Introduction to SPSS
September 12 @ 9:00 am - 12:30 pm
Modern Languages Building (MLB), Room 2001A
Audience: Never before SPSS users who will be using SPSS for Windows. Those using SPSS for Unix or Macintosh should email the instructor at email@example.com before enrolling.
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.
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.
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.