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Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence by Judith D. Singer and John B. Willett Chapter 2: Exploring Longitudinal Data on Change | SPSS Textbook Examples


Figure 2.1, page 18

get file ="c:aldatolerance.sav".
list cases.

      ID    TOL11    TOL12    TOL13    TOL14    TOL15     MALE EXPOSURE
    9.00     2.23     1.79     1.90     2.12     2.66      .00     1.54
   45.00     1.12     1.45     1.45     1.45     1.99     1.00     1.16
  268.00     1.45     1.34     1.99     1.79     1.34     1.00      .90
  314.00     1.22     1.22     1.55     1.12     1.12      .00      .81
  442.00     1.45     1.99     1.45     1.67     1.90      .00     1.13
  514.00     1.34     1.67     2.23     2.12     2.44     1.00      .90
  569.00     1.79     1.90     1.90     1.99     1.99      .00     1.99
  624.00     1.12     1.12     1.22     1.12     1.22     1.00      .98
  723.00     1.22     1.34     1.12     1.00     1.12      .00      .81
  918.00     1.00     1.00     1.22     1.99     1.22      .00     1.21
  949.00     1.99     1.55     1.12     1.45     1.55     1.00      .93
  978.00     1.22     1.34     2.12     3.46     3.32     1.00     1.59
 1105.00     1.34     1.90     1.99     1.90     2.12     1.00     1.38
 1542.00     1.22     1.22     1.99     1.79     2.12      .00     1.44
 1552.00     1.00     1.12     2.23     1.55     1.55      .00     1.04
 1653.00     1.11     1.11     1.34     1.55     2.12      .00     1.25

Number of cases read:  16    Number of cases listed:  16

Creating a person-period data set from a balanced person-level data set and bottom part of Figure 2.1 with person-period data.

* reshaping data into person period data file.
varstocases  
 /make tol from tol11 tol12 tol13 tol14 tol15
 /index=measure(5)
 /keep=id exposure male.
compute age=measure+10.
compute time=age-11.
execute.

list cases /var=id age tol male exposure.
      ID      AGE      TOL     MALE EXPOSURE

    9.00    11.00     2.23      .00     1.54
    9.00    12.00     1.79      .00     1.54
    9.00    13.00     1.90      .00     1.54
    9.00    14.00     2.12      .00     1.54
    9.00    15.00     2.66      .00     1.54
   45.00    11.00     1.12     1.00     1.16
   45.00    12.00     1.45     1.00     1.16
   45.00    13.00     1.45     1.00     1.16
   45.00    14.00     1.45     1.00     1.16
   45.00    15.00     1.99     1.00     1.16
 ...........................................
 1653.00    11.00     1.11      .00     1.25
 1653.00    12.00     1.11      .00     1.25
 1653.00    13.00     1.34      .00     1.25
 1653.00    14.00     1.55      .00     1.25
 1653.00    15.00     2.12      .00     1.25

Number of cases read:  80    Number of cases listed:  80

Table 2.1 on page 20, estimated bivariate correlations among tolerance scores.

corr /var=tol11 to tol15.

Image


Figure 2.2 , page 25

formats age tol (f3.0) id (f2.0).
GGRAPH
  /GRAPHDATASET NAME="GraphDataset" VARIABLES= tol age id 
  /GRAPHSPEC SOURCE=INLINE INLINETEMPLATE=[ "<setWrapPanels/>"].
BEGIN GPL
SOURCE: s=userSource( id( "GraphDataset" ) )
DATA: tol=col( source(s), name( "tol" ) )
DATA: age=col( source(s), name( "age" ) )
DATA: id=col( source(s), name( "id" ), unit.category() )
GUIDE: text.title( label( "Figure 2.2" ) )
GUIDE: axis( dim( 1 ), label( "age" ) )
GUIDE: axis( dim( 2 ), label( "tol" ) )
GUIDE: axis( dim( 3 ), label( "id" ), opposite() )
SCALE: linear( dim( 1 ), min( 11 ), max( 15 ) )
SCALE: linear( dim( 2 ), min( 0 ), max( 4 ) )
ELEMENT: point( position( summary.mode( age * tol * id ) ) )
END GPL.

