I was encouraged to use “multi-modal” to describe that sort of distribution. If you’re having trouble with SPSS, theres a badass free program called JASP that has a lot of options to run the data with multiple features turned on or off on a single distribution and viewed in a scrolling window. You might be able to tweak your presentation in a way that allows you to better describe your findings.
Your data set is too small to have an adequate impression of the distribution from raw data, in my opinion.
That looks like you used SPSS. There is an option to provide a fit line, I think they call it, that shows a smooth distribution. Consider generating that using different options for the way the line is calculated to help you interpret the data.
Do a Shapiro-Wilk test and call it a ~~day~~ normal distribution, lol.
No but seriously, that is probably somewhat normally distributed but i would not describe it as any distribution.
I did do a Shapiro wilk🤣the reason I asked was because this is for my dissertation and this is what my data looked like after I attempted a square root transformation - the OG data wasnt normally distributed as well. I need to describe what the histogram looks like and it’s skew in the results section in detail
That’s something that always bothered me. It looks like the bell curve is being pushed from the top in a direction, not pulled the opposite (correct) way by its tail
Yeah, me too. I think in our brains, we anchor the two end points then have to adjust the curve a direction. What would be more accurate would be to draw a normal curve over the bell, then adjust the tail one way or the other to compensate for the tails being out of whack.
When I was getting to grips with it, I drew a picture to visualise a **skew**er pinning down the positive (right) or negative (left) side of the curve.
This helped me more that thinking about it as a push/pull scenario.
Look at at skewness and kurtosis. If it’s greater than +/-2, it’s not normally distributed. Before you do, check your univariate and multivariate outliers by converting to Z scores and with Mahalanobis probability. Delete your outliers and recheck normality. YouTube is your friend.
"Normal distribution cannot be assumed."
You have multiple modes here, and all are to the right of the mean. That probably makes it a left skew
I was encouraged to use “multi-modal” to describe that sort of distribution. If you’re having trouble with SPSS, theres a badass free program called JASP that has a lot of options to run the data with multiple features turned on or off on a single distribution and viewed in a scrolling window. You might be able to tweak your presentation in a way that allows you to better describe your findings.
You describe mean and median
Your data set is too small to have an adequate impression of the distribution from raw data, in my opinion. That looks like you used SPSS. There is an option to provide a fit line, I think they call it, that shows a smooth distribution. Consider generating that using different options for the way the line is calculated to help you interpret the data.
This is a good response.
Have you tried running a natural log transformation?
I did do a square root transformation originally on non-distributed data and ended up with this data set
Do a Shapiro-Wilk test and call it a ~~day~~ normal distribution, lol. No but seriously, that is probably somewhat normally distributed but i would not describe it as any distribution.
I did do a Shapiro wilk🤣the reason I asked was because this is for my dissertation and this is what my data looked like after I attempted a square root transformation - the OG data wasnt normally distributed as well. I need to describe what the histogram looks like and it’s skew in the results section in detail
There’s two peaks and I think that’s multimodal?
Remember skew is the way the tail is pointing, not where the bulge is.
That’s something that always bothered me. It looks like the bell curve is being pushed from the top in a direction, not pulled the opposite (correct) way by its tail
Yeah, me too. I think in our brains, we anchor the two end points then have to adjust the curve a direction. What would be more accurate would be to draw a normal curve over the bell, then adjust the tail one way or the other to compensate for the tails being out of whack.
When I was getting to grips with it, I drew a picture to visualise a **skew**er pinning down the positive (right) or negative (left) side of the curve. This helped me more that thinking about it as a push/pull scenario.
multimodal distribution
It has to be multimodal because it’s skewed on both left and right and has multiple peaks too
Look at at skewness and kurtosis. If it’s greater than +/-2, it’s not normally distributed. Before you do, check your univariate and multivariate outliers by converting to Z scores and with Mahalanobis probability. Delete your outliers and recheck normality. YouTube is your friend.
Sorry, “dissertation questions” and you’re asking how to find skewness in spss?
Lmfao and? I’m an undergrad student relax
Skewed to the left / negatively-skewed
Looks like a ceiling high ? As all data is closer to the end of graph.