Open source credits
The open-source library call behind each computation is listed below for all 60 methods, together with the call name, library version, licence and citation.
Libraries we use
t-tests, ANOVA, non-parametric tests, correlation, chi-square, normality tests.
Virtanen, P., Gommers, R., Oliphant, T. E., et al. (2020). SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261-272.
Regression, ANCOVA, MANOVA, repeated measures ANOVA, Tukey HSD.
Seabold, S., & Perktold, J. (2010). statsmodels: Econometric and statistical modeling with Python. Proceedings of the 9th Python in Science Conference, 92-96.
Cluster analysis, cluster quality metrics, standardization.
Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
Structural equation modeling, confirmatory factor analysis, path and mediation analysis.
Igolkina, A. A., & Meshcheryakov, G. (2020). semopy: A Python package for structural equation modeling. Structural Equation Modeling, 27(6), 952-963.
Numerical core; coefficients without a library equivalent are computed with these operations.
Harris, C. R., Millman, K. J., van der Walt, S. J., et al. (2020). Array programming with NumPy. Nature, 585, 357-362.
Data frame, grouping, missing value handling, descriptive statistics.
McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference, 56-61.
Method mapping
Inside the app each method shows its full credits, with every call it makes and the running library versions.
| Method | Computing call | Library | License | Method source |
|---|---|---|---|---|
| Descriptive Statistics | pandas.DataFrame.describe | pandas | BSD-3 | pandas (McKinney, 2010) |
| Missing Value Analysis | pandas.DataFrame.isnull | pandas | BSD-3 | Rubin (1976) |
| Linear Regression | statsmodels.api.OLS | statsmodels | BSD-3 | statsmodels (Seabold & Perktold, 2010) |
| Logistic Regression | statsmodels.api.Logit | statsmodels | BSD-3 | statsmodels (Seabold & Perktold, 2010) |
| Independent Samples t-Test | scipy.stats.ttest_ind | SciPy | BSD-3 | Student (1908) |
| Paired Samples t-Test | scipy.stats.ttest_rel | SciPy | BSD-3 | Student (1908) |
| One-Way ANOVA | scipy.stats.f_oneway | SciPy | BSD-3 | Fisher (1925) |
| Two-Way ANOVA | statsmodels.stats.anova.anova_lm | statsmodels | BSD-3 | Fisher (1935) |
| ANCOVA | statsmodels.stats.anova.anova_lm | statsmodels | BSD-3 | Fisher (1935) |
| MANOVA | statsmodels.multivariate.manova.MANOVA | statsmodels | BSD-3 | statsmodels (Seabold & Perktold, 2010) |
| MANCOVA | statsmodels.multivariate.manova.MANOVA | statsmodels | BSD-3 | statsmodels (Seabold & Perktold, 2010) |
| Repeated Measures ANOVA | statsmodels.stats.anova.AnovaRM | statsmodels | BSD-3 | Fisher (1935) |
| Mann-Whitney U Test | scipy.stats.mannwhitneyu | SciPy | BSD-3 | Mann & Whitney (1947) |
| Wilcoxon Signed-Rank Test | scipy.stats.wilcoxon | SciPy | BSD-3 | Wilcoxon (1945) |
| Kruskal-Wallis H Test | scipy.stats.kruskal | SciPy | BSD-3 | Kruskal & Wallis (1952) |
| Friedman Test | scipy.stats.friedmanchisquare | SciPy | BSD-3 | Friedman (1937) |
| Correlation Matrix | pandas.DataFrame.corr | pandas | BSD-3 | Pearson (1896) |
| Pearson Correlation | scipy.stats.pearsonr | SciPy | BSD-3 | Pearson (1896) |
| Spearman Correlation | scipy.stats.spearmanr | SciPy | BSD-3 | Spearman (1904) |
| Kendall Tau Correlation | scipy.stats.kendalltau | SciPy | BSD-3 | Kendall (1938) |
| Chi-Square Test | scipy.stats.chi2_contingency | SciPy | BSD-3 | Pearson (1900) |
| Cross Tabulation | pandas.crosstab | pandas | BSD-3 | pandas (McKinney, 2010) |
| Frequency Analysis | pandas.Series.value_counts | pandas | BSD-3 | pandas (McKinney, 2010) |
| Factor Analysis | numpy.linalg.eighImplemented from the formula | NumPy | BSD-3 | Kaiser (1958) |
| Principal Component Analysis (PCA) | scipy.linalg.svd | SciPy | BSD-3 | Hotelling (1933) |
| K-Means Cluster Analysis | sklearn.cluster.KMeansLibrary plus our own layer | scikit-learn | BSD-3 | MacQueen (1967) |
