Chapter VIII PATTERNS OF COMMUNICATION BEHAVIOUR ON MAC PLANTS
8.2 Multivariate Analysis: OVERALS 1 OVERALS Canonical Correlation Analysis
OVERALS is an explanatory analysis technique. Stability of OVERALS results can be obtained using the ‘Boothstrap method’ (cf. Van der Burg & De Leeuw, 1988). In general, the
eigenvalues (and the canonical correlation coefficients) are very stable, if the sample size is not too small. Van der Burgh, Noordenmeer & De Haes (1944) state that confidence intervals for Component Loadings are larger than eigenvalues but still stable. This analysis is carried out using the canonical correlation coefficients and component loadings in the description of the analysis results.
OVERALS explores the internal interactions between the independent, intervening and the dependent variables. Thereafter, the multiple regression analysis is used to examine the values of the correlations between the various ‘blocks’ of the model. In other words, it assesses the interactions between the seventeen variables in the two data-sets.
The results of the above-mentioned bivariate analysis of cross-tabulations between the quantitative data from the household surveys suggest certain relationships between predisposing, enabling, perceived, institutional and intervening factors and the dependent factors of the communication behaviour on MAC plant knowledge and practice. The model is adapted to transcultural health care utilisation behaviour in Sub-Saharan Africa by Slikkerveer (1990; 2001), providing the basis for the present analytical model of advanced multivariate analysis of communication behaviour on medicinal plants knowledge behaviour: the Non- linear Canonical Correlation Analysis, OVERALS.
The non-linear canonical correlation analysis renders it possible to determine the coherence between categories of independent and intervening variables and dependent variables of communication on MAC plants for health promotion and illness prevention, and the communication on MAC plants for treatment in Lembang and subsequently interprets this coherence by incorporating it into the final explanatory model.
This method can be regarded as a factor analysis of two sets of categories of variables in which the variable from the first set should have a maximum correlation with the variable from the second set. The correlation between the two variables is called the canonical correlation (r).
The OVERALS programme for the quantitative data of the household surveys is implemented in the canonical correlation model of the communication on MAC plants for health promotion and illness prevention and of the communication on MAC plants for
treatment for the seventeen variables, grouped into eight ‘blocks’ as described in Chapter III (Figure 3.1). The canonical correlation analysis of the two sets, 1 and 2, of the variables via alternating least squares not only has the advantage of specifying the number of sets with variables in each set, but also the number of dimensions or solutions. The plot resulting from the projection of variables in the canonical space indicates the category quantifications and the category co-ordinates. Similar to the situation in the multiple regression and canonical correlation analyses, OVERALS focuses on the relationship between the two sets of variables. The OVERALS analysis consists of a list of variables, listing the 17 variables used in the analysis, the number of categories which each variable possesses, as well as their ordinal or single nominal scaling levels. The list of variables and their labels, as described in the previous section, can be grouped into the following ‘blocks’ of the model: Block 1 consisting of socio-demographic variables: ‘Age’ (label ‘Age’), ‘Education’ (label ‘Edu’), ‘Occupation’ (label ‘Occup’), ‘Membership of sort of institution’ (label ‘Memins’) and ‘Number of household members’ (label ‘Numhhm’). Block 2 consisting of psycho-social variables: ‘Knowledge level of MAC plant’ (label ‘MACpknow’), ‘Opinion of role of arisan in MAC plants’ (label ‘Opinarol’), ‘Belief in MAC plants’ (label ‘BelMACpk’), ‘Need of MAC plants’ (label ‘NeedMACk’). Block 3 encompasses enabling factors: ‘Socio-economic status’ (label ‘SES’). Block 4 consists of the perceived need factors: ‘Satisfaction with arisan
activities’ (label ‘Satisfac’). Block 5 contains the institutional factors: ‘Frequency of arisan
meetings per month’ (label ‘Freqam’), ‘Attendance at arisan’s presentations about MAC plants’ (label ‘Attpresa’), and ‘Participation in arisan activities’ (label ‘Partca’). Block 6
consists of the intervening factors: ‘Exposure to MAC plants from the media (label ‘Expomed’). Finally, Blocks 7 and 8 include the dependent variables, respectively ‘Communication on MAC plants for health promotion and illness prevention’ (label ‘Cpromprev’) and ‘Communication on MAC plants for treatment’ (label ‘Ctreatm’).
