Research Assistant: Social Inequality & Stereotypes (STERN)

Owen Thomas B Corp; Greater London; Hybrid; GBP 28,000 - 42,000; Full time

Benefits offered by this job

41 Days holiday

Relocation scheme

On-site nursery

On-site gym

Cycle to work scheme

Season ticket loan

Immigration loan

CARE pension scheme

Enhanced parental pay

Employee assistance programme

Job summary

University College London is seeking a Research Assistant to join the ST E R N project in London. The role focuses on applied stereotypes, social networks, and inequalities, contributing to data collection and analysis in English schools.

The successful candidate will hold a master's in a relevant social science and have expertise in quantitative methods, with an interest in how stereotypes affect education and well-being. London-based, full-time, with involvement in multidisciplinary teamwork.

Qualifications

• Master's degree or equivalent research experience in a relevant social science field.
• Experience with quantitative research methods and data analysis.
• Interest in social inequalities, social networks, or stereotypes.

Responsibilities

• Develop the questionnaire and potential new measures for the study.
• Select the sample and recruit participating schools.
• Collect and manage data for the project.
• Collaborate with the team and contribute to data analysis where possible.
• Participation in academic activities and potential own research projects.

Skills

Quantitative research methods

Statistical analysis

Interest in social inequalities

Education

Master's degree in sociology / social psychology / education / behavioural science or related field

Tools

Statistical software (R)

Job description

University College London is seeking a Research Assistant to join the ST E R N project in London. The role focuses on applied stereotypes, social networks, and inequalities, contributing to data collection and analysis in English schools.

The successful candidate will hold a master's in a relevant social science and have expertise in quantitative methods, with an interest in how stereotypes affect education and well-being. London-based, full-time, with involvement in multidisciplinary teamwork.