Dallas Novakowski, Ph.D.

Dallas Novakowski, Ph.D.

Hello there.

I am a behavioral scientist interested in leveraging data science, consumer research, and evolutionary psychology to help people and organizations make better decisions.

In my research, I primarily investigate how social, cognitive, and emotional forces intersect to influence consumer behaviour.

My current projects examine the impact of explanations on people’s reactions to algorithmic decisions, effects of inequality on risk-taking behaviors (e.g., gambling, consumption of security products), and the effects early traumatic events on substance abuse.

View my CV

I am also excited to use and share open science practices. My research materials are available through GitHub and the Open Science Framework.

Contact me at dallasnovakowski@gmail.com

Interests
  • Evolutionary psychology
  • Inequality
  • Risk-taking
  • Competition and cooperation
Education

  • Ph.D. in Management (Marketing), 2023

    University of Calgary, Haskayne School of Business

  • M.A. in Experimental and Applied Psychology, 2016

    University of Regina

  • B.A. in Psychology, 2014

    University of Regina

Recent Posts

Creating Report-Ready Charts for Group Comparisons in R: A Step-By-Step Guide
This post is a rundown of a workflow that I use for preparing group-comparison graphs that are ready to include in nearly any kind of report. This post is sectioned out piece-by-piece to better demonstrate the impacts of each function and argument for ggplot, and also incorporates statistical hypothesis tests.
Last updated on Nov 9, 2023 77 min read
Creating Report-Ready Charts for Group Comparisons in R: A Step-By-Step Guide
Simplifying Transparent Data Visualizations Using Faded Dotplots and Shadeplots
Building off of raincloud and fadecloud plots, I introduce two new styles for plotting data transparently: (1) the faded dotplot and (2) the shadeplot. Both leverage the r package ggdist to present stacked raw data, overall distribution, and summary stats while minimizing visual clutter.
Last updated on Oct 19, 2023 20 min read dataviz, r, tutorial, tidyverse, ggplot2, factorial, experiments
Simplifying Transparent Data Visualizations Using Faded Dotplots and Shadeplots
Efficient data visualization with faded raincloud plots
The raincloud plot is probably the ‘gold standard’ in transparent data vizualization, but it requires charting a lot of objects. To improve the legibility of the raincloud, we’ll explore a modification that colors in the density distribution according to meaningful cut-off points. The result is a ‘fadecloud’ plot, which can be presented without the boxplot. Done primarily with ggplot and ggdist.
Last updated on Aug 31, 2022 12 min read dataviz, r, tutorial, tidyverse, ggplot2, factorial, experiments
Efficient data visualization with faded raincloud plots
Running oTree on Prolific for Beginners
oTree is an open-source, Python-based framework that lets you build Multiplayer strategy games, behavioral experiments, and surveys/quizzes. What I especially like is that entire projects can be saved in their raw code form, being easily shared, reproduced, and modified.
Last updated on Jul 27, 2023 14 min read github, otree, prolific, heroku
Running oTree on Prolific for Beginners