Vital and health statistics. Series 2, Data evaluation and methods research最新文献_第5页

Education reporting and classification on death certificates in the United States. 美国死亡证明的教育报告和分类。

Vital and health statistics. Series 2, Data evaluation and methods research Pub Date : 2010-05-01

Brian L Rostron, John L Boies, Elizabeth Arias

{"title":"Education reporting and classification on death certificates in the United States.","authors":"Brian L Rostron, John L Boies, Elizabeth Arias","doi":"","DOIUrl":"","url":null,"abstract":"Objectives-This report analyzes education reporting and classification on the death certificate and their effect on estimates of mortality and life expectancy by education level in the United States. Methods-The National Longitudinal Mortality Study (NLMS) was used to analyze education information on the death certificate for participants in the Current Population Survey (CPS) from 1992 through 1998 who died by the end of 1998. Educational attainment reported on the death certificate for these persons was compared to their educational attainment reported in the CPS. NLMS wasalsousedtocalculate classification ratios consisting of the number of deaths by education level in the CPS compared to the number of deaths by education level on the death certificate. These classification ratios were then used to produce estimates of life expectancy by education level for the United States in 2005, adjusted for probable education misreporting on the death certificate. Results-Comparison of educational attainment from the death certificate and CPS shows differences due to the different classification systems used in the two sources and probable misreporting on the death certificate. The difference is most pronounced regarding graduation from high school. Black and Hispanic persons at the high school graduate level appear to be more likely than other racial or ethnic groups to have their educational attainment underreported on the death certificate as less than 4 years of high school completed. Adjusted estimates for the U.S. population show a large disparity in life expectancy by education level, on the order of 10-12 years for females and 11-16 years for males. ","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 151","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2010-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32561306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linkage of the Third National Health and Nutrition Examination Survey to air quality data. 第三次全国健康与营养调查与空气质量数据的联动。

Vital and health statistics. Series 2, Data evaluation and methods research Pub Date : 2008-11-01

Nataliya Kravets, Jennifer D Parker

{"title":"Linkage of the Third National Health and Nutrition Examination Survey to air quality data.","authors":"Nataliya Kravets, Jennifer D Parker","doi":"","DOIUrl":"","url":null,"abstract":"Objectives: This report describes the linked data file obtained as a result of combining air pollution data and National Health and Nutrition Examination Survey (NHANES) III data.Methods: Average annual air pollution exposures to particulate matter consisting of particles smaller than 10 micrometers in diameter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) were created for NHANES III examined persons by averaging values from monitors within a 5-, 10-, 15-, and 20-mile radius from the block-group centroid of their residence and in the county of their residence. Percentage records geocoded to block-group level, percentage records linked to air pollution, and distributions of exposure values were estimated for the total sample and various demographic groups.Results: The percentages of respondents who were assigned countywide air pollution values ranges from a low of 43 percent in the case of NO2 data to a high of 68 percent in the case of PM10 data. Among the pollutants considered, PM10 data provides the best coverage. Of all the metrics created, the highest coverage is achieved by averaging readings of monitors located within a 20-mile distance from the centroid of respondents' block groups. Among the demographic variables analyzed, differences in air pollution coverage and exposure levels occur most often among groups defined by race and Hispanic origin, region, and county level of urbanization. However, differences among groups depend on the pollutant and geographic linkage method. The linked dataset provides researchers with opportunities to investigate the relationship between air pollution and various health outcomes.","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 149","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"28117902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mortality experience of the 1986-2000 National Health Interview Survey Linked Mortality Files participants. 1986-2000年全国健康访谈调查相关死亡率档案参与者的死亡率经验。

Vital and health statistics. Series 2, Data evaluation and methods research Pub Date : 2008-10-01

