27.10.2014 Views

Russel-Research-Method-in-Anthropology

Russel-Research-Method-in-Anthropology

Russel-Research-Method-in-Anthropology

SHOW MORE
SHOW LESS

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

688 Chapter 21<br />

each pair of items is for this set of <strong>in</strong>formants. If we run the MDS on the<br />

aggregate matrix we’d get a mental map for the group.<br />

Susan Weller (1983) asked 20 Guatemalan women to list as many illnesses<br />

as they could th<strong>in</strong>k of. Then she took the 27 most frequently named illnesses,<br />

put each named illness on a card, and asked 24 other women (some urban,<br />

some rural) to sort the cards <strong>in</strong>to piles, accord<strong>in</strong>g to similarity. Weller created<br />

an aggregate proximity matrix from the 24 <strong>in</strong>dividual matrices and did a multidimensional<br />

scal<strong>in</strong>g to represent how her <strong>in</strong>formants collectively perceived<br />

the 27 illnesses. Figure 21.6 shows the MDS graph for Weller’s urban sample<br />

RheumatismArthritis<br />

Allergies<br />

Rubella<br />

Small pox<br />

Measles Chicken pox<br />

Mumps<br />

Polio<br />

Whoop<strong>in</strong>g<br />

cough<br />

Typhoid<br />

Tonsilitis<br />

Flu<br />

Malaria<br />

Tuberculosis<br />

Kidney pa<strong>in</strong><br />

Diarrhea Intest<strong>in</strong>al<br />

<strong>in</strong>fection<br />

Gastritis<br />

Ameobas Colic<br />

Hepatitis<br />

Diphtheria<br />

Cancer<br />

Diabetes<br />

Tetanus<br />

Appendicitis<br />

Figure 21.6. MDS representation of 27 illnesses for urban Guatemalan women.<br />

SOURCE: S. Weller, ‘‘New Data on Intracultural Variability: The Hot-Cold Concept of Medic<strong>in</strong>e and Illness,’’<br />

Human Organization, Vol. 42, pp. 249–257. 1983. Repr<strong>in</strong>ted with permission of the Society for Applied<br />

<strong>Anthropology</strong>.<br />

of the pile-sort data. The MDS program converts similarities (percentages, <strong>in</strong><br />

this case) <strong>in</strong>to distances. The illness terms that were judged to be similar are<br />

closer together <strong>in</strong> figure 21.6, and the terms judged to be dissimilar by <strong>in</strong>formants<br />

are farther apart.<br />

This is the fun part. Look at figure 21.6. Keep look<strong>in</strong>g at it. Th<strong>in</strong>k about the<br />

distribution of those items (the illness names). Do you see any pattern? We’re<br />

look<strong>in</strong>g for arrays, ordimensions of illnesses across the plot and clumps, or<br />

clusters of illnesses scattered around the plot. S<strong>in</strong>ce the MDS program was<br />

able to plot the items <strong>in</strong> two dimensions with acceptably low stress, we are<br />

look<strong>in</strong>g for two arrays. There can be any number of clumps. Interpretation<br />

means figur<strong>in</strong>g out what the dimensions and the clumps are.<br />

Try nam<strong>in</strong>g the clusters <strong>in</strong> figure 21.6. There is a clump on the right that<br />

might be called ‘‘gastro<strong>in</strong>test<strong>in</strong>al disorders.’’ On the left there is a clump of

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!