Ethkt4 (version in 5.1 without some figures and tables. Mar. 4, 1998 version)
for Cultural Domain Analysis
by Stephen P. Borgatti, University of Kentucky
Daniel S. Halgin, University of Kentucky
[HEAD: LIST, BULLETS
ELICITING CULTURAL DOMAINS USING FREE LISTS
ELICITING DOMAIN STRUCTURE USING PILESORTS
ELICITING DOMAIN STRUCTURE USING TRIADS]
We are grateful to H. Russell Bernard, Pertti Pelto, A. Kimball Romney, and
Gery Ryan for helping shape our views on cultural domain analysis, which is not
to say they necessarily agree with anything we have written. We are also
grateful to Mark Fleisher and to John Gatewood for giving us permission to use
their data to illustrate concepts. Finally, we thank Jay Schensul and Marki
LeCompte for their many helpful comments on earlier drafts.
The techniques described in this chapter are used to understand cultural domains
[DEFINITION: MARGIN, A cultural domain is a set of items or things that are all of
the same type or category] (Lounsbury, 1964; Spradley 1979; Weller & Romney,
1988). A cultural domain is a mental category like “animals” or “illnesses”. It is a set of
items that are all alike in some important way. Humans in all cultures classify the world
around them into cognitive domains, and the way they do this affects the way they
interact with the world. Not all cultures classify things the same way. For example,
English-speakers recognize a category called “shrubs” which is different from “trees” and
“grasses”. But many other cultures do not recognize the “shrub” category at all: they
divide up the plant kingdom differently. Even when cultures have the same domains, the
contents may be somewhat different. For example, many cultures have a domain called
“illnesses”, but often these cultures include as illnesses things that most Americans would
regard as imaginary, such as “evil eye”, or things that Americans regard as symptoms,
such as “stomach pains”. Ethnographers often begin their studies by trying to identify and
describe the cultural domains that are used by the people they are studying.
The techniques described in this chapter are used to (a) elicit the items in a
cultural domain, (b) elicit the attributes and relations that structure the domain, and (c)
measure the positions of the items in the domain structure. These techniques, which
include freelists, pilesorts, triads, multidimensional scaling, and graph layout algorithms
have been incorporated into two commercially available computer programs called
Anthropac (Borgatti, 1992) and UCINET (Borgatti, Everett & Freeman, 2002).
3 Elicitation Techniques
Defining Cultural Domains
There are several ways to define a cultural domain. A good starting point is: a set
of items all of which a group of people define as belonging to the same type. For
example, “animals” is a cultural domain. The members of the domain of animals are all
the animals that have been named, such as dogs, cats, horses, lions, tigers, etc. But there
is more to the idea than just a set of items of the same type. Implicit in the notion is also
the idea that membership in the cultural domain is determined by more than the
individual respondent -- the domain exists “out there” either in the language, in the
culture or in nature. Hence, the set of colors that a given individual likes to wear is not
what we mean by a cultural domain.
One rule of thumb for distinguishing cultural domains from other lists is that
cultural domains are about people’s perceptions rather than people’s preferences. Hence,
“my favorite foods” is not a cultural domain, but “things that are edible” is. Another way
to put it is that cultural domains are about things “out there” in reality, so that, in
principle, questions about the members of a domain have a right answer. Consider, for
example, the cultural domain of animals. If asked whether a tiger is an animal, the
respondent feels that she is discussing a fact about the world outside, not about herself. In
contrast, if she is asked whether “vanilla” is one of her favorite ice cream flavors, the
respondent feels that she is revealing more about herself than about vanilla ice cream. In
this sense, cultural domains are experienced as outside the individual and shared across
Elicitation Techniques 4
The fact that cultural domains are shared across individuals does not mean that all
members of a given population are in complete agreement on which items belong to a
given cultural domain. The extent to which a cultural domain is actually shared in any
given population is an empirical question --- that is, a question that is open to testing.
Another aspect of cultural domains is that they have internal structure.
[DEFINITION:MARGIN: THE INTERNAL STRUCTURE OF A CULTURAL
DOMAIN REFERS TO THE RELATIONSHIPS THAT EXIST AMONG THE
ITEMS OR THINGS IN IT] That is, they are systems of items related by a web of
relationships. For example, in the domain of animals, some animals are understood to eat
other animals. The relation here is “eats”, and every pair of animals can be evaluated to
Conversely, simple agreement about a set of items does not imply that the set is a cultural
domain. If we ask 1,000 randomly sampled informants in our own culture about their ten
favorite foods and every one of them happens to give the same list, it is still not a cultural
domain because personal preferences are not the kind of thing which in principle
a cultural domain. In contrast, responses to the question, "What foods are preferred in
your community?" could
be a cultural domain.
Some people use the term “cognitive domain” to refer to
domains which are not necessarily shared. For example, a
psychologist might make an in-depth study of one person’s
understanding of nature. Since no other respondents were studied,
the psychologist might refer to the person’s categories as cognitive
domains rather than cultural domains. However, it is important to
realize that whether they are shared or not, cognitive domains have
all the same properties as cultural domains, including being
experienced as outside the individual, as outlined above. In this
sense, we can think of cognitive domains as the general category,
and cultural domains as a member of that category.
5 Elicitation Techniques
see if the first animal eats the second. Another relation applicable to animals, recognized
by biologists at least, is “competes with”.
A relation of particular importance, which seems to be common to all cultural
domains, is the relation of similarity. It appears that, for all cultural domains, respondents
can readily indicate which pairs of items they consider similar, and which they consider
dissimilar. Another relation that seems to apply to most domains is co-occurrence, as in
which foods “go with” which others, or which animals live in the same habitats with
Relations among things are a fundamental aspect of how humans think about the
world. Lists of “universal” relations have been made by many researchers, including
Casagrande and Hale (1967) and Spradley (1979). Spradley’s list includes [LIST:
--cause-and-effect (X causes Y, Y is the result of X),
--inclusion (X is a kind of Y),
--rationale (X is a reason for doing Y),
--means-end (X is a way to accomplish Y),
--sequence (X follows Y)
--function (X is used for Y)
--spatial (X is a part of Y), (X is a place in Y)
--attribution (X is a characteristic of Y)
--location for action (X is a place for doing Y)]
Elicitation Techniques 6
Most of these, however, are not relations among items in the same domain, but rather
relate the items from one domain to the items in another domain. For example, <italic>
location for action <italic> relates a place, such as "Madrid" with an activity, such as
"bullfighting"; places and activities typically belong to different cognitive domains.
