I have a passion for research and working with data. I regularly conduct research in different areas of psychology, following the research process from beginning to end (i.e., establishing a research question, literature review, survey design, data collection/analysis, and report writing).

In addition to conducting research, I also have experience working as a data analyst. My previous work has led to me work in the video game industry as a user researcher and data analyst, and more recently as a performance analyst.

Current Projects

The Role of Machiavellianism in Emotional Intelligence and Perceived Control

An investigation into the role of Machiavellianism (i.e., manipulative individuals) and their feelings of power and control.

Text Analysis of Black Metal Lyrics

A text analysis of lyrics from the original black metal movement in the 1990’s in Norway.

I Agree… Sort Of

This project focuses on addressing two of Kuzon et al.’s ‘seven deadly sins’ of statistical analysis.

Past Projects

I have a diverse background with my previous research projects.

  • Personality Differences in Hope and Optimism
  • Nonsuicidal Self-Injury as an Addiction: A Literature Review
  • Waypoint Finding, Spatial Navigation, and Signage Use in a Local Children’s Hospital
  • Where Do People Sit on Public Transport Systems?
  • The Role of Family History on Adolescent Depression
  • The Moderating Role of Honors Student Status in Academic Perfectionism
  • Suitability of the Trojan Player Typology for Categorizing Mobile Game Players

Data Analysis Versus Data Science

Is there a difference?

Yes! … Somewhat.

Data analysts and data scientists do similar work: extract data from databases, programming/coding, and write reports. But analysts and scientists have their own specialties, too.

Data scientists are highly-skilled in database design and management, programming and writing various code, and structuring data. Most data scientists are experts in machine learning and provide insights into key issues using advanced statistical techniques. Scientists are usually skilled in languages such as SQL and python, and maybe R or another statistical language, and can write queries to extract data from a massive database in order for someone to make sense of it. They are typically mathematically-adept folks who solve complex problems. Data scientists make up the ‘hard skills’ of data work.

Data analysts tap into the business-side of things. While most analysts can build databases if needed, can write SQL and optimize their queries, and many dabble (or are proficient!) in one or more programming languages, the analyst excels in the insight and analyzing part of a project. Analysts critically examine the data, use statistical techniques, and provide actionable insights into the data they are working with – the act as an in-between between the tech-savvy data scientists and the business-minded managers. Analysts think critically about how the results of the data can directly apply to the business and help make important decisions. They are expert communicators; report writing is only one part of it, the data analyst tells a story with data. Data analysts make up the ‘soft skills’ of data work.

In many organizations the data scientist is also an analyst or vice versa. Some organizations do not need a full-time data scientist while others have a scientist who can write reports and communicate the results to others in an effective manner. There is a substantial amount of overlap between data analysts and data scientists and where you will find a data analyst or a data scientists is largely interchangeable. Where the difference becomes more transparent is when the need arises to lean on a specific skill set: hard skills or soft skills?