scholarly journals Analytical Modeling in Complex Surveys of Work Practices

ILR Review ◽  
2005 ◽  
Vol 59 (1) ◽  
pp. 82-100 ◽  
Author(s):  
Jerome P. Reiter ◽  
Elaine L. Zanutto ◽  
Larry W. Hunter
Keyword(s):  
Analytical Modeling ◽  
Industrial Relations ◽  
Complex Surveys ◽  
Independent Variables ◽  
Complex Sampling ◽  
Model Based ◽  
Unequal Probability ◽  
Indicator Variables ◽  
National Organizations ◽  
Complex Sampling Designs

Quantitative industrial relations research frequently relies on data collected from large surveys of establishments that use complex sampling designs, such as stratified and unequal probability sampling. The authors analyze two complex surveys of establishments, the National Organizations Survey and the National Survey of Establishments. They discuss design-based (survey-weighted) and model-based (unweighted) strategies for analyzing these data. They show that the choice of strategy can affect inferences about parameters, and hence conclusions drawn from analyses. They discuss the advantages of model-based approaches that include independent variables corresponding to design features, such as functions of size measures or indicator variables for strata or clusters, relative to purely design-based approaches.

scholarly journals Simulation Studies for Complex Sampling Designs

2016 ◽  
Vol 35 (4) ◽  
Author(s):  
Helga Wagner ◽  
Doris Eckmair
Keyword(s):  
Simulation Study ◽  
Variance Estimation ◽  
Sampling Error ◽  
Estimation Method ◽  
Sampling Plan ◽  
Simulation Studies ◽  
Complex Surveys ◽  
Variance Estimators ◽  
Complex Sampling ◽  
Complex Sampling Designs

Choosing the appropriate variance estimation method in complex surveys is a difficult task since there exist a variety of techniques which usually cannot be compared mathematically. A relatively easy way to accomplish such a comparison is on the basis of simulation studies. Though simulation studies are widely used in statistics, they are not a standard tool for investigating properties of estimators in complex survey sampling designs. In this paper we describe the setup for a simulation study according to the sampling plan of the Austrian Microcensus (AMC), used 1994–2003 which is an example for a very complex sampling plan. To illustrate the proceeding we conducted a simulation study comparing basic variance estimators. Results of the study reveal the extent to which simple variance estimators may underestimate the true sampling error in close to reality situations.

scholarly journals Modelos alométricos na determinação da área foliar de Bauhinia monandra Kurz

2016 ◽  
Vol 7 (3) ◽  
pp. 415
Author(s):  
Edilson Romais Schmildt ◽  
Omar Schmildt ◽  
Rodrigo Sobreira Alexandre ◽  
Adriano Alves Fernandes ◽  
Marcio Paulo Czepak
Keyword(s):  
Mathematical Models ◽  
Leaf Area ◽  
Linear Model ◽  
Potential Model ◽  
Leaf Surface ◽  
Linear Quadratic ◽  
Potential Models ◽  
Independent Variables ◽  
Model Based ◽  
Area Determination

The aim of this study was to evaluate the efficiency of the adjustment of mathematical models for determining Bauhinia monandra leaf area using the length and/or width of the leaves as independent variables. Leaves from plants with three years were used to the estimative of equations in linear, quadratic and potential models. The validation from the estimated leaf area as a function of the observed leaf area showed that the linear model based on the product of length and width of the largest leaf surface is the model that best fits. However, the leaf area determination can be represented by using only the length or width of the leaves with little loss of accuracy. A representation that better estimates Bauhinia monandra leaf area with easy application is the potential model in which xi represents the length of one of the symmetrical leaf lobes.

scholarly journals The Influence of Individual Characteristics toward Benefit Recipients’ Participation of Program Keluarga Harapan

2020 ◽  
Vol 3 (1) ◽  
pp. 29-37
Author(s):  
Tryas Wardani Nurwan ◽  
Helmi Hasan
Keyword(s):  
Data Analysis ◽  
Quantitative Method ◽  
Individual Characteristics ◽  
Individual Characteristic ◽  
Indicator Variable ◽  
Level Of Education ◽  
Independent Variables ◽  
The Family ◽  
Indicator Variables

The purpose of the study was to determine the effect of individual characteristic toward benefit recipients’ participation of Program Keluarga Harapan (PKH) in Nagari Pematang Panjang, Sijunjung District, West Sumatera. This study used quantitative method with a questionnaire and data analysis using SPSS 21. Based on Slovin’s theory, the respondents in this study were 131 from the 194 benefit recipients. Indicator variable Participation as the dependent variable is participation in the implementation of P2K2 and participation in taking PKH fund benefits. While the indicator variables of individual characteristics as independent variables are the level of education (X1), age (X2), and number of dependents of the Family (X3). The results showed that the three individual characteristic variables influence recipients’ participation.

