Abstract: Background: Manually, when a dependent variable is affected by a
large number of independent variables, the number of parameters helps researchers
determine the number of independent variables to be considered in an analysis.
However, when there are many parameters to be estimated in a model, the manual
counting is tedious and time consuming. Thus, this study derives a method to
determine the number of parameters systematically in a model. Methods:
The model building procedure in this study involves removing variables due to
multicollinearity and insignificant variables. Eventually, a selected model
is obtained with significant variables. Results: The findings of this
study would enable researchers to count the number of parameters in a resulting
model (selected model) with ease and speed. On top of that, models which fulfill
the assumption are considered in the statistical analysis. In addition, human
errors caused by manual counting can also be minimised and avoided by implementing
the proposed procedure. Conclusion: These findings will also undoubtedly
help many researchers save time when their analyses involve complex iterations.
H.J. Zainodin, Noraini Abdullah and S.J. Yap, 2014. Number of Parameters Counting in a Hierarchically Multiple Regression Model. Science International, 2: 37-43.
