OmegaPy
Summary
Simple implementation in Python of the reliability measures: Omega Total, Omega Hierarchical and Omega Hierarchical Total.
| Name | Link |
|---|---|
| Omega Total |
Tell us how muhc variance can the model explain |
| Omega Hierarchcal |
|
| Omega Hierarchycal Limit |
|
| Cronbach's alpha |
See Documentation
Quick Start
import pandas as pd
import numpy as np
from omegapy import reliability_analysis
correlations_matrix = pd.DataFrame(np.matrix([[1., 0.483, 0.34, 0.18, 0.277, 0.257, -0.074, 0.212, 0.226],\
[0.483, 1., 0.624, 0.26, 0.433, 0.301, -0.028, 0.362, 0.236],\
[0.34, 0.624, 1., 0.24, 0.376, 0.244, 0.233, 0.577, 0.352],\
[0.18, 0.26, 0.24, 1., 0.534, 0.654, 0.165, 0.411, 0.306],\
[0.277, 0.433, 0.376, 0.534, 1., 0.609, 0.041, 0.3, 0.239],\
[0.257, 0.301, 0.244, 0.654, 0.609, 1., 0.133, 0.399, 0.32],\
[-0.074, -0.028, 0.233, 0.165, 0.041, 0.133, 1., 0.346, 0.206],\
[0.212, 0.362, 0.577, 0.411, 0.3, 0.399, 0.346, 1., 0.457],\
[0.226, 0.236, 0.352, 0.306, 0.239, 0.32, 0.206, 0.457, 1.]]))
reliability_report = reliability_analysis(correlations_matrix=correlations_matrix)
reliability_report.fit()
print('here omega Hierarchical: ',reliability_report.omega_hierarchical)
print('here Omega Hierarchical infinite or asymptotic: ',reliability_report.omega_hierarchical_asymptotic)
print('here Omega Total',reliability_report.omega_total)
print('here Alpha Cronbach total',reliability_report.alpha_cronbach)
Context
It is common to try check the reliability, i.e.: the consistency of a measure, particular in psychometrics and surveys analysis.
R has packages for this kind of analysis available, such us psychby Revelle (2017). python goes behind on this. The closes are factor-analyser and Pingouin. As I write this there is a gap in the market since none of the above libraries currently implement any omega related reliability measure. Although Pingouin implements Cronbach's alpha
References
- Flora, David B. "Your coefficient alpha is probably wrong, but which coefficient omega is right? A tutorial on using R to obtain better reliability estimates." Advances in Methods and Practices in Psychological Science 3.4 (2020): 484-501. https://journals.sagepub.com/doi/pdf/10.1177/2515245920951747
- Revelle, Willian. Manuscrip. 2021. An introduction to psychometric theory with applications in R. https://personality-project.org/r/book/Chapter7.pdf
- Revelle, William R. "psych: Procedures for personality and psychological research." (2017).
- Omega Implementation in R. https://github.com/cran/psych/blob/master/R/omega.R
- Schmid-Leiman in R. https://github.com/cran/psych/blob/master/R/schmid.R
- Starkweather, Jon (2013). Hierarchical Factor Analysis. https://it.unt.edu/sites/default/files/hierfa_l_jds_apr2013.pdf
- Vallat, R. (2018). Pingouin: statistics in Python. Journal of Open Source Software, 3(31), 1026, https://doi.org/10.21105/joss.01026
- Wolff, Hans-Georg, and Katja Preising. "Exploring item and higher order factor structure with the Schmid-Leiman solution: Syntax codes for SPSS and SAS." Behavior Research Methods 37.1 (2005): 48-58.
Acknowledgement
- real-statistics.com
- Factor Analyzer. Python library. https://github.com/EducationalTestingService/factor_analyzer
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