I used a logistic regression on a variable indicating whether a person of an address-dataset took part in a survey (1), or not (0). I extracted the probabilities of each person to participate and calculated the inverse-probability (hence the name of the weighting method - inverse propensity score weighting).
What irritates me, is, that my smallest survey-weight is 1.901. I expected the smallest survey weight to at least be below "1".
I hope somebody can help me and either find out where i made a mistake, or assure me, that i´m on the right track. Any help is greatly appreciated! Thank you!
#Calculate logistic regression glm2<-glm(indicator ~ var1 + varx,family=binomial,data=sampleframe) #extract inverse probability of every case sampleframe$weight<-glm2$fitted^-1 #combine the survey-weight to the survey-data surveydata<-left_join(surveydata,sampleframe, by="ID") #diagnostics: #summary of the weights for the complete sampleframe Min. 1st Qu. Median Mean 3rd Qu. Max. 1.901 2.810 3.247 3.616 3.836 12.070 #summary of the survey-weights of the participants Min. 1st Qu. Median Mean 3rd Qu. Max. 1.925 2.686 3.078 3.308 3.502 12.070 #comparison of mean-weight for participants (1) / non-participants (0) indicator weight.mean 0 3.755967 1 3.295854