Deborah D Ingram, Kimberly A Lochner, Christine S Cox

{"title":"Mortality experience of the 1986-2000 National Health Interview Survey Linked Mortality Files participants.","authors":"Deborah D Ingram, Kimberly A Lochner, Christine S Cox","doi":"","DOIUrl":"","url":null,"abstract":"The National Center for Health Statistics (NCHS) has produced the 1986-2000 National Health Interview Survey (NHIS) Linked Mortality Files by linking eligible adults in the 1986-2000 NHIS cohorts through probabilistic record linkage to the National Death Index to obtain mortality follow-up through December 31, 2002. The resulting files contain more than 120,000 deaths and an average of 9 years of survival time. To assess how well mortality was ascertained in the linked mortality files, NCHS has conducted a comparison of the mortality experience of the 1986-2000 NHIS cohorts with that of the U.S. population. This report presents the results of this comparative mortality assessment. Methods The survival of each annual NHIS cohort was compared with that of the U.S. population during the same period. Cumulative survival probabilities for each annual NHIS cohort were derived using the Kaplan-Meier product limit method, and corresponding cumulative survival probabilities were computed for the U.S. population using information from annual U.S. life tables. The survival probabilities were calculated at various lengths of follow-up for each age-race-sex group of each NHIS cohort and for the U.S. population. Results As expected, mortality tended to be underestimated in the NHIS cohorts because the sample includes only civilian, noninstitutionalized persons, but this underestimation generally was not statistically significant. Statistically significant differences increased with length of follow-up, occurred more often for white females than for the other race-sex groups, and occurred more often in the oldest age groups. In general, the survival experience of the age-race-sex groups of each NHIS cohort corresponds quite closely to that of the U.S. population, providing support that the ascertainment of mortality through the probabilistic record linkage accurately reflects the mortality experience of the NHIS cohorts.","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 147","pages":"1-37"},"PeriodicalIF":0.0,"publicationDate":"2008-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"27851264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The validity of race and Hispanic origin reporting on death certificates in the United States. 美国死亡证明上种族和西班牙裔报告的有效性。

Vital and health statistics. Series 2, Data evaluation and methods research Pub Date : 2008-10-01

Elizabeth Arias, William S Schauman, Karl Eschbach, Paul D Sorlie, Eric Backlund

{"title":"The validity of race and Hispanic origin reporting on death certificates in the United States.","authors":"Elizabeth Arias, William S Schauman, Karl Eschbach, Paul D Sorlie, Eric Backlund","doi":"","DOIUrl":"","url":null,"abstract":"Objectives: This report presents the results of an evaluation study of the validity of race and Hispanic origin reporting on death certificates in the United States and its impact on race- and Hispanic origin-specific mortality estimates.Methods: The National Longitudinal Mortality Study (NLMS) was used to evaluate death certificate classification of race and Hispanic origin by comparing death certificate with survey race-ethnicity classifications for a sample of decedents identified in NLMS. NLMS consists of a series of annual Current Population Survey files (1973 and 1978-1998) linked to death certificates for years 1979-1998. To identify and measure the effect of race-ethnicity misclassification on death certificates on mortality estimates, pooled 1999-2001 vital statistics mortality data and population data from the 2000 census were used to estimate and compare observed and corrected (for death certificate misclassification) race-ethnicity specific death rates.Results: Race and ethnicity reporting on the death certificate continues to be excellent for the white and black populations. It remains poor for the American Indian or Alaska Native (AIAN) population but is reasonably good for the Hispanic and Asian or Pacific Islander (API) populations. Decedent characteristics such as place of residence and nativity have an important effect on the quality of reporting on the death certificate. The effects of misclassification on mortality estimates were most pronounced for the AIAN population, where correcting for misclassification reverses a large AIAN over white mortality advantage to a large disadvantage. Among the Hispanic and API populations, adjustment for death certificate misclassification did not significantly affect minority-majority mortality differentials.","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 148","pages":"1-23"},"PeriodicalIF":0.0,"publicationDate":"2008-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"27852509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating healthy life expectancies using longitudinal survey data: methods and techniques in population health measures. 利用纵向调查数据估计健康预期寿命:人口健康措施的方法和技术。

