Aging, Neuropsychology, and Cognition, 16: 721–744, 2009
http://www.psypress.com/anc
ISSN: 1382-5585/05 print; 1744-4128 online
DOI: 10.1080/13825580902912739
Level of Education and Category Fluency
Task among Spanish Speaking Elders:
Number of Words, Clustering, and
Switching Strategies
1744-4128
1382-5585/05
NANC
Aging,
Neuropsychology, and Cognition
Cognition, Vol. 1, No. 1, April 2009: pp. 1–36
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Education
Mónica
Rosselli
and Verbal
et al. Fluency
MÓNICA ROSSELLI1, RUTH TAPPEN2, CHRISTINE WILLIAMS2, JUDY SALVATIERRA3,
1
AND YARON ZOLLER
1
Department of Psychology, Charles Schmidt College of Science, Florida Atlantic University,
Davie, FL, USA, 2Christine E. Lynn College of Nursing, Florida Atlantic University, Boca
Raton, FL, USA, and 3Department of Psychology, Miami Dade College, Miami, FL, USA
ABSTRACT
It has been well documented that education influences the individual’s performance on
category fluency tasks but it is still unclear how this effect may differ across the different
types of category tasks (i.e., animals, fruits, vegetables and clothing). This study aims
(1) to analyze the effect of the level of education on four different types of category
fluency tasks among elder Hispanic Americans and (2) to provide normative information on a population with different education levels that was previously screened for
neurological and psychiatric conditions. In addition this study examines the semantic
strategies used by these individuals to complete the fluency tasks. The sample included
105 healthy Hispanic individuals (age 55–98; 29 males and 76 females) divided into
three education groups (<6, 6–11 and >11 years of education). Results showed that after
controlling for age and gender, education has a main effect and is a strong predictor of
performance in verbal fluency for the categories animals and clothing with increasing
educational attainment being associated with higher category fluency scores and with
more switches between categories. These findings suggest that the category fruit is less
influenced by level of education than the other three semantic categories and may be a
more appropriate test across different educational groups. Results from this study provide
a reference for clinicians assessing verbal fluency in Spanish speaking populations.
Keywords: Fluency; Assessment; Culture; Education; Language; Spanish; Elders.
Address correspondence to: Mónica Rosselli, Ph.D., Department of Psychology, Charles Schmidt
College of Science, Florida Atlantic University, 2912 College Ave., Davie, FL 33314-7714, USA.
E-mail: [email protected]
© 2009 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business
722 MÓNICA ROSSELLI ET AL.
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INTRODUCTION
Category fluency tasks are frequently used in neuropsychological assessment of normal and abnormal populations (Ardila, Bernal, Ostrosky-Solis,
& Bernal, 2006; Lezak, Howieson, & Loring, 2004; Mitrushina, Boone,
Razani & D’Elia, 2005; Strauss, Sherman, & Spreen, 2006). To complete the
task, the individual is required to recall as many words as possible belonging
to a particular category (e.g., animals) within a restricted time limit (60 s).
Category fluency tests are believed to assess verbal initiative (Lezak et al.,
2004). In addition, successful performance on fluency tasks requires executive functions such as inhibition of words that do not conform to the rules of
the task (Anderson, Levi, & Jacobs, 2002) and mental set switching (Rende,
Ramsberger, & Miyake, 2003). Performance on category tasks has been
shown to be affected by a variety of brain pathologies including frontal
(Herrmann, Ehlis, & Fallgatter, 2003) and temporal damage (Troyer,
Moscovitch, Winocur, Alexander, & Stuss, 1998), Parkinson’s disease
(Donovan, Siegert, McDowall, & Abernethy, 1999) and Alzheimer’s disease
(Coen et al., 1999; Salvatierra, Rosselli, Acevedo, & Rajan, 2007).
Cognitive strategies such as semantic clustering and shifting have been
proposed to underlie the generation of words in fluency tasks. The individual
first searches for meaningful semantic subcategories and then clusters them
for more efficient recall (Gruenewald & Lockhead, 1980; Robert et al.,
1998). A subcategory is a subdivision that has general distinguishing characteristics within a larger category. For example, ‘birds’ and ‘insects’ are subcategories of the larger category of ‘animal’ (Salvatierra et al., 2007).
A semantic cluster is defined as two or more consecutive words belonging to
a particular subcategory (e.g., insects). The ability to shift effectively from
one subcategory to another has been suggested to underscore an efficient
verbal fluency test performance (Troyer, Moscovitch, & Winocur, 1997).
Though both clustering and switching have been proposed to be positively
correlated with the total number of words produced within a semantic category (Robert et al., 1998; Troyer et al., 1997), they underlie different brain
processes. Clustering involves semantic searching and is impaired in cases
of temporal pathology while switching involves a more active strategy and
tends to be reduced in cases of frontal damage (Troyer et al., 1998).
From a cognitive perspective, verbal fluency is, therefore, considered a
multifactorial task and report of the number of correct words alone does not
address the different processes involved in this task (Robert et al., 1998).
The qualitative analysis of cognitive strategies such as switching and clustering provide added insights on the underlying cognitive deficits in different
clinical disorders (Gomez & White, 2006; Ho et al., 2002; Raskin, Sliwinski,
& Borod, 1992; Robert et al., 1998). For example, clustering and switching
remain stable over time in patients with Huntington’s disease (Ho et al.,
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EDUCATION AND VERBAL FLUENCY
723
2002) while difficulties in switching have been observed in Parkinson’s
patients (Donovan et al., 1999). Moreover, clustering has been found to be a
better predictor than the total number of words for early Alzheimer’s disease
(Fagundo et al., 2008).
Previous research has shown that demographic variables such as age,
gender, literacy and level of education may be crucial determinants of the individual’s performance on verbal fluency tasks (Acevedo et al., 2000; BenitoCuadrado, Estaba-Catillo, Boehm, Cejudo-Boloivar, & Pena-Casanova, 2002;
Kave, 2005; Kosmidis, Vlahou, Panagiotaki, & Kiosseoglou, 2004;
Ostrosky-Solis, Ardila, Rosselli, Lopez-Arango, & Uriel-Mendoza, 1998;
Reis & Castro-Caldas, 1997; Rosselli, Ardila, & Rosas, 1990; Troyer, 2000)
although the influence of these demographic variables is not consistent
across different types of category tests. For example, researchers found that
higher levels of education correlated with better performance on category
tests such as animals and fruits but was not correlated with vegetables
(Acevedo et al., 2000). In the study conducted by Acevedo’s research team, a
differential effect was reported for gender. Females scored significantly
higher than males in vegetables and fruits but no gender effect was observed
for animals and clothing. Several studies have also found that persons who
were illiterate scored lower than literates on the semantic verbal fluency tests
when animals and furniture categories were used (Ardila, Rosselli, & Rosas,
1990; Gonzalez da Silva, Peterson, Faisca, Ingvar, & Reis, 2004; Reis &
Castro-Caldas, 1997). No effect of literacy was reported for supermarket item
recall (Petersson, Reis, & Ingvar, 2001; Reis, Guerreiro, & Petersson, 2001).
