In Problem 7.3 we will graph the slopes using the data from the output from Problem 7.2.

- What do the slopes look like when
*previous experience (months)*is moderating the relationship between*beginning salary*and*current salary?*

Follow these steps:

- Open a new and blank SPSS data file by going to
**File → Data → New**. - Click on
**Variable View**in the lower left corner. - Under
**Name**enter the variable name*salbegin*and then hit the return button. - In the second row under
**Name**enter - In the third row under
**Name**, enter*currentsal*. - Next, click on
**Data View**in the lower left corner. - Get the data from Problem 7.2 listed under
**Data for visualizing conditional effect of X of Y**.

As you can see from the data, in the first column under *salbegin* there are three distinct values of 7870.6382, .0000, and *–*7870.6382. These values represent high, medium, and low values for beginning salary. Instead of entering these values, we will enter 1 for low (whenever *–*7870.6382 is listed), 2 for medium (whenever .0000 is listed), and 3 for high (whenever 7870.6382 is listed).

We will repeat this procedure for the *prevexp* data. For the values *–*95.8608 we will enter a 1 for low, .0000 we will enter 2 for medium, and 95.8608 we will enter 3 for high.

- Enter the data as described above for
*salbegin*and - Click on Label and label these variables with 1 = low, 2 = medium, and 3 = high. See Appendix A if you need help with this step.
- Next, enter the data as it is shown in the output for
*yhat,*which we have renamed - Click on
**Graph → Chart Builder**. The Chart Builder window will open. - On the bottom left of the window, be sure the
**Gallery tab**is highlighted. - Under
**Choose from:**click on**Line**. Two pictures of line graphs will appear to the right of this box. - Click on the picture with three lines and drag it to the large white space where it says
**Drag a Gallery chart here to use it as your starting point OR Click on the Basic elements tab to build a chart element by element**. - From the variable list under
**Variables**: click on*currentsal*and drag it to the large white space with the box**Y-Axis?**. - Click on the variable
*beginingsal*and drag it to the box**X-Axis?**. - Click on the variable
*prevexp*and drag it to the box**Set color**. Note that the lines in the graph use example data: this is not what our graph will look like! - Click on
**Continue**. - Click on
**OK**.

Compare your output and syntax to Output 7.3.

**Output 7.3: Graphing the Simple Slopes for a Moderation Analysis**

* Chart Builder.

GGRAPH

/GRAPHDATASET NAME=”graphdataset” VARIABLES=beginsal

MEAN(currentsal)[name=”MEAN_currentsal”] prevexp MISSING=LISTWISE REPORTMISSING=NO

/GRAPHSPEC SOURCE=INLINE.

BEGIN GPL

SOURCE: s=userSource(id(“graphdataset”))

DATA: beginsal=col(source(s), name(“beginsal”), unit.category())

DATA: MEAN_currentsal=col(source(s), name(“MEAN_currentsal”))

DATA: prevexp=col(source(s), name(“prevexp”), unit.category()) GUIDE: axis(dim(1), label(“beginsal”))

GUIDE: axis(dim(2), label(“Mean currentsal”))

GUIDE: legend(aesthetic(aesthetic.color.interior), label(“prevexp”))

SCALE: cat(dim(1), include(“1.00”, “2.00”, “3.00”))

SCALE: linear(dim(2), include(0))

SCALE: cat(aesthetic(aesthetic.color.interior), include(“1.00”, “2.00”, “3.00”)) ELEMENT: line(position(beginsal*MEAN_currentsal), color.interior(prevexp), missing.wings()) END GPL.

**Graph**

**Interpretation of Output 7.3**

The graph represents how *previous experience* moderates the relationship between *beginning salary* and *current salary.* The graph shows what we found in Problem 7.2: when *previous experience* is low, there is a statistically significant positive relationship between *beginning salary* and *current salary,* when *previous experience* is at the mean, there is a statistically significant positive relationship between *beginning salary* and *current salary,* and when *previous experience* is high, there is a statistically significant positive relationship between *beginning salary* and *current salary.*

**Example of How to Write About Output 7.2 and 7.3**

Results

Multiple regression was conducted to determine if previous experience (in months) moderates the relationship between beginning salary and current salary. (Assumptions of linearity, normally distributed errors, and uncorrelated errors were checked and met.) A statistically significant interaction was found, *F*(3, 470) = 263.46, *p* < .001, *R* squared = .82. According to Cohen (1988) this is a large effect size. It was found that there is a statistically significant positive relationship between beginning salary and current salary, *b* = 2.43, 95% CI [2.18, 2.67], *t* = 19.77, *p* < .001. When previous experience is at the mean, there is a statistically significant positive relationship between beginning salary and current salary, *b* = 2.03, 95% CI [1.88, 2.18], *t* = 27.14, *p* < .001. Finally, when previous experience is high, there is a statistically significant positive relationship between beginning salary and current salary, *b* = 1.60, 95% CI [1.43, 1.76], *t* = 18.79, *p *< .001.

Source: Leech Nancy L. (2014), *IBM SPSS for Intermediate Statistics*, Routledge; 5th edition;

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