Jesus Salcedo

This Cheat Sheet is a handy reference to some of the most commonly used data preparation techniques in SPSS Statistics. It also includes information about the different types of graphs you can create, given the level of measurement of the variables. You'll also find some of the questions you should ask yourself when first looking at a data set in SPSS Statistics.

After you bring data into SPSS Statistics, the next step is to select a procedure. The Analyze menu contains a list of reporting and statistical analysis categories. Most of the categories are followed by an arrow, which indicates that several analytical procedures are available in the category; these appear on a submenu when the category is selected.

IBM SPSS Statistics is an application that performs statistical analysis on data. To perform statistical analyses correctly, you need to know the level of measurement of the variables because it defines which summary statistics and graphs should be used. It also helps to know the most commonly used procedures in the Analyze menu and possible conclusions that you can reach after conducting a statistical test.

The following table provides a list of some of the most commonly used procedures in the Analyze menu in SPSS Statistics. Menu Submenu Useful For Code Book Reports Provides a quick look at all your variables at once. The level of measurement automatically controls which summary statistics are displayed. Frequencies Descriptives Tells you how many of each category value you have.

When choosing a graph, you need to know the level of measurement of the variables. The following table shows some of the graphs that can be used to display relationships between different types of variables. Categorical Dependent Scale Dependent Categorical Independent Clustered bar or paneled pie Error bar

Level of measurement defines which summary statistics and graphs should be used. The following table provides definitions, examples, appropriate summary statistics, and graphs for variables based on their level of measurement. Nominal Ordinal Scale Definition Unordered categories Ordered categories Both inte

When conducting a statistical test, too often people jump to the conclusion that a finding “is statistically significant” or “is not statistically significant.” Although that is literally true, it doesn't imply that only two conclusions can be drawn about a finding.What if in the real world no relationship exists between the variables, but the test found that there was a significant relationship?