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SPSS Complex Samples
SPSS Complex Samples™ 16.0 enhancements
SPSS Complex Samples now includes the Cox Regression technique for time-to-event data. If you have data based on a complex sample design, you can use this technique to accurately predict the time to a specific
event—how long a high-value customer remains active, for example, or how long people fitting a certain profile will survive a certain medical condition.
SPSS Complex Samples Cox Regression (CSCOXREG) enables you to more easily analyze differences in subgroups as well as the effects of a set of predictors. The procedure takes the sample design into account when
estimating variances, and can handle data involving multiple cases, such as multiple patient visits, encounters, and observations.
In addition, the Complex Samples Select (CSSELECT) routine is multithreaded, to improve performance on computers with multiple processors and multi-core processors.
Correctly and easily compute statistics for complex sampling.
Do you analyze data from survey or market research, public health datasets or government agencies? Do you use sample survey methodology in your research, or are your data likely to come from a public-use dataset that
includes complex sample designs? Are you confident that the statistical methods you use to analyze sample survey data provide you with the most accurate results?
If you're working with complex sample designs, such as stratified, clustered or multistage sampling, you need specialized statistical techniques to account for the sample design and its associated standard errors.
The new SPSS Complex Samples add-on module for SPSS 15.0
provides the specialized planning tools and statistics you need when working with sample survey data. It enables you to make more statistically valid inferences for a population by incorporating the sample design into survey analysis. It's an indispensable statistical tool for survey and market researchers, public opinion researchers or social scientists, and enables you to reach more accurate conclusions when working with sample survey methodology.
Work efficiently and easily using SPSS Complex Samples
Only SPSS Complex Samples
makes understanding and working with your complex sample survey results easy. Through the intuitive interface, you can analyze data and interpret results. When you're finished, you can publish public-use datasets and include your sampling and analysis plans. These plans act as a template and allow you to save all the decisions made when creating the plan - define it once and you're done. This saves time and accuracy for yourself and others who may want to plug your plans into the data to replicate results or pick up where you left off.
To begin your work in SPSS Complex Samples, use the wizards, which prompt you for the many factors you must consider before you start planning. If you are creating your own samples, use the Sampling Wizard to
define the scheme and draw the sample. If you're using public-use datasets that already have samples, such as those provided by the Centers for Disease Control and Prevention (CDC), use the Analysis Plan Wizard to
specify how the samples were defined and how standard errors should be estimated. Once you create a sample or specify standard errors, you can create plans, analyze your data and produce results (see diagram below
for workflow).
Accurate analysis of survey data is easy in SPSS Complex Samples. Start with one of the
wizards (which one depends on your data source) and then use the interactive interface to create plans, analyze data and interpret results.
You can use the following types of sample design information with SPSS Complex Samples:
- Stratified sampling - increase the precision of your sample or ensure a representative sample from key groups by choosing to sample within
subgroups of the survey population. For example, subgroups might be a specific number of males or females or contain people in certain job categories, people of a certain age group and so on.
- Clustered sampling - select clusters which are groups of sampling units for your survey. Clusters can include schools, hospitals or geographic areas with sampling units that might be students, patients
or citizens. Clustering often helps makes surveys more cost-effective.
- Multistage sampling - select an initial or first-stage sample based on groups of elements in your population; then create a second-stage sample by drawing a sub-sample from each selected unit in the first
-stage sample. By repeating this option, you can select a higher-stage sample. For example, in a face-to-face survey, you might sample individuals within households and city blocks.
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