Statistician for Dissertation and Thesis Statistics and Data Analysis Help
PhD Statistician for Dissertation and Thesis Statistical Consulting
Dr. Su is a freelance statistician and statistics consultant who provides statistical consulting services and data analysis help for both quantitative and qualitative PhD dissertations and Master's theses. Postgraduate research often involves statistical analysis. However, graduate students may encounter challenges when performing data analysis or writing their dissertation/thesis. Dr. Su aims to provides the dissertation statistics help services graduate students always long for.
Dissertation Statistics Help Services Dr. Su Provides
Perform both quantitative and qualitative data analysis to answer your research questions
Help you determine appropriate statistical analysis techniques
Assist with relevant data collection
Resolve issues regarding data coding
Offer aid for statistical software (e.g., SPSS, SAS, R, AMOS, EXCEL, Minitab, and NVivo)
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
Why Dr. Su Statistics
Dr. Su has great experience and expertise of statistical consultation for PhD dissertations and Master's theses regarding the introduction, literature review, methods, results, and discussion and conclusion chapters.
You will get 1-on-1 help analyzing and interpreting your qualitative or quantitative data from the PhD statistician, Dr. Yuhua Su.
Dr. Su will perform all data analyses needed to answer your research questions and achieve your research goals.
Dr. Su will provide you with a clear written report on the results of the statistical analysis and help you understand the data analysis results.
Dr. Su is an expert of commonly used statistical software such as, SAS, SPSS, AMOS, Excel, Minitab, R, and NVivo. Hence, Dr. Su can provide a tutorial and step-by-step instructions to teach you how to analyze your data using the desired software.
Dr. Su has an excellent track record of positive results when it comes time for clients’ thesis or dissertation defense.
Need a Statistician for Dissertation/Thesis
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 hire a statistician now to receive PhD-level statistics and data analysis help for your dissertation/thesis research.
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.
"Hello Dr. Su, I just wanted you to know that today i received the final approval for my dissertation. Thank you for your all your help and support. I could not have done it without your assistance. I look forward to referring other students to you in the future. You have done an excellent job on my analysis. I am so glad that I found your website. You were the right statistician for my data analysis. You provided with me multiple consultations for free during which you clearly explained to me how you were going to approach my data analysis to make it more presentable to my dissertation committee members and AQR. Also, the fee for your service is exactly what students can afford. You have gone beyond and above expectations to make a sense out of my data analysis to the point where Chapter 4 was pretty much all set for approval. Once again, thank you for your excellent service." -- United States
Statistical Data Analysis Methods Utilized by Dr. Su for Dissertation/Thesis and Research Projects Statistics Help
Common Statistical Data Analysis Methods for Dissertation/Thesis and Research Projects
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 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 and Research Projects
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)
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
Moderator and mediator analysis
Monte Carlo simulation
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, and vector autoregressive (VAR) models)
Two-stage least squares (2SLS) simultaneous equations
Variability assessment using the bootstrap method