I work with researchers in the social, behavioral, and health sciences on the quantitative side of their studies: designing the analysis, fitting the models, and making the results clear enough to publish, present, and fund.
Some projects are a single conversation about model fit. Others run from the first draft of an aim through the published paper. Both are welcome, and both start the same way - with a short email about what you are working on.
Join at any point; earlier is usually better
Framing aims as models, choosing waves and measures, and sizing the sample before anything is collected.
Establishing that a scale holds up - factor structure, reliability, and invariance across groups and time.
Fitting the model the question calls for, with the missing data, nesting, and attrition handled honestly.
Methods and results sections, tables and figures, and replies to the reviewer who questions the approach.
Analysis plans, power, and budget language for the next proposal - often the same study, one stage on.
Most engagements are some combination of these four
Running the models, or making sense of output you already have - what the fit statistics support, what they do not, and how to say it in the language of your research question.
Writing and structuring the quantitative sections, preparing tables and figures to journal specification, and drafting responses to methodological reviews.
Working through a method with a student, a lab, or a department - including annotated syntax you keep and can run again on the next study.
Slides, posters, and diagrams for conferences and defenses, and a rehearsal of the methods questions that tend to follow them.
Proposals are rarely turned down for the idea. They are turned down for an approach section that does not convince a reviewer the design can answer the question.
There is more room to help a month or two out than in the final week, though I have done both.
Each aim tied to a model and a contingency, written so a methodologist on the panel can follow it.
Justification built on the model you will actually fit, with attrition and clustering accounted for.
Approach and measurement sections drafted or tightened so the design’s strengths are explicit.
Statistical effort costed across the project years, plus a candid critique before you submit.
Grouped by the question they answer - you do not need to know which one you need
Latent growth curve models, multilevel modeling, latent transition analysis, and repeated-measures designs.
Latent class analysis, latent class growth analysis, growth mixture modeling, and profile comparison.
Confirmatory factor analysis, item response theory, measurement invariance, and scale development.
Structural equation modeling, path analysis, mediation and moderation, and regression.
Discrete-time and continuous-time survival analysis, with time-varying covariates.
Analyses are run in Mplus, R, SAS, Stata, SPSS, or LISREL - whichever your team already works in - and you get the annotated syntax back with the results.