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
Principal Research Economist
|Francisco Gonzales Carreras
What works in reducing pay gaps?
Equality and Human Right Commission (EHRC)
'Prevent in Further Education' data analysis
Department for Business Innovation and Skills
Centre for Vocational Education Research (CVER)
Department for Business, Innovation and Skills
Evaluation of the Pathways to Apprenticeship programme
Welsh Assembly Government (WAG)
Evaluation of the Paramedic Pre-Degree Pilot
NHS Health Education England
Careers Information Advice and Guidance Impact Tracking
The Careers & Enterprise Company (C&EC)
Evaluation of the new jobcentre plus approach to providing support for 16-17 year olds
Department for Work and Pensions