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It describes the different types of panel datasets commonly used for empirical analysis, and how to use them for cross sectional, panel, and event history analysis. Longhi and Nandi then guide the reader through the data management and estimation process, including the interpretation of the results and the preparation of the final output tables.
Using existing data sets and structured as hands-on exercises, each chapter engages with practical issues associated with using data in research. These include:
- Data cleaning
- Data preparation
- Computation of descriptive statistics
- Using sample weights
- Choosing and implementing the right estimator
- Interpreting results
- Preparing final output tables
- Graphical representation
Written by experienced authors this exciting textbook provides the practical tools needed to use panel data in research.
Автор жөнүндө
Simonetta Longhi is Research Fellow at the Institute for Social and Economic Research at the University of Essex. She is also Research Fellow at the Institute for the Study of Labour (IZA) and Research Affiliate at the Centre for Research and Analysis of Migration (CReAM). Her research interests are on migration and labour economics and she has published various journal articles using individual and household datasets. For many years she has taught courses on how to analyse the British Household Panel Survey and the Understanding Society datasets, and she is currently teaching a course on applications of data analysis.
Alita Nandi is Research Fellow at the Institute for Social and Economic Research. Her research interests include issues of ethnicity, identity, personality, family formation and dissolution. She has conducted empirical research in these areas using cohort and household panel datasets and published in various journals. She has been part of the Ethnicity Strand of Understanding Society since 2007. For the last few years she has taught courses on applications of data analysis and on how to use and analyse the British Household Panel Survey and Understanding Society datasets.
