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I know that I can determine the correlation / association / dependency between two variables (X= smoker, and Y=cancer) using chi-squared with a 2x2 contingency table: 

        Y=yes   Y=no
X=yes     n11    n12
X=no      n21    n22

where

  • n11 is the number of times when X happened, Y also happened
  • n12 is the number of times only X happened
  • n21 is the number of times only Y happened
  • n22 is the number of times when X did not happen, Y also did not happen

Then I follow the chi-squared calculation:
$$ \chi^2 = \sum\frac{(\rm observed - expected)^2}{\rm expected} $$

But what if: instead of one outcome Y, I have more than one outcome? For example: y1=cancer, y2=irregular heartbeat, y3=High blood pressure, etc. How can I represent these in contingent table?

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This is actually a pretty advanced topic. There are a couple of possible shortcuts. If all Y variables are independent, you could simply run several chi-squared tests. You could also collapse your Y variables into a single new Y variable with 8 levels (y1-no; y2-no; y3-no, y1-no; y2-no; y3-yes, y1-no; y2-yes; y3-no, etc.) and run a chi-squared test on the 2x8 contingency table. Neither of these are likely to be very satisfactory, however.

The preferred analysis is to construct a multi-way contingency table, and fit a log-linear model. The contingency table that you present is a two-way table with counts in rows and columns. With one X and three Y variables, you would have a four-way contingency table, where each cell holds the count of the number of times that combination of X, Y1, Y2, and Y3 values happened. Since it is hard to visualize a four-way table, it might also be convenient to display it as a flat table: 

x-no;  y1-no;  y2-no;  y3-no:   n1111
x-no;  y1-no;  y2-no;  y3-yes:  n1112
x-no;  y1-no;  y2-yes; y3-no:   n1121
x-no;  y1-no;  y2-yes; y3-yes:  n1122
x-no;  y1-yes; y2-no;  y3-no:   n1211
x-no;  y1-yes; y2-no;  y3-yes:  n1212
x-no;  y1-yes; y2-yes; y3-no:   n1221
x-no;  y1-yes; y2-yes; y3-yes:  n1222
x-yes; y1-no;  y2-no;  y3-no:   n2111
x-yes; y1-no;  y2-no;  y3-yes:  n2112
x-yes; y1-no;  y2-yes; y3-no:   n2121
x-yes; y1-no;  y2-yes; y3-yes:  n2122
x-yes; y1-yes; y2-no;  y3-no:   n2211
x-yes; y1-yes; y2-no;  y3-yes:  n2212
x-yes; y1-yes; y2-yes; y3-no:   n2221
x-yes; y1-yes; y2-yes; y3-yes:  n2222

A log-linear model is essentially a Poisson regression model that compares models that take various combinations of the variables into account and compares their fit to the saturated model. If you use R, there is a nice tutorial here.

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  • $\begingroup$ Thank you so much for all your help and useful information. What I did is: I built 2x2 contingency table, x-yes; (y1, and y2, and y3)-yes: n11 x-yes; (y1, and y2, and y3)-no: n12 x-no; (y1, and y2, and y3)-yes: n21 x-no; (y1, and y2, and y3)-no: n22 I guess i did wrong $\endgroup$ – phoenix Mar 29 '15 at 14:32
  • $\begingroup$ @phoenix, that's right: a chi-squared test is not appropriate for your data. You need to use a log-linear model. What software are you using? $\endgroup$ – gung - Reinstate Monica Mar 29 '15 at 14:37
  • $\begingroup$ I wrote my program using Java $\endgroup$ – phoenix Mar 29 '15 at 14:40
  • $\begingroup$ @phoenix, Oh, OK. I don't know if there are statistical libraries in Java for log-linear models. You may have to code it up from scratch (which will be very difficult). Or you could use some other software (like R). $\endgroup$ – gung - Reinstate Monica Mar 29 '15 at 14:43
  • $\begingroup$ Thank you, the reason I did that, I read a paper and they gave a solution using a chi-squared test for combined (X's), so I thought it will be the same (statistically) for combined (Y's): [1]: store2.up-00.com/2015-03/1427642138361.png $\endgroup$ – phoenix Mar 29 '15 at 15:19

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