Directions: Answer each question completely, showing all your work. Refer to the SPSS tutorials as needed (see all attachments). Copy and Paste the SPSS output into the word document for the calculati
S P S S Tu t o r i a l 01 Multiple Linear Regression Regression begins to explain behavior by demonstrating how dif- ferent variables can be used to predict outcomes. Multiple regres - sion gives you the ability to control a third variable when investi- gating association claims. To explore Multiple Linear Regression, let’s work through the following example.Example:
A researcher is interested in studying four different variables: GPA, motivational score, IQ, and hours of study. The following data was collected on a simple random sample of students.
The researcher wants to examine the re- lationship between the dependent variable GPA and the independent variables of Moti- vational score, IQ, and hours of study.
To begin the analysis, enter the data into SPSS. There are four variables, so we need four columns. For questions entering data, please review the data entry tutorial. Once the data are entered, click on Ana- lyze – Regression – Linear . 02 The Linear Regression pop-up window will open. Move GPA to the Dependent box and the other three variables to the Indepen - den t box. Then click OK . 03 The output will be displayed in the output window. There are four boxes of output. The first is a description of the variables entered. The second box is the Model Summary.
This gives the correlation coefficient r, the coefficient of determination r2, ad- justed r2, and the standard error of the estimate. The correlation coefficient tells the strength and direction of the linear relationship. Since r = .968, it is a strong positive relationship (a positive number and close to one). The coefficient of deter- mination tells the proportion of variation that is account for by the linear relation- ship between the dependent and indepen- dent variables. The third box gives an analysis to show if there is a statistically significant linear relationship between the dependent and independent variables. The p-value of .000 (Sig.) indicates a statistically significant linear relationship since it is less than 0.05. 04 The last output box gives an individual look at each of the independent variables as predictors of GPA. To interpret this review the p-value (Sig.) for each of the variables. We can see that Motivation Score and IQ are statistically significant predictors of GPA (with p-value <0.05) but Hours of Study is not (p-value > 0.05). From these results, we see that collectively these three predictor variables explain a significant amount of variability in GPA, and individ- ually, because the slope values are all positive (the B column in the output table), motivation, and IQ signifi - cantly predict GPA (as motivation and IQ increase, so does GPA).