Some authors (Gonzalez da Silva et al., 2004) have looked at demographic group differences in terms of cognitive strategies used in the recall
of supermarket and animal names. Results demonstrated that literates produced a higher number of isolated words and in consequence used a higher
number of switches between subcategories for animal and supermarket
when compared to the illiterate group. More clusters and more subcategories
were seen in the illiterate group compared with the literate group for the animal category only. The authors concluded that the literate group adopted a
more active cognitive strategy in searching for words than did the illiterate
group but that difference in cognitive strategy seemed to depend on the type
of experience with the semantic category. Gonzalez et al.’s findings may
generalize to individuals with 1 or 2 years of formal education. In fact,
Ostrosky et al. (1998) found no significant differences in fluency category
(animals) between illiterates and literates with a few years of formal education (less than 4 years), yet when comparing the illiterates with the literates
with more than 5 years of schooling, differences emerged.
In summary, it is still unclear how individuals with different levels of
education perform across different fluency tasks and how differences in task
performance across education groups are related to specific semantic criteria.
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724 MÓNICA ROSSELLI ET AL.
Moreover, there is little information on how levels of education influence
the cognitive strategies involved in semantic fluency tasks. Few studies have
provided normative data for elderly Hispanics with little education (for a
review see Strauss et al., 2006) yet in the United States, 28% of the Hispanics older than 55 have 6 years of formal education or less (U.S. Census
Bureau, 2007).
The specific aims of this study were (1) to analyze the effect of the
level of education on four different types of category fluency tasks (i.e.,
animals, fruits, vegetables and clothing) among elder Hispanic Americans
and (2) to provide normative information on a population with different educational levels that was previously screened for neurological and psychiatric
conditions that may affect cognition. In addition, this study examines the
semantic strategies (clustering, switching, number and size of semantic subcategories) employed by individuals with dissimilar educational attainments
to successfully perform the fluency tasks. Most researchers who have examined the influence of demographic variables on verbal fluency strategies have
used only one or two fluency categories (Ostrosky et al., 2000; Gonzalez da
Silva et al., 2004) or have used a total score from several category tests without analyzing the effects on each specific category fluency task (Kave, 2005;
Kosmidis et al., 2004). To overcome these limitations, we examined the
effects of four categories. Based on previous research, our prediction was
that level of education would predict performance on animals but not on
fruits, vegetables or clothing. In most cases people have daily contact with a
variety of fruits and vegetables during meals, when shopping at supermarkets
or at restaurants. Their experience generates ‘ecologically’ valid categories
across education groups. The same may be true with clothing items. Animal
names on the other hand, may be more influenced by level of education.
People living in urban areas are not ‘naturally’ exposed to many animals in
daily activities and may acquire knowledge about animal names in books,
school and travel that are more accessible to people with higher education
levels. Current literature gives very little information about the influence of
education on verbal fluency tasks. Results from this study can be used as a
reference point for clinicians assessing verbal skills in Spanish speaking
patients rather than inappropriately relying on results from English speaking
samples. Moreover, the data from this study will add needed normative
information on elderly Hispanics with less than 6 years of education.
In addition, by providing information about clustering and switching strategies in normal aging, data from this study could assist clinicians who use this
analysis as a helpful tool to make differential diagnosis between normal
aging and early Alzheimer’s disease (Fagundo et al., 2008). This study provides a reference for the type of strategies (clustering and switching) that
aged Hispanic individuals with different education attainment but with no
neurological or psychiatric disorders would perform.
EDUCATION AND VERBAL FLUENCY
725
METHOD
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Participants
One hundred and five cognitively unimpaired Hispanic elders age 55–98
(29 males, 76 females) were recruited from various community sites in
Miami-Dade County, Florida. Individuals were recruited through verbal
and written notices placed at different housing complexes and senior
centers throughout the county. Twenty-nine percent of the participants were
married, 29% divorced, 3% separated, 30% widowers and 8% had never
married. Most of the participants came from low socioeconomic environments; 76% reported yearly household income of $10 000 or less, 11%
between $10 000 and $15 000, 5% between $15 000 and $20 000, 6%
between $20 000 and $25 000; only 1% reported a year income between
$35 000 and $45 000. The majority of the participants (88%) reported
living in urban areas while growing up and 12% grew up in rural areas.
All participants resided in an urban area of Miami-Dade County at the time
of testing.
All participants had normal scores on the Mini-Mental State Examination
(MMSE) (Ardila, Rosselli, & Puente, 1994; Folstein, Folstein, & McHugh,
1975; Ostrosky, Lopez-Arango, & Ardila, 2000). Exclusion criteria were
history of neurological or psychiatric disease and alcohol or drug abuse. This
information was collected using the participants’ self-report. The DSM-IV
structured clinical interview (SCID-I) (First, Spitzer, Gibbon, & Williams,
1997) was used to ruled out mood and anxiety disorders and dementia All
subjects were living independently and reported independence in the basic
activities of daily living (Katz, Ford, & Moskowitz, 1963) and in instrumental activities of daily living (Lawton, 1969).
Participants were divided into three education groups using years of
formal education: 1–5 (n = 21, 15 females and 6 males); 6–11 (n = 41, 29
females and 12 males) and 12 or more (n = 43, 31 females and 12 males).
All subjects had normal scores on the Fuld Object-Memory Evaluation
(FOME) (Fuld, 1981). This test has been shown to provide sufficient
discrimination between normal and impaired Spanish speaking elders
(Loewenstein, Duara, Arguelles, & Arguelles, 1995). No age differences
across education groups were seen, F(3, 102) = 0.16, p = .86. The groups
also did not differ on FOME scores, F(3, 102) = 1.13; p = .33 and gender, c2
(2, N = 105) = 0.21, p = .90 and SES, c2(10, N = 105) = 13.55; p = .19.
All participants were American immigrants born in a Latin American country with an average number of years in the US of 27.11 (SD = 15.35). The
majority of them were born in Cuba (75%). Others were born in Colombia
(11%), Puerto Rico (6%), Nicaragua (5%), Peru (3%), Honduras (3%), and
Argentina (2%). All reported that Spanish was their dominant language.
Many did not speak any English at all.
726 MÓNICA ROSSELLI ET AL.
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Instrument/Measures
Verbal fluency was tested using four categories: animals, vegetables,
fruits and clothing. Participants were tested in Spanish and required to name
words that belonged to a particular category (e.g., animals) in a 1-min trial
(Ardila et al., 1994; Spreen & Strauss, 1998). Participants were instructed to
avoid repetitions and variations of the same word (for example, perro, perrito).
All subjects’ responses were recorded. The following scores were calculated
per category: total number of correct words, number of semantic subcategories, number of switches, number of semantic clusters and cluster size.
1. Total number of correct words included total number of correct
words generated excluding intrusions and repetitions. Although most participants were Spanish monolinguals, the effect of living in the US was evident.
Occasionally, words were generated in English. Examples are blueberry as a
fruit or belt as a clothing item. These words were accepted as legitimate
responses and were counted as correct. Credit was also given for superordinate
categories (e.g., birds). Specific exemplars of that category were also
accepted. For example, if the participant said ‘birds’ or ‘eagle’, both were
accepted as correct. Gender distinctions (e.g., caballo [horse], yegua [mare])
and age distinctions (e. g. vaca [cow], ternero [calf]) were also accepted.
