The use of weights to account for non-response and drop-out.
BACKGROUND
Empirical studies in psychiatric research and other fields often show substantially high refusal and drop-out rates. Non-participation and drop-out may introduce a bias whose magnitude depends on how strongly its determinants are related to the respective parameter of interest.


METHODS
When most information is missing, the standard approach is to estimate each respondent's probability of participating and assign each respondent a weight that is inversely proportional to this probability. This paper contains a review of the major ideas and principles regarding the computation of statistical weights and the analysis of weighted data.


RESULTS
A short software review for weighted data is provided and the use of statistical weights is illustrated through data from the EDSP (Early Developmental Stages of Psychopathology) Study. The results show that disregarding different sampling and response probabilities can have a major impact on estimated odds ratios.


CONCLUSIONS
The benefit of using statistical weights in reducing sampling bias should be balanced against increased variances in the weighted parameter estimates.
Höfler
Michael
M
Max-Planck-Institut of Psychiatry, Clinical Psychology and Epidemiology, Kraepelinstr. 2-10, 80804, München, Germany. hoefler@mpipsykl.mpg.de
Pfister
Hildegard
H
Lieb
Roselind
R
Wittchen
Hans-Ulrich
HU
D000293:Adolescent; D005260:Female; D006801:Humans; D008297:Male; D001523:Mental Disorders / Q000453:epidemiology / Q000628:therapy*; D008605:Mental Health Services / Q000706:statistics & numerical data*; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D012984:Software; D013223:Statistics as Topic / Q000379:methods; D011795:Surveys and Questionnaires