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Key words: spss, computer, program, spss, database, spss, data, analysis, spss
SPSS Complex Samples Data Analysis
Everything you need for data analysis
As a researcher, you want to be confident about your results. Performing data analysis in SPSS Complex Samples
helps you to achieve more statistically valid inferences for populations measured in your complex sample data. SPSS Complex Samples
provides you with better results because, unlike most conventional statistical software, it incorporates the sample design into survey analysis. And, it easily plugs into SPSS Base
so you can seamlessly work in the SPSS environment.
SPSS Complex Samples provides you with two procedures to analyze data from sample survey data.
Complex Samples Descriptives (CSDESCRIPTIVES):
Estimates means, sums and ratios, and computes standard errors, design effects, confidence intervals hypothesis tests for samples drawn by complex methods. The procedure estimates variances by taking into account the sample design used to select the sample, including equal probability and probability proportionate to size (PPS) methods, and without replacement (WOR) sampling procedures. Optionally,
CSDESCRIPTIVES performs analyses for subpopulations.
You can also use CSDESCRIPTIVES to specify how to handle missing data:
- Base each statistic on all valid data for the analysis variable(s) used in computing the statistic. Compute ratios using all cases with valid data for both of the specified variables. You may base statistics for
different variables on different sample sizes.
- Base only cases with valid data for all analysis variables when computing statistics. Always base statistics for different variables on the same sample size.
- Exclude user-missing values among the strata, cluster and subpopulation variables.
- Include user-missing values among the strata, cluster and subpopulation variables. Treat user-missing values for these variables as valid data.
Complex Sample Tabulate (CSTABULATE):
Displays one-way frequency tables or two-way crosstabulations and associated standard errors, design effects, confidence intervals and hypothesis tests, for samples drawn by complex sampling methods. The procedure estimates variances by taking into account the sample design used to select the sample, including equal probability and PPS methods, and with replacement (WR) and WOR sampling procedures. Optionally,
CSTABULATE creates tables for subpopulations.
Use the following statistics within the table:
- Population size
- Standard error
- Row percentages
- Column percentages
- Table percentages
- Coefficient of variation
- Design effects
- Square root of the design effects
- Confidence interval
- Unweighted counts
- Cumulative population size estimates
- Cumulative percentages
- Expected population size estimates
- Pearson residuals
- Adjusted Pearson residuals
Use the following statistics and tests for the entire table:
- Test of homogeneous proportions
- Test of independence
- Odds ratio
- Relative risk
- Risk difference
Like CSDESCRIPTIVES, you can also use CSTABULATE to specify how to handle missing data. You can:
- Base each table on all valid data for the tabulation variable(s) used in creating the table. You may base tables for different variables on different sample sizes.
- Use only cases with valid data for all tabulation variables in creating the tables. Always base tables for different variables on the same sample size.
- Exclude user-missing values among the strata, cluster and subpopulation variables.
- Include user-missing values among the strata, cluster and subpopulation. variables. Treat user-missing values for these variables as valid data.
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Key words: spss, computer, program, spss, database, spss, data, analysis, spss
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