The calculated correlations, represented as component loadings in Table 8.6, show that both dimensions did indeed confirm a significantly high correlation between Set 1 of independent and intervening variables and Set 2 of dependent variable not only for the communication on MAC plants for health promotion and illness prevention but also for the communication on MAC plants for treatment (resp. -.901 and .214 versus -.638 and -.635). Four strong factors influence the communication behaviour on MAC plants for health promotion and illness prevention and for treatment in the first dimension: namely, ‘Belief in MAC plants’ (-.599), ‘Knowledge about MAC plants’ (.-502), ‘Participation in arisan
activities’ (-.448) and the intervening factors ‘Exposure to the media’ (-.533). These variables are related to the knowledge and communication on MAC plants. Knowledge about MAC plants and belief in MAC plants plus the need of MAC plants exert quite a strong influence on the communication on MAC plants for health promotion, illness prevention and for treatment. This high correlation in the component loadings also bolsters the close linkage relationship between the knowledge, belief, perception and opinion on the communication on MAC plants for health promotion, illness prevention and for treatment in the study area.
Most component loadings in the first dimension confirm the results of the bivariate analysis, indicating that variables with a significant relationship to be those strongest in the solution. Among the predisposing variables of the analytical model, the variables ‘Belief in MAC plants’ and ‘Knowledge level of MAC plants’ are identified as strong (Pearson Chi- Square .000 and Component loadings on Dimension 1 = -.599 & Pearson Chi-Square .000 and Component loadings on Dimension 1 = -.502).
Table 8.6 Distribution of the component loadings (c) for both dimensions between
the first set and the second set of the total number of 17 variables in the survey (N=120). Set Variable D i m e n s i o n 1 2 1 Age (a,b) -.369 .032 Educ (a,b) -.094 .027 Occup (c,b) .205 -.056 Memins (c,b) -.008 .238 Numhhm (d,b) -.206 .100 MACpknow (a,b) -.502 (5) -.082 Opinarol (a,b) -.402 .427 (2) BelMACpk (a,b) -.599 (3) .204 NeedMACk (a,b) -.330 .216 SES (a,b) -.230 -.255 Satisfac (a,b) .136 -.086 Freqam (d,b) .038 .102 Attpresa (d,b) -.402 .149 Partca (a,b) -.448 (6) -.363 (3) Expomed (a,b) -.553 (4) 307 (4) 2 Cpromprev (a,b) -.901 (1) .214 Ctreatm (a,b) -.638 (2) -.635 (1) a = Optimal Scaling Level: Ordinal
b = Projections of the Single Quantified Variables in the Object Space c = Optimal Scaling Level: Single Nominal
d = Optimal Scaling Level: Numerical
The institutional variables ‘Attendance at arisan presentations about MAC plants’ and ‘Participation in arisan activities’ are identified as strong (Pearson Chi-Square .000 and Component loadings on Dimension 1 = -.402 & Pearson Chi-Square .000 and Component loadings on Dimension 1=-.448).
Furthermore, the intervening variable, ‘Exposure to MAC plants from the media’, is also identified as strong (Pearson Chi-Square .000 and Component loading on Dimension 1 = - .553). In the second dimension, of all independent variables, the variable ‘Opinion about the role of the arisan in MAC plants’ is the strongest in the solution (Component loadings on Dimension 2 = .427).
8.2.2 Projection of Variables and Objects in the Canonical Space
In order to gain a better understanding of the complex coherence between all seventeen variables, a graphic representation of all the variables already described can be constructed by placing the final projections of the correlations as points on the canonical space, as shown in Figure 8.1 below (Component Loadings). The plot of all seventeen variables, which includes the two dependent variables ‘Communication on MAC plants for health promotion and illness prevention’ (Cpromprev) and ‘Communication on MAC plants for treatment’ (‘Ctreatm’) and the fifteen predictor variables, are projected onto the canonical space represented in Figure 8.1. This figure shows the divergence between ‘Communication on MAC plants for health promotion and illness prevention’ (‘Cpromprev’) and ‘Communication on MAC plants for
treatment’ (‘Ctreatm’) and therefore strongly supports the initial methodology of dividing these two, shown earlier in the qualitative surveys.
