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THE STATE AND AFRICAN INDEPENDENT CHURCHES IN BOTSWANA
A statistical and qualitative analysis of the application of the 1972 Societies’ Act

PART III

Wim van Binsbergen

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2. Towards a quantitative profile of Botswana churches (b)

Aspects of registration among the African Independent churches.

When the preceding analyses are repeated for our 233 African Independent churches only, the following patterns become discernible:

                        Membership and year of registration. With regard to membership and year of registration the relatively poor quality of the data only allow us to contrast two registration categories: the registered churches and those which saw their registration cancelled; within this narrow framework, there turns out to be a statistically significant association between registration status and size of church membership: the registered churches tend to be much larger than the cancelled ones.[1] That the registered African Independent churches also have a significantly later date of registration than those which saw their registration cancelled[2] appears to be a different problem: if we look at registration, and subsequent cancellation of registration, as a cycle into which an increasing number of African Independent churches are drawn in their contact with the Registrar of Societies, it is clear that those churches which were registered at an earlier also have the greatest chance of having already reached the later stage of that cycle, i.e. cancellation.

                        Registration status and number of congregations: total, urban and rural. Among the African Independent churches, there is no longer a statistically significant association between number of congregations, and registration status.[3] The relationship between number of urban congregations and registration status is still statistically significant, now highlighting particularly the difference between registered and never-registered churches, with the cancelled churches somewhere in between.[4] However, with regard to number of rural congregations, registration status no longer makes a difference.[5]

                        Registration status and urban orientation. Registration status and ‘urbanity’ of the African Independent churches is again significantly associated: the registered churches score highest, followed by the cancelled ones, while the never-registered churches close the line.[6] With regard to the ‘rurality’ scale this pattern is of course identical but reversed.[7]

 

Factor analysis of the variables in the data set (for African Independent churches only)

The above results show the same few variables from a number of complementary points of view. It is useful to attempt to arrive at a more comprehensive view. Factor analysis constitutes a powerful statistical technique that allows us to reduce the number of variables in a data set and to identify underlying factors which may in complex ways influence the behaviour of the surface variables and their interaction. For instance, if churches would tend to register immediately after emergence, and if churches would tend to grow at the same rate, then any association found between date of registration and church size would have to be attributed to an underlying factor ‘date of origin’, not directly measured in the data collection. Factor analysis upon the variables in the data set would mathematically construct such a factor, calculate the ‘loading’ (between -1 and +1) of each surface variable upon that still anonymous factor, and allow us to suggest the nature of by an inspection of the pattern of loadings allow us to suggest the nature of that fact, e.g. ‘date of origin’. For students of African divination this technique is easy to understand, inspiring and exhilarating.

                        Factor analysis upon the variables in our data set comes up against the relatively poor quality of the data: the correlation matrix on which the analysis is based is only meaningful if missing cases have been deleted ‘listwise’, i.e. if a missing value on only one of the variables leads to exclusion of that case from the entire analysis. One strategy is to exclude variables with many missing cases from the analysis, thus reducing but not invalidating the matrix. Not only quality of the data collection but also the specific design of the analysis is involved here: e.g. churches which were never registered will inevitably show a missing value on the variable ‘year of registration’ regardless of the quality of the data. After some trial and error I limited my analysis to the African Independent churches in the data set, basing it on 13 variables and 93 cases which had no missing values on any of these variables. Most of these variables have been discussed above; some however particularly refer to Francistown: measuring a church’s presence (at the congregation and headquarters level) in that town and in the rural region which surrounds it, and contrasting that information with other towns and other rural areas in Botswana. In view of the national-level focus in this paper findings relating to these variables have not been presented here, but the variables as such are part of the data set and can help to highlight such underlying factors as it contains. Finally, either of the pair of variables ‘urbanity’ and ‘rurality’ had to be omitted in order to avoid problems of multicollinearity: spurious results due to the confusion of deliberately constructed arithmetical relations between variables on the one hand, and stochastic association on the other.

                        Factor analysis[8] yielded the results presented in table 4.

                        The following abbreviations have been used for the names of variables: NOBRANCH, number of branches; URBELS, number of urban branches in Botswana outside Francistown; URBRA, total number of urban branches in Botswana; RURBRA, total number of rural branches in Botswana; RURELS, number of rural branches in Botswana outside the Francistown region; RURFT, number of rural branches in the Francistown region; MEMBERS, membership; YEARREG, year of registration; CHUFT, does this church occur with at least one urban or rural branch in the Francistown region?; HQFT, does this church have its headquarters in Francistown?; URBFT, does this church have an urban branch in Francistown?; URBANITY, number of urban branches as fraction of total number of branches; RIG, as stated above, is a slightly modified form of the variable REG = registration status. With the exception of RIG, all these variables have been measured on an interval or a dichotomous nominal scale, which justifies their inclusion in the matrix. RIG measures a church’s positive interaction with the Registrar of Societies on an ordinal scale, from ‘exemption’ (1), via ‘registration’ (2), ‘doubt/has to give proof of existence’ (3) and ‘registration cancelled’ (4) to ‘never registered’ (5); strictly speaking such an ordinal variable does not belong in a factor analysis matrix, but the intuitive conceptual unilinearity of the scale, and the variable’s behaviour in the analysis once entered, yet would appear to justify its inclusion.

