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Selecting Domain-Specific Informants

This brings up a really interesting use of consensus analysis: selecting domain-specific informants. Table 16.13, from Weller and Romney (1988), shows the number of informants you need to produce valid and reliable data about particular cultural domains, given that the three conditions of the model are more-or-less met. (I say ‘‘more-or-less’’ because the model is very robust, which means that it produces very similar answers even when its conditions are more-or-less, not perfectly, met.) Just 10 informants, with an average competence of .7 have a 99% probability of answering each question on a true- false test correctly, with a confidence level of .95. Only 13 informants, with a relatively low average competence of .5 are needed if you want a 90% probability of answering each question on a test correctly, with a confidence level of .95.

And, as table 16.14 shows, when you have interval level data, if you interview 10 informants whose responses correlate .49, then the aggregate of their answers are likely to correlate .95 with the true answers. Consensus analysis, shows that: (1) Only a relatively small sample of informants are needed for studying particular cultural domains; and (2)

Informant

Reason

REC

SOR

HEL

CUS

REL

EAT

FRI

GIF

CON

GOO

CHU

COF

1

4

1

2

1

6

12

11

8

5

10

3

9

2

1

5

12

4

11

7

3

2

8

9

10

6

3

4

5

3

9

2

12

11

8

6

2

10

7

4

3

4

2

10

5

12

6

7

1

11

9

8

5

2

11

3

10

4

12

5

6

1

7

8

9

6

3

8

9

2

7

12

10

6

5

1

4

1

7

1

8

10

6

5

12

7

4

2

9

3

11

8

3

7

5

8

1

9

2

11

4

10

6

12

9

3

2

4

11

1

12

8

5

7

10

9

6

10

1

5

3

2

10

12

11

8

4

7

6

9

11

2

4

7

1

5

12

11

6

3

8

9

10

12

1

2

4

6

9

12

8

5

7

3

10

11

13

3

4

1

5

11

9

7

6

8

2

12

10

14

4

10

2

6

1

12

7

9

3

8

5

11

15

2

5

3

1

11

12

10

9

4

6

8

7

16

5

3

6

4

11

12

8

7

1

2

9

10

17

7

4

8

6

9

12

10

3

5

1

2

11

18

1

8

3

6

5

12

4

11

2

7

9

10

19

5

4

1

8

9

12

3

7

10

11

2

6

20

1

10

2

11

8

12

5

4

6

3

9

7

21

2

5

8

7

6

12

1

11

4

9

3

10

22

3

4

2

1

6

12

7

9

5

11

8

10

23

1

3

5

11

7

12

10

2

4

6

8

9

24

12

10

5

8

1

11

2

7

3

4

6

9

25

2

1

7

3

4

12

8

10

5

9

6

11

26

1

7

3

2

5

9

8

6

4

10

12

11

27

1

4

2

9

5

12

8

3

11

7

6

10

28

1

7

4

2

5

12

9

11

10

8

3

6

29

3

4

9

5

1

12

2

8

6

10

7

11

30

1

2

4

6

5

12

11

8

3

7

9

10

The list of reasons are: REC = reciprocity (attending a specific person's funeral so that his or her family members will attend your family's funerals), SOR = sorrow, HEL = to help the family of the deceased with funeral preparations, CUS = going out of custom, REL = going because ego is a relative of the deceased, EAT = to eat the requisite funeral feast, FRI = attending because ego is a friend of the deceased, GIF = to bring gifts to the family of the deceased, CON = to console the family of the deceased, GOO = to say good-bye to the deceased, CFIU = going because ego is a member of the same church as the deceased, COF = to carry the coffin.

SOURCE: A. Kis, NAPA Bulletin 27 pp. 129-140, 2007. Table 1, p. 134. Used by permission.

Table 16.12 Correlation Matrix for the First 10 Rows of Table 16.11

1

2

3

4

5

6

7

8

9

10

1

1.000

-0.231

0.399

0.650

0.273

0.245

0.406

0.336

0.601

0.699

2

-0.231

1.000

-0.364

-0.028

-0.098

0.070

0.273

-0.147

-0.007

0.105

3

0.399

-0.364

1.000

0.420

0.462

0.378

0.084

0.175

0.587

0.364

4

0.650

-0.028

0.420

1.000

0.748

-0.049

0.441

0.601

0.741

0.455

5

0.273

-0.098

0.462

0.748

1.000

0.245

0.587

0.622

0.448

0.336

6

0.245

0.070

0.378

-0.049

0.245

1.000

0.608

0.042

-0.112

0.622

7

0.406

0.273

0.084

0.441

0.587

0.608

1.000

0.483

0.280

0.469

8

0.336

-0.147

0.175

0.601

0.622

0.042

0.483

1.000

0.406

0.140

9

0.601

-0.007

0.587

0.741

0.448

-0.112

0.280

0.406

1.000

0.196

10

0.699

0.105

0.364

0.455

0.336

0.622

0.469

0.140

0.196

1.000

FIGURE 16.22.

Factor analysis of the complete 30 X 30 matrix implied by table 16.12.

Table 16.13 Minimal Number of Informants Needed to Classify a Desired Proportion of Questions with a Specified Confidence Level for Different Levels of Cultural Competence

Proportion of questions

Average level of cultural competence

.5

.6

.7

.8

.9

.95 confidence level

0.80

9

7

4

4

4

0.85

11

7

4

4

4

0.90

13

9

6

4

4

0.95

17

11

6

6

4

0.99

29

19

10

8

4

.99 confidence level

0.80

15

10

5

4

4

0.85

15

10

7

5

4

0.90

21

12

7

5

4

0.95

23

14

9

7

4

0.99

>30

20

13

8

6

SOURCE: S. C. Weller and A. K. Romney, Systematic Data Collection, p. 77. © 1988. Reprinted by permission of Sage Publications.

There will be variation in knowledge among informants who are competent in a cultural domain.

Consensus analysis is great for finding top people who can talk about well-defined areas of cultural knowledge. But if you are doing general descriptive ethnography and you’re looking for all-around good informants, consensus analysis is not a substitute for

Table 16.14 Agreement among Individuals and Estimated Validity of Aggregating Their Responses for Different Samples

Agreement

Validity

0.80

0.85

0.90

0.95

0.99

0.16

10

14

22

49

257

0.25

5

8

13

28

148

0.36

3

5

8

17

87

0.49

2

3

4

10

51

SOURCE: S. C. Weller and A. K. Romney, Systematic Data Collection, p. 77. © 1988. Reprinted by permission of Sage Publications.

the time-honored way that ethnographers have always chosen key informants: luck, intuition, and hard work by both parties to achieve a working relationship based on trust (Further Reading: consensus analysis).

 
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