Vital and health statistics. Series 2, Data evaluation and methods research Pub Date : 2008-05-01

Michael T Molla, Jennifer H Madans

{"title":"Estimating healthy life expectancies using longitudinal survey data: methods and techniques in population health measures.","authors":"Michael T Molla, Jennifer H Madans","doi":"","DOIUrl":"","url":null,"abstract":"Objective-Summary measures of population health are statistics that combine mortality and morbidity to represent overall population health in a single index. Such measures include healthy life expectancy, also called disability-free life expectancy and active life expectancy. Healthy life expectancy can be calculated using cross-sectional or longitudinal survey data. This report presents a comprehensive discussion of a method for calculating healthy life expectancy using data from longitudinal surveys. Methods-Healthy life expectancies are calculated using the multistate life table model. Expected life in various states of health is estimated using data from the Second Longitudinal Study of Aging and the Medicare Current Beneficiary Survey to illustrate the calculation of the statistics and the discussion of data and methodology related issues. Results-The study shows that estimating summary measures of population health using longitudinal survey data provides the opportunity of using incidence rather than prevalence rates. Health measures estimated based on incidence reflect the most recent health status of the population. Models that use longitudinal survey data measure transitions from good to poor health as well as poor to good health. That is, the models account for recovery from morbidity or illness. Longitudinal survey data canalsobeusedtocalculate healthy or active life expectancies by initial health states. ","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 146","pages":"1-24"},"PeriodicalIF":0.0,"publicationDate":"2008-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32561330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linkage of the National Health Interview Survey to air quality data. 全国健康访谈调查与空气质量数据的联系。

Vital and health statistics. Series 2, Data evaluation and methods research Pub Date : 2008-02-01

Jennifer D Parker, Nataliya Kravets, Tracey J Woodruff

{"title":"Linkage of the National Health Interview Survey to air quality data.","authors":"Jennifer D Parker, Nataliya Kravets, Tracey J Woodruff","doi":"","DOIUrl":"","url":null,"abstract":"Objective: This report describes the linkage between the National Health Interview Survey (NHIS) and air monitoring data from the U.S. Environmental Protection Agency (EPA). There have been few linkages of these data sources, partly because of restrictions on releasing geographic detail from NHIS on public-use files in order to protect participant confidentiality.Methods: Pollution exposures for NHIS respondents were calculated by averaging the annual average exposure estimates from EPA air monitors both within 5, 10, 15, and 20 miles of the respondent's block-group location (which is available on restricted NHIS data files) and by county of residence. The 1987-2005 linked data files--referred to as NHIS-EPAAnnualAir--were used to describe the percentage of NHIS respondents linked and the median exposures by linkage method, survey year, and pollutant. Using the 2005 NHIS-EPAAnnualAir data file, the percentage linked and median exposure were described by respondent characteristics, linkage method, and pollutant.Results: Many decisions were made to define pollution exposures for NHIS respondents, including monitor selection, location assignment for NHIS respondents, and geographic linkage criteria. Geographic linkage criteria for assigning area-level exposure estimates affected the percentage and composition of respondents included in the resulting linked sample. Median exposure estimates, however, were similar among geographic linkage methods.Conclusion: NHIS-EPAAnnualAir data files for 1985 through 2005 are currently available to users in the NCHS Research Data Center.","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 145","pages":"1-24"},"PeriodicalIF":0.0,"publicationDate":"2008-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"27330960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Statistical match of the March 1996 Current Population Survey and the 1995 National Health Interview Survey. 1996年3月当期人口调查与1995年全国健康访问调查的统计匹配。

Vital and health statistics. Series 2, Data evaluation and methods research Pub Date : 2008-01-01

