“Multiple Regression” Strategy to Deploy in Your PhD Study
PhD students need a dissertation or thesis for getting a doctorate. Multiple regression is a boon to help PhD students to find out the correlation of the variables. It predicts the size and direction of any relationship with variables. And thus, it helps in a proficient prediction of dependent variables. It streamlines solutions to bring into focus the much influential factors that need attention. It is possible by preventing the unnecessary costs for solutions that are not concerned with the problem.
What is Multiple Regression?
Multiple regression being an extension of the linear regression predicts the unknown value of the dependant from the known value of two or more exploratory variables or independent predictors. An excellent example of multiple regression is the calculation of the yield of wheat per acre. Here the unknown exploratory variable is the total yield, which depends on the quality of seeds, soil fertility, amount of use of pesticides and fertilizers, rainfall, and temperature. One can even find out the individual influence of each known variable on the total yield.
Types of Multiple Regression analysis:
Multiple regression strategies depend on the nature and variables of the research. The types of regression analysis rely on the way of entering the variables in the regression equation, which include:
- Simple Regression Analysis:
In simple regression analysis, the predictor known exploratory variables is entered altogether. The statistical software will take it as one by one entry of the variables.
- Hierarchical Regression Analysis:
In hierarchical research analysis, the statistical software is fine-tuned to treat each of the known exploratory variables in order.
- Step by step regression analysis:
Here the order of the entry of known variables is more of a statistical decision than based on the theory of the dissertation.
How to determine the particular type of multiple regression analysis strategies for research in PhD studies?
The type of multiple regression to use for analyzing the data depends on the topic or question or theory of the dissertations. It may be summarized as follows:
- If the dissertation is based on theories of a particular order, then hierarchical regression will suit for the analysis.
- If the research paper is not in the precise order of entry for the unknown predictor variables, then simple dissertation is appropriate.
- Step by step regression is the most commonly used multiple regression analysis. It is because it often capitalizes on chance and the results will not generalize like the other similar samples.
The type of regression is readily available in most of the software packages by way of a drop-down menu.
How to find if the regression for a PhD study is right?
As soon as the formation of a regression equation, it is easy to check it in terms of predictive ability by examining the coefficient of determination or R 2. The thumb rule for good dissertation is for the R2 to be between the range of 0 – 1. The best multiple regression is one with R2 as close to 1 as possible.
The values of multiple regression and their applications for PhD study:
- R-square denotes the extent of the input variables influence on the variation of the output value.
- ANOVA table finds the variations in the data and the place of it to compare mean differences between 2 or more variables.
- ANOVA computes the F-ratio statistics to find if the regression is fit for the data
- In predicted models, the t-value and p-value coefficients help in knowing each variable estimates the dependent variable.
- For the independent variable, it is the Beta coefficient and the interval estimator.
Multiple regression strategies are useful by the ways mentioned above for a productive and successful PhD study.