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Furlow
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/ COMPARING INDICATORS OF KNOWLEDGE
ARTICLE
Comparing Indicators of Knowledge
within and between Cultural Domains
CHRISTOPHER A. FURLOW
University of Florida
This article examines the use of the cultural consensus model to estimate individual
informants’ knowledge of cultural domains. The author compares informants’
knowledge scores generated by the cultural consensus model derived from triad data
and rating data with each other and with free list lengths in two closely related
domains—brands of bicycles and the greatest cyclists of all time. Results indicate
that individual informant competency scores correlated at highly variable levels
(.11–.75). These results raise questions concerning the model’s validity related to
individual informant competency scores. More research is needed concerning the
range of domain consensus and interinformant variability in genuine knowledge that
produces valid informant competency scores and the impact that different research
instruments have on competency scores.
Keywords: intracultural variation; cultural consensus model; validity; informant
competency; cultural domain analysis
Intracultural variation in informants’ knowledge is a major area of concern
for anthropologists and other social researchers interested in accurately representing cultural domains. Of all the informants available to the researcher,
which individuals know the most about the specific topic of interest and how
can researchers determine this? One method developed to address this problem is the cultural consensus model. Romney, Weller, and Batchelder (1986)
proposed the cultural consensus model (1) to estimate the degree of consensus among informants about particular cultural domains, (2) to estimate the
correct answer to each question asked to informants, and (3) to estimate
informant knowledge or “cultural competency” within cultural domains. In
this article, I am interested in the use of the cultural consensus model to estimate individuals’ knowledge.
Anthropologists have applied the cultural consensus model to a wide variety of cultural domains including folk medicine (Weller 1984; Garro 1986,
I thank Mark Papa for assisting in data gathering and entry. I also thank Bryan Byrne, H. Russell
Bernard, and two anonymous reviewers for their comments, which greatly improved this article.
Field Methods, Vol. 15, No. 1, February 2003 51–62
DOI: 10.1177/1525822X02239570
© 2003 Sage Publications
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1988) and ethnobiology (Boster 1986; Boster and Johnson 1989) among others (see also Boster, Johnson, and Weller 1987; Romney, Batchelder, and
Weller 1987; Brewer, Romney, and Batchelder 1991; Furlow and Papa n.d.).
They have used a variety of data collection techniques, including true-false
(Batchelder and Romney 1986, 1988; Romney, Weller, and Batchelder
1986), multiple choice (Romney, Weller, and Batchelder 1986), fill in the
blank (Boster 1986), rank-order tasks (Romney, Batchelder, and Weller
1987; Brewer 1993, 1995), triads (Brewer 1995; Furlow and Papa n.d.), and
pile sorts (Boster, Johnson, and Weller 1987; Boster and Johnson 1989). In
each case, the cultural consensus model was successful.
The cultural consensus model has also been tested in other ways. Weller
(1984) and Brewer, Romney, and Batchelder (1991) have demonstrated that
the more internally consistent informants are more likely to agree with other
internally consistent informants. Likewise, Boster (1985) demonstrated the
reliability of the cultural consensus model using a test-retest research design.
In other studies that included multiple cultural domains, researchers have
shown that informant knowledge is domain specific. Thus, an informant’s
knowledge about animals has no relationship to that same individual’s
knowledge about things students do to get good grades (Brewer, Romney,
and Batchelder 1991). In contrast to the relationship between consistency
and consensus, test-retest reliability, and the relationship between informant
knowledge of different cultural domains, the validity of the cultural consensus model for measuring individual informants’ knowledge within a single
cultural domain is still relatively taken for granted.
Boster (1985) reported a correlation between informants’ agreement with
each other and test-retest reliability. Brewer (1995) attempted to evaluate
cultural competency scores generated using the cultural consensus model
through comparison with other indicators of knowledge. Specifically,
Brewer measured correlations between competency scores in five cultural
domains generated from triad tests and a ranking exercise with free listing
lengths, self-ratings of knowledge, and in some cases, self-reported recognition ability. Brewer concluded that these comparisons further validated the
cultural consensus model and that free list length may be a good proxy for
measuring informant knowledge. Brewer’s strongest evidence for the validity of the consensus model derives from the fact that the highest correlations
occurred between informant competency scores from matching tasks and the
various other indicators of knowledge because it is more difficult for informants to guess correctly on matching tasks.
