Sanne Smith
Director of the MS in Education Data Science (EDS)
Initiative Centers & Program
Bio
Sanne Smith is the director of the master’s program in education data science and a lecturer at Stanford Graduate School of Education.
Her research interests center around the study of social networks and the creation of thriving, diverse contexts. She has studied, for example, why adolescents tend to segregate within schools, how interdisciplinary centers boost scholar productivity and how social capital within neighborhoods offsets ethnic health inequalities. Her work has appeared in the American Journal of Sociology, Social Networks, Research Policy and Social Science Research.
Her teaching includes courses that introduce students to coding, data wrangling and visualization, supervised and unsupervised machine learning (e.g., hierarchical linear modeling, structural equation modeling, factor and principal component analysis), and interpretation of quantitative data.
Prior to directing the GSE master’s program in education data science, she worked as a research scientist at the Center for Population Health Sciences at Stanford and carried out national survey collection for the Children for Immigrants Longitudinal Survey in Four European Countries in the Netherlands.
Academic Appointments
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Lecturer, Initiative Centers & Program
Professional Education
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PhD, Utrecht University, Sociology (2015)
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MSc, Utrecht University, Sociology (2010)
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BSc, Utrecht University, Interdisciplinary Social Sciences (2008)
2024-25 Courses
- Advanced Regression Analysis
EDUC 326, SOC 384 (Spr) - Education Data Science Capstone Projects
EDUC 259D (Aut) - Education Data Science Capstone Projects
EDUC 259E (Win) - Education Data Science Capstone Projects
EDUC 259F (Spr) - Education Data Science Seminar
EDUC 259A (Aut) - Education Data Science Seminar
EDUC 259B (Win) - Education Data Science Seminar
EDUC 259C (Spr) - Introduction to Education Data Science: Data Analysis
EDUC 423B, SOC 302B (Win) - Introduction to Education Data Science: Data Processing
EDUC 423A, SOC 302A (Aut) -
Independent Studies (6)
- Curricular Practical Training
EDUC 437 (Aut, Win, Spr, Sum) - Directed Reading
EDUC 480 (Aut, Win, Spr, Sum) - Directed Reading in Education
EDUC 180 (Aut, Win, Spr, Sum) - Directed Research
EDUC 490 (Aut, Win, Spr, Sum) - Directed Research in Education
EDUC 190 (Aut, Win, Spr, Sum) - Supervised Internship
EDUC 380 (Aut, Win, Spr, Sum)
- Curricular Practical Training
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Prior Year Courses
2023-24 Courses
- Advanced Regression Analysis
EDUC 326, SOC 384 (Spr) - Education Data Science Capstone Projects
EDUC 259D (Aut) - Education Data Science Capstone Projects
EDUC 259E (Win) - Education Data Science Capstone Projects
EDUC 259F (Spr) - Education Data Science Seminar
EDUC 259A (Aut) - Education Data Science Seminar
EDUC 259B (Win) - Education Data Science Seminar
EDUC 259C (Spr) - Introduction to Education Data Science: Data Analysis
EDUC 423B, SOC 302B (Win) - Introduction to Education Data Science: Data Processing
EDUC 423A, SOC 302A (Aut)
2022-23 Courses
- Advanced Regression Analysis
EDUC 326, SOC 384 (Spr) - Education Data Science Capstone Projects
EDUC 259D (Aut) - Education Data Science Capstone Projects
EDUC 259E (Win) - Education Data Science Seminar
EDUC 259A (Aut) - Education Data Science Seminar
EDUC 259B (Win) - Education Data Science Seminar
EDUC 259C (Spr) - Introduction to Data Analysis and Interpretation
EDUC 200A (Aut) - Introduction to Education Data Science: Data Processing
EDUC 423A, SOC 302A (Aut)
2021-22 Courses
- Education Data Science Seminar
EDUC 259A (Aut) - Education Data Science Seminar
EDUC 259B (Win) - Education Data Science Seminar
EDUC 259C (Spr) - Introduction to Data Analysis and Interpretation
EDUC 200A (Aut) - Introduction to Education Data Science: Data Analysis
EDUC 423B, SOC 302B (Win) - Introduction to Education Data Science: Data Processing
EDUC 423A, SOC 302A (Aut)
- Advanced Regression Analysis
Stanford Advisees
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Master's Program Advisor
Khaulat Abdulhakeem, Ari An, Ruishi Chen, Michael Chrzan, Vryan Feliciano, Kazunori Fukuhara, Matías Hoyl, Samin Khan, Lucia Langlois, Tracy Li, Michelle Liu, Xinman Liu, Andrea Mock, Dominik Moehrle, Savira Dwia Nadela, Mitsutoshi Nozaki, Mayank Sharma, Yimei Shen, Teah Shi, Ziqi Shu, Alexa Sparks, Jason Zhang, Yiling Zhao -
Doctoral Dissertation Reader (NonAC)
Radhika Kapoor
All Publications
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Poverty and mental health among migrants: When is ingroup exposure more protective than social ties?
