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From |
Emmanuel Koku <[email protected]> |

To |
[email protected] |

Subject |
Re: st: RE: Creating a Non-Self Mean & Proportions |

Date |
Tue, 30 Jun 2009 01:24:04 -0400 |

Dear, Nick, thanks for attempting to resolve my question, though the code fragment did not help. Please, below are 20 cases, covering 3 different strata, as well as their respective values on the index variable of interest (i.e., score), for your examination. What I want is the count or proportion of each value of the index variable (i.e., score) in each strata. Cluster Hhold# Line# Strata score 46 7 1 1 0 122 20 4 1 0 122 6 2 1 1 46 1 1 1 2 122 11 2 1 1 122 6 1 1 0 122 16 6 2 0 46 13 1 2 1 46 12 1 2 2 122 17 1 2 0 122 8 2 2 1 46 13 3 2 1 122 5 2 2 0 46 6 3 3 1 122 16 2 3 0 122 15 1 3 2 46 4 2 3 0 46 19 3 3 1 122 4 2 3 0 46 20 4 3 2 So from this scratch data we can have the following distribution of the 3 score values by strata: ScoreValues Strata1 Strata2 Strata3 Total 0 3 3 3 9 1 2 3 2 7 2 1 1 2 4 N 20 Thus, we can state that across all strata 9 individuals had a score of 0, 7 had a score of 1 and 4 had a score of 2 -- that is we aggregate or count each of the values of the index variable (Score) across or over all strata. In this way I can describe the distribution of the scores over the strata. I hope this makes it a bit clearer. If, not please let me know and I can try and provide further clarification. rgds, Em ------------------------------------------------------------------ On Mon, Jun 29, 2009 at 4:30 PM, Nick Cox <[email protected]> wrote: > > A meta-question here is why no one has replied to this since it was > posted. An answer to that is that trying to work out what is wanted > requires too much effort. Why not give an example of your data? Why make > everyone try to imagine what you have? > > Anyway, my guesses are: > > 1. You have a long data structure with personal id (say -id-) and > stratum id (say -stratum-). (One stratum, several strata.) > > 2. You have a variable (say -score-) with values 0,1,2. (What that > variable represents, how many negative attitudes the others in the > stratum have, it seems, is not important here.) > > 3. You want to summarize the fractions of 0, 1, 2 in each stratum. > > If these guesses are all correct, then one answer is > > bysort stratum score : gen freq = _N > by stratum: gen total = freq / _N > > You may also want to -collapse- the data. > > If these guesses are wrong, you may need to try again with more > information. > > Nick > [email protected] > > Emmanuel Koku > > I have created a summed scale of 2 dichotomous items coded originally > 0 1. The new variable now has values ranging from 0 to 2. > > Since I am using a survey that I want to create summary measures like > non-self mean or non-self proportions that represent other members of > the respondents strata but excluding the respondent. Eg. I would want > to know the proportion of the respondents strata who hold no negative > attitudes - ie 0 in the summed scale; the proportion who hold only one > of the negative attitudes, ie, 1 in the summed scale as well as those > members of the strata who hold both negative values or 2 in the summed > scale. > > Through help received here and from a statafaq article, i have been > able to create similar summary measures for dichotomous items, but i'm > stumped with this 3-category variable, and would appreciate any > assistance. > > > * > * 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/ * * 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/

**Follow-Ups**:**RE: st: RE: Creating a Non-Self Mean & Proportions***From:*"Nick Cox" <[email protected]>

**AW: st: RE: Creating a Non-Self Mean & Proportions***From:*"Martin Weiss" <[email protected]>

**References**:**st: Creating a Non-Self Mean & Proportions***From:*Emmanuel Koku <[email protected]>

**st: RE: Creating a Non-Self Mean & Proportions***From:*"Nick Cox" <[email protected]>

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