It is important to note, however, that cultural competency scores from the
triad and ranking exercises correlated at relatively low levels with each other
and with other indicators of knowledge. Brewer (1995) hypothesized that the
Furlow / COMPARING INDICATORS OF KNOWLEDGE
53
low level may be due to minimal interinformant variation in knowledge and
that therefore variations in competency scores were due to guessing and sampling variability rather than being representative of real differences in
knowledge.
Brewer (1995) tested whether the competency scores represented genuine
intracultural variation by comparing the standard deviations in competency
scores with standard deviations from simulated informants with the same
underlying mean competency as observed among his real informants. If the
actual standard deviation was larger than the range of standard deviations
from ten simulations, Brewer considered this to represent genuine
intracultural variation in knowledge. Brewer concluded that four of five triad
tests and one of five ranking tests in both high salience and low salience lists
represented genuine variation.
However, even among the competency scores considered genuine, correlations were only modest. For example, the mean r of exercises with genuine
intracultural variation include r = .44 (seven triads × free list), r = .21 (seven
triads × self-rating), and r = .28 (three triads × recognition). Significantly,
while the mean correlation between triads competency and ranking competency was r = .29, the only correlation between competency scores of triad
and ranking exercises that Brewer (1995) considered genuine in the same
domain was at an r = .02 level.
Boster, Johnson, and Weller (1987) also compared informant competency
scores derived from free pile sort and triad data as part of a social networks
research project. They found that informants’ competency scores in the two
tasks correlated at a moderate level (r = .47). Thus, there is a significant need
for additional research.
In this article, I compare indicators of knowledge within and between two
closely related cultural domains—brands of bicycles (bike domain) and the
greatest cyclists of all time (cyclist domain). Specifically, I compare informants’ knowledge scores generated by the cultural consensus model derived
from triad data and rating data from seven-point Likert-type scales with each
other and with free list lengths.
METHOD
The research findings presented here are part of a larger cultural domain
analysis project among road cyclists in Gainesville, Florida (see Furlow and
Papa n.d.). As part of this larger project, I collected a variety of data, including demographic information, free list exercises, unconstrained pile sort
tasks, triad tests, and rating data on the bike and cyclist domains. Most data
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were gathered at weekly club meetings. During the first week, individuals
provided demographic information and completed free lists for both the bike
and cyclist domain. In subsequent weeks, individuals completed bike pile
sorts, cyclist pile sorts, bike triad tests, cyclist triad tests, and four ratings
tasks using seven-point Likert-type scales.
In the bike domain, twenty-five informants sorted thirty 3″ × 5″ index
cards containing the brand names of the thirty most frequently listed bicycles
from the free list exercise (all brands listed by at least 20% of free list respondents) into piles containing similar bikes using whatever criteria they wished;
the only restrictions were that they must have at least two piles and that each
bike could not be in a separate pile. Two weeks later, thirty-three informants
completed a triad test. The triad test included thirteen bicycle brands and used
a lambda 2 design generated using ANTHROPAC 4.0 (Burton and Nerlove
1976; Borgatti 1992). The thirteen brands were selected from the twenty-two
bicycle brands listed by at least 30% of respondents to maximize variation on
qualities such as cost, frame material, usage, and country of manufacture. In
subsequent weeks, informants completed three seven-point Likert-type scale
questionnaires to elicit rating information regarding prestige (twenty-five
respondents), expense (twenty-two respondents), and exoticness of frame
material (twenty-two respondents) for each of the thirty most frequently
listed bike brands.
In the cyclist domain, thirty-one informants were asked to sort fourteen 3″
× 5″ index cards containing the names of cyclists named by at least two of the
respondents in the free list exercise and were given the same instructions as
for the bike domain. Two weeks later, twenty-nine informants completed a
triad test that included the names of thirteen cyclists and used a lambda 2
design generated using ANTHROPAC 4.0 (Burton and Nerlove 1976;
Borgatti 1992). The triad test included ten cyclists named by at least three
respondents and three of the five cyclists named by two respondents. One
week later, twenty-seven informants completed a seven-point Likert-type
scale questionnaire to elicit rating information regarding the era in which
cyclists competed (i.e., how recently each cyclist raced).
