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Political Protest and Power Distance

Towards a Typology of Political Participation
Protestation politique et distance de pouvoir – Vers une typologie de la participation politique
Erik H. Cohen et José Valencia
p. 54-72

Résumés

L’analyse des données de protestation politique collectées par la « World Values Survey » produit une typologie de la participation politique non-institutionalisée. La « Smallest Space Analysis” (SSA) des variables de protestation politique produit une sructure claire à une dimension allant du moins impiqué et moins exigeant des activités, au plus impliqué et plus exigeant. Dans une SSA à deux dimensions, la variable « participer à une manifestion légale » est le point de rupture entre les types de participation. Une analyse par échelles multidimensionnelles à ordre partial (MPOSAC) montre comment des pays à distance de pouvoir basse, moyenne et haute sont situés dans une structure à deux dimensions. Cette typologie de pays est analysée par raport au concept d’Hofstede de distance de pouvoir.

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Texte intégral

Introduction: Political Participation and Power Distance

1Members of a society may try to affect change through a variety of types of action: institutional (i.e. voting and making political contributions) and non-institutional (which may range from petitioning to armed revolution). The types of political actions prevalent in a given society are related to the ways in which the culture addresses fundamental issues including the relationship with authority and ways of resolving conflicts (Inkeless and Levinson, 1969; Prados and Tejada, 2003). These constructs influence how people socialized within a particular culture perceive events and help determine what behaviours are considered appropriate in various social situations.

2Hofstede’s (1991a) seminal survey on work values identified factors corresponding to these basic social issues. One of these was termed Power Distance. Power distance refers to the extent to which members of a culture expect and accept that power is distributed unequally in society. Members of societies with low power distance expect relatively equitable distribution of power (examples of low power distance countries in Hofstede’s study included Denmark and New Zealand). Members of societies with high power distance expect and accept greater inequalities of power (examples of high power distance countries in Hofstede’s study included Malaysia and Guatemala). Hofstede (1991b) asserts that in high power distance countries the political spectrum tends towards extremes with a weak moderate-center, and political change is generally made by violent overthrow of the existing power structure (revolution). In low power distance countries there tends to be a strong moderate-center, and political change may be made by legal, non-violent change of laws and rules (evolution). Hofstede’s scores continue to show high concurrent validity with current cross-cultural research (see for example, Bond and Smith, 1996; Fernandez, Carlson, Stepina and Nicholson, 1997; Fiske, Markus, Kitayama and Nisbett, 1998; Miller-Loessi, 2003).

3There is a generalised agreement among the scholars of democracy, from Jean-Jacques Rousseau through James Madison, John Stuart Mill, Robert Dahl, Benjamin Barber, and David Held that mass participation is essential for the life of a representative democracy (Cunningham, 2002); a critical link between the citizenry and the governing process. In the words of Verba, Schlozman, and Brady (1995:1) “…political participation provides the mechanism by which citizens can communicate information about their interests, preferences, and needs and generate pressure to respond.”

4Social trust and personal, face-to-face interaction between individuals impacts the level and type of political participation (Tarrow, 2000; Benson and Rochon, 2004). Connections also have been found between the likelihood that an individual will take part in protest and the individual’s resources for participation (Verba, et al., 1995; Skocpol and Fiorina, 1999); grievances toward the political regime (Klandermans, 1984; Valencia, 1990), and tolerance displayed by the regime (Tarrow, 1994). However, less work has been directed to the understanding of non-institutional political participation (Drury and Scott, 2001; Kaase, 1999; Mazie and Woods, 2003; McAdam, Tarrow and Tilly, 2001; Prados and Tejada, 2003) or to in-depth analysis of the relationship between political protest and the cultural syndrome of power distance.

5In this article we analyze data related to participation in various types of political protest in the surveyed countries, creating a typology of political participation. This typology is used to investigate the relationship between power distance and political protest.

Methods

Sample and Data

6The sample consisted of the 54 countries of the third wave of the World Values Survey.

7The data on political protest consisted of the mean score by country or region for each of five items of political protest included in the World Values Survey (1996). Respondents were asked whether they ever have or would participate in each of five types of non-institutional political protest: sign a petition, join a boycott, attend a lawful demonstration, join an unofficial strike, and occupy a building (1 = never, 2 = might; 3 = I have done). An index of political protest was composed by summing up the five scores and dividing them by five.

8Hofstede´s (1991b) scores on power distance were computed and coded such that 1 = 0 through 0.49; 2 = 0.50 through .65; and 3= 0.66 through 1.0. Higher numbers indicate higher power distance. The data is given in Table 1.

Table 1: Political Protest, and Hofstede’s Power Distance Index

Table 1: Political Protest, and Hofstede’s Power Distance Index

Political Protest refers to the average of the responses to the five items of political participation from the 1996 World Values Study. (1 = never done & never would do, 2 = never done but might do; 3 = have done).

Power Distance refers to Hofstede’s scores where 1 = 0- 0.49; 2 = 0.50 through .65; 3= 0.66 through 1.0 Higher numbers indicate higher power distance.

* These ten countries were not included in Hofstede’s (1991b) survey, and therefore data for power distance is not available.

** Data on political protest is not available for these three countries because the relevant items were not included in the national surveys.

9The sample showed good concurrent validity for power distance, as well as expected correlations of power distance with political protest.

Data Analysis Techniques

Factor Analysis

  • 1 First, this procedure assumes that all the variance in a measure is potentially explicable by the f (...)

10An exploratory factor analysis using Principle Component Analysis was conducted on the five items related to political protest from the World Values Survey. It should be noted that several problems have been identified with this method.1 Of particular concern to the present study is the limited ability of Factor Analysis to portray the structural relationships between the variables (Guttman, 1992; Levy, 1994; Maraun, 1997).

11To explore the internal structure of the data, two multi-dimensional analytic techniques were applied: the Smallest Space Analysis (SSA) and the Multidimensional Partial Order Scalogram Analysis (MPOSAC). Each of these techniques has been used with great success in the social sciences (Canter, 1985; Cohen, 2005; Levy 1994).

Smallest Space Analysis

12Smallest Space Analysis (SSA) is a multi-dimensional scaling technique developed by Guttman (1968) as part of his Facet Theory. This technique simultaneously compares a large number of variables and graphically portrays the underlying structure of the data.

13The first step in the SSA procedure involves the calculation of a correlation matrix of the chosen variables. Based on this correlation matrix, the SSA program plots points representing the variables on a cognitive ‘map’ according to an intuitively understandable principle: the higher the correlation between two variables, the closer they will be placed in the map and conversely, the lower the correlation between two variables, the further apart they will be placed in the map. The map is then interpreted, based on the theoretical outlook of the study, revealing distinct and contiguous regions of correlated data (Guttman, 1968). Since by definition, for n variables a structure may be found in n-1 dimensions, the lower the dimensionality necessary to recognize a structure, the stronger the result of the analysis.

14Smallest Space Analysis has been used in the study of a wide variety of psycho-social phenomenon such as voting behavior (Ben-Sira, 1977), values and advertising appeal (Hetsroni, 2000), teacher’s perceptions (Maslovaty et al., 2001), ethnic identity (Cohen, 2004), tour guiding (Cohen, Ifergan and Cohen, 2002) and the widely-cited Schwartz typology (Schwartz & Bilsky, 1987, 1990). For previous applications of SSA published in this journal, see Cohen (2007); Cohen, Meir, Segal and Amar (2003); Cohen, Sagee and Reichenberg (2005).

Multidimensional Partial Order Scalogram Analysis

15Multidimensional Partial Order Scalogram (MPOSAC) is a multidimensional scale also developed by Guttman. While the SSA technique considers the content variables of the data, POSAC considers the data from the perspective of the subjects. The MPOSAC technique compares and ranges the various subject profiles. In this case each subject is one of the countries in the sample. The response to each of the variables defines each subject’s profile.