Image


Figure 2.3, page 27

GGRAPH
  /GRAPHDATASET NAME="GraphDataset" VARIABLES= tol age id 
  /GRAPHSPEC SOURCE=INLINE INLINETEMPLATE=["<setWrapPanels/>"].
BEGIN GPL
SOURCE: s=userSource( id( "GraphDataset" ) )
DATA: tol=col( source(s), name( "tol" ) )
DATA: age=col( source(s), name( "age" ) )
DATA: id=col( source(s), name( "id" ), unit.category() )
GUIDE: text.title( label( "Figure 2.3" ) )
GUIDE: axis( dim( 1 ), label( "age" ) )
GUIDE: axis( dim( 2 ), label( "tol" ) )
GUIDE: axis( dim( 3 ), label( "id" ), opposite() )
SCALE: linear( dim( 1 ), min( 11 ), max( 15 ) )
SCALE: linear( dim( 2 ), min( 0 ), max( 4 ) )
ELEMENT: line( position( smooth.spline( summary.mode( age * tol * id  ) ) ))
ELEMENT: point( position( summary.mode( age * tol * id ) ) )
END GPL.

Image


Table 2.2, page 30

Separate regressions for Table 2.2. The first table of Model Summary gives the R-square column. The second table of ANOVA gives the residual variance column which is the Mean Square column for residuals. The last table of Coefficients gives the columns for Initial status and for the rate of change. The last two columns of Table 2.2 can be obtained from the original data set.

sort cases by id.
split file by id.
regress /dep=tol /meth=enter time.
split file off.

Image Image Image


Figure 2.4 on page 31, the top part. We can make use of the option from regression to save the parameter estimates to a data file and use this data set for the stem-and-leaf plots. The data set does not contain either R-square or the residual variance for the bottom part of Figure 2.4. So we skip the bottom part now.

GET FILE='D:aldaatsdataspsstolerance_pp.sav'.
sort cases by id.
split file by id.

REGRESSION
  /DEPENDENT toleranc
  /METHOD=ENTER time
  /OUTFILE=COVB('D:aldaspsstable2.2.sav') .
GET FILE='D:aldaspsstable2.2.sav'.
USE ALL.
COMPUTE filter_$=(rowtype_ = "EST").
FILTER BY filter_$.
EXECUTE .
examine variables=const_ time/plot=stemleaf.

Constant

Constant Stem-and-Leaf Plot
 Frequency    Stem &  Leaf
     1.00        0 .  9
     9.00        1 .  001111234
     6.00        1 .  555789
 Stem width:      1.00
 Each leaf:       1 case(s)

TIME

TIME Stem-and-Leaf Plot
 Frequency    Stem &  Leaf
     2.00       -0 .  59
     1.00       -0 .  3
     3.00        0 .  224
     1.00        0 .  5
     2.00        1 .  14
     3.00        1 .  557
     2.00        2 .  34
     1.00        2 .  6
     1.00 Extremes    (>=.63)
 Stem width:       .10
 Each leaf:       1 case(s)

Figure 2.5, page 32. 

formats age tol (f3.0).
GGRAPH
  /GRAPHDATASET NAME="GraphDataset" VARIABLES= tol age id 
  /GRAPHSPEC SOURCE=INLINE INLINETEMPLATE=["<setWrapPanels/>"].
BEGIN GPL
SOURCE: s=userSource( id( "GraphDataset" ) )
DATA: tol=col( source(s), name( "tol" ) )
DATA: age=col( source(s), name( "age" ) )
DATA: id=col( source(s), name( "id" ), unit.category() )
GUIDE: text.title( label( "Figure 2.5" ) )
GUIDE: axis( dim( 1 ), label( "age" ) )
GUIDE: axis( dim( 2 ), label( "tol" ) )
GUIDE: axis( dim( 3 ), label( "id" ), opposite() )
SCALE: linear( dim( 1 ), min( 11 ), max( 15 ) )
SCALE: linear( dim( 2 ), min( 0 ), max( 4 ) )
ELEMENT: point( position( age * tol * id ) )
ELEMENT: line( position(smooth.linear( age * tol * id ) ))
END GPL.

Image



Figure 2.6, page 34, left-hand panel

format tol age (f2.0). 
GGRAPH 
  /GRAPHDATASET NAME="iGraphDataset" VARIABLES= tol age id 
  /GRAPHSPEC SOURCE=INLINE . 
BEGIN GPL 
SOURCE: s=userSource( id( "iGraphDataset" ) ) 
DATA: tol=col( source(s), name( "tol" ) ) 
DATA: age=col( source(s), name( "age" ) ) 
DATA: id=col( source(s), name( "id" ), unit.category() ) 
GUIDE: text.title( label( "Figure 2.6, left panel" ) ) 
GUIDE: axis( dim( 1 ), label( "age" ) ) 
GUIDE: axis( dim( 2 ), label( "tol" ) ) 
GUIDE: legend( aesthetic( aesthetic.shape.interior ), null() ) 
SCALE: linear( dim( 1 ), min( 11 ), max( 15 ) ) 
SCALE: linear( dim( 2 ), min( 0 ), max( 4 ) ) 
ELEMENT: line( position( smooth.linear( summary.mode( age * tol ) ) ), shape.interior( id )) 
ELEMENT: line( position( smooth.spline( summary.mean( age * tol ) ) ), color( color.red )) 
END GPL.

