* Encoding: UTF-8.
* Graphing Interactions Using the PROCESS Macro in SPSS.

* slides 12 and 13.
get file "D:\data\Seminars\Graphing_Interactions_SPSS_PROCESS\exercise.sav".
dataset name exercise.

* slides 12 and 13.
get file "D:\data\Seminars\Graphing_Interactions_SPSS_PROCESS\hsbmoderation.sav".
dataset name hsbm.
dataset activate hsbm.

* slide 18.
glm opinion by binaryvr bcovar
    /design binaryvr bcovar binaryvr*bcovar
    /print parameter
    /emmeans = tables(binaryvr*bcovar)
    /plot = profile(binaryvr*bcovar) errorbar = se.

* notice that binaryvr and bcovar come after "with"; notice change to ref group.
* slide 31.
dataset activate hsbm.
glm opinion with binaryvr bcovar
    /design binaryvr bcovar binaryvr*bcovar
    /print parameter.

* two binary predictors.
* slide 33.
dataset activate hsbm.
process y = opinion /x = binaryvr /w = bcovar /model = 1 /plot = 1. 

DATA LIST FREE/ 
   binaryvr   bcovar     opinion    . 
BEGIN DATA. 
     1.0000      .0000    51.0260 
     2.0000      .0000    56.0000 
     1.0000     1.0000    52.7363 
     2.0000     1.0000    53.8333 
END DATA. 
GRAPH/SCATTERPLOT= 
 binaryvr WITH     opinion  BY       bcovar   .
* within the graph editor, select fit line for subgroups to get the lines to connect the dots and remove label.

* slide 42.
GGRAPH
  /GRAPHDATASET NAME="GraphDataset" VARIABLES= opinion bcovar binaryvr
  /GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource( id( "GraphDataset" ) )
DATA: opinion=col( source(s), name( "opinion" ) )
DATA: bcovar=col( source(s), name( "bcovar" ), unit.category() )
DATA: binaryvr = col(source(s), name("binaryvr"), unit.category())
GUIDE: axis( dim( 1 ), label( "binaryvr" ) )
GUIDE: axis( dim( 2 ), label( "opinion" ) )
ELEMENT: point( position( binaryvr * opinion )) , shape(bcovar))
ELEMENT: line(position(smooth.linear(binaryvr * opinion)), shape(bcovar))
END GPL.

* binary by three-level.
* slide 46.
dataset activate hsbm.
glm opinion by binaryvr detail3
    /design = binaryvr detail3 binaryvr*detail3
    /print = parameter
    /emmeans = tables(binaryvr*detail3)
    /plot = profile(binaryvr*detail3) errorbar = se.

* slide 51.
dataset activate hsbm.
process y = opinion /x = binaryvr /w = detail3 /mcw = 1 /model = 1 /plot = 1. 

DATA LIST FREE/ 
   binaryvr   detail3    opinion    . 
BEGIN DATA. 
     1.0000      .0000    43.9804 
     2.0000      .0000    47.2500 
     1.0000     1.0000    51.3500 
     2.0000     1.0000    53.3333 
     1.0000     2.0000    59.7193 
     2.0000     2.0000    58.7692 
END DATA. 
GRAPH/SCATTERPLOT= 
 binaryvr WITH     opinion  BY       detail3  . 

* binary by continuous interaction.
* slide 58.
dataset activate hsbm.
glm opinion with age binaryvr
    /design = age binaryvr age*binaryvr
    /print = parameter.

* slide 62.
dataset activate hsbm.
process y = opinion /x = age /w = binaryvr /model = 1 /plot = 1. 

DATA LIST FREE/ 
   age        binaryvr   opinion    . 
BEGIN DATA. 
    41.0000      .0000    43.7102 
    54.0000      .0000    53.2345 
    62.0000      .0000    59.0956 
    41.0000     1.0000    52.2194 
    54.0000     1.0000    54.5113 
    62.0000     1.0000    55.9217 
END DATA. 
GRAPH/SCATTERPLOT= 
 age      WITH     opinion  BY       binaryvr . 

* where do the values of age of 41, 54 and 62 come from?.
* they are the 16th, 50th and 84th percentile.
* slide 67.
dataset activate hsbm.
freq var = age
    /statistics = median
    /percentiles = 16.0 84.0
    /format = notable.

* continuous by 3-level.
* slide 70.
dataset activate hsbm.
glm opinion with age by detail3
    /design = age detail3 age*detail3
    /print = parameter.

* we can see from the above output that the interaction term is not stat sig.
* so we will include the intprobe = 1 option.
* slide 73.
dataset activate hsbm.
process y = opinion /x = age /w = detail3 /mcw = 1 /model = 1 /plot = 1 /intprobe = 1. 

DATA LIST FREE/ 
   age        detail3    opinion    . 
BEGIN DATA. 
    41.0000      .0000    42.0830 
    54.0000      .0000    48.2141 
    62.0000      .0000    51.9870 
    41.0000     1.0000    46.9477 
    54.0000     1.0000    52.4784 
    62.0000     1.0000    55.8819 
    41.0000     2.0000    48.5857 
    54.0000     2.0000    56.4992 
    62.0000     2.0000    61.3690 
END DATA. 
GRAPH/SCATTERPLOT= 
 age      WITH     opinion  BY       detail3  .

* two continuous predictors with the pick-a-point approach.
* slide 81.
dataset activate exercise.
process y = loss /x = hours /w = effort /model = 1 /plot = 1.

