Controlling Confounding Variables in Gender Bias Studies of Remote Pair Programming

Written by pairprogramming | Published 2024/09/16
Tech Story Tags: pair-programming | remote-pair-programming-bias | implicit-bias-in-tech | remote-pair-programming | educational-technology | twincode | women-in-tech | gender-bias-in-tech

TLDRThis section addresses how the study controls for confounding variables, such as subjects' technical skills and the difficulty of programming exercises, to ensure accurate and unbiased results in remote pair programming research.via the TL;DR App

Table of Links

Abstract and 1 Introduction

1.1 The twincode platform

1.2 Related Work

2 Research Questions

3 Variables

3.1 Independent Variables

3.2 Dependent Variables

3.3 Confounding Variables

4 Participants

5 Execution Plan and 5.1 Recruitment

5.2 Training and 5.3 Experiment Execution

5.4 Data Analysis

Acknowledgments and References

3.3 Confounding Variables

The confounding variables that will be controlled during the experiment are the following.

Subject’s technical skills To control the variability caused by each subject on their partner, pairs are kept the same during the entire experiment, although the subjects are not informed about this fact until the end of the experiment. Ideally, this would make the conditions of the two in–pair tasks the same except for the programming exercises (see below) and for the perceived gender in the case of the experimental group.

Programming exercises In order to avoid potential differences among the programming exercises used during in–pair tasks, they are of similar difficulty and are randomly assigned.

Authors:

(1) Amador Durán, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (amador@us.es);

(2) Pablo Fernández, SCORE Lab, I3US Institute, Universidad de Sevilla, Sevilla, Spain (pablofm@us.es);

(3) Beatriz Bernárdez, I3US Institute, Universidad de Sevilla, Sevilla, Spain (beat@us.es);

(4) Nathaniel Weinman, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (nweinman@berkeley.edu);

(5) Aslı Akalın, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (asliakalin@berkeley.edu);

(6) Armando Fox, Computer Science Division, University of California, Berkeley, Berkeley, CA, USA (fox@berkeley.edu).


This paper is available on arxiv under CC BY 4.0 DEED license.


Written by pairprogramming | Pair Programming
Published by HackerNoon on 2024/09/16