Analyze statistical data and interpret survey results from complex samples
IBM® SPSS® Complex Samples helps market researchers, public opinion researchers and social scientists make more statistically valid inferences by incorporating sample design into their survey analysis. SPSS Complex Samples provide the specialized planning tools and statistics you need when working with complex sample designs, such as stratified, clustered or multistage sampling.
Incorporate sample design into survey analysis
- Increase the precision of your sample or ensure a representative sample from key groups.
- Select clusters or groups of sampling units to make your surveys more cost-effective.
- Employ multistage sampling to select a higher-stage sample.
Retain survey planning parameters for future use
- Publish public-use data sets that include your sampling and analysis plans.
- Use published plans as a template in order to save decisions made when creating the plan.
- Make plans available to others in the organization so they can replicate results or pick up where you left off.
Manage complex survey data
- Display one-way frequency tables or two-way cross-tabulations and associated standard errors, design effects, confidence intervals and hypothesis tests.
- Build linear regression, analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models.
- Estimate means, sums and ratios, and compute standard errors, design effects confidence intervals and hypothesis tests for samples drawn by complex sampling methods.
- Perform binary logistic regression analysis and multiple logistic regression (MLR) analysis.
- Apply Cox proportional hazards regression to analysis of survival times.
Use an intuitive interface and helpful wizards
- Use the Analysis Preparation Wizard to specify how the samples are defined and how standard errors should be estimated.
- When creating your own samples, use the Sampling Plan Wizard to define the scheme and draw the sample.
- Use the IBM SPSS Complex Samples Selection (CSSELECT) procedure to select complex, probability-based samples from a population while mitigating the risk of over-representing or under-representing a subgroup.
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