IndependentSamples Proportions
The IndependentSamples Proportions procedure provides tests and confidence intervals for the difference in two independent binomial proportions. The data are assumed to be from a simple random sample, and each hypothesis test or confidence interval is a separate test or individual interval. Output includes observed proportions, estimates of differences in population proportions, asymptotic standard errors of population differences under null and alternative hypotheses, specified test statistics with twosided probabilities, and specified confidence intervals for differences in proportions.
 Example
 Statistics
 AgrestiMin, BonettPrice, Newcombe, Wald, Wald (continuity corrected), Exact Binomial, Midp Adjusted Binomial, McNemar, McNemar (continuity corrected).
Data Considerations
 Data

 At least one dependent variable and a single variable to identify the two groups to be compared are required.
 The grouping variable can be either numeric or string.
 Assumptions
Obtaining IndependentSamples Proportions tests
This feature requires the Statistics Base option.
 From the menus choose:
 Select one or more quantitative test variables.
 Select a single Grouping Variable that identifies the two groups to be compared.
 Optionally, specify settings for the selected Grouping
Variable.
 When Value(s) is selected, you can specify two numeric or string values within parentheses for the values to be compared. String values should be enclosed in single quotes. Cases with other values are ignored.
 Midpoint applies only to numeric variables. Cases at or above the midpoint of the distribution of the grouping variable are assigned to the second group, cases below the midpoint are assigned to the first group.
 Cut Point applies only the numeric variables and allows specification with parentheses of a single numeric value. Cases at or above the cut point on the grouping variable are assigned to the second group, cases below the cut point are assigned to the first group.
 Optionally, you can:
 Select success criteria settings under the Define
Success section:
 Last Value
 The last or highest value among the sorted distinct values in the data is used. This applies to numeric or string variables. This is the default setting.
 First Value
 The first or lowest value among the sorted distinct values in the data is used. This applies to numeric or string variables.
 Value(s)
 One or more parenthesized specific values. Multiple values must be separated by spaces. This applies to numeric or string variables. String variable values should be enclosed in single quotes.
 Midpoint
 Values at or above the middle of the range of observed values in the data. This applies only to numeric data.
 Cut Point
 Values at or above a specified value. This applies only to numeric data.
 Click Confidence Intervals... to specify which types of confidence intervals are displayed, or to suppress all confidence intervals.
 Click Tests... to specify which types of test statistics are displayed, or to suppress all tests.
 Click Missing Values... to control the treatment of missing data.
 Click Bootstrap... for deriving robust estimates of standard errors and confidence intervals for estimates such as the mean, median, proportion, odds ratio, correlation coefficient or regression coefficient.
 Select success criteria settings under the Define
Success section:
 Click OK.
This procedure pastes PROPORTIONS command syntax.