Estimation of response errors in complex sample surveys

Download
index.pdf
2013
Fahmi, Fidan Mahmut
Show full item record
164
views
102
downloads
In this thesis, simple and correlated response errors are investigated. The optimum interviewer allocation settings has been investigated by using different experimental design models (Nested design, Nested and factorial factors design and Split plot design). The proposed designs considers several stages of an interactive process. Several linear and multiplicative models have been used to explain the factors effecting nonsampling errors. This naturally includes response error models. The response error models are generally evaluated as simple response errors, and some other proposals are used to evaluate them as correlated response errors. Correlated response errors, naturally takes the interaction between the interviewer and the respondent into account. In this study, after an exhaustive literature review, the interviewer fieldwork allocation techniques are investigated by the proposed experimental design models to study the sources of error. The pilot survey and main survey results are presented in detail.
Correlation (Statistics)., Sampling (Statistics)., Random data (Statistics)., Social surveys
http://etd.lib.metu.edu.tr/upload/12616196/index.pdf
https://hdl.handle.net/11511/22816
Graduate School of Natural and Applied Sciences, Thesis

Suggestions

Estimation and hypothesis testing in multivariate linear regression models under non normality
İslam, Muhammed Qamarul (Informa UK Limited, 2017-01-01)
This paper discusses the problem of statistical inference in multivariate linear regression models when the errors involved are non normally distributed. We consider multivariate t-distribution, a fat-tailed distribution, for the errors as alternative to normal distribution. Such non normality is commonly observed in working with many data sets, e.g., financial data that are usually having excess kurtosis. This distribution has a number of applications in many other areas of research as well. We use modifie...
Pairwise multiple comparisons under short-tailed symmetric distribution
Balcı, Sibel; Akkaya, Ayşen; Department of Statistics (2007)
In this thesis, pairwise multiple comparisons and multiple comparisons with a control are studied when the observations have short-tailed symmetric distributions. Under non-normality, the testing procedure is given and Huber estimators, trimmed mean with winsorized standard deviation, modified maximum likelihood estimators and ordinary sample mean and sample variance used in this procedure are reviewed. Finally, robustness properties of the stated estimators are compared with each other and it is shown that...
Effect of estimation in goodness-of-fit tests
Eren, Emrah; Sürücü, Barış; Department of Statistics (2009)
In statistical analysis, distributional assumptions are needed to apply parametric procedures. Assumptions about underlying distribution should be true for accurate statistical inferences. Goodness-of-fit tests are used for checking the validity of the distributional assumptions. To apply some of the goodness-of-fit tests, the unknown population parameters are estimated. The null distributions of test statistics become complicated or depend on the unknown parameters if population parameters are replaced by ...
Estimation in the simple linear regression model with one-fold nested error
Ülgen, Burçin Emre; Güven, Bilgehan; Department of Statistics (2005)
In this thesis, estimation in simple linear regression model with one-fold nested error is studied. To estimate the fixed effect parameters, generalized least squares and maximum likelihood estimation procedures are reviewed. Moreover, Minimum Norm Quadratic Estimator (MINQE), Almost Unbiased Estimator (AUE) and Restricted Maximum Likelihood Estimator (REML) of variance of primary units are derived. Also, confidence intervals for the fixed effect parameters and the variance components are studied. Finally, ...
Analysis of Covariance with Non-normal Errors
ŞENOĞLU, BİRDAL; Avcioglu, Mubeccel Didem (Wiley, 2009-12-01)
P>Analysis of covariance techniques have been developed primarily for normally distributed errors. We give solutions when the errors have non-normal distributions. We show that our solutions are efficient and robust. We provide a real-life example.
Citation Formats
F. M. Fahmi, “Estimation of response errors in complex sample surveys,” Ph.D. - Doctoral Program, Middle East Technical University, 2013.
METU IIS - Integrated Information Service open@metu.edu.tr