Directors' liability
Einde inhoudsopgave
Directors' liability (IVOR nr. 101) 2017/3.3.4:3.3.4 Statistical analysis plan
Directors' liability (IVOR nr. 101) 2017/3.3.4
3.3.4 Statistical analysis plan
Documentgegevens:
N.T. Pham, datum 09-01-2017
- Datum
09-01-2017
- Auteur
N.T. Pham
- JCDI
JCDI:ADS402002:1
- Vakgebied(en)
Ondernemingsrecht / Rechtspersonenrecht
Toon alle voetnoten
Voetnoten
Voetnoten
Combrink-Kuiters (1998, p. 3) argue that a small sample of carefully selected cases may be more representative of all possible cases than a large sample with a bias towards a particular group of cases.
Combrink-Kuiters 1998, p. 212.
As I have noted, the total sample size of this research involves 158 coded cases. The logistic regression analysis presented in Table 7 was conducted on the basis of 99 coded cases and 5 variables.
Nagel 1963.
Haar et al. 1977.
See also Givelber & Farrell 2008 p. 31-52.
Deze functie is alleen te gebruiken als je bent ingelogd.
Two important considerations in jurimetrics are sample size and the potential problem of over-determination. The sample size in this research cannot be said to be very large, especially given that it is used for the purpose of regression analysis.1 I acknowledge that many rules of thumb exist with regard to sample size when undertaking multiple regression analysis. In the context of jurimetrics, it has been argued that the number of cases should be more than 2(u-1), where u is the number of variables.2 My data set complies with this criterion.3 More importantly, several methods have been developed in legal scholarship to overcome the potential problem of over-determination. For instance, Nagel focused on a limited set of predictor variables for conducting a multiple regression analysis.4 Haar et al. selected only highly significant variables.5 Accordingly, I chose to apply logistic regression analysis on a limited set of factors – behavioural and contextual legal case factors – which I had derived from existing case law and subsequently only made use of the highly significant variables.6