The data were analyzed using ANTHROPAC 4.0 (Borgatti 1992), which
generated eigenvalues and eigenvalue ratios, an answer key, and informant
competency scores for triad and rating data in each domain.1 Table 1 presents
a summary of descriptive statistics for the two domains. Triad data were analyzed using the multiple-choice method, while rating data were analyzed
using the interval method. The informant competency scores on each task in
each domain were then compared using a Pearson product–moment correlation. Competency scores were then further compared with free list length (in
Furlow / COMPARING INDICATORS OF KNOWLEDGE
55
TABLE 1
Descriptive Statistics for Indicators of Knowledge
Variable
Triad eigenvalue ratio
Prestige rating eigenvalue ratio
Exoticness rating eigenvalue ratio
Expense rating eigenvalue ratio
Era rating eigenvalue ratio
Free listing length mean
SD
Length range
Bikes
Cyclists
4.942
12.776
9.945
14.954
—
21.68
11.54
5–60
3.869
—
—
—
20.829
3.57
2.94
0–12
NOTE: Dashes indicate that no data were gathered.
the bike domain only).2 Overall, thirty-eight informants participated in at
least one of the exercises.
Because the bike and cyclist domains are in essence subsets of a general
cycling domain, the competency scores were also compared across these two
closely related domains. For example, my experience among cyclists indicates that bikes, races, and racers are the most common topics of conversation. While I would never advocate comparing informant competency scores
about flowers and animals, comparing informant knowledge about bikes and
cyclists, especially among cultural specialists (i.e., racing cyclists who are
members of a cycling club) seems justified.
RESULTS
The informant competency scores on each of the six exercises are presented along with summary statistics in Table 2. Within the bike domain, the
results of the Pearson product–moment correlations (see Table 3) indicate
high variability among correlation levels between informant competency
scores on the five exercises. These scores ranged from an r = .11 correlation
between the competency scores on the prestige rating and free list tasks to an
r = .75 correlation between the expense and exoticness rating tasks, with a
mean r of .38. Within the cyclist domain, the Pearson product–moment correlation (see Table 3) was r = .45 between competency scores from the triad
and era-ranking tasks. The mean r for the eleven correlations is .39. Competency correlations between the bike and cyclist domains were smaller than
within domain correlations, with a mean r of .07 including negative correlations in four of ten cases.
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TABLE 2
Informant Competency Scores and Aggregate Statistics Calculated
by ANTHROPAC 4.0 from Bike Triads (BTR), Bike Prestige Vector (PRE),
Bike Exoticness Vector (EXO), Bike Expense Vector (EXP),
Cyclist Triads (CTR), and the Cyclist Era Vector (ERA)
Bikes
Cyclists
Informant
BTR
PRE
EXO
EXP
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
.65
.72
.44
.06
.39
.77
.19
.26
.10
.75
.26
.69
.67
.55
.57
.61
.68
.59
.57
.59
.75
.63
.65
.53
.35
.51
.68
.38
.69
.39
.62
—
.74
—
.73
—
.85
—
.89
.63
.68
.77
.85
.79
.80
.94
.89
.93
.82
.85
.87
.72
—
.90
.91
.72
.92
—
.58
—
—
.63
—
.66
—
—
—
—
.86
.76
—
.75
.84
.79
.94
.71
.89
.65
.87
.78
—
.85
.77
.91
.85
—
.77
—
.85
—
.81
.69
—
—
—
.58
—
.47
.76
—
—
.50
—
—
.87
—
—
.91
.82
.94
.93
.87
.85
.82
.89
.87
—
.96
.90
.92
.88
—
.79
—
.89
—
.90
.78
—
—
—
.61
—
.76
.90
—
—
.37
—
—
.91
—
—
.86
CTR
.41
.60
.60
.55
.35
.37
.30
.68
.35
.60
.70
.55
.60
.19
.13
.35
.49
.32
.17
.62
.48
—
—
.45
.41
—
—
.64
—
.04
.57
.55
.33
—
.44
—
ERA
.91
—
.94
.97
.91
.90
.96
.98
.98
.90
.94
.95
.91
.87
.77
.76
.93
.98
.92
.95
.93
—
—
.91
—
.88
—
.96
—
—
—
—
.96
.85
—
.73
Furlow / COMPARING INDICATORS OF KNOWLEDGE
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TABLE 2 (continued)
Bikes
Cyclists
Informant
BTR
PRE
EXO
EXP
M
SD
Minimum
Maximum
n
.54
.19
.06
.77
33
.80
.10
.58
.94
25
.78
.13
.47
.94
22
.84
.13
.37
.96
22
CTR
ERA
.44
.17
.04
.70
29
.91
.07
.73
.98
26
NOTE: Dashes indicate that no data were gathered.
