Ralph Rippe is a tenured Associate Professor in he Research Methods and Statistics group at the Institute of Child and Education Studies, Leiden University. He is the Datamanager in the same institute.

More information about Ralph Rippe

Short CV

Ralph Rippe is a tenured Associate Professor in the Research Methods and Statistics group at the Institute of Child and Education Studies, Leiden University. He obtained his MSc degree (cum laude) in Methodology and Statistics from Leiden University (2006), and his  PhD degree in Genetical Statistics in Social Sciences in 2012.

Furthermore, he works as an expert in quantitative modelling in a team of methodologists and statisticians (holding a PhD) that research provides consultation on the above as well as on (models for) categorical data and nonlinear approaches, meta-analysis and machine learning, case-series (N=1) in developmental or clinical designs and qualitative research designs, such as focus groups and semi-structured interviews.

He has published over 50 articles, among which both first-, last- and co-authorships in high-impact journals. Furthermore, he has international collaborations with researchers in Germany, the UK and the USA. He currently appointed as visiting researcher with the Deutsches Herzzentrum Berlin, department of Kinderkardiologie (with prof. Schmitt and dr. Ferentzi), from November 2022 to August 2025.

As of September 1st 2024 he is the Datamanager for the Institute of Education and Child Studies.

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Research

Applied in a wide range of (inter)national collaborations, his main interests lie at the intersection of statistics and (any interdisciplinary) application in child education, child behavior and child health, mental health, curriculum development, child fostering evaluation and intervention efficacy.

For example, he is intrigued by how internal factors (e.g. genetics) and environment interplay in their influence on the stress children or families (have) experience(d), on how children form (inadequate) attachment bonds, how groups of at-risk children might benefit from early interventions on literacy and numeracy and how they form (un)healthy eating behaviors. As he works at the front of these research topics, new and unsolved statistical and design issues are encountered on a regular basis.

His main focus is on gaining insight into the combined effects of sample size, high signal-to-noise data (for example coded observations of behavioral data as well as self-reports), and skewed scales on estimation bias and Type I/II errors in complex models. Such models range from such as (within-subjects) interactions, mediation, multilevel models (observations nested within families, schools, day care centers, and clinics) and path analysis (for example group measurements on child abuse, obesity, bullying, eating behavior) over time, with time-dependent covariates.

Interests lie in studies of Robustness, Skewness, Measurement Error, Differential Susceptibility (GxE interactions), Data Mining, Machine Learning, SNP genotyping and Copy Number estimation, Genome-Wide Association Studies (GWAS) and Statistical issues in obesity research, Multilevel Models, Component Analysis, Factor Analysis, Missing Data and Imputation.

Awards

• Best Student Presentation (ISWM 2010)
• Best Student Paper (IWSM 2009)
• Best Poster (Channel Network Conference, 2009)

Grants

• 2018: Wellcome Trust Grant (GBP 30,000) for collaboration with drs. Duschinsky and Reijman (Cambridge University)
• 2016: LUF grant for fraternity research
• 2013: Young Scholarship grant (CHF 120,000) joint main applicant - Jacobs Foundation
• 2013: Young Scholarship grant (CHF 80,000) co-applicant - Jacobs Foundation

Publications