SPSS Statistics - Complex Samples

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.