PubMed · 14374353
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M D JEFFREYS. 1955. A census factor.. https://pubmed.ncbi.nlm.nih.gov/14374353/
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In the areas of epidemiology, psychology, sociology, and other social and behavioural sciences, researchers often encounter situations where there are not only many variables contributing to a particular phenomenon, but there are also strong relationships among many of the predictor variables of interest. By using the traditional multiple regression on all the predictor variables, it is possible to have problems with interpretation and multicollinearity. As an alternative to multiple regression, we explore the use of a latent variable model that can address the relationship among the predictor variables. We consider two different methods for estimation and prediction for this model: one that uses multiple regression on factor score estimates and the other that uses structural equation modelling. The first method uses multiple regression but on a set of predicted underlying factors (i.e. factor scores), and the second method is a full-information maximum-likelihood technique that incorporates the complete covariance structure of the data. In this tutorial, we will explain the model and each estimation method, including how to carry out prediction. A data example will be used for demonstration, where respiratory disease death rates by county in Minnesota are predicted by five county-level census variables. A simulation study is performed to evaluate the efficiency of prediction using the two latent variable modelling techniques compared to multiple regression.
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OBJECTIVE: To reduce cost and time associated with household sampling process and to assess the feasibility of shared use of address data file of census enumeration areas in several epidemiological surveys using updated information from the National Survey of Households (PNAD). METHODS: Address data file comprising 72 census enumeration areas was kept as primary sampling units for the city of S o Paulo. During the period 1995-2000, three distinct household samples were drawn using the two-stage cluster sampling procedure. Geographic Information System (GIS) technology allowed delimiting boundaries, blocks and streets for any primary sampling unit and printing updated maps for selected sub-samples. RESULTS: Twenty-five thousand dwellings made up the permanent address data file of the master sample. A cheaper and quicker selection of each sample, plus gathering information on demographic and topographical profiles of census enumeration areas were the main contribution of the study results. CONCLUSIONS: The master sample concept, integrated with GIS technology, is an advantageous alternative sampling design for household surveys in urban areas. Using the list of addresses from the PNAD updated yearly, although limiting its application to the most populated Brazilian cities, avoids the need of creating an independent sampling procedure for each individual survey carried out in the period between demographic censuses, and it is an important contribution for planning sampling surveys in public health.