PROC MDS: Jacobowitz Body Parts Data from Children and Adults :: SAS/STAT(R) 9.2 User's Guide, Second Edition

The MDS Procedure

Example 53.1 Jacobowitz Body Parts Data from Children and Adults

Jacobowitz (1975) collected conditional rank-order data regarding perceived similarity of parts of the body from children of ages 6, 8, and 10 years and from college sophomores. The data set includes data from 15 children (6-year-olds) and 15 sophomores. The method of data collection and some results of an analysis are also described by Young (1987, pp. 4–10). The following statements create the input data set:

   data body;
      title  'Jacobowitz Body Parts Data from 6-Year-Olds and Adults';
      title2 'First 15 Subjects (obs 1-225) Are Children';
      title3 'Second 15 Subjects (obs 226-450) Are Adults';
      input  (Cheek Face Mouth Head Ear Body Arm Elbow Hand
              Palm Finger Leg Knee Foot Toe) (2.);
      if _n_ <= 225 then Subject='C'; else subject='A';
      datalines;
    0  2  1  3  4 10  5  9  6  7  8 11 12 13 14
    2  0 12  1 13  3  8 10 11  9  7  4  5  6 14
    3  2  0  1  4  9  5 11  6  7  8 10 13 12 14
   
   ... more lines ...   

   10 12 11 13  9 14  8  7  4  6  2  3  5  1  0
   ;

The data are analyzed as row conditional (CONDITION=ROW) at the ordinal level of measurement (LEVEL=ORDINAL) by using the weighted Euclidean model (COEF=DIAGONAL) in three dimensions (DIMENSION=3). The final estimates are displayed (PFINAL). The estimates (OUT=OUT) and fitted values (OUTRES=RES) are saved in output data sets. The following statements produce Output 53.1.1:

   ods graphics on;
   proc mds data=body condition=row level=ordinal coef=diagonal
            dimension=3 pfinal out=out outres=res;
      subject subject;
      title5 'Nonmetric Weighted MDS';
   run;
   ods graphics off;

Output 53.1.1 Analysis of Body Parts Data

Jacobowitz Body Parts Data from 6-Year-Olds and Adults

First 15 Subjects (obs 1-225) Are Children

Second 15 Subjects (obs 226-450) Are Adults

Nonmetric Weighted MDS

Multidimensional Scaling: Data=WORK.BODY.DATA

Shape=SQUARE Condition=ROW Level=ORDINAL

Coef=DIAGONAL Dimension=3 Formula=1 Fit=1

Mconverge=0.01 Gconverge=0.01 Maxiter=100 Over=2 Ridge=0.0001 Alternate=MATRIX

Iteration	Type	Badness- of-Fit Criterion	Change in Criterion	Convergence Measures
Iteration	Type	Badness- of-Fit Criterion	Change in Criterion	Monotone	Gradient
0	Initial	0.5938	.	.	.
1	Monotone	0.2344	0.3594	0.4693	0.4028
2	Gau-New	0.2080	0.0264	.	.
3	Monotone	0.1963	0.0118	0.0556	0.2630
4	Gau-New	0.1927	0.003592	.	.
5	Monotone	0.1797	0.0130	0.0463	0.1544
6	Gau-New	0.1779	0.001809	.	.
7	Monotone	0.1744	0.003430	0.0225	0.1210
8	Gau-New	0.1736	0.000807	.	.
9	Monotone	0.1717	0.001929	0.0161	0.1128
10	Gau-New	0.1712	0.000474	.	.
11	Monotone	0.1698	0.001413	0.0135	0.1119
12	Gau-New	0.1696	0.000188	.	.
13	Monotone	0.1684	0.001261	0.0121	0.1121
14	Gau-New	0.1683	0.000117	.	.
15	Monotone	0.1672	0.001096	0.0111	0.1064
16	Gau-New	0.1670	0.000131	.	.
17	Monotone	0.1661	0.000902	0.0103	0.0965
18	Gau-New	0.1660	0.000160	.	.
19	Monotone	0.1652	0.000736	0.009740	0.0980
20	Gau-New	0.1651	0.000169	.	0.1062
21	Gau-New	0.1645	0.000542	.	0.0161
22	Gau-New	0.1645	4.2645E-6	.	0.009969

Convergence criteria are satisfied.

