# Search results

1. ### Question in interpreting Effects Size - versus p - Very large data sets

thanks for the comments. I am now trying Cohen's d, that that is much more manageable and expandable . As you can surely tell, I was just introduced to effect size 2 weeks ago. I did a lot of calculations before I was told it was available in JASP
2. ### Question in interpreting Effects Size - versus p - Very large data sets

Here is an interesting review - free pdf Breaugh, J. A. (2003). Effect size estimation: Factors to consider and mistakes to avoid. Journal of Management, 29(1), 79-97. https://people.kth.se/~lang/Effect_size.pdf the pdf
3. ### Question in interpreting Effects Size - versus p - Very large data sets

Good morning, Happy Memorial from the US My question is how to interpret Effects Size statistics - eta squared - JASP application Data Set very large >22,000 data observations Am told in the literature that the p-values will be very small and useless. I have looked at a dozen publication on...
4. ### Homogeneity requirements for Linear regression

Thanks for the input. very helpful. Eases my mind. JDB
5. ### Homogeneity requirements for Linear regression

I have a large data set >22,000 obs. This is industrial data with many unequal cells. Some cells have 1000's of obs, and some have 10's of obs. The Levene's test for Equality of Variances has a very sig p for non-homogeneity. Regardless of all that, the analysis makes very good sense, and...
6. ### ANOVA - 1 dependent Variable - 3 category variables

Thanks, I see I was unclear. I am running an ANOVA in JASP with 1 dependent variable and 3 categorical variables. > 22,000 obs. ALL categorical variables are very significant in ANOVA. The Q_Q plot easily pass the fat pencil test for normality. These are industrial observations. The 3...
7. ### ANOVA - 1 dependent Variable - 3 category variables

In a hypothesis testing checklist, it says, "equal variance were assumed for the analysis". What does that actually mean? With only one scalar variable does one just ignore that statement. The Q-Q plot is normal. It is a huge data set, > 22,000 observations, and I trimmed out the outliers >...
8. ### How to Interpreting beta or standardized regression coefficients

I have read up further on this topic, and it appears I am all wet with regard to the question. It seems to be meaningless to get a standardized coef on a 0,1 categorical variable. Sorry for the post.
9. ### How to Interpreting beta or standardized regression coefficients

I am comparing machinery and fish species. I am using dummy variables (0,1) for 2 variables with 3 values each. The standardized coef's make sense. I would like to explain the standardized variables in the following way. D-CAP has about 4 times more impact than D-VPC-T (0.342/0.085) = 4.02...