Professional Statistical Analysis
Analyze your survey data in depth with 40+ scientific analysis methods, from regression to structural equation modeling, factor analysis to reliability tests.
Descriptive Statistics
Explore the general structure of your data. Measures of central tendency, dispersion, and data quality assessment.
Descriptive Statistics
Mean, standard deviation, minimum, maximum, quartiles, and basic distribution measures.
Missing Data Analysis
Count and percentage of missing values for each variable. Data quality reporting.
Validity Analysis
Scientifically test the validity of your scales. CFA, AVE, CR, discriminant, content, and face validity.
Confirmatory Factor Analysis (CFA)
Verification of the scale factor structure. Fit indices (CFI, TLI, RMSEA), factor loadings, and model fit.
Average Variance Extracted (AVE)
Average variance extracted for each factor. Convergent validity assessment with values above 0.50.
Composite Reliability (CR)
CR coefficient measuring internal consistency of factors. Reliability and validity with values above 0.70.
Discriminant Validity
Test distinctiveness between factors. Fornell-Larcker criterion and cross-loading analysis.
Content & Face Validity
Evaluate the content and face validity of the scale with expert opinion and item coverage.
Regression Analysis
Measure the effect of independent variables on dependent variables. Improve your prediction power with linear and logistic regression models.
Linear Regression (OLS)
OLS regression analysis for continuous dependent variables. R², F-statistic, AIC/BIC, coefficient confidence intervals, and residual analysis.
Logistic Regression
Logistic regression for binary outcome variables. Odds ratios, pseudo-R², classification metrics, and ROC analysis.
Correlation Analysis
Determine the direction and strength of relationships between variables. Parametric and non-parametric correlation coefficients.
Pearson Correlation
Pearson r coefficient measuring linear relationship between two continuous variables. With P-value and confidence interval.
Spearman Correlation
Spearman rho coefficient measuring rank-based monotonic relationships. Does not require normal distribution assumption.
Correlation Matrix
Matrix visualization of correlations between multiple variables. Pearson, Spearman, and Kendall methods supported.
Parametric Tests (ANOVA & t-test)
Compare group means. Independent and paired sample tests, one-way and two-way analysis of variance.
Independent Samples t-Test
Comparing means of two independent groups. Welch correction, Cohen's d effect size, and confidence intervals.
Paired Samples t-Test
Comparing measurements of the same group at two different times. Ideal for pre-post designs.
One-Way ANOVA
Comparing means of three or more groups. F-statistic, eta-squared effect size, and homogeneity test.
Two-Way ANOVA
Main effects and interaction effects of two factors. Partial eta-squared, SS Type III calculation, and detailed ANOVA table.
Post-Hoc & Effect Size
Post-ANOVA pairwise group comparisons and multi-purpose effect size calculations.
Tukey HSD Test
Comparison of all group pairs after ANOVA. Reliable multiple comparison with family-wise error rate control.
Bonferroni Correction
Bonferroni corrected multiple t-tests. Detailed results for each pair with Cohen's d effect size.
Effect Size Analysis
Cohen's d, Hedges' g, Glass' delta, eta², omega², Cohen's f, and CLES. Two-group, correlation, and ANOVA scenarios supported.
Categorical Data Analysis
Work with categorical variables. Frequency distributions, crosstabs, and independence tests.
Chi-Square Test of Independence
Test relationship between two categorical variables. With Cramér's V effect size and expected frequencies.
Crosstab (Cross Tabulation)
Contingency table with row, column, and total percentages. Visualize distribution between two variables.
Frequency Analysis
Frequency distribution, cumulative count, and percentages for each variable. Basic distribution summary of categorical data.
Factor Analysis & PCA
Discover latent factors underlying observed variables. Dimensionality reduction and scale validity analyses.
Exploratory Factor Analysis (EFA)
Varimax, promax, and quartimax rotation methods. KMO adequacy measure, Bartlett test of sphericity, and explained variance ratios.
Principal Component Analysis (PCA)
SVD-based dimensionality reduction. Component selection with Kaiser criterion, component loadings, and cumulative explained variance ratios.
Non-Parametric Tests
Robust tests not requiring normal distribution assumption. Group comparisons in ordinal and ranked data.
Mann-Whitney U Test
Comparing rank means of two independent groups. Non-parametric alternative to independent samples t-test.
Wilcoxon Signed-Rank Test
Difference test in paired samples. Non-parametric alternative to paired t-test.
Kruskal-Wallis H Test
Comparing rank means of three or more independent groups. Non-parametric alternative to one-way ANOVA.
Friedman Test
Non-parametric variance analysis in repeated measures. With Kendall's W coefficient of concordance.
Distribution & Normality Tests
Check distribution characteristics of your data. Normality, homogeneity of variance, and outlier detection.
Normality Test
Shapiro-Wilk, Kolmogorov-Smirnov, and D'Agostino-Pearson tests. Comprehensive normality assessment with skewness and kurtosis values.
Homogeneity of Variance Test
Check variance equality between groups with Levene and Bartlett tests. Necessary for ANOVA assumption verification.
Outlier Detection
Detect outliers with IQR, Z-score, and Modified Z-score (MAD) methods. Improve data quality.
Runs Test
Test randomness of sequential values in data series. Serial dependence and pattern analysis with Wald-Wolfowitz Runs test.
Reliability Analysis
Measure internal consistency and reliability of your scales. Cronbach Alpha, split-half reliability, and inter-rater agreement.
Cronbach Alpha
Internal consistency coefficient. Item-total correlations, alpha values if item deleted, and reliability assessment.
Split-Half Reliability
Reliability with Split-half method. Spearman-Brown correction and Guttman lambda-4 coefficient.
Inter-Rater Reliability
Measure rater/scorer consistency with Cohen's Kappa and ICC (Intraclass Correlation Coefficient).
Composite Reliability (CR)
Factor-based composite reliability (CR) coefficient. Preferred reliability measure in structural equation models as an alternative to Cronbach Alpha. CR ≥ 0.70 is considered reliable.
Structural Equation Modeling (SEM)
Test measurement and structural models together. Confirmatory factor analysis, path analysis, and mediation effects.
Structural Equation Modeling (SEM)
Full structural equation modeling. Lavaan-like syntax, fit indices (CFI, TLI, RMSEA, SRMR), and parameter estimates.
Confirmatory Factor Analysis (CFA)
Test hypothesized factor structures. Composite reliability (CR), average variance extracted (AVE), and discriminant validity.
Path Analysis
Calculate direct, indirect, and total effects between observed variables. Causality modeling.
Mediation Analysis
Test mediator variable effect. a, b, c' paths, Sobel test, and mediation ratio to determine full and partial mediation.
Transform Your Data into Meaningful Insights
Analyze your survey data professionally with 40+ statistical analysis methods. Share your results with everyone using comprehensive reporting.