Similarly, the cause of a given effect is not necessarily a member of the same cognitive
domain as the effect. For instance, making love may result in getting AIDS, but most
respondents think of these as belonging to different domains. In this chapter we
concentrate on only those relations that relate items within a single cognitive domain.
Other largely universal relations are semantic relations among the terms used to
label items in a cultural domain. These are relations such as synonymy (same meaning)
and antonymy (opposite meaning). For example, in the domain of illnesses, there is often
more than one term for a given illness (such as a folk name and a medical term). While
the line separating relations among terms
from relations among the items themselves may
be difficult to draw, in principle our interest here is in the relations among the items
rather than among the terms we use to describe them.
An important class of relations among items is the kind that can be reduced to a
single attribute. For example, in the domain of illnesses, some illnesses are seen as “more
contagious” than other illnesses. This relation is based on a single property of each illness
in the domain, which is how contagious it is. This is different from the relation of
(perceived) similarity, which is indivisible. We cannot attach a similarity score to an
individual item -- it is always attached to a pair. For example, we can say that the
similarity between “pneumonia” and “flu” is 8 on a scale of 1 to 10, but it doesn’t make
7 Elicitation Techniques
sense to assign a similarity score to just one of the illnesses by itself (as in, ‘the flu has a
similarity score of three’). In contrast, it does make sense to assign an individual illness a
contagious score: we don’t have to do it in pairs. The difference between attributes of
individual items and relations among pairs of items becomes more clear as we go along
in this chapter.
In general, an attribute that makes sense for some items in a cultural domain will
make sense for all items. In other words, if “sweetness” is a sensible attribute of fruit,
then it is meaningful to ask ‘how sweet is ____?’ of all fruit in the domain. If the attribute
cannot be applied to all items, this is sometimes because not all the items are at the same
level of contrast, which in turn means that there are subdomains. For example, if the
domain of “animals” contains the items “squirrel”, “ant”, and “mammal”, informants will
be confused if asked whether squirrels are faster than mammals. The real test for items of
different levels of contrast, however, is to look at the semantic “is a kind of” relation
(Casagrande and Hale, 1967; Spradley, 1979). If any item in a domain is a kind of any
other item in the domain (e.g., squirrel is a kind of mammal) then you know that the latter
item is actually a cover term (a gloss) for a subdomain. [DEFINITION: MARGIN, Cover
terms are summary terms encompassing all the items in a domain or subdomain]
Even if all the items are of the same level of contrast, however, the inability to
apply an attribute to all items is sufficient to suggest that the domain has a hierarchical
taxonomic structure and that the attribute belongs to items in one particular class. For
example, the attribute “shape of wings” can be applied to some animals, but not to others.
This means that the domain of animals contains at least two types — animals with wings
Elicitation Techniques 8
and animals without — and within the set of those with wings, we can ask what shape the
wings are (see Figure 1).
INSERT FIGURE 1 HERE
Eliciting Cultural Domains Using Freelists
The freelist technique is used to elicit the elements or members of a cultural
domain. For domains that have a name or are easily described, the technique is very
simple: just ask a set of informants to list all the members of the domain. For example,
you might ask them to list all the names of illnesses that they can recall. If you don’t
know the name of a domain, you may have to elicit that first. For example, you can ask
“what is a mango?” and very likely you will get a response like “it’s a kind of fruit”.
Then you can ask, “what other kinds of fruit are there?” Note that if a set of items does
not have a name in a given culture, it is likely that it is not (yet) a domain in that culture.
However, you can still obtain a list of related items by asking questions like “what else is
there that is like a mango?”.
At first glance, the freelist technique may appear to be the same as any open-
ended question, such as “What illnesses have you had?” The difference is that freelisting
is used to elicit cultural domains, and open-ended questions are used to elicit information
about individual informants (see Table 1). In principle, the freelists from different
respondents who belong to the same culture should be comparable and similar because
the stimulus question is about something outside themselves and which they have in
9 Elicitation Techniques
common with other members. In contrast, an open-ended question could easily generate
only unique answers.
INSERT TABLE 1 HERE
Collecting Freelist Data
Ordinarily, freelists are obtained as part of a semi-structured interview, not an
informal conversation. With literate informants, it is easiest to ask the respondents to
write down all the items they can think of, one item per line, on a piece of paper. The
exact same question is asked of the entire sample of respondents (see below for a
discussion of sample size). We then count the number of times each item is mentioned
and sort the items in order of decreasing frequency. For example, I asked 14
undergraduates at Boston College to list all the animals they could think of. On average,
each person listed 21.6 animal terms. The top twenty terms are given in Table 2.
INSERT TABLE 2 HERE
Elicitation Techniques 10
The number of informants needed to establish a cultural domain depends on the
amount of cultural consensus in the population of interest — if every informant gives the
exact same answers, you only need one — but a conventional rule-of-thumb is to obtain
lists from a minimum of 30 lists. One heuristic for determining whether it is necessary to
interview more informants, recommended by Gery Ryan
Dr. Ryan is a Senior Behavioral Scientist at RAND and an
Adjunct Assistant Professor in the Department of Psychiatry and
Biobehavioral Sciences at UCLA
(personal communication), is
to compute the frequency count after obtaining 20 or so lists from randomly chosen
informants, then repeat the count after 30 lists. If the relative frequencies of the top items
have not changed, this suggests that no more informants are needed. In contrast, if the
relative frequencies have changed, this indicates that the structure has not yet stabilized
and you need more informants. This procedure only works if the respondents are being
sampled at random from the population of interest. If, for example, the domain is
illnesses and the first 20 respondents are all nurses, the method might indicate that no
more respondents are needed. Yet if the results are intended to represent more than just
nurses, more (non-nurse) respondents will be needed.
11 Elicitation Techniques
The frequency of items is usually interpreted in terms of their salience to
informants. That is, items that are frequently mentioned are assumed to be highly salient
to respondents, so that few forget to mention those items. Another aspect of salience,
however, is how soon the respondent recalls the item. Items recalled first are assumed to
be more salient than items recalled last. The second column from the right in Table 2
gives the average position or rank of each item on each individual’s list. With sufficient
respondents (more than used in Table 2), it is often the case that a strong negative
correlation exists between the frequency of the items and their average rank, at least for
the items mentioned by a majority of respondents. This means that the higher the
probability that a respondent mentions an item, the more likely it is that they will mention
it early. This supports the notion of salience as a latent property that determines both
whether an item is mentioned and when. In recognition of this, some researchers like to
combine frequency and average rank into a single measure
Once the freelists have been collected and tabulated, it usually becomes apparent
that there are a few items that are mentioned by many respondents, and there are a huge
number of items that are mentioned by just one person. For example, I collected freelist
data on the domain of “bad words” from 92 undergraduate students at the University of
South Carolina. A total of 309 distinct items were obtained, of which 219 (71%) were
mentioned by only one person (see Figure 2). As discussed near the end of this section,
domains seem to have a core/periphery sort of structure with no absolute boundaries. The
One such measure, Smith’s S (Smith, 1997), is given in the rightmost column of Table 2. The measure is
essentially a frequency count that is weighted inversely by the rank of the item in each list. In practice,
Smith’s S tends to be very highly correlated with simple frequency.
