[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
"Rosenthal, James A." <[email protected]> |

To |
<[email protected]> |

Subject |
st: complex survey wi mult dep vars |

Date |
Wed, 29 Jun 2005 15:06:34 -0500 |

Dear Statalist member: I have data from a complex survey. The appropriate svyset code is: svyset [pweight = xweight] , strata (xstratum), psu (xpsu) I have done some analyses of the data most often with SVYREG. These analyses used data from only the survey's first wave. The survey has about 100 different PSU's. it has about 50 cases within each PSU . I am using version 8 of STATA. the analysis that I need to do now is more complex in two ways: 1) the survey has 3 waves and 2) I want to analyze simultaneously 6 different dependent variables at each wave. the six different variables are different measures of children's behavior. I will do some appropriate transformations of the measures to make their scaling similar (I will likely generate z scores of each variable). My primary interest is in fixed effects, so I don't need to implement a random effects (linear mixed model), though I would be open to doing so. Again, in the more complex analysis I have: 6 different behavioral measurements within 3 different waves within (an average of about) 50 cases within 100 psu's. There are numerous situations of missing waves for cases and of missing measurements within waves. Each line of my data set represents a different outcome measure (so, I have up to 6 lines of data per wave, fewer when the outcome measure is missing) Let me say a couple of things (and I may be wrong). GEE won't work as it build a specialized correlation matrix (very nice but not essential for my work) at the level of the wave and my standard errors need to be developed at the level of the PSU (GEE won't (to my knowledge) do clustered standard errors at a higher level than the level of the correlation matrix. Linear mixed modeling implemented in STATA 9 won't work as it doesn't handle sampling weights and doesn't have robust standard errors. GLLAMM would likely work but I probably don't want to use it as it is so slow computationally. Basically, my best options appear to be REGRESS and SVYREG. Could I simply specify REGRESS with robust standard errors at the level of the PSU and with the strata variables included as dummies. It seems to me that the only problem that I would have would be that I couldn't develop a specialized correlation matrix as in GEE. On, the other hand, could I use SVYREG? If so, would I need to rewrite the SVYSET statement to include the waves? the multiple observations within waves? What other modifications would be needed? Related to the just-mentioned procedures is my own lack of practical and statistical knowledge about sampling weights. Here is my concern (probably ungrounded): the weights for my survey were (so far as I know) designed to be used under the presumption that there would be one outcome variable for each case in any given analysis. Now, I could have as many as 18 outcomes for each case (as many as 6 per wave). So, my sample size (number of lines in the data set) will be many times larger than the 5000 or so on which the sampling weights were presumably built. So, if I don't alter the weights, won't standard errors be inaccurate? Or will SVYREG somehow adjust to the just-described issue and generate accurate standard errors. I have the same concern if I use REGRESS. I worry that my standard errors will be many times too small. Any comments on how and whether this data can be analyzed in STATA will be greatly appreciated. Thank-you. Jim Rosenthal Professor University of Oklahoma School of Social Work 1005 Jenkins Avenue Norman OK 73069 405-325-1401 fax: 405-325-7072 [email protected] * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

- Prev by Date:
**st: RE: Counting unique values over moving time windows** - Next by Date:
**st: kaplan-meier curves** - Previous by thread:
**st: RE: Counting unique values over moving time windows** - Next by thread:
**st: kaplan-meier curves** - Index(es):

© Copyright 1996–2024 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |