Journal of Psycholinguistic Research, Vol. 4, No. 1, 1975
Semantic Organization in Deaf and Hearing Subjects 1 Ryan D. Tweney, 2,3 Harry W. Hoemann, 2 and Carol E. Andrews 2
Received February 11, 1974
Hierarchical cluster analysis o f data from the sorting o f noun words was used to compare semantic structures in 63 profoundly deaf and 63 hearing adolescents. In the first study, performance differed only for a set o f words referring to sounds, where deaf persons have no experience, and not for a set o f common noun words and pictures. In the second study, differences between matched sets o f high. and low-imagery words were comparable for 63 deaf and 63 hearing subjects. It is concluded that deaf subfects manifested abstract hierarchical relations and were not dependent on "visual mediators" or hindered by the absence of "acoustic mediators."
INTRODUCTION Miller (1969) and Anglin (1970) have recently demonstrated that abstract, conceptual relations are reflected in the subjective organization of items in a person's lexicon. Miller showed that the presence or absence of higher-order, "abstract" relations can be detected by means of hierarchical cluster analyses This investigation was supported in part by National Institutes of Health Research Grant NS-09590-03 from the National Institute of Neurological Diseases and Stroke and in part by a Faculty Research Grant from Bowling Green State University. 1Porfions of Study 1 were previously reported at the Eighty-first Annual Convention of the American Psychological Association, Montreal, 1973. 2Department of Psychology, Bowling Green State University, Bowling Green, Ohio. 3Requests for reprints should be addressed to the first author, whose address is Department of Psychology, Bowling Green State University, Bowling Green, Ohio 43403. 61 @)1975 Plenum Publishing Corporation, 227 West 17th Street, New York, N.Y. 10011. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any f o r m o r by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise, without written permission of the publisher.
62
Tweney, Hoemann, and Andrews
of sorting data, a technique developed further and described in detail by Fillenbaum and Rapaport (1971). Anglin (1970) showed that such techniques can be applied to the analysis of the development of semantic relations in children. Using sorting data, free recall, and word association techniques, Anglin reported evidence of progressively more abstract structure in children's subjective lexicons as a function of age. The present investigation explored the organization of items in the subjective lexicons of deaf and hearing adblescents. Two studies were conducted to determine the amount of hierarchical structure present in the sorting-of deaf subjects and to compare their performance with that of hearing subjects of the same age. In the first study, semantic domains were chosen to assess the relative contribution of specific experiences to deaf persons' lexical structures. Three domains were used: common noun words, which would presumably be familiar to both deaf and hearing subjects; line drawings corresponding to the noun words (to determine if there is a uniquely visual component involved in the structure of deaf subjects' lexicons); and words referring to sounds (hiss, crash, roar, etc.), for which deaf subjects should have limited or no experience. It was expected that deaf subjects would show less hierarchical structure than hearing subjects when sorting sound words but not when sorting noun words and their corresponding pictures. This prediction follows from the argument of Furth (1971) that deaf children perform as well as hearing children on cognitive tasks for which the experiential background of deaf and hearing children is matched. While it is frequently found that deaf subjects do more poorly than age-matched hearing subjects (e.g., see DiFrancesca, 1972; Blanton, 1968; Myklebust, 1964), Furth has noted that there does not seem to be a consistent pattern associated with the cognitive tasks in which deaf children show poorer performance and those tasks in which deaf children perform as well as their hearing age peers. Furth has argued that the experiential deficit associated with a profound hearing loss and with society's treatment of deaf children is the most parsimonious explanation for the observed developmental lags. If the thinking processes of deaf persons are as logical and as abstract as those of hearing persons, then their subjective lexicon ought to reflect the same amount of hierarchical structure as long as the stimuli are equally familiar to both deaf and hearing subjects. Thus the sorting data generated for common noun words and pictures ought to reflect the same levels of conceptual thinking in deaf and hearing subjects, while the sorting data for sound words ought to maximize differences between the groups.
Semantic Organization in Deaf and Hearing Subjects
63
Since both the pictures and the noun words used in the first study were concrete and high in imagery value, a second study was conducted in which two different word lists were selected that were either very high or very low in imagery value as measured by published norms (Paivio et aL, 1968b). With the role of imagery thereby controlled, the possible influence of acoustic mediators could be assessed by comparing deaf and hearing sorts of the low-imagery items. Since the lack of acoustic mediation in deaf subjects has been cited to explain their poorer performances in, for example, tasks measuring sequential memory (Koh et al., 1971), the outcome of this comparison was considered especially important. In addition, as a partial replication, the common noun words from the first study were administered to the subjects in the second study.
