# Estimation of regression and causal models with and without correction for measurement errors

Our example in the introduction about the factors that explain the level of satisfaction with democracy is based on the ESS Round 61 rotating module proposal [Kri10]. In this chapter, we are not going to use all the variables proposed, but only the variables mentioned in the introduction. These have been evaluated with respect to quality in Chapter 3 and we have corrected the correlation matrix for measurement errors in the previous chapter. We are going to start with the estimation of a simple regression model visualized in Figure 5.1.

The model presented in this figure illustrates direct effects from all the explanatory and control variables on satisfaction with democracy. The correlations between the exogenous variables are taken into account in the analysis. The regression analysis for these variables can be done in different ways. By downloading the raw data, it could be done in SPSS and Stata or any other statistical package. However, it is also possible to run the regression analysis using the correlation matrices provided above. This can be done using different programs2 but, here, we will illustrate the use of the programs Stata and LISREL. Both programs can perform regression analysis using the raw data; however, as data for the analysis we prefer to use the correlation matrix from Table 4.2 (without corrections) and Table 4.7 (with corrections), as input for our analysis.

#### Footnotes

- [1] ESS Round 6: European Social Survey (2013): ESS-6 2012 Documentation Report. Edition 2.0. Bergen, European Social Survey Data Archive, Norwegian Social Science Data Services.
- [2] For example Structural Equation Modeling (SEM) programs like: EQS, R, LISREL, Stata, IBM SPSS Amos, etc.

#### References

- [Kri10] Kriesi, H., Molino, L., Magalhaes, P., Alonso, S. and Ferrin, M. (2010).Europeansâ€™ understanding and evaluation of democracy.
*ESS Round 6 democracy module proposal*.