| Hierarchical Cluster Analysis | sklearn.cluster.AgglomerativeClustering | scikit-learn | BSD-3 | Ward (1963) |
| Cronbach's Alpha | pandas.DataFrame.varImplemented from the formula | pandas | BSD-3 | Cronbach (1951) |
| Split-Half Reliability | scipy.stats.pearsonrLibrary plus our own layer | SciPy | BSD-3 | Spearman (1910); Brown (1910) |
| Normality Test | scipy.stats.shapiro | SciPy | BSD-3 | Shapiro & Wilk (1965) |
| Outlier Detection | pandas.Series.quantileImplemented from the formula | pandas | BSD-3 | Tukey (1977) |
| Runs Test | scipy.stats.normImplemented from the formula | SciPy | BSD-3 | Wald & Wolfowitz (1940) |
| Structural Equation Modeling (SEM) | semopy.Model | semopy | MIT | Bollen (1989) |
| Confirmatory Factor Analysis (CFA) | semopy.Model | semopy | MIT | Jöreskog (1969) |
| Path Analysis | semopy.Model | semopy | MIT | Wright (1934) |
| Mediation Analysis | semopy.ModelLibrary plus our own layer | semopy | MIT | Baron & Kenny (1986); Sobel (1982) |
| Moderation Analysis | statsmodels.api.OLS | statsmodels | BSD-3 | Aiken & West (1991) |
| MaxDiff (Best-Worst Scaling) | pandas.DataFrame.groupbyImplemented from the formula | pandas | BSD-3 | Louviere ve ark. (2015) |
| Conjoint Analysis | pandas.DataFrame.groupbyImplemented from the formula | pandas | BSD-3 | Green & Srinivasan (1978) |
| One-Sample t-Test | scipy.stats.ttest_1samp | SciPy | BSD-3 | Student (1908) |
| Welch's ANOVA (Unequal Variances) | statsmodels.stats.oneway.anova_oneway | statsmodels | BSD-3 | Welch (1951) |
| Dunn's Test (Post-Hoc) | scipy.stats.rankdataImplemented from the formula | SciPy | BSD-3 | Dunn (1964) |
| Chi-Square Goodness of Fit | scipy.stats.chisquare | SciPy | BSD-3 | Pearson (1900) |
| Fisher's Exact Test | scipy.stats.fisher_exact | SciPy | BSD-3 | Fisher (1922) |
| McNemar Test | statsmodels.stats.contingency_tables.mcnemar | statsmodels | BSD-3 | McNemar (1947) |
| Cochran's Q Test | statsmodels.stats.contingency_tables.cochrans_q | statsmodels | BSD-3 | Cochran (1950) |
| Proportion Test | statsmodels.stats.proportion.proportions_ztest | statsmodels | BSD-3 | statsmodels (Seabold & Perktold, 2010) |
| Partial Correlation | statsmodels.api.OLSLibrary plus our own layer | statsmodels | BSD-3 | Fisher (1924) |
| Ordinal Logistic Regression | statsmodels.miscmodels.ordinal_model.OrderedModel | statsmodels | BSD-3 | McCullagh (1980) |
| Multinomial Logistic Regression | statsmodels.api.MNLogit | statsmodels | BSD-3 | McFadden (1974) |
| Hierarchical (Block-wise) Regression | statsmodels.api.OLSLibrary plus our own layer | statsmodels | BSD-3 | Cohen ve ark. (2003) |
| Inter-Rater Agreement (Kappa, ICC) | statsmodels.stats.inter_rater.cohens_kappaLibrary plus our own layer | statsmodels | BSD-3 | Cohen (1960); Fleiss (1971) |
| McDonald's Omega | numpy.linalg.eighImplemented from the formula | NumPy | BSD-3 | McDonald (1999) |
| Parallel Analysis (Number of Factors) | numpy.linalg.eigvalshImplemented from the formula | NumPy | BSD-3 | Horn (1965) |
| Multiple Response Analysis | pandas.DataFrame.sumImplemented from the formula | pandas | BSD-3 | pandas (McKinney, 2010) |
| Banner Table (with Significance Letters) | pandas.crosstabImplemented from the formula | pandas | BSD-3 | Pearson (1900) |
| Weighting (Rim Weighting) | numpy.ndarrayImplemented from the formula | NumPy | BSD-3 | Deming & Stephan (1940); Kish (1965) |
| TURF Analysis (Reach and Frequency) | numpy.ndarrayImplemented from the formula | NumPy | BSD-3 | Miaoulis ve ark. (1990) |
| Van Westendorp Price Sensitivity | numpy.percentileImplemented from the formula | NumPy | BSD-3 | Van Westendorp (1976) |
| Net Promoter Score (NPS) | pandas.Series.value_countsImplemented from the formula | pandas | BSD-3 | Reichheld (2003) |
Evaluate it with your own data
Each result states the call that produced it, and the citations can be copied directly.