The was superior nature of presidential rule.

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). “Perils of Presidentialism” (Linz, 1990) is
an example of small-N analysis. Linz considers the consequence that
presidential and parliamentary government types have on states’ democratic
ability. Linz’s research was carried out through selected cases (countries)
from Western Europe (e.g. Italy, Spain and France), Latin America (such as
Chile, Argentina and Brazil) and North America. His hypothesis was based on
proving if the nature of parliamentary rule was superior nature of presidential
rule. Small-N analysis enabled him to intentionally select case studies that
had alike characteristics to aide specific hypothesis testing. The
Comparative Method (Collier, 1993) 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. This means that analysis can be more
rigorous in small-N studies, unlike statistical analysis in large-N studies. Small-N
studies also save time and resources, as collecting mass data can be extremely
difficult due to the size of the study. 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. 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 which conveys the superiority of large-N analysis. 


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