Elicitation Techniques 12
more respondents you have, the longer the periphery (the right-hand tail in Figure 2)
grows, though ever more slowly.
INSERT FIGURE 2
From a practical point of view, of course, it is usually necessary to determine a
boundary for the domain one is studying. [LIST: BOX, BULLETS
Ways to Determine a Domain Boundary
--Include all items mentioned more by more than one
--Look for a natural break or grouping.
--Define a boundary arbitrarily.]
One natural approach is to count as members of the domain all items that are mentioned
by more than one respondent. This is logical because cultural domains are shared at least
to some extent, and it is hard to argue that an item mentioned by just one person is
shared. However, this approach usually does not cut down the number of items enough
for further research. Another approach is to look for a natural break or “elbow” in the
sorted list of frequencies.
Or salience, as captured by Smith’s S.
This is most easily done by plotting the frequencies in what is
known as a “scree plot” (see Figure 2). When such a break can be found it is very
convenient, and may well reflect a real difference between the culturally shared items of
the domain and the idiosyncratic items. But if no break is present, it is ultimately
necessary to arbitrarily choose the top N items, where N is the largest number you can
really handle in the remaining part of the study. In Figure 2, no really clear breaks are
13 Elicitation Techniques
present, but there are three “mini-breaks” that one might consider. In the sorted list of
words, they occur after the 20
, and 40
One problem that must be dealt with before computing frequencies is the
occurrence of synonyms, variant spellings, subdomain names, and the use of modifiers.
For instance, in the “bad words” domain, some of the terms elicited were “whore”, “ho”,
and “hore”. It seems likely that “whore” and “hore” are variant spellings of the same
word, and therefore pose no real dilemma. In contrast, “ho”, which was used primarily by
African-American students, could conceivably have a somewhat different meaning.
(There is always this potential when a word is used more often by one ethnic group than
by others.) Similarly, in the domain of animals, the terms “aardvark” and “anteater” are
synonymous for most people, but for some (including biologists), “anteater” refers to a
general class of animals of which the aardvark is just one. Whether they should be treated
as synonyms or not will depend on the purposes of the study. It may be necessary, before
continuing, to ask respondents whether “aardvark” means the same thing as “anteater”.
Occasionally, respondents will fall into a response set in which they list a class of
items separated by modifiers. For example, they may name “grizzly bear”, “Kodiak
bear”, “black bear”, and “brown bear”. Obviously, these constitute subclasses of bear that
are at a lower level of contrast than other terms in their lists. Occasionally, these kinds of
items may lead respondents to generalize the principle to other items, so that they then
list such items as “large dog”, “small dog”, and “hairless dog”. In general this is not a
problem because these kinds of items will be mentioned by just one person, and so will
be dropped from further consideration.
Elicitation Techniques 14
Analyzing Freelist Data
While the main purpose of the freelisting exercise is to obtain the membership list
for a domain, the lists can also be used as ends in themselves. That is, several interesting
analyses can be done with such lists.
INSERT TABLE 3 HERE
Once we have a master list of all items mentioned, we can arrange the freelist data
as a matrix in which the rows are informants and the columns are items (see Table 3).
The cells of the matrix can contain ones (if the respondent in a given row mentioned the
item in a given column) or zeros (that respondent did not mention that item). Taking
column sums of the matrix would give us the item frequencies. Taking column averages
would give us the proportion of respondents mentioning each item. Taking row sums
would give us the number of items in each person’s freelist.
The number of items in an individual’s freelist is interesting in itself. Although
perhaps confounded by such variables as respondent intelligence, motivation and
personality, it seems plausible that the number of items listed reflects a person’s
familiarity with the domain (Gatewood, 1984). For example, if we ask people to list all
sociological theories of deviance they can think of, we should expect to find that
professional sociologists have longer lists than most other people. Similarly, dog fanciers
are likely to produce longer lists of dog breeds than ordinary people. Yet length of list is
obviously not perfectly correlated with domain familiarity, as respondents who are
15 Elicitation Techniques
relatively unfamiliar with a domain can produce impressively long lists of very unusual
items -- items with which other respondents would not agree.
To construct a better measure of domain familiarity (or “cultural domain
competence”) we could weight the items in an individual freelist by the proportion of
respondents who mention the item. Adding up the weights of the items in a respondent’s
freelist then gives a convenient measure of what might be called “cultural competence”.
Respondents score high on this measure if they mention many high-frequency items and
avoid mentioning low-frequency items.
INSERT TABLE 4
INSERT TABLE 5
Another way to analyze freelist data -- now focusing on the items rather than the
respondents -- is to examine the co-occurrences among freelisted items. Table 4 gives an
excerpt from a respondent-by-item freelist matrix. There are four items labeled A through
D. Consider items A and B. Each are mentioned by four respondents. Three respondents
mention both of them. That is, A and B co-occur in three of the six freelists. By
comparing every pair of items, we can construct the item-by-item matrix given in Table
5. This matrix can then be displayed via multidimensional scaling (MDS), as shown in
Figure 3. In a multidimensional scaling map of this kind, two items are close together to
the extent that many respondents mentioned both items. Items that are far apart on the
map were rarely mentioned by the same respondents.
INSERT FIGURE 3
Elicitation Techniques 16
Typically, such maps will have a core/periphery structure in which the core
members of the domain (i.e., the most frequently mentioned) will be at the center, with
the rest of the items spreading away from the core and the most idiosyncratic items
located on the far periphery. The effect is similar to a fried egg.
While not an artifact, exactly, of the column sums of the matrix
(i.e., some items are mentioned more often than others), the
core/periphery structure of co-occurrence matrices is made visible
by not controlling for the sums. It is also useful to examine the
pattern obtained by controlling for these sums. One way to do this
is to simply compute Pearson correlations among the columns.
Another way is to count both matches of the ones and the zeros.
This occurs in part
because odd items can be odd in so many ways that they tend to be different from each
other as well. In contrast, core items are very similar to each other. Another factor is that
core items tend to be mentioned so much more often, there is a greater chance of
overlapping with other items.
17 Elicitation Techniques
There are a number of other ways to analyze freelist data. As Henley (1969)
noticed, the order in which items are listed by individual respondents is not arbitrary.
