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The idea that brings both the large-N design and case studies together is the practice of small-N studies. Small-N analysis examines a small number of cases in depth, which are all selectively handpicked. One of the main strengths of these types of studies are that they are “specified, complex models that are sensitive to variations by time and place.” (Coppedge, 1999). Small-N analysis can be found in Juan Linz’s “Perils of Presidentialism” (1990) where the effect that presidential and parliamentary regime types has on democratic ability is studied. Linz’s research was carried out through selected cases (countries) from Western Europe and Latin America, with a focus on the USA. The hypotheses he wanted to test was that “the superior historical performance of parliamentary democracies is no accident.” The strength in this project was that he was able to intentionally select case studies that had similar characteristics to aide specific hypothesis testing. The Comparative Method by Collier, argues that small-N designs such as Linz’s enable the intensive analysis of a few cases with less energy expenditure, financial resources and time. Therefore, intensive analysis can be more productive than superficial statistical analysis, which can be time consuming and difficult to successfully execute as the collection of large date can be extremely difficult. A benefit of utilising small-N instead of large-N is that the studies can be operationalised at a lower level and consequently the results are likely to be valid as the concepts chosen are being accurately measured. Small-N scientists are critical of the case study method as they believe that patterns must come from theory or observation which is “validated by intimate knowledge of the detail, nuance, and history of the small number of cases” (Paul et al. 2013). However, once the number of cases expands, analysts can no longer “hold all the cases in their head” and the information is too large to be compared holistically and qualitatively without expecting a margin of error. Lijphart argues that this is because small-N analyses can focus on “comparable cases” that are matched on many variables that are not central to the study. This means that they can effectively ‘control’ these variables. They can then choose countries which differ in terms of key variables that are the focus of the study which allows a more reliable assessment of their influence. Yet, small-N analysis has various weaknesses which make it inferior to its large-N counterpart. Goggin (1986) comments on the nature of small-N analysis, as there are many variables yet a small number of cases. Therefore, it is more efficient to study more countries and consequently conduct a large-N study instead. As a result, Linz’s study has come under great criticism for its underdevelopment. Kerlinger (1973) argues that the ideal research design must answer the research question, introduce the element of control for extraneous independent variables and permit the investigator to generalize from their findings. Small-N studies are incapable of fulfilling these criteria. However, Prezworski et. al in Democracy and Development (2000) studies 150 countries over 40 years to achieve a similar objective to Linz. Conversely, unlike Linz’s analysis, this study complies with Kerlinger’s ideal research design as it allows generalisation due to the increased scale of the project and randomisation of case studies.