Structural Equations with Latent Variables.

https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.0040297&type=printable

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalizability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results, and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies, and cross-sectional studies, and 4 are specific to each of the 3 study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors, and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, 1 or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated Web site (www.strobe-statement.org) should be helpful resources to improve reporting of observational research.

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Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalizability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers.This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated web site (http://www.strobe-statement.org) should be helpful resources to improve reporting of observational research.

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.

Large-scale surveys using complex sample designs are frequently carried out by government agencies. The statistical analysis technology available for such data is, however, limited in scope. This study investigates and further develops statistical methods that could be used in software for the analysis of data collected under complex sample designs. First, it identifies several recent methodological lines of inquiry which taken together provide a powerful and general statistical basis for a complex sample, structural equation modeling analysis. Second, it extends some of this research to new situations of interest. A Monte Carlo study that empirically evaluates these techniques on simulated data comparable to those in largescale complex surveys demonstrates that they work well in practice. Due to the generality of the approaches, the methods cover not only continuous normal variables but also continuous nonnormal variables and dichotomous variables. Two methods designed to take into account the complex sample structure were

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Complex Sample Data in Structural Equation Modeling

The limiting distribution of the regression coefficients calculated from a correlation matrix that has been corrected for attenuation is obtained. Methods of estimating the covariance matrix of the vector of regression coefficients are presented. Nonnormal regression variables and nondiagonal error matrices are considered. The procedures are illustrated with data on the socioeconomic career.

Regression Estimation after Correcting for Attenuation

Approximate cut-off rules for stratifying a population into a take-all and take-some universe have been given by Dalenius (1952) and Glasser (1962) Their cut-off value (the value that delineates the take-all and take-some boundary) was derived on the assumption that all units from the take-all universe were included in the sample and that a simple random sample of fixed size was to be drawn without replacement from the population of take-some units In the present context, exact and approximate cut-off rules are worked out in terms of required precision expected from the overall sample

The Construction of a Self-Representing Stratum of Large Units in Survey Design

In estimating means, totals, and other parameters for small domains of a finite population, the survey statistician is usually faced with a domain sample size that is random rather than controlled at the selection stage. Often a sensible approach is to make design-based inference conditionally on the realized sample size in the domain (nd ). In this article, we suggest and analyze some small domain estimators and their design-based conditional confidence intervals. The conditional outlook leads us to some new small domain estimators that (a) are based on regression of pertinent auxiliary information and are therefore efficient to the extent that the auxiliary information is strong; (b) are nearly design unbiased, conditionally on nd , as well as unconditionally; and (c) give rise to design-based confidence intervals that are valid conditionally as well as unconditionally. The conditional properties of these new regression estimators are first derived theoretically, then confirmed through a Monte ...

Small Domain Estimation: A Conditional Analysis

The problem considered is the estimation of the population total of some characteristic from a simple random sample containing a few large or extreme observations. The effect of these large units in the sample is to distort the estimate of the population total. It is therefore important to correct the weights for such units or deflate their values at the estimation stage once they have been sampled and identified as unusually large units. In this paper, three estimators that alter the usual sampling weights have been considered. The efficiencies of these estimators have been worked out in terms of the ratio of the mean squared error of the usual estimator of the population total to the mean squared error of these estimators. A numerical study of these estimators is also discussed.

Some Estimators of a Population Total from Simple Random Samples Containing Large Units

Sample allocation issues are studied in the context of estimating sub-population (stratum or domain) means as well as the aggregate population mean under stratified simple random sampling A non-linear programming method is used to obtain “optimal” sample allocation to strata that minimizes the total sample size subject to specified tolerances on the coefficient of variation of the estimators of strata means and the population mean The resulting total sample size is then used to determine sample allocations for the methods of Costa, Satorra and Ventura (2004) based on compromise allocation and Longford (2006) based on specified “inferential priorities” In addition, we study sample allocation to strata when reliability requirements for domains, cutting across strata, are also specified Performance of the three methods is studied using data from Statistics Canada’s Monthly Retail Trade Survey (MRTS) of single establishments

Michael A. Hidiroglou

Papers

Regression Estimation after Correcting for Attenuation

The Construction of a Self-Representing Stratum of Large Units in Survey Design

Small Domain Estimation: A Conditional Analysis

Some Estimators of a Population Total from Simple Random Samples Containing Large Units

On sample allocation for efficient domain estimation