In our example, each of the predictors added with the exception of Perceived ease of use improved the model, hence the adjusted r square increased. Most often, adjusted r square is reported for a sufficiently complex model with a lot of predictors. Adjusted R square, as the name implies, adjusts the number of independent variables in the model and only improves when the new variable added improves the model decreases when the new variable does not affect the model. To curb this situation, an adjusted R square was introduced. In other words, R square increases with an increase in the number of independent variables. Adjusted R SquareĪssuming you need a higher R square value, you can simply increase the number of independent variables in your model. Since this is about human behaviour and because there are several uncontrollable factors influencing User Behaviour, it is reasonable for the predictor variables to have a 27.6% change in User Behaviour. In other words, the R square value is not different from the one in model 2, so Perceived ease of use added no change to the study model. Similarly, in model 2, 27.6% of the changes in User Behaviour is accounted for by both Quality of information in Wikipedia and the Sharing Attitude of University faculty members.įinally, we can see that 27.6% of the changes in User Behaviour is accounted for by all the predictor variables in our model. 270 means 27.0% of the variation or change in User Behaviour is accounted for by the Quality of information in Wikipedia. It is obtained by squaring the R-value described above. It ranges from 0 to 1 but typically expressed as a percentage during interpretation. It is also known as the coefficient of determination explains the variations in the dependent variable accounted for by the independent variable. In the final block, there is a 41.9% chance that all the predictor variable increases user behaviour of University faculty members.
Their combined effect has a 0.374 (37.4%) chance of increasing User behaviour. In the Second Model, both Quality of information and Sharing Attitude have a positive relationship with User behaviour. In other words, there exist a 0.346 (34.6%) chance that the Quality of information in Wikipedia will enhance the user behaviour of University faculty members. Most often, it is expressed in percentages.įrom table 1, in the first Model, there is a positive relationship between the Quality of information in Wikipedia and the University faculty member’s User Behaviour. closer to -1 or +1, the better the relationship. So, depending on your study, the higher the R-value, i.e. Thus, an R-value of 0 shows that there is no relationship between these variables. An R-value of -1 and +1 indicates respectively a perfect negative and positive relationship between the independent and dependent variable. R in a regression analysis is called the correlation coefficient and it is defined as the correlation or relationship between an independent and a dependent variable.
Concepts can be easily understood when they are explained in a non-technical way with good examples.
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