STUDY 1
Method Subjects. The subjects were 63 severely and profoundly deaf adolescents (16-18 years old) enrolled at the Kentucky School for the Deaf in Danville, Kentucky, and 63 hearing adolescents (16-18 years old) enrolled in the Danville, Kentucky, City High School. Adolescents were used to facilitate obtaining comparable groups of subjects. No age trends were present in the data for any of the variables discussed below. The data were accordingly pooled across ages. Procedure. Three sets of stimulus items were prepared on index cards: 30 sound words, 30 common noun words drawn from a variety of conceptual categories, and 30 ink drawings corresponding to the list of noun words (see Table I). Subjects were tested in groups not larger than five. They were instructed to place words they did not know in a separate pile and then to sort each set into categories of similar meaning, using as many or as few categories as desired and assigning as many or as few items to each category as desired. The instructions were presented to the deaf subjects in both American Sign Language and spoken English by an experimenter who was a fluent user of American Sign Language. Order of administration was counterbalanced across stimulus type, with each subject receiving all three types of items. There were no effects on any of the indices as a function of order of administration. The data were therefore pooled across orders.
Tweney, Hoemann, and Andrews
64
Table I. Stimulus Words Used in Study 1 Common noun words key table chair picture house window kite fire flower tree leaf cup apple egg bread t
Sound words
horse bear soldier clown baby car airplane train hammer lawnmower book letter shoes purse money
swish tinkle hiss ring toot whisper whistle roar bark meow whack whine thunder crack crash
slam bang boom bump pop thump twang splat snap tap shout laugh cough rustle crackle
i
Table II. Mean Number of Categories, Mean Category Sizes, and Overlap Coefficients for Deaf and Hearing Subjects in Study 1 Stimulus type Noun words Response measure
Drawings
Sound words
Deaf
Hearing
Deaf
Hearing
Deaf
Hearing
12.95
13.10
12.35
12.51
9.37
11.30
Category size
2.46
2.48
2.63
2.78
2.72 a
3.09
Overlap
0.627
0.539
0.706
0.653
0.328
0.449
Number of categories
aCategory size for sound words sorted by deaf subjects excludes items placed in the "don't know" category.
Results Table II presents the means of the number o f categories and category sizes used in the sortings. For the number of categories, deaf-hearing differences were significant ( F = 4.04; d f = 1, 123; p < 0.05). Stimulus type was significant ( F = 51.03; df = 2, 246; p < 0.005), and the interaction was significant ( F = 11.53; df = 2 , 2 4 6 ; p < 0.005). Scheff~ tests revealed that the
Semantic Organization in Deaf and Hearing Subjects
65
deaf-hearing difference and the interaction were due to the small number of categories used by deaf subjects for sound words. Thus a second analysis was conducted using mean category size to correct for unequal numbers of categories. For deaf subjects sorting sound words, the large "don't know" category was excluded. Analysis of variance for this case showed no significant deaf-hearing difference (F = 1.07; df = 1, 123; p > 0.05) and no interaction (F = 1.24; df = 2, 246; p ;> 0.05). A significant difference due to stimulus type was found (F = 7.09; df = 2,246; p < 0.005). Scheffd tests showed that the latter difference was due only to the somewhat large category size used b y hearing subjects sorting sound words. Similar values for numbers of categories and category sizes in the deaf and hearing samples (excluding sound words) indicated that both groups understood the instructions similarly and responded to the task demands of the situation in a similar manner. For each group, all sorts were tallied to produce a 30 • 30 frequency matrix showing the number of times each pair of items was combined in one cluster. Thus if a particular subject placed the words iron, nail, and machine in a single category, a tally mark was placed in the matrix at the cells for iron-nail, nail-machine, and iron-machine. The maximum cell entry was 63, equal to the number of subjects. The matrices were submitted to hierarchical duster analysis using Johnson's (1967) algorithm. In brief, the algorithm seeks those items most often sorted together, combines them into a cluster, recomputes the similarity matrix using the new duster as though it were a single item, and begins anew by searching for the next most commonly sorted pair of items or clusters. The process continues until all items have been combined into a single large cluster. As output, the program produces a tree diagram indicating which items duster together. Solutions are necessarily hierarchical, regardless of the nature of the data. When nonhierarchical structure is present, this can be discovered by executing two different variants of the algorithm, the "diameter" method and the "connectedness" method. Each produces identical solutions when the data are perfectly hierarchical. "Noise" in the data, stemming from nonhierarchical structure or inconsistencies among subjects, produces differences between the solutions and results in nonshared clusters between the two trees. Hence the degree of overlap for each pair of solutions can serve as an index of the amount of hierarchical structure present in a given data set (Miller, 1969). For each group of subjects, cluster analyses were performed using each variant of the algorithm separately for each type of stimulus item. Overlap between solutions was calculated using the formula Proportion overlap = (2 x number of nodes in common) + (number of nodes in diameter solution + number of nodes in connectedness solution).
