- The data
were collected under NSF grants GS-1418, GS-2533, and
GS-27081 for the International Comparative Political
Parties Project at Northwestern University, with Kenneth
Janda as Principal Investigator. The data have been
deposited with the Inter-University Consortium for
Political and Social Research as ICPSR Study 7534 (Janda,
1979). The variables and coding procedures are discussed
thoroughly in Janda (1980).
- This usage
of analysis of variance differs from Cox's description of
the aim of regionalization as the allocation of places to
regions 'in such a way as to minimize the within-region
variance! between-region variance ratio within the
constraint of the number of regions required' (1969: 70).
Although Cox also employs the analysis of variance model,
his approach seeks to delineate regions. This study
evaluates the utility of regions once regions have been
delineated on other grounds.
parties were selected for study if they met minimum
criteria for strength and stability. For legal parties,
we required that they win at least 5 per cent of the
seats in two elections from 1950 to 1962. For illegal
parties, we required evidence of support from at least 10
per cent of the population over five years. The selection
criteria are discussed in Janda (1980: 5-7).
eta-squared statistic is also known as the correlation
ratio. It is the ratio of the explained sum of squares to
the total sum of squares in an analysis of variance. It
expresses the proportion of variation in the dependent
variable that is due to the groupings on the independent
to the theory of regional explanations of party politics,
region constitutes the independent variable and party
characteristics the dependent variable. In the
conventional use of discriminant analysis, however, the
nominal variable (region) constitutes the dependent
variable. In truth, discriminant analysis is blind to
causal ordering, and the authors can conceive of the
analysis in the reverse direction.
have been noted in using the 'three world' classification
scheme. Roth and Wilson suggest that this scheme is
'neither neat nor analytically precise' (1976: 5). The
problem lies in part in the fact that classification into
the First and Second Worlds is based primarily on a
country's political system and its dominant ideology,
while Third World countries are defined by the extent of
their social, political, and economic development.
However, Horowitz (1969: 39-46) discusses the three
worlds in terms of four factors: economy, polity,
society, and military. The First World is 'dominated by
the United States, including allies in Western Europe and
satellites in Latin-America and elsewhere'. The key
traits of First World countries are an industrialized,
capitalist economy, parliamentary democracy, a highly
urbanized society, and a professionalized military that
executes the orders of the political elites. The Second
World is 'dominated by the Soviet Union, including allies
andlor satellites in Eastern Europe and parts of Asia'.
Second World nations are characterized by an
industrialized, socialist economy, democratic centralism,
high urbanization, and a professionalized military that
works with the political elites. Third World nations are
'non-aligned and non-satellite nations with a general
tendency toward clustering in Africa, Asia and
Latin-America--a spectrum conventionally covering Algeria
to Yugoslavia in economy and India to China in polity'.
In spite of the variation, Horowitz suggests that Third
World nations are characterized by low development, a
mixed economy tending toward socialism, mass democracy,
an urbanizing society, and a politically active
discriminant analysis rontine in SPSS was used for this
analysis. The canonical functions were not rotated.
Unfortunately, the SPSS program does not calculate the
total structure coefficients reported in this paper. They
were computed by correlating the variables with a
composite score created from the unstandardized
discriminant function coefficients.