By Hans-Jürgen Andreß, Katrin Golsch, Alexander W. Schmidt

Many monetary and social surveys are designed as panel reports, which supply very important facts for describing social adjustments and trying out causal kin among social phenomena. This textbook indicates tips on how to deal with, describe, and version these types of facts. It provides versions for non-stop and specific established variables, focusing both at the point of those variables at various closing dates or on their switch over the years. It covers mounted and random results types, versions for swap rankings and occasion historical past versions. All statistical tools are defined in an application-centered kind utilizing learn examples from scholarly journals, which might be replicated via the reader via info supplied at the accompanying site. As all types are in comparison to one another, it offers important advice with selecting the best version in utilized learn. The textbook is directed at grasp and doctoral scholars in addition to utilized researchers within the social sciences, psychology, enterprise management and economics. Readers might be conversant in linear regression and feature a great knowing of normal least squares estimation.

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Extra resources for Applied Panel Data Analysis for Economic and Social Surveys

Example text

3 10 2009 1 | 9. | 3 10 2010 1 | |------------------------| 10. | 4 11 2008 0 | 11. | 4 11 2009 0 | 12. | 4 11 2010 0 | +------------------------+ . save myfirstpanel . use house08 . append using house09 house10 . sort HHNR Year . list, sepby(HHNR) +----------------------+ | HHNR Year Income | |----------------------| 1. | 10 2008 3000 | 2. | 10 2009 3500 | 3. | 10 2010 4000 | |----------------------| 4. | 11 2008 4000 | 5. | 11 2009 4500 | 6. | 11 2010 5000 | +----------------------+ . save mysecondpanel The first two commands append all data sets including the variable Sex.

If data is merged together, the data sets have to include observations that fit together. If one data set is appended to another, the data sets have to have the same variables. Abstractly speaking, data sets can be combined if there is an identifying element either in the columns (appending) or in the rows (merging) of the data matrices. From a technical point of view, the variables (observations) have to have the same names (identification numbers) if they are equal and different names (identification numbers) if they are not.

For the analysis of income poverty in the examples below, we will have to combine data from most of these SOEP files. The data files used in the examples are not the original SOEP files. For reasons of data privacy protection we altered some variables and use only a part of the full SOEP sample. We generated random identification variables and added a random error to the sensitive information on income. In total, we use 25 % of the full SOEP sample. Nevertheless, all the important features of real survey data are still in the data.

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