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SPSS Statistics 25 - Complex Samples versions & prices
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IBM SPSS Complex Samples incorporates complex sample designs into data analysis, with specialized planning tools and statistics, reducing the risk of reaching incorrect or misleading inferences for stratified, clustered or multistage sampling.

This module is indispensable for survey and market researchers, public opinion researchers or social scientists seeking to reach more accurate conclusions when working with sample survey methodology.

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IBM SPSS Statistics - 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. 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 for more accurate results.
  • Retain survey planning parameters for future use to speed analysis and increase efficiency.
  • Manage complex survey data for thorough, detailed analysis.
  • Use an intuitive interface and helpful wizards to analyze data and interpret survey results faster.

Desktop-Systems

  Windows® Mac® OS X Linux®
Further Requirements Super VGA-Monitor (800x600) or higher Resolution
For a connection to SPSS Statistics Base Server, you will need a network adapter for TCP/IP-Network protocol
Internet Explorer
Super VGA-Monitor (800x600) or higher Resolution
Webbrowser: Mozilla Firefox
Super VGA-Monitor (800x600) or higher Resolution
Webbrowser: Mozilla Firefox
Operating System Windows XP, Vista, 7, 8, 10 (32-/64-Bit) Mac OS X 10.7 (32-/64-Bit), Mac OS X 10.8 (only 64-Bit!) Debian 6.0 x86-64, Red Hat Enterprise Linux (RHEL) 5 Desktop Editions, Red Hat Enterprise Linux (RHEL) Client 6 x86-64:
  • Linux (64 bit) kernel 2.6.28-238.e15 or higher
  • FORTRAN version libgfortran.so.3
  • C++ Version libstdc++.so.6.0.10
Min. CPU Intel or AMD-x86-Processor 1 GHz or better Intel-Processor (32-/64-Bit) Intel or AMD-x86-Processor 1 GHz or better  
Min. RAM 1 GB RAM + 1 GB RAM + 1 GB RAM +
Festplattenplatz Min. 800 MB Min. 800 MB Min. 800 MB

Server-Systems

  SPSS Statistics Server
Further Requirements For Windows-, Solaris-PC's: Network adapter with TCP/IP-Network protocol
For System z-PC's: OSA-Express3 10 Gigabit Ethernet, OSA-Express3 Gigabit Ethernet, OSA-Express3 1000BASE-T Ethernet
Operating System Windows Server 2008 or 2012 (64-Bit), Red Hat Enterprise Linux 5 (32-/64-Bit), SUSE Linux Enterprise Server 10 and 11 (32-/64-Bit)

Details can be found in the the following PDF-document:System Requirements SPSS Statistics Server 22
Min. CPU  
Min. RAM 4 GB RAM +
Disk Space ca. 1 GB for the installation. Double the amount may be needed.

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.