![]() The third column, "Beta," provides a standardized version of the slope coefficient (in a bivariate regression, this is also the correlation coefficient or "r"). Error," provides a standard error for the slope coefficient. This tells us that the relationship between the two variables we noticed in the scatterplot was accurate - the relationship is positive. ![]() 506 point increase in the spirituality scale. In our example, every 1 point increase in the 10 point religiosity scale results in a. What this means is that, for every 1 unit change in our independent variable, there is an XX unit change in the dependent variable. The first column, "B", in the second row (not the first row labeled "(Constant)") provides the slope coefficient for the independent variable. The fourth table provides the regression statistics of most interest to our present efforts. The third table is an ANOVA, which is useful for a variety of statistics, but we are going to skip it in this chapter at the present. The second table provides a "Model Summary," which we'll return to in a moment. The first table simply tells you which variables are included in the analysis and how they are included (i.e., which is the independent and which is the dependent variable). ![]() Thus, choose "OK" and you'll get the following in the Output Window: While there are a number of additional options that can be selected, the basic options are sufficient for example. In our example it is "relscale." We move that over to the "Independent(s):" box: In our example it is "sprscale." We move that over to the "Dependent" box with the arrow. The "Linear Regression" window will open: To run a regression analysis in SPSS, select "Analyze" -> "Regression" -> "Linear": Having checked the scatterplot, we can now proceed with the regression analysis. The relationship also appears to be linear, which is good for regression analysis. In this case, what we can see in the scatterplot is that there appears to be a dark line run from the bottom left to the upper right, suggesting a positive relationship between religiosity and spirituality - as one increases, so does the other. SPSS tries to make this a little easier by making the dots with lots of occurrences darker. Scatterplots with lots of values are often hard to interpret. We then select "OK" and get the following in our Output Window: In our example, since we are using religiosity to predict spirituality, we drag relscale to the X axis and sprscale to the Y axis. Choose the one at the upper left of the choices, which is called "Simple Scatter." To choose it, you'll need to drag it up to the box above that says "Chart preview uses example data." You'll then be presented with two axes - a Y axis and an X axis. If you hover over those, they will identify the type of scatterplot they will generate. To the right of the options you'll see 8 boxes. In the Chart Builder window, toward the middle of the screen make sure you've selected the "Gallery" tab, then select "Scatter/Dot" from the list of options. Once you select on "Chart Builder," you'll get the "Chart Builder" window, which looks like this: This is done in SPSS by going to "Graphs" -> "Chart Builder": A scatterplot will help us determine if the relationship between the two variables is linear or non-linear, which is a key assumption of regression analysis. In the analysis below, we are going to see how well religiosity predicts spirituality.īefore we calculate the regression line for religiosity and spirituality for genetic counselors, the first thing we should do is examine a scatterplot for the two variables. Genetic counselors were asked to rate how religious and spiritual they consider themselves on a 10 point scale - higher values indicate more religious or more spiritual. These same variables were used in some of the other chapters. To illustrate how to do regression analysis in SPSS, we will use two interval variables from the sample data set. Using a basic line formula, you can calculate predicted values of your dependent variable using your independent variable, allowing you to make better predictions. For example, because there is a linear relationship between height and weight, if you know someone's height, you can better estimate their weight. The basic idea of linear regression is that, if there is a linear relationship between two variables, you can then use one variable to predict values on the other variable. ![]() If the relationship is not linear, OLS regression may not be the ideal tool for the analysis, or modifications to the variables/analysis may be required. OLS regression assumes that there is a linear relationship between the two variables. Ordinary Least Squares (OLS) regression (or simply "regression") is a useful tool for examining the relationship between two or more interval/ratio variables.
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