DATA LIST FREE/ 
   hours      effort     loss       . 
BEGIN DATA. 
     1.5165    24.5391     6.2483 
     2.0051    24.5391     6.3835 
     2.4859    24.5391     6.5165 
     1.5165    29.6340     8.8784 
     2.0051    29.6340     9.9930 
     2.4859    29.6340    11.0894 
     1.5165    34.7141    11.5008 
     2.0051    34.7141    13.5919 
     2.4859    34.7141    15.6489 
END DATA. 
GRAPH/SCATTERPLOT= 
 hours    WITH     loss     BY       effort   .

* adds a small section to the output.
* slide 90.
dataset activate exercise.
process y = loss /x = hours /w = effort /model = 1 /plot = 1 /jn = 1.

* moments = 1 means that the mean, +/- sd are used instead of percentiles 16, 50, 84.
* slide 92.
dataset activate exercise. 
means var = effort.
process y = loss /x = hours /w = effort /model = 1 /plot = 1 /moments = 1.

* wmodvalu = #, # allows the user to provide values at which the predicted values are calculated.
* slide 96.
dataset activate exercise.
process y = loss /x = hours /w = effort /model = 1 /plot = 1 /wmodval = 15,40. 

* the resulting graphing commands.
* same as before, as expected.
DATA LIST FREE/ 
   hours      effort     loss       . 
BEGIN DATA. 
     1.5165    15.0000     1.3241 
     2.0051    15.0000     -.3743 
     2.4859    15.0000    -2.0451 
     1.5165    40.0000    14.2295 
     2.0051    40.0000    17.3366 
     2.4859    40.0000    20.3932 
END DATA. 
GRAPH/SCATTERPLOT= 
 hours    WITH     loss     BY       effort   .

* slide 100.
GGRAPH
  /GRAPHDATASET NAME="GraphDataset" VARIABLES= hours loss effort
  /GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource( id( "GraphDataset" ) )
DATA: loss=col( source(s), name( "loss" ) )
DATA: hours=col( source(s), name( "hours" ), unit.category() )
DATA: effort = col(source(s), name("effort"), unit.category())
GUIDE: axis( dim( 1 ), label( "hours" ) )
GUIDE: axis( dim( 2 ), label( "effort" ) )
ELEMENT: point( position( hours * loss )) , shape(effort))
ELEMENT: line(position(smooth.linear(hours * loss)), color(effort))
END GPL.

* two interaction terms in the same model.
 * slide 105.
dataset activate exercise.
glm loss with hours effort female
    /design = hours effort female hours*effort hours*female 
    /print  = parameter.
 
* slide 108.
dataset activate exercise.
process y = loss /x = hours /w = effort /z = female /model = 2 /intprobe = 1 /plot = 1.

DATA LIST FREE/ 
   hours      effort     female     loss       . 
BEGIN DATA. 
     1.5165    24.5391      .0000     6.8235 
     2.0051    24.5391      .0000     6.3407 
     2.4859    24.5391      .0000     5.8658 
     1.5165    24.5391     1.0000     5.7279 
     2.0051    24.5391     1.0000     6.4139 
     2.4859    24.5391     1.0000     7.0888 
     1.5165    29.6340      .0000     9.4508 
     2.0051    29.6340      .0000     9.9717 
     2.4859    29.6340      .0000    10.4841 
     1.5165    29.6340     1.0000     8.3552 
     2.0051    29.6340     1.0000    10.0449 
     2.4859    29.6340     1.0000    11.7071 
     1.5165    34.7141      .0000    12.0705 
     2.0051    34.7141      .0000    13.5921 
     2.4859    34.7141      .0000    15.0889 
     1.5165    34.7141     1.0000    10.9748 
     2.0051    34.7141     1.0000    13.6653 
     2.4859    34.7141     1.0000    16.3119 
END DATA. 
GRAPH/SCATTERPLOT= 
 hours    WITH     loss     BY       effort   /PANEL   ROWVAR=  female   .

* a three-way interaction.
* slide 122.
dataset activate exercise.
process y = loss /x = hours /w = effort /z = female /model = 3 /plot = 1.

DATA LIST FREE/ 
   hours      effort     female     loss       . 
BEGIN DATA. 
     1.5165    24.5391      .0000     6.5816 
     2.0051    24.5391      .0000     5.9660 
     2.4859    24.5391      .0000     5.3604 
     1.5165    24.5391     1.0000     5.9598 
     2.0051    24.5391     1.0000     6.7880 
     2.4859    24.5391     1.0000     7.6028 
     1.5165    29.6340      .0000     9.4641 
     2.0051    29.6340      .0000     9.9590 
     2.4859    29.6340      .0000    10.4457 
     1.5165    29.6340     1.0000     8.3551 
     2.0051    29.6340     1.0000    10.0374 
     2.4859    29.6340     1.0000    11.6924 
     1.5165    34.7141      .0000    12.3383 
     2.0051    34.7141      .0000    13.9403 
     2.4859    34.7141      .0000    15.5162 
     1.5165    34.7141     1.0000    10.7434 
     2.0051    34.7141     1.0000    13.2773 
     2.4859    34.7141     1.0000    15.7699 
END DATA. 
GRAPH/SCATTERPLOT= 
 hours    WITH     loss     BY       effort   /PANEL   ROWVAR=  female   .

* adding covariates.
* slide 131.
dataset activate hsbm.
process y = opinion /x = detail /w = age /cov = ccovar bcovar /model = 1 /plot = 1.

* using heterscedastic SEs.
* slide 134.
dataset activate hsbm.
process y = opinion /x = detail /w = age /hc = 4 /model = 1 /plot = 1.

* changing the number of decimal places shown, decimals = F8.4.
* slide 136.
dataset activate hsbm.
process y = opinion /x = detail /w = age /decimals = f10.2 /model = 1 /plot = 1.