Question

# Do effect size heuristics for standardised beta's exist?

Are there any justifiable method/heuristic for assessing the rough effect size (e.g. small, medium or large) of standardized beta coefficients from multiple regressions and path analysis?

2nd Sep, 2021
Clemens Koob
Catholic University of Applied Sciences Munich, Munich, Bavaria, Germany
Christian Young Christian, since I have a similar issue, which "rules of thumb" did you use in the end for the standardised regression coefficients?
I took a look at your paper but I could not find anything for the Betas. It says "We followed conventional rules of thumb for effect sizes [29] and deemed medium effect sizes as: Cohen’s d = .5, zero-order correlation coefficient r = |.3|, and odds ratios = 2 or .5; large effect sizes were defined as Cohen’s d = .8, zero-order correlation coefficient r = |.5|, and odds ratios = 5 or.2. All other statistics were interpreted within the context of the study."
Elissa J Hamlat Elissa, did you find a workable solution?

22nd May, 2016
Kelvyn Jones
University of Bristol
In that case you may want to look at
Standardised regression coefficient as an effect size index in summarising
findings in epidemiological studies; Epidemiology Biostatistics and Public Health - 2013, Volume 10, Number 4
6 Recommendations

21st May, 2016
Béatrice Marianne Ewalds-Kvist
Stockholm University
Dear Christian,
"Cohen’s d is a good example of a standardized effect size measurement. It’s equivalent in many ways to a standardized regression coefficient (labeled beta in some software). Both are standardized measures-they divide the size of the effect by the relevant standard deviations. So instead of being in terms of the original units of X and Y, both Cohen’s d and standardized regression coefficients are in terms of standard deviations."
2 Recommendations
21st May, 2016
Kelvyn Jones
University of Bristol
You may want to have a look at this current debate on how to compare effect size in regression in favour of unstandardized coefficients
1 Recommendation
22nd May, 2016
Christian Young
The New South Wales Department of Health
Thanks for your answers. To clarify, I am conducting a systematic review and am hoping to give a rough approximation of effect size based on multiple types of statistics (r’s, odds ratios, beta’s, b’s, etc.) I have conventions for correlations, t and F tests, and odds ratios. But am struggling to find and effect size convention (small, medium, large) for standardised regression coefficients (beta) as reported using multiple regressions and path analysis. As I understand, beta is the standard deviation change in the DV with one standard deviation change in the change in the IV, holding all other IV’s constant. Is there a justifiable method for assessing the effect size of beta in this context? Thanks.
2 Recommendations
22nd May, 2016
Kelvyn Jones
University of Bristol
In that case you may want to look at
Standardised regression coefficient as an effect size index in summarising
findings in epidemiological studies; Epidemiology Biostatistics and Public Health - 2013, Volume 10, Number 4
6 Recommendations
6th Mar, 2020
Elissa J Hamlat
University of California, San Francisco
Christian Young Did you ever find a satisfactory solution? I am conducting a meta-analysis and have a similar issue. Thanks.
8th Mar, 2020
Daniel P. Moriarity
University of California, Los Angeles
"Acock (2014) also argues that they can be interpreted similar to correlations: β^∗<0.2β^∗<0.2 is considered a weak, 0.2<β^∗<0.50.2<β^∗<0.5 moderate, and β^∗>0.5β^∗>0.5 strong effect (p.272)"
8th Mar, 2020
Elissa J Hamlat
University of California, San Francisco
Daniel P. Moriarity Thanks Daniel! I've also found an article that suggests a formula for conversion from β to r. However, it's not helpful in the case of large β, as some β can be over 1.
My goal is to ultimately convert all to Hedges g' for meta-analysis.
8th Mar, 2020
Christian Young
The New South Wales Department of Health
Elissa J Hamlat I never really did, though the paper Daniel P. Moriarity has linked to above looks useful and is very similar to rules of thumb that I used in the end.
18th Aug, 2021
Uyen-Phuong Nguyen
Mahidol University
Although unusual, beta weights can even exceed one when cooperative suppression is present. Thanks y'all for useful resources.

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