2. Semantic subcategories – A subcategory is a subdivision that has
general distinguishing characteristics within a larger category. For example,
‘birds’ and ‘insects’ are subcategories of the larger category of ‘animal’.
The following eight animal subcategories were adapted from Gonzalez
da Silva et al. (2004) and Salvatierra et al. (2007): reptiles, birds, rodents,
pets, water, farm, insects and wild. Salvatierra et al. (2007) suggested three
additional subcategories: fish, forest and pre-historic. Our participants
produced few responses corresponding to these three subcategories, so we
incorporated fish in the water subcategory and forest in the wild category.
No responses were generated for the pre-historic category, therefore it was
eliminated. Gonzalez da Silva et al. (2004) divided the wild subcategory in
two: European and non-European wild animals. We incorporated all wild
animals in one category. All eight subcategories used in our study were part
of the 17 animal subcategories used by Roberts and Le Dorze (1997).
For fruits, the following six subcategories were defined: Citrus, Dried,
Tropical, Non-tropical/Traditional, Berries, and Melons. The first three subcategories were adopted from Robert et al. (1998) although the Exotic subcategory used in that study was modified and was called Tropical after
Kosmidis et al. (2004). Three additional subcategories were identified
(Non-tropical/Traditional, Berries and Melons) using the participants’ own
responses, following Roberts and Le Doze (1997), Kosmidis et al. (2004) and
Rende et al. (2003) empirical method of identifying semantic subcategories
(see Appendix for examples of each subcategory).
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EDUCATION AND VERBAL FLUENCY
727
Finally, for vegetables and clothing, three Hispanic judges analyzed
the participants’ responses on each category and defined the subcategories,
following Roberts and Le Dorze (1997) methodology. This same method has
previously been used by Gonzalez da Silva et al. (2004) in analyzing the
supermarket category, by Kosmidis et al. (2004) in studying the category
objects and by Rende et al. (2003) in the analysis of the categories fruits and
vegetables. The judges were fluent Spanish speakers who were familiar with
cultural factors influencing the type of items generated by this sample.
For the vegetable category, the following seven subcategories were
empirically defined based on the words generated by the participants: Root/
Tubers, Fruitlike, Green Stem, Spices, Flower, and Peas/Beans/Grains/Nuts.
Finally, for the clothing category, we analyzed the participants’ responses in
this category from all participants following Roberts and Le Dorze’s (1997)
methodology and created the following seven subcategories: Accessories,
Footwear, Underwear, Outerwear, Upper Body, and Lower Body.
The Appendix has examples of words that were included in each of the
subcategories used in this study. In some cases, one response could belong
to two subcategories. For example, pollo (Chicken) could be a bird and a
farm animal. In those cases, the anteceding and preceding animal were used
to determine the subcategory of pollo. For example, if pollo (Chicken) came
after águila (eagle) and before condor (condor), it was considered under the
birds subcategory; if the preceding and antecedent responses were vaca
(cow) and caballo ( horse), then pollo was considered a farm animal.
3. Number of switches – The number of switches was determined by
the number of times the subject moved from one subcategory to another.
4. Number of semantic clusters – A semantic cluster was defined as
three or more consecutive words belonging to a particular subcategory (e.g.,
insects). For example, consecutive responses like mosco (fly), hormiga (ant)
and mariposa (butterfly) would make one cluster. Following Gonzalez da
Silva et al. (2004) and Kosmidis et al. (2004), we also made the distinction
of strongly associated pairs, i.e., two words belonging to the same subcategory such as tigre (tiger) and león (lion) and counted them as a two item
cluster (‘strong pair’) only if used by more than three participants. See
Appendix for the description of strong pairs under each category.
5. Mean cluster size – Mean cluster size was calculated as the average
number of responses per cluster per subject per category.
All participants’ responses were transcribed and scored by one judge.
A second judge independently verified the transcriptions and scored all
responses. Inter-judge agreement for total number of correct responses was
.99. Three judges verified the scoring for subcategories, semantic clusters,
cluster size and number of switches and the inter-judge reliability coefficients were .98, .95, .95, and .97, respectively. There was no systematic error
across variables.
728 MÓNICA ROSSELLI ET AL.
Interrater reliability of r = .98 has been reported for verbal fluency tests
(Norris, Blankenship-Reuter, Snow-Turek, & Finch, 1995). Test–retest reliability with retesting occurring 6 months later has produced a reliability
coefficient of r = .74 (Ruff, Light, Parker, & Levin, 1996). Retesting of a
group of elderly individuals after 1 year yielded a reliability coefficient of
r = .71 (Coen et al., 1999; Snow, Tierney, Zorzitto, Fisher, & Reid, 1988).
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Statistical Procedures
A 3 × 4 repeated measures multivariate analysis of covariance was performed for each of the five dependent measures. The between subjects factor
was level of education (3 groups) and the within subjects factor was the type
of category test (animals, fruits, vegetables and clothing). In addition,
univariate analyses of covariance were performed to examine the influence of
education on each dependent variable. Age was included in all analyses as a
covariate to partition out its influence over the dependent measures. Post-hoc
analyses were used to assess the mean group differences. The effect sizes of
the univariate and multivariate F values were analyzed using partial eta
squares (ph2). Two models of multiple regression analyses were performed
to examine the predictive value of years of education on the dependent measures over and above gender and age. In the first model gender and age were
entered as independent measures and in the second model gender, age and
education were included. R2 values were calculated for both models. Finally,
Pearson’s correlations were used to examine the association between the
dependent measures. Type I error probability was set to 0.05 for all analyses.
RESULTS
Table 1 shows the means, standard deviations and the univariate and multivariate effects of education and the fluency task on all dependent measures.
The effects of the covariate age are also indicated. Multivariate analysis of
covariance (MANCOVA) showed that the education main effect was significant for the total number of words within categories and number of
switches. More words and switches were produced by the better educated
group (>12 years of education). The groups with lowest level of education
generated less number of words across categories than the group with 6 or
more years of education (p = .048) and the group with more than 12 years of
education (p = .004). No significant differences were found between the
middle educated group and the highest educated one. The main effect of the
type of fluency task was not significant for any of dependent measures in the
multivariate analyses of covariance. The interactions between education and
category task (i.e., animals, fruits, vegetables and clothing) were not significant for any of the dependent measures. Age was a significant covariate in
the multivariate analyses for total number of words and number of clusters.