16A ‘perfect’ order or scale may be found if every pair of profiles within the sample is comparable, that is if they vary in only one direction (elements of one profile are the same or higher but none are lower than the elements of another profile). Perfect orders are rare. In most cases, profiles vary in several directions. For example, the hypothetical profiles 1-2-1 and 1-1-1 are comparable because all of the items in the first are either equal to or greater than the item in the same place in the second profile. The profiles 1-2-1 and 2-1-1 are not comparable, because some items in the first are greater and some are lesser than the corresponding item in the second profile. A list of non-comparable profiles can not be ranked in a simple highest-to-lowest scale. The MPOSAC is designed to deal with sets of profiles which contain both comparable and non-comparable profiles. The MPOSAC uses a computer program to find the best “fit” among non-comparable profiles. This best fit is called a partial order. Like the SSA, the MPOSAC procedure creates a graphic representation of the data. The graphic representation of the partial order of a set of profiles which are only partially comparable is called a scalogram. For a more detailed, mathematical description and explanation of this approach see Shye and Amar (1985) and Levy and Amar (2000).

17The POSAC procedure has been used, for example, to develop typologies of values and beliefs (Cohen and Sabbagh, 2001; Sabbagh, Cohen and Levy, 2003; Wiley and Levi-Martin, 1999), survival among cancer patients (Levy and Guttman, 1985) and drug use (Levy, 1998; Levy and Cohen Bar-on, 2003).

Results

18Pearson correlations showed that Power Distance was significantly and negatively related with political participation (r (42) = −.57, p ≤ .00) as well as with Social Trust (r (45) = -.49, p ≤ .00). However, Social Trust was positively related with Political Participation (r (45) = .43, p ≤ .00). Results show the main role that this cultural niche plays in the relationship between political participation and social trust for the development of countries’ political dynamics.

Factor Analysis

19The results of the exploratory factor analysis are shown in Table 2. Three important results emerge: a) the communalities were medium or high loads; b) the first variable (Signed Petition) explained a 56% of variance and c) each of the five variables was extracted with loads higher than 0.60.

Table 2: Factor analysis-- Communalities, Eigenvalues, Percentage of Explained Variance and Factor loadings of items

Table 2: Factor analysis-- Communalities, Eigenvalues, Percentage of Explained Variance and Factor loadings of items

Extraction: PCA.

20All five variables load on a single factor. This is consistent with the few previous cross-cultural analyses of political protest using factor analysis, which consistently got only one factor on political protest (See for example, Norris, 2002; Park and Kwon, 2001; Montero and Torcal, 1994; Fernandez-Prados and Rojas, 2003). While this result is interesting, indicating the strong link between the variables, it is not helpful in distinguishing between sub-groups of variables and it seems that the data should allow for recognition of more subtle details and differentiations.

21Additionally, ’occupy buildings’ has a quite low communality (.382) and two others ’attend a lawful demonstration’ and ’join an unofficial strike’ are close to .50. Since our sample consists of 54 countries or regions, relying on Factor Analysis as the sole technique for interpreting the data is problematic.

Multi-Dimensional Scaling and Smallest Space Analysis

22The input correlation matrix for the five variables of political protest is shown in Table 3. All the correlations are strongly positive. An SSA, shown in Figure 1, was produced in only one dimension, with a coefficient of alienation of .24. It is highly unusual for such a clear structure with such a low coefficient of alienation to be found in one dimension. This reinforces the uni-dimensionality reflected in the Factor Analysis, in which all the items loaded on a single factor. However, the Factor Analysis does not show the sequentiality of the items. In the SSA, the five items are placed in a logical sequential order starting with ‘signed a petition’ at the left, followed by ‘joined a boycott’, ‘attended a lawful demonstration’, ‘joined an unofficial strike’ and finally at the right extreme ‘occupied a building or factory’. The sequence may be interpreted as moving from the least involved and least demanding form of activity (signing a petition) to the most involved and most demanding (occupying a building). Additionally, it may be interpreted as portraying a spectrum from the type of activity requiring the least face-to-face interaction with others through the activity demanding the greatest level of face-to-face interaction with others.

