample size calculations to be covered include:

* comparisons of two means or proportions

* analysis of variance (ANOVA) designs

* repeated measures designs

* regression designs

* case-control study designs.

Special requests from those attending the workshop are welcome.

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.

Introduction to Survey Design: Data Collection, Questionnaire Design and Response Processes-Lecture

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This lecture-format workshop will present an overview of available modes and methods of survey data collection as well as an introduction to the survey response process and implications for questionnaire design.  Participants will gain an appreciation of the tradeoffs inherent in survey design decisions and how design can affect data quality and survey errors. Topics will include:

  • Survey errors, in particular measurement, coverage, and nonresponse error.
  • What to consider when selecting a data collection method for a particular research question.
  • Measurement (response) error and how to reduce it through question wording/format and questionnaire structure.
  • The role of the interviewer and interviewer effects.

Applied Survival Analysis

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This workshop, held over two days, covers basic concepts of 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).

Statistical Analysis with R

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R is a free and open source environment for data analysis and statistical computing.  While R contains many built-in statistical procedures, a powerful feature of R is the facility for users to extend these procedures to suit their own needs.  Excellent graphing capability is another reason R is gaining wide popularity.


  • How to Obtain R
  • Help Tools
  • Importing / Exporting Data
  • Data Management
  • Descriptive and Exploratory Statistics
  • Common Statistical Analyses (t-test, Regression Modeling, ANOVA, etc.)
  • Graphics
  • Creating Functions

Issues in Analysis of Complex Sample Survey Data

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This workshop will provide participants with an introductory, hands-on overview of issues frequently encountered when conducting secondary computer analyses of survey data collected from samples with complex, multi-stage designs (e.g., PSID, NHANES, NCS), including design-based weight determination, software choice, and proper analysis methods.  The workshop is not intended for participants looking to design a survey, but rather for participants who have a desire to analyze complex sample survey data.  Topics to be covered include:


  • Recognizing a sample with a complex design
  • Calculation of sample weights based on sample designs / non-response / post-stratification
  • Calculation of new weights for subgroups / longitudinal analyses
  • Weighted vs. unweighted analyses
  • Calculation of correct confidence intervals for population quantities
  • Hypothesis Testing based on sample estimates
  • Design Effects
  • Software packages capable of complex sample survey data analysis
  • Common analysis methods (linear modeling, descriptive statistics), interpretation of results
  • Hands-on examples using procedures in SAS and Stata to analyze real survey data (web links to example code in other software packages, such as SPSS and R, will be provided as well)