This video demonstrates how to perform a McNemar test in SPSS. The McNemar test is used to analyze dichotomous variables and is often used with pretest and posttest data.
What test would be appropriate if I had PRE and POST dichotomous variable (IV) and A likert scale Dependent variable (4 choice). The problem is that my PRE and POST groups are NOT independent but actually are the same group of people (their responses are NOT paired together). If they were paired it would simply be a matched samples t-test or Wicoxon. If they were independent it would be an indpendent t-test or Mann Whitney U. Problem is they are NOT independent and NOT paired! Thanks, Jeff
Thank you for the video, however i tried running it using the syntax provided and it keeps saying something is missing or the SPSS cannot access file or variable or FORMAT wrong
Thank you very much for the great lessons. What in case if we have 2 groups and would like to do pretest posttest analysis for dichotomous data, which test to use?
Warnings The McNemar Test for PreCadScoreTotal & PostCadScoreTotal is not performed because both variables are not dichotomous with the same values. Why did i get this?
Hello Dr. Grande. I wonder if you can help me? i have to study a sample where 1 dicothomous variable variable is measured at 3 points on time: at baseline, at 12 months and after 24 months of treatment. How should i proceed? thanks in advance.! Great as always.
hi giorgeto my take would be when you have more than two time series i would sggest mc nemars tet wont be suitable but you can use cochran q test instead
So, similar to a Chi-square but you might use the McNemar specifically if you have a pretest/posttest design and you meet the other assumptions.
The video content is so excellent, congratulations
Great details, very helpful, thanks.
What to use for non dichotomous variables? Can anyone help? : )
Thank you very much! you are my savior!
This is a helpful explanation. Thank you.
What test would be appropriate if I had PRE and POST dichotomous variable (IV) and A likert scale Dependent variable (4 choice). The problem is that my PRE and POST groups are NOT independent but actually are the same group of people (their responses are NOT paired together). If they were paired it would simply be a matched samples t-test or Wicoxon. If they were independent it would be an indpendent t-test or Mann Whitney U. Problem is they are NOT independent and NOT paired! Thanks, Jeff
dear dr, tq for your video. what if the result is continuity corrected, is it we can accept the results?
Thank you for the video, however i tried running it using the syntax provided and it keeps saying something is missing or the SPSS cannot access file or variable or FORMAT wrong
Very helpful, explicit explanation :) thank you
Thank you very much for the great lessons. What in case if we have 2 groups and would like to do pretest posttest analysis for dichotomous data, which test to use?
The second way to conduct the test In SPSS provides much less output. It's like a quick and dirty version when you just want the significance
Warnings
The McNemar Test for PreCadScoreTotal & PostCadScoreTotal is not performed because both variables are not dichotomous with the same values.
Why did i get this?
It appears that the variables in your analysis have more than two levels. Check the number of levels in the variable view.
When I did the cross tabs, it gave me this huge table of numbers. i have everything as Ordinal. (It is a likert scale.) I have no idea what I am doing, Obviously.
PreCadScoreTotal * PostCadScoreTotal Crosstabulation
PostCadScoreTotal Total
14 17 18 20 21 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 43
PreCadScoreTotal 15 Count 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
% within PreCadScoreTotal 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.9%
19 Count 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3
% within PreCadScoreTotal 0.0% 33.3% 33.3% 33.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 100.0% 50.0% 33.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 5.7%
20 Count 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
% within PreCadScoreTotal 0.0% 0.0% 50.0% 50.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 50.0% 33.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3.8%
21 Count 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.9%
23 Count 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 50.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.9%
24 Count 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
% within PreCadScoreTotal 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 33.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.9%
25 Count 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 50.0% 50.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 33.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3.8%
26 Count 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 50.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.9%
27 Count 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 3
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 33.3% 0.0% 33.3% 0.0% 0.0% 33.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 33.3% 0.0% 25.0% 0.0% 0.0% 25.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 5.7%
28 Count 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 25.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.9%
29 Count 0 0 0 0 0 1 0 0 0 0 2 1 2 1 0 1 0 0 0 0 0 0 0 8
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 12.5% 0.0% 0.0% 0.0% 0.0% 25.0% 12.5% 25.0% 12.5% 0.0% 12.5% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 50.0% 16.7% 66.7% 25.0% 0.0% 25.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 15.1%
30 Count 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 2
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 33.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 3.8%
31 Count 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 4
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 25.0% 25.0% 0.0% 0.0% 0.0% 25.0% 0.0% 0.0% 25.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 25.0% 16.7% 0.0% 0.0% 0.0% 25.0% 0.0% 0.0% 25.0% 0.0% 0.0% 0.0% 0.0% 7.5%
32 Count 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 1 1 1 0 0 0 0 0 6
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 16.7% 0.0% 0.0% 0.0% 0.0% 0.0% 33.3% 16.7% 16.7% 16.7% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 33.3% 0.0% 0.0% 0.0% 0.0% 0.0% 66.7% 25.0% 50.0% 33.3% 0.0% 0.0% 0.0% 0.0% 0.0% 11.3%
33 Count 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 1 1 0 0 0 0 0 6
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 16.7% 0.0% 16.7% 0.0% 16.7% 16.7% 0.0% 16.7% 16.7% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 16.7% 0.0% 25.0% 33.3% 0.0% 50.0% 33.3% 0.0% 0.0% 0.0% 0.0% 0.0% 11.3%
34 Count 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 4
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 25.0% 0.0% 0.0% 25.0% 0.0% 25.0% 0.0% 0.0% 25.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 33.3% 0.0% 0.0% 25.0% 0.0% 33.3% 0.0% 0.0% 100.0% 0.0% 0.0% 7.5%
35 Count 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 25.0% 0.0% 0.0% 0.0% 0.0% 1.9%
36 Count 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 2
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 50.0% 0.0% 0.0% 0.0% 50.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 25.0% 0.0% 0.0% 0.0% 100.0% 3.8%
37 Count 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 2
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 50.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 50.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 16.7% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 3.8%
39 Count 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 0.0% 0.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 25.0% 0.0% 0.0% 0.0% 0.0% 1.9%
41 Count 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1
% within PreCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 100.0%
% within PostCadScoreTotal 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 100.0% 0.0% 1.9%
Total Count 1 1 2 3 1 1 2 1 3 1 4 6 3 4 3 4 2 3 4 1 1 1 1 53
% within PreCadScoreTotal 1.9% 1.9% 3.8% 5.7% 1.9% 1.9% 3.8% 1.9% 5.7% 1.9% 7.5% 11.3% 5.7% 7.5% 5.7% 7.5% 3.8% 5.7% 7.5% 1.9% 1.9% 1.9% 1.9% 100.0%
% within PostCadScoreTotal 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
Hello Dr. Grande. I wonder if you can help me? i have to study a sample where 1 dicothomous variable variable is measured at 3 points on time: at baseline, at 12 months and after 24 months of treatment. How should i proceed? thanks in advance.! Great as always.
hi giorgeto my take would be when you have more than two time series i would sggest mc nemars tet wont be suitable but you can use cochran q test instead
Can we use one-tail test here in McNemar Dr. Todd Grande?
How to resolve "P must greater than 1«
Thank you so much,
Is there is a test for more than 2 related categorical samples
Cochran's Q test.
very helpful!!
Thank you!