729
EDUCATION AND VERBAL FLUENCY
TABLE 1. Multivariate analyses of education and type of fluency tasks and univariate effects of
education on each dependent measure using age as a covariate on all analyses
Education group
1–5
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Category
Total Words+
Animals
Fruits
Vegetables
Clothing
Subcategories+
Animals
Fruits
Vegetables
Clothing
Clusters+
Animals
Fruits
Vegetables
Clothing
Cluster Size+
Animals
Fruits
Vegetables
Clothing
Switches+
Animals
Fruits
Vegetables
Clothing
6–11
Effects
>12
Mean
Total
Mean
SD
Mean
SD
SD
Mean
SD
12.76
11.71
8.48
10.33
3.03
2.91
2.50
2.49
14.49
11.63
8.46
12.15
3.10 15.26 3.69
2.29 12.65 3.31
3.19 9.74 3.18
2.82 13.21 3.29
14.46
12.07
8.99
12.21
3.44
2.89
3.10
3.12
4.19
4.14
3.52
4.19
1.40
0.72
0.98
1.28
4.85
3.61
3.93
4.23
1.21
0.92
1.23
0.86
4.86
3.95
4.28
4.45
1.37
0.98
1.15
0.77
4.72
3.86
3.99
4.31
1.33
0.92
1.17
0.93
2.24
2.00
1.14
2.29
0.70
1.09
0.79
1.18
2.53
1.93
1.10
2.37
1.10
1.10
.92
1.00
2.60
2.26
1.30
2.43
0.96
0.95
0.94
1.19
2.50
2.07
1.19
2.38
0.97
1.03
0.90
1.11
4.19
2.88
2.35
2.68
1.92
1.03
1.36
0.67
3.38
2.70
2.01
2.75
.94
1.31
1.40
0.63
3.40
2.96
2.13
2.90
1.00
1.05
1.14
0.79
3.55
2.84
2.13
2.80
1.25
1.15
1.28
0.70
4.67
6.10
4.57
4.95
2.67
1.97
1.98
1.46
6.24
6.00
4.78
6.07
2.25
1.74
2.14
2.09
6.70
6.02
5.86
6.29
2.68
2.45
2.45
2.01
6.11
5.98
5.18
5.93
2.60
2.07
2.30
2.00
Fluency
Task
Education
2
F
ph
4.68**
3.75*
1.40
2.02
6.36**
2.70
1.51
2.17
2.98
0.74
1.04
.96
1.00
0.57
0.12
2.08
3.70*
0.10
0.45
0.81
4.69**
4.58**
0.05
3.17*
3.31*
.09
.07
.03
.04
.11
.06
.05
.04
.06
.01
.02
.01
.02
.01
.00
.04
.06
.01
.01
.00
.08
.08
.00
.06
.06
F
2.38
.30
2.29
2.09
.05
ph
Age
2
F
.06 4.74*
5.32*
.69
3.05
.30
.00 2.25
5.47*
.00
.161
2.31
.36 4.14*
1.99
1.93
.00
2.53
.41 .21
.12
4.02*
.24
.01
.00 1.95
.22
1.27
1.78
1.06
ph2
.05
.05
.00
.03
.00
.02
.05
.00
.01
.02
.04
.01
.01
.00
.02
.00
.00
.04
.00
.00
.01
.00
.01
.01
.01
+Multivariate effects; *p < .05; ** p< .01; ph2 corresponds to partial eta square.
Univariate analyses revealed that the main effect of education was not
seen for any of the dependent measures under fruits. Significant effects
emerged under vegetables for number of switches, with a greater number of
switches observed in the group with more than 6 years of education in comparison to the group with less than 6 years of education (mean differences
significant at .05 using post-hoc Tukey). In the animals category, significant
education effects were evident for the total number of words, mean cluster
size and number of switches. The most educated group (>12 years) produced
more words, smaller clusters and more switches than the least educated
group (<6 years of education) (mean differences significant at .05 using
post-hoc Tukey). Also, the group with 6–11 years of education had significantly more switches and smaller clusters under animals than the group with
less than 6 years of education. More words and switches in the clothing category were produced for the most educated group in comparison to the least
educated group (mean differences significant at .05 using post-hoc Tukey)
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730 MÓNICA ROSSELLI ET AL.
and for the middle educated group in comparison to the least educated group
(mean differences significant at .05 using post-hoc Tukey). No significant
differences were observed between the group with 6–11 years of education
and the group with more than 12 years of education in any of the dependent
measures. Results revealed that age was a significant covariate in the effects
of education for total number of words and subcategories under animals and
for cluster size under fruits.
To better characterize the sample by age and education, each of the
education groups was divided into two age groups using a median split
procedure. Since the median age of the whole group was 74 years old, the
following two age groups were defined 55–74 and 75–99 years. The means
and standard deviations on each of the dependent measures for these groups
are presented on Table 2. Further analysis of the interaction between age and
education using these age groups is limited by the small number of individuals
included in each age cell in the lowest educated group.
Unfortunately the analysis of the interaction between gender and
education is also limited by the irregular distribution of gender in the sample
with significantly more females than males and therefore by the very small
representation of males in each education group. However, means by education and gender are provided on Table 3.
Multiple regression analyses showed that years of education was a
significant predictor for the number of words under animals and clothing,
for cluster size and number of switches under animals and for number of
subcategories, and number of switches under vegetables and cluster size and
switching under clothing The R2 showed that education explained a significant proportion of the variance for all the above dependent measures beyond
the effects of age and gender (Table 4). For example, for total words under
animals, gender and age simultaneously explained 5% of the variance; however when education is entered with age and gender 11% of the variance is
explained, meaning that education alone adds 6% of the variance. Age was a
significant predictor for total words under animals and clothing and for
cluster size under fruits. Gender, on the other hand, significantly predicted
subcategories under animals, number of switches under fruits, total words,
subcategories, and number of switches under vegetables and number of total
words, and clusters for clothing. The beta coefficients presented on Table 4
and the analyses of the means presented on Tables 2 and 3 suggest that being
younger and having a higher level of education contributes to better scores in
