Proceso de creación de una s.r.l

In contrast to the deterministic understanding of a cause, a probabilistic cause is one that usually but not necessarily produces an effect.²⁴⁵ The probabilistic view on causality, as its name suggests, derives from the probability theory, maintaining that an event occurs with a certain probability, not as a pre-ordained fact. Under the probabilistic philosophy, a factor (independent variable) can be considered a cause if its presence increases the odds of a change in the outcome (dependent variable).

Probabilistic approaches are most widely associated with the statistical methods of analysis, normally operating with variables on a continuous scale and a large number of cases. A typical probabilistic (statistical) analysis computes the degree to which values on the independent variables explain or predict change in values on the dependent variable. And more importantly, statistical tests let us estimate to what

240 “Carsten Schneider and Claudius Wagemann, “Standards of Good Practice in Qualitative Comparative Analysis (QCA) and Fuzzy-Sets,” Comparative Sociology 9 (2010): 8.

241 Ibid.

242 Rihoux and De Meur, “Crisp-Set Qualitative Comparative Analysis (csQCA),” 34.

243 Schneider and Wagemann, Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis, 277.

244 Schneider and Wagemann, “Standards of Good Practice in Qualitative Comparative Analysis (QCA) and Fuzzy-Sets,” 23.

245 Burnham et al., Research Methods in Politics, 174.

extent that degree of relationship is non-random by also computing the significance or p-values attached to the coefficient of relationship.

In my article on the democratic governance substance of EU TG cooperation, I utilise two different statistical approaches: regression analysis and t-statistics. Both of these parametric tests focus on the measures of central tendency, rather than dispersion. Hence, they suit well my data on democratic governance substance, which have no significant outliers and may be represented more intuitively by the means, rather than the medians.²⁴⁶ The multivariate linear regression, relying on the principle of correlation between variables, predicts a dependent variable from multiple independent ones, measured on a continuous level.²⁴⁷ Regression analysis also enables one to determine the overall fit of the model by computing the relative contribution of each of the independent variables to the respective variance on the dependent variable.²⁴⁸ The regression equation basically models an optimal straight line in an XY coordinate system, plotting data points for the dependent and independent variables.

In the example with four independent variables X1, X2, X3, and X4 (as is the case in Article 2), a multiple regression equation looks as follows:

Y = β0 + β1X1 + β2X2 + β3X3 + β4X4+ ε,

where β0 represents the intercept (also known as the constant), β1 is the slope parameter or the partial regression coefficient for X1, and so forth, and ε represents the errors.²⁴⁹ Each β, a partial regression coefficient, indicates the effect of the independent variable on the dependent variable, while controlling for all other variables in the model.²⁵⁰ In such a way, for example, if a partial regression coefficient β3 equals zero, the independent variable X3 does not explain the dependent variable, as hypothesised. Alternatively, if β3 is not a zero, it shows the percentage of the variance explained in Y with a one-unit change in X, as well as signals the direction of relationship (depending on whether it is positive or negative). In order to estimate the prediction power of an entire equation (model), statisticians also use R² (R Squared), or the coefficient of multiple determination, which returns the value for the total variance explained in Y by X1, X2, X3, and X4 combined.²⁵¹ The regression analysis also produces a p-value or significance level, indicating whether the derived coefficients are not different from zero merely by chance.

While the multivariate regression analysis controls well for the combined effects of multiple continuous variables on the dependent variable, it does not always factor in the differences between the specific categories of independent variables, especially

246 parametric statistics are less robust and are more commonly used for non-normally distributed data. Non-parametric tests also use the median as a point of reference rather than the mean.

247 Michael Kutner et al., Applied Linear Statistical Models, 5th ed. (Boston, MA: McGraw-Hill, 2005); Adam Lund and Mark Lund, “Laerd Statistics,” 2016, https://statistics.laerd.com/.

248 Ibid.; O’Sullivan, Rassel, and Berner, Research Methods for Public Administrators.

249 Lund and Lund, “Laerd Statistics.”

250 O’Sullivan, Rassel, and Berner, Research Methods for Public Administrators, 439.

251 Ibid., 441.

if those categories are measured on a nominal scale.²⁵² While it is interesting to know whether a level of country’s political liberalisation generally predicts the democratic governance substance of Twinning projects, it is even more insightful to glean how Armenia and Ukraine, for example, differ in terms of the democratic governance substance and whether that difference is statistically significant (i.e., greater than zero). To accomplish that research objectives, I complemented the multivariate regression analysis with independent-sample t-tests, which determined whether and how much the means of democratic governance substance of Twinning projects in Ukraine differed from those in Armenia, and other pairs of countries. Similarly, t-tests were used in each of the countries in order to confirm the differences in democratic governance substance in politicised and regular projects, those with EU sectoral conditionality and those without, technically complex and regular.

An alternative approach to gauging the country- and sector-based dynamics in the sample of Twinning fiches would be to introduce a series of dummy variables for different countries and policy sectors. That would enable me to capture the unaccounted effects of those variables on the democratic governance substance of Twinning and its indicators. Upon closer inspection, however, I realised that the effects of countries and policy sectors on the dependent variables strongly correlated with the effects of political liberalisation and sector politicisation and technical complexity, respectively. That mainly manifested through a high degree of multicollinearity (VIF value) when all the above variables were tested together with the dummy variables within one model. ²⁵³ That led me to conclude that some of those variables simply duplicated one another and hence were redundant in the model. For that reason, variables such as sector and country were excluded from the actual regression analysis, and used instead for describing the data sample and clustering data during t-testing.

However, political liberalisation, sector politicisation, and sector technical complexity, because of their deeper theoretical grounding, remained in the model. Finally, with the help of the dummy variables, I tested the time-series dimension of democratic governance substance in order to capture any effects occurring during the years when different Twinning fiches were created.

To sum up, combining various methodological approaches allows me to look at the subject matter from unusual angles and produce specific types of inferences not accessible to any single approach. Moreover, the real-world data are often messy and do not readily lend themselves to a specific type of causal inference. In selecting the method for analysis, I was mainly guided by the specific research questions, the nature of my data, the hypothesised character of causality, and the type of results my reader would be interested in knowing. On a more pragmatic level, my methodological choices were determined by the sample size. Where the sample size was small (Article 3), I

252 Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 153; O’Sullivan, Rassel, and Berner, Research Methods for Public Administrators, 378; Lund and Lund, “Laerd Statistics.”

253 Lund and Lund, “Laerd Statistics.”

resorted to deterministic methods, which depended epistemologically on the quality rather than the quantity of data collected. In the medium-size dataset of Twinning projects in Ukraine (Article 4), configurational approaches carried greater value, insofar as they offered a good level of parsimony and idiosyncrasy at the same time. Finally, in a large-N study of democratic governance substance of EU TG cooperation, quantitative methods, relying on probabilistic causality, were the most suitable to address my research questions (Article 2).