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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). “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 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

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