 

ROTATED LOADINGS

 

FACTORS

 

1

2

3

4

NOBRANCH

0.974

0.146

0.107

0.036

URBELS

0.936

0.005

-0.180

0.029

URBRA

0.925

0.185

-0.206

0.046

RURBRA

0.924

0.107

0.309

0.027

RURELS

0.889

-0.007

0.299

0.012

RURFT

0.736

0.333

0.241

0.052

MEMBERS

0.690

0.142

0.034

0.319

YEARREG

-0.594

0.004

0.232

0.147

CHUFT

-0.297

-0.923

-0.028

0.011

HQFT

0.237

-0.890

-0.155

0.053

URBFT

0.360

0.809

-0.192

0.092

URBANITY

-0.081

-0.015

-0.931

0.097

RIG

-0.044

0.011

0.091

-0.961

VARIANCE EXPLAINED BY ROTATED COMPONENTS

5.977

2.497

1.322

1.075

PERCENT OF TOTAL VARIANCE EXPLAINED

45.979

19.208

10.166

8.266

Table 4. Factor analysis on the data set: African Independent churches only (high loadings underlined)

The four rotated factors together explain more than five sixth (83.619%) of the variance in the data set for African Independent churches, which is a very high percentage. The nice grouping of the variables with regard to their loadings on the factors makes it rather easy to interpret them.

                        Church size. Factor 1 clearly amounts to an overall factor church size, as directly reflected in a church’s number of total, rural and urban congregations, and its membership. It is noteworthy that also year of registration should have a significant loading on this factor: the larger the size of an African Independent church, the earlier its date of registration. Pending the collection of further data on churches’ dates of origin and rates of growth, we might suggest various interpretations for this association: considering the truly exponential growth of Independent Churches in Botswana over the past quarter of a century, older churches have had more time to grow large. And, as a complementary explanation, larger churches are more conspicuous, cannot escape the Registrar of Societies’ attention, and also have specific problems (the acquisition of building plots, conflicts over movable and immovable property in case of church schisms, the organization of raffles, etc.) for which registration is required and is positively sought by them; in the light of the latter explanation it might even be said that registration is a precondition for African Independent churches to grow large in the first place. The fact that the loading of the registration date on factor 1 is relatively slight suggests that factor 1 primarily measures numbers of people, and not (contrary to my example when introducing factor analysis) a time dimension concealed by membership figures which might increase over the years merely as a function of time.

                        Francistown bias. Factor 2 seems to reflect little else than the inevitable Francistown bias in my data: although the majority of data also for cancelled and never-registered churches derive from other parts of the country, intensive field-work in one particular town and region fortunately cannot fail to yield data of a precision and scope not available from national-level bureaucracies and surveys. Meanwhile it remains remotely possible that, apart from the data collection factor, Francistown is genuinely exceptional with regard to Botswana Independency, as it has been with another type of Botswana voluntary associations: political parties. In a country whose Protectorate status largely reduced it to a remote labour reserve far away from direct confrontation with capitalist relations of production, Francistown has a unique history of capitalist encroachment and exploitation (through the early mines, the massive passage of labour migrants from all over Southern Africa, and the iron rule of the Tati Company which for almost a century monopolized and alienated land, agricultural production and trade in the Francistown region).[9] It is no accident that most political movements in Botswana originated in this town. It was also (cf. Lagerwerf 1982; Chirenje 1977) the first place for African Independent churches to become active, in the beginning of the twentieth century. However, any specific Francistown factor that cannot be relegated to the Francistown bias in my data collection can only be identified once we are satisfied that for other parts of Botswana data have been collected of the same overall quality as for Francistown. It is perhaps regrettable that the Francistown bias in my data seems to be responsible for 20% of the variance in the data set; on the other hand, we are lucky that that factor has been made explicit, and that it leaves more than 80% of the variance to be explained in more systematic terms.

                        Rural orientation and its complement, urban orientation. That it is not the urban nature of such of the Francistown environment that is involved in Factor 2, is clear from the minimal loading of the ‘urbanity’ variable on this factor. Factor 3 meanwhile is largely confined to this variable, and therefore should be taken to measure the rural orientation of the African Independent churches in the data set: when ‘urbanity’ is high, Factor 3 (because of its negative sign) is low.

