From the help desk: It's all about the sampling

Effective estimation and inference, when the data are collected using complex survey designs, requires estimators that fully account for the sampling design. This article explores, by means of Monte Carlo simulations of the power of simple hypothesis tests, the consequences of parameter estimation and inference when naive estimators are employed with survey data. Copyright 2002 by Stata Corporation.cluster, design, power, strata, svy, svymean, svyset

Survey data analysis in Stata

In this presentation, I cover how to use Stata for survey data analysis assuming a fixed population. We will begin by reviewing the sampling methods used to collect survey data, and how they affect the estimation of totals, ratios, and regression coefficients. We will then cover the three variance estimators implemented in Stata's survey estimation commands. Strata with a single sampling unit, certainty sampling units, subpopulation estimation, and poststratification will be also covered in some detail.

Investigating the effects of factor variables

Stata has a rich set of operators for specifying factor variables in linear and nonlinear regression models. I will show how to test for the effects of factor variables in these models. I will also show how to compare and contrast these effects using linear combinations of the model coefficients.

Analysis of survey data and correlated data

This talk discusses Stata's features for analyzing survey data and correlated data, and will explain how and when to use the three major variance estimators for survey and correlated data: the linearization estimator, balanced repeated replications, and the clustered jackknife (the latter two having been added in Stata 9). The talk will also discuss sampling designs and stratification, including Stata's new features for estimation with data from multistage designs and for applying poststratification. A theme of the seminar will be how you can make inferences with correct coverage from data collected by single stage or multistage surveys or from data with inherent correlation, such as data from longitudinal studies.

Survey data analysis in Stata

In this presentation, I cover how to use Stata for survey data analysis assuming a fixed population. We will begin by reviewing the sampling methods used to collect survey data, and how they affect the estimation of totals, ratios, and regression coefficients. We will then cover the three variance estimators implemented in Stata’s survey estimation commands. Strata with a single sampling unit, certainty sampling units, subpopulation estimation, and poststratification will be also covered in some detail.

New factor variables features in Stata

In this presentation, I cover how to use the new factor variables features in Stata 11. Stata’s new factor variables notation allows you to identify categorical covariates as factor variables, provides a convenient notation for specifying indicator variables without having to generate them, and allows interactions of factor variables with other factor variables or continuous covariates. We will also cover the new margins postestimation command. margins is a powerful yet easy-to-use command for computing expected marginal means, predictive margins, adjusted predictions, average marginal effects, and conditional marginal effects. Standard errors in margins can be estimated conditionally on the observed/specified covariate values or unconditionally via linearization.