66
Tweney, Hoemann, and Andrews
For identical trees, overlap equals 1.00; for trees having no common clusters, overlap equals 0.0. Miller (1969) reports a Monte Carlo simulation in which pseudorandom data produced mean overlaps of 0.149, establishing a chance baseline. Overlap coefficients for all groups are shown in Table II. The values for common noun words and pictures were very high for this type of task, with deaf subjects showing slightly higher values than hearing subjects. The values for sound words were somewhat lower for the hearing subjects and much lower for the deaf subjects, No statistical test of these differences exists. Subjective inspection of the trees produced by deaf and hearing subjects confirmed the results of the overlap coefficients. The deaf and hearing subjects differed in only minor ways for noun words and pictures, but greatly for sound words. Even where some structure seemed to be present for the sound words among deaf subjects, it was not always based on semantic relations. Thus meow-bark and laugh-cough clustered together for deaf subjects, but whack-whine, a somewhat less frequent cluster, may have been based only on visual letter similarity. This kind of sort was not found for noun words or pictures, suggesting that the deaf subjects resorted to such criteria for clustering only when they lacked adequate semantic grounds. Comparison of noun words and pictures revealed no substantial differences: both groups sorted in essentially the same fashion regardless of the nature of the stimulus item. STUDY 2 The results of the first study may be limited in generality because the noun words were very concrete. Paivio (1971) has argued that such words may be processed differently than words having little concrete reference. To support this, Paivio and others have shown that scaled ratings of the extent to which lexical items have associated imagery (Paivio's/) are a major predictor of learning ease (Paivio et al., 1968a) and retention of meaning (Begg and Paivio, 1969). It is therefore possible that the results of the first study show comparable performance among deaf and hearing subjects only because the words used were high in imagery. The second study attempted to determine if this was, in fact, the case. Method
Subjects. Subjects were 63 severely and profoundly deaf adolescents (16-18 years old) enrolled in the Kentucky School for the Deaf and 63
Semantic Organization in Deaf and Hearing Subjects
67
hearing adolescents (14-17 years old) enrolled in Boyle County High School, Danville, Kentucky. Approximately half of the deaf subjects had been subjects in the first experiment, conducted 6 months earlier. As in the first study, no age trends were present for any of the variables discussed. Groups were the same size as in the first study to facilitate comparison of the hierarchical trees in the two studies. Stimulus Materials. The list of stimulus words was prepared using the Paivio et al. (1968b) norms. Each list consisted of 19 words. The high-imagery list had mean imagery values 0.50 SD or more above the mean for the entire distribution (4.97), while the low-imagery list had mean imagery values 0.50 SD or more below the mean. For the high-imagery list, shown in Fig. 1, all [ ~> 5.93, mean [ = 6.28, SD = 0.249. For the low-imagery list shown in Fig. 2, all I < 4.01, mean I = 3.18, SD = 0.530. In selecting the stimulus words, an attempt was made to keep frequency, signability (in American Sign Language), familiarity, and meaningfulness as constant as possible. In each list, 13 words were AA in frequency (Thorndike and Lorge, 1944), and the remaining six were A. Twelve words in the low-imagery list and t 3 words in the high-imagery list were indexed by Stokoe et al. (1965) as having American Sign Language gesture equivalents. Silverman-Dresner and Guilfoyle's (1972) norms were used to determine the likelihood that the meaning of each word was familiar to the deaf subjects and that the two lists were matched on this dimension for the deaf subjects. The norms are based on a paper-and-pencil test (essentially a reading-recognition test) given to 13,207 deaf students (8-17 years old) from 89 schools for the deaf in the United States. Using Silverman-Dresner and Guflfoyle's norms for 16- and 17-year-old deaf students, the low-imagery list had a mean score of 62.6 and the high-imagery list a mean score of 63.0, values quite a bit toward the high end on the norms for that age. The difference was not statistically significant. Mean meaningfulness (Nobte's m; as reported in the Paivio