animals and clothing. Higher scores in vegetables seem to be associated with
being a female whereas higher scores in fruits appear to be independent from
education, age and gender. Demographic variables were in general not good
predictors for clustering, except for vegetables, in which being a female
contributed to a higher number of clusters. On the contrary, switching was
predicted from level of education, meaning that having a high level of
EDUCATION AND VERBAL FLUENCY
731
TABLE 2. Mean and standard deviations (in parentheses) for each of the dependent measures by
education and age
Education group
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Category
1–5
6–11
>12
Total
Age in years
Age in years
Age in years
Age in years
55–74
N = 12
75–98
N=9
55–74
N = 18
75–98
N = 23
55–74
N = 26
75–98
N = 17
55–74
N = 56
75–98
N = 49
Total Words
Animals
13.9 (3.2) 11.2 (2.0) 15.3 (3.4) 13.7 (2.7) 15.0 (3.9) 15.8 (4.7) 14.9 (3.6) 13.9 (3.1)
Fruits
12.9 (3.0) 10.1 (1.6) 12.2 (2.1) 11.1 (2.3) 12.0 (3.4) 13.5 (2.91) 12.3 (3.0) 11.7 (2.7)
Vegetables 9.1 (2.3) 7.5 (2.5) 9.3 (3.1)
7.7 (3.1)
8.9 (2.9) 10.9 (2.2)
9.1 (2.4)
8.8 (3.3)
Clothing
10.7 (2.7) 9.7 (2.2) 12.6 (3.2) 11.7 (2.3) 12.8 (3.2) 13.8 (3.3) 12.34 (3.2) 12.06 (3.1)
Subcategories
Animals
4.4 (1.5) 3.9 (1.1) 4.61 (1.1) 5.0 (1.1)
4.8 (1.5) 5.0 (1.0)
4.6 (1.4)
4.8 (1.2)
Fruits
4.0 (.7)
4.3 (.7)
3.9 (.8)
3.3 (.8)
3.9 (1.0) 3.9 (.8)
3.9 (1.0)
3.9 (.8)
Vegetables 4.0 (.9)
2.8 (.6)
4.0 (1.1)
3.8 (1.3)
4.1 (1.2) 4.5 (1.0)
4.0 (1.1)
3.0 (1.2)
Clothing
4.3 (1.2) 4.0 (1.4) 4.1 (.6)
4.3 (.9)
4.5 (.8)
4.3 (.6)
4.3 (.90)
4.2 (.9)
Clusters
Animals
2.2 (.86) 2.2 (.44) 3.1 (1.1)
2.1 (.92)
2.5 (1.1) 2.7 (.7)
2.6 (1.1)
2.3 (.8)
Fruits
2.2 (1.2) 1.6 (.7)
2.4 (1.1)
1.5 (.9)
2.0 (.97) 2.5 (.87)
2.2 (1.07) 1.9 (.9)
Vegetables 1.3 (.77) 0.8 (.78) 1.2 (.94)
1.0 (.90)
1.1 (.6)
1.6 (1.1)
1.2 (.8)
1.2 (.98)
Clothing
2.6 (1.1) 1.6 (1.1) 2.6 (1.2)
2.1 (.6)
2.2 (1.1) 2.6 (1.2)
2.4 (1.1)
2.2 (1.0)
Cluster Size
Animals
4.7 (2.4) 3.5 (.6)
3.4 (.87)
3.3 (1.0)
3.2 (1.1) 3.5 (.70)
3.6 (1.5)
3.4 (.8)
Fruits
2.9 (1.2) 2.7 (.6)
2.6 (.8)
2.7 (1.6)
2.6 (1.0) 3.4 (0.9)
2.7 (1.0)
2.9 (1.2)
Vegetables 2.5 (1.1) 2.1 (1.6) 2.3 (1.4)
1.7 (1.3)
1.9 (1.3) 2.3 (.8)
2.2 (1.3)
2.0 (1.2)
Clothing
2.5 (.5)
2.8 (.8)
2.7 (.47)
2.7 (.6)
2.9 (.8)
2.8 (.7)
2.8 (.6)
2.7 (.7)
Switches
Animals
5.2 (3.3) 3.8 (1.2) 6.1 (1.9)
6.3 (2.4)
6.6 (2.9) 6.5 (2.3)
6.2 (2.7)
5.9 (2.4)
Fruits
6.5 (2.3) 5.5 (1.2) 6.3 (1.3)
5.74 (1.9) 6.0 (2.6) 6.0 (2.1)
6.2 (2.2)
5.8 (1.9)
Vegetables 5.1 (2.0) 3.8 (1.8) 4.9 (2.0)
4.6 (2.2)
5.4 (2.4) 6.4 (2.3)
5.2 (2.2)
5.1 (2.3)
Clothing
5.0 (1.1) 4.7 (1.8) 6.0 (2.2)
6.1 (2.0)
5.9 (1.9) 6.8 (2.1)
5.7 (1.9)
6.1 (2.1)
education predicts less switching under animals, vegetables and fruits. These
results also suggest that being a female contributes to the number of
switches under fruits and vegetables.
Pearson’s correlations are presented in Table 5. Total number of words
evidenced a significant positive correlation with most of the other dependent
measures across semantic categories. As the number of words increased, so
did the number of subcategories, clusters and switches. Significant negative
correlations were observed between switches and cluster size for all categories
except vegetables; larger clusters were associated with fewer switches in animals, fruits and clothing. Significant positive correlations were found between
the total number of words and the total number of switches across categories
for each education group (r = .68, .64 and .87, respectively) and between the
732 MÓNICA ROSSELLI ET AL.
TABLE 3. Mean and standard deviation (in parentheses) for each of the dependent measures by
education and gender
Education group
1–5
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Category
Males
N=5
6–11
Females
N = 16
Males
N = 12
>12
Females
N = 29
Males
N = 12
Total Words
Animals
14.0 (4.1) 12.3 (2.6) 14.6 (1.8)
14.41 (3.5) 15.1 (4.7)
Fruits
12.8 (4.0) 11.71 (2.9) 11.3 (2.3)
11.62 (2.2) 13.4 (2.2)
Vegetables 7.2 (.83)
8.8 (2.7)
7.6 (2.3)
8.7 (3.4)
8.8 (2.7)
Clothing
11.2 (2.4) 10.0 (2.5) 11.6 (1.7)
12.3 (3.1) 11.0 (3.7)
Subcategories
Animals
4.2 (2.1)
4.1 (1.2)
4.3 (1.1)
5.1 (1.1)
4.1 (1.1)
Fruits
4.0 (1.0)
4.2 (.65)
3.5 (.79)
3.7 (1.1)
3.6 (1.1)
Vegetables 3.0 (.7)
3.6 (1.0)
3.5 (1.3)
4.1 (1.1)
3.8 (1.2)
Clothing
4.6 (.54)
4.0 (1.4)
4.4 (.7)
4.1 (.8)
4.1 (.7)
Clusters
Animals
2.4 (1.1)
2.19 (.54) 2.67 (1.07) 2.4 (1.1)
2.5 (1.3)
Fruits
2.6 (1.5)
1.8 (.9)
2.3 (.9)
1.7 (1.1)
2.1 (1.0)
Vegetables 1.2 (.44)
1.1 (.88)
1.0 (.85)
1.1 (.9)
1.0 (.8)
Clothing
2.29
1.18
2.35
1.00
2.43
Cluster Size
Animals
4.85 (2.9) 4.0 (1.5)
3.0 (.6)
3.5 (1.0)
3.1 (1.3)
Fruits
2.95 (.7)
2.87 (1.1) 2.94 (1.1)
2.58 (1.3) 2.9 (1.4)
Vegetables 2.9 (.7)
2.18 (1.4) 2.1 (1.5)
1.96 (1.3) 2.08 (1.5)
Clothing
2.33 (.3)
2.79 (.72) 2.79 (.81)
2.74 (.54) 2.81 (.76)
Switches
Animals
4.6 (3.7)
4.69 (2.4) 6.3 (2.1)
6.2 (2.32) 6.8 (2.8)
Fruits
5.8 (2.1)
6.1 (1.9)
5.6 (1.3)
6.1 (1.8)
4.2 (3.0)
Vegetables 3.6 (0.0)
4.8 (2.0)
4.2 (2.1)
5.0 (2.1)
4.9 (2.2)
Clothing
5.2 (1.3)
4.8 (1.5)
6.7 (1.8)
5.7 (2.1)
5.5 (2.0)
Total
Females
N = 31
15.3 (3.3)
12.6 (3.3)
10.1 (3.3)
14.0 (2.7)
Males
N = 29
Females
N = 76
14.7 (3.5) 14.3 (3.4)
11.6 (3.6) 12.2 (2.5)
8.0 (2.3) 9.4 (3.2)
11.3 (2.7) 12.5 (3.12)
5.2 (1.3)
4.1 (.8)
4.4 (1.0)
4.2 (.9)
4.2 (1.2)
3.6 (.9)
3.5 (1.1)
4.3 (.7)
4.93 (1.3)
3.9 (.89)
4.1 (1.1)
4.2 (.9)
2.6 (.7)
2.3 (.9)
1.4 (.9)
1.19
2.5 (1,1)
2.2 (1.0)
1.0 (.7)
2.0 (1.0)
2.4 (.9)
2.0 (1.0)
1.2 (.9)
2.5 (1.1)
3.5 (.8)
2.9 (.9)
2.1 (.9)
2.94 (.80)
3.3 (1.5)
2.9 (1.1)
2.2 (1.4)
2.72 (.7)
3.6 (1.1)
2.7 (1.1)
2.09 (1.2)
2.8 (.6)
6.6 (2.6)
6.7 (1.7)
6.2 (2.4)
6.5 (1.9)
6.2 (2.7)
5.1 (2.3)
4.4 (2.0)
6.0 (1.8)
6.0 (2.5)
6.3 (1.8)
5.4 (2.3)
5.9 (2.0)
total number of words and the total number of clusters across semantic categories for each education group (r = .68, .71 and .60, respectively).
