Descriptions of Statistical Computing Workshops
Introduction to Statistical Computing with SPSS, SAS, and Stata
In this hands-on workshop, participants will explore the world of statistical computing by taking a tour of the top three statistical software packages used at USC. This class will compare and contrast the strengths and weaknesses of each and discuss how to choose the right software for a particular project.
No prior experience with statistical software is needed.
Pre-analysis Data Screening with SPSS
Cleaning and screening data is an important part of the data analysis process. In this hands-on workshop, participants will screen a data set in preparation for analysis using SPSS. Topics such as techniques for dealing with missing data and non-normal distributions will be covered.
No experience with SPSS or previous data screening experience is required to participate.
Data Analysis Planning and Preparation (including Power Analysis)
This workshop will discuss how to plan in advance for the data analysis phase of your study and how to prepare your data for analysis once it is collected. Topics will include how to handle missing values, how to do a power analysis, and how to deal with complex or unbalanced samples.
Statistical computing experience is helpful, but not required, for this workshop.
Statistical Computing with SPSS I
SPSS is user-friendly software for statistical analysis with a convenient point-and-click interface. In this hands-on workshop, users will take a tour of SPSS’s basic functions. Participants will learn to enter data and set up variables, do some descriptive statistics, and have an introduction to advanced functions available in the IBM/SPSS Standard GradPack.
No prior experience with SPSS is needed to participate.
Statistical Computing with SPSS II
In this workshop, participants will use SPSS to conduct ANOVA and regression analysis. The class will also discuss how to become a more advanced user of SPSS and take a guided tour of some of the more advanced features in SPSS.
Some experience with SPSS is helpful, but not necessary in order to benefit from this workshop.
Statistical Computing with SAS I
SAS is a powerful and flexible suite of products for data management and analysis. The goal of this hands-on workshop is to become familiar with SAS 9.2 and SAS Enterprise Guide. Getting data into SAS and SAS Enterprise Guide will be demonstrated and participants will practice basic SAS programming using DATA steps and PROC steps to analyze descriptive statistics and beginning inferential statistics.
No prior experience with SAS or with programming needed to participate.
Statistical Computing with SAS II
In this hands-on workshop, participants will explore more procedures in SAS and learn more about the endless possibilities for working in the SAS environment.
This is still a beginning-level SAS workshop, so please feel free to join us even if you do not have any experience with SAS.
Choosing Appropriate Statistical Techniques
In this workshop, participants will discuss the process of decision making in data analysis planning and execution. Questions that will be address include:
- What types of research questions can be answered with the various statistical techniques?
- What type of variables are appropriate to study using a given type of analysis?
- What is the best way to explore data prior to analysis?
- What are some appropriate post-hoc tests to do after a main analysis?
- What are some ways to get around problems with data such as non-normal distributions, or lack of equality of variances in group comparisons?
This workshop in applied statistics is appropriate for researchers of all levels who have a grasp of basic descriptive and inferential statistics. Experience with statistical software is helpful, but not required.
Statistical Computing with JMP by SAS, Inc.
JMP (pronounced "jump") is user-friendly software made by SAS that enables users to explore and visualize data using a variety of tools for statistical analysis and interactive graphing. Compared to SAS, JMP is easier to install, takes up less hard drive space, and requires less RAM. While JMP can not handle the large amounts of data that SAS can, JMP has many advantages and is great for those who prefer a streamlined graphical user-interface as opposed to a programming approach to statistical computing. JMP is also available at no charge to all USC faculty, staff, and students.
In this workshop we will take a tour of JMP and discuss situations where JMP may be the proper tool to use for statistical computing.
No previous experience with JMP or other statistical computing software is required.