etal., 1968b, norms) for the high-imagery list was 6.42, SD = 0.818, and for the low-imagery list was 5.50; SD = 0.691. This difference was significant (t = 3.75, df = 36, p < 0.005). It was not possible to equalize meaningfulness values at the very high levels of frequency and imagery used. As Paivio etal. (1968b) point out, high imagery values are closely associated with high meaningfulness values, with meaningfulness varying over a greater range at low imagery values. Thus the construction of balanced lists is fully attainable only when relatively nonextreme values are used. For the present study, such lists would have had the disadvantage of including substantially greater numbers of nonsignable words and words low on the familiarity norms for deaf subjects. It was felt that the present lists represented the best compromise, especially
68
Tweney, Hoemann, and Andrews
since a number of studies have shown that meaningfulness has little effect when imagery is controlled (Paivio, 1971). Procedure. Three stimulus lists were used; the 30 noun words from Study 1 and the high- and low-imagery lists of 19 items each. Instructions used were identical to those in the first experiment. As before, order of administration was counterbalanced across stimulus type, with each subject sorting all three types. Subjects were run in small groups of four or five. Results
Mean number of categories and category sizes are shown in Table III. Analysis of variance revealed no difference between deaf and hearing in number of categories (F = 0.09; df = 1, 124; p > 0.05) and no interaction (F = 2.74; df = 2, 248; p > 0 . 0 5 ) , but a significant difference due to stimulus type (F = 160.18; df = 2, 248; p < 0.001). Since there were more items in the common noun words category, this difference is to be expected if subjects adjust to more items by creating more categories of a similar size. Scheff6 tests confirmed that the only differences were between the noun words and the low- and high-imagery items. Slightly fewer categories were used for low-imagery than for high-imagery words by both groups, but the difference was not significant. Sorting data were combined to produce similarity matrices as in the first study, and the matrices were analyzed using Johnson's algorithm. Overlap values are given in Table III. Values for noun words were somewhat higher than the corresponding values from the first study, but the differences were slight. There were moderate differences between low- and high-imagery items for both groups, and very slight or no differences between deaf and hearing Table III. Mean Number of Categories, Mean Category Sizes, and Overlap Coefficients for Deaf and Hearing Subjects in Study 2 Stimulus type Response
High imagery
Low imagery
Noun words
measure
Deaf
Hearing
Deaf
Hearing
Deaf
Hearing
Number of categories
8.29
8.97
9.16
9.46
13.33
12.79
Category size
2.29
2.12
2.07
2.01
2.25
2.34
Overlap
0.500
0.500
0.611
0.666
0.644
0.689
69
Semantic Organization in Deaf and Hearing Subjects
subjects for the two types of items. Thus differences between high- and low-imagery items appeared much greater than differences between deaf and hearing subjects within any given word category. The diameter-method solutions for concrete and abstract words are shown in Figs. 1-4. The filled circles represent clusters that were also found in the connectedness solutions. The node values for hearing subjects were, in general, somewhat higher tha~ those for deaf subjects, perhaps reflecting less commonality of meanings among deaf subjects. This difference did not, however, affect the overlap values, which directly reflect the type of structure used. Comparison of the details of the clusterings manifest in the figures is revealing. Some differences are apparent, particularly for low4magery words. Note, for instance, that deaf subjects included freedom and law in a single cluster, whereas hearing subjects sorted freedom together with hope, and law together with duty. Again, where hour was sorted by hearing subjects into a kind of measurement cluster with length, cost, and amount, deaf subjects placed length and style together, reflecting (perhaps) a relation between such things as hem length and style in dress. The measurement cluster is still found for deaf subjects-it simply lacks the one element length.
/
i
d
:o {o # a. ~ - ~ ~
7--
o-
i
l o.
Fig. 1. Diameter-method hierarchical tree for hearing subjects sorting high-imagery words. Filled circles represent nodes also present in cannectedness-method solution. Numbers represent node height.