DISCUSSION
Results of this study showed that the generation of words in Spanish under
the semantic fluency categories of vegetables and fruits was less influenced
by education than clothing and animals. Education was a strong predictor of
performance in verbal fluency in the categories animals and clothing with
increasing educational attainment being associated with higher category fluency scores. Consistent with previous results (Ostrosky et al., 1998), group
differences emerged when the lowest and the highest educational groups
were compared. Number of words under vegetables and fruits was not
affected by level of education.
EDUCATION AND VERBAL FLUENCY
733
TABLE 4. Predictive value of years of formal education on category fluency tests using linear
regression analysis
Coefficients
Downloaded By: [Florida Atlantic University] At: 18:37 19 October 2009
Fluency test
Animals
Total Words
Gender
Age
Education
Subcategories
Gender
Age
Education
Clusters
Gender
Age
Education
Cluster Size
Gender
Age
Education
Number of Switches
Gender
Age
Education
Fruits Total Words
Gender
Age
Education
Subcategories
Gender
Age
Education
Clusters
Gender
Age
Education
Cluster Size
Gender
Age
Education
Number of Switches
Gender
Age
Education
Vegetables
Total Words
Gender
Age
Education
Model
B
SE B
b
R2(1)
−.19
−.09
1.12
.72
.04
.42
−.02
−.21
.24
.05
.74
−.01
.31
.28
−.01
.16
.25
.03
.17
.06*
−.03
−.02
.15
.21
.01
.12
−.01
−.13
.12
.02
.21
−.01
.34
.27
.01
.16
.07
−.04
.21
.00
−.11
−.01
.92
.55
.03
.33
−.02
−.04
.26
.00
.69
−.03
.53
.63
.03
.37
.11
−.09
.14
.02
.33
−.01
−.03
.20
.01
.12
.16
−.08
−.02
.03
−.24
−.01
.14
.22
.01
.13
−.10
−.13
.10
.03
−.21
.03
.08
.24
.01
.14
−.08
.19
.05
.04
1.32
−.03
−.02
.44
.02
−.26
.28
−.13
.01
.09**
1.44
−.07
.70
.65
.03
.38
.21
−.18
.17
.07
R2(2)
t
p
.11**
−.27
−2.26
2.62
.786
.026
.010
.09
2.66
−.30
1.86
.009
.762
.066
.03
−.169
−1.38
1.24
.866
.169
.218
.05*
.780
−.431
2.09
.437
.668
.039
.08**
−.210
.431
2.80
.834
.667
.006
.04
1.08
−.953
1.43
.280
.343
.155
.03
1.67
−.859
−.265
.097
.392
.791
.04
−1.08
−1.32
1.06
.279
.188
.292
.04
−.846
2.04
.556
.444
.044
.579
.08
3.00
−1.40
−.105
.003
.164
.916
.10
2.22
−1.96
1.82
.029
.053
.071
1.90
(Continued)
734 MÓNICA ROSSELLI ET AL.
TABLE 4. (Continued)
Coefficients
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Fluency test
Subcategories
Gender
Age
Education
Clusters
Gender
Age
Education
Cluster Size
Gender
Age
Education
Number of Switches
Gender
Age
Education
Clothing
Total Words
Gender
Age
Education
Subcategories
Gender
Age
Education
Clusters
Gender
Age
Education
Cluster Size
Gender
Age
Education
Number of Switches
Gender
Age
Education
Model
2
B
SE B
b
.69
−.02
.37
.24
.01
.14
.26
−.14
.24
.22
.00
.10
.19
.01
.11
.11
.00
.08
.01
−.14
−.01
−.08
.28
.01
.16
−.05
−.04
−.04
.01
1.15
−.04
.69
.47
.02
.28
.22
−.15
.23
.07*
1.36
−.07
1.36
.63
.03
.37
.19
−.18
.33
.07*
−.02
−.01
.08
.20
.01
.16
−.01
−.15
.04
.02
.50
−.02
.06
.23
.01
.14
.29
−.17
.04
.06*
.11
.00
.11
.15
.00
.09
.07
.01
.12
.01
−.03
−.02
.58
.43
.02
.25
−.01
−.09
.22
.01
2
R (1)
R2(2)
t
p
.14**
2.89
−1.53
2.60
.005
.129
.010
.01
1.11
.23
.860
.269
.982
.392
.01
−.522
−.472
−.486
.603
.638
.628
.12*
2.41
−1.59
2.45
.017
.115
.016
.18**
2.17
−2.02
3.64
.032
.046
.001
.01
−.108
−1.52
.486
.914
.132
.628
.06
2.12
−1.77
.453
.036
.079
.651
.02
.764
.180
1.27
.447
.858
.206
.06*
−.070
−.993
2.28
.944
.323
.025
.09**
2
*p < .05, ** p < .01; R (1) entering gender and age into the model, R (2) entering gender, age and
education into the model.
In a sample of cognitively normal Spanish and English elders, Acevedo et
al. (2000) showed that level of education was a significant predictor of fluency
measures such as animals and fruits but not vegetables. After entering age in a
stepwise regression analysis, they found that education was able to predict an
additional 2.8 and 3.5% of the total variance for animals and fruits, respectively. Similarly, our results showed that education predicted an additional 2%
EDUCATION AND VERBAL FLUENCY
735
TABLE 5. Correlations between difference dependent measures within each semantic category
Downloaded By: [Florida Atlantic University] At: 18:37 19 October 2009
Words
Animals
Total Words
Subcategories
Clusters
Cluster Size
Fruits
Total Words
Subcategories
Clusters
Cluster Size
Vegetables
Total Words
Subcategories
Clusters
Cluster Size
Clothing
Total Words
Subcategories
Clusters
Cluster Size
Subcategories
Clusters
Cluster size
Switches
1.00
.48**
1.00
.56**
.23*
1.00
.06
.34**
−.07
1.00
.59**
.70**
.05
−.45**
1.00
.35**
1.00
.57**
.01
1.00
.29*
−.17
.35**
1.00
.66**
.60**
.06
−.25**
1.00
.56**
1.00
.59**
.08
1.00
.31*
−.17
.60**
1.00
.83**
.75**
.20*
−.06
1.00
.46**
1.00
.57**
.09
1.00
.26**
−.01
.15
1.00
.64**
.46**
−.02
−.24**
*p < .05, **p < .01.
of the total variance for fruit performance once age and gender were entered
into the regression model. Our results differed from those reported by Acevedo’s team, however, in that we found that education explained double the
variance (6%) for fluency in the animals category after controlling for age and
gender. One possible explanation for the discrepancy in results is the difference
in the language of the tasks. While our analysis was of items generated by
Spanish speakers, Acevedo et al.’s study included verbal performance mean
scores of Spanish and English speakers. Language effects on fluency tasks have
been previously reported (Dick, Teng, Kempler, Davis, & Taussing, 2002).
