HI,
I hope someone could help.
My experiment:
within subject (time) with 3 level
between subject (condition) with 2 levels
In each condition 10 subjects (total 20)
I have different DP variables but I want to analyse these one by one.
Unfortunately my data are not normally distributed (as expected)and even with the correction I cannot achieve this assumption. The idea to use a mixed anova is not possible. For sure I could use MANN-WHITNEY test.
I was wondering if there is a different way to analyse the data or a sort of nonparametric GLM.
Thank you very much
L
On Monday, 17 November 2014 03:51:55 UTC-8, luigi.b...@gmail.com wrote:
HI,
I hope someone could help.
My experiment:
within subject (time) with 3 level
between subject (condition) with 2 levels
In each condition 10 subjects (total 20)
I have different DP variables but I want to analyse these one by one.
Unfortunately my data are not normally distributed (as expected)and even with the correction I cannot achieve this assumption. The idea to use a mixed anova is not possible. For sure I could use MANN-WHITNEY test.
I was wondering if there is a different way to analyse the data or a sort of nonparametric GLM.
Thank you very much
L
Hey I was wondering how did you solve your problem at the end?
In fact, I have a very similar problem right now where I have a design in which it has both between subject and within subject component, and the distribution are not at all normal. I was struggling in deciding which non-parametric test to use.
How did you tackle the stat at the end in your research? because if we tease apart the groups and within subject level, we can't detect interaction effect.
best
J
It is highly unlikely that the hit-and-run questioner from 2014
is still reading this group. So don't expect to hear what he did.
If anyone is interested, the original two replies (from Bruce and
from me) are at
https://groups.google.com/forum/#!topic/comp.soft-sys.stat.spss/ZCppmHoKMNE
They read pretty well.
On Tue, 12 May 2020 17:40:28 -0700 (PDT), yzz_812@hotmail.com wrote:
On Monday, 17 November 2014 03:51:55 UTC-8, luigi.b...@gmail.com wrote:
HI,
I hope someone could help.
My experiment:
within subject (time) with 3 level
between subject (condition) with 2 levels
In each condition 10 subjects (total 20)
I have different DP variables but I want to analyse these one by one.
Unfortunately my data are not normally distributed (as expected)and even with the correction I cannot achieve this assumption. The idea to use a mixed anova is not possible. For sure I could use MANN-WHITNEY test.
I was wondering if there is a different way to analyse the data or a sort of nonparametric GLM.
Thank you very much
L
Hey I was wondering how did you solve your problem at the end?
In fact, I have a very similar problem right now where I have a design in which it has both between subject and within subject component, and the distribution are not at all normal. I was struggling in deciding which non-parametric test to use.
How did you tackle the stat at the end in your research? because if we tease apart the groups and within subject level, we can't detect interaction effect.
best
J
To the Questioner: Why is your distribution "not at all normal"?
More importantly, do equal point-differences describe "equal
intervals" of whatever is important in outcome? (If not, why
not, and can't you do something sensible about that.)
There is certainly not a one-size-fits all solution, especially when
the problem arises from design that did not foresee it.
It is highly unlikely that the hit-and-run questioner from 2014
is still reading this group. So don't expect to hear what he did.
If anyone is interested, the original two replies (from Bruce and
from me) are at
https://groups.google.com/forum/#!topic/comp.soft-sys.stat.spss/ZCppmHoKMNE
They read pretty well.
Googling showed me a similar question in another forum, but I
haven't looked at it.
--
Rich Ulrich
I have used the Kolmogorov-Smirnov normality test.
the reason the data was not normal largely because there are a lot of zeros in the data (continuous numeric data with absolute zero), which make it skewed positively. My
Hello Rich,understanding both your and Bruce's comments.
it was very nice of you to reply my question, as I actually didn't expect any response since it was a 6 year-old post.