Tweney~ Hoemann, and Andrews
70
.I
x
.
Semantic Organization in Deaf and Hearing Subjects 1 Ryan D. Tweney, 2,3 Harry W. Hoemann, 2 and Carol E. Andrews 2
Received February 11, 1974
Hierarchical cluster analysis o f data from the sorting o f noun words was used to compare semantic structures in 63 profoundly deaf and 63 hearing adolescents. In the first study, performance differed only for a set o f words referring to sounds, where deaf persons have no experience, and not for a set o f common noun words and pictures. In the second study, differences between matched sets o f high. and low-imagery words were comparable for 63 deaf and 63 hearing subjects. It is concluded that deaf subfects manifested abstract hierarchical relations and were not dependent on "visual mediators" or hindered by the absence of "acoustic mediators."
INTRODUCTION Miller (1969) and Anglin (1970) have recently demonstrated that abstract, conceptual relations are reflected in the subjective organization of items in a person's lexicon. Miller showed that the presence or absence of higher-order, "abstract" relations can be detected by means of hierarchical cluster analyses This investigation was supported in part by National Institutes of Health Research Grant NS-09590-03 from the National Institute of Neurological Diseases and Stroke and in part by a Faculty Research Grant from Bowling Green State University. 1Porfions of Study 1 were previously reported at the Eighty-first Annual Convention of the American Psychological Association, Montreal, 1973. 2Department of Psychology, Bowling Green State University, Bowling Green, Ohio. 3Requests for reprints should be addressed to the first author, whose address is Department of Psychology, Bowling Green State University, Bowling Green, Ohio 43403. 61 @)1975 Plenum Publishing Corporation, 227 West 17th Street, New York, N.Y. 10011. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any f o r m o r by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise, without written permission of the publisher.
62
Tweney, Hoemann, and Andrews
of sorting data, a technique developed further and described in detail by Fillenbaum and Rapaport (1971). Anglin (1970) showed that such techniques can be applied to the analysis of the development of semantic relations in children. Using sorting data, free recall, and word association techniques, Anglin reported evidence of progressively more abstract structure in children's subjective lexicons as a function of age. The present investigation explored the organization of items in the subjective lexicons of deaf and hearing adblescents. Two studies were conducted to determine the amount of hierarchical structure present in the sorting-of deaf subjects and to compare their performance with that of hearing subjects of the same age. In the first study, semantic domains were chosen to assess the relative contribution of specific experiences to deaf persons' lexical structures. Three domains were used: common noun words, which would presumably be familiar to both deaf and hearing subjects; line drawings corresponding to the noun words (to determine if there is a uniquely visual component involved in the structure of deaf subjects' lexicons); and words referring to sounds (hiss, crash, roar, etc.), for which deaf subjects should have limited or no experience. It was expected that deaf subjects would show less hierarchical structure than hearing subjects when sorting sound words but not when sorting noun words and their corresponding pictures. This prediction follows from the argument of Furth (1971) that deaf children perform as well as hearing children on cognitive tasks for which the experiential background of deaf and hearing children is matched. While it is frequently found that deaf subjects do more poorly than age-matched hearing subjects (e.g., see DiFrancesca, 1972; Blanton, 1968; Myklebust, 1964), Furth has noted that there does not seem to be a consistent pattern associated with the cognitive tasks in which deaf children show poorer performance and those tasks in which deaf children perform as well as their hearing age peers. Furth has argued that the experiential deficit associated with a profound hearing loss and with society's treatment of deaf children is the most parsimonious explanation for the observed developmental lags. If the thinking processes of deaf persons are as logical and as abstract as those of hearing persons, then their subjective lexicon ought to reflect the same amount of hierarchical structure as long as the stimuli are equally familiar to both deaf and hearing subjects. Thus the sorting data generated for common noun words and pictures ought to reflect the same levels of conceptual thinking in deaf and hearing subjects, while the sorting data for sound words ought to maximize differences between the groups.
Semantic Organization in Deaf and Hearing Subjects
63
Since both the pictures and the noun words used in the first study were concrete and high in imagery value, a second study was conducted in which two different word lists were selected that were either very high or very low in imagery value as measured by published norms (Paivio et aL, 1968b). With the role of imagery thereby controlled, the possible influence of acoustic mediators could be assessed by comparing deaf and hearing sorts of the low-imagery items. Since the lack of acoustic mediation in deaf subjects has been cited to explain their poorer performances in, for example, tasks measuring sequential memory (Koh et al., 1971), the outcome of this comparison was considered especially important. In addition, as a partial replication, the common noun words from the first study were administered to the subjects in the second study.
