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- Calculate Effect Sizes Add-in

May 20, 2016 11:28 PM

This add-in ingests estimates from an ordinary least squares report (produced by Fit Model) and saves a table of model effect estimates, F-ratios, p-values, and three measures of effect size as formula columns (** Eta Squared, Partial Eta Squared, Omega Squared)**. If pairwise analyses are present with 'Ordered Difference' reports available, a combined data table of estimates including pairwise effect sizes (

**Instructions **(after installing add-in):

- Fit an ordinary least squares model using Analyze > Fit Model
- Go to Add-Ins > Calculate Effect Sizes > From Least Squares Report (Fit Model)

- If more than one Fit Model Least Squares report is open, a dialog will appear to select which analysis to use.

For models with a categorical factor:

- If pairwise analyses (e.g. Student's t-test) and "Ordered Differences' reports are available, Cohen's d including confidence intervals for available pairwise effects will be calculated and saved to a combined data table
- If no pairwise analyses have been produced (or "Ordered Differences' reports have not been produced) a prompt will appear to add these analyses and calculate these effect sizes.
**Output:**

- Table of model effects, effect sizes, and False Discovery Rate adjusted p-Value.
If requested, table of pairwise effect sizes, confidence intervals, and False Discovery Rate adjusted p-Values.

Note: Overall model statistics are saved to each table as table variables for use in formulas or additional measures of model fit you may wish to add.

**Statistical Notes:**

- This add-in provides estimated effect sizes, confidence intervals, and FDR adjusted p-values for ordinary least squares models (models with random effects are not supported).
- Effect Sizes Produced:

**Model Effect Sizes:**

*Eta Squared*

** **

*Partial Eta Squared*

** **

*Omega Squared*

- Negative values of omega squared are possible and are automatically set to 0

**Pairwise Effect Size:**

*Cohen's d*

note: In multifactor models, as well as one-factor models with more than two levels, the pooled within-cell error term of the model (RMSE) is used as SDpooled for calculating pairwise effect sizes. In these cases, it is typical to refer to the resulting effect sizes as **Root Mean Square Standardized Effects** (RMSSE). This is appropriate (and preferable) since the RMSE is the most statistically efficient (lowest variance) estimator of the population sigma in cases where the assumption of homogeneity of variance has not been violated.

**Update History**

- v0.03 - Added Cohen's d calculation for pairwise differences, and dialog to produce pairwise analyses if not present
- v0.04 - Added code to suppress prompt to add pairwise comparisons for models without categorical factor
- v0.05 - Fixed defect for running on Windows (Fit Least Squares reports were not detected). Added support dialog for selecting from available Least Squares reports:
- v0.06 - Added support for confidence intervals around Cohen's D
- v.0.07 - Added support for False Discovery Rate p-Values

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05-26-2016
11:32 AM

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05-26-2016
01:16 PM

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05-26-2016
02:02 PM

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05-26-2016
02:51 PM

This is Great! It's easy enough to calculate these by hand, but its even easier to have it calculated automatically! I also love that it includes Eta^2, Partial Eta^2 and Omega^2.

For those who are not familiar with these can also see how they're calculated but right clicking the column and selecting "Formula"!

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07-09-2016
02:31 PM

Thanks for this add-in, this is extremely useful. And being able to see the actual formula used is indeed a great plus.

Do you have any plans for creating a effect size add-in that also works with repeated measures or mixed models? This would be quite valuable, especially when using JMP for teaching to non-statisticians - I just discovered your repeated measures add-in, and having a way to produce effect sized more easily for repeated measures and mixed designs would be a great plus.

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07-18-2016
01:53 PM

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07-18-2016
03:04 PM

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02-05-2018
03:11 PM

Hi there,

I was wondering if there might be any updates on computing effect sizes for repeated measures or mixed models in JMP - would be super-useful! (it's a pity to have to go back to SPSS for calculating those...)