Typically, respondents produce runs of similar items separated by visible pauses. Even if
we do not record the timing, we can recover a great deal of information about the
cognitive structuring of the domain by examining the relative position of items on the list.
Two factors seem to affect position on the list. First, as mentioned earlier, the more
central items tend to occur first. When we ask North Americans to list all animals, “cat”
and “dog” tend to be at the top of each person’s list, and they tend to be mentioned by
A second pattern is that related items tend to be mentioned near each other (i.e.,
the difference in their ranks is small). Hence, we can use the differences in ranks for each
pair of items as a rough indicator of the cognitive similarity of the items. To do this, we
construct a new person-by-item matrix in which the cells contain ranks rather than ones
and zeros. For example, if respondent “Jim” listed item “Deer” as the 7
item on his
freelist, then we would enter a “7" in the cell corresponding to his row and the deer
column. If a second respondent named "Fred" did not mention an item at all, we enter a
special code in Fred's column denoting a missing value (NOT a zero). Then we compute
correlations (or distances) among the columns of the matrix. The result is an item-by-item
matrix indicating how similarly items are positioned in different people’s lists, when they
occur at all. This can then be displayed using multidimensional scaling. It should be
noted, however, that if uncovering similarities is the primary interest of the study, it
would be wiser to use more direct methods, such as those outlined in the next section.
Elicitation Techniques 18
It should also be noted that while we reserve the term “freelisting” for the
relatively formal elicitation task described here, the basic idea of asking informants for
examples of a conceptual category is very useful even in informal interviews (Spradley
1979). For example, in doing an ethnography of an academic department, we might find
ourselves asking an informant “You mentioned that there are a number of ways that
graduate students can get into difficulty. Can you give me some examples?” Rather than
eliciting all the members of the domain, the objective might be simply to elicit just one
element, which then becomes a vehicle for further exploration.
It is also possible to reverse the question and ask the respondent if a given item
belongs to the domain, and if not, why not. The negative examples help to elicit the
taken-for-granted characteristics that are shared by all members of the domain and which
therefore might otherwise go unmentioned.
Eliciting Domain Structure Using Pilesorts
The pilesort task is used primarily to elicit from respondents judgments of
similarity among items in a cultural domain. It can also be used to elicit the attributes that
people use to distinguish among the items. There are many variants of the pilesort sort
technique. We begin with the free pilesort.
Collecting Free Pilesort Data
The typical free pilesort technique begins with a set of 3-by-5 cards on which the
name or short description of a domain item is written. For example, for the cultural
19 Elicitation Techniques
domain of illnesses, we might have a set of 80 cards, one for each illness. For
convenience, a unique ID number is written on the back of each card. The stack of cards
is shuffled randomly and given to a respondent with the following instructions: “Here are
a set of cards representing kinds of illnesses. I’d like you to sort them into piles according
to how similar they are. You can make as many or as few piles as you like. Go!”
In some cases, it is better to do it in two steps. First you ask the respondent to look
at each card to see if they recognize the illness. Ask them to set aside any cards
representing illnesses that they are unfamiliar with. Then, with the remaining cards, have
them do the sorting exercise.
Sometimes, respondents object to having to put a given item into just one pile.
They feel that the item fits equally well into two different piles. This is perfectly
acceptable. In such cases, the researcher can simply take a blank card, write the name of
the item on the card, and let the respondent put one card in each pile. As discussed in a
later section, putting items into more than one pile causes no problems for analyzing the
data, and may correspond better to the respondents’ views. The only problem it creates is
that it makes it more difficult later on to check whether the data were entered into
computer files correctly, since having an item appear in more than one pile is usually a
sign that someone has mistyped an ID code.
Instead of writing names of items on cards, it is sometimes possible to sort
pictures of the items (see Figure 4), or even the items themselves (e.g., when working
with the folk domain of “bugs”). However, in our experience, for literate respondents, the
written method is always best. Showing pictures or using the items themselves tends to
Elicitation Techniques 20
bias the respondents toward sorting according to physical attributes such as size, color
and shape. For example, sorting pictures of fish yields sorts based on body shape and
types of fins (Boster and Johnson, 1989). In contrast, sorting names of fish allows non-
visible attributes to affect the sorting (such as taste, where the fish is found, what it is
used for, how it is caught, what it eats, how it behaves, etc.).
INSERT FIGURE 4
Normally, the pilesort exercise is repeated with at least 30 respondents
Analyzing Pilesort Data
the number depends on the amount of variability in responses. For example, if everyone
in a society would give exactly the same answers, you would only need one respondent.
But if there is a great deal of variability, you may need hundreds of sorts to get a good
picture of the modal answers (i.e., the most common responses), and so that you can cut
the data into demographic subgroups so that you can see how different groups sort things
Pilesort data are tabulated and interpreted as follows. Every time a respondent
places a given pair of items in the same pile together, we count that as a vote for the
similarity of those two items (see Table 6). In the domain of animals, if all of the
respondents place “coyote” and “wolf” in the same pile, we take that as evidence that
these are highly similar items. In contrast, if no respondents put “salamander” and
“moose” in the same pile, we understand that to mean that salamanders and moose are
The number 30 is merely a convention -- a rule of thumb. More
respondents is always more desirable but involves more time and
21 Elicitation Techniques
not very similar. We further assume that if an intermediate number of respondents put a
pair of items in the same pile this means that the items are of intermediate similarity.
INSERT TABLE 6 HERE
This interpretation of agreement as monotonically
--They use a similarity metric or measure.
related to similarity is not
trivial and is not widely understood. It reflects the adoption of a set of simple process
models for how respondents go about solving the pilesort task.[LIST: BOX BULLETS Process Models
--They "bundle" or clump together items with similar attribute
This means that there is a 1-to-1 correspondence between the
rank orders of the data. That is, the pair placed most often in the
same pile is the most similar, the pair placed second-most often in
the same pile is the second-most similar, etc.
Elicitation Techniques 22
One such model is the metric model. Each respondent is seen as having the equivalent of
a similarity metric in her head (e.g., she has a spatial map of the items in semantic space).
However, the pilesort task essentially asks her to state, for each pair of items, whether the
items are similar or not. Therefore, she must convert a continuous measure of similarity
or distance into a yes/no judgment. If the similarity of the two items is very high, she
places, with high probability, both items in the same pile. If the similarity is very low, she
places the items, with high probability again, in different piles. If the similarity is
intermediate, she essentially flips a coin (i.e., the probability of placing in the same pile is
near 0.5). This process is repeated across all the respondents, leading the highly similar
items to be placed in the same pile most of the time, and the dissimilar items to be placed
in different piles most of the time. The items of intermediate similarity are placed
together by approximately half the respondents, and placed in separate piles by the other
half, resulting in intermediate similarity scores.