STUDY 1
Method Subjects. The subjects were 63 severely and profoundly deaf adolescents (16-18 years old) enrolled at the Kentucky School for the Deaf in Danville, Kentucky, and 63 hearing adolescents (16-18 years old) enrolled in the Danville, Kentucky, City High School. Adolescents were used to facilitate obtaining comparable groups of subjects. No age trends were present in the data for any of the variables discussed below. The data were accordingly pooled across ages. Procedure. Three sets of stimulus items were prepared on index cards: 30 sound words, 30 common noun words drawn from a variety of conceptual categories, and 30 ink drawings corresponding to the list of noun words (see Table I). Subjects were tested in groups not larger than five. They were instructed to place words they did not know in a separate pile and then to sort each set into categories of similar meaning, using as many or as few categories as desired and assigning as many or as few items to each category as desired. The instructions were presented to the deaf subjects in both American Sign Language and spoken English by an experimenter who was a fluent user of American Sign Language. Order of administration was counterbalanced across stimulus type, with each subject receiving all three types of items. There were no effects on any of the indices as a function of order of administration. The data were therefore pooled across orders.
Tweney, Hoemann, and Andrews
64
Table I. Stimulus Words Used in Study 1 Common noun words key table chair picture house window kite fire flower tree leaf cup apple egg bread t
Sound words
horse bear soldier clown baby car airplane train hammer lawnmower book letter shoes purse money
swish tinkle hiss ring toot whisper whistle roar bark meow whack whine thunder crack crash
slam bang boom bump pop thump twang splat snap tap shout laugh cough rustle crackle
i
Table II. Mean Number of Categories, Mean Category Sizes, and Overlap Coefficients for Deaf and Hearing Subjects in Study 1 Stimulus type Noun words Response measure
Drawings
Sound words
Deaf
Hearing
Deaf
Hearing
Deaf
Hearing
12.95
13.10
12.35
12.51
9.37
11.30
Category size
2.46
2.48
2.63
2.78
2.72 a
3.09
Overlap
0.627
0.539
0.706
0.653
0.328
0.449
Number of categories
aCategory size for sound words sorted by deaf subjects excludes items placed in the "don't know" category.
Results Table II presents the means of the number o f categories and category sizes used in the sortings. For the number of categories, deaf-hearing differences were significant ( F = 4.04; d f = 1, 123; p < 0.05). Stimulus type was significant ( F = 51.03; df = 2, 246; p < 0.005), and the interaction was significant ( F = 11.53; df = 2 , 2 4 6 ; p < 0.005). Scheff~ tests revealed that the
Semantic Organization in Deaf and Hearing Subjects
65
deaf-hearing difference and the interaction were due to the small number of categories used by deaf subjects for sound words. Thus a second analysis was conducted using mean category size to correct for unequal numbers of categories. For deaf subjects sorting sound words, the large "don't know" category was excluded. Analysis of variance for this case showed no significant deaf-hearing difference (F = 1.07; df = 1, 123; p > 0.05) and no interaction (F = 1.24; df = 2, 246; p ;> 0.05). A significant difference due to stimulus type was found (F = 7.09; df = 2,246; p < 0.005). Scheffd tests showed that the latter difference was due only to the somewhat large category size used b y hearing subjects sorting sound words. Similar values for numbers of categories and category sizes in the deaf and hearing samples (excluding sound words) indicated that both groups understood the instructions similarly and responded to the task demands of the situation in a similar manner. For each group, all sorts were tallied to produce a 30 • 30 frequency matrix showing the number of times each pair of items was combined in one cluster. Thus if a particular subject placed the words iron, nail, and machine in a single category, a tally mark was placed in the matrix at the cells for iron-nail, nail-machine, and iron-machine. The maximum cell entry was 63, equal to the number of subjects. The matrices were submitted to hierarchical duster analysis using Johnson's (1967) algorithm. In brief, the algorithm seeks those items most often sorted together, combines them into a cluster, recomputes the similarity matrix using the new duster as though it were a single item, and begins anew by searching for the next most commonly sorted pair of items or clusters. The process continues until all items have been combined into a single large cluster. As output, the program produces a tree diagram indicating which items duster together. Solutions are necessarily hierarchical, regardless of the nature of the data. When nonhierarchical structure is present, this can be discovered by executing two different variants of the algorithm, the "diameter" method and the "connectedness" method. Each produces identical solutions when the data are perfectly hierarchical. "Noise" in the data, stemming from nonhierarchical structure or inconsistencies among subjects, produces differences between the solutions and results in nonshared clusters between the two trees. Hence the degree of overlap for each pair of solutions can serve as an index of the amount of hierarchical structure present in a given data set (Miller, 1969). For each group of subjects, cluster analyses were performed using each variant of the algorithm separately for each type of stimulus item. Overlap between solutions was calculated using the formula Proportion overlap = (2 x number of nodes in common) + (number of nodes in diameter solution + number of nodes in connectedness solution).
