Module stikpetP.other.thumb_cohen_d

Functions

def th_cohen_d(d, qual='sawilowsky')

Rules Of Thumb For Cohen D

This function will give a qualification (classification) for Cohen d

Parameters

d : float

the Cohen d value

qual : {"sawilowsky", "cohen", "lovakov", "rosenthal", "brydges"} optional

the rule of thumb to be used. Default is "sawilowsky"

Returns

pandas.DataFrame

A dataframe with the following columns:

• classification, the qualification of the effect size
• reference, a reference for the rule of thumb used

Notes

The following rules-of-thumb can be used:

"cohen" => Uses Cohen (1988, p. 40):

|d|	Interpretation
0.00 < 0.20	negligible
0.20 < 0.50	small
0.50 < 0.80	medium
0.80 or more	large

"lovakov" => Lovakov and Agadullina (2021, p. 501):

|d|	Interpretation
0.00 < 0.15	negligible
0.15 < 0.35	small
0.35 < 0.65	medium
0.65 or more	large

"rosenthal" => Rosenthal (1996, p. 45):

|d|	Interpretation
0.00 < 0.20	negligible
0.20 < 0.50	small
0.50 < 0.80	medium
0.80 < 1.30	large
1.30 or more	very large

"sawilowsky" => Sawilowsky (2009, p. 599):

|d|	Interpretation
0.00 < 0.01	negligible
0.01 < 0.20	very small
0.20 < 0.50	small
0.50 < 0.80	medium
0.80 < 1.20	large
1.20 < 2.00	very large
2.00 or more	huge

"brydges" => Brydges (2019, p. 5):

|d|	Interpretation
0.00 < 0.15	negligible
0.15 < 0.40	small
0.40 < 0.75	medium
0.75 or more	large

Before, After and Alternatives

Cohen d for one-sample and Hedges g could be converted to Cohen d * es_convert to convert the effect size measure * es_cohen_d_os for Cohen d' * es_hedges_g_os for Hedges g

References

Brydges, C. R. (2019). Effect size guidelines, sample size calculations, and statistical power in gerontology. Innovation in Aging, 3(4), 1–8. doi:10.1093/geroni/igz036

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). L. Erlbaum Associates.

Lovakov, A., & Agadullina, E. R. (2021). Empirically derived guidelines for effect size interpretation in social psychology. European Journal of Social Psychology, 51(3), 485–504. doi:10.1002/ejsp.2752

Rosenthal, J. A. (1996). Qualitative descriptors of strength of association and effect size. Journal of Social Service Research, 21(4), 37–59. doi:J079v21n04_02

Sawilowsky, S. (2009). New effect size rules of thumb. Journal of Modern Applied Statistical Methods, 8(2). https://doi.org/10.22237/jmasm/1257035100

Author

Made by P. Stikker

Companion website: https://PeterStatistics.com
YouTube channel: https://www.youtube.com/stikpet
Donations: https://www.patreon.com/bePatron?u=19398076

Examples

>>> es = 0.6
>>> th_cohen_d(es)
  classification                  reference
0         medium  Sawilowsky (2009, p. 599)

Expand source code

def th_cohen_d(d, qual="sawilowsky"):
    '''
    Rules of Thumb for Cohen d
    --------------------------
     
    This function will give a qualification (classification) for Cohen d
    
    Parameters
    ----------
    d : float
        the Cohen d value
    qual : {"sawilowsky", "cohen", "lovakov", "rosenthal", "brydges"} optional 
        the rule of thumb to be used. Default is "sawilowsky"
        
    Returns
    -------
    pandas.DataFrame
        A dataframe with the following columns:
    
        * *classification*, the qualification of the effect size
        * *reference*, a reference for the rule of thumb used
   
    Notes
    -----
    The following rules-of-thumb can be used:
    
    *"cohen"* => Uses Cohen (1988, p. 40):
    
    |\\|d\\|| Interpretation|
    |---|----------|
    |0.00 < 0.20 | negligible |
    |0.20 < 0.50 | small |
    |0.50 < 0.80 | medium |
    |0.80 or more | large |
    
    *"lovakov"* => Lovakov and Agadullina (2021, p. 501):
    
    |\\|d\\|| Interpretation|
    |---|----------|
    |0.00 < 0.15 | negligible |
    |0.15 < 0.35 | small |
    |0.35 < 0.65 | medium |
    |0.65 or more | large |
    
    *"rosenthal"* => Rosenthal (1996, p. 45):
    
    |\\|d\\|| Interpretation|
    |---|----------|
    |0.00 < 0.20 | negligible |
    |0.20 < 0.50 | small |
    |0.50 < 0.80 | medium |
    |0.80 < 1.30 | large |
    |1.30 or more | very large |
    
