Factor analysis is employed to identify the important dimensions of the observed variables in this study. It also streamlines the data by reducing its complexities to a more parsimonious model, the goal being to find a smaller number of theoretical variables. It is assumed that some underlying factors that are smaller in number than the number of observed variables are responsible for the co-variation among the observed items (Kim and Mueller, 1978). We chose confirmatory factor analysis rather than exploratory factor analysis to reduce the number of variables and identify the pattern between them. The method applies the principal axis factoring extraction with Oblimin rotation. The computation process relies on SPSS 19 software.
As the factor analysis was intended to find a substantial construction to enable comparison between Muslims and Christians, we tested it three times on the observed variables. First we ran it on national-level data, ignoring differences between religious traditions. Next, we ran it on the Muslim and Christian groups separately. Other religious groups (Buddhist, Hindu and some smaller religious denominations) were excluded from the analysis. When the factors had been defined from these tests, the final test was run again on the national level to get an identical
solution for the respective religious traditions. The distinctive characteristics of the groups were filtered out, since we were looking only for the commensurable factor for Muslims and Christians (Anthony et al., 2007: 111-112). The process resulted in a comparable model solution for the two religious groups. In the process, the outcomes of communality, Eigenvalue, structure of the pattern matrix and item- correlations became the focus for the data interpretation.
Commonality refers to the proportion of common variance present in a variable. It measures the variance of an observed variable accounted for by common factors. It is equivalent to the sum of the squared factor loadings. For the commonality (h2), the score must be higher than .20, as a rule of thumb, as it indicates the lowest point of the shared variance in the tested variable. In the process, the score of Eigenvalue also comes into consideration. Eigenvalue is the mathematical property of a matrix used in relation to decomposition of a covariance matrix. It accounts for the variances explained by a given dimension and is a criterion for determining the number of factors to extract. In this sense, the Eigenvalue must be greater than or equal to 1, because it represents a substantial amount of variation, i.e., larger than the variance of a standardized variable (Field, 2009: 637-642).
The scale construction is also determined by the factor loadings in the pattern matrix. The factor loading indicates the correlation coefficient between the item and the factor. It must be relatively high, which is, as a rule of thumb, greater than .30. Factor loadings in more than one dimension or cross-loadings are avoided. If the item loads in two dimensions, the higher value must be selected for the respective dimension. Likewise, the positive direction is chosen if the item loads in two dimensions that appear both positive and negative. Furthermore, the correlation between factors is highlighted so that a reasonable number of them can be selected. If the correlation is high (>.50), the factors can be combined together. Next, there is a check as to whether the items in one factor show a conceptually meaningful dimension; this dimension can then be labelled substantially.
The reliability of the factor is also tested by measuring Cronbach’s alpha (α). This score refers to internal consistency, i.e., how closely a set of items are related as a group. The inter-item correlation determines the value of α. If the average inter- item correlation in the set is high, Cronbach’s α will be high as well. So, the higher the score of the Cronbach’s α, the more reliable the scale. Generally speaking, a score of less than .40 is not reliable; .41 -.60 is quite reliable (moderate); and a score greater than .61 is reliable (Nunnally, 1978; Robinson, Shaver & Wrightsman, 1991). In our process, Cronbach’s α was tested in the groups of Muslims and Christians as well as at national level. The descriptive statistics of the scale results are presented to discover the mean and its standard deviation.
Analysis of variance is run to test whether there are any differences between groups for particular variables. It specifically looks into significant correlations
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between the dependent and independent variables as well as the dependent and control variables and the intermediate determinants. All dimensions are examined to reveal group differences and tendencies. This test assumes that the scores in each group are normally distributed and the variances in all groups are equal (Te Grotenhuis and Van der Weegen, 2009: 77); we take into account the score of F, the significance level, and the correlation among the tested variables. The significance level should be less than .05 (Field, 2009: 660).
Furthermore, the linearity (or deviance from it) between the tested variables is observed. This tells us if there is a linear or non-linear relationship between them. If the relation is linearly significant, Pearson’s correlations are applied. The statistic of eta is used when the relation is deviant from linearity. In this test, the mean values of the dependent variables and of the independent, control and intermediate determinant variables are calculated and possibly recoded, simply to get more normal distribution and to avoid low frequencies.
4.1. Support for intergroup violence
Support for intergroup violence, the key dependent variable, consists of four dimensions: support for harm to persons, harm to property, public criticism and demonstrations. The questionnaire contained twenty items, with five for each dimension (q218-q237).
The factor analysis was run in three steps to generate a cross-religious comparative measurement of support for violence towards Muslims and Christians. In the first step, the analysis combined Muslim and Christian respondents; in the second step, they were analyzed separately. In the final step we combined the two again. The twenty items were loaded in three factors corresponding to support for public criticism, demonstrations and harm. Among the twenty items, eight were loaded clearly in the first factor and seven were loaded clearly in the second. One item (q227) was loaded both in the first and second factors while four items (q345, q235, q236, q237) were loaded both in the second and third. Q227 was removed because the score was higher than .30 and loaded in two factors. However, q345, q235, q236, q237 were retained for the next analysis because they were all positive in the second factor but negative in the third (<. 30). In the next run, two factor solutions consisting of harm to persons and property and public criticism and demonstrations were found.
In the second step, q224 was eliminated because of double loadings in the Muslim sub-sample. Then the scale was readjusted by removing q234, q235, q236, q237 because these items loaded in two factors; q234 and q235 loaded both in the second and third factor and q236 and q237 in the first and third. After deleting these items and running the program again, q220 was also excluded because of the double loadings. Overall, q220, q224, q227, q234, q235, q236, q237 were removed
in the new scale. In the last step, this scale was then tested again using Muslims and Christians combined; the result remained consistent in two dimensions, as shown in Table 4.1.
Table 4.1 Support for harm and demonstration: scales Muslims & Christians
h2
Factor loading pattern matrix Harm strationsDemon-
233. I would support harm to persons to enforce free
access to education for my religious group .73 .85 229. I would support harm to persons to enforce the
political influence of my religious group .71 .84 232. I would support the damaging of property to enforce
free access to education for my religious group .67 .82 228. I would support the damaging of property to enforce
the political influence of my religious group .65 .80 221. I would support harm to persons to obtain more jobs
for my religious group .51 .71
225. I would support harm to persons to fight abuse of
political power against my religious group .46 .67 223. I would support demonstrations that protest against
abuse of political power that threatens my religious
group .51 .71
222. I would support public criticism of abuse of political
power that threatens my religious group .47 .69 231. I would support demonstrations that protest against
my religious group’s lack of free access to education .48 .68 226. I would support public criticism of actions that
undermine the political influence of my religious
group .41 .64
230. I would support public criticism of my religious group’s
lack of free access to education .41 .64
219. I would support demonstrations that protest against
job discrimination of my religious group .40 .63 218. I would support public criticism of job discrimination
of my religious group .33 .56
Initial Eigenvalues 4.12 3.53
% of variance explained 28.81 22.85
The reliability of Muslims for harm is .88 and for demonstrations is .81, while the reliability of Christians for harm is .93 and for demonstrations is .85.
The result does not match our expectation that support for intergroup violence has four dimensions; instead, the dimension of harm to persons and property merge