IBM® SPSS® Complex Samples

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. IBM® 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.

Need help leveraging the power of IBM® SPSS® software? Let us help you with our analytical expertise and experience - contact us today.