While the French longed for Liberation, they also feared its destructive and divisive potential. Given diminutive conventional French forces, de Gaulle counted on popular resistance to symbolize the participation of the French people in their own liberation. However, the fear that a “national insurrection” would cause great slaughter, and benefit the communists, caused planners to define down the concept. The STO crisis and emergence of the maquis phenomenon seemed to offer political opportunities to various players. However, the réduits maquisards played at best a marginal role in the Liberation, while their destruction offered another “black legend” of betrayal by the Gaullists and the Allies, one promoted by the PCF. The liberation of Paris served as one of the Second World War’s iconic moments, both as a milestone in the rollback of Nazi power and as a consecration of France’s republican resurrection. De Gaulle’s GPRF moved rapidly to assume the levers of national power, forcing a resistance that had consecrated, democratized, and legitimized him to step back into the ranks. De Gaulle’s acclamation removed any lingering reservations, even in Washington, that he was the legitimate leader of France. The levée was finished, the emergency over, and former FFI would henceforth fight the Germans “amalgamated” as soldiers in the regular army, not serve the political ambitions of resistance leaders and communists facilitated by the interface services.

In Chapter 13, a few examples which were evaluated in the preceding chapters of this book are reanalysed with different software programmes such as SAS, R and SPSS. For all examples, both output and syntax is provided. The most important conclusion of all the analyses is that mixed model analysis and GEE analysis with a continuous outcome variable are very stable and lead to the same results, independent of the software programme used. This also holds for logistic GEE analysis, but is totally different for logistic mixed model analysis. The results of the logistic mixed model analysis are highly dependent on the estimation procedure used. The most important estimation procedures used are the Gauss-Hermite quadrature method and the residual pseudo-likelihood method. It is argued that the Gauss-Hermite estimation procedure provides the most valid results of a logistic mixed model analysis.