Although it is named "SPSS Tutorials," there is a great deal of information about how statistical tests work that goes beyond SPSS. Created by Kent State University Statistical Consultations.
Section 1: Intro to the SPSS Environment:
Section 2: Working with Data covers data manipulation and cleaning of all kinds.
Section 3: Exploring the Data.
Section 4: Analyzing the Data. This section incorporates hypothesis testing.
Data is the raw ingredient(s) from which statistics are created. Statistics are useful when you just need a few numbers to support an argument (ex. In 2003, 98.2% of American households had a television set--from Statistical Abstract of the United States). Statistics are usually presented in tables. Statistical analysis can be performed on data to show relationships among the variables collected. Through secondary data analysis, many different researchers can re-use the same data set for different purposes.
Aggregate/Macro Data vs. Microdata
Aggregate or Macro Data are higher-level data that have been compiled from smaller units of data. For example, the Census data that you find on Explore Census Data have been aggregated to preserve the confidentiality of individual respondents. Microdata contain individual cases, usually individual people, or in the case of Census data, individual households. The Integrated Public Use Microdata Sample (IPUMS) for the Census provides access to the actual survey data from the Census, but eliminates information that would identify individuals.
Data Sets, Studies, and Series
In data archives, a data set or study is made up of the raw data file and any related files, usually the codebook and setup files. The codebook is your guide to making sense of the raw data. For survey data, the codebook usually contains the actual questionnaire and the values for the responses to each question. The setup files help will not display properly.
Types of Data
Cross-Sectional describes data that are only collected once.
Time Series study the same variable over time. The National Health Interview Survey is an example of time series data because the questions generally remain the same over time, but the individual respondents vary.
Longitudinal Studies describe surveys that are conducted repeatedly, in which the same group of respondents are surveyed each time. This allows for examining changes over the life course. The Project on Human Development in Chicago Neighborhoods (PHDCN) Series contains a longitudinal component that tracks changes in the lives of individuals over time through interviews.
(Originally from Sue Erickson at Vanderbilt University)
R is a language and environment for statistical computing and graphics which provides a wide variety of statistical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, etc. The program uses graphical techniques, and is highly extensible. R is similar to the S language and environment developed at Bell Laboratories (now Lucent Technologies), and most S code also runs in R.
R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of platforms, including Window,s MacOS, UNIX, FreeBSD, and Linux.
Tutorials - Paired Sample T Test
Tutorials - One Sample T Test
Tutorials from Universities
Tutorials - Independent Samples T-Test
Tutorials - One-Way ANOVA
Tutorials - More One-Way ANOVA