User Guide

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Chapter 34

NPAR TESTS Command Additional Features (Chi-Square Test)

The command language also allows you to:

 Specify different minimum and maximum values or expected frequencies for

different variables (with the

CHISQUARE subcommand).

 Test the same variable against different expected frequencies or use different

ranges (with the

EXPECTED subcommand).

See the SPSS Command Syntax Reference for complete syntax information.

Binomial Test

The Binomial Test procedure compares the observed frequencies of the two categories

of a dichotomous variable to the frequencies expected under a binomial distribution

with a specified probability parameter. By default, the probability parameter for

both groups is 0.5. To change the probabilities, you can enter a test proportion for

the first group. The probability for the second group will be 1 minus the specified

probability for the first group.

Example. When you toss a dime, the probability of a head equals 1/2. Based on this

hypothesis, a dime is tossed 40 times, and the outcomes are recorded (heads or tails).

From the binomial test, you might find that 3/4 of the tosses were heads and that the

observed significance level is small (0.0027). These results indicate that it is not

likely that the probability of a head equals 1/2; the coin is probably biased.

Statistics. Mean, standard deviation, minimum, maximum, number of nonmissing

cases, and quartiles.

Data. The variables tested should be numeric and dichotomous. To convert string

variables to numeric variables, use the Automatic Recode procedure, available on the

Transform menu. A dichotomous variable is a variable that can take on only two

possible values: yes or no, true or false, 0 or 1, and so on. If the variables are not

dichotomous, you must specify a cut point. The cut point assigns cases with values

greater than the cut point to one group and the rest of the cases to another group.

Assumptions. Nonparametric tests do not require assumptions about the shape of the

underlying distribution. The data are assumed to be a random sample.