SPSS Stepwise Regression - Simple Example

SPSS tutorials. Statistical Tests. Data Analysis. Editing Data. Stepwise Regression in SPSS – Example. A magazine wants to improve their customer satisfaction. They surveyed some readers on their overall satisfaction as well as satisfaction with some quality aspects. Their basic question is “which aspects have most impact on customer satisfaction?” We'll try to answer this question with regression analysis. Overall satisfaction is our dependent variable (or criterion) and the quality aspects are our independent variables (or predictors). The data have already been prepared and can be downloaded from magazine_reg.sav. Preliminary Settings. Our data contain a FILTER variable which we'll switch on with the syntax below. We also want to see both variable names and labels in our output so we'll set that as well. *1. Switch filter variable on. filter by filt1. *2. Show variable names and labels in output. set tvars both. SPSS ENTER Regression. We'll first run a default linear regression on our data as shown by the screenshots below. Note (below) that we usually select E xclude cases pairwise because it uses as many cases as possible for computing the correlations on which our regression is based. Clicking P aste results in the syntax below. We'll run it right away. SPSS ENTER Regression - Syntax. SPSS ENTER Regression - Output. In our output, we first inspect our coefficients table as shown below. Some things are going dreadfully wrong here: The b coefficient of -0.075 suggests that lower “reliability of information” is associated with higher satisfaction. However, these variables have a positive correlation (r = 0.28 with a significance level of 0.000). This weird b-coefficient is not statistically significant: there's a 0.063 probability of finding this coefficient in our sample if it's zero in the population . This goes for some other predictors as well. This problem is known as multicollinearity : we entered too many intercorrelated predictors into our regression model. The (limited) R square gets smeared out over 9 predictors here. Therefore, the unique contributions of some predictors become so small that they can no longer be distinguished from zero. The confidence intervals confirm this: it includes zero for three b-coefficients. A rule of thumb is that Tolerance METHOD=STEPWISE in the last line. Like so, we end up with the syntax below. We'll run it and explain the main results. SPSS Stepwise Regression - Variables Entered. This table illustrates the stepwise method: SPSS starts with zero predictors and then adds the strongest predictor, sat1, to the model if its b-coefficient in statistically significant (p. Let me know what you think! This tutorial has 13 comments. By Ruben Geert van den Berg on March 23rd, 2018. I'd simply say something like "factor A accounts for . % of the total impact on . ". Which is technically not entirely correct. But it may be the best answer you can give to the question being asked. Especially in market research, your client may be happier with an approximate answer than a complicated technical explanation -perhaps 100% correct- that does not answer the question at all because it strictly can't be answered. In such cases, being a little less strict probably gets you further. By Lisa on March 23rd, 2018. Thank you! It is much clearer now. Just one more quick question please :) What is the correct way to interpret the data where the b coefficient is x% of total coefficients? By Ruben Geert van den Berg on March 22nd, 2018. With real world data, you can't draw that conclusion. The problem is that predictors are usually correlated. So some of the variance explained by predictor A is also explained by predictor B. To which predictor are you going to attribute that? That is, if A has r-square = 0.3 and B has r-square = 0.3, then A and B usually have r-square lower than 0.6 because they overlap. Most of the variance explained by the entire regression equation can be attributed to several predictors simultaneously. So the truly unique contributions to r-square don't add up to the total r-square unless all predictors are uncorrelated -which never happens. A better idea is to add up the beta coefficients and see what percentage of this sum each predictor constitutes. Or do the same thing with B coefficients if all predictors have identical scales (such as 5-point Likert). Last, keep in mind that regression does not prove any causal relations. So b = 1 means that one unit increase in b is associated with one unit increase in y (correlational statement). You can not conclude that one unit increase in b will result in one unit increase in y (causal statement). In fact, the latter will rarely be the case.