Deborah D Ingram, Christopher L Moriarity, John F O'Hare, Joan Turek

{"title":"Statistical match of the March 1996 Current Population Survey and the 1995 National Health Interview Survey.","authors":"Deborah D Ingram, Christopher L Moriarity, John F O'Hare, Joan Turek","doi":"","DOIUrl":"","url":null,"abstract":"Objectives: Statistical matching is a method used to combine two files when it is unlikely that individuals on one file are also on the other file. The objectives of this report are to document and evaluate statistical matches of the March 1996 Current Population Survey (CPS) and the 1995 National Health interview Survey (NHIS) and give recommendations for improving future matches. The CPS-NHIS match was motivated by the need for a data set with data on health measures and family resources for use in policy analyses.Methods: Three statistical matches between the March 1996 CPS and the 1995 NHIS are described in this report. All three matches used person-level constrained matching with partitioning and a predictive mean matching algorithm to link records on the two files. For two of the matches, the CPS served as the Host file and the NHIS served as the Donor file; for the third match, the NHIS was the Host file and the CPS was the Donor file.Results: The results suggest that the constrained predictive mean matches of the March 1996 CPS and the 1995 NHIS successfully combined some of the information on the two files, but that relationships among some Host and Donor variables on the matched file may be distorted. The evaluation of the matches suggested that the variables used to partition the Host and Donor files prior to matching and the variables involved in the predictive mean matching play an important role in determining whether relationships among variables on the matched file correctly represent relationships among those variables in the population. The evaluation also indicated that estimates for small subgroups may be especially subject to error. The results reinforce the need to proceed cautiously when exploring relationships among Host and Donor variables on a statistically matched file.","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 144","pages":"1-50"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"27363179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Statistical match of the March 1996 Current Population Survey and the 1995 National Health Interview Survey. 1996年3月当期人口调查与1995年全国健康访问调查的统计匹配。

Vital and health statistics. Series 2, Data evaluation and methods research Pub Date : 2008-01-01 DOI: 10.1037/e414732008-001

D. D. Ingram, C. Moriarity, John F. O'Hare, Joan L. Turek

{"title":"Statistical match of the March 1996 Current Population Survey and the 1995 National Health Interview Survey.","authors":"D. D. Ingram, C. Moriarity, John F. O'Hare, Joan L. Turek","doi":"10.1037/e414732008-001","DOIUrl":"https://doi.org/10.1037/e414732008-001","url":null,"abstract":"OBJECTIVES Statistical matching is a method used to combine two files when it is unlikely that individuals on one file are also on the other file. The objectives of this report are to document and evaluate statistical matches of the March 1996 Current Population Survey (CPS) and the 1995 National Health interview Survey (NHIS) and give recommendations for improving future matches. The CPS-NHIS match was motivated by the need for a data set with data on health measures and family resources for use in policy analyses. METHODS Three statistical matches between the March 1996 CPS and the 1995 NHIS are described in this report. All three matches used person-level constrained matching with partitioning and a predictive mean matching algorithm to link records on the two files. For two of the matches, the CPS served as the Host file and the NHIS served as the Donor file; for the third match, the NHIS was the Host file and the CPS was the Donor file. RESULTS The results suggest that the constrained predictive mean matches of the March 1996 CPS and the 1995 NHIS successfully combined some of the information on the two files, but that relationships among some Host and Donor variables on the matched file may be distorted. The evaluation of the matches suggested that the variables used to partition the Host and Donor files prior to matching and the variables involved in the predictive mean matching play an important role in determining whether relationships among variables on the matched file correctly represent relationships among those variables in the population. The evaluation also indicated that estimates for small subgroups may be especially subject to error. The results reinforce the need to proceed cautiously when exploring relationships among Host and Donor variables on a statistically matched file.","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":"144 1","pages":"1-50"},"PeriodicalIF":0.0,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57762933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

National Survey of Family Growth, Cycle 6: sample design, weighting, imputation, and variance estimation. 全国家庭成长调查第六周期:样本设计、加权、归算和方差估计。

Vital and health statistics. Series 2, Data evaluation and methods research Pub Date : 2006-07-01 DOI: 10.1037/e414742008-001