Configuration
	Dim1	Dim2	Dim3
Cheek	0.25	1.47	2.06
Face	0.36	0.32	0.33
Mouth	0.78	0.08	1.08
Head	2.10	1.07	-0.01
Ear	0.26	0.72	-0.34
Body	1.51	-0.61	-0.68
Arm	-0.74	2.07	-0.59
Elbow	-0.51	-0.21	0.01
Hand	0.46	-1.50	-0.60
Palm	-1.44	-0.81	1.48
Finger	-0.24	-0.24	-0.81
Leg	-1.68	0.26	-0.05
Knee	-1.19	-1.19	-1.36
Foot	0.16	-0.03	-1.56
Toe	-0.10	-1.39	1.02

Dimension Coefficients
Subject	1	2	3
C	1.00	1.12	0.86
C	0.96	1.02	1.01
C	0.98	1.05	0.98
C	1.02	1.08	0.89
C	0.95	1.04	1.01
C	0.99	1.12	0.89
C	1.07	1.00	0.93
C	1.04	1.02	0.94
C	0.99	1.15	0.83
C	0.89	1.11	0.99
C	1.04	1.03	0.92
C	1.06	1.01	0.93
C	0.92	1.24	0.78
C	0.97	0.98	1.05
C	1.03	1.00	0.97
A	0.93	1.17	0.88
A	0.89	1.12	0.97
A	0.88	1.17	0.94
A	0.81	1.14	1.02
A	0.90	1.11	0.98
A	0.90	1.17	0.91
A	0.92	1.17	0.88
A	0.97	1.19	0.80
A	0.95	1.16	0.87
A	1.08	1.07	0.83
A	0.95	1.20	0.81
A	1.00	0.97	1.02
A	0.89	1.18	0.91
A	0.97	1.15	0.86
A	0.93	1.21	0.82

Subject	Number of Nonmissing Data	Weight	Badness-of-Fit Criterion	Distance Correlation	Uncorrected Distance Correlation
C	160	0.03	0.15	0.86	0.99
C	163	0.03	0.19	0.78	0.98
C	166	0.03	0.20	0.79	0.98
C	158	0.03	0.16	0.84	0.99
C	173	0.03	0.18	0.83	0.98
C	164	0.03	0.14	0.90	0.99
C	158	0.03	0.20	0.77	0.98
C	170	0.03	0.18	0.83	0.98
C	156	0.03	0.15	0.88	0.99
C	165	0.03	0.18	0.79	0.98
C	153	0.03	0.19	0.79	0.98
C	162	0.03	0.17	0.83	0.98
C	161	0.03	0.14	0.90	0.99
C	164	0.03	0.17	0.83	0.99
C	161	0.03	0.18	0.81	0.98
A	163	0.03	0.15	0.87	0.99
A	174	0.04	0.17	0.85	0.99
A	172	0.03	0.15	0.89	0.99
A	175	0.04	0.17	0.85	0.98
A	171	0.03	0.15	0.87	0.99
A	163	0.03	0.16	0.86	0.99
A	173	0.03	0.14	0.90	0.99
A	160	0.03	0.14	0.89	0.99
A	164	0.03	0.14	0.90	0.99
A	158	0.03	0.16	0.86	0.99
A	165	0.03	0.16	0.87	0.99
A	168	0.03	0.18	0.82	0.98
A	175	0.04	0.15	0.89	0.99
A	172	0.03	0.16	0.88	0.99
A	175	0.04	0.15	0.90	0.99
- All -	4962	1.00	0.16	0.85	0.99

Often the output of greatest interest in an MDS analysis is the graphical output. The first plots show two-dimensional view of the three-dimensional configuration. Next, the coefficients are plotted. The last plot is the fit plot.

In the fit plot, the transformed data are plotted on the vertical axis, and the distances from the model are plotted on the horizontal axis. If the model fits perfectly, all points lie on a diagonal line from lower left to upper right. The vertical departure of each point from this diagonal line represents the residual of the corresponding observation.

The configuration has a tripodal shape with Body at the apex. The three legs of the tripod can be distinguished in the plot of dimension 2 by dimension 1, which shows three distinct clusters with Body in the center. Dimension 1 separates head parts from arm and leg parts. Dimension 2 separates arm parts from leg parts. The plot of dimension 3 by dimension 1 shows the tripod from the side. Dimension 3 distinguishes the more inclusive body parts (at the top) from the less inclusive body parts (at the bottom).

The plots of dimension coefficients show that children differ from adults primarily in the emphasis given to dimension 2. Children give about the same weight (approximately 1) to each dimension. Adults are much more variable than children, but all have coefficients less than 1.0 for dimension 2, with an average of about 0.7. Referring back to the configuration plot, you can see that adults consider arm parts to be more similar to leg parts than children do. Many adults also give a high weight to dimension 1, indicating that they consider head parts to be more dissimilar from arm and leg parts than children do. Dimension 3 shows considerable variability for both children and adults.

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