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 3.1 Independent Variables group nominal factor representing the group (experimental or control) subjects are randomly allocated to. time nominal factor representing the moment (t1 and t2) in which the first and second in–pair tasks are performed by the subjects. induced partner’s gender (ipgender) nominal factor representing the induced partner’s binary gender (man or woman for the experimental group, and none for the control group, in which gender is not revealed) during the in–pair tasks. This variable, which is directly related to the gender bias treatment, is operationalized by means of the instructions provided at the beginning of the tasks (“. . .work with your partner. She is . . . ”) and by the gendered avatar that is visible during the in–pair tasks for the experimental group and that is swapped between tasks. Subjects in the control group receive no treatment, i.e., they do not see any information about the gender of their partners in any way, neither textual nor graphical. gender nominal factor representing subject’s gender, which may be man, woman, or any other option as freely expressed in the demographic form during registration. 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. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 The twincode platform 1.1 The twincode platform 1.2 Related Work 1.2 Related Work 2 Research Questions 2 Research Questions 3 Variables 3 Variables 3.1 Independent Variables 3.1 Independent Variables 3.2 Dependent Variables 3.2 Dependent Variables 3.3 Confounding Variables 3.3 Confounding Variables 4 Participants 4 Participants 5 Execution Plan and 5.1 Recruitment 5 Execution Plan and 5.1 Recruitment 5.2 Training and 5.3 Experiment Execution 5.2 Training and 5.3 Experiment Execution 5.4 Data Analysis 5.4 Data Analysis Acknowledgments and References Acknowledgments and References 3.1 Independent Variables group nominal factor representing the group (experimental or control) subjects are randomly allocated to. group time nominal factor representing the moment (t1 and t2) in which the first and second in–pair tasks are performed by the subjects. time induced partner’s gender (ipgender) nominal factor representing the induced partner’s binary gender (man or woman for the experimental group, and none for the control group, in which gender is not revealed) during the in–pair tasks. This variable, which is directly related to the gender bias treatment, is operationalized by means of the instructions provided at the beginning of the tasks (“. . .work with your partner. She is . . . ”) and by the gendered avatar that is visible during the in–pair tasks for the experimental group and that is swapped between tasks. Subjects in the control group receive no treatment, i.e., they do not see any information about the gender of their partners in any way, neither textual nor graphical. induced partner’s gender (ipgender) gender nominal factor representing subject’s gender, which may be man, woman, or any other option as freely expressed in the demographic form during registration. gender 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). Authors: 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. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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Defining Variables for Analyzing Gender Bias in Remote Pair Programming

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