Hypothesis Testing in Regression Analysis

5.4 Hypothesis Testing
The main effect hypothesis was tested by using OLS hierarchical regression as detailed by
Baron and Kenny (1986). In this work there are multiple studies which can raise the concern
of multicollinearity. It can be a problem interpreting the result accurately as it confounds the
effect of each independent variable on the dependent variable. It can expand the standard
error of the regression coefficient and violate their value. Signs of multicollinearity include
large standard error combined with high R-squared (R2), high correlation between
independent variables (Haire et al., 1998), and high correlation between the estimated
coefficients.
To prevent the problem multicollinearity, (Field, 2009) recommended inspecting the average
scores for each item and inspect the multicollinearity between dependent and the in before
proceeding with multiple regression analysis. Therefore, the regression analysis between
dependent variable (purchase intension) and the independent variables (fit, cause affiliate and
the brand equity) was carried out in this study. The means, standard deviations and pair-wise
correlations among the variables, using SPSS version 20 are presented in the appendix 1of
this work. The results does not list any outstanding regression or correlation between the
values of the predictors i.e. fit, cause affiliate and brand equity. (R > 0.9) (Field, 2009; Hair
et al., 1998; Tabachnick et al., 2001).
In the multiple regression analysis, there are a few important values to consider. The beta
values, which indicate the degree of variance of each independent variable to trust. The R2
value, which identifies how much dose the predictor contributes to the variability in the
outcome. The adjusted R-squared (R2) value, the degree of possibly generalising the results.

It also indicates which variable is the more influential in the model by less than expected by
chance.
To further understand the interaction between the variables, moderation analyses is
conducted. The method used for analysing the moderation effect is Haye’s process model for
moderation analysis was adopted (Hayes, 2012; Preacher & Hayes, 2008). A moderation
analysis extends the exploration of each of the individual predictors in the linear model to
that of the combined effect of two, or more, predictor variables on an outcome (Field, 2009).
A moderation occurs when the relationship between two variables changes as a function of a
third variable. Moderation is tested using a regression in which the outcome is predicted from
a predictor (antecedent dimensions of trust), the moderator (business experience) and the
interaction of these variables (Field, 2009). If the interaction is significant, then moderation is
present and the analysis will be followed up with a simple slopes analysis to examine the
relationship between the predictor and outcome at low, mean and high levels of the
moderator.
5.4.2 Regression Analysis
The multiple regression analysis is an extension of the simple linear regression that can be
used in predicting the value of the dependent variable based on the value of two or more
independent variables. In relation to case, multiple regression analysis will be conducted
since there exists more than one independent variables (i.e. fit, cause affiliation and brand
equity).
The variable we want to predict in our case is the purchase intension which the dependent
variable). Multiple regression analyses thus were conducted to examine the relationship
between the dependent variable (purchase intentions) and independent variables (fit, cause

affiliation and brand equity). Table 5.4.2 below summarizes the descriptive statistics analysis
of the results obtained.
From the results, the regression models of the combined predictors is obtained R2 = .654.
This value indicates a better regression fit for the data. The value of the standard error of the
estimate of the model with the three predictors is 0.903756 which is value close to 1. Thus it
implies a more accuracy of the predictions.
By examining the coefficient table, the constant of the regression model is -0.178. The
coefficients of the predictors are given as; Fit = 0.379, cause affiliate = 0.607 and that of the
brand equity is 0.037. It therefore follows that there exist association between the dependent
and the independent variables. The regression equation for this model is given as;
Purchase intension = -0.178 + 0.379*fit + 0.607*Aflcrs + 0.037*(Brand Equity).
By examining the p-values of all the predictors, both the fit and the affiliation cause has p-
values of 0.000 which are less than 0.05 while the brand equity has p–value of 0.652. With p-
values <0.05 (alpha value), the predictor variables of fit and cause affiliate are significant
because both of their p-values are 0.000.
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .811a .658 .654 .90375
Model Sum of Squares df Mean Square F Sig.
1 Regression 395.666 3 131.889 161.478 .000b