I am obtaining different results from what I believe is the same survey weighted logistic regression on the same data set.
The main difference is that one says "Population size = 25,327" and the other "Population size = 43,676
Can anyone explain why this might be the case? I thought my weights were the same in each sample but I puzzled as to why it is generating a different population size.
Here is my code followed by a sample output:
mi estimate, or: svy: logistic visa foreigner fujian pregnant otherpaidtrip othersapply age male migrant agent disadveth geotravex1 geotravex2 pargeotravex1 pargeotravex2 ocpr0 lsal satsal educyear schpc9foreig parocpr pareducyear ifoplive lwealthwosqm humanities socialscience professions stem business married numsib jobyear chimin parret numfaminus famtiegravsponsor
Multiple-imputation estimates Imputations = 40
Survey: Logistic regression Number of obs = 2,395
Number of strata = 1 Population size = 43,676
Number of PSUs = 2,395
Average RVI = 0.0792
Largest FMI = 0.2823
Complete DF = 2394
DF adjustment: Small sample DF: min = 387.79
avg = 1,697.47
max = 2,361.48
Model F test: Equal FMI F( 35, 2368.0) = 5.58
Within VCE type: Linearized Prob > F = 0.0000
example coefficient:
ifoplive | 1.420166 .2310829 2.16 0.031 1.031706 1.954889
mi estimate, or: svy: logistic visa foreigner fujian pregnant otherpaidtrip othersapply age male migrant agent disadveth geotravex1 geotravex2 pargeotravex1 pargeotravex2 ocpr0 lsal satsal educyear schpc9foreig parocpr pareducyear ifoplive lwealthwosqm humanities socialscience professions stem business married numsib jobyear chimin parret numfaminus famtiegravsponsor
Multiple-imputation estimates Imputations = 40
Survey: Logistic regression Number of obs = 2,395
Number of strata = 1 Population size = 25,327
Number of PSUs = 2,395
Average RVI = 0.0939
Largest FMI = 0.3811
Complete DF = 2394
DF adjustment: Small sample DF: min = 232.51
avg = 1,616.43
max = 2,363.46
Model F test: Equal FMI F( 35, 2359.5) = 5.67
Within VCE type: Linearized Prob > F = 0.0000
ifoplive | 1.365883 .2233156 1.91 0.057 .9907228 1.883105
Thanks!
The main difference is that one says "Population size = 25,327" and the other "Population size = 43,676
Can anyone explain why this might be the case? I thought my weights were the same in each sample but I puzzled as to why it is generating a different population size.
Here is my code followed by a sample output:
mi estimate, or: svy: logistic visa foreigner fujian pregnant otherpaidtrip othersapply age male migrant agent disadveth geotravex1 geotravex2 pargeotravex1 pargeotravex2 ocpr0 lsal satsal educyear schpc9foreig parocpr pareducyear ifoplive lwealthwosqm humanities socialscience professions stem business married numsib jobyear chimin parret numfaminus famtiegravsponsor
Multiple-imputation estimates Imputations = 40
Survey: Logistic regression Number of obs = 2,395
Number of strata = 1 Population size = 43,676
Number of PSUs = 2,395
Average RVI = 0.0792
Largest FMI = 0.2823
Complete DF = 2394
DF adjustment: Small sample DF: min = 387.79
avg = 1,697.47
max = 2,361.48
Model F test: Equal FMI F( 35, 2368.0) = 5.58
Within VCE type: Linearized Prob > F = 0.0000
example coefficient:
ifoplive | 1.420166 .2310829 2.16 0.031 1.031706 1.954889
mi estimate, or: svy: logistic visa foreigner fujian pregnant otherpaidtrip othersapply age male migrant agent disadveth geotravex1 geotravex2 pargeotravex1 pargeotravex2 ocpr0 lsal satsal educyear schpc9foreig parocpr pareducyear ifoplive lwealthwosqm humanities socialscience professions stem business married numsib jobyear chimin parret numfaminus famtiegravsponsor
Multiple-imputation estimates Imputations = 40
Survey: Logistic regression Number of obs = 2,395
Number of strata = 1 Population size = 25,327
Number of PSUs = 2,395
Average RVI = 0.0939
Largest FMI = 0.3811
Complete DF = 2394
DF adjustment: Small sample DF: min = 232.51
avg = 1,616.43
max = 2,363.46
Model F test: Equal FMI F( 35, 2359.5) = 5.67
Within VCE type: Linearized Prob > F = 0.0000
ifoplive | 1.365883 .2233156 1.91 0.057 .9907228 1.883105
Thanks!