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04-05-2018
02:28 AM

Hello,

I have used this on previous versions and just updated to 07 and now I dont get the ES calc (see pics). Also the summary data table doesnt collate either. I used the same steps as I had previously with the add-in (which I found great by the way). Yet no love on this attempt

Just wondering if there is something I am doing wrong? I have un-installed the add in and re-installed...no love.

Any help would be greatly appreciated.

Cheers

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07-03-2018
09:07 PM

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07-05-2018
09:03 PM

Hi @benson_munyan,

I can't replicate this issue on Windows or Mac, with either JMP 14 or JMP 13. Can you provide additional details about your setup and anything that might be unique about your data table? We'll get to the bottom of this.

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08-23-2018
11:24 AM

Great tool. Pity random effects isn't supported. Is it possible in the future?

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08-27-2018
09:32 AM

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08-27-2018
09:47 AM

Yes!

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04-23-2019
04:18 PM

Julian,

Not sure why my first post didn't take...

I calculated d using the fit model route as you mentioned, vs manually from values of group means and pooled est. of standard deviation (from OneWay - comparing means of 2 groups). The d value for the fit model was slightly lower than the manual calculation. My original objective is to show difference between 2 group means ( I have 7 people rating a product before and after stability protocol to make sure there is no detectable difference). Was I wrong to take the shortcut (fit model route with addin)?

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04-23-2019
04:26 PM

Hi @TCM ,

I think I can guess what might be happening here. Do you have more than two groups in your table, but are calculating d for the difference between just two of them? If that's the case, you were not wrong at all to go this route, and the small difference you found in the values of d you calculated is due to where the estimate for the population standard deviation is coming from. When you go the fit model route, the estimates for d are based on the pooled standard error across all groups. I have a note in the add-in description about this, but I should give it some more prominence. It says "note: In multifactor models, as well as one-factor models with more than two levels, the pooled within-cell error term of the model (RMSE) is used as SDpooled for calculating pairwise effect sizes. In these cases, it is typical to refer to the resulting effect sizes as **Root Mean Square Standardized Effects** (RMSSE). This is appropriate (and preferable) since the RMSE is the most statistically efficient (lowest variance) estimator of the population sigma in cases where the assumption of homogeneity of variance has not been violated."

So, if it is the case that you have multiple groups, the estimates from manually calculating d from just two of those groups, and the calculation from this add in, will differ slightly. Does this explain the difference? If not, let's dig into this and figure out what is happening.

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04-23-2019
04:46 PM

Julian,

Thanks for the speedy response. I am not a statistician (I am a data scientist, which falls under Computer Science- but people assume they are the same :)), so apologies for some knowledge gaps.

Not sure if we are using the term groups the same way. What I have are 7 people. These people rated a product at room temperature. Another day, the same 7 people rated the same product but one that was subjected to accelerated aging. The objective was to see if there were perceptible differences in product features (there are many that were rated by these people) between the room temp and aged product.

I performed Oneway fit y by x with the feature as response and product (there are 2 levels- RT and aged). In the fit model scheme, I did fit model with the same variables as the oneway. I like the idea of the fit model because I don't have to manually calculate cohen's d (calculation from your "Subliminal Message" tutorial). NB: I didn't check for normality; I just assumed it. I figured I could since I am assuming the same in both analyses. I could send you a short dataset if it helps (only 14 rows and 1 response).

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04-23-2019
04:50 PM

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04-23-2019
05:01 PM

Awesome!

Sorry, I can't find the insert attachment button. I only see insert/edit link.

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04-23-2019
05:21 PM

Here it is. And I am certainly open to using the best way to detect differences between products. BTW, there are many more responses, but there is no order of importance among them that we know of.

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04-23-2019
05:23 PM

Hi @TCM ,

Hmm, don't see the attachment, and I think comments can't have attachments. Feel free to email me directly, Julian.Parris@jmp.com, and we can continue the conversation there, and then I'll update this thread with whatever insights we get to.

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