An alternative model, not inconsistent with the first one, is that respondents
conceptualize domain items as bundles of features or attributes. When asked to place
items in piles, they place the ones that have mostly the same attributes in the same piles,
and place items with mostly different attributes in separate piles. Items that share some
attributes and not others have intermediate probabilities of being placed together, and this
results in intermediate proportions of respondents placing them in the same pile.
Both these models are plausible. However, even if either or both is true, there is
still a problem with how to interpret intermediate percentages. Just because intermediate
similarity implies intermediate consensus does not mean that the converse is true, namely
23 Elicitation Techniques
that intermediate consensus implies intermediate similarity. For example, suppose half
the respondents clearly understand that shark and dolphin are very similar (because they
are large ocean predators) and place them in the same pile, while the other half are just as
clear that shark and dolphin are quite dissimilar (because one is a fish and the other is a
mammal). Under these conditions, 50% of respondents would place shark and dolphin in
the same pile, but we would NOT want to interpret this as meaning that 100% of
respondents believed shark and dolphin to be moderately similar. In other words, the
validity of measuring similarity by aggregating pilesorts depends crucially on the
assumption of underlying cultural consensus (Romney, Weller and Batchelder, 1986), a
topic we take up in more detail a bit further along.
INSERT FIGURE 5 HERE
We can record the proportion of respondents placing each pair of items in the
same pile using an item-by-item matrix, as shown in Table 6. This matrix can then be
represented spatially via non-metric multidimensional scaling, or analyzed via cluster
Figure 5 shows a multidimensional scaling of pilesort similarities among 30
crimes collected by students of Mark Fleisher
An excellent introduction to multidimensional scaling is
provided by Kruskal and Wish (1978). For an introduction to
cluster analysis, we recommend Everitt (1980).
. In general, the purpose of such analyses
would be to [LIST, BULLETS
The data were collected specifically for inclusion in this chapter
by: Jennifer Teeple, Dan Bakham, Shannon Sendzimmer, and
Amanda Norbits. We are grateful for their help.
Elicitation Techniques 24
-- reveal underlying perceptual dimensions that people use to distinguish among
the items, and
--detect clusters of items that share attributes or comprise subdomains. ]
Let us discuss the former goal first. One way to uncover the attributes that
structure a cultural domain is to ask respondents to name them as they do the pilesort
In addition, it is possible that the research objectives may not require that we
know how the respondent completes the sorting task but merely that we can accurately
One approach is to ask respondents to “think aloud” as they do the sort. This is useful
information but should not be the only attack on this problem. Respondents can typically
come up with dozens of attributes that distinguish among items, but it is not easy for
them to tell you which ones are important. In addition, many of the attributes will be
highly correlated with each other if not directly synonymous, particularly as we look
across respondents. It is also possible that respondents do not really know why they
placed items into the piles that they did: when a researcher asks them to explain, they
cannot directly examine their unconscious thought processes and instead go through a
process of justifying and reconstructing what they must have done. For example, all
native speakers of a language are good at constructing grammatically well-formed
sentences, but they need not have conscious knowledge of grammar to do this.
It is best to use a different sample of respondents for this
purpose, or wait until they have finished the sort and then ask them
to discuss the reasons behind their choices. Otherwise, the
discussion will influence their sorts. You can also have them sort
the items twice: the first time without interference, the second time
discussing the sort as they go. The results of both sorts can be
recorded and analyzed, and compared.
25 Elicitation Techniques
predict the results. In general, scientists build descriptions of reality (theories) that are
expected to make accurate predictions, but are not expected to be literally true, if only
because these descriptions are not unique and are situated within human languages
utilizing only concepts understood by humans living at one small point in time. This is
similar to the situation in artificial intelligence where if someone could construct a
computer that could converse in English so well that it could not be distinguished from a
human, we would be forced to grant that the machine understood English, even if the way
it did so could not be shown to be the same as the way humans do it. What is common to
both scientific theories and artificial intelligence is that we evaluate truth (success) in
terms of the behavioral outcomes, not an absolute yardstick.
To discover the underlying perceptual dimensions people use to distinguish
among items in a cultural domain, we begin by collecting together the attributes elicited
directly from respondents. Then we look at the multidimensional scaling (MDS) map to
see if the items are arrayed in any kind of order that is apparent to us.
It is important to remember that since the axes of MDS pictures
are arbitrary; perceptual dimensions can run along any angle, not
just horizontal or vertical.
For example, in
the crime data shown in Figure 5, it appears that as we move from right to left on the
map, the crimes become increasingly serious. This suggests the possibility that
respondents use the attribute “seriousness” to distinguish among crimes. Of course, the
idea that the leftmost crimes are more serious than the rightmost crimes is based on the
researcher’s perceptions of the crimes, not the informants’. Furthermore, there are other
attributes that might arrange the crimes in roughly the same order (such as violence). The
Elicitation Techniques 26
first question to ask is whether respondents have the same view of the domain as the
To resolve this issue, we then take all the attributes, both those elicited from
Sometimes MDS maps do not yield much in the way of interpretable dimensions.
One way this can happen is when the MDS map consists of a few dense clusters
separated by wide open space. This can be caused by the existence of sets of items that
happen to be extremely similar on a number of attributes. Most often, however, it signals
the presence of subdomains (which are like categorical attributes that dominate res-
and those proposed by researchers, and administer a questionnaire to a
(possibly new) sample of respondents asking them to rate each item on each attribute.
This way we get the informants’ views of where each item stands on each attribute. Then
we use a non-linear multiple regression technique called PROFIT (Kruskal and Wish,
1975) to statistically relate the average ratings provided by respondents to the positions of
the items on the map. Besides providing a statistical test of independence (to guard
against the human ability to see patterns in everything), the PROFIT technique allows us
to plot lines on the MDS map representing the attribute so that we can see in what
direction the items increase in value on that attribute. Often, several attributes will line up
in more or less the same direction. These are attributes that have different names but are
highly correlated. The researcher might then explore whether they are all manifestations
of a single underlying dimension that respondents may or may not be aware of.
Either as part of the pilesort exercise, or by showing the MDS
map to informants and asking them to interpret it.
27 Elicitation Techniques
pondents’ thinking). For example, a pilesort of a wide range of animals, including birds,
land animals, and water animals will result in tight clumps in which all the
representatives of each group are seen as so much more similar to each other than to other
animals that no internal differentiation can be seen. An example is given in Figure 6. In
such cases, it is necessary to run the MDS on each cluster separately. Then, within
clusters, it may be that meaningful dimensions will emerge.
