Complex Samples
What SPSS Complex Samples can do for your business
IBM® SPSS® Complex Samples software can compute statistics and standard errors from complex sample designs by incorporating the designs into survey analysis.
- This solution helps you produce a more accurate picture by allowing subpopulation assessments to consider other subpopulations.
- SPSS Complex Samples offers planning tools such as stratified, clustered or multistage sampling.
- It's designed to help you reach correct point estimates, predict numerical and categorical outcomes from nonsimple random samples, and account for up to 3 stages when analyzing data from a multistage design.
This module is included in the SPSS Premium package and the IBM® SPSS® Statistics Premium edition for traditional license use and as part of the IBM® SPSS® Complex Sampling and Testing add-on for subscription plans.
Try the interactive product tour of SPSS Statistics to see how easily you can extract actionable insights to optimize your decisions.
Benefits of SPSS Complex Samples
Increase the precision of your sample or help ensure a representative sample from key groups. Employ multistage sampling to select a higher-stage sample.
Retain survey planning parameters. Publish data sets and reuse plans as templates. Select clusters or groups of sampling units to make surveys more cost-effective.
Display one-way frequency tables or two-way cross-tabulations. Build linear regression with ANOVA and ANCOVA. Estimate means, sums, ratios and more.
Use helpful wizards to specify how samples are defined and standard errors estimated. Define schemes and draw samples. Select complex, probability-based samples from a population.
Feature spotlights
Start with one of the wizards. Then, use the interactive interface to create plans, analyze data and interpret results. Each plan acts as a template and enables you to save decisions. Then, use the procedures specifically developed for complex samples to predict numerical, ordinal and categorical outcomes or time to a specific event.
Predict categorical outcomes (for example, who is most likely to buy your product) while taking the sample design into account to more accurately identify groups through logistic regression. Ordinal regression: predicts ordinal outcomes such as customer satisfaction (low, medium or high). Cox regression: predicts time to an event for samples drawn by complex sampling methods. General linear models: predicts numerical outcomes while taking the sample design into account.
Have an intuitive sampling wizard guide you through the process of designing and drawing a sample. The analysis preparation wizard helps prepare public-use datasets for analysis, such as the National Health Inventory Survey data from the Centers for Disease Control and Prevention (CDC).
Increase the precision of your sample or help ensure a representative sample from key groups by choosing to sample within subgroups of the survey population through stratified sampling. Clustered sampling: select groups of sampling units (clusters). Multistage sampling: select an initial sample based on groups of elements in the population. Then, create a second-stage sample by drawing a subsample from each selected unit in the first-stage sample.
Technical details
IBM SPSS Complex Samples requires a valid IBM SPSS Statistics base license.
- Prerequisite: IBM SPSS Statistics
- Processor: 2 GHz or faster
- Display: 1024 × 768 or higher
- Memory: 4 GB of RAM required, 8 GB of RAM or more recommended
- Disk space: 2 GB or more
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