My main programming language is R. R is open source which supports reproducability, a part of the open science movement. R also allows me to create pipelines from data cleaning to data analysis to re-run analyses in an efficient, seamless, and reproducible manner.

I am also familiar with other statistical software such as SPSS, SAS, and Mplus.

I am familiar with a range of statistical techniques to answer empirical research questions, including:

• Structural Equation Modelling (SEM): How do certain behavioral, physiological, or psychological influence each other? In these models, we can also use latent variable modelling to increase precision of our findings. This approach includes mediation and moderation models.
• Latent Variable Modelling: How do individual data sources represent or measure larger theoretical concepts (latent variables)? Latent Variable Modelling includes Confirmatory Factor Analysis (CFA), meaning we are testing if certain items may represent subcategories, or subscales of an overall measure.
• Latent Growth Modelling (Longitudinal Data): Are there changes in these variables over time? Do certain variables or interventions impact change?
• Latent Profile Analysis: Within this population, are there distinct groups based on certain characteristics (variables)?
• Meta-Analysis: How does a relationship between two variables, including an intervention and various outcomes, vary across research studies? How robust is this relationship?

Many of the following approaches are based on regression modelling, which is relies on certain types of data. I am also familiar with techniques for non-normally distributed or non-continuous data (for example, logistic regression). I often consult with researchers to ensure research questions are compatible with data, whether pre or post data collection.

Other specialities:

• Reliability Statistics (Kappa, ICC, Chronbach’s alpha)
• Odds Ratios, Risk Ratios
• Specificity / Sensitivity (AOC)

• Data Visualization: Bringing data to life using graphics. There are many pre-exisiting packages built in R that maximize visual insights specific to the data type or analysis. I can adjust color schemes and font to match your institution or business branding.
• Data Cleaning: Converting data from data collection into variables that are useful in analyses.
• Data Missingness: There are many approaches to data missingness, which is often dependent on your data and analytic plan. I am familiar with a range of imputation methods to help create full datasets when needed, or the use of analytic approaches that account for missingness (FIML).
• Webscraping: Extracting data from online sources into a spreadsheet that can be used for decision making, data visualizations, or data analyses.
• Text-Based Data: Turning qualitative data into a quantitative format. This might include automated coding of free text response items in a survey, or short interview transcripts.

• Hourly Rate = $75/hr
• Discounted rate available for graduate students and early career researchers ($50/hr).
• Data Analysis projects resulting in a full manuscript usually take around 20 hours.
• Once a manuscript is submitted, I will not charge for minor edits during the revision process.
• I can accept checks or online payment (3% processing fee).