Multiple Linear Regression Analysis Using SPSS – Untold Guide
Multiple linear regression is a statistical technique which is inclined towards the use of dependent as well as an independent variables. In this case, the number of independent variables is always two or more than two, and the aim of using multiple independent variables is to identify authentic results of a dependent variable designed in research. if you are good at using linear regression, then multiple linear regression is not a big thing for you, because it is the extension of linear regression. For the analysis of this regression type, use of SPSS is very frequently. As per its frequent use, this article aims to discuss multiple linear regression analysis using SPSS.
What Is Multiple Linear Regression?
Multiple regression works well to identify the fitness of data in the research work. You can find its use in so many domains including education as well as in the health sector. Let’s understand them with the help of an example.
In the case of education, you can take the example of a class. Multiple regression can help you determine the performance of students and the reason behind their way of acting in a particular way. Based on the problem, you have to see which variables are best to use.
Similarly, in the example of health sector, drug addicts can be targeted. You can study what time, amount of dosage and age factor can be affected by drugs at a high rate. This is how, you can set multiple variables and determine the final required results of a study by proper analysis.
When Should We Use in Multiple Linear Regression Analysis?
Linear regression is only suitable when the aim of study is to deal with two continuous variables only. Here one of the variables is dependent, while the other one is independent. On the other hand, when the study demands to target multiple dimensions and get insights into the problem, then multiple linear regression analysis of the collected data is a good option. For analysis, the easiest and most precise way is to use SPSS.
How Do You Go For Multiple Linear Regression Analysis In SPSS?
At the time of multiple linear regression analysis, you have to follow some steps that can lead you towards effective outcomes. In SPSS, you have to follow the standard path otherwise end would not be favorable. For analysis, data and the factors associated with it play a vital role. The factors are about the accuracy and relatability of data. You cannot use any data for analysis, but you have to ensure that the data is perfectly fine for multiple linear regression.
If you do not know how to check the perfection of data, you can seek help to buy dissertation online. Otherwise, you have to know about the assumptions to see if you can use data for multiple linear regression analysis in SPSS. Let’s discuss these assumptions and understand the analysis process.
- First of all, you have to come up with multiple independent variables. As per the name of the analysis technique, it is clear that you should have two or more than two variables. After dealing with the number of variables, you have to work on the category of variables. As the independent variable is supposed to be more than two, here you have to check the category of an independent variable. It should be ordinal or nominal.
- Now, you have to see if the dependent variable is there. After that, you should be focused on the category of the dependent variable. The category of the dependent variable should be opposite to the category of an independent variable. It can be an interval variable or ratio variable.
- Thirdly, you need to get the independence of observation in SPSS. While using SPSS, the best way to check this point is the use of the Durbin-Watson test. In the guide of SPSS, you should not ignore it at all. So, you have to be careful about this test.
- Identify if the linear regression is existing between the dependent and independent variables. At this point, you have to check the relationship of one dependent variable with multiple independent variables separately. In SPSS, you can have more than one test including a scatterplot. Also, you can use a partial regression plot for this purpose.
- Now, you need to determine the homoscedasticity of the data to be used for multiple linear regression analysis. it helps you determine the variance of data and its movement along the best fit line. These steps also have great importance in the standard guide of multiple regression. If your data meet this assumption, it is best. Otherwise, it is not a big deal to continue your analysis.
- On the next, the data collected for analysis should show multicollinearity. This is only possible when your variables are closely related to each other and you can use correlation coefficient and tolerance/VIF value.
- The second last assumption for multiple linear regression analysis is about the influential points. There can be other forms of unusual points and every unfamiliar point create negative impressions on analysis. So, you need to detect these points in the data.
- Lastly, you have to determine the residuals in the data. For this, you can use SPSS and go for the methods like Normal P-P, Normal Q-Q or histogram as well.
If you find that data is not meeting all of the assumptions, you can have many options to make it suitable. So, do not think that data is useless but make efforts to shape it well.
Final Thoughts
The above-mentioned points can help you guide well for multiple linear regression analysis using SPSS. Your grip on the assumptions can help you a lot in every expect. Once you meet the standard assumptions, rest of the procedure is very easy to manage. SPSS is very easy to use because of its simple features. You do not need to achieve any extra milestones which is why you can see its frequent use in research work.