Table 3: Correlation matrix for five types of political protest

Table 3: Correlation matrix for five types of political protest

Figure 1: SSA of variables of political protest, dimensionality 1, coefficient of alienation: .24

Figure 1: SSA of variables of political protest, dimensionality 1, coefficient of alienation: .24

23In order to verify these results, the same correlation matrix was also analyzed using another MDS program (Proxscal in SPSS). The results are very similar to the SSA, and show the same order of the variables. In the SSA, the two items ‘signed a petition’ and ‘joined a boycott’ are slightly separated, whereas in the Proxscal they appeared almost in the same place.

Figure 2: Proxscal analysis of five items of political protest

Figure 2: Proxscal analysis of five items of political protest

24A second SSA in two dimensions was generated, as shown in Figure 3. The two-dimensional structure allows for a more detailed exploration of the data. In this map, the two items ‘signed a petition’ and ‘joined a boycott’ are placed in almost exactly the same spot, at the left-hand extreme of the map. The same sequence from left to right may be recognized. The addition of the second dimension reveals a difference in placement along the vertical axis. The item ‘attended a lawful demonstration’ is placed at the bottom edge of the map. The subsequent items ‘joined an unofficial strike’ and ‘occupied a building’ follow an upward curve. The item ‘attended a lawful demonstration’, then, may be considered the inflexion point or point at which the curve of a graph changes direction or theoretical significance.

Figure 3: SSA of variables of political protest, dimensionality 2, coefficient of alienation: .00

Figure 3: SSA of variables of political protest, dimensionality 2, coefficient of alienation: .00

Multidimensional Partial Order Scalogram Analysis

25Next, the MPOSAC technique was applied to the data on political protest in order to attempt to establish a typology of the various countries or regions. The data base was first re-coded as follows:

26Eighteen different profiles emerged, as shown in Table 4.

Table 4: POSAC--Profiles of countries for five variables of political protest

Table 4: POSAC--Profiles of countries for five variables of political protest

Number of Posac variables ...... 5
Number of read cases ......... 55
Number of rejected cases ..... 3 (insufficient data)
Number of retained cases ..... 52
Proportion of profile-pairs correctly represented (CORREP coefficient) .9842 (= 1184 / 1203(

27The most common profile includes 17 countries or regions. This profile represents countries and regions which had a mean response of between 1.5 and 1.99 (code 4) for the items ‘signed a petition’ and ‘attended a lawful demonstration’ and a mean response of between 1.0 and 1.49 (code 3) for the items: ‘joined a boycott’, ‘joined an unofficial strike’ and ‘occupied a factory or building’. Eight countries or regions had unique profiles, not fitting any other country, illustrating the widely varying levels and types of political participation in the surveyed regions.

28The graphic representation of the MPOSAC is shown in Figure 4. In two dimensions, 100% of the comparable pairs and 87% of the incomparable pairs were correctly represented by the MPOSAC. Overall, 98% of the profile pairs were correctly represented: an excellent result.

Figure 4: Partial order of the 52 countries or regions

Figure 4: Partial order of the 52 countries or regions

29The countries and regions are roughly dispersed along a diagonal from the lower left hand corner (the lowest level of political protest) to the upper right hand corner (the highest level of political protest). In the lower left hand corner we find the Philippines, which had the lowest level of overall political protest (code 3 for all five variables). Close by are: Taiwan, Venezuela, Nigeria, Belarus and Azerbaijan. In the upper right hand corner is the profile representing the highest level of political protest, which did not correspond to any of the actual subjects. The countries and regions with the highest levels of political protest are Basque Country and Sweden. The most common profile, representing 17 countries or regions, is found close to the lower-left hand corner, the region of low political protest.

30The correlation between the scalogram and each of the five items is shown in Table 5. The most discriminating item for axis X is ‘signed a petition’. The item ’occupied a building’ helps to detail this basic distinction. The most discriminating variable for axis Y is ‘joined an unofficial strike’. Referring back to the two-dimensional SSA, we see that ‘signed a petition’ was placed to the left of the inflexion point and ’joined an unofficial strike’ was placed to its right. Thus the two items identified as axes in the POSAC cover the range of the structure in the SSA.

