Iris Eekhout, PhD

Statistician

TNO

Biography

Iris Eekhout holds a double master in clinical psychology and methodology and statistics of psychology (Leiden University). She obtained her PhD at the Department of Epidemiology and Biostatistics of the VU University medical centre in Amsterdam. Her dissertation work resulted in novel ways of dealing with missing data in questionnaire items and total scores. Currently, Iris teaches a course on missing data analysis in the epidemiology masters program at VU University medical centre. At TNO, Iris works on a variety of projects as a methodologist and statistical analyst related to child health, e.g., measuring child development (D-score) and adaptive screenings for psycho-social problems (psycat). Her main interests and expertise are missing data methods (multiple imputation), longitudinal (multilevel) modeling, R programming, developing shiny applications, interactive visualizations, psychometrics and measurement.

Interests

Missing data methods (multiple imputation)
Longitudinal (Multilevel) modelling
R programming/shiny apps
Psychometrics and measurement

Education

PhD in Epidemiology & Biostatistics, 2015
VU University, Amsterdam
MSc in Methodology and Statistics, 2010
Leiden University
MSc in Clinical Psychology, 2010
Leiden University
BSc in Psychology, 2008
Leiden University

Posts

Child Development with the D-score

Our blog introduces the revolutionary book Child Development with the D-score, unveiling a pioneering framework that harmonizes childrens diverse milestones onto a single scale. It promises unparalleled comparisons across populations and individuals. The book explores the significance of the D-score, from its construction to computation, catering not only to child development professionals but also policymakers and data scientists. Its an empowering guide to understand and enhance child development. Readers are encouraged to grab this transformative book and embark on an enlightening journey into the world of the D-score!

Edited by Stef van Buuren & Iris Eekhout

Last updated on Jan 9, 2024 2 min read

Missing data mechanisms

In research, missing data occur when a data value is unavailable. Many empirical studies encounter missing data. Missing data can occur in many stages of research due to many different causes in many different forms. Each type of missing data may have different reasons, and also different implication for the methods to deal with the missing data.The underlying reasons for missing data can be described as missing data mechanisms.

Last updated on Jan 9, 2024 15 min read

Missing value analysis

Missing observations are defined as NA in R. Missing data can have different implications for data summaries, analyses and conclusions based on the data with missing values. In this post, different types of missing data are reviewed and explored in data examples.

Last updated on Jan 9, 2024 7 min read

Passive multiple imputation

This post demonstrates how to perform passive multiple imputation to deal with missing items in a multi-item questionnaire.

Last updated on Jan 9, 2024 10 min read

Agreement for dichotomous outcomes

This document describes the use of the Agree package for the data example that was used in the paper on specific agreement on dichotomous outcomes in the situation of more than two raters.

Last updated on Jan 9, 2024 3 min read

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Projects

Don’t Miss Out!

Missing data occurs in many empirical studies. It is vital for study results to handle the missing data correctly. The best solution to deal with missing data depends on the reasons for the occurrence of missing data and on the analysis that is planned. In the project a guide was developed to find the best way to deal with missing data in multi-item questionnaires. The website www.missingdata.nl also provides a lot of information about missing data and methodology.

D-score

Children learn to walk, speak, and think at an astonishing pace. The D-score captures this process as a one-number summary. Application of the D-score enables comparisons in child development across populations, groups and individuals.

Rater agreement & reliability

In studies where more than one rater gives a judgement on a certain characteristic, the agreement between the judgements is of interest. Historically, mostly a kappa statistic is used to assess the agreement. However, the kappa statistic is a reliability measure instead of an agreement measure. It is more informative to use the percentage of absolute agreement instead. The reliability of ratings can also be obtained via different methods. The choice between the ICC oneway, ICC consistency and ICC agreement depends on the study design and goals.

Publications

Stef van Buuren, Iris Eekhout (2022). Child development with the D-score: turning milestones into measurement. Gates Open Research.

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Gareth McCray, Melissa Gladstone, Vanessa Cavallera, Dana Mccoy, Marcus Waldman, Iris Eekhout, Stef van Buuren, Mag-Dalena Janus, Patricia Kariger, Maureen Black, others (2022). Combining expert judgment and psychometric information to create the Global Scale for Early Development (GSED). QUALITY OF LIFE RESEARCH.

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Gerben Hulsegge, Iris Eekhout, Hardy van de Ven, Alex Burdorf, Karen M Oude Hengel (2022). Educational inequalities in self-rated health and emotional exhaustion among workers during the COVID-19 pandemic: a longitudinal study. International Archives of Occupational and Environmental Health.

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Roberto AAL Traversari, Robert A Bezemer, Stefan PM van Heumen, Karin Kompatscher, Menno J Hinkema, Iris Eekhout (2022). Effect of different setups, protective screens and air supply systems on the exposure to aerosols in a mock-up restaurant. International Journal of Ventilation.

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Berend Terluin, Iris Eekhout, Caroline B Terwee (2022). Improved adjusted minimal important change took reliability of transition ratings into account. Journal of Clinical Epidemiology.

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Catherina PB Van der Ploeg, Manon Grevinga, Iris Eekhout, Eline Vlasblom, Caren I Lanting, HM van Minderhout, J van Dijk-van der Poel, ME van den Akker-van Marle, PH Verkerk (2022). Kosten en effecten van visusscreening en fotoscreening in de jeugdgezondheidszorg bij kinderen van 3 tot 6 jaar. JGZ Tijdschrift voor jeugdgezondheidszorg.

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Gerjanne Vennegoor, Patricia van Assema, Iris Eekhout, Jeanette Lezwijn, Gerard Molleman, Maria Jansen (2022). Measuring implementation of health promoting school (HPS) programs: development and psychometric evaluation of the HPS implementation questionnaire. Journal of school health.

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Lidwine B Mokkink, Henrica de Vet, Susanne Diemeer, Iris Eekhout (2022). Sample size recommendations for studies on reliability and measurement error: an online application based on simulation studies. Health Services and Outcomes Research Methodology.

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Jacqueline V Stam, Iris Eekhout, Marit Koenen, Victor L Kallen (2022). Susceptibility for criminal facilitation in social housing: a psychological perspective. Security Journal.

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Meinou HC Theunissen, Marianne S de Wolff, Iris Eekhout, Cathelijne L Mieloo, Lisanne L Stone, Sijmen A Reijneveld (2022). The Strengths and Difficulties Questionnaire Parent Form: Dutch norms and validity. BMC pediatrics.

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Contact

R Packages & shiny

The projects highlighted on the website have R packages attached. The D-score project has multiple R packages available and several shiny application that demonstrate functionailies. The agreement project evolves around one R package and one shiny application.

Reliability and Agreement

Agree: An R package with functions to compute the Reliability and Agreement between multiple raters.
ICC power: A shiny application that can assist sample size decisions for studies on reliability.

Child development with the D-score

Missing data

Missing data mechanism