Dissertation and Thesis Statistics Help Services
Dissertation and Thesis Statistics Help Services
Many PhD and Master’s candidates require guidance for statistics or data analysis during during the course of their research. Student researchers also rarely find time to learn the nuances of statistical tools and tests needed for their data. Hence, there is a need for trustworthy guidance which I offer through the dissertation/thesis statistics help service.
Since 2011, Dr. Su has been working as a freelance statistician and statistics consultant to provide PhD-level statistical consulting services for both quantitative and qualitative dissertations and theses. Typically, Dr. Su assists graduate students with the methods and results sections of their dissertation/thesis, but oftentimes helps in other areas as well.
Dr. Su's experience and expertise of statistics consultation for dissertations and theses includes assistance with the introduction, literature review, methods, results, and discussion and conclusion chapters. Dr. Su has an excellent track record of positive results when it comes time for clients’ thesis or dissertation defense.
Dr. Su can help you with the following:
Perform both quantitative and qualitative data analysis to answer your research questions
Help complete your analysis and methods/results chapter
Ensure that the results are accurate and that the methods chosen are appropriate and supported by the relevant literature
Explain the results to you and coach you for your proposal and final defenses
Identify your specific school requirements and edit and format your dissertation/thesis accordingly
Assistance with other needs and chapters of your thesis or dissertation
Hire a statistician now to receive PhD-level statistics and data analysis help for your dissertation, thesis, or research project. Writing a thesis or dissertation can be a lengthy, time-consuming journey. Let Dr. Su help you complete this journey so you will continue to build toward your next achievement! Email Dr. Su (drsu.statistics@gmail.com) and get started on your dissertation/thesis research!
"Hi Yuhua, I am happy to inform you that my dissertation Chairperson has approved of my dissertation. With that I have convinced my friend to get in touch with you for her data analysis." -- United States
Need a statistician/statistical consultant for your dissertation/thesis? Contact Dr. Su via email (drsu.statistics@gmail.com) or phone (808-4941545) for a free quote.
Statistical Data Analysis Methods Utilized by Dr. Yuhua Su for Dissertation/Thesis Statistics Help
Common Statistical Data Analysis Methods for Dissertation/Thesis Research
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
Advanced Statistical Data Analysis Methods for Dissertation/Thesis Research
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