Beyond Treating Complex Sampling Designs as Simple Random Samples: Data Analysis and Reporting

2015 ◽  
Author(s):  
Sonya K. Sterba ◽  
Sharon L. Christ ◽  
Mitchell J. Prinstein ◽  
Matthew K. Nock
Keyword(s):  
Data Analysis ◽  
Complex Sampling ◽  
Random Samples ◽  
Complex Sampling Designs

scholarly journals Tantangan UKM Menghadapi Pandemi Covid-19: Pengaruh Kepemimpinan dan Pelatihan terhadap Kinerja

2021 ◽  
Vol 4 (2) ◽  
pp. 91
Author(s):  
Mei Rani Amalia
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Leadership Training ◽  
Raw Materials ◽  
Regression Analyses ◽  
Business Units ◽  
Random Samples ◽  
Perceived Effects ◽  
The Impact ◽  
Data Analysis Methods

<p><em>SMES have a very important role, so it can be said that SMES are the backbone of the Indonesian economy. BPS Data informs that about 99% of existing business units are SMES capable of absorbing manpower about 96.3% of the amount of productive workforce available. But since the outbreak of Covid-19 struck Indonesia from the end of December 2019, the business of SMES is one of the most experienced sectors, such as sales that go down, difficult to obtain raw materials, the disbanding of production and capital are some of the perceived effects of SMES during the pandemic.  To be able to continue to maintain its performance, it takes good cooperation from all parties from government, private, and community participation. This research is conducted with the aim to know the impact of training and leadership on the performance of SME Kab. Tegal during the Covid-19 pandemic. The study took 100 random samples and data collection was conducted through observations, interviews, and questionnaires. Data analysis methods use multiple linear regression analyses. The results showed that partial training had no significant effect on performance, while leadership had a significant influence on MSME performance. Simultaneously the training and leadership significantly affect the performance of SME Kab. Tegal during the Covid-19 pandemic.  Training to the SMES will help improve the performance during the pandemic.</em></p><p><em> </em></p><p><em>Keywords: </em><em>SMES, leadership, training, pandemic covid-19, performance</em><em></em></p>

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 On the estimation of the Lorenz curve under complex sampling designs

2019 ◽  
Vol 29 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Pier Luigi Conti ◽  
Alberto Di Iorio ◽  
Alessio Guandalini ◽  
Daniela Marella ◽  
Paola Vicard ◽  
...  
Keyword(s):  
Lorenz Curve ◽  
Complex Sampling ◽  
The Lorenz Curve ◽  
Complex Sampling Designs
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