Multiple regression is a powerful statistical test used in finding the relationship between a given dependent variable and a set of independent variables. The use of multiple regression requires a dedicated statistical software like SPSS, Statistica, Microstat, among other statistical packages. It will be near impossible to do the calculations manually.
However, a common spreadsheet application like Microsoft Excel will enable you to compute the relationship between the dependent variable and a set of predictor or independent variables. To activate the multiple regression add-in in MS Excel, you may refer to my article in another site here.
Example of a Research Using Multiple Regression Analysis
I will illustrate the use of multiple regression by citing the actual research activity that my graduate students undertook two years ago. This is about the identification of the factors predicting a current problem among high school students, that is, the long hours spent in using the internet for various reasons. This is to address the concern of many parents on their difficulty of weaning their children away from the lures of online games, social networking and other interesting virtual activities.
The graduate students discovered, upon reviewing articles on internet use, that there are very few studies conducted on the subject. Studies on problems associated with internet use are still at its infancy.
The brief study using multiple regression is a broad study or analysis of the reasons or underlying factors that significantly relate to the number of hours devoted by high school students in using the internet. The regression analysis is broad in the sense that it only focuses on the total number of hours devoted by high school students to activities online in relation to their personal profile which are composed of many variables; hence the term “multiple”.
The statement of the problem in this study is: “Is there a significant relationship between the total number of hours (the dependent variable) and students’ age, gender, relationship with their mother, and relationship with their father (the independent variables)?” The relationship with their parents was gauged using a scale of 1 to 10, 10 being the best experience with parents.
Notice that in multiple regression, there is only one dependent variable involved, that is, the total number of hours spent by high school students in using the internet. Although there are many studies available that identified factors influencing the use of the internet, it is standard practice that the profile of the respondents are included among the set of predictor variables. Hence, the variables age and gender are included in the multiple regression analysis. In this instance, only the relationship between children and their parents were tested just to find out if time spent by parents to establish bonding between them and their children matters.
Findings of the Study
What are the findings of this brief study? An interesting finding was revealed by the multiple regression analysis. The number of teaching hours relates significantly to the number of hours spent by a parent, specifically the mother, with her child. These two factors are inversely or negatively correlated, meaning, the greater the number of hours spent by the mother with her child to establish a close emotional bond between them, the lesser the number of hours spent by her child in using the internet.
While this may be a significant finding, the mother-child bond accounts for only a small percentage of the variance in total hours spent by the child online. This means that there are other factors which need to be addressed to resolve the problem of long waking hours and abandonment of serious study of lessons by children. But establishing a close bond between mother and child is a good start.
This example of multiple regression analysis demonstrates that the statistical tool is useful in predicting the behavior of dependent variables. In the above case, this is the number of hours spent by students online. When the significant predictors are identified, then intervention can be figured out to resolve the problem. Using the multiple regression approach will save on costs for remedies which do not address an issue or a problem. Thus, in general, research streamlines solutions and brings into focus those influential factors that must be given attention.
©2012 November 10 Patrick Regoniel