Image

Figure 2.6, page 34, right-hand panel

GGRAPH
  /GRAPHDATASET NAME="GraphDataset" VARIABLES= tol age id 
  /GRAPHSPEC SOURCE=INLINE .
BEGIN GPL
SOURCE: s=userSource( id( "GraphDataset" ) )
DATA: tol=col( source(s), name( "tol" ) )
DATA: age=col( source(s), name( "age" ) )
DATA: id=col( source(s), name( "id" ), unit.category() )
GUIDE: text.title( label( "Figure 2.6, right panel" ) )
GUIDE: axis( dim( 1 ), label( "age" ) )
GUIDE: axis( dim( 2 ), label( "tol" ) )
GUIDE: legend( aesthetic( aesthetic.shape.interior ), null() )
SCALE: linear( dim( 1 ), min( 11 ), max( 15 ) )
SCALE: linear( dim( 2 ), min( 0 ), max( 4 ) )
ELEMENT: line( position( smooth.linear( summary.mode( age * tol ) ) ), shape.interior( id ))
ELEMENT: line( position( summary.mean( age * tol ) ), color(color.red) )
END GPL.

Image


Table 2.3 on page 37. We have created a data set for Figure 2.4 and we can use it here.

GET FILE='D:aldaspsstable2.2.sav'.
USE ALL.
COMPUTE filter_$=(rowtype_ = "EST").
FILTER BY filter_$.
EXECUTE .
CORRELATIONS
  /VARIABLES=const_ time
  /PRINT=TWOTAIL NOSIG
  /STATISTICS DESCRIPTIVES.

Correlations

Image Image


Figure 2.7, page 38, upper portion of graph.

compute hiexp = 0.
if (exposure >= 1.145) hiexp = 1 .
execute.

value labels male 0 "male" 1 "female".
GGRAPH
  /GRAPHDATASET NAME="GraphDataset" VARIABLES= tol age id male 
  /GRAPHSPEC SOURCE=INLINE 
      INLINETEMPLATE=["<setWrapPanels/>"].
BEGIN GPL
SOURCE: s=userSource( id( "GraphDataset" ) )
DATA: tol=col( source(s), name( "tol" ) )
DATA: age=col( source(s), name( "age" ) )
DATA: id=col( source(s), name( "id" ), unit.category() )
DATA: male=col( source(s), name( "male" ), unit.category() )
GUIDE: text.title( label( "Figure 2.7, top row" ) )
GUIDE: axis( dim( 1 ), label( "age" ) )
GUIDE: axis( dim( 2 ), label( "tol" ) )
GUIDE: axis( dim( 3 ), label( "male" ), opposite() )
GUIDE: legend( aesthetic( aesthetic.shape.interior ), null() )
SCALE: linear( dim( 1 ), min( 11 ), max( 15 ) )
SCALE: linear( dim( 2 ), min( 0 ), max( 4 ) )
ELEMENT: line( position( smooth.linear( summary.mode( age * tol * male) ) ), shape.interior( id ))
END GPL.

Image

Figure 2.7, page 38, lower portion of graph.

compute hiexp = 0.
if (exposure >= 1.145) hiexp = 1 .
execute.
value labels hiexp 0 "Low exposure" 1 "High exposure".
GGRAPH
  /GRAPHDATASET NAME="GraphDataset" VARIABLES= tol age id hiexp 
  /GRAPHSPEC SOURCE=INLINE 
      INLINETEMPLATE=["<setWrapPanels/>"].
BEGIN GPL
SOURCE: s=userSource( id( "GraphDataset" ) )
DATA: tol=col( source(s), name( "tol" ) )
DATA: age=col( source(s), name( "age" ) )
DATA: id=col( source(s), name( "id" ), unit.category() )
DATA: hiexp=col( source(s), name( "hiexp" ), unit.category() )
GUIDE: text.title( label( "Figure 2.7, bottom row" ) )
GUIDE: axis( dim( 1 ), label( "age" ) )
GUIDE: axis( dim( 2 ), label( "tol" ) )
GUIDE: axis( dim( 3 ), label( "hiexp" ), opposite() )
GUIDE: legend( aesthetic( aesthetic.shape.interior ), null() )
SCALE: linear( dim( 1 ), min( 11 ), max( 15 ) )
SCALE: linear( dim( 2 ), min( 0 ), max( 4 ) )
ELEMENT: line( position( smooth.linear( summary.mode( age * tol * hiexp) ) ), shape.interior( id ))
END GPL.

