ACCC offers hands-on statistical workshops for current employees and students of UIC. This fall features a SAS workshop series and an SPSS workshop series will be offered in the spring. There is no charge for these workshops. Please review our new Statistical Software Workshop Guidelines and Registration Procedures. To sign up, click on the date of the workshop that you like to attend, review the event information, and email stats@uic.edu with the requested information.

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.

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

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.

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.

Recommended 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.

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” workshops; OR equivalent knowledge acquired through academics and/or workplace experience. Comfort with using a syntax programming language.

This class will cover Logistic Binary Regression, which is employed to build predictive models when the outcome of research interest is dichotomous (e.g., “patient died/survived” or “customer purchased/didn’t purchase”). This class is comprised of hands-on, applied, SAS programming exercises. To instill the course with “real world” context, all instruction will revolve around the same dataset.

Recommended prerequisites: completion of 400-level statistics classes, an aptitude for and an interest in multivariate statistical testing; and, a level of competency writing SAS programming language which is equivalent to having completed the other four ACCC-sponsored SAS workshops.

Due to its particular usefulness as a hypothesis-test for evaluating experimental treatments in human-subjects studies over time, r-ANOVA is widely used for clinical research as well as many other topically diverse areas like agriculture, economics, engineering, marketing, and psychology.

This workshop will cover 1) a brief refresher of the r-ANOVA formula and of the "mathematical mechanics" that it performs, 2) some conditions under which r-ANOVA may - or may not - be the most appropriate hypothesis-test for a dataset, 3) a few basic diagnostic protocols to help determine if, and how much, the mathematical properties of a dataset could weaken or even confound r-ANOVA test-results, 4) practical examples of some types of research confounds that cannot be directly assessed through statistical diagnostics-tests, 5) methods commonly used to partially-compensate for some violations of assumptions, 6) the SAS programming syntax required to perform metric-diagnostic tests, run the F-Test and alternatives to the F-Test, run post-hoc tests and alternative post-hoc tests, generate graphs for diagnostic-assessments and presentation of r-ANOVA test-results; 7) reporting of test-results and interpretations.

Recommended prerequisites: completion of graduate-level statistical coursework which covered the mathematics and assumptions of r-ANOVA, a level of competency writing SAS programming language.

Last updated:

August 31, 2015