European Parliament Elections After Eastern Enlargement by Hermann Schmitt

Author:Hermann Schmitt [Schmitt, Hermann]
Language: eng
Format: epub
Tags: Political Science, American Government, General
ISBN: 9780415556750
Google: e7dDAQAAIAAJ
Goodreads: 10838568
Publisher: Routledge
Published: 2010-01-15T08:58:01+00:00

Appendix A

The stacked matrix, combining party preferences for the fifty-eight parties from eight political systems has a total of 56,080 units of analysis, after deletion of missing cases in the dependent variable. To estimate the parameters of the regression models, units of analyses are weighted in two steps. As a result of the weight factor applied in the first step respondents in each system are weighted in such a way that their party choice in the European Elections 2004 reflects exactly the actual election results. In the second step this weight variable is multiplied by a (different) constant for each system, so that the eight systems in the stacked matrix contain the same number of cases. This weight variable was used for the analyses in which all parties from the eight different political systems are analysed simultaneously. Each time groups of parties are selected, the variable generated in the first stage is multiplied by yet different constants for each system, so that in all regressions presented in Table 1 the eight systems in the stacked matrix contain the same number of units of analysis each.

Because we stacked the data, the unit of analysis is no longer the individual respondent, but the respondent/party combination. Since these are not independent observations, we computed panel-corrected standard errors, and reported significance on the basis of these tests. To be precise, we did these analyses in STATA, using the robust estimate of variance (known as the Huber/White/Sandwich estimate of variance) and the ‘cluster’ option to adjust for the dependency among observations pertaining to the same respondent (Rogers 1993; Williams 2000). Each of the 7,470 respondents was defined as a separate cluster.

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