                        Bureaucratic recognition. The last factor which was sufficiently powerful to be retained in the analysis is Factor 4, which again has a significant loading for only one variable: RIG; when RIG is low (when the church is registered, or better still exempted), Factor 4 is high, and it can be said to measure the degree of positive interaction with the Registrar of Societies’ office, or let us say bureaucratic recognition.

                        It is important to realize that, as mathematical constructs, these four factors have been computed to be be mutually uncorrelated and irreducible. In other words, while the factor analysis identifies

 (a) church size,

 (b) rural orientation and

 (c) bureaucratic recognition

as major dimensions along which any African Independent church in Botswana can be located in our data set and hopefully in social reality too, such statistical associations between these dimensions as we may find in the present data set will remain slight: the factor analysis shows that in principle these factors are independent.

 


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[1]       INDEPENDENT SAMPLES T-TEST ON  ‘MEMBERS’     GROUPED BY      ‘REG’

GROUP  N  MEAN  SD
1.000 77 979.013  1916.456
4.000 33 251.061 355.683

SEPARATE VARIANCES T = 3.207 DF =       87.4 P = .002

        POOLED VARIANCES T = 2.161 DF =              108 P = .033

123 missing cases

[2]       INDEPENDENT SAMPLES T-TEST ON  ‘YEARREG’     GROUPED BY      ’REG’

GROUP  N  MEAN  SD
1.000  144  80.319 5.401
4.000 43  76.930 3.173

SEPARATE VARIANCES T = 5.129 DF = 119.8 P =0.000

        POOLED VARIANCES T = 3.914 DF =              185 P =0.000

46 missing cases

[3]BARTLETT TEST FOR HOMOGENEITY OF GROUP VARIANCES, CHI-SQUARE = 203.155, DF= 2, P =  .000

 

ANALYSIS OF VARIANCE

 SOURCE  SUM OF SQUARES DF MEAN SQUARE  F  P
 BETWEEN GROUPS  98.204  2  49.102  1.405  .248
 WITHIN GROUPS 7303.267 209  34.944      

 

REG reg canc never
NO CASES 133 40 39
MEAN 2.887 2.100 1.128

21 missing cases

[4]BARTLETT TEST FOR HOMOGENEITY OF GROUP VARIANCES: CHI-SQUARE = 44.606, DF= 2, P = 0.000

 

ANALYSIS OF VARIANCE

 SOURCE  SUM OF SQUARES DF MEAN SQUARE F  P
 BETWEEN GROUPS  20.269  2  10.135  3.455  .033
 WITHIN GROUPS  607.259 207 2.934      

 

reg reg canc never
no cases 132 40 38
mean 1.477 1.175 .658

23 missing cases

[5]BARTLETT TEST FOR HOMOGENEITY OF GROUP VARIANCES: CHI-SQUARE = 176.207, DF= 2, P =  .000

  ANALYSIS OF VARIANCE
 SOURCE  SUM OF SQUARES DF MEAN SQUARE F  P
 BETWEEN GROUPS  27.333  2  13.667 .640  .528
 WITHIN GROUPS 4482.441 210  21.345    

 

REG reg canc never
NO CASES 135 40 38
MEAN 1.393 .925 .474

18 missing cases

[6]BARTLETT TEST FOR HOMOGENEITY OF GROUP VARIANCES: CHI-SQUARE = 3.246, DF= 2, P =  .197

 

ANALYSIS OF VARIANCE

 SOURCE  SUM OF SQUARES DF MEAN SQUARE F  P
 BETWEEN GROUPS 1.185  2 0.593  3.265  .040
 WITHIN GROUPS 37.581 207 0.182    

 

reg reg canc never reg
no cases 132 40 38
       
mean .73 .6 .553

23 missing cases

[7]BARTLETT TEST FOR HOMOGENEITY OF GROUP VARIANCES: CHI-SQUARE = 3.246, DF= 2,        P =  .197

  ANALYSIS OF VARIANCE
 SOURCE  SUM OF SQUARES DF MEAN SQUARE F  P
 BETWEEN GROUPS 1.185  2 0.593  3.265  .040
 WITHIN GROUPS 37.581 207 0.182    

 

reg reg canc never reg
no cases 132 40 38
mean .27 .4 .447

23 missing cases

[8]       The rotation method used is known as varimax. The criterion eigenvalue for retention of factors was set at .9; 1.0 would have been more elegant, statistically, but would have sacrificed the important fourth factor.

[9]       Cf. Kerven 1977; Mogotsi 1983; Mupindu 1983; Schapera 1943, 1971; Tapela 1976, 1982; Werbner 1970, 1971.


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