Are you tired of “winging it” and ready to build a storehouse of real
statistical knowledge to further your career?
As president and founder of The Analysis Factor, I’ve been supporting researchers like you through their statistical planning, analysis, and interpretation since 1997.
With master’s degrees in both applied statistics and social psychology, I’ve been honored to work with everyone from undergrad honors students to Ivy League professors, and from non-profit evaluators to corporate data analysts.
After seeing so many smart people get nervous, uncertain, and downright phobic about analyzing their data, I made it my mission to remove the barrier between research and statistical analysis.
I want to banish the jargon that makes eyes glaze over, and instead explain statistical terminology in plain English.
My goal is to help you improve your statistical literacy so you can bring your important research results into the light with confidence.
Audrey first realized her love for research and, in particular, data analysis when she made a career move from clinical psychology to dementia research. She has particular expertise in biostatistics, including inter-rater reliability, case control studies, and linear models, and uses SAS and SPSS.
Mentoring researchers in Statistically Speaking brings together Audrey’s two passions: conducting research and helping people.
For Jeff Meyer, being an effective stats mentor takes more than just a knowledgeable and logical approach to analyzing data. You have to also enjoy working with people – and most importantly, care about their success.
Jeff is the go-to resource for Statistically Speaking members working with Stata’s multiple imputation model for missing data. He’s also well-versed in confidence intervals and effect sizes. Jeff’s main areas of focus include multivariate, logistic, mixed models, truncated, censored, Poisson, and negative binomial regression.
Kim has more than a decade of professional and academic experience in the fields of regression and linear models, categorical data, generalized linear models, mixed effects models, nonlinear models, repeated measures, and experimental design.
While she enjoys working with all clients, Kim particularly enjoys working with those who feel they have a less-than-perfect relationship with statistics. Her goal is to spread an appreciation of statistics across many fields of study, starting by making it understandable to those who interact with it.