Analyze statistical data and interpret survey results from complex samples

 

The Complex Samples Module allows you to build sample designs by providing statistical estimates tailored for complex data samples, enhancing both the precision and representativeness of the sample.

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It can be used in the context of:

• Healthcare research: Analyzing large datasets to disseminate information on public health, national or global nutrition, alcohol consumption trends, or the incidence of road accidents as a cause of hospitalizations due to injuries.

• Social sciences: Conducting public polls or grouping certain attitudes toward political issues.

• Market surveys: Analyzing customer satisfaction data.

Statistics Complex Samples provides advanced statistical and planning tools for designing complex sampling plans, allowing you to:

• Integrate sampling designs into survey analysis for more accurate results.

• Save survey planning parameters for future use, speeding up analysis and increasing efficiency.

• Manage complex survey data for in-depth and detailed analysis.

• Use an intuitive interface and helpful wizards to facilitate the analysis and interpretation of survey results.

 

Key Functionalities

Integrate sampling designs into survey analysis:

• Increase sample precision and obtain representative samples from the starting population.

• Select clusters to conduct more cost-effective surveys.

Store survey planning parameters for future use:

• Save complex sampling plans as templates for use with new populations.

• Share and save sample designs for later retrieval.

Manage complex survey data:

• View frequency tables or cross-tabulations with associated standard errors, design effects, confidence intervals, and hypothesis tests.

• Develop linear regression models, analysis of variance (ANOVA), and analysis of covariance (ANCOVA).

• Estimate means, sums, and percentages; calculate standard errors, design effect confidence intervals, and hypothesis tests for complex sample models.

• Perform binary logistic regression and multiple logistic regression (MLR) analyses.

• Apply Cox proportional hazards regression for survival analysis.

Use an intuitive interface and wizards:

• Utilize the Analysis Preparation Wizard (APW) to define how samples are defined and standard errors are estimated.

• Use the Sampling Plan Wizard to define sampling patterns and design samples for custom templates.

• Apply the Complex Samples Selection (CSSELECT) procedure to select complex samples based on probabilities, reducing the risk of subset misrepresentation.