How do you do a chaid analysis in SPSS?

Posted on by Muna Meyer

How do you do a chaid analysis in SPSS?

Building the CHAID Tree Model

  1. To run a Decision Tree analysis, from the menus choose: Analyze > Classify > Tree…
  2. Select Credit rating as the dependent variable.
  3. Select all the remaining variables as independent variables.

Can chaid handle missing values?

CHAID and Exhaustive CHAID treat all system- and user-missing values for each independent variable as a single category. For cases in which the value for that variable is missing, other independent variables having high associations with the original variable are used for classification.

What is chaid decision tree?

Chi-square automatic interaction detection (CHAID) is a decision tree technique, based on adjusted significance testing (Bonferroni testing). Like other decision trees, CHAID’s advantages are that its output is highly visual and easy to interpret.

How do you list cases in SPSS?

Overview (LIST command)

  1. Selecting and Ordering Variables. You can specify a list of variables to be listed using the VARIABLES subcommand.
  2. Format. You can limit each case listing to a single line, and you can display the case number for each listed case with the FORMAT subcommand.
  3. Selecting Cases.

How do you select cases in SPSS with two variables?

You go to Data->Select Cases->and Click on ‘If condition is satisfied’ You then click on the ‘IF’ push button, highlight my variable, and click on the middle arrow to bring it over to the Expression box. You then specify ‘var=1’ AND ‘var=2’. When you do so, all the cases become unselected.

What is CHAID decision tree?

What is CHAID model?

Chi-square Automatic Interaction Detector (CHAID) was a technique created by Gordon V. Kass in 1980. CHAID is a tool used to discover the relationship between variables. CHAID analysis builds a predictive medel, or tree, to help determine how variables best merge to explain the outcome in the given dependent variable.

What is variance calculation?

In statistics, variance measures variability from the average or mean. It is calculated by taking the differences between each number in the data set and the mean, then squaring the differences to make them positive, and finally dividing the sum of the squares by the number of values in the data set.