Statistical Workshops

All statistical workshops are free and registration is first-come/first-seated.  Attendees must contact the instructor 24 hours before the class starts to request data and syntax files be emailed to them (stats@uic.edu).

Introductory-level Workshops

Introduction to SAS (Sept 15 | Sept 18)

Topics covered in this workshop include an overview of the SAS layout, defining and creating SAS libraries, importing Excel spreadsheets into SAS, SAS statements “DATA”, “INFILE”, “INPUT”; creating SAS variables, row & column include/exclude commands, variable labels, date and currency definition options, PROC statements for “PRINT”, “MEANS”, “FREQ”, and tables-options.  This workshop is offered twice this semester and the same material will be covered in each session.

Recommended prerequisite: Completion of a 200-level course in statistics.

Introduction to SPSS (Sept 22 | Sept 25)

Topics covered in this workshop include the different kinds of SPSS files, navigating the SPSS Graphical User Interface, using SPSS syntax, the variable-attributes screen, FREQUENCIES dialogue, importing Excel and text files into SPSS, the partial-correlation formula, cross-tabulations, and some basic graph functions. This workshop is offered twice this semester and the same material will be covered in each session.

Recommended prerequisite: Completion of a 200-level course in statistics.

Intermediate-level Workshops

Linear Multiple Regression (SPSS, Sept 29 | SAS, Oct 2)

Linear Multiple Regression: A start-to-finish example of some of the various procedures and options for univariate screening of data prior to producing a multiple-regression model (e.g., addressing common confounds to producing models, decision-rules for addressing missing-data issues, formulas to partially-ameliorate issues arising from data Missing Completely At Random, regression-selection methods, diagnostic plots, and detection of multicolinearity).

Recommended prerequisites: SAS or SPSS basic skills, comfort with using a syntax programming language, and completion of a 400-level course in statistics.

Cleaning Dirty Data (SPSS, Oct 6 | SAS, Oct 9)

Taught as a hands-on lesson with a realistic sample dataset and narrative context, this workshop teaches programming protocols for the screening and cleaning of various types of data errors (e.g., missing or invalid primary-keys, alphabetical entries in numeric fields, invalid values, data mis-keyed into incorrect fields). Lessons are useful for all professionals that manage and/or analyze data.

Prerequisites: Completion of ACCC's “Introduction to SAS/SPSS” and “Linear Multiple Regression” workshops, OR equivalent knowledge acquired through academics and/or workplace experience. Comfort with using a syntax programming language.

One-Way Analysis of Variance (SPSS, Oct 13 | SAS, Oct 16)

Analysis of Variance (ANOVA) is one of the most commonly-used statistical tests.  This class will cover “One-Way-Between-Subjects”, one of the most basic and frequently used models, employed to assess whether group-averages differ meaningfully due to some attribute(s) specific to one or more groups being studied.

Topics covered in this workshop include importing various genres of research data into SAS or SPSS, mathematical assumptions that must be met in order for ANOVA test results to be valid and reliable, relative effects of mathematical assumption violations on ANOVA testing, relative effectiveness of widely-accepted countermeasures to ANOVA assumption violations, using tabular or graphical means to identify assumption violations, and producing ANOVA graphs.

Recommended prerequisites: Completion of a 300- or 400-level course in statistics, completion of ACCC's “Introduction to SAS/SPSS” and “Linear Multiple Regression"; OR equivalent knowledge acquired through academics and/or workplace experience. Comfort with using a syntax programming language.

Logistics Regression (SAS, Oct 23)

TBA.

Last updated: 

July 25, 2014

Please consider providing an email address so that we may contact you for additional clarification and assistance.
By submitting this form, you accept the Mollom privacy policy.