Image


Figure 2.8 on page 40 can be created based on the data set we created for Figure 2.4. The data set is table2.2.sav. We will have to merge it back with the original data set to have all the variables in one data set.

GET
  FILE='D:aldaspsstable2.2.sav'.

FILTER OFF.
USE ALL.
SELECT IF(rowtype_ = "EST").
EXECUTE .

MATCH FILES /FILE=*
 /RENAME (depvar_ rowtype_ varname_ = d0 d1 d2)
 /FILE='D:aldaspsstolerance.sav'
 /RENAME (tol11 tol12 tol13 tol14 tol15 = d3 d4 d5 d6 d7)
 /BY id
 /DROP= d0 d1 d2 d3 d4 d5 d6 d7.
EXECUTE.
CORRELATIONS
  /VARIABLES=const_ time male exposure.

Image

rename variables CONST_ = const.
formats const (f4.1) male (f1.0).
exe.

GGRAPH 
  /GRAPHDATASET NAME="graphdataset" VARIABLES=male const 
  /GRAPHSPEC SOURCE=INLINE. 
BEGIN GPL 
SOURCE: s=userSource(id("graphdataset")) 
DATA: male=col(source(s), name("male"), unit.category()) 
DATA: const=col(source(s), name("const")) 
GUIDE: text.title( label( "Figure 2.8, top left" ) )
GUIDE: axis(dim(1), label("Male")) 
GUIDE: axis(dim(2), label("Constant")) 
GUIDE: text.footnote(label("r = 0.01")) 
SCALE: linear(dim(2), min(.5), max(2.5)) 
ELEMENT: point(position(male*const)) 
END GPL.

Image aldach2_8_a

formats exposure (f1.0).
GGRAPH 
  /GRAPHDATASET NAME="graphdataset" VARIABLES=exposure const 
  /GRAPHSPEC SOURCE=INLINE. 
BEGIN GPL 
SOURCE: s=userSource(id("graphdataset")) 
DATA: exposure=col(source(s), name("exposure")) 
DATA: const=col(source(s), name("const")) 
GUIDE: text.title( label( "Figure 2.8, top right" ) )
GUIDE: axis(dim(1), label("Exposure"), delta(1)) 
GUIDE: axis(dim(2), label("Constant")) 
GUIDE: text.footnote(label("r = 0.19")) 
SCALE: linear(dim(1), min(0), max(2)) 
SCALE: linear(dim(2), min(.5), max(2.5)) 
ELEMENT: point(position(exposure*const)) 
END GPL.

Image aldach2_8_b

formats time (f3.1).
GGRAPH 
  /GRAPHDATASET NAME="graphdataset" VARIABLES=male time 
  /GRAPHSPEC SOURCE=INLINE. 
BEGIN GPL 
SOURCE: s=userSource(id("graphdataset")) 
DATA: male=col(source(s), name("male"), unit.category()) 
DATA: time=col(source(s), name("time")) 
GUIDE: text.title( label( "Figure 2.8, bottom left" ) )
GUIDE: axis(dim(1), label("Male")) 
GUIDE: axis(dim(2), label("Time"), delta(.4)) 
GUIDE: text.footnote(label("r = 0.23")) 
SCALE: linear(dim(2), min(0), max(.8)) 
ELEMENT: point(position(male*time)) 
END GPL.

Image aldach2_8_c

GGRAPH 
  /GRAPHDATASET NAME="graphdataset" VARIABLES=exposure time 
  /GRAPHSPEC SOURCE=INLINE. 
BEGIN GPL 
SOURCE: s=userSource(id("graphdataset")) 
DATA: exposure=col(source(s), name("exposure")) 
DATA: time=col(source(s), name("time")) 
GUIDE: text.title( label( "Figure 2.8, bottom right" ) )
GUIDE: axis(dim(1), label("Exposure"), delta(1)) 
GUIDE: axis(dim(2), label("Time"), delta(.4)) 
GUIDE: text.footnote(label("r = 0.44")) 
SCALE: linear(dim(1), min(0), max(2)) 
SCALE: linear(dim(2), min(0), max(.8)) 
ELEMENT: point(position(exposure*time)) 
END GPL.

Image aldach2_8_d

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