So what if just one predictor has a curvilinear relation with the outcome variable? This curvilinearity will be diluted by combining predictors into one variable -the predicted values. The reason is that predicted values are (weighted) combinations of predictors. Residual plots are useless for inspecting linearity. Inspect Scatterplotsĭo our predictors have (roughly) linear relations with the outcome variable? Basically all textbooks suggest inspecting a residual plot: a scatterplot of the predicted values (x-axis) with the residuals (y-axis) is supposed to detect non linearity. By default, SPSS regression uses only such complete cases -unless you use pairwise deletion of missing values (which I usually recommend). Valid N (listwise) is the number of cases without missing values on any variables in this table. If this is the case, you may want to exclude such variables from analysis. The descriptives table tells us if any variable(s) contain high percentages of missing values. Running the syntax below creates all of them in one go.
This is a super fast way to find out basically anything about our variables.
We'll do so by running histograms over all predictors and the outcome variable. Right, before doing anything whatsoever with our variables, let's first see if they make any sense in the first place. Which quality aspects predict job satisfaction and to which extent? Employees also rated some main job quality aspects, resulting in work.sav. Inspect variables with unusual correlations.Ī company held an employee satisfaction survey which included overall employee satisfaction. Look for influential cases.Įxclude cases if needed.
0 kommentar(er)
