Inferencia basada en el diseño mediante diseños de cuestionarios divididos
ilustraciones, tablas
- Autores:
-
Lozano González, Jose Alberto
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81379
- Palabra clave:
- 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
Diseño de cuestionario dividido
Total poblacional
Muestreo basado en el diseño
Split questionnaire design
Total population
Sampling based on design
Análisis de datos
Procesamiento de datos
Muestreo
Data analysis
Data processing
Sampling
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Inferencia basada en el diseño mediante diseños de cuestionarios divididos |
| dc.title.translated.eng.fl_str_mv |
Design based inference using split questionnaire designs |
| title |
Inferencia basada en el diseño mediante diseños de cuestionarios divididos |
| spellingShingle |
Inferencia basada en el diseño mediante diseños de cuestionarios divididos 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Diseño de cuestionario dividido Total poblacional Muestreo basado en el diseño Split questionnaire design Total population Sampling based on design Análisis de datos Procesamiento de datos Muestreo Data analysis Data processing Sampling |
| title_short |
Inferencia basada en el diseño mediante diseños de cuestionarios divididos |
| title_full |
Inferencia basada en el diseño mediante diseños de cuestionarios divididos |
| title_fullStr |
Inferencia basada en el diseño mediante diseños de cuestionarios divididos |
| title_full_unstemmed |
Inferencia basada en el diseño mediante diseños de cuestionarios divididos |
| title_sort |
Inferencia basada en el diseño mediante diseños de cuestionarios divididos |
| dc.creator.fl_str_mv |
Lozano González, Jose Alberto |
| dc.contributor.advisor.none.fl_str_mv |
Trujillo Oyola, Leonardo |
| dc.contributor.author.none.fl_str_mv |
Lozano González, Jose Alberto |
| dc.subject.ddc.spa.fl_str_mv |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas |
| topic |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Diseño de cuestionario dividido Total poblacional Muestreo basado en el diseño Split questionnaire design Total population Sampling based on design Análisis de datos Procesamiento de datos Muestreo Data analysis Data processing Sampling |
| dc.subject.proposal.spa.fl_str_mv |
Diseño de cuestionario dividido Total poblacional Muestreo basado en el diseño |
| dc.subject.proposal.eng.fl_str_mv |
Split questionnaire design Total population Sampling based on design |
| dc.subject.unesco.spa.fl_str_mv |
Análisis de datos Procesamiento de datos Muestreo |
| dc.subject.unesco.eng.fl_str_mv |
Data analysis Data processing Sampling |
| description |
ilustraciones, tablas |
| publishDate |
2022 |
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2022-03-24T20:23:25Z |
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2022-03-24T20:23:25Z |
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2022-03-24 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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https://repositorio.unal.edu.co/handle/unal/81379 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/81379 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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Adams, L. M. & Darwin, G. (1982), ‘Solving the quandary between questionnaire length and response rate in educational research’, Research in Higher Education 17, 231–240. Bethel, J. (1989), ‘An optimal allocation algorithm for multivariate surveys’, Survey Methodology 15, 47–57. Chipperfield, J. O. & Steel, D. G. (2009), ‘Design and estimation for split questionnaire surveys’, Journal of Official Statistics 25 (2), 227–244. Chromy, J. (1987), ’Design optimization with multivariate objectives’. Proceedings of the Survey Research Section, American Statistical Association, 194-199. Cochran, W. (1977), Sampling Techniques, New York: John Wiley and Sons. DANE (2019), Encuesta de Convivencia y Seguridad Ciudadana, Departamento Administrativo Nacional de Estadística - Gobierno Nacional. Dillman, D. A., Sinclair, M. D. & Clark, J. R. (1993), ‘Effects of questionnaire length, respondent-friendly design, and a difficult question on response rates for occupant-addressed census mail surveys’, Public Opinion Quarterly 57, 289–304. Fuller, W. A. (1990), ‘Analysis of repeated surveys’, Survey Methodology 16, 167–180. Goodman & Kish, L. (1950), ‘Controlled selection –a technique in probability sampling’, Journal of the American Statistical Association 45 pp. Herzog, A. R. & Bachman, J. G. (1980), ‘Effects of questionnaire length on response quality’, Public Opinion Quarterly 45, 549–559. Holland, P. W. & Thayer, D. T. (1985), ‘Section pre-equating in the presence of practice effects’, Journal of Educational Statistics 10, 109–120. Holland, P. W. & Wightman, L. E. (1982), Section Pre-Equating: A Preliminary Investigation in Test Equating, New York: Academic Press, p271-297. Kokan, A. R. & Khan, S. (1967), ‘Optimal allocation in multivariate surveys: An analytical solution’, Journal of the Royal Statistical Society Series B(29), 115–125. Kovar, J. G., Rao, J. N. K. & Wu, C. F. J. (1988), ‘Bootstrap and other methods to measure errors in survey estimates’, The Canadian Journal of Statistics 16, Supplement, 25–45. Merkouris, T. (2004), ‘Combining independent regression estimators from multiple surveys’, Journal of the American Statistical Association 99, 1131–1139. Munger, G. F. & Lloyd (1988), ‘The use of multiple matrix sampling for