Skewness and Kurtosis with SPSS Tutorial (SPSS Tutorial Video #11)

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In this video, I show you how to determine and interpret the SKEWNESS and KURTOSIS of a distribution. These are two useful metrics for describing the shape of a distribution. I also give you a chance to try and to it yourself.

This SPSS tutorial series is designed to teach you the basics of how to analyze and interpret the results of data using SPSS. I will cover everything from the very basics of the main windows within SPSS, to manipulating data, to running and interpreting meaningful analyses like t-tests, ANOVA, regression, and many more, and visualizing results.

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Комментарии
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Thank you for the clear explanation! I completely forgot how to calculate skewness and kurtosis on SPSS. 😅

lilydarcy
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Thanks a lot. its really help me. its really really different from what general Indonesian Youtuber I see explain

Yudo
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Great video, very clear explanation! I have 3 variables with a too high curtosis (according to literature should be between -2 en 2). How can I fix that? Have been trying to apply LogLinear Transformation, but that only seems to make things worse.. Any tips?

liekev.
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Hi
Thankyou so much for explaining everything so simply. I have a question that can skewness and kurtosis be applied on likert scale?

omerashahnawaz
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How do you find the skewness and kurtosis for say half of the statistics of one variable?

lukebreedon
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Are there thresholds that we can use to say that data is normal using these? i.e. how to decide whether we can then go on and treat the data as normally distributed

petertucker
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The higher the skewness number the higher the bars in the graph?

leonvdb
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Kurtosis does not measure flatness or peakedness at all. You can have infinitely peaked distributions with low kurtosis (e.g. beta(.5, 1)), and you can have distributions that appear perfectly flat-topped over nearly all the data with infinite kurtosis (eg .9999U(0, 1) + .0001Cauchy).

Kurtosis measures tail weight (or outlier propensity) only. The only reason that high kurtosis distributions appear "peaked" is because the outliers stretch the horizontal scale, as in your graph. In other words, kurtosis measures the outliers, not the peak.

Reference:
"Kurtosis as peakedness: 1905 - 2014. RIP", The American Statistician.

peterwestfall