Dr. REBECCA ANDREWS holds a PhD in Biometrics and Statistics with specialization in non-parametric methods. With over 20 publications, she is the co-author and the biostatistician of studies published in top rank medical journals such as the Journal of the American Medical Association (JAMA), the American Journal of Public Health (AJPH) and Pediatric Emergency Care and Vaccine.
During her graduate years she worked as a fellow researcher at the London School of Economics. After she earned her doctorate Dr. Andrews worked as biostatistician at Columbia University Medical Center and at the Mailman School of Public Health. During her extensive career as biostatistician she has advised many graduate students with their dissertations in the fields of medicine, sociology, nursing, and psychology.
Dr. Andrews walks her students through all the steps needed for completing solid and sound research: (1) formulating the study hypothesis; (2) selecting and collecting data (e.g., interviews, surveys, administrative records); (3) designing a sample plan (simple random, systematic, stratified or clustered sampling, or more complex designs); (4) database structure; (5) data cleaning and consistency checks; (6) appropriate analysis from data description and bivariate analysis (student t-test, chi-square, McNemar test of marginal homogeneity) to multivariable analysis (linear regressions, MANOVA, mixed-effects or multilevel models). She helps students select the most appropriate techniques given the nature of their data—continuous or categorical variables, small or large samples.
Dr. Andrews has expertise in working and programming with various statistical packages such as SPSS and Stata.Feedback
"It was a pleasure working with Rebecca. I particularly appreciated her flexibility in scheduling meetings, her promptness in responding to my statistical questions, and her assistance with conducting sophisticated analyses related to my thesis project." Annika Hofstetter, Research Fellow, New York- Presbyterian Hospital, Columbia University Medical Center
“Rebecca provided me with invaluable technical, statistical support and training for my dissertation. She challenged my thinking, conducted flawless analytical work, and made me a stronger quantitative researcher. Rebecca's mastery of statistics and different modeling and analytical techniques made solving complex data challenges easy and manageable. She was 100 percent reliable, thoughtful, understanding, and engaged. She often re-arranged her schedule to meet last-minute dissertation deadlines. Even though Rebecca was not a formal member of my dissertation committee, I consider her my most active member. I am certain that by working with Rebecca, I saved about 6 months of time.” Allison Goldberg, Mailman School of Public Health Health, PhD graduate
DR. BRIONES, a graduate from Columbia University and former fellow at the Harvard Kennedy School of Government, taught Applied Regression Analysis and Research Methods to graduate students. In the last eight years, Victoria has worked as a dissertation and statistics consultant, helping graduate students in psychology, education, nursing, biology, and business formulate/hone their study hypotheses, arrive at better operational definitions for their study variables, improve procedures to increase the internal and the external validity of their studies, test their study hypotheses, analyze their resulting data, and understand their results.
In addition to having a strong grasp of research methods, Victoria's primary areas of expertise are in testing moderation and mediation hypotheses (using SPSS) and in testing path, measurement (via a confirmatory factor analysis or CFA), and structural models using the AMOS, LISREL, EQS, and MPlus programs. She is also able to explain relatively complex procedures and results to clients who have minimal knowledge of research methods and statistics.
Victoria is familiar with non-parametric procedures such as Mann-Whitney, Kruskal-Wallis, and chi-square tests. But her strengths lie in conducting basic (e.g., Pearson correlations, t-tests, and ANOVAs) and complex parametric procedures such as exploratory factor analysis (EFA), regression (i.e., linear, logistic, and multinomial), mixed-ANOVA, MANOVA, and discriminant analysis. Moreover, Victoria provides her clients with concise and coherent summaries of the results of these procedures.
Scope: research methods, reliability analyses, t-tests, ANOVA, repeated-measures ANOVA, ANCOVA, exploratory and confirmatory factor analyses, multiple linear regression, logistic regression, MANOVA, structural equation modeling (AMOS, LISREL, and EQS).
LYNETTA CAMPBELL has a passion for finding the stories that are hidden in the numbers and for sharing this love of what statistics reveals with students at all levels. She holds an M.S. in statistics, an M.S. in Mathematics, an M.S. in Management Science, and a B.S. in Chemical Engineering.
