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Introduction to SPSS Complex Samples Procedures. Properties of Complex Samples A complex sample can differ from a simple random sample in many ways. In a simple random sample, individual sampling units are selected at random with equal probability and without replacement (WOR) directly from the entire population. By contrast, a given complex sample can have some or all of the following features: Stratification. Stratified sampling involves selecting samples independently within non-overlapping subgroups of the population, or strata. For example, strata may be socioeconomic groups, job categories, age groups, or ethnic groups. With stratification, you can ensure adequate sample sizes for subgroups of interest, improve the precision of overall estimates, and use different sampling methods from stratum to stratum. Sampling from a Complex Design. The Sampling Wizard guides you through the steps for creating, modifying, or executing a sampling plan file. Before using the Wizard, you should have a well-defined target population, a list of sampling units, and an appropriate sample design in mind. Complex Samples Crosstabs. The Complex Samples Crosstabs procedure produces crosstabulation tables for pairs of selected variables and displays two-way statistics. Optionally, you can request statistics by subgroups, defined by one or more categorical variables. Complex Samples Ratios The Complex Samples Ratios procedure displays univariate summary statistics for ratios of variables. Optionally, you can request statistics by subgroups, defined by one or more categorical variables. Complex Samples General Linear Model. Example. A grocery store chain surveyed a set of customers concerning their purchasing habits, according to a complex design. Given the survey results and how much each customer spent in the previous month, the store wants to see if the frequency with which customers shop is related to the amount they spend in a month, controlling for the gender of the customer and incorporating the sampling design. Statistics. The procedure produces estimates, standard errors, confidence intervals, t tests, design effects, and square roots of design effects for model parameters, as well as the correlations and covariances between parameter estimates. Measures of model fit and descriptive statistics for the dependent and independent variables are also available. Additionally, you can request estimated marginal means for levels of model factors and factor interactions.
Tags : different sampling methods, target population, sampling units, grocery store chain, stratified sampling, categorical variables, sampling plan, customers shop, model example, socioeconomic groups, summary statistics, linear model, random sample, stratum, stratification
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