SSM - population health
2020; 11: 100599
Abstract
•Ingroup exposure in residential areas and social ties are typically positively linked to mental health among migrants.•We argue that whether migrants can reap the benefits of these protective factors depends on their poverty status.•Findings show that migrants below the poverty line do not benefit from ingroup exposure or social ties.•However, compared to natives, migrants above the poverty line do benefit from social ties.•We conclude that migrants might benefit more from protective factors when they are equipped to invest in them.
View details for DOI 10.1016/j.ssmph.2020.100599
View details for PubMedID 32518815
View details for PubMedCentralID PMC7270188
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Superstars in the making? The broad effects of interdisciplinary centers
RESEARCH POLICY
2018; 47 (3): 543–57
View details for DOI 10.1016/j.respol.2018.01.014
View details for Web of Science ID 000427664000001
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Befriending the same differently: ethnic, socioeconomic status, and gender differences in same-ethnic friendship
JOURNAL OF ETHNIC AND MIGRATION STUDIES
2018; 44 (11): 1858–80
View details for DOI 10.1080/1369183X.2017.1374168
View details for Web of Science ID 000445201600006
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Ethnic composition of the school class and interethnic attitudes: a multi-group perspective
JOURNAL OF ETHNIC AND MIGRATION STUDIES
2018; 44 (3): 482–502
View details for DOI 10.1080/1369183X.2017.1322501
View details for Web of Science ID 000424263200008
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Ethnic Composition and Friendship Segregation: Differential Effects for Adolescent Natives and Immigrants
AMERICAN JOURNAL OF SOCIOLOGY
2016; 121 (4): 1223-1272
Abstract
Ethnically diverse settings provide opportunities for interethnic friendship but can also increase the preference for same-ethnic friendship. Therefore, same-ethnic friendship preferences, or ethnic homophily, can work at cross-purposes with policy recommendations to diversify ethnic representation in social settings. In order to effectively overcome ethnic segregation, we need to identify those factors within diverse settings that exacerbate the tendency toward ethnic homophily. Using unique data and multiple network analyses, the authors examine 529 adolescent friendship networks in English, German, Dutch, and Swedish schools and find that the ethnic composition of school classes relates differently to immigrant and native homophily. Immigrant homophily disproportionately increases as immigrants see more same-ethnic peers, and friendship density among natives has no effect on this. By contrast, native homophily remains relatively low until natives see dense groups of immigrants. The authors' results suggest that theories of interethnic competition and contact opportunities apply differently to ethnic majority and minority groups.
View details for Web of Science ID 000369717400006
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From neighbors to school friends? How adolescents' place of residence relates to same-ethnic school friendships
SOCIAL NETWORKS
2016; 44: 130–42
View details for DOI 10.1016/j.socnet.2015.07.004
View details for Web of Science ID 000367423900012
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Parental Influence on Friendships Between Native and Immigrant Adolescents
JOURNAL OF RESEARCH ON ADOLESCENCE
2015; 25 (3): 580–91
View details for DOI 10.1111/jora.12149
View details for Web of Science ID 000358083000013
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Ethnic ingroup friendships in schools: Testing the by-product hypothesis in England, Germany, the Netherlands and Sweden
SOCIAL NETWORKS
2014; 39: 33–45
View details for DOI 10.1016/j.socnet.2014.04.003
View details for Web of Science ID 000340338600004
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Irreconcilable differences? Ethnic intermarriage and divorce in the Netherlands, 1995-2008
SOCIAL SCIENCE RESEARCH
2012; 41 (5): 1126–37
Abstract
This study uses population data of the Netherlands (municipality registers) between 1995 and 2008 to describe and explain the occurrence of divorce among recently newlywed interethnic and mono-ethnic couples (N=116,745). In line with homogamy theory, divorce risks are higher for interethnic couples, in particular if the spouses were born and raised in countries that are culturally distant from each other. In addition, the effect of cultural distance is smaller for second generation immigrants than for first generation immigrants. There is no evidence for a higher risk of divorce among Black-White marriages. In line with convergence theory, results show that the higher the divorce propensity in the wife's origin country, the higher the divorce risk of a couple is.
View details for DOI 10.1016/j.ssresearch.2012.02.004
View details for Web of Science ID 000306620600010
View details for PubMedID 23017922