J. Lepkowski, W. Mosher, K. Davis, R. Groves, John van Hoewyk, Jennifer Willem

{"title":"National Survey of Family Growth, Cycle 6: sample design, weighting, imputation, and variance estimation.","authors":"J. Lepkowski, W. Mosher, K. Davis, R. Groves, John van Hoewyk, Jennifer Willem","doi":"10.1037/e414742008-001","DOIUrl":"https://doi.org/10.1037/e414742008-001","url":null,"abstract":"OBJECTIVES\u0000Cycle 6 of the National Survey of Family Growth (NSFG) was conducted by the National Center for Health Statistics in 2002 and early 2003. This report describes how the sample was designed, shows response rates for various subgroups of men and women, describes how the sample weights were computed to make national estimates possible, shows how missing data were imputed for a limited set of key variables, and describes the proper ways to estimate sampling errors from the NSFG. The report includes both nontechnical summaries for readers who need only general information and more technical detail for readers who need an in-depth understanding of these topics.\u0000\u0000\u0000METHODS\u0000The NSFG Cycle 6 was based on an independent, national probability sample of men and women 15-44 years of age. It was the first NSFG ever to include a national sample of men 15-44 as well as a sample of women. Fieldwork was carried out by the University of Michigan's Institute for Social Research (ISR) under a contract with NCHS. In-person, face-to-face interviews were conducted by professional female interviewers using laptop computers. In all, 12,571 women and men-7,643 females and 4,928 males-were interviewed, the largest NSFG ever done.\u0000\u0000\u0000RESULTS\u0000Analysis of NSFG Cycle 6 data requires the use of sampling weights and estimation of sampling errors that accounts for the complex sample design and estimation features of the survey. Examples of how to use several available software packages that incorporate complex design features in estimation, such as SAS, SUDAAN, and STATA, are presented.","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":"142 1","pages":"1-82"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57763004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 142

National Survey of Family Growth, Cycle 6: sample design, weighting, imputation, and variance estimation. 全国家庭成长调查第六周期:样本设计、加权、归算和方差估计。

Vital and health statistics. Series 2, Data evaluation and methods research Pub Date : 2006-07-01

James M Lepkowski, William D Mosher, Karen E Davis, Robert M Groves, John van Hoewyk, Jennifer Willem

{"title":"National Survey of Family Growth, Cycle 6: sample design, weighting, imputation, and variance estimation.","authors":"James M Lepkowski, William D Mosher, Karen E Davis, Robert M Groves, John van Hoewyk, Jennifer Willem","doi":"","DOIUrl":"","url":null,"abstract":"Objectives: Cycle 6 of the National Survey of Family Growth (NSFG) was conducted by the National Center for Health Statistics in 2002 and early 2003. This report describes how the sample was designed, shows response rates for various subgroups of men and women, describes how the sample weights were computed to make national estimates possible, shows how missing data were imputed for a limited set of key variables, and describes the proper ways to estimate sampling errors from the NSFG. The report includes both nontechnical summaries for readers who need only general information and more technical detail for readers who need an in-depth understanding of these topics.Methods: The NSFG Cycle 6 was based on an independent, national probability sample of men and women 15-44 years of age. It was the first NSFG ever to include a national sample of men 15-44 as well as a sample of women. Fieldwork was carried out by the University of Michigan's Institute for Social Research (ISR) under a contract with NCHS. In-person, face-to-face interviews were conducted by professional female interviewers using laptop computers. In all, 12,571 women and men-7,643 females and 4,928 males-were interviewed, the largest NSFG ever done.Results: Analysis of NSFG Cycle 6 data requires the use of sampling weights and estimation of sampling errors that accounts for the complex sample design and estimation features of the survey. Examples of how to use several available software packages that incorporate complex design features in estimation, such as SAS, SUDAAN, and STATA, are presented.","PeriodicalId":23577,"journal":{"name":"Vital and health statistics. Series 2, Data evaluation and methods research","volume":" 142","pages":"1-82"},"PeriodicalIF":0.0,"publicationDate":"2006-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"26273076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0