66
Tweney, Hoemann, and Andrews
For identical trees, overlap equals 1.00; for trees having no common clusters, overlap equals 0.0. Miller (1969) reports a Monte Carlo simulation in which pseudorandom data produced mean overlaps of 0.149, establishing a chance baseline. Overlap coefficients for all groups are shown in Table II. The values for common noun words and pictures were very high for this type of task, with deaf subjects showing slightly higher values than hearing subjects. The values for sound words were somewhat lower for the hearing subjects and much lower for the deaf subjects, No statistical test of these differences exists. Subjective inspection of the trees produced by deaf and hearing subjects confirmed the results of the overlap coefficients. The deaf and hearing subjects differed in only minor ways for noun words and pictures, but greatly for sound words. Even where some structure seemed to be present for the sound words among deaf subjects, it was not always based on semantic relations. Thus meow-bark and laugh-cough clustered together for deaf subjects, but whack-whine, a somewhat less frequent cluster, may have been based only on visual letter similarity. This kind of sort was not found for noun words or pictures, suggesting that the deaf subjects resorted to such criteria for clustering only when they lacked adequate semantic grounds. Comparison of noun words and pictures revealed no substantial differences: both groups sorted in essentially the same fashion regardless of the nature of the stimulus item. STUDY 2 The results of the first study may be limited in generality because the noun words were very concrete. Paivio (1971) has argued that such words may be processed differently than words having little concrete reference. To support this, Paivio and others have shown that scaled ratings of the extent to which lexical items have associated imagery (Paivio's/) are a major predictor of learning ease (Paivio et al., 1968a) and retention of meaning (Begg and Paivio, 1969). It is therefore possible that the results of the first study show comparable performance among deaf and hearing subjects only because the words used were high in imagery. The second study attempted to determine if this was, in fact, the case. Method
Subjects. Subjects were 63 severely and profoundly deaf adolescents (16-18 years old) enrolled in the Kentucky School for the Deaf and 63
Semantic Organization in Deaf and Hearing Subjects
67
hearing adolescents (14-17 years old) enrolled in Boyle County High School, Danville, Kentucky. Approximately half of the deaf subjects had been subjects in the first experiment, conducted 6 months earlier. As in the first study, no age trends were present for any of the variables discussed. Groups were the same size as in the first study to facilitate comparison of the hierarchical trees in the two studies. Stimulus Materials. The list of stimulus words was prepared using the Paivio et al. (1968b) norms. Each list consisted of 19 words. The high-imagery list had mean imagery values 0.50 SD or more above the mean for the entire distribution (4.97), while the low-imagery list had mean imagery values 0.50 SD or more below the mean. For the high-imagery list, shown in Fig. 1, all [ ~> 5.93, mean [ = 6.28, SD = 0.249. For the low-imagery list shown in Fig. 2, all I < 4.01, mean I = 3.18, SD = 0.530. In selecting the stimulus words, an attempt was made to keep frequency, signability (in American Sign Language), familiarity, and meaningfulness as constant as possible. In each list, 13 words were AA in frequency (Thorndike and Lorge, 1944), and the remaining six were A. Twelve words in the low-imagery list and t 3 words in the high-imagery list were indexed by Stokoe et al. (1965) as having American Sign Language gesture equivalents. Silverman-Dresner and Guilfoyle's (1972) norms were used to determine the likelihood that the meaning of each word was familiar to the deaf subjects and that the two lists were matched on this dimension for the deaf subjects. The norms are based on a paper-and-pencil test (essentially a reading-recognition test) given to 13,207 deaf students (8-17 years old) from 89 schools for the deaf in the United States. Using Silverman-Dresner and Guflfoyle's norms for 16- and 17-year-old deaf students, the low-imagery list had a mean score of 62.6 and the high-imagery list a mean score of 63.0, values quite a bit toward the high end on the norms for that age. The difference was not statistically significant. Mean meaningfulness (Nobte's m; as reported in the Paivio etal., 1968b, norms) for the high-imagery list was 6.42, SD = 0.818, and for the low-imagery list