Statistics and Data Analysis

(Compared to the last post on risk to attempt suicide, the main difference is that the interaction effect is not significant.) The hypothesis is:  Are the sexual minority (LGBT) kids who use the computer more often (meaning > 5 hours), more likely to be sad or depressed?  Again, this question is an incomplete one. The big picture in front: why >5 hours is a reasonable break.  For all the kids, do kids who use PC more than __ hours more likely to be depressed than kids who use PC less than such hours?  2 hours or 3 hours are not significant. 4 hours is weakly significant, 5 hours is significant. OR =  1.63 (1.44 – 1.84).  41% of all the kids use PC more than 5 hours, which is a large enough amount. I see no reason to go into the more detailed breakdowns within 5 hours. 8% of all the kids belong to the LBGT group. Since there are 13,862 kids in total, 8% represents about 1,145 kids.  In the LGBT group, 52% use PC more often, while...

A three-way contingency table can also be interpreted as a logistic regression with two binary independent variables. I have just encountered a very good example illustrating how logistic regression and contingency tables will obtain the same odds ratio. The study is on 2015 dataset from Youth Risk Behavior Surveillance System ( YRBSS ). It is a national school-based survey conducted by CSD on more than 10,000 youth and students in the United States. We start with a plausible hypothesis: does the sexual minority (LGBT) group who use the computer more often (meaning > 5 hours), more likely to be attempt suicide? First of all, this question is incomplete.  There are 3 ways to complete it: Is the LGBT group who use the computer more often, more likely to attempt suicide than the majority group who also use the computer more often?  Is the LGBT group who use the computer more often, more likely to attempt suicide than the LGBT group who use the...