Important Info
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We are looking for a highly motivated postdoctoral scholar to join the Adolescent Ethnic-Racial Identity Development (AERID) Lab at Stanford University and lead advanced statistical analyses for multiple longitudinal, multi-site, and cross-national projects. The successful candidate will contribute to ongoing research examining ethnic-racial-cultural identity development and adolescent adjustment across diverse cultural and national contexts. The position offers an exciting opportunity to work as part of a large, collaborative, interdisciplinary, and international research team. This is a fully in-person position based at Stanford University. The Fellow will report directly to Dr. Umaña-Taylor but will also be connected to the broader Graduate School of Education and Stanford Communities.
Detailed Description
The Postdoctoral Analyst will play a central role in leading statistical analyses and supporting grant writing and dissemination efforts focused on adolescent development, ethnic-racial-cultural identity, socialization experiences, and youth adjustment. The successful candidate will work closely with a team of doctoral students, another postdoctoral fellow, Dr. Adriana Umaña-Taylor, and other PIs at collaborating sites to analyze data, write grants, and contribute to dissemination efforts in peer-reviewed scientific journals.
Primary Responsibilities
· Lead the development and execution of data analytic plans for longitudinal, multi-site projects
· Lead the design and planned missingness strategies for a multi-site longitudinal study
· Lead data harmonization across multiple datasets and study sites
· Analyze longitudinal and multi-site data using statistical approaches such as:
o Latent variable modeling (e.g., growth curve models, latent trajectory or latent class analyses, latent transition analysis) and other multilevel modeling approaches that account for nested data (both within individuals and across sites)
o Mediation, moderation, multigroup modeling, and measurement invariance testing
o Missing data estimation methods (e.g., multiple imputation, FIML) and sensitivity analyses
· Conduct apriori and post hoc power analyses
· Communicate the findings of the above research in oral (e.g., conference presentations) and written (e.g., peer-reviewed scientific journals) form
· Lead and support manuscript preparation, conference presentations, research reports, and collaborative grant writing
· Participate in research team meetings and collaborative scientific discussions
· Support the analytic training of graduate research assistants
What We Offer
· Mentorship in Team Science, grant writing, and dissemination
· A collaborative and supportive research environment in the AERID Lab and at Stanford University
· An opportunity to work on innovative, cross-national, and highly collaborative research projects involving teams across the U.S. and multiple continents
· An opportunities to contribute to high-impact publications, grant proposals, and collaborative global initiatives that will support your professional development.
Review of applications will begin immediately and continue until the position is filled. Applicants advancing in the review process may be asked to submit additional materials and may be invited to interview and/or complete a task-based interview. Please direct all questions to Casey Chamberlain at caseyc02@stanford.edu.
- · A Ph.D. with strong quantitative training in Psychology or a related social science discipline
- · Experience analyzing data and publishing findings based on studies using accelerated longitudinal (cohort sequential) designs
- · Experience with advanced quantitative analyses such as latent variable modeling, multi-level models, time-varying effects modeling, longitudinal mediation, moderation, multi-group analyses, and measurement invariance
- · Proficiency in multiple statistical packages such as Mplus, R, Stata, or SAS.
- · Demonstrated ability to communicate complex quantitative analyses and findings in a clear and accessible manner, both orally and in written form
- · Evidence of ability to work independently and proactively while contributing effectively to a complex, multi-site, collaborative research team.
- · Highly detail-oriented and exceptionally organized, with the ability to manage and support multiple complex projects simultaneously
Preferred Qualifications
- · Experience working with cross-national datasets
1. Cover letter describing relevant training and experience specific to the required qualifications listed above, as well as interest in and alignment with the position
2. Curriculum vitae (CV)
3. One representative writing sample
4. Contact information for three professional references who are willing to submit letters of recommendation on the candidate’s behalf, if requested.