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.
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.)
- Creating Functions
R is an extremely powerful tool for data modeling, visualization, and general programming. In many practical applications of statistics, the vast majority of time is spent preparing the data for eventual analysis. However, this also where many practitioners who use R often have relatively little training. In recent years, a variety of packages have become available to make data wrangling, summarizing, generation and other common operations more straightforward, and easier to read for future use (e.g. via piping and clearer syntax). In addition, some newer visualization packages work these approaches, allowing one to go quite seamlessly from raw data to interactive graphics. This workshop will introduce participants to a handful of tools that can make their data exploration and analytical flow more streamlined and reproducible.
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.
This workshop will help participants develop skills in defining, estimating and testing graphical and latent variable models, and structural equation models in particular. After a general overview of concepts, regression approaches with observed variables will be demonstrated (e.g. path analysis). This will be followed by an overview of latent variable modeling techniques (‘factor analysis’), including categorical latent variables (mixture models). The second part of the course will focus on SEM generally, including model construction, interpretation, and issues in assessing such models. Specific time may be spent on a popular class of SEM, latent growth curve models, and time permitting, an overview of extensions and related techniques will be provided.