Structural equation modelling with complex sampling designs and non-random attrition: A tutorial using lavaan and Mplus

2021 ◽  
Author(s):  
Aja Louise Murray ◽  
Anastasia Ushakova ◽  
Helen Wright ◽  
Tom Booth ◽  
Peter Lynn
Keyword(s):  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Cluster Sampling ◽  
Complex Sampling ◽  
Latent Trajectory ◽  
Adequate Sampling ◽  
Complex Sampling Designs ◽  
Selection Probabilities ◽  
Pseudo Maximum Likelihood

Complex sampling designs involving features such as stratification, cluster sampling, and unequal selection probabilities are often used in large-scale longitudinal surveys to improve cost-effectiveness and ensure adequate sampling of small or under-represented groups. However, complex sampling designs create challenges when there is a need to account for non-random attrition; a near inevitability in social science longitudinal studies. In this article we discuss these challenges and demonstrate the application of weighting approaches to simultaneously account for non-random attrition and complex design in a large UK-population representative survey. Using an auto-regressive latent trajectory model with structured residuals (ALT-SR) to model the relations between relationship satisfaction and mental health in the Understanding Society study as an example, we provide guidance on implementation of this approach in both R and Mplus is provided. Two standard error estimation approaches are illustrated: pseudo-maximum likelihood robust estimation and Bootstrap resampling. A comparison of unadjusted and design-adjusted results also highlights that ignoring the complex survey designs when fitting structural equation models can result in misleading conclusions.

Bootstrap confidence limits for groundfish trawl survey estimates of mean abundance

1997 ◽  
Vol 54 (3) ◽  
pp. 616-630 ◽  
Author(s):  
S J Smith
Keyword(s):  
Confidence Intervals ◽  
Probability Model ◽  
Small Sample ◽  
Trawl Survey ◽  
Confidence Limits ◽  
Complex Sampling ◽  
Survey Estimates ◽  
Small Sample Sizes ◽  
Complex Sampling Designs ◽  
Trawl Surveys

Trawl surveys using stratified random designs are widely used on the east coast of North America to monitor groundfish populations. Statistical quantities estimated from these surveys are derived via a randomization basis and do not require that a probability model be postulated for the data. However, the large sample properties of these estimates may not be appropriate for the small sample sizes and skewed data characteristic of bottom trawl surveys. In this paper, three bootstrap resampling strategies that incorporate complex sampling designs are used to explore the properties of estimates for small sample situations. A new form for the bias-corrected and accelerated confidence intervals is introduced for stratified random surveys. Simulation results indicate that the bias-corrected and accelerated confidence limits may overcorrect for the trawl survey data and that percentile limits were closer to the expected values. Nonparametric density estimates were used to investigate the effects of unusually large catches of fish on the bootstrap estimates and confidence intervals. Bootstrap variance estimates decreased as increasingly smoother distributions were assumed for the observations in the stratum with the large catch. Lower confidence limits generally increased with increasing smoothness but the upper bound depended upon assumptions about the shape of the distribution.

scholarly journals Large‐Sample Properties of Minimum Discriminant Information Adjustment Estimates Under Complex Sampling Designs

2020 ◽  
Vol 2020 (1) ◽  
pp. 1-20
Author(s):  
Lili Yao ◽  
Shelby Haberman ◽  
Daniel F. McCaffrey ◽  
J. R. Lockwood
Keyword(s):  
Large Sample ◽  
Complex Sampling ◽  
Complex Sampling Designs ◽  
Discriminant Information

scholarly journals International migration beyond gravity: A statistical model for use in population projections

2008 ◽  
Vol 105 (40) ◽  
pp. 15269-15274 ◽  
Author(s):  
Joel E. Cohen ◽  
Marta Roig ◽  
Daniel C. Reuman ◽  
Cai GoGwilt
Keyword(s):  
International Migration ◽  
Generalized Linear Model ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Population Projections ◽  
Parameter Estimates ◽  
Independent Variables ◽  
Indicator Variables ◽  
Starting Model ◽  
Country Specific

International migration will play an increasing role in the demographic future of most nations if fertility continues to decline globally. We developed an algorithm to project future numbers of international migrants from any country or region to any other. The proposed generalized linear model (GLM) used geographic and demographic independent variables only (the population and area of origins and destinations of migrants, the distance between origin and destination, the calendar year, and indicator variables to quantify nonrandom characteristics of individual countries). The dependent variable, yearly numbers of migrants, was quantified by 43653 reports from 11 countries of migration from 228 origins and to 195 destinations during 1960–2004. The final GLM based on all data was selected by the Bayesian information criterion. The number of migrants per year from origin to destination was proportional to (population of origin)0.86(area of origin)−0.21(population of destination)0.36(distance)−0.97, multiplied by functions of year and country-specific indicator variables. The number of emigrants from an origin depended on both its population and its population density. For a variable initial year and a fixed terminal year 2004, the parameter estimates appeared stable. Multiple R2, the fraction of variation in log numbers of migrants accounted for by the starting model, improved gradually with recentness of the data: R2 = 0.57 for data from 1960 to 2004, R2 = 0.59 for 1985–2004, R2 = 0.61 for 1995–2004, and R2 = 0.64 for 2000–2004. The migration estimates generated by the model may be embedded in deterministic or stochastic population projections.

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