• 검색어에 아래의 연산자를 사용하시면 더 정확한 검색결과를 얻을 수 있습니다.
  • 검색연산자
검색연산자 기능 검색시 예
() 우선순위가 가장 높은 연산자 예1) (나노 (기계 | machine))
공백 두 개의 검색어(식)을 모두 포함하고 있는 문서 검색 예1) (나노 기계)
예2) 나노 장영실
| 두 개의 검색어(식) 중 하나 이상 포함하고 있는 문서 검색 예1) (줄기세포 | 면역)
예2) 줄기세포 | 장영실
! NOT 이후에 있는 검색어가 포함된 문서는 제외 예1) (황금 !백금)
예2) !image
* 검색어의 *란에 0개 이상의 임의의 문자가 포함된 문서 검색 예) semi*
"" 따옴표 내의 구문과 완전히 일치하는 문서만 검색 예) "Transform and Quantization"
쳇봇 이모티콘
ScienceON 챗봇입니다.
궁금한 것은 저에게 물어봐주세요.

논문 상세정보

무응답이 있는 설문조사연구의 접근법 : 한국노인약물역학코호트 자료의 평가

An Approach to Survey Data with Nonresponse: Evaluation of KEPEC Data with BMI


Objectives : A common problem with analyzing survey data involves incomplete data with either a nonresponse or missing data. The mail questionnaire survey conducted for collecting lifestyle variables on the members of the Korean Elderly Phamacoepidemiologic Cohort(KEPEC) in 1996 contains some nonresponse or missing data. The proper statistical method was applied to evaluate the missing pattern of a specific KEPEC data, which had no missing data in the independent variable and missing data in the response variable, BMI. Methods : The number of study subjects was 8,689 elderly people. Initially, the BMI and significant variables that influenced the BMI were categorized. After fitting the log-linear model, the probabilities of the people on each category were estimated. The EM algorithm was implemented using a log-linear model to determine the missing mechanism causing the nonresponse. Results : Age, smoking status, and a preference of spicy hot food were chosen as variables that influenced the BMI. As a result of fitting the nonignorable and ignorable nonresponse log-linear model considering these variables, the difference in the deviance in these two models was 0.0034(df=1). Conclusion : There is a lot of risk if an inference regarding the variables and large samples is made without considering the pattern of missing data. On the basis of these results, the missing data occurring in the BMI is the ignorable nonresponse. Therefore, when analyzing the BMI in KEPEC data, the inference can be made about the data without considering the missing data.

참고문헌 (12)

  1. Bishop YM, Feinberg SE, Holland PW. Discrete Multivariate Analysis, Cambridge, MA : MIT Press; 1975 
  2. Park TS, Brown MB. Models for Categorical Data with Nonignorable Nonresponse. JASA 1994; 89: 44-52 
  3. Chambers RL, Welsh AH. Log-linear Models for Survey Data with Nonignorable Non-response. JRSS B 1993; 55(1): 157-170 
  4. Park TS, Lee SY. Analysis of Categorical Data with Nonresponses. Korean J Appl Statistics 1998; 11: 83-95 (Korean) 
  5. Little RJA, Rubin DB. Statistical Analysis with Missing Data, New York: John Wiley & Son; 1987 
  6. Dempster AP, Laird NM, Rubin DB. Maximum Likelihood Estimation from Incomplete Data via the EM Algorithm. JRSS B 1977; 39: 1-38 
  7. Park BJ, Kim DS, Koo HW, Bae JM. Reliability and Validity of a Life Style Questionnaire for Elderly People. Korean J Prev Med 1998; 31(1): 49-58 (Korean) 
  8. Fey RE. Causal Models for Patterns of Nonresponse. JASA 1986; 81:354-365 
  9. Baker SG, Laird NM. Regression Analysis for Categorical Variables with Outcome Subject to Nonignorable Nonresponse. JASA 1988; 83: 62-69 
  10. Pregibon D. Typical Survey Data: Estimation and Imputation. Survey Methodology 1977; 2: 70-102 
  11. Little RJA. Models for Nonresponse in Sample Survey. JASA 1982; 77: 237-250 
  12. Park TS. An Approach to Categorical Data with Nonignorable Nonresponse. Biometrics 1998; 54: 1579-1590 

이 논문을 인용한 문헌 (0)

  1. 이 논문을 인용한 문헌 없음


원문 PDF 다운로드

  • ScienceON :

원문 URL 링크

원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다. (원문복사서비스 안내 바로 가기)

상세조회 0건 원문조회 0건

DOI 인용 스타일