    *"sawilowsky"* => Sawilowsky (2009, p. 599):
    
    |\\|d\\|| Interpretation|
    |---|----------|
    |0.00 < 0.01 | negligible |
    |0.01 < 0.20 | very small |
    |0.20 < 0.50 | small |
    |0.50 < 0.80 | medium |
    |0.80 < 1.20 | large |
    |1.20 < 2.00 | very large |
    |2.00 or more | huge |
    
    *"brydges"* => Brydges (2019, p. 5):
    
    |\\|d\\|| Interpretation|
    |---|----------|
    |0.00 < 0.15 | negligible |
    |0.15 < 0.40 | small |
    |0.40 < 0.75 | medium |
    |0.75 or more | large |
    
    Before, After and Alternatives
    ------------------------------
    Cohen d for one-sample and Hedges g could be converted to Cohen d
    * [es_convert](../effect_sizes/convert_es.html#es_convert) to convert the effect size measure
    * [es_cohen_d_os](../effect_sizes/eff_size_cohen_d_os.html#es_cohen_d_os) for Cohen d'
    * [es_hedges_g_os](../effect_sizes/eff_size_hedges_g_os.html#es_hedges_g_os) for Hedges g
    
    References
    ----------
    Brydges, C. R. (2019). Effect size guidelines, sample size calculations, and statistical power in gerontology. *Innovation in Aging, 3*(4), 1–8. doi:10.1093/geroni/igz036
    
    Cohen, J. (1988). *Statistical power analysis for the behavioral sciences* (2nd ed.). L. Erlbaum Associates.
    
    Lovakov, A., & Agadullina, E. R. (2021). Empirically derived guidelines for effect size interpretation in social psychology. *European Journal of Social Psychology, 51*(3), 485–504. doi:10.1002/ejsp.2752
    
    Rosenthal, J. A. (1996). Qualitative descriptors of strength of association and effect size. *Journal of Social Service Research, 21*(4), 37–59. doi:J079v21n04_02
    
    Sawilowsky, S. (2009). New effect size rules of thumb. *Journal of Modern Applied Statistical Methods, 8*(2). https://doi.org/10.22237/jmasm/1257035100
    
    Author
    ------
    Made by P. Stikker
    
    Companion website: https://PeterStatistics.com  
    YouTube channel: https://www.youtube.com/stikpet  
    Donations: https://www.patreon.com/bePatron?u=19398076
    
    Examples
    --------
    >>> es = 0.6
    >>> th_cohen_d(es)
      classification                  reference
    0         medium  Sawilowsky (2009, p. 599)
    
    '''
            
    #Cohen (1988, p. 40).
    if (qual=="cohen"):
        ref = "Cohen (1988, p. 40)"
        if (abs(d) < 0.2):
            qual = "negligible"
        elif (abs(d) < 0.5):
            qual = "small"
        elif (abs(d) < 0.8):
            qual = "medium"
        else:
            qual = "large"
            
    #Lovakov and Agadullina (2021, p. 501).
    elif (qual=="lovakov"):
        ref = "Lovakov and Agadullina (2021, p. 501)"
        if (abs(d) < 0.15):
            qual = "negligible"
        elif (abs(d) < 0.35):
            qual = "small"
        elif (abs(d) < 0.65):
            qual = "medium"
        else:
            qual = "large"
    
    #Rosenthal (1996, p. 45).
    elif (qual=="rosenthal"):
        ref = "Rosenthal (1996, p. 45)"
        if (abs(d) < 0.2):
            qual = "negligible"
        elif (abs(d) < 0.5):
            qual = "small"
        elif (abs(d) < 0.8):
            qual = "medium"
        elif (abs(d) < 1.3):
            qual = "large"
        else:
            qual = "very large"
            
    #Sawilowsky (2009, p. 599).
    elif (qual=="sawilowsky"):
        ref = "Sawilowsky (2009, p. 599)"
        if (abs(d) < 0.1):
            qual = "negligible"
        elif (abs(d) < 0.2):
            qual = "very small"
        elif (abs(d) < 0.5):
            qual = "small"
        elif (abs(d) < 0.8):
            qual = "medium"
        elif (abs(d) < 1.2):
            qual = "large"
        elif (abs(d) < 2.0):
            qual = "very large"        
        else:
            qual = "huge"
    
    #Brydges (2019, p. 5).
    elif (qual=="brydges"):
        ref = "Brydges (2019, p. 5)"
        if (abs(d) < 0.15):
            qual = "negligible"
        elif (abs(d) < 0.40):
            qual = "small"
        elif (abs(d) < 0.75):
            qual = "medium"
        else:
            qual = "large"
    
    results = pd.DataFrame([[qual, ref]], columns=["classification", "reference"])
    
    return results