There are some very exciting things and some very worrying things about this proposal. First, to start with the exciting aspects, the use of panel data by students and researchers in their projects is already taking off at rapid speed now that a number of longitudinal panel surveys are available online. I have met countless academics who have told me recently that they commonly direct their students to panel studies such as the BHPS instead of asking them to design and collect survey data of their own. So we should be confident of their being a good market for a practical guide to using panel data in research. Secondly, Essex University is the THE centre for panel survey expertise in Europe and so the fact that these two authors are part of such a highly-respected team will help sales of the book. But, as flagged above, there is a big ‘BUT’ to this proposal, and that is the authors' dogged determination to support this book with Stata software, rather than with, ideally, SPSS or R. Stata is not widely used in the UK in the social sciences and I fear there could be an impact on sales if stata is too prominent. The authors have agreed to include an appendix on R and will have some coverage of how to use SPSS in analysing panel data on a modest website. Other changes to the chapter structure make the book more accessible and practical, and the agreement to include a range of international panel studies in the guide will help overseas sales. But there is no getting around the fact that the stata dimension is far from helpful. As a consequence I have sought to control costs and I would suggest Indian printing and a very modest royalty offer. It is also I think a Mod in it's market potential, but will have a sales pattern more characteristic of a supp. I want a Guide to Panel Data to support the list, and Essex is the ideal department to supply authors for such a text, but this is not quite the ideal book.

A First Look at the Data Using Stata

Aim

In this chapter we give a short introduction to Stata and to its basic commands, including best practice tips. These are followed by a description of how to open different types of datasets and how to deal with datasets provided in different formats. We then describe the commands commonly used to inspect the data, compute descriptive statistics, recode values, create new variables, label variables and values, and produce graphs.

Introduction

Any analysis starts with a first look at the data. It is good practice to familiarise ourselves with the dataset before starting any new analysis: what variables are available, how are they coded, how many missing values do they have? For large datasets, looking at the data ...

  • Loading...
locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles