The Sage handbook of regression analysis and causal inference

By H. Best, C. Wolf. 2014

306 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f43882d3087c0d7a70eae56c13bfe3912cb75c90

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. J. Brüderl
2. V. Ludwig

In Management Science and Operations Research

1. K. Möhring
2. Katrin Auspurg
3. J. Brüderl
4. A. Schneck
5. Zoltan J. Acs
6. T. Hinz
7. V. Ludwig

In Sociology and Political Science

1. Katrin Auspurg
2. T. Hinz
3. K. Möhring
4. J. Brüderl
5. Zoltan J. Acs
6. K. Hank
7. V. Ludwig

Citations in the corpus

• Age and entrepreneurship: nuances from entrepreneur types and generation effects (2018, background): “For binary-dependent variables (0 or 1), logistic regression models are often preferred over LPM because in LPM, some values can lead to prediction outside the range of probability (between 0 and 1) and because the variance of the LPM error term correlates with the dependent variables and results in”
• Closed doors everywhere? A meta-analysis of field experiments on ethnic discrimination in rental housing markets (2018, background): “These fixed effects regressions (for a general introduction e.g. Brüderl and Ludwig 2015) allowed us to explore exactly what we are interested in: Does a higher amount of (positive) information on applicants’ social status – all else of the experimental trial being equal – lead to a lower level of…”
• Changes in employment and relationship satisfaction in times of the COVID-19 pandemic: Evidence from the German family Panel (2020, methodology): “We estimated fixed-effects linear regression models (e.g. Brüderl and Ludwig 2014) in order to explore changes in relationship satisfaction between wave 11 and the COVID-19 survey for different employment groups.”
• The COVID-19 pandemic and subjective well-being: longitudinal evidence on satisfaction with work and family (2020, background): “Unobserved heterogeneity in time-varying factors might still cause bias as first difference models rely on the assumption that the growth rate in satisfaction over time does not systematically vary between groups (Brüderl and Ludwig 2014).”
• Is There a Male Marital Wage Premium? New Evidence from the United States (2018, methodology): “The procedure to estimate the FEIS model is straightforward (see Wooldridge 2010:377– 81; for more details, see Brüderl and Ludwig 2015).”