INSERT FIGURE 6
We may also be interested in comparing respondents’ views of the structure of a
domain. One way to think about the pilesort data for a single respondent is as the answers
to a list of yes/no questions corresponding to each pair of items. For example, if there are
N items in the domain, there are N(N-1)/2 pairs of items, and for each pair, the
respondent has either put them in the same pile (call that a YES) or a different pile (call
that a NO). Each respondent’s view can thus be represented as a string of ones ("yesses")
and zeros ("no's"). We can then, in principle, compare two respondents’ views by
correlating these strings.
However, there are problems caused by the fact that some people create more
piles than others. This is known as the “lumper/splitter” problem. For example, suppose
two respondents have identical views of what goes with what. But one respondent makes
many piles to reflect even the finest distinctions (he’s a “splitter”), while the other makes
just a few piles, reflecting only the broadest distinctions (she’s a “lumper”). Correlating
their strings would yield very small correlations, even though in reality they have
identical views. Another problem is that the strings of two splitters can be fairly highly
Elicitation Techniques 28
correlated even when they disagree a great deal because both say “no” so often (i.e., most
pairs of items are NOT placed in the same pile together). Some analytical ways to
ameliorate the problem have been devised, but they are beyond the scope of this chapter.
The best way to avoid the lumper/splitter problem is to force all respondents to
make the same number of piles. One way to do this is to start by asking them to sort all
the items into exactly two piles, such that all the items in one pile are more similar to
each other than to the items in the other pile. Record the results. Then ask the respondents
to make three piles, letting them rearrange the contents of the original piles as
A more sophisticated approach was proposed by Boster (1994). In order to
preserve the freedom of a free pilesort while at the same time controlling the
lumper/splitter problem, he begins with a free pilesort. If the respondent makes N piles,
The new results are then recorded. The process may be repeated as many
times as desired. The data collected can then be analyzed separately at each level of
splitting, or combined as follows. For each pair of items sorted by a given respondent, the
researcher counts the number of different sorts in which the items were placed together.
Optionally, the different sorts can be weighted by the number of piles, so that being
placed together when there were only two piles doesn’t count as much as being placed
together when there were 10 piles. Either way, the result is a string of values (one for
each pair of items) for every respondent, which can then be correlated with each other to
determine which respondents had similar views.
An alternative here is to ask them to divide each pile in two.
This is repeated as often as desired.
29 Elicitation Techniques
the researcher then asks the respondent to split one of the piles, making N+1 in total. He
repeats this process as long as desired. He then returns to the original sort and asks the
respondent to combine two piles so that there are N-1 in total. This process is repeated
until there are only two piles left.
Both of these methods, which we can describe as successive pilesorts, yield very
rich data, but they are time-consuming and can potentially require a lot of time to record
the data. The the respondent also has a long wait while data are recorded. In Boster’s
method, because piles are not rearranged at each step, it is possible to record the data in
an extremely compact format without making the respondent wait at all. However, it
requires extremely well-trained and alert interviewers to do it.
Eliciting Cultural Domain Structure Using Triads
An alternative to pilesorts for measuring similarity is the triad test. [LIST: BOX,
Triads are used for:
-- very small domains (12 items or less)
-- testing hypotheses where it is important that every respondent make an active
judgment regarding the similarities among a certain set of items, or
--getting people to define what attributes they use to distinguish among items.]
Elicitation Techniques 30
In a triads test, the items in a domain are presented to the respondent in groups of three.
For each triple, the respondent must pick out the one she judges to be the most different.
For example, one triple drawn from the domain of animals might be:
Picking any item is equivalent to voting for the similarity of the other two. Hence,
choosing “dog” would indicate that “seal” and “shark” were similar, while choosing
“shark” would indicate that “seal” and “dog” were similar. If all possible triples are
presented, each pair of items will occur N-2 times
Again, N is the number of items in the domain.
, each time “against” a different item.
If a pair of items is really similar it will “win” each of those contests and will be voted
most similar a total of N-2 times. If the pair is extremely dissimilar, it will never win. For
example, “oyster” and “elephant” might occur in the following triples:
31 Elicitation Techniques
In the first one, the respondent might choose “oyster” as the most different. In the
second, the respondent might choose “elephant”. In the third, the respondent might
choose “oyster” again, and so on. Hence, the triad test in which every possible triple is
presented will yield a similarity score for each pair of items that ranges from zero to N-2,
where N is the number of items. For example, if there 10 items, then each pair will occur
against all 10-2 = 8 remaining items.
The problem with presenting all possible triples is that there are N(N-1)(N-2)/6 of
them, which is a quantity that grows with the cube of the number of items. For example,
if the domain has 30 items in it, the number of triples is 30 times 29 times 28 divided by
6, which is 4,060, which is too many for an informant to respond to, even over a period of
days. The solution is to take a manageable sample of triples. For example, out of the
4,060 triples, we might randomly select 200 for the respondent to work with. However, a
random sample would allow some pairs of items to appear in several triples, and allow
others not to occur it all. The latter would be a real problem because the purpose of the
task is to measure the perceived similarity between every pair of items.
The solution is to use a balanced incomplete block or BIB design (Burton and
Nerlove, 1976). In a BIB design, every pair of items occurs a fixed number of times. The
number of times the pair occurs is known as lambda (λ). In a complete design (where all
Elicitation Techniques 32
possible triples occur), λ obviously equals N-2, since each pair occurs against every other
item in the domain. When λ equals 1, we have the smallest possible BIB design, where
each pair of items occurs only once. For a domain with 30 items, a λ=1 design would
have only 435 triads — still a lot, but a considerable savings over 4,060.
In general, however, λ=1 designs should be avoided, because the similarity of
each pair of items will be completely determined by their relation to whichever item
happens to turn up as the third item. For example, if “elephant” and “mouse” occur in this
it is likely that they will be measured as not similar, since “elephant” is likely to be
chosen as most different. But if instead they happen to occur in this triple:
it is likely that they will be measured as similar. Thus, it is much better to have at least a
λ=2 design, where each pair of items occurs against two different third items. The only
exception to this rule of thumb is when you give each respondent in a culturally
homogeneous sample a completely different triad test, based on the same set of items but
containing different triples. For example, respondent #1 might get “mouse” and
“elephant” paired with “oyster”, but respondent #2 might get “mouse” and “elephant”
paired with “dog”. In a way, this is like taking a complete design and spreading it out
33 Elicitation Techniques
across multiple respondents. This can work well, but means that you cannot compare
respondents’ answers with each other to assess similarity of views, since each person was
given a different questionnaire.