The total number of words produced under animals, fruits and vegetables generated in our sample is lower than the mean scores reported by
Acevedo and associates for the same categories in a Spanish speaking sample of similar age. Differences in the level of education may explain this
discrepancy. The sample in Acevedo et al.’s study had higher levels of
education, 13.4 years, compared to the sample in our study that had a mean
of 9.37 years of formal education. It is likely that the samples from the two
studies differed not just in years of schooling but also in socioeconomic
level since ‘years of education’ is only one of many components of socioeconomic status.
The animal fluency scores of the lowest educated group in our study,
with a mean education level of 3.0 ± 1.4 years, were below the performance
of a Portuguese sample of female elders with an average of 5 ± 1.9 years of
Downloaded By: [Florida Atlantic University] At: 18:37 19 October 2009
736 MÓNICA ROSSELLI ET AL.
education described by Gonzalez da Silva et al. (2004). These females in
their study produced 16.7 animal names while the participants in our study
generated only 12.76. Age differences of the sample from the two studies
may account for the score discrepancies. Our low educational level sample’s
mean age is 8 years higher than Gonzalez da Silva et al.’s sample. Age is a
strong predictor of animal category fluency scores (Acevedo et al., 2000)
and may very well account for the difference in the scores. When compared
to participants from a Colombian (Ardila et al., 1994) and Mexican older
adult sample (Ostrosky et al., 1999) with equivalent age and education level,
no discrepancies were found in the animal fluency scores.
Results of the current study indicated that age was a significant predictor for fluency scores under animals and clothing and it was very close to
significance under vegetables. In other words, increasing age was associated
with lower scores in these fluency tasks. These findings corroborate previous research by Acevedo et al. (2000) and Benito-Cuadrado et al. (2002) that
found that fluency scores are influenced by age in addition to education.
However, our findings differ from the results of these studies in two aspects:
first, different from Acevedo et al. we did not find any age effect on fruits
and secondly, the amount of variance that is explained by age in our study is
smaller compared to these studies’ findings. For example, while in our study
age predicts 5% of the variance for animals, in Acevedo et al.’s age alone
explains 6.9% and in Benito-Cuadrado 10.5% for the same category fluency
task. Differences in results may be explained by variations in the type of
regression analyses between studies. Also, the inclusion of gender simultaneously with age as a predictor in the current study may account for these
discrepancies.
Gender was also a significant predictor for words under vegetables and
clothing. Although there is little evidence of gender differences in category
fluency (for a review see Strauss et al., 2006), some have reported gender
effect for fruits (Kosmidis et al., 2004) and vegetables (Acevedo et al., 2000)
with females producing more words than males. Also, our findings suggest
that gender is a significant predictor of the number of subcategories and
switching strategies for vegetables. However, due to the uneven gender distribution of our sample, these findings should be viewed with some caution.
Results from our study indicate an educational effect on the use of
semantic strategies. Education was a significant predictor of number of
switches under animals, vegetables and clothing and cluster size under animals. For animals, the less educated group (<6 years) evidenced larger mean
clusters and fewer switches between subcategories. Gonzalez da Silva et al.
(2004) found that switching and cluster size were sensitive to literacy. The literate group in their study had a significantly greater number of switches
between semantic subcategories while the illiterate group tended to generate
larger semantic clusters. The authors suggested that literacy may be associated
Downloaded By: [Florida Atlantic University] At: 18:37 19 October 2009
EDUCATION AND VERBAL FLUENCY
737
with a more active cognitive search strategy. Results from this study support
the association between years of schooling and a more active strategy of
searching for words in the animal, vegetable and clothing fluency tasks.
Clustering on semantic category fluency tasks involve semantic categorization and is thought to be an automatic process. Switching involves shifting from one category to the next and has been considered an effortful task
(Troyer, 2000). Both processes are thought to contribute to the efficient recall
of words during fluency tasks and, in fact, high correlations exist between
clustering and switching and the number of recalled words in healthy adults.
The results from our study suggest that clustering is not affected by education
level while switching is. This effect, however, is influenced by the category
task. Generating words from fruits categories seems to require less effort
from participants across education groups than producing words in the animals, vegetables and clothing categories. Mayr (2002) concluded that switching between categories is based on semantic processing and is influenced by
the level of difficulty of the semantic criterion. The influence of the semantic
category on switching is supported by our findings.
Consistent with previous research from Kosmidis et al. (2004), we
found an association between the number of words produced and both clustering and switching, suggesting that efficient use of these two strategies
enhances word production These associations were found in all education
groups. However, the highest correlation between switching and total number of words was found in the group with more than 12 years of education
(.87). This may suggest that the effort of shifting is more sensitive to environmental influences than clustering and improves with higher education.
Clustering correlated with the total number of words in an equal manner
across education groups suggesting little effect of education over this process that has been considered by some as more automatic in nature. Recent
research with children suggests a greater effect of schooling on analytical
processes that are slow and effortful compared to those analogical and intuitive processes that are quick and effortless (Cahan, Greenbaum, Artman,
Deluya, & Gappel-Gilon, 2008). Regrettably, however, scarce research has
been done with adults.
No previous reports on the analysis of strong pairs in category fluency
using Spanish speakers were found. However, it is interesting to note that
many of the strong pairs under animals produced by our Spanish speaking
participants were also generated by Portuguese (Gonzalez da Silva et al.,
2004) and Greek (Kosmidis et al., 2004) samples. In addition, the strong
pairs of fruits recalled by our sample were similar to those strong pairs
reported by Kosmidis et al. These similarities may suggest common experiences with these particular items by individuals from diverse culture
groups.
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738 MÓNICA ROSSELLI ET AL.
The present study may have clinical application in that the categories
vegetables and fruits may be useful in testing Spanish speaking elders with
lower levels of education. However, the suitability of vegetables is questionable due to other issues. For example, significantly fewer words were
produced by our participants under vegetables compared to the number of
words generated under the other three categories indicating a greater level
of difficulty for this category. Previously Acevedo et al. (2000) found that
Spanish speakers produced fewer words than English speakers under vegetables (Acevedo et al., 2000). Differences in meaning of the word vegetables in the two languages have been suggested (Rosselli et al., 2002). The
Spanish word ‘vegetales’ includes anything related to plants (Real Academia Española, 2001) while in English the noun in its popular use refers
more frequently to a plant cultivated for an edible part, although it could
also mean a member of the vegetable kingdom (Agnes, 2000). Therefore,
this semantic category in Spanish may be more general and less well
defined than in English. Moreover, our results indicated that the category
vegetales in Spanish did not exclude fruits. For example, many participants
included limón (lemon) under vegetables. In English, it is likely that it
would only be considered a fruit. In addition, our participants generated
names of nuts (e.g., chestnuts, peanuts) under fruits and vegetables. It is
unlikely that English speakers would call a nut a fruit. Our findings suggest
linguistic differences in the popular use of semantic categories. Future
research analyzing the influence of linguistic effects in combination with
education is needed.