survey research’, Journal of Experimental Education 56, 187–191. Raghunathan, T. & Grizzle, J. (1995), ‘A split questionnaire survey design’, Journal of the American Statistical Association 90, 54–63. Rahim, M. A. & Currie, S. (1993), ‘Optimizing sample allocation for multiple response variables’, Proceedings of the Survey Research Section, American Statistical Association pp. 364–351. Rao, J. (1973), Linear Statistical Inference and its Applications, John Wiley and Sons. Renssen, R. & Nieuwenbroek, N. (1997), ‘Aligning estimates for common variables in two or more surveys’, Journal of the American Statistical Association 92, 368–374. Roszkowski, M. J. & Bean, A. G. (1990), ‘Believe it or not: Longer questionnaires have lower response rates’, Journal of Business and Psychology 4, 495–509. Royall, R. M. & Cumberland, W. G. (1978), ‘Variance estimation in finite population sampling’, Journal of the American Statistical Association 73, 351–358. Shoemaker, D. M. (1973), Principles and Procedures of Multiple Matrix Sampling, Cambridge, MA: Ballinger. Sitter, R. R. (1992b), ‘Comparing three bootstrap methods for survey data’, The Canadian Journal of Statistics 20, 135–154. Sitter, R. R. (1997), ‘Variance estimation for the regression estimator in two-phase sampling’, Journal of the American Statistical Association 12, 780–787. Srivastava, M. S. & Carter, E. M. (1986), ‘The maximum likelihood method for non-response in sample surveys’, Survey Methodology 12, 61–72. Särndal, C., Swensson, B. & Wretman, J. (1992), Model Assisted Survey Sampling, Springer, Verlag. Team, R. C. (2020), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/ Wretman, J. (1994), ‘Estimation in sample surveys with split questionnaires’, Research Report, University of Stockholm 3, 1–11. |
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Derechos reservados al autor, 2022 |
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Reconocimiento 4.0 Internacional |
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info:eu-repo/semantics/openAccess |
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Reconocimiento 4.0 Internacional Derechos reservados al autor, 2022 http://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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vii, 154 páginas |
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application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ciencias - Maestría en Ciencias - Estadística |
| dc.publisher.department.spa.fl_str_mv |
Departamento de Estadística |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ciencias |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Universidad Nacional de Colombia |
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Reconocimiento 4.0 InternacionalDerechos reservados al autor, 2022http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Trujillo Oyola, Leonardo3071a809cf31e3bc9635cf923ab5132dLozano González, Jose Albertod9843f75cc36218f335ea8214dca974e2022-03-24T20:23:25Z2022-03-24T20:23:25Z2022-03-24https://repositorio.unal.edu.co/handle/unal/81379Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, tablasAl usar una estrategia de muestreo (combinación de diseño y estimador) cualesquiera, se asume implícitamente en la literatura la regla de recoger todas las variables en todos los respondientes. En la actualidad las encuestas tienden a tener menores tasas de respuestas, en ocasiones debido a la longitud de los cuestionarios; se demanda una mayor cantidad de información a ser recolectada; se cuenta con la disponibilidad de fuentes secundarias más baratas como registros administrativos o big data que actúan como sustitutos de los datos de encuestas. Por lo tanto, es necesario buscar análisis que combinen eficiencia y flexibilidad como la técnica de diseños de cuestionarios divididos que reemplaza el supuesto de recoger todas las variables para todos los respondientes por recoger un subconjunto de la información requerida para cada unidad en la muestra. Sin embargo, bajo este tipo de estrategias estimadores de tipo Horvitz–Thompson o Hansen–Hurwitz para el total poblacional no han sido descritos para diseños muestrales complejos (probabilidades desiguales, estratificados, conglomerados y varias etapas). Con este trabajo se pretende incorporar la metodología de diseños de cuestionario dividido a los diseños de muestreo más conocidos y así, determinar la forma del estimador del total poblacional y su estimador de varianza. (Texto tomado de la fuente)By using a sampling strategy (combination of design and estimator), possibly the rule of collecting all variables in all respondents is duly assumed in the literature. Currently, surveys tend to have lower response rates, sometimes due to the length of the questionnaires; a greater amount of information is demanded to be collected; cheaper secondary sources such as administrative records or big data are available to act as surrogates for survey data. Therefore, it is necessary to look for analyzes that combine efficiency and flexibility, such as the technique of replaceable designs of divided questionnaires, which assumes the collection of all the variables for all the respondents by collecting a subset of the information required for each unit in the sample. However, under this type of strategy, Horvitz-Thompson or Hansen-Hurwitz type estimators for the total population have not been described for complex sample designs (unequal probabilities, stratified, conglomerates and various stages). The aim of