Quantitative and econometric analysis

IES researchers use a wide repertoire of statistical and econometric analysis techniques to analyse numerical data including:

  • The weighting and grossing of survey data, for sampling and response bias, inter-wave attrition in longitudinal studies etc.
  • Bivariate and multivariate analysis of cross-section data, including: logistic regression; probit; tobit; loglinear modelling; structural equation modelling; factor analysis; reliability analysis; analysis of variance; cluster analysis; discriminant analysis.
  • Longitudinal and panel data analysis, including multivariate modelling; survivor/event history analysis (non-parametric and parametric modelling, Cox regressions etc).
  • Regression models (including panel data, dynamic GMM); selection models (including extensions eg control function estimators); and non- and semi-parametric matching (propensity score matching, kernel matching).

We pride ourselves on being able to present and explain complex analysis to non-technical audiences and use a range of infographic techniques to get the key findings across.

Contact: Helen Gray

IES experts

Helen Gray Matthew Williams
Helen Gray
Principal Research Economist
Matthew Williams
Senior Research Fellow

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