Dissertation and Thesis Statistics Help Services

Analysis of variance (ANOVA)

Analysis of covariance (ANCOVA)

Categorical data analysis (e.g., Chi-square test, Fisher's exact test, Cochran-Armitage trend test, Cochran-Mantel-Haenszel (CMH) test, Kappa statistics, and McNemar's test)

Correlation coefficients (e.g., Pearson correlation, Kendall rank correlation, Spearman correlation, and Point-Biserial correlation) 

Descriptive statistics

Factor analysis (e.g., exploratory factor analysis and confirmatory factor analysis)

Hierarchical regression

Linear regressions (e.g., simple and multiple linear regressions)

Logistic regressions (e.g., binary logistic regression, ordinal logistic regression, multinomial logistic regression, and baseline-category logit models)  

Multivariate analysis of variance (MANOVA)

Multivariate analysis of variance (MANCOVA)

Multivariate regression

Non-parametric tests (e.g., Mann-Whitney U test, Wilcoxon signed-rank test, Kruskal-Wallis test, and Friedman test)

Principal component analysis

Qualitative data analysis (e.g., thematic analysis)

Reliability analysis (e.g., Cronbach's alpha)

Repeated measures analysis of variance (RMANOVA)

T-tests (e.g., two-sample or independent samples t-test and paired t-test)

Z-test or binomial test for proportions

Canonical correlation analysis

Data envelopment analysis (DEA)

Diagnostic test evaluation (e.g., sensitivity, specificity, positive predictive value, negative predictive value, and the 'exact' Clopper-Pearson confidence intervals) 

Discriminant analysis (e.g., linear/quadratic discriminant analysis)

Doubly multivariate repeated measures design (i.e., Repeated measures multivariate analysis of variance (MANCOVA))

Equivalence and noninferiority testing

Generalized linear models (e.g., Poisson regression and probit regression)

Generalized linear mixed effects models

Item analysis

Latent class growth modelling

Linear mixed-effects models

Machine learning methods (e.g., regression trees and k-nearest neighbors)

Meta-analysis

Methods for statistical quality control

Missing data analysis

Moderator and mediator analysis

Monte Carlo simulation

Multiple imputation

Non-linear regression

Panel data analysis

Path analysis

Person-fit statistics (e.g., lz)

Six sigma process improvement methods

Structural equation modeling

Survival analysis (e.g., Kaplan-Meier method, log-rank tests, and Cox proportional odds model)

Tobit regression

Time series analysis (e.g., interrupted time series analysis (ITS), time series forecasting, vector autoregressive (VAR) models, autoregressive error models, and autoregressive conditional heteroscedasticity (ARCH) models)

Two-stage least squares (2SLS) simultaneous equations

Variability assessment using the bootstrap method