One-way ANOVA in SPSS - checking normality assumption
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// One-way ANOVA in SPSS
// One-way ANOVA in SPSS - checking normality assumption //
The main requirement of the one-way ANOVA, next to the interval or ratio level for the test variable, is normal distribution. However, not the test variable has to be normally distributed, but rather the residuals.
Residuals are essentially what the model (ANOVA) predcits for your test variable compared to what your test variable actually looks like in your dataset.
Despite there being three ways to test for normal distribution, the Shapiro-Wilk-test, a histogram or a Q-Q-plot, I will only show the latter for the following reason:As with all analytical tests, large samples have more power and will “find” significant deviations from normal distribution, even if those deviations are negligible. Therefore, caution is advised when blindly trusting a p-value.
Please refer, among many other publications, to Lantz, B. (2013). The large sample size fallacy. Scandinavian journal of caring sciences, 27(2), 487-492.
Eventually, put emphasis on the plots, mainly a histogram or a q-q-plot. I prefer the latter since one can manipulate the histogram with a proper "bin width".
Final note: a z-standardization before plotting a histogram or q-q-plot is optional.
You will only see a slightly different histogram (reminder of the bin width) with less cliffs on the inside. The q-q-plot is not affected, hence my advise to use this as a test for normal distribution.
⏰ Timestamps:
==============
0:00 Introduction and overview
0:22 Shapiro-Wilk-Test - why not to use
0:42 Q-Q-Plot creation and interpretation
If you have any questions or suggestions regarding checking the normal distribution for the one-way ANOVA in SPSS, please use the comment function. Thumbs up or down to decide if you found the video helpful.#statorials
Support channel? 🙌🏼
===================
Paypal donation: https://www.paypal.com/paypalme/Bjoer...
Amazon affiliate link: https://amzn.to/49BqP5H
The main requirement of the one-way ANOVA, next to the interval or ratio level for the test variable, is normal distribution. However, not the test variable has to be normally distributed, but rather the residuals.
Residuals are essentially what the model (ANOVA) predcits for your test variable compared to what your test variable actually looks like in your dataset.
Despite there being three ways to test for normal distribution, the Shapiro-Wilk-test, a histogram or a Q-Q-plot, I will only show the latter for the following reason:As with all analytical tests, large samples have more power and will “find” significant deviations from normal distribution, even if those deviations are negligible. Therefore, caution is advised when blindly trusting a p-value.
Please refer, among many other publications, to Lantz, B. (2013). The large sample size fallacy. Scandinavian journal of caring sciences, 27(2), 487-492.
Eventually, put emphasis on the plots, mainly a histogram or a q-q-plot. I prefer the latter since one can manipulate the histogram with a proper "bin width".
Final note: a z-standardization before plotting a histogram or q-q-plot is optional.
You will only see a slightly different histogram (reminder of the bin width) with less cliffs on the inside. The q-q-plot is not affected, hence my advise to use this as a test for normal distribution.
⏰ Timestamps:
==============
0:00 Introduction and overview
0:22 Shapiro-Wilk-Test - why not to use
0:42 Q-Q-Plot creation and interpretation
If you have any questions or suggestions regarding checking the normal distribution for the one-way ANOVA in SPSS, please use the comment function. Thumbs up or down to decide if you found the video helpful.#statorials
Support channel? 🙌🏼
===================
Paypal donation: https://www.paypal.com/paypalme/Bjoer...
Amazon affiliate link: https://amzn.to/49BqP5H
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