TABLE 3
Pearson Product–Moment Correlations Calculated by ANTHROPAC 4.0
Using the Informant Competency Scores from Bike Triads (BTR),
Bike Prestige Rating (PRE), Bike Exoticness Rating (EXO),
Bike Expense Rating (EXP), Cyclist Triads (CTR), and the
Cyclist Era Vector (ERA) in Table 2 and Bike Free List Length (BFL)
BTR
PRE
EXO
EXP
BFL
CTR
ERA
BTR
PRE
EXO
EXP
BFL
CTR
ERA
—
.28
—
.13
.58
—
.16
.64
.75
—
.14
.11
.46
.54
—
–.01
–.16
.30
.54
–.04
—
–.36
.14
.00
.24
.04
.45
—
Gatewood (1984) originally suggested free list length as a good proxy for
domain knowledge. Brewer (1995) confirmed these findings despite relatively high variability in correlations between competency scores and free
list length. I also found correlations between individual competency scores
and free list lengths to be highly variable. The data in Table 3 indicate that for
the bike domain, free list length correlations within the bike domain range
between .11 and .54, with a mean r of .31. This is comparable to Brewer’s
mean r of .29 between free list length and triads/ranking competency scores.
The correlations between bike free list length and cyclist triads (r = –.04) and
era ratings (r = .04) were insignificant and may indicate that informant
knowledge may be domain specific even in the case of two closely related
domains.
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DISCUSSION
In their article outlining the cultural consensus theory, Romney, Weller,
and Batchelder (1986) defined validity to mean “that our measures relate in
known and precise ways to other variables that we accept as measuring substantially the ‘same’ thing as we think we are measuring” (p. 329). In other
words, two measures of the same thing, such as informant knowledge or cultural competence, correspond with each other or produce comparable findings. For example, the validity of the cultural consensus model’s informant
competency scores depends on the model’s ability to produce consistent
within-domain results—regardless of the instrument used to gather the data,
assuming the model is applicable to the data type.
The research presented here compares several indicators of informant
knowledge, including competency scores from six different exercises plus
one free list exercise. The validity of the model is based on the comparison of
indicators of informant knowledge within a single domain. Between-domain
consistency is irrelevant to the validity of the model as one would not expect
informants to be equally competent in all domains or perhaps even in closely
related domains such as bikes and cyclists. The data presented here, consisting of highly variable correlation levels (rs ranging from .11–.75), seemingly
raise questions concerning the validity of the cultural consensus model’s
competency scores. However, the issue of the potential impact of low genuine intracultural variability in informant competency must be addressed.
Brewer (1995) has indicated that informant competency scores correlate
weakly with other indicators of informant knowledge in cases where there
appears to be low intracultural variability in informant knowledge levels
(i.e., in which the effects of guessing and sampling could not be distinguished
from genuine differences in informant knowledge). I did not attempt to test
for low intracultural variation among my informants using the simulation
described in Brewer and first used by Weller (1987). However, I can say that
informant experience ranged from novice (less than six months of riding) to
long-term cyclists (more than a decade). In discussions and interviews, individuals displayed a wide range of knowledge abilities as judged by my own
decade of riding and racing experience at the time the research was conducted and based on formal and informal peer evaluations, including a focus
group.
Second, the lack of correlation between competency scores may be the
result of the inability of the tasks to capture the variability rather than a flaw
in the cultural consensus model itself. Brazill, Romney, and Batchelder
(1995), for example, found that interinformant reliability varied among four
methods used to collect perceived similarity data. Therefore, different tasks
Furlow / COMPARING INDICATORS OF KNOWLEDGE
59
may do a relatively better or worse job of capturing and distinguishing
between informants’ underlying knowledge. I believe that there was extensive intracultural variation in knowledge in this case, although I am not fully
convinced that the tasks were adequate in distinguishing cultural competency between individuals.
Clearly, more research is needed because the problem of a lack of genuine
intracultural variation in knowledge has significant implications for the cultural consensus model. For example, if (1) moderately high interinformant
consensus is required, by definition, for a cultural domain to exist but, at the
same time, precludes valid informant competency scores and (2) low
interinformant consensus precludes the existence of a cultural domain but, at
the same time, enables valid informant competency scores, that leaves only a
narrow range of variability between instance 1 and instance 2 (how narrow
remains to be determined) in which the model can determine that both a cultural domain does indeed exist and, at the same time, provide valid and therefore useful informant competency scores.
Perhaps the most useful product of consensus analysis is to assist
researchers in selecting the most knowledgeable informants who can then be
interviewed in depth when research time is limited. If individual informants’
competency scores are highly variable across data-gathering instruments
within a single domain, informant selection becomes problematic. On the
purely pragmatic level, however, I must point out that there may be a way
around this problem. In cases of moderately high to high interinformant consensus in which the validity of informant competency scores may be negatively affected by a lack of genuine intracultural variation in knowledge,
researchers may use the competency scores as a guide to whom to interview
in depth, with only minor concerns precisely because there are just minimal
differences in knowledge from one informant to the next.
It is important to restate that the cultural consensus model produces three
products. The first product is a score, based on the ratio between the first and
second eigenvalues of factor analysis, which measures overall domain consensus. The second product is an answer key for the test or research instrument used. The third product is a list of competency scores for each informant. I have addressed only the third product—individual informant
competency scores.
In summary, the results presented here suggest that individual informant
competency scores derived from a variety of instruments within two closely
related domains correlated at highly variable levels (.11–.75). These results
raise questions concerning the model’s validity related to individual informant competency scores, specifically concerning the range of domain consensus and interinformant variability in genuine knowledge that produces
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valid informant competency scores and the impact that different research
instruments have on competency scores. I conclude, then, by suggesting that
more research is needed to test the validity of the model using more tasks for
comparison with more items on each task and more difficult tasks such as fill
in the blank questions, matching, rank ordering, true-false, and multiplechoice questions, in addition to triads and ratings.
NOTES
1. Competency scores were also generated from the pile sort data. However, it is important to
note that the individual informant competency scores should not be used from single, free pile
sort data because these data may be bound up with lumper-splitter problems. To test for lumpersplitter influences, I compared competency scores from the free pile sorting exercises with the
number of piles informants made. These comparisons yielded some of the highest correlations
among any of the variables—.71 in the bike domain and .79 in the cyclist domain. Indeed, even
the between-domain correlations (i.e., bike pile sort competency and number of piles of cyclists
[.40] and cyclist pile sort competency and number of piles of bikes [.50]) were among the highest
correlations, as was the correlation between the bike pile sort and cyclist pile sort competency
scores (.49).
In addition, I compared competency scores derived from both the multiple-choice matching
and the covariance methods of consensus analysis as recommended in Romney (1999). The correlations were r = .23 and r = –.25 for the bike and cyclist domains, respectively, and the cyclist
domain’s ratio dropped to 1.306, indicating that the data violate the consensus model when analyzed using the covariance method. This indicates to me that the lumper-splitter problem significantly influenced individual competency scores for these free pile–sorting tasks and also was
responsible for the relatively high correlation between bike pile sort and cyclist pile sort competency scores.
The importance of not using competency scores derived from free pile sort data cannot be
emphasized enough because the only published materials that explicitly discuss cultural consensus analysis and pile sort data (Boster, Johnson, and Weller 1987; Boster and Johnson 1989) use
consensus analysis to measure and even to compare individual informant competency scores.
Boster and Johnson (1989) reported in a footnote that Romney recommended they use consensus
analysis on their pile sort data and on the pile sort data in their collaborative project with Weller
(Boster, Johnson, and Weller 1987). Given that two of the originators of the cultural consensus
model have either used or reportedly recommended the use of consensus analysis on free pile
sort data in the only published materials dealing directly with this topic, it is important to state
unequivocally that free pile sort data should not be used to compare individual competency
scores because individual differences in competency are overwhelmed by lumper-splitter
effects. By extension, therefore, these data may not be indicative of the model’s validity as a
whole (see Weller and Romney 1988; Boster and Johnson 1989).
2. It is important to note that the free-listing exercise for the cyclist domain is not a traditional
free-listing exercise in that it asked participants to list the greatest cyclists of all time rather than
all the cyclists the individual could think of, thus requiring informants to make a judgment based
on knowledge of cultural norms concerning what counts as greatness and, in effect, moderating
the importance of length as an indicator of knowledge. Because length is therefore not a reliable
Furlow / COMPARING INDICATORS OF KNOWLEDGE
61
indicator of knowledge, I did not compare competency scores on other tasks with the cyclist free
list lengths.
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CHRISTOPHER A. FURLOW is a doctoral student in the Department of Anthropology
at the University of Florida. His research interests include the anthropology of science,
technology, and knowledge; globalization and identity; Islamic science; and
intracultural variation. He is currently completing his dissertation, Islam, Science, and
Modernity: From Northern Virginia to Kuala Lumpur, and he is the author of “The
Islamization of Knowledge: Philosophy, Legitimation, and Politics” (1996, Social
Epistemology).