The dependent variable ‘Communication on MAC plants for health promotion and illness prevention’ (‘Cpromprev’) expresses the strongest coherence with the independent variables in the second Dimension, while ‘Communication on MAC plants for treatment’ (‘Ctreatm’) emerges most strongly in the first dimension. The plot also exposes the significance in the first Dimension of variable ‘Participation in arisan activities’ (‘Partca’), which represents respondents who participate actively in arisan activities. As explained earlier, ‘Belief in MAC plants’ (‘BelMACpk’) and ‘Exposure to MAC plants from the media’ (‘Expomed’) exert an enormous influence on the communication on MAC plants in Lembang, since they are the initial determinants of the degree to which the community is made aware of the significance of communication on MAC plants for health promotion, illness prevention and for treatment. Moreover, knowledge of particular plants which are useful to a particular need is within their reach in their immediate environment.
From the projection of the OVERALS canonical correlation analysis presented in Figure 8.1, it is clear that there is a very strong coherence between the psycho-social variables ‘Knowledge level of MAC plants knowledge’ (‘MACknow’), ‘Belief in MAC plants’ (‘BelMACpk’) and the dependent variable ‘Communication on MAC plants for health promotion and illness prevention’ (‘Cpromprev’). Meanwhile, the intervening variable ‘Exposure to MAC plants from the media’ (‘Expomed’) shows strong coherence with the dependent variable ‘Communication on MAC plants for health promotion and illness prevention’ (‘Cpromprev’). The plot also reveals that the variable ‘Participation in arisan
activities’ (‘Partca’) expresses strong coherence with ‘Communication on MAC plants for treatment (‘Ctreatm’).
The comparison of the projections of variables in Figure 8.1 and objects in Figure 8.2 on the canonical space confirms the existence of a strong interaction and prediction in both dimensions between the location of the objects of two comparable sub-groups in the sample survey in relation to their scores as variables of the related communication behaviour on MAC plants for health promotion and illness prevention, and communication behaviour on MAC plant for treatment in the study area of Lembang.
Consequently, Figure 8.1 reveals that the independent variables ‘Age’ (‘Age’), ‘Education’ (‘Edu’), ‘Occupation’ (‘Occup’), ‘Membership of sort of institution’ (‘Memins’), ‘Number of household members’ (‘Numhhm’) and ‘Satisfaction with arisan activities’ (‘Satisfac’) do not show significant interactions with ‘Communication on MAC plants for health promotion and illness prevention’ (Cpromprev’) and ‘Communication on MAC plants for treatment’ (‘Ctreatm’) in the First Dimension.
Figure 8.1 OVERALS analysis of communication on MAC knowledge for health promotion, illness prevention & communication on MAC plants for treatment in Lembang. Projection of the 17 optimally scaled variables of set 1 and 2 on the canonical space (variables are labelled).
Simultaneously, there is no significant interaction between the independent variables ‘Education’ (‘Edu’), ‘Age’ (‘Age’), and ‘Membership of sort of institution’ (‘Memins’), ‘Social-economic status’ (‘SES’) and ‘Frequency of arisan meeting’ (‘Freqam’) in the Second Dimension on ‘Communication on MAC plants for health promotion and illness prevention’ (‘Cpromprev’) as well as on ‘Communication on MAC plants for treatment’ (‘Ctreatm’).
After the determination of the significant interaction of the independent variables in Set 1 with the dependent variables in Set 2 in both the First and Second Dimension, it has become possible to project the objects or individuals in the sample survey onto the canonical space as shown in Figure 8.2. In this figure the position of each respondent is the projection of each individual (n=120) as a function of their scores in all the 17 variables in the analytical model.
The plot reveals that the projection of the variables in Figure 9.1 and the projection of objects onto the canonical space in Table 9.2 contribute to the confirmation of the strong
relationship existing between communication behaviour on MAC plants and communication behaviour on non-MAC plants in Lembang.
Figure 8.2 Projection of respondents in the sample surveys as objects on the canonical space, specified according to their relevant variables in the sample surveys.