Doctoral Students & Educational Researchers
Statistical Consulting & Analysis Services
Welcome, Fellow Researcher!
I am looking forward to the opportunity to assist you in quantitative analysis for your study. This page provides a brief overview of what to expect from this service.
What We Offer
How the Process Works
Examples of Completed Analysis Projects
Frequently Asked Questions
*NEW: One-on-One Quantitative Refresher Courses
If you have any questions about this process, please feel free to reach out to me at Crystal@theLondonBridgED.com or leave your information on my contact page. If you would like to speak in person, click here to access my schedule for a free consultation.
Ready to get started? Click here to complete the Intake Form or here to schedule a free one-on-one introductory consultation.
Thank you for allowing me to help you reach your education and/or authorship goals!
Dr. Crystal London
Quantitative Analysis *Most Popular*
Review of Quantitative Analyses
Editorial Review for Dissertation/Thesis and Professional Articles
One-on-One Refresher Courses
My initial goal is to ensure that I provide you with the service that you have requested.
I will meet with you for a preliminary interview to confirm details specific to the service that you are requesting. Prior to scheduling, please complete the Customer Intake Form. This form will guide and assist our discussion in the preliminary interview.
Once service details are confirmed, a 50% deposit will be required to begin processing your analysis.
Please allow four to six weeks for completion.
Once your analysis is complete, I will schedule an exit interview with you to confirm results and to deliver your report and supporting documents.
Cleaning & Coding
Preliminary/ancillary testing, as necessary
Internal consistency reliability
Full Reports; includes
APA formatted tables
Clearly defined rejection or acceptance of the null statement(s)
Summary of Key Findings
Updated SPSS file (includes all new and recoded variables, etc.)
Original output files
Extended services available
Analysis of Covariance (ANCOVA) to determine political engagement and linguistic integration among groups of gender, citizenship status, education, and income, while controlling for other demographic variables;
Created two new composite variables
Exploratory Factor Analysis to confirm construct validity
Cronbach's alpha to test for internal consistency reliability of each scale.
Analysis of variance (ANOVA) and Post-hoc testing to determine differences in career readiness among varying levels of ethnicity.
Logistic regression to determine the odds of community college graduates accepting a post-graduate job.
Logistic regression to determine the odds of community college graduate students' acceptance into a postgraduate bachelor's program.
Descriptive statistics to create sample demographics table.
Descriptive statistics to determine student use of career services at a college.
Analysis of variance (ANOVA) to determine the difference in experiences among levels of ethnicity for students attending Historically Black Universities and Colleges (HBCUs).
Multiple regression to determine whether demographic characteristics could predict overall satisfaction/self-reported success with doctoral journey.
Descriptive statistics using boxplots to summarize values for nitrate, chloride, sulfate, pH, temperature, and conductivity.
Kruskal-Wallis to determine differences in the concentrations of nitrate, chloride, and sulfate among the different levels (low, mid, high) of total suspended matter.
Ternary plot to provide a visual representation of proportional relationships among variable elements in watershed samples.
Descriptive statistics to summarize the perceptions of youth unemployment and opportunities for college education.
Pearson Correlations to understand relationships between stakeholder attitude, subjective norms, perceived behavioral control each in relation to intent to support the establishment of a two-year college.
Spearman rho to test for the relationship between reproduction and resistance constructs in public elementary and secondary school students.
Multiple linear regressions to determine whether reproduction and resistance constructs can predict race-based achievement and/or race-based disciplinary gaps.
Logistic regression to determine the odds of elementary and secondary students prioritizing career readiness or entrepreneurial readiness.
Descriptive statistics to determine the relationship between grade level and resistance & grade level and reproduction.
Analysis of data from primary and secondary sources.
Explanatory, sequential mixed methods combining interview and survey data with secondary data from national archives.