Iris Eekhout, PhD

Iris Eekhout, PhD




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.

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



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 also provides a lot of information about missing data and methodology.
Don't Miss Out!
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.
Rater agreement & reliability

Recent Publications

(2019). Childhood trauma and the role of self-blame on psychological well-being after deployment in male veterans. European Journal of Psychotraumatology.

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(2019). Comparison of logistic-regression based methods for simple mediation analysis with a dichotomous outcome variable. BMC Medical Research Methodology.

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(2019). Development of Prediction Models for Sick Leave Due to Musculoskeletal Disorders. Journal of Occupational Rehabilitation.

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(2019). Educational inequalities in the impact of chronic diseases on exit from paid employment among older workers: A 7-year prospective study in the Netherlands. Occupational and Environmental Medicine.

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(2019). Self-reported adverse childhood experiences and quality of life among children in the two last grades of Dutch elementary education. Child Abuse and Neglect.

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    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