A nice feature of the triads task is that, unlike the simple pilesort, it yields degrees
of similarity for pairs of items for each respondent. In the simple pilesort, each
respondent essentially gives a “yes they are similar” or “no they are not” vote. In the
triads, the range of values obtained for each pair of items goes from zero to λ. Hence, for
a λ=3 design, each pair of items is assigned an ordinal similarity score of 0, 1, 2, or 3.
This means that we can sensibly construct separate multidimensional scaling maps for
One problem with triad tasks is that respondents often find them tiring and
repetitive. They will swear that a certain triad has already occurred, and will suspect that
you are trying to see if they are responding consistently, which makes them nervous.
Another problem is that respondents tend to become aware of their own thought
processes as they proceed, and start feeling uncomfortable about using varying criteria
(which is unavoidable) to pick the item most different in each triple. This makes them
feel that they are not doing a good job. In general, triads are only useful for very small
domains (12 items or less) or for testing hypotheses (where it is important that every
respondent make an active judgment regarding the similarities among a certain set of
The same was true for the successive pilesort techniques
Elicitation Techniques 34
Analyzing Triad Data
Perhaps the most interesting use of triads was by Romney and D’Andrade (1964)
who used them to test two theories of cognition about American male kinship roles, such
as grandfather, father, son, grandson, uncle, brother, nephew and cousin. One theory, by
Wallace and Atkins (1960), held that Americans use two attributes --- generation and
lineality [STEVE DEFINE HERE -- COLLINEAL, ABLINEAL, EGO,
RECIPROCAL, DIRECT, AND COLLATERAL] --- to distinguish among the roles,
as shown in Table 7. In the table, “lineal” refers to kin who are either ancestors or
descendants of the speaker, who by convention is labeled “ego”. The term “collineal”
refers to non-lineal kin whose set of ancestors include or are included by ego’s set of
ancestors. The term “ablineal” refers to all other blood relatives.
INSERT TABLE 7
If the theory is true, in a triads test that included the triple
Americans should choose “grandson” as the one most different because grandfather and
grandson are the least different with respect to the two attributes in the model (all of the
terms are lineal, differing only on generation, where “grandfather” and “father” are
adjacent, but “grandson” is a step removed).
In contrast, Romney and D’Andrade propose a model with three attributes ---
generational distance, lineality, and reciprocal roles --- as shown in Table 8. In the table,
“direct” refers to kin that share the same ancestors as ego, and “collateral” are all others.
35 Elicitation Techniques
INSERT TABLE 8
According to the Romney and D’Andrade model, when faced with the same triad
given above, Americans should choose, with equal probability, either “grandson” or
“father” as the item most different, and should never choose “grandfather”. Given these
predictions, it was a simple matter to test the theories by giving the triads to a sample of
Americans and seeing which theory best predicted the actual answers on the triads test.
Overall, the best theory turned out to be the Romney and D’Andrade model.
Informal Use of Triads
So far, I have only described the formal use of the triads task, which results in the
generation of similarities among items. Another way to use triads is as a device to spark
discussion of the underlying attributes that people use to distinguish among items in the
domain. To do this, we present informants with a small random sample of triples, one at a
time. For each triple, the informant is asked to explain in what ways each item is different
from the other two. This is an extraordinarily effective way to elicit the attributes that
people use to think about the domain. For example, consider this triple:
This triple can elicit a number of perceived attributes of illnesses including seriousness
(“cancer is fatal”), age of the afflicted (“measles is something that kids get”), morality
(“you get syphilis from sleeping around too much”), contagiousness (“you can catch
Elicitation Techniques 36
syphilis and measles from other people”), and so on. It is easy to see that it only requires
a handful of triples to elicit dozens and dozens of attributes.
In the study of cultural domains, the researcher must be aware of issues of cultural
variability among respondents. It is impossible to interpret the results of triads and
pilesorts if fundamentally different systems of classification are in use among different
respondents. One way to determine whether variability among respondents reflects
different systems of classification is the consensus methodology of Romney, Weller and
Batchelder (1986). This method, available in both UCINET and Anthropac, provides a
way to (a) discover whether a set of questions has multiple correct answer keys
(corresponding to different cultures), (b) uncover what the culturally “correct” answers
are for each culture, and (c) assess the extent of cultural domain knowledge possessed by
a given member of a culture. Thinking back to the example of dolphins and sharks,
consensus analysis would indicate whether our sample includes subgroups of respondents
with systematically different answers across all similarity judgments (e.g., because some
are sorting on the basis of habitat others are sorting based on phylogeny), or just
individual variability, where some people simply know more about a given domain than
Once consensus analysis has been used to identify culturally homogeneous groups
of respondents, it can then be applied within in each group to determine the amount of
knowledge regarding the cultural domain possessed each respondent in that group. In
37 Elicitation Techniques
effect, the theory underlying consensus analysis distinguishes between two kinds of
variability in people’s responses. One kind is systematic difference that we attribute to
cultural differences. The other kind is random or piecemeal difference which we attribute
to individual differences in knowledge of the domain. For example, within a culture,
people may have similar understandings of types of flowers, but some people simply
have more of that cultural knowledge than others. They know more names of flowers,
they know more about which are used in what occasions (such as weddings or funerals or
romances), and so on. This does not mean they know more in the sense of Truth with a
capital “t”, but rather that they know more of their own culture.
Consider the study of crimes discussed earlier in this chapter in which 30
respondents were asked to sort crimes into piles based on how similar the crimes were to
each other. As discussed, we can use the pilesort method to uncover various types of
crimes (see figure X for a MDS plot), and then use consensus analysis to determine
whether there was agreement among respondents. Consensus analysis methods of pilesort
data using UCINET and Anthropac use individual proximity matrices as input. In this
example, the input is an item-by-item matrix for each of the 30 respondents in which
=1 if the respondent placed crime i and crime j in the same pile. The program then
correlates the 30 item-by-item matrices with each other and runs a factor analysis. The
program output includes factor loadings for all respondents. If one factor is predominant,
we can conclude that there is a single culture. In this example, the largest eigenvalue
=14.440 and the 2
largest eigenvalue = 1.653 indicating strong cultural consensus. If
the respondents were not culturally homogeneous we would find multiple factors
Elicitation Techniques 38
indicating systematically different response patterns. We are also able to identify
respondent 3 as an individual with the greatest amount of cultural knowledge (see table X
for other competence scores) because he or she has the highest loading on the first factor.
We also see that respondent 22 has a significantly lower competence score than all the
others – it is possible the respondent did not understand the task.
