Module stikpetP.other.thumb_kaiser_b
Expand source code
import pandas as pd
def th_kaiser_b(b, qual="kaiser"):
'''
Rule of thumb for Kaiser b
--------------------------
Simple function to use a rule-of-thumb for the Kaiser b variation measure.
Parameters
----------
b : float
the Kaiser b value
qual : {"kaiser"}, optional
indication which set of rule-of-thumb to use. Currently only "kaiser" (default)
Returns
-------
results : a pandas dataframe with.
* *classification*, the qualification of the effect size
* *reference*, a reference for the rule of thumb used
Notes
-----
Kaiser's rule of thumb for Kaiser b (1968, p. 212):
|\\|b\\|| Interpretation|
|---|----------|
|0.00 < 0.70 | terrible |
|0.70 < 0.80 | poor |
|0.80 < 0.90 | fair |
|0.90 < 0.95 | good |
|0.95 < 1.00 | excellent |
See Also
--------
stikpetP.measures.meas_qv.me_qv : to determine Kaiser b
References
----------
Kaiser, H. F. (1968). A measure of the population quality of legislative apportionment. *American Political Science Review, 62*(1), 208–215. doi:10.2307/1953335
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
'''
if (qual=="kaiser"):
ref = "Kaiser (1968, p. 212)"
if (abs(b)<0.70):
qual = "terrible"
elif (abs(b)<0.80):
qual = "poor"
elif (abs(b)<0.90):
qual = "fair"
elif (abs(b)<0.95):
qual = "good"
else:
qual = "excellent"
results = pd.DataFrame([[qual, ref]], columns=["classification", "reference"])
return(results)
Functions
def th_kaiser_b(b, qual='kaiser')
-
Rule Of Thumb For Kaiser B
Simple function to use a rule-of-thumb for the Kaiser b variation measure.
Parameters
b
:float
- the Kaiser b value
qual
:{"kaiser"}
, optional- indication which set of rule-of-thumb to use. Currently only "kaiser" (default)
Returns
results : a pandas dataframe with.
- classification, the qualification of the effect size
- reference, a reference for the rule of thumb used
Notes
Kaiser's rule of thumb for Kaiser b (1968, p. 212):
|b| Interpretation 0.00 < 0.70 terrible 0.70 < 0.80 poor 0.80 < 0.90 fair 0.90 < 0.95 good 0.95 < 1.00 excellent See Also
me_qv()
- to determine Kaiser b
References
Kaiser, H. F. (1968). A measure of the population quality of legislative apportionment. American Political Science Review, 62(1), 208–215. doi:10.2307/1953335
Author
Made by P. Stikker
Companion website: https://PeterStatistics.com
YouTube channel: https://www.youtube.com/stikpet
Donations: https://www.patreon.com/bePatron?u=19398076Expand source code
def th_kaiser_b(b, qual="kaiser"): ''' Rule of thumb for Kaiser b -------------------------- Simple function to use a rule-of-thumb for the Kaiser b variation measure. Parameters ---------- b : float the Kaiser b value qual : {"kaiser"}, optional indication which set of rule-of-thumb to use. Currently only "kaiser" (default) Returns ------- results : a pandas dataframe with. * *classification*, the qualification of the effect size * *reference*, a reference for the rule of thumb used Notes ----- Kaiser's rule of thumb for Kaiser b (1968, p. 212): |\\|b\\|| Interpretation| |---|----------| |0.00 < 0.70 | terrible | |0.70 < 0.80 | poor | |0.80 < 0.90 | fair | |0.90 < 0.95 | good | |0.95 < 1.00 | excellent | See Also -------- stikpetP.measures.meas_qv.me_qv : to determine Kaiser b References ---------- Kaiser, H. F. (1968). A measure of the population quality of legislative apportionment. *American Political Science Review, 62*(1), 208–215. doi:10.2307/1953335 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 ''' if (qual=="kaiser"): ref = "Kaiser (1968, p. 212)" if (abs(b)<0.70): qual = "terrible" elif (abs(b)<0.80): qual = "poor" elif (abs(b)<0.90): qual = "fair" elif (abs(b)<0.95): qual = "good" else: qual = "excellent" results = pd.DataFrame([[qual, ref]], columns=["classification", "reference"]) return(results)