(2008). Introduction to Variance Estimation. Journal of the American Statistical Association: Vol. 103, No. 483, pp. 1324-1325.

Introduction to Variance Estimation

Survey Errors and Survey Costs

The general principles of Bayesian data analysis imply that models for survey responses should be constructed conditional on all variables that affect the probability of inclusion and nonresponse, which are also the variables used in survey weighting and clustering. However, such models can quickly become very complicated, with potentially thousands of poststratification cells. It is then a challenge to develop general families of multilevel probability models that yield reasonable Bayesian inferences. We discuss in the context of several ongoing public health and social surveys. This work is currently open-ended, and we conclude with thoughts on how research could proceed to solve these problems.

Struggles with survey weighting and regression modeling

Introduction to Variance Estimation (2nd ed.)

Heavy clay soils are globally widespread but their poor drainage and poor aeration limit their use for agriculture. This study was designed to test the effect of the amendment of biochar (BC) from woody shrubs on drainage/saturated hydraulic conductivity (Ksat), soil aeration/air capacity, available water capacity and biomass and grain yields of maize. In a field experiment, BC from Gliricidia sepium was applied in planting basins or rip lines at 2.5% and 5% w/w in addition to a control without BC. The maize biomass and grain yields were higher in BC treated plots compared to control (p 0.05). In contrast, bulk density decreased linearly by 0.011±0.002 g cm-3 per percent BC added (p<0.001). Ksat and air capacity of the soil were 288 cm day-1 and 30.9%, respectively falling within the generally accepted optimal range. Both Ksat and air capacity followed a significant quadratic relation (p<0.05) upon BC addition, decreasing at low BC doses, reaching a minimum at 3-5% BC and increasing at higher doses. Results allowed a partial attribution of the yield increases to changes in soil physical properties such as changes in bulk density and not clearly to Ksat and air capacity.

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The potential of biochar in improving drainage, aeration and maize yields in heavy clay soils.

Secondary analyses of survey data collected from large probability samples of persons or establishments further scientific progress in many fields. The complex design features of these samples improve data collection efficiency, but also require analysts to account for these features when conducting analysis. Unfortunately, many secondary analysts from fields outside of statistics, biostatistics, and survey methodology do not have adequate training in this area, and as a result may apply incorrect statistical methods when analyzing these survey data sets. This in turn could lead to the publication of incorrect inferences based on the survey data that effectively negate the resources dedicated to these surveys. In this article, we build on the results of a preliminary meta-analysis of 100 peer-reviewed journal articles presenting analyses of data from a variety of national health surveys, which suggested that analytic errors may be extremely prevalent in these types of investigations. We first perform a meta-analysis of a stratified random sample of 145 additional research products analyzing survey data from the Scientists and Engineers Statistical Data System (SESTAT), which describes features of the U.S. Science and Engineering workforce, and examine trends in the prevalence of analytic error across the decades used to stratify the sample. We once again find that analytic errors appear to be quite prevalent in these studies. Next, we present several example analyses of real SESTAT data, and demonstrate that a failure to perform these analyses correctly can result in substantially biased estimates with standard errors that do not adequately reflect complex sample design features. Collectively, the results of this investigation suggest that reviewers of this type of research need to pay much closer attention to the analytic methods employed by researchers attempting to publish or present secondary analyses of survey data.

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How Big of a Problem is Analytic Error in Secondary Analyses of Survey Data

In this article, we review current state-of-the art software enabling statisticians to apply design-based, model-based, and so-called “hybrid” approaches to the analysis of complex sample survey data. We present brief overviews of the similarities and differences between these alternative approaches, and then focus on software tools that are presently available for implementing each approach. We conclude with a summary of directions for future software development in this area.

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Guy Alain S. Aurelien

Papers

How Big of a Problem is Analytic Error in Secondary Analyses of Survey Data

Accounting for Complex Sampling in Survey Estimation: A Review of Current Software Tools