Microdata Analysis includes several interrelated areas such as Artificial Intelligence, Decision Support Systems, Management of Limited Resources, Data Modeling, Design of Experiments, Focus Groups, Geographic Information Systems, Visualization, Measurement Techniques, Optimization, Forecasting, Simulation and Statistical Inference.

Doctoral Studies: Goals

The complex processes in industry and the built environment that are studied through microdata analysis can be schematically illustrated by a figure, see below.

Microdata Analysis Process Diagram

Illustration showing the complex processes within business, industry and society

The first part is the collection of data. This requires knowledge and skills in various measurement techniques, as well as design of experiments.

The second and third parts are data capture, processing, and storage: these require skills in advanced database techniques such as multi-dimensional database searching and understanding of the importance of metadata.

The fourth part in the analysis is commonly in the form of mathematical modeling of data and requires skills in statistical modeling, forecasting, simulation techniques, visualization and data mining.

The fifth part is decision-making and action, and requires an understanding of techniques such as benchmarking and counterfactual analysis. It also concerns economic decision-making and the dissemination of information within organizations.


Doctoral studies in Microdata Analysis are aimed at students who wish to acquire general skills in all parts of the process and, in addition, expert skill in any single part.

Contact Kenneth Carling if you would like to be informed when an opening in this field of study is to be advertised.


Kenneth Carling
Director of Doctoral Studies
Professor Microdata Analysis
Telephone: 023-77 89 67

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