I use SAS 9.4 for windows. Does this answer your question?   ... View more

Thank you @ballardw for your thoughts and suggestions. I've added my thoughts to your comments below:   You may get an easier to read for your purpose if you reverse the order of the variables in the tables statement. Try   proc surveyfreq data = &data VARHEADER = NAMELABEL nosummary; tables Fruits * Kids_gender / nostd nocellpercent row row(cl) cv chisq; weight WEIGHT_scale; run; Then the boy/girl responses for the same level of the question will be closer together and easier to read but the information isn't going to change. I tried this but I noticed it will give me different percentages than what I want. I want % girls among all girls who said they would use less, use more, ... but if I switch the order of the variables to Fruits * Kids_gender then I get %boys and girls who responded use less ... Use of fruits and vegetables Kids gender Frequency Weighted Frequency Percent CV for Percent Row Percent 95% Confidence Limits for Row Percent CV for Row Percent Use less Girls 748 688.4 4.1253 0.044 41.9 38.8 45.0 0.0379   Boys 647 955.1 5.7232 0.045 58.1 55.0 61.2 0.0273   Total 1395                 I don't see anything related to "change in behavior" though. That would require some sort of Before/After response time indicator. At this stage, I am interested in the different patterns of behavior of girls and boys after the intervention/workshop. Your point is a good idea but answers a different question.   I would also tend to be a bit concerned over your nearly 6% "Don't know" response. That might indicate the way that particular data point is collected wasn't very clear to a lot of respondents to the survey.  I would consider creating another response variable where the "Don't Know" are set to missing so you can compare the responses among those respondents that made and actual choice. In this survey Don't Know is a legitimate answer because the kids are guessing on how the workshop would affect their behavior in the future and some kids are not sure how or whether the workshop would change their behavior in practice.    The CHI-square test statistic tells you if there is/is not a significant difference in the distribution of values overall. Thanks for confirming. Now that my overall test of patterns of behavior for boys and girls are different, I am interested to know whether the 10.7% of boys who replied they will use less is significantly higher than the 8.9% of the girls who also responded to use less and similar comparisons for other levels (if 19.5% boys are significantly more than 15.6% girls, ...) Thanks ... View more

Thank you @sbxkoenk for your thoughts. I have a question about your suggestion. If I use the  by pattern_of_consumption approach to slice the comparison for the subgroups of levels of consumption for fruits and vegies then for example, for the kids who said use less, I am comparing 748 girls who said they use less with 647 boys who said use less and I would compare 41.9% girls with 58.1% boys vs. I wanted to compare 8.9% girls with 10.7% boys from the overall sample of girls and boys (9315 girls and 6537 boys). My concern/question is if it is correct to use the p-value from this BY analysis below in my MULTTEST procedure to adjust for type I error.    Kids Gender Frequency Weighted Frequency Percent CV for Percent Girls 748 688 41.9 0.0379 Boys 647 955 58.1 0.0273 Total 1395 1644 100.0   Rao-Scott Chi-Square Test Pearson Chi-Square 36.7227 Design Correction 1.4406     Rao-Scott Chi-Square 25.4908 DF 1 Pr > ChiSq <.0001     F Value 25.4908 Num DF 1 Den DF 1394 Pr > F <.0001 Sample Size = 1395   The percentages I want to compare   The percentages I compare with BY analysis     MULTTEST adjustment Patterns of consumption Female Male Female Male P-value Adjusted p-value Use less 8.9 10.7 41.9 58.1 0.0001     Use more 15.6 19.5 41.2 58.8 0.0001     No difference, would use the same amount 58.7 54.0 48.6 51.4 0.0367     Not applicable / I do not use this 11.1 9.9 49.3 50.7 0.6779     Don't know 5.7 6.0 45.6 54.4 0.038     Thanks ... View more

Thank you @GraphGuy for your thoughts on this. I agree with your suggestions. Thanks ... View more

Thank you @ballardw for your thoughts. I have respondents in all categories but a Venn diagram is too busy and very hard to interpret. I am using simpler graphs to show the popular combinations. Thanks ... View more

Thanks @Reeza for your message. I was checking first to see if anyone has done this before and if I can get help to start it. I even do not know yet how to do a 4-way Venn diagram in SAS. I found that R can do higher-order Venn diagrams. Also, when I checked other examples, the 5-way or higher order Venn diagrams are very hard to read. I am thinking about other ways to present/visualize My results for now but have a Venn diagram in my mind as well. Thanks ... View more

Thank you @Madelyn_SAS  for your suggestion! ... View more

Hello,   I have a survey for boys and girls who attended an education series for healthy eating and based on the following chi square test the pattern of change in behavior is different for boys and girls. We asked the kids how the education affected their use of fruits and vegetables. I want to compare the same answers for patterns of consumption for girls and boys. For example, if the % boys who said they will use more vegies are significantly more than % girls who replied they will use more vegies (19.5% vs. 15.6%). I am not interested to test different levels for boys and girls, for example, if more boys said "use more" than girls who said "use less" (19.5% vs. 8.9%)   Could you please help me with this test?   proc surveyfreq data = &data VARHEADER = NAMELABEL nosummary; tables Kids_gender*Fruits / nostd nocellpercent row row(cl) cv chisq; weight WEIGHT_scale; run;   Kids gender Use of fruits and vegetables Frequency Weighted Frequency CV for Percent Row Percent 95% Confidence Limits for Row Percent CV for Row Percent Girls Use less 748 688.4 0.044 8.9 8.1 9.6 0.0430   Use more 1490 1214.1 0.031 15.6 14.7 16.5 0.0292   No difference, would use the same amount 5706 4559.3 0.015 58.7 57.4 59.9 0.0109   Not applicable / I do not eat them 912 860.8 0.040 11.1 10.2 11.9 0.0386   Don't know 459 445.6 0.056 5.7 5.1 6.4 0.0547   Total 9315 7768.3 0.011 100.0       Boys Use less 647 955.1 0.045 10.7 9.8 11.6 0.0442   Use more 1211 1735.6 0.032 19.5 18.3 20.6 0.0306   No difference, would use the same amount 3743 4812.7 0.016 54.0 52.5 55.4 0.0139   Not applicable / I do not eat them 596 884.3 0.048 9.9 9.0 10.8 0.0474   Don't know 340 532.4 0.063 6.0 5.2 6.7 0.0622   Total 6537 8920.1 0.009 100.0       Total Use less 1395 1643.5 0.031           Use more 2701 2949.7 0.022           No difference, would use the same amount 9449 9372.0 0.009           Not applicable / I do not eat them 1508 1745.1 0.031           Don't know 799 978.0 0.042           Total 15852 16688.4           Frequency Missing = 3   Rao-Scott Chi-Square Test Pearson Chi-Square 67.5914 Design Correction 1.6183     Rao-Scott Chi-Square 41.7675 DF 4 Pr > ChiSq <.0001     F Value 10.4419 Num DF 4 Den DF 63404 Pr > F <.0001 Sample Size = 3   Thanks ... View more

Thank you @Tom for your comprehensive explanation! ... View more

Thank you, @Tom. 1- Then, with this addition, PROC IMPORT saves the value labels in a dataset called FORMATS in the WORK library? 2 - And, I cannot import the variable labels, right? Thanks ... View more