this work is to incorporate the split questionnaire design methodology into the best-known test designs and thus determine the shape of the total population estimator and its variance estimator.MaestríaMagíster en Ciencias - Estadísticavii, 154 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaDepartamento de EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasDiseño de cuestionario divididoTotal poblacionalMuestreo basado en el diseñoSplit questionnaire designTotal populationSampling based on designAnálisis de datosProcesamiento de datosMuestreoData analysisData processingSamplingInferencia basada en el diseño mediante diseños de cuestionarios divididosDesign based inference using split questionnaire designsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAdams, L. M. & Darwin, G. (1982), ‘Solving the quandary between questionnaire length and response rate in educational research’, Research in Higher Education 17, 231–240.Bethel, J. (1989), ‘An optimal allocation algorithm for multivariate surveys’, Survey Methodology 15, 47–57.Chipperfield, J. O. & Steel, D. G. (2009), ‘Design and estimation for split questionnaire surveys’, Journal of Official Statistics 25 (2), 227–244.Chromy, J. (1987), ’Design optimization with multivariate objectives’. Proceedings of the Survey Research Section, American Statistical Association, 194-199.Cochran, W. (1977), Sampling Techniques, New York: John Wiley and Sons.DANE (2019), Encuesta de Convivencia y Seguridad Ciudadana, Departamento Administrativo Nacional de Estadística - Gobierno Nacional.Dillman, D. A., Sinclair, M. D. & Clark, J. R. (1993), ‘Effects of questionnaire length, respondent-friendly design, and a difficult question on response rates for occupant-addressed census mail surveys’, Public Opinion Quarterly 57, 289–304.Fuller, W. A. (1990), ‘Analysis of repeated surveys’, Survey Methodology 16, 167–180.Goodman & Kish, L. (1950), ‘Controlled selection –a technique in probability sampling’, Journal of the American Statistical Association 45 pp.Herzog, A. R. & Bachman, J. G. (1980), ‘Effects of questionnaire length on response quality’, Public Opinion Quarterly 45, 549–559.Holland, P. W. & Thayer, D. T. (1985), ‘Section pre-equating in the presence of practice effects’, Journal of Educational Statistics 10, 109–120.Holland, P. W. & Wightman, L. E. (1982), Section Pre-Equating: A Preliminary Investigation in Test Equating, New York: Academic Press, p271-297.Kokan, A. R. & Khan, S. (1967), ‘Optimal allocation in multivariate surveys: An analytical solution’, Journal of the Royal Statistical Society Series B(29), 115–125.Kovar, J. G., Rao, J. N. K. & Wu, C. F. J. (1988), ‘Bootstrap and other methods to measure errors in survey estimates’, The Canadian Journal of Statistics 16, Supplement, 25–45.Merkouris, T. (2004), ‘Combining independent regression estimators from multiple surveys’, Journal of the American Statistical Association 99, 1131–1139.Munger, G. F. & Lloyd (1988), ‘The use of multiple matrix sampling for survey research’, Journal of Experimental Education 56, 187–191.Raghunathan, T. & Grizzle, J. (1995), ‘A split questionnaire survey design’, Journal of the American Statistical Association 90, 54–63.Rahim, M. A. & Currie, S. (1993), ‘Optimizing sample allocation for multiple response variables’, Proceedings of the Survey Research Section, American Statistical Association pp. 364–351.Rao, J. (1973), Linear Statistical Inference and its Applications, John Wiley and Sons.Renssen, R. & Nieuwenbroek, N. (1997), ‘Aligning estimates for common variables in two or more surveys’, Journal of the American Statistical Association 92, 368–374.Roszkowski, M. J. & Bean, A. G. (1990), ‘Believe it or not: Longer questionnaires have lower response rates’, Journal of Business and Psychology 4, 495–509.Royall, R. M. & Cumberland, W. G. (1978), ‘Variance estimation in finite population sampling’, Journal of the American Statistical Association 73, 351–358.Shoemaker, D. M. (1973), Principles and Procedures of Multiple Matrix Sampling, Cambridge, MA: Ballinger.Sitter, R. R. (1992b), ‘Comparing three bootstrap methods for survey data’, The Canadian Journal of Statistics 20, 135–154.Sitter, R. R. (1997), ‘Variance estimation for the regression estimator in two-phase sampling’, Journal of the American Statistical Association 12, 780–787.Srivastava, M. S. & Carter, E. M. (1986), ‘The maximum likelihood method for non-response in sample surveys’, Survey Methodology 12, 61–72.Särndal, C., Swensson, B. & Wretman, J. (1992), Model Assisted Survey Sampling, Springer, Verlag.Team, R. C. (2020), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/Wretman, J. (1994), ‘Estimation in sample surveys with split questionnaires’, Research Report, University of Stockholm 3, 1–11.EstudiantesInvestigadoresMaestrosPúblico generalORIGINAL1062676254.2022.pdf1062676254.2022.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf682115https://repositorio.unal.edu.co/bitstream/unal/81379/3/1062676254.2022.pdfd6d007f2faf8842f41cdee2bfc441762MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81379/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1062676254.2022.pdf.jpg1062676254.2022.pdf.jpgGenerated Thumbnailimage/jpeg3706https://repositorio.unal.edu.co/bitstream/unal/81379/5/1062676254.2022.pdf.jpgb1294aaa2be713561ac6d96c29241534MD55unal/81379oai:repositorio.unal.edu.co:unal/813792024-08-05 23:10:21.183Repositorio Institucional Universidad Nacional de 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