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In the figure below, the results of the regression analysis regarding the second model (percentage of income from commercial activities) can be found.

Figure 33: regression model regarding the percentage of income from commercial activities.

Model Summaryb

Model R R Square Adjusted R Square Std. Error of the

Estimate

Durbin-Watson

a. Predictors: (Constant), Extent of social entrepreneurial projects, Percentage of income from private contributions, donations and (government)subsidies, Percentage of service club members setting up commercial activities, Extent to which commercial activity is an alternative for increased competition for private contributions, donations and (government)subsidies, Extent of dependency on private contributions, donations and (government)subsidies, Extent of income stability due to the use of commercial activity, Extent to which service club members with commercial background set up commercial activity

b. Dependent Variable: Percentage of income from commercial activities

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 287,295 7 41,042 7,500 ,000b

Residual 580,038 106 5,472

Total 867,333 113

a. Dependent Variable: Percentage of income from commercial activities

b. Predictors: (Constant), Extent of social entrepreneurial projects, Percentage of income from private contributions, donations and (government)subsidies, Percentage of service club members setting up commercial activities, Extent to which commercial activity is an alternative for increased competition for private contributions, donations and

(government)subsidies, Extent of dependency on private contributions, donations and (government)subsidies, Extent of income stability due to the use of commercial activity, Extent to which service club members with commercial background set up commercial activity

Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. 95,0% Confidence Interval for B B Std. Error Beta Lower Bound Upper Bound 1 (Constant) 3,256 ,765 4,258 ,000 1,740 4,773

the commercial background of service club members – 1

,329 ,194 ,160 1,691 ,094 -,057 ,714

percentage of service club members setting up commercial activities

,109 ,103 ,093 1,056 ,293 -,096 ,314

reduced revenue volatility ,393 ,152 ,242 2,589 ,011 ,092 ,694

increased competition for private contributions, donations and subsidies

,152 ,136 ,100 1,118 ,266 -,118 ,421

extent of dependency on private contributions, donations and (government)subsidies

-,196 ,151 -,120 -1,294 ,198 -,495 ,104

percentage of income from private contributions, donations and (government)subsidies

-,194 ,087 -,212 -2,247 ,027 -,366 -,023

social entrepreneurial projects ,086 ,159 ,045 ,543 ,589 -,229 ,401

a. Dependent Variable: Percentage of income from commercial activities

The Durbin-Watson statistic in the model summary can vary between 0 and 4; a value of 2 means that the residuals are uncorrelated and thus independent. Since this value is 2.103 and close to 2, the conclusion can be drawn that the errors are independent. Multicollinearity can be checked by which predictors correlate too highly (r > .9) with each other. There are no predictors that correlate so high. Moreover, the average of the VIF values (I exclude them from the coefficients table) of all predictors should not be substantially greater than 1. The VIF values are: 1.411, 1.240, 1.384, 1.261, 1.365, 1.404 and 1.109. The average is 1.3106 and is not substantially greater than 1. So, there is no cause for concern regarding multicollinearity. A histogram and a normal probability plot (i.e. P-P Plot) help to test the normality of residuals (see figure 34 below). The P-P Plot shows deviations from normality

normally distributed. Moreover, the P-P plot shows that the data points do not deviate substantially away from the diagonal.

Figure 34: histogram and normal P-P plot regarding standardised residuals

A scatterplot regarding standardised residuals against standardised predicted values is helpful in spotting heteroscedasticity and non-linearity. There should be no systematic relationship between the errors in the model and what the model predicts. According to Field (2013, p. 192), in a scatterplot the dots may not funnel out or show a curve; they should look like a random array of dots. As one can see in the scatterplot below, the dots do not funnel out or show a curve, so the assumptions of linearity and homoscedasticity have been met.

5.4.4 Results

R2 shows how much variance is explained by the model compared to how much variance there is to explain in the first place; it is the proportion of variance in the outcome variable that is shared by the predictor variables. R2 = .331 and means that 33.1% of the variance in the percentage of income from commercial activities is explained by the predictor variables. The F-ratio shows how much variability the model can explain relative to how much it cannot explain; it is the ratio of how good the model is compared to how bad it is. According to Field (2013, p. 337), the F-ratio is ‘…calculated by dividing the average improvement in prediction by the model (MSM) by the average difference between the

model and the observed data (MSR)’; a ratio that is greater than 1 means that the improvement due

to fitting the regression model is much greater than the inaccuracy within the model. The F-ratio regarding our model is 7.500, p < .001. This means that the model significantly improved our ability to predict the outcome variable compared to not fitting the model.

As mentioned earlier, the b-values tell us to what degree each predictor affects the outcome if the effects of all other predictors are held constant. In our model, there are two predictor (of the seven) that significantly (α = .05) predicts the outcome variable. These variables are:

- the extent of income stability due to the use of commercial activity (b = .393, p = .011); - the percentage of income from private contributions, donations and (government)subsidies

(b = -.194, p = .027).

The regression analysis results in the following equation:

Percentage of income from commercial activitiesi = 3.256 + (0.329 commercial background-1i) + (0.109 percentage of service club members setting up commercial activitiesi) + (0.393 reduced revenue volatilityi)* + (0.152 increased competitioni) + (-0.196 extent of dependency on private contributions, donations and (government)subsidiesi) + (-0.194 percentage of income private contributions, donations and (government)subsidiesi)* + (0.086 social entrepreneurial projectsi) *

= significant at .05 – level.

5.5 Conclusion

Based on the results described in section 5.2 and 5.3, the hypotheses H2a, H3a, H4a and H6b, as

formulated in section 3.4.1, can be tested. However, before testing these hypotheses it is noteworthy to recapitulate some interesting specific conclusions resulting from some (non- )parametric tests as described in this chapter and from the regression analyses in section 5.4.