Assist in the Development of a New Intergroup Measure

Dr. Geoffrey Leonardelli, Professor

Mentor: Dr. Geoffrey Leonardelli



Project Description

Organizations have been investing significant resources over the past decade to make themselves more receptive to diversity. It has not been an easy path, however, to creating inclusiveness (Dobbins & Kalev, 2016). My lab has recently begun investigating social perception that we think are tied to creating a sense of diversity and inclusion, which we call “intergroup complexity” and “outgroup complexity. ” The first refers to the degree to which people see overlap between their group memberships (called ingroups) and those of other groups (called outgroups), and the second, overlap between different outgroups. Such differences in the complexity of intergroup settings are believed to reflect as well as shape the disadvantaged status of some groups. I am looking for an RA to help me identify some new ways to program such measures so as to better identify ways to measure intergroup and outgroup overlap. It will also likely involve literature searches to supplement what survey programming is needed. These activities will be conducted over the 8-week period.

Mentorship Statement

Researchers in my lab can be exposed to survey development, access to online populations, experimental methods, linguistic analysis, field methods, ANOVA, linear and logistic regression analysis, scale development, statistical moderation and mediation, open science practices, research ethics and more. Some exposure to theory. Training objectives entail exposure to a research area, basic skill development, and instilling interest to pursue graduate studies. There will be some training in participant ethics and treatment, and proper application of methodological procedures involving delegation. As time permits, there will also be application of procedures involving surveying working populations. In both years, under the oversight of the entire lab group, the assistants will be exposed to experimental methods, data integrity practices, and single-blind procedures to reduce exposure to and unintended confirmation of the test’s hypotheses, as well as participant recruiting and compensation procedures.

Project ID 168