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Fernanda, I think you need to back up a bit. You will need to take into account the fact that the variance of the outcome is not constant across the logit equations, in order to get unbiased estimates of c - c'. Actually, I think it might be a better idea to do a path analysis with -cmp- (i.e., test your full model simultaneously) and try to get specific indirect effects for your two mediators from that model, rather than estimating a global reduction in the direct effect, which isn't very informative about where the mediational actions are taking place. Anyway, I think you have some more homework to do before you proceed with this analysis. See the following: Karlson, A.F.B., Holm, A., & Breen, R. (December 23, 2010). Total, Direct, and Indirect Effects in Logit Models. CSER Working Paper Series, No. 0005. Aarhus, Denmark: Centre for Strategic Educational Research DPU, Aarhus University.http://www.cser.dk/fileadmin/www.cser.dk/wp_005rbkkahfin_01.pdf Buis, M.L. (2010). Direct and indirect effects in a logit model. The Stata Journal, 10(1), 11?29.https://teamsite.smu.edu.sg/wiki/stats/Shared%20Documents/Stata%20Journals/2010/sj10-1.pdf MacKinnon, D. P., & Dwyer, J. H. (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17, 144-158. MacKinnon, D.P., Lockwood, C.M., Brown, C.H., Wang, W., & Hoffman, J.M. (2007). The intermediate endpoint effect in logistic and probit regression. Clinical Trials, 4(5), 499-513.http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2857773/pdf/nihms-173365.pdf Li, Y, Schneider, J.A., & Bennett, D.A. (2007). Estimation of the mediation effect with a binary mediator. Statistics in Medicine, 26(18), 3398-3414. Jasti, S., Dudley, W.N., & Goldwater, E. (2008). SAS macros for testing statistical mediation in data with binary mediators or outcomes. Nursing Research, 57(2), 118-122. Huang, B., Sivaganesan, S., Succop, P., & Goodman, E. (2004). Statistical assessment of mediational effects for logistic mediational models. Statistics in Medicine, 23(17), 2713?2728. Kuha, J., & Goldthorpe, J.H. (2010). Path analysis for discrete variables: the role of education in social mobility. Journal of the Royal Statistical Society: Series A (Statistics in Society), 173(2), 351?369. Eshima, N., Tabata, M., & Zhi, G. (2001). Path analysis with logistic regression models: effect analysis of fully recursive causal systems of categorical variables. Journal of the Japanese Statistical Society, 31(1), 1-14. http://www.scipress.org/journals/jjss/pdf/3101/31010001.pdf Roodman, D. (2011). Fitting fully observed recursive mixed-process models with cmp. The Stata Journal, 11(2), 159-206.http://www.stata-journal.com/article.html?article=st0224http://ideas.repec.org/c/boc/bocode/s456882.html Cam 2012/4/2 Fernanda Queiros <nandaqueiros@gmail.com>: > Dear list participants, > > First of all, I am a PhD student with limited statistical knowledge > compared to what I?ve seen here at the list. Also, English is not my > first language; so, forgive me if any part of the message is not > crystal clear. > > I am trying to estimate the mediation effect for an ordinal outcome > with 10 levels (ladderw4). The two mediators I?m interested are > categorical. My main independent variable (IV) is categorical (four > levels), as well as all my co-variates. I am using complex survey > data, with 132 PSU?s and 4 strata. I saw here at the list some > suggestions applying econometrics methods to a question about testing > mediation with categorical mediators, which is beyond my knowledge. > > Thus, I am trying to compute the difference of my main IV?s > coefficients (from the models with and without the mediator) to > measure the change and, therefore, mediation effect. I am thinking > about using the jackknife procedure to estimate the variance of my > coefficients. However, the problem I am facing is that I don?t know > how to compute the variance of the difference? > > That is how I am establishing the survey design using Jackknife (Stata 11.2): > > . svyset psuscid [pweight=gswgt4_2], strata(region) vce(jackknife) > > I am, then, using svy: ologit to run my models: (1) without the > mediator and (2) with it: > > (1) xi: svy,subpop(subpop2): ologit ladderw4 i.type_disability age_w4 > bio_sex_ i.racew1_r i.parented i.famst3 > > (2) xi: svy,subpop(subpop2): ologit ladderw4 i.type_disability age_w4 > bio_sex_ i.racew1_r i.parented i.famst3 deprew1_dic > > The output gives me the ?final? coefficients and Jackknife SE?s for my > IV?s. My question is: > > - Is there a way to get the coefficients/SE?s for each one of the 132 > replications Stata is running in this case? I think that with this > information it would be possible to compute the variance of the > difference between the two coefficients of my main IV... > > - Also, does any one have experience on testing mediation with > similar types of variables (using complex survey data) and would > suggest me a different approach from what I am trying to do? > > Looking forward to hearing your ideas/suggestions! > > Many thanks, > > Fernanda > > > > -- > Fernanda Queirós > Me., Fonoaudióloga / M.S., Brazilian Speech-Language Pathologist > Doutoranda - Bolsista CAPES/Fulbright / Ph.D. Candidate - > CAPES/Fulbright grantee > University of North Carolina at Chapel Hill > Gillings School of Global Public Health > Department of Maternal and Child Health > > > Para ser grande, sê inteiro: nada teu exagera ou exclui. Sê todo em > cada coisa. Põe quanto és > no mínimo que fazes.... > (Fernando Pessoa) -- Fernanda Queirós Me., Fonoaudióloga / M.S., Brazilian Speech-Language Pathologist Doutoranda - Bolsista CAPES/Fulbright / Ph.D. Candidate - CAPES/Fulbright grantee University of North Carolina at Chapel Hill Gillings School of Global Public Health Department of Maternal and Child Health Para ser grande, sê inteiro: nada teu exagera ou exclui. Sê todo em cada coisa. Põe quanto és no mínimo que fazes.... (Fernando Pessoa) * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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