Visualization of Cultural Domains
Implicit in these data collection and analysis techniques is the idea of the cultural
domain as a network of items related by semantic relations, or families of linked
meanings. Thus, we can use visualization and analytic techniques drawn from social
network analysis to investigate cultural domains. For example, network visualization
tools can be used to elucidate the internal structure of a cultural domain and identify how
the position of items within this structure distinguishes the items from each other and
gives them their unique meanings.
39 Elicitation Techniques
Network visualization is based on graph layout algorithms (GLAs) which locate
nodes in space and connect them with lines indicating a close relationship (cite dejordy et
al?) . In the case of cultural domain analysis, the lines are determined by the level of
similarity among items determined by the researcher (e.g., how often two items appeared
in the same pile, the correlation coefficient of two items, etc.). [something about
choosing the level] Boston College student Heidi Stokes used the pilesort method to
collect data on the perceived similarity of 24 holidays as part of an undergraduate
research methods class. Figure 6 is a GLA representation of the holiday data in which an
edge is shown connecting holidays deemed similar by at least 50% of the respondents.
The edges allow the viewer to clearly identify various groupings that exist within the
domain of holidays among Boston College undergraduates. We can see that there is a
large grouping of religious holidays and a large grouping of patriotic holidays. The GLA
also reveals structure within these groupings. The existence of a line from the 4
to Memorial Day but no other holidays in the grouping of Patriotic holidays indicates that
respondents considered the 4
of July more similar to Memorial Day than to other
holidays such as Columbus Day and Martin Luther King Day.
Elicitation Techniques 40
Researchers can use GLAs to investigate the perceived similarity of two items at
various strengths to better determine the structure of items within groupings and possible
attributes that might give items their unique meanings. For example, we can draw a GLA
in which a line is shown connecting holidays deemed similar by at least 70% of the
respondents (or any other percentage decided by the researcher). Figure X indicates that
respondents in the Catholic school deemed Easter more similar to Christmas than to
Jewish holidays Hanukkah, Yom Kippur, and Passover. We can also see that Christmas
is deemed more similar to Hanukkah than to other Jewish holidays perhaps because both
holidays occur at the same time of year.
41 Elicitation Techniques
We have presented basic techniques for eliciting data concerning cultural
domains. The freelist technique is primarily used to elicit the basic elements of the
domain. The pilesort and triad tasks are used both to elicit similarities among the items,
and to elicit attributes that describe the items. Consensus analysis can be used to uncover
the culturally correct items in a cultural domain in the face of certain kinds of intra-
cultural variability, and enables the researcher to assess the extent of knowledge
possessed by an informant about a given cultural domain. In addition, we have touched
on the use of multidimensional scaling and graph layout algorithms to graphically
illustrate the structure of the domain, and locate each item’s position in that structure.
SUGGESTED ADDITIONAL RESOURCES
Borgatti, S.P. 1992. ANTHROPAC 4.0. Columbia, SC: Analytic Technologies.
Elicitation Techniques 42
Anthropac is a menu-driven computer program for cultural domain
analysis. The program’s capabilities include all the techniques discussed
in this chapter. More information is available on the web at
Kruskal, J.B. and M. Wish. 1978. Multidimensional Scaling. Newbury Park: Sage
This is perhaps the clearest book available on the mathematics and
interpretation of multidimensional scaling.
Romney, A.K., S.C. Weller, and W.H. Batchelder. 1986. “Culture as consensus: A theory
of cultural and informant accuracy.” American Anthropologist 88(2):313-338.
This is a brilliant paper on the theory of consensus analysis. A seminal
article in the field.
Scott, J. 1991. Social Network Analysis: A Handbook. Newbury Park: Sage Publications.
Scott’s book is a popular introduction to the techniques of social network
analysis. It discusses everything from data management techniques to
advanced analytical methods.
Spradley, J. 1979. The Ethnographic Interview. NY: Holt, Rinehart & Winston.
Spradley’s book is perhaps the definitive book on interviewing technique in
the context of cultural domain analysis. Extremely well-written with lots of
43 Elicitation Techniques
Borgatti, S.P. 1992. ANTHROPAC 4.0. Columbia, SC: Analytic Technologies.
Borgatti, S.P., Everett, M.G. and Freeman, L.C. 2002. Ucinet for Windows: Software for
Social Network Analysis. Harvard, MA: Analytic Technologies.
Boster, J.S. 1994. “The successive pilesort.” CAM: Cultural Anthropology Methods
Boster, J.S. and J.C. Johnson. 1989. “Form or function: A comparison of expert and
novice judgments of similarity among fish.” American Anthropologist 91:866-
Burton, M.L. and S.B. Nerlove. 1976. “Balanced Designs for Triads Tests: Two
Examples from English.” Social Science Research 5:247-267.
Casagrande, J.B. and K.L. Hale. 1967. “Semantic relations in Papago folk-definitions.” In
Studies in Southwestern Ethnolinguistics. D. Hymes and W.E. Bittle, (eds.) Pp.
165-196. The Hague: Mouton.
Chavez, L.R., J.M. McMullin, R.G. Martinez, S.I. Mishra, F.A. Hubbell. 1995. “Structure
and meaning in models of breast and cervical cancer risk factors: A comparison of
perceptions among Latinas, Anglo women and physicians. Medical
Anthropological Quarterly 9:40-74.
Dejordy et al
Everitt, B. 1980. Cluster analysis. New York : Halsted Press.
Gatewood, J. 1984. “Familiarity, vocabulary size, and recognition ability in four semantic
domains.” American Ethnologist, 11:507-527.
Elicitation Techniques 44
Henley, N.M. 1969. “A psychological study of the semantics of animal terms.” Journal of
Verbal Learning and Verbal Behavior. 8:176-184.
Kruskal, J.B. and M. Wish. 1978. Multidimensional Scaling. Newbury Park: Sage
Lounsbury, F. 1964. “The structural analysis of kinship semantics.” In H.G. Lunt (Ed.)
Proceedings of the ninth international congress of linguists. The Hague: Mouton.
Romney, A.K. and R.G. D’Andrade. 1964. “Cognitive aspects of English kin terms.”
American Anthropologist 66(3):146-170.
Romney, A.K., S.C. Weller, and W.H. Batchelder. 1986. “Culture as consensus: A theory
of cultural and informant accuracy.” American Anthropologist 88(2):313-338.
Scott, J. 1991. Social Network Analysis: A Handbook. Newbury Park: Sage Publications.
Spradley, J. 1979. The Ethnographic Interview. NY: Holt, Rinehart & Winston.
Weller, S.C., and A.K. Romney. 1988. Systematic Data Collection. Newbury Park: Sage