Quantitative Dissertation Statistics Help

Data Management 

Our statistical consultants can input, organize, code, merge, clean, and manage your data. In addition, we can get your dataset ready for analysis. Additionally, we create composite scores, work with missing data, and label data. Our statistical consultants can detect and correct corrupt or inaccurate records from a database. The process of data cleaning includes data auditing, workflow specification, workflow execution, post-processing, and controlling. We can use popular methods (e.g., parsing, data transformation, duplicate elimination). Additionally, we run outlier analyses such as a box plot. In addition, our statistical consultants can examine any standardized residual greater than about 3 in absolute value. Finally, we can find unexpected and erroneous values by analyzing the data.

Validity and Reliability 

We ensure that the results are reliable and valid. For example, we can test reliability (e.g., Cronbach’s alpha, test-retest reliability, split-half reliability, inter-rater reliability) and validity (e.g., content validity, construct validity, criterion validity).

Data Analysis

Our dissertation consultants specify specific statistics to address the research questions. We are experts in statistical analysis, including but not limited to the following:

  • linear, logistic, and hierarchical regression analysis
  • correlation analysis
  • chi-square testing
  • t‑tests, ANOVA, MANOVA, ANCOVA, MANCOVA
  • structural equation modeling (SEM), multilevel SEM, confirmatory factor analysis (CFA), multilevel CFA
  • factor analysis, principal component analysis
  • time series analysis
  • Cox regression, Kaplan-Meier survival analysis
  • hierarchical linear modeling (HLM)
  • meta-analysis
  • Bayesian analysis
  • latent class analysis
  • longitudinal growth modeling
  • mixture models
  • linear mixed models
  • distribution analysis
  • predictive analytics
  • ensemble analysis
  • de-identification
  • trend analysis
  • sensitivity analysis
  • negative binomial regression
  • interim analysis
  • decision trees
  • item analysis
  • nonparametric tests
  • statistical and decision modeling
  • MaxDiff
  • segmentation and cluster analysis
  • conjoint/discrete choice
  • propensity score analysis

Software

We use various software packages, including but not limited to the following:

  • SAS
  • SPSS
  • Stata
  • HLM
  • Mplus
  • R
  • SPSS Amos
  • SPSS Modeler
  • Azure
  • JMP
  • Python
  • WinBUGS
  • Minitab
  • SYSTAT
  • LISREL
  • EQS
  • Smart PLS
  • WarpPLS
  • EViews

Interpretation of the Results

Our statistical consultants interpret the research results. We also explain the results of the study. In addition, we write up all the results, including tables and figures. Our statisticians also provide syntax and the raw output file. Additionally, we ensure you are comfortable with the analyses. Finally, our statistical consultants allow unlimited email and phone support to ensure that you completely understand the analysis results. Typical results sections include:

Conduct descriptive statistics;
Test the assumptions of the analyses;
Perform analyses to examine the research questions;
Draft results write-up, which includes documenting assumptions, interpreting findings, stating support or non-support for the hypotheses, and providing tables and figures.

Chapter 4: Results

 

Introduction
Review the purpose, research questions, and hypotheses.

Preview the organization of chapter 4.

Results
Report descriptive statistics that appropriately characterize the sample.

Evaluate statistical assumptions as appropriate to the study.

Report statistical analysis findings, organized by research questions and hypotheses.

Include tables and figures to illustrate results, as appropriate, and per the current edition of the Publication Manual of the American Psychological Association.

Summary
Summarize answers to research questions.

Provide transitional material from the findings and introduce the reader to the rigid material in chapter 5.