Lynetta worked twelve years in the Chemical Process Industry, during which time she participated in the design, construction, and start-up of projects in polystyrenics. However, it was her work in the implementation of statistical quality control programs that lead her to focus on data collection and analysis.
In the course of earning her masters degree in statistics, Lynetta gained extensive knowledge in programming with R. She greatly enjoys working with conventional data analysis tests as well as customized simulations using this language.
Lynetta has assisted graduate students with their data analysis in fields as diverse as engineering, education, and public health. She can assist clients with their initial exploratory data analysis, usually with a graphical approach to viewing the data. She makes sure each client understands the basics, such as how to properly state the null and alternative hypotheses, and how to test for equivalence using procedures such as independent sample or paired sample t-tests. She routinely helps clients in the selection of the proper regression methods to employ, helping them to understand generalized linear models, logistic regression, and logit and loglinear models. Her ultimate goal is always that the client gain full understanding of what his or her data has to say. In this manner, she has worked with clients whose data was highly qualitative, such as survey data, and she has assisted students with highly quantitative data involving modeling and forecasting. In addition to working with R, she is highly proficient with SPSS, and JMP, statistical software and programming languages.
Linda Deacon has served as an independent statistical consultant since 1985. She has coordinated the data management and statistical analysis of state and federally funded longitudinal and multi-center research. She has co-authored several articles published in the fields of medicine and psychology and has assisted in the editing of statistical textbooks and documentation for statistical software. She has designed and assisted in the development of survey instruments currently in use at various medical centers, universities and county regional centers. She regularly advises doctoral candidates on the formulation, statistical analysis and interpretation of research hypotheses.
Survey design, formulating hypotheses, content organization, data analysis, results analysis, graphs and tables, PowerPoint presentations.
"Without a doubt Linda Deacon is an incredible statistician! Over the past 10 years Linda has assisted me in countless research projects. I would not be where I am in my career today without her expertise. Not only is she the warmest human being around, she is brilliant and easy to work with."
Susan Regas, PhD
Distinguished Professor of Clinical Psychology
Statistics | Inferential Statistics | Psychology Statistics | Hypotheses Development | Research Design | Research Methods
Database Management | Statistical Analysis | SPSS | STATA
Bryan Hamilton offers individual statistics tutoring with an introduction to the basic methods of collecting, organizing, and analyzing data. You will learn a variety of descriptive and inferential statistical techniques. The inferential techniques include an emphasis on statistical inference (e.g., t tests, F tests, and selected non-parametric statistics).
Bryan is a Certified Lean Six Sigma Black Belt and can help you prepare for your test. He uses the DMAIC project methodology, which has five phases. He will take you through the methodology and help you learn to understand and apply it
RICK OAKS studied psychology and sociology at Harvard, received his Ph.D. in clinical psychology from Michigan, then did two post-doctoral fellowships at Yale. Since then he has taught many courses on research methods at both the undergraduate level and in a doctoral program in higher education leadership.
He is an expert in mixed methods research; that is, the sort that combines interviewing and questionnaires.
Although Rick teaches statistics (among other things), his first love is interview research. He has published three books and many articles in the field. These include studies on the meaning of work in the lives of mid-career businessmen, the family tensions that sometimes arise when first-generation college students become more educated than their parents, and the way that students can sometimes discover who they really are by temporarily dropping out of college.
RICHARD POLLARD (Ph.D. Harvard University) has held professorships at Northeastern University, University of Arizona, and Lewis & Clark College. He has served as chair or reader on numerous doctoral dissertations in management, organizational development, leadership, nursing, homeland security, and social/environmental sustainability.
He designed the doctoral programs at Colorado Technical University and served as the founding Chancellor of CTU's Institute for Advanced Studies, where he oversaw all dissertation chairs and readers. He has taught statistics and research methods at all levels from undergraduate through doctoral.
His primary areas of expertise include organizational behavior, organization development, small groups and teams, leadership, psychology, sociology, research methods and statistics.