was 5.50; SD = 0.691. This difference was significant (t = 3.75, df = 36, p < 0.005). It was not possible to equalize meaningfulness values at the very high levels of frequency and imagery used. As Paivio etal. (1968b) point out, high imagery values are closely associated with high meaningfulness values, with meaningfulness varying over a greater range at low imagery values. Thus the construction of balanced lists is fully attainable only when relatively nonextreme values are used. For the present study, such lists would have had the disadvantage of including substantially greater numbers of nonsignable words and words low on the familiarity norms for deaf subjects. It was felt that the present lists represented the best compromise, especially
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Tweney, Hoemann, and Andrews
since a number of studies have shown that meaningfulness has little effect when imagery is controlled (Paivio, 1971). Procedure. Three stimulus lists were used; the 30 noun words from Study 1 and the high- and low-imagery lists of 19 items each. Instructions used were identical to those in the first experiment. As before, order of administration was counterbalanced across stimulus type, with each subject sorting all three types. Subjects were run in small groups of four or five. Results
Mean number of categories and category sizes are shown in Table III. Analysis of variance revealed no difference between deaf and hearing in number of categories (F = 0.09; df = 1, 124; p > 0.05) and no interaction (F = 2.74; df = 2, 248; p > 0 . 0 5 ) , but a significant difference due to stimulus type (F = 160.18; df = 2, 248; p < 0.001). Since there were more items in the common noun words category, this difference is to be expected if subjects adjust to more items by creating more categories of a similar size. Scheff6 tests confirmed that the only differences were between the noun words and the low- and high-imagery items. Slightly fewer categories were used for low-imagery than for high-imagery words by both groups, but the difference was not significant. Sorting data were combined to produce similarity matrices as in the first study, and the matrices were analyzed using Johnson's algorithm. Overlap values are given in Table III. Values for noun words were somewhat higher than the corresponding values from the first study, but the differences were slight. There were moderate differences between low- and high-imagery items for both groups, and very slight or no differences between deaf and hearing Table III. Mean Number of Categories, Mean Category Sizes, and Overlap Coefficients for Deaf and Hearing Subjects in Study 2 Stimulus type Response
High imagery
Low imagery
Noun words
measure
Deaf
Hearing
Deaf
Hearing
Deaf
Hearing
Number of categories
8.29
8.97
9.16
9.46
13.33
12.79
Category size
2.29
2.12
2.07
2.01
2.25
2.34
Overlap
0.500
0.500
0.611
0.666
0.644
0.689
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Semantic Organization in Deaf and Hearing Subjects
subjects for the two types of items. Thus differences between high- and low-imagery items appeared much greater than differences between deaf and hearing subjects within any given word category. The diameter-method solutions for concrete and abstract words are shown in Figs. 1-4. The filled circles represent clusters that were also found in the connectedness solutions. The node values for hearing subjects were, in general, somewhat higher tha~ those for deaf subjects, perhaps reflecting less commonality of meanings among deaf subjects. This difference did not, however, affect the overlap values, which directly reflect the type of structure used. Comparison of the details of the clusterings manifest in the figures is revealing. Some differences are apparent, particularly for low4magery words. Note, for instance, that deaf subjects included freedom and law in a single cluster, whereas hearing subjects sorted freedom together with hope, and law together with duty. Again, where hour was sorted by hearing subjects into a kind of measurement cluster with length, cost, and amount, deaf subjects placed length and style together, reflecting (perhaps) a relation between such things as hem length and style in dress. The measurement cluster is still found for deaf subjects-it simply lacks the one element length.
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Fig. 1. Diameter-method hierarchical tree for hearing subjects sorting high-imagery words. Filled circles represent nodes also present in cannectedness-method solution. Numbers represent node height.
Tweney~ Hoemann, and Andrews
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