Table 5: Coefficient of weak monotonicity between each observed item and the types of political protest

Table 5: Coefficient of weak monotonicity between each observed item and the types of political protest
  • 2 For reasons of space these maps are not included. They are available upon request from the authors.

31To further explore these results, a separate scalogram was created for each of the five items2. This enabled us to verify the accuracy of the scalogram for each variable. In all five of these individual-item scalograms we found a clear distinction between low and high profiles, with few ‘misplaced’ items. Only the direction of the division between categories differed. Significantly, whereas in four of the scalograms the division between categories was either horizontal or vertical, in the scalogram created for the variable ‘attended a lawful demonstration’, the division of categories was along a diagonal. In the SSA, this variable was found to be the inflexion point, at which the curve of the diagram changed direction. Again, we see this type of action to be pivotal, somewhere between the anonymous and undemanding actions of signing a petition or joining a boycott and the personally involved and demanding actions of joining a strike or occupying a building.

Power Distance and the Typology of Political Protest

  • 3 Again, for reasons of space these maps are not included and are available upon request from the aut (...)

32The scalogram was re-considered according to the percentage of the countries or regions included in each profile-point on the map which correspond to Hofstede’s Power Distance Index categories3. This exercise verified that the MPOSAC classified correctly countries of low, medium and high levels of power distance according to the level of political protest of the categories of countries.

Conclusions and Discussion

33The results of the study show that the expectation and acceptance of unequal power distribution within a culture are related to participation in various types of political protest.

34The one-dimensional SSA / MDS showed a structure of political participation ranging from the least involved and least demanding form of activity, requiring the least face-to-face interaction with others (signing a petition) to the most involved and most demanding, demanding a great level of face-to-face interaction with others (occupying a building). This adduces the results of the exploratory factor analysis in which all items loaded on a single factor, in the sense that the SSA portrays a simplex, a structure in a single dimension.

35The two-dimensional SSA shows the important role played by the item ’lawful protest’ in differentiating between illegal forms of protest which may possibly lead to conflict, such as strikes and occupying buildings, with more benign forms of political participation such as petitions and boycotts. The item ’attended a lawful demonstration’ may be considered the inflexion point or point at which the curve of a graph changes direction or theoretical significance.

36The MPOSAC classified the countries according to six levels of protest in which the countries and regions were roughly dispersed along a diagonal from the lower left hand corner (the lowest level of political protest) to the upper right hand corner (the highest level of political protest). This highlights the contribution of the MPOSAC, which is a unique feature of the HUDAP; while there is no radical specificity in the SSA (its results being almost identical to that of the Proxscal), the MPOSAC makes a specific contribution. The MPOSAC may be used in future research to differentiate between various regions within a single country, contributing to a deeper understanding of observed differences such as were found in Prados and Tejada’s (2003) study of political action in regions of Spain.

37Finally, this analysis of the third wave of the World Values Survey shows the role that power distance plays in political protest.

38The application of the SSA and MPOSAC techniques go beyond verification of the findings of the Factor Analysis. The loading of the five types of protest on a single factor was confirmed by the simplex. Additionally the one-dimensional SSA showed the order of the five categories from least- to most-involved type of activity. Additionally, the subsequent two-dimensional SSA and the MPOSAC revealed specific functions of types certain acts. Lawful demonstrations function as an inflexion point between the more and less involved types of actions. Signing petitions and joining strikes serve to discriminate between profiles of the countries in the survey. Thus the structural relationships between the data portrayed by these techniques revealed significant aspects of non-institutional political participation. Similarly, Maraun (1997) found that SSA enabled the development of a rich graphic portrayal of the five-factor model of personality traits. The five factors had been repeatedly verified through Factor Analysis, but aspects of their structural relationship only became apparent through use of SSA.

39Future research may explore the impact of new means of organizing and carrying out political protest, most significantly via the internet (Ducke, 2007; Oates, Owen and Gibson, 2006; Shane, 2004; Zhou, 2006). ‘Virtual’ political participation through informal internet networks are not strictly ‘face-to-face’ and raise interesting questions regarding social trust, yet may be particularly important in countries with high power distance where participation may be difficult or dangerous.

