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Multiple Regression Dissertation Sample

Data analysis using multiple regression analysis is a fairly common tool used in statistics. Many people find this too complicated to understand. In reality, however, this is not that difficult to do especially with the use of computers.

How is multiple regression analysis done? This article explains this very useful statistical test when dealing with multiple variables then provides an example to demonstrate how it works.

Multiple regression analysis 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 analysis requires a dedicated statistical software like the popular Statistical Package for the Social Sciences (SPSS), Statistica, Microstat, among other sophisticated statistical packages. It will be near impossible to do the calculations manually.

However, a common spreadsheet application like Microsoft Excel can help you compute and model the relationship between the dependent variable and a set of predictor or independent variables. But you cannot do this without activating first the set of statistical tools that ship with MS Excel. To activate the add-in for multiple regression analysis in MS Excel, view the Youtube tutorial below.

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. The study pertains to the identification of the factors predicting a current problem among high school students, that is, the long hours they spend online for a variety of reasons. The purpose is to address the concern of many parents on their difficulty of weaning their children away from the lures of online gaming, social networking, and other interesting virtual activities.

Upon reviewing the literature, the graduate students discovered that there were very few studies conducted on the subject matter. Studies on problems associated with internet use are still in 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. The time they spent online was correlated with their personal profile. The students’ profile consisted of more than two independent variables; hence the term “multiple”. The independent variables are age, gender, relationship with the mother, and relationship with the father.

The statement of the problem in this study is:

“Is there a significant relationship between the total number of hours spent online and the students’ age, gender, relationship with their mother, and relationship with their father?”

The relationship with their parents was gauged using a scale of 1 to 10; 1 being a poor relationship, and 10 being the best experience with parents. The figure below shows the paradigm of the study.

Notice that in multiple regression studies such as this, there is only one dependent variable involved. That is the total number of hours spent by high school students online. Although many studies have identified factors that influence the use of the internet, it is standard practice to include the profile of the respondents among the set of predictor or independent variables.

Hence, the common variables age and gender are included in the multiple regression analysis. Also, among the set of variables that may influence internet use, only the relationship between children and their parents were tested. The intention is to find out if parents spend quality time to establish strong emotional bonds between them and their children.

Findings of the Study

What are the findings of this exploratory study? The multiple regression analysis revealed an interesting finding.

The number of hours spent online 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. The relationship means that the greater the number of hours spent by the mother with her child to establish a closer emotional bond, the lesser the number of hours spent by her child in using the internet. The number of hours spent online relates significantly to the number of hours spent by the mother with her child

The number of hours spent online relates significantly to the number of hours spent by the mother with her child

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 observation means that there are other factors that 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.

Conclusion

The above 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.

The identification of significant predictors can help determine the correct intervention resolve the problem. The use of multiple regression approaches prevents unnecessary costs for remedies that do not address an issue or a problem.

Thus, in general, research employing multiple regression analysis streamlines solutions and brings into focus those influential factors that must be given attention.

©2012 November 11 Patrick Regoniel

Cite this article as: Regoniel, Patrick A. (November 11, 2012). Example of a Research Using Multiple Regression Analysis. In SimplyEducate.Me. Retrieved from http://simplyeducate.me/2012/11/11/example-of-a-research-using-multiple-regression-analysis/

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Multiple Regression in Dissertation & Thesis Research

 

     For your dissertation or thesis, you might want to see if your variables are related, or correlated. A correlation indicates the size and direction of any relationship between variables. If, however, your hypothesis involves prediction (such as variables "A", "B", and "C" predict variable "D"), then a regression is the statistic you will use in your analysis.

     If you have only one independent variable and one dependent variable, you would use a bivariate linear regression (the straight line that best fits your data on a scatterplot) for your analysis. When your research involves more than one independent variable and you want to see if it predicts one dependent variable, you can use a multivariate, or multiple regression equation, although we won't discuss the mathematical equation here.

Types of Regression Analysis

     There are several types of regression analysis -- simple, hierarchical, and stepwise -- and the one you choose will depend on the variables in your research. The big difference between these types of regression analysis is the way the variables are entered into the regression equation when analyzing your data. (Note: In most statistical software packages, you simply select the type of regression you want to use for your analysis from a drop-down menu.)

     In a simple regression analysis, all of your predictor variables are entered together. The statistical software will treat each of the predictor/independent variables as though it had been entered after each of the other predictor variables. To use a hierarchical regression in analysis, you must tell the statistical software what order to put your predictor variables into the regression equation. For an analysis using step-wise regression, the order in which you enter your predictor variables is a statistical decision, not a theory on which your dissertation is based.

     To determine which of these regressions you should use to analyze your data, you must look to the underlying question or theory on which your dissertation or thesis is based. If your paper is based on a theory that suggests a particular order in which your predictor variables should be entered, then use a hierarchical regression for the analysis.

     If your theory doesn't really suggest a clear order of entry for your predictor variables, then use a simple regression for your analysis. For reasons we won't go into here, it is not normally recommended that you analyze your data using a step-wise regression, as it often capitalizes on chance, and your results may not generalize to other similar samples.

     To illustrate these regression analyses, let's say that your research has led you to believe that alcohol use, socioeconomic status, and education (independent variables) are related to the incidence of child abuse (dependent variable). Your dissertation hypothesizes that these three variables predict the incidence of child abuse. From your research, you learn that there is a strong correlation between alcohol use and the incidence of child abuse. Your research also has indicated that socioeconomic status is correlated with child abuse, but not as much as alcohol use. Let's say that your research did not provide any clear evidence that education was related to child abuse, but you think it is.

     Based on your research, an order of entry is suggested for your analysis, so you would use a hierarchical regression for your analysis. As your research has indicated that alcohol use is the biggest predictor of child abuse, you would enter that predictor variable into the regression equation first. Since your background suggests that socioeconomic status also contributes to child abuse, but not as much as alcohol use, you would enter that predictor variable next. Given that your research didn't produce any indication that education was related to child abuse, you would enter that predictor variable last. The incidence of child abuse would be entered as your dependent variable.

     After you enter all your variables and run the analysis, your statistical software package should provide a significance value (p-value). Using your preset alpha level (.01 or .05, usually), you can determine if your results are significant. If the p-value obtained by your analysis is less than this, then your results are significant, and your variable (education level) is a significant predictor of child abuse, even when your other variables (alcohol use and socioeconomic status) are accounted for!

     If your research did not indicate that any of your independent variables (alcohol use, socioeconomic status, education) were related to your dependent variable (child abuse), then there is no clear theory on which your dissertation is based to dictate what order you should enter these variables in the regression equation. If this is the case, then use a simple regression for the analysis.

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