回复： 回复：[mpluser] 求unweighted means？

[星牧童]

marginal mean weighting 附件文献

This article has systematically introduced MMW-S as an alternative to the existing propensity score-based causal inference methods for multilevel educational data. 

加权的有公式，没有软件。你可以问作者。公式很复杂，看不懂

加权的比没加权的复杂多了，SPSS输出的通常没有加权吧，手动回归残差求的也没有加权。

发送

你问文献里面的大牛啦 

帮不了你

------------------ 原始邮件 ------------------

发件人: "xinxi813";<xinx...@126.com>;

发送时间: 2014年4月19日(星期六) 晚上7:55

收件人: "mpluser"<mpl...@googlegroups.com>;

主题: 回复: 回复：[mpluser] 求unweighted means？

谢谢牧童啊。

 

这个marginal是weighted过的还是没有weighted过的呀？求文献。

 

关键是，在multilevel的情况下，这个平均数的standard error也不知道怎么求。

 

你旁边有高手么，帮忙扩散这个问题吧。求扩散。。。。

 

---

ya

 

发件人： 星牧童

发送时间： 2014-04-19 14:43

收件人： xinxi813

主题： 回复：[mpluser] 求unweighted means？

据说，可以先拿协变量对研究关心的变量做回归，得到的残差代替这些变量的原始值来统计，得到的就是Marginal

------------------ 原始邮件 ------------------

发件人: "xinxi813";<xinx...@126.com>;

发送时间: 2014年4月19日(星期六) 晚上7:00

收件人: "mpluser"<mpl...@googlegroups.com>;

主题: [mpluser] 求unweighted means？

大家，

 

请问有人知道这个东西怎么通过SEM的方式来求么？

 

在SPSS里面，这个被叫做Estimated Marginal means（EM means），SAS里面叫做Least Square means（LS means），通常是anova结果的一部分。

 

求指点。。。。

 

---

ya

--
-- 本小组推荐：
http://www.statmodel.com/
http://quantpsy.org/
http://www.kthau.com/?page_id=81
http://www.caldar.org/html/2010%20Speakers/mcardle.html
http://courses.nus.edu.sg/course/psycwlm/internet/
（4 TEACHING------4.2 Some notes from previous workshops）
http://blog.sina.com.cn/u/2142257021
http://lavaan.ugent.be/
http://personality-project.org/r/r.guide.html#Intro


You received this message because you are subscribed to the Google
Groups "Mplus学习小组" group.
To post to this group, send email to mpl...@googlegroups.com
To unsubscribe from this group, send email to
mpluser+u...@googlegroups.com
For more options, visit this group at
http://groups.google.com/group/mpluser?hl=zh-TW
---
You received this message because you are subscribed to the Google Groups "Mplus学习小组" group.
To unsubscribe from this group and stop receiving emails from it, send an email to mpluser+u...@googlegroups.com.
For more options, visit https://groups.google.com/d/optout.

--
-- 本小组推荐：
http://www.statmodel.com/
http://quantpsy.org/
http://www.kthau.com/?page_id=81
http://www.caldar.org/html/2010%20Speakers/mcardle.html
http://courses.nus.edu.sg/course/psycwlm/internet/
（4 TEACHING------4.2 Some notes from previous workshops）
http://blog.sina.com.cn/u/2142257021
http://lavaan.ugent.be/
http://personality-project.org/r/r.guide.html#Intro
 
 
You received this message because you are subscribed to the Google
Groups "Mplus学习小组" group.
To post to this group, send email to mpl...@googlegroups.com
To unsubscribe from this group, send email to
mpluser+u...@googlegroups.com
For more options, visit this group at
http://groups.google.com/group/mpluser?hl=zh-TW
---
You received this message because you are subscribed to the Google Groups "Mplus学习小组" group.
To unsubscribe from this group and stop receiving emails from it, send an email to mpluser+u...@googlegroups.com.
For more options, visit https://groups.google.com/d/optout.

--
-- 本小组推荐：
http://www.statmodel.com/
http://quantpsy.org/
http://www.kthau.com/?page_id=81
http://www.caldar.org/html/2010%20Speakers/mcardle.html
http://courses.nus.edu.sg/course/psycwlm/internet/
（4 TEACHING------4.2 Some notes from previous workshops）
http://blog.sina.com.cn/u/2142257021
http://lavaan.ugent.be/
http://personality-project.org/r/r.guide.html#Intro


You received this message because you are subscribed to the Google
Groups "Mplus学习小组" group.
To post to this group, send email to mpl...@googlegroups.com
To unsubscribe from this group, send email to
mpluser+u...@googlegroups.com
For more options, visit this group at
http://groups.google.com/group/mpluser?hl=zh-TW
---
You received this message because you are subscribed to the Google Groups "Mplus学习小组" group.
To unsubscribe from this group and stop receiving emails from it, send an email to mpluser+u...@googlegroups.com.
For more options, visit https://groups.google.com/d/optout.

[ya]

同学，这个文章其实说的不是我的那个问题，不过确实提了个醒。
实际上需要的是在level-1的within-cluster里不要根据各treatment combination的sample size来加权，但是在整个模型里，的确要weight stratification。文章说的其实是后面这个问题。
我发了邮件了。。。
继续求搭理。。。。。