Figure 5: Partial order of the 52 countries or regions with high-medium and low-power distance scores indicated

Figure 5: Partial order of the 52 countries or regions with high-medium and low-power distance scores indicated
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Notes

1 First, this procedure assumes that all the variance in a measure is potentially explicable by the factors that are derived. The product-moment coefficient is corrected for communality as a lower bound estimate of score reliability depending on this correction for the reduction of dimensions (Fabrigar et al., 1999; Russel, 2002; Guttman, 1982a, 1982b). Additionally, there is a minimum sample size needed to conduct a factor analysis. Small sample sizes may affect the factor analysis by making the solution unstable: the addition of more data may cause the variables to switch from one factor to another. In particular, large samples may be required if average communalities are low (around .50 or lower) (MacCallum et al. 1999; Meade and Lautenschlager 2004). The large number of observations recommended for each item makes Factor Analysis inappropriate for many studies.

2 For reasons of space these maps are not included. They are available upon request from the authors.

3 Again, for reasons of space these maps are not included and are available upon request from the authors

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Table des illustrations

Titre Table 1: Political Protest, and Hofstede’s Power Distance Index
Légende Political Protest refers to the average of the responses to the five items of political participation from the 1996 World Values Study. (1 = never done & never would do, 2 = never done but might do; 3 = have done).
URL http://bms.revues.org/docannexe/image/2833/img-1.png
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Titre Table 2: Factor analysis-- Communalities, Eigenvalues, Percentage of Explained Variance and Factor loadings of items
Légende Extraction: PCA.
URL http://bms.revues.org/docannexe/image/2833/img-3.png
Fichier image/png, 2,1k
Titre Table 3: Correlation matrix for five types of political protest
URL http://bms.revues.org/docannexe/image/2833/img-4.png
Fichier image/png, 1,4k
Titre Figure 1: SSA of variables of political protest, dimensionality 1, coefficient of alienation: .24
URL http://bms.revues.org/docannexe/image/2833/img-5.png
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Titre Figure 2: Proxscal analysis of five items of political protest
URL http://bms.revues.org/docannexe/image/2833/img-6.png
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Titre Figure 3: SSA of variables of political protest, dimensionality 2, coefficient of alienation: .00
URL http://bms.revues.org/docannexe/image/2833/img-7.png
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URL http://bms.revues.org/docannexe/image/2833/img-8.png
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Titre Table 4: POSAC--Profiles of countries for five variables of political protest
Légende Number of Posac variables ...... 5Number of read cases ......... 55Number of rejected cases ..... 3 (insufficient data) Number of retained cases ..... 52Proportion of profile-pairs correctly represented (CORREP coefficient) .9842 (= 1184 / 1203(
URL http://bms.revues.org/docannexe/image/2833/img-9.png
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Titre Figure 4: Partial order of the 52 countries or regions
URL http://bms.revues.org/docannexe/image/2833/img-11.png
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Titre Table 5: Coefficient of weak monotonicity between each observed item and the types of political protest
URL http://bms.revues.org/docannexe/image/2833/img-12.png
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Titre Figure 5: Partial order of the 52 countries or regions with high-medium and low-power distance scores indicated
URL http://bms.revues.org/docannexe/image/2833/img-13.png
Fichier image/png, 72k
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Pour citer cet article

Référence papier

Erik H. Cohen et José Valencia, « Political Protest and Power Distance », Bulletin de méthodologie sociologique, 99 | 2008, 54-72.

Référence électronique

Erik H. Cohen et José Valencia, « Political Protest and Power Distance », Bulletin de méthodologie sociologique [En ligne], 99 | 2008, mis en ligne le 01 juillet 2011, consulté le 25 mars 2017. URL : http://bms.revues.org/2833

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Auteurs

Erik H. Cohen

University of Bar Ilan; ehcohen@mail.biu.ac.il

Articles du même auteur

José Valencia

University of the Basque Country

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