Postgraduate Thesis uoadl:1309800 1397 Read counter

Κατεύθυνση Βιοστατιστική

Library of the School of Health Sciences

Library of the School of Health Sciences

2015-06-29

2015

Μαργετάκη Αικατερίνη

Αναπλ.Καθηγήτρια Γ.Τουλούμη, Καθηγητής Α.Μπουρνέτας, ΕΔΙΠ Ν.Πανταζής

Ανασκόπηση και συγκριτική αξιολόγηση στατιστικών μεθόδων ανάλυσης πληθυσμιακών ερευνών με διαφορετικό δειγματοληπτικό κλάσμα ανά στρώμα παρουσία μη ανταπόκρισης

Greek

When the sample design of a survey is complex, design-based analysis should be

performed. This kind of analysis takes into account the stages of the complex

design and the survey weights. In order to deal with unit nonresponse, the

survey weights are properly adjusted so that the respondent weighted sample

is representative of the population. To perform such a correction the

researcher must collect data for non responders. Then, using the available

information, weighting classes are created. Within these classes response

probabilities are considered constant. The corresponding adjusted weight is the

product of the inverse of the estimated response probability and the survey

weight. Subsequent adjustments can be performed in order to conform the

respondent sample distribution to distributions from an external source, such

as population census.

The second type of non response we dealt with, is item non response. Two broad

categories of methods are usually employed to address this issue. Inverse

probability weighting and multiple imputation.

Standard multiple imputation methods can be modified to incorporate design

features. We used the fully adjusted weights to incorporate design features

into the imputation model first by weighting the imputation model, and second

by including them as a predictor in the imputation model. Alternative

approaches that we did not implement are stratified imputations and multilevel

imputation models.

Inverse probability weighting method and doubly robust estimator are easily

implemented using statistical software, and yielded similar estimates as

multiple imputation. The main drawback, is that variance estimates and

confidence intervals were computed using bootstrap methods, thus they were not

as precise as the ones obtained using multiple imputation. Moreover, the

structure of the missing data in a survey setting, do not allow the use of

these methods.

To conclude, when handling item non response in a survey setting, a good

practice is to perform multiple imputation using the fully adjusted weights as

a covariate. This practice is both theoretically sound and easily implemented

using statistical software such as Stata.

performed. This kind of analysis takes into account the stages of the complex

design and the survey weights. In order to deal with unit nonresponse, the

survey weights are properly adjusted so that the respondent weighted sample

is representative of the population. To perform such a correction the

researcher must collect data for non responders. Then, using the available

information, weighting classes are created. Within these classes response

probabilities are considered constant. The corresponding adjusted weight is the

product of the inverse of the estimated response probability and the survey

weight. Subsequent adjustments can be performed in order to conform the

respondent sample distribution to distributions from an external source, such

as population census.

The second type of non response we dealt with, is item non response. Two broad

categories of methods are usually employed to address this issue. Inverse

probability weighting and multiple imputation.

Standard multiple imputation methods can be modified to incorporate design

features. We used the fully adjusted weights to incorporate design features

into the imputation model first by weighting the imputation model, and second

by including them as a predictor in the imputation model. Alternative

approaches that we did not implement are stratified imputations and multilevel

imputation models.

Inverse probability weighting method and doubly robust estimator are easily

implemented using statistical software, and yielded similar estimates as

multiple imputation. The main drawback, is that variance estimates and

confidence intervals were computed using bootstrap methods, thus they were not

as precise as the ones obtained using multiple imputation. Moreover, the

structure of the missing data in a survey setting, do not allow the use of

these methods.

To conclude, when handling item non response in a survey setting, a good

practice is to perform multiple imputation using the fully adjusted weights as

a covariate. This practice is both theoretically sound and easily implemented

using statistical software such as Stata.

Survey data, Nonresponse, Missing data, Inverse probability weighting, Multiple imputation

No

0

Yes

65

viii, 100