Statistician for DNP Capstone Projects Statistics and Data Analysis Help

Doctor of Nursing Practice (DNP) Capstone projects have unique and specific requirements that differ from a traditional dissertation. Dr. Su understands that most nursing students have a hard time working on the data analysis for their DNP projects. Therefore, Dr. Su endeavors to provide the statistical consultation services needed to assist DNP students in producing high quality data analysis results. 

Dr. Su provides a wide range of PhD-level statistical consultation and data analysis services for DNP students, including 

• reviewing/revising the methodology section, 

• refining PICOT,

• performing power analysis to determine the minimal sample size needed, 

• conducting all required data analyses,  

• interpreting data analysis results, 

• creating a write-up for with high quality figures and tables for results, 

• identifying your specific school requirements and edit and format the DNP capstone paper accordingly, 

• explaining the results to you and coaching you for your proposal and final defenses, 

• helping with the discussion section, and

• providing assistance for other needs of the DNP project.

Dr. Su is an expert of common statistical software, such as SPSS, SAS, Intellectus Statistics, EXCEL, R, and NVivo, and knowledgeable about statistics. Regardless of your PICOTs, methods, or analysis plans, Dr. Su can help you analyze your data. Dr. Su has helped many researchers in nursing practice complete their DNP capstone projects. Let Dr. Su help you, too! Email Dr. Su (drsu.statistics@gmail.com) and hire a statistician now to receive PhD-level statistics and data analysis help for your DNP capstone project.

• Two-sample t-test (or independent samples t-test)

• Paired t-test

• Categorical data analysis (Chi-square test, Fisher's exact test, Cochran-Armitage trend test, Cochran-Mantel-Haenszel (CMH) test, Kappa statistic, and McNemar's test)

• Descriptive statistics

• Pearson's correlation coefficient 

• Analysis of variance (ANOVA)

• Analysis of covariance (ANCOVA)

• Multivariate analysis of variance (MANOVA)

• Repeated measures ANOVA

• Exploratory factor analysis

• Confirmatory factor analysis

• Linear regression

• Logistic regression

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

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