GRAPH /SCATTERPLOT(BIVAR)=whours WITH salary /MISSING=LISTWISE. *Minimal scatterplot syntax from legacy dialogs. Running it creates our first basic scatterplot. The aforementioned steps result in the syntax below. We'll first run our scatterplot the way most users find easiest: by following the screenshots below. However, a scatterplot will show that there's way more to this relation. Since we already inspected this data file (and set missing values) we can simply runĪnd see that the correlation is 0.648, quite a strong linear relation. How (strongly) is monthly salary related to working hours?

But we'd like to know more about this relationship so our research question is It seems obvious that working hours are related to monthly salaries: employees who work more hours earn more money. The survey included the number of hours people work per week and their gross monthly salaries. They carried out a survey, the results of which are in bank_clean.sav. If you've any remarks, please throw me a comment below.SPSS Scatterplot Tutorial By Ruben Geert van den Berg under Charts in SPSSĪ large bank wants to gain insight into their employees’ job satisfaction. I hope you enjoyed this quick tutorial as much as I have. Right, so those are the main options for obtaining scatterplots with fit lines in SPSS. This is especially relevant forĪ very simple tool for precisely these purposes is downloadable from and discussed in SPSS - Create All Scatterplots Tool. However, we often want to check several such plots for things like outliers, homoscedasticity and linearity. Most methods we discussed so far are pretty good for creating a single scatterplot with a fit line. It (probably) won't replicate in other samples and can't be taken seriously. However, keep in mind that these are only a handful of observations the curve is the result of overfitting. The main exception is upper management which shows a rather bizarre curve. Most groups don't show strong deviations from linearity. STATS REGRESS PLOT YVARS=salary XVARS=whours COLOR=jtype /OPTIONS CATEGORICAL=BARS GROUP=1 INDENT=15 YSCALE=75 /FITLINES CUBIC APPLYTO=GROUP. *FIT CUBIC MODELS FOR SEPARATE GROUPS (BAD IDEA). Running the syntax below verifies the results shown in this plot and results in more detailed output. This handful of cases may be the main reason for the curvilinearity we see if we ignore the existence of subgroups. Sadly, the styling for this chart is awful but we could have fixed this with a chart template if we hadn't been so damn lazy.Īnyway, note that R-square -a common effect size measure for regression- is between good and excellent for all groups except upper management. simple slopes analysis in moderation regression.inspecting homogeneity of regression slopes in ANCOVA and.BEGIN GPL SOURCE: s=userSource(id('graphdataset')) DATA: whours=col(source(s), name('whours')) DATA: salary=col(source(s), name('salary')) DATA: jtype=col(source(s), name('jtype'), unit.category()) GUIDE: axis(dim(1), label('On average, how many hours do you work per week?')) GUIDE: axis(dim(2), label('Gross monthly salary')) GUIDE: legend(aesthetic(), label('Current job type')) GUIDE: text.title(label('Scatter Plot of Gross monthly salary by On average, how many hours do ', 'you work per week? by Current job type')) SCALE: cat(aesthetic(), include( '1', '2', '3', '4', '5')) ELEMENT: point(position(whours*salary), color.interior(jtype)) END GPL.

GGRAPH /GRAPHDATASET NAME='graphdataset' VARIABLES=whours salary jtype MISSING=LISTWISE REPORTMISSING=NO /GRAPHSPEC SOURCE=INLINE /FITLINE TOTAL=NO SUBGROUP=YES. *SCATTERPLOT WITH LINEAR FIT LINES FOR SEPARATE GROUPS.

Spss scatter plot with trend line