Timeline for Taking into account population size differences in chi-squared test
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
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| Mar 3 at 16:31 | answer | added | Nick Cox | timeline score: 1 | |
| Feb 23, 2015 at 12:47 | comment | added | Matthew | Thank you, both, for your comments. I was actually more interested in the characteristics of the areas they come from, e.g. deprivation. Using population data and calculating rates has been much more useful. You have both answered my question so I'd happily accept if you want to post as such. | |
| Feb 16, 2015 at 20:59 | comment | added | whuber♦ | There are good ways to run this test. I can guarantee that if you are located near London, have any competitors, and have more than a tiny amount of data, then you will find there is "significant" variation--people will have a greater tendency to come from nearby locations and locations with no nearby competitor. The proper response to such an obvious conclusion should be "so what?". What is it that you are really trying to find out about your patients? | |
| Feb 16, 2015 at 17:50 | comment | added | Nick Cox | It's back therefore to expected number of patients being proportional to population at risk. Using total population in each local authority is better than using equal frequencies, but using some tailored count (e.g. with adjustments for age and sex) may be better still. | |
| Feb 16, 2015 at 17:44 | comment | added | Matthew | Yes but if I do try and take them into account, it will only be a very crude way of doing it as I have no data directly on point. I thought about a regression but I'm not sure that will really help, either, due to the lack of relevant data. Therefore, I thought I may as well stick with a simple technique. | |
| Feb 16, 2015 at 17:40 | comment | added | Nick Cox | The edited version, mentioning various other predictors, implies that a chi-squared test is just a dead end here, as it can't be extended beyond some very basic comparisons. | |
| Feb 16, 2015 at 17:38 | history | edited | Matthew | CC BY-SA 3.0 |
added 321 characters in body; edited title
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| Feb 16, 2015 at 17:20 | comment | added | Nick Cox | Expecting that different local authorities produce the same total numbers of patients can't be taken seriously, even as a null hypothesis. You just have to ask if you would defend that if the populations were even more variable than they are. A null hypothesis should be based on the population at risk, but exactly how that is defined is a more subtle question. | |
| Feb 16, 2015 at 17:08 | history | asked | Matthew | CC BY-SA 3.0 |