The OP's question basically hit most of the concerns I have with my current data analysis. unfortunately, I don't have a strong background in statistic and am in a process in self-learning most of the statistic knowledge. So i was having hard time
I have used the Kolmogorov-Smirnov normality test.transformation. however, I have done both and yet the most of the dependent variables still violate assumption of normality.
the reason the data was not normal largely because there are a lot of zeros in the data (continuous numeric data with absolute zero), which make it skewed positively. My supervisor advice me to try clean up the outlier, if possible, and then try data
so I turned to non-parametric solution. my research has between subject component (2 groups), and time (3 time points) as within subject component.there is any way to work around that. i wonder if i still can analyze the interaction effect (group x time) under this context?
Since there is no non-parametric equivalence of mixed design ANOVA, I have to find a solution that is similar to what parametric ANOVA does and self-learn how to do that on SPSS.
I have examined Friedman's test, Mann-Whitney U test, Kruskal-Wallis H test, and Wilcoxon signed rank test. as i saw it, most of them are based on the rank of the dataset and is only partial solution to my analyzing goal. So I was trying to find if
If run the between group and within group tests separately on my data, what problems/issue would follow by doing so?
that's why I post the question and try to see how other researchers usually deal with these kind of situation.
On Wednesday, 13 May 2020 12:28:43 UTC-7, Rich Ulrich wrote:
On Tue, 12 May 2020 17:40:28 -0700 (PDT), yzz...@hotmail.com wrote:
On Monday, 17 November 2014 03:51:55 UTC-8, luigi.b...@gmail.com wrote: >> HI,
I hope someone could help.
My experiment:
within subject (time) with 3 level
between subject (condition) with 2 levels
In each condition 10 subjects (total 20)
I have different DP variables but I want to analyse these one by one. >>
Unfortunately my data are not normally distributed (as expected)and even with the correction I cannot achieve this assumption. The idea to use a mixed anova is not possible. For sure I could use MANN-WHITNEY test.
I was wondering if there is a different way to analyse the data or a sort of nonparametric GLM.
Thank you very much
L
Hey I was wondering how did you solve your problem at the end?
In fact, I have a very similar problem right now where I have a design in which it has both between subject and within subject component, and the distribution are not at all normal. I was struggling in deciding which non-parametric test to use.
How did you tackle the stat at the end in your research? because if we tease apart the groups and within subject level, we can't detect interaction effect.
best
J
To the Questioner: Why is your distribution "not at all normal"?
More importantly, do equal point-differences describe "equal
intervals" of whatever is important in outcome? (If not, why
not, and can't you do something sensible about that.)
There is certainly not a one-size-fits all solution, especially when
the problem arises from design that did not foresee it.
It is highly unlikely that the hit-and-run questioner from 2014
is still reading this group. So don't expect to hear what he did.
If anyone is interested, the original two replies (from Bruce and
from me) are at
https://groups.google.com/forum/#!topic/comp.soft-sys.stat.spss/ZCppmHoKMNE
They read pretty well.
Googling showed me a similar question in another forum, but I
haven't looked at it.
understanding both your and Bruce's comments.--Hello Rich,
Rich Ulrich
it was very nice of you to reply my question, as I actually didn't expect any response since it was a 6 year-old post.
The OP's question basically hit most of the concerns I have with my current data analysis. unfortunately, I don't have a strong background in statistic and am in a process in self-learning most of the statistic knowledge. So i was having hard time
I have used the Kolmogorov-Smirnov normality test.transformation. however, I have done both and yet the most of the dependent variables still violate assumption of normality.
the reason the data was not normal largely because there are a lot of zeros in the data (continuous numeric data with absolute zero), which make it skewed positively. My supervisor advice me to try clean up the outlier, if possible, and then try data
so I turned to non-parametric solution. my research has between subject component (2 groups), and time (3 time points) as within subject component.there is any way to work around that. i wonder if i still can analyze the interaction effect (group x time) under this context?
Since there is no non-parametric equivalence of mixed design ANOVA, I have to find a solution that is similar to what parametric ANOVA does and self-learn how to do that on SPSS.
I have examined Friedman's test, Mann-Whitney U test, Kruskal-Wallis H test, and Wilcoxon signed rank test. as i saw it, most of them are based on the rank of the dataset and is only partial solution to my analyzing goal. So I was trying to find if
If run the between group and within group tests separately on my data, what problems/issue would follow by doing so?
that's why I post the question and try to see how other researchers usually deal with these kind of situation.
John
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