One possible limitation of this study is the heterogeneity of the sample
with regard to the country where the education was received, suggesting that
differences in the educational systems may have accounted for some of the
effects attributed to years of education. However, this argument is not
supported when the education characteristics of the sample are reviewed.
The majority of the participants in each group were educated in urban
schools and each education groups had similar representation from Cuba,
Central America (Honduras or Nicaragua) and South America (Colombia,
Argentina or Peru). Therefore it is expected that the effect of type of education will be similar across education groups. The only difference across the
groups with regard to country of origin was with Puerto Ricans, who were
not represented in the lowest education group.
One important shortcoming of this study is that the gender distribution
was not ideal. Also, our sample had an uneven age distribution across education groups that limited the analyses of the interactions between age and
education. A larger study with a range in age, education, and cognitive ability and a balance in gender would produce more definitive results if we
could analyze all of these interactions at once to determine the least biased
category to use.
EDUCATION AND VERBAL FLUENCY
739
ACKNOWLEDGEMENTS
This project was supported by a grant (1R01 NR04477) from the National Institute of Nursing
Research (PI: R. Tappen). We are thankful to Andrea Barquero and Dayana Sanchez for their
help with the analysis.
Original manuscript received May 12, 2008
Revised manuscript accepted March 18, 2009
First published online June 2, 2009
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742 MÓNICA ROSSELLI ET AL.
APPENDIX
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Animals
1. Subcategories
a. Reptiles: all types of snakes, iguana, tortuga (turtles), lagartija
(lizards), cocodrilo (crocodile), caimán (caiman).
b. Birds: all types of birds (i.e., cacatúa, canario, codorniz,
guacamaya paloma, pollo, pavo, ruiseñor, etc.). Pingüino
(penguin) and murciélago (bat), although not real birds,
they were recalled by participants always in association to
birds.
c. Insects & Arachnoids: abeja (bee), araña (spider), etc.
d. Rodents: conejo (rabbit), liebre (haer), mapache (raccoon), ratón
(mouse), etc.
e. Pets: perro (dog), gato (cat), conejo (rabbit).
f. Water: all type of fish, cretaceous [e.g., cangrejo] and mammals
living under water [e.g., delfin (dolphin), ballena (whale)].
g. Farm: asno/burro (donkey), caballo (horse), ternero (calf), buey
(ox), cabra (goat), gallo (roster), gallina (hen), perro (dog), vaca
(cow) etc.
h. Wild: forest animals such as venado (deer), ardilla (squirrel) etc.,
African animals such as camello (camel), cebra (zebra), león
(lion), tigre (tiger), and Australian animals such as canguro (kangaroo) and koala.
2. Strong pairs:
perro-gato (dog-cat); león-tigre (lion-tiger); serpiente-cocodrilo
(snake-crocodile); paloma-ave (dove-bird); jirafa-elefante (giraffeelephant); caballo-vaca (horse-cow); cebra-león (zebra-lion); ballenatiburón (whale-shark); perro-caballo (dog-horse).
Fruits
1. Subcategories
a. Citrus: limón (lemon), mandarina (tangerine), naranja (orange),
toronja (grapefruit), etc.
b. Tropical: aguacate (avocado), anón, different types of banana/
platano, (plantain), chirimoya, coco (coconut), guanábana,
guayaba (guava), lechosa/papaya (papaya), mamoncillo, mango,
níspero, piña (pineapple), tamarindo, etc.
c. Dry: different type of nuts [e.g., almendra (almonds),
castañas (chestnut)], dátiles (dates), higos (fig) uvas pasas
(plum).
d. Melons: different type of melons (i.e., de castilla, cantalope),
sandía (water melon).
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EDUCATION AND VERBAL FLUENCY
743
e. Non tropical/Traditional: albaricoque (apricot), cereza (cherry),
ciruela (plum), durazno (peach), manzana (apple), melocotón
(nectarine), pera (pear) etc.
f. Berries: blueberry, cranberry, frambuesa (raspberry), fresa
(strawberry), mora (blackberry). Kiwi and uvas (grapes)
were associated with the berry group and they were therefore
included under this category, although technically they are
not berries.
2. Strong Pairs:
Pera-manzana (pear-apple); uva-fresa (grape-strawberry); mango-piña
(mango-pineapple); sandía-melón (watermelon-melon); naranja-mandarina
(orange-tangerine); manzana-uva (apple-grape); mango-banano/
platano (mango-banana); melón-cantaloupe (melon-cantaloupe);
naranja-toronja (orange-grapefruit); mamey-mango (mamey-mango);
melocotón-albaricoque (nectarine-apricot); fresa-frambuesa (strawberry-raspberry); mamey-piña (mamey-pineapple); papaya-piña
(papaya-pineapple); cereza-ciruela (cherry-plum).
Vegetables
1. Subcategories
a. Roots/Tubers: batata (sweet potato), rábanos (radish),
remolacha (beet), malanga (malanga/type of sweet potato), nabo
(turnip), ñame (yam), papa/patata (potato), yuca (yucca), zanahoria (carrot), cebolla (onion), ajo (garlic), etc.
b. Fruitlike: calabaza (pumpkin), pepino/cohombro (cucumber),
plátano (platain), tomate (tomato), aguacate (avocado), aceituna
(olive), etc.
c. Green: aguacate (avocado), different types of leafy vegetables
[(alfalfa, berros (watercress) albahaca, cilantro (coriander),
lechuga (lettuce)], arveja (pea), apio (celery), etc.
d. Stem: espárrago (asparagus), hongo/seta (mushrooms), apio
(celery). Onion and garlic were frequently associated with the
stem vegetables.
e. Condiments/spices: pimienta (pepper), soya (soy), comino
(cumin), tomillo (thyme), ají (chilli pepper), orégano
(oregano), etc.
f. Flower: coliflor (cauliflower), bróculi (broccoli), repollo
(cabbage).
g. Peas/beans/grains/nuts: arveja/ guisante (peas), chícharo (pea),
frijoles (beans) gandules (pigean pea), garbanzos (chickpea),
habas (fava or broad bean), lenteja (lentils), maíz (corn), arroz
(rice), trigo (wheat), nueces (nuts), maní (peanuts).
744 MÓNICA ROSSELLI ET AL.
2. Strong pairs:
Cebolla-ajo (onion-garlic); zanahoria-remolacha (carrot-beet);
tomate-pepino (tomato-pickle/cucumber); bróculi-coliflor (broccolicauliflower); tomate-ají (tomato-chilli pepper); berro-lechuga
(watercress-lettuce); malanga-papa (malanga/sweetpotato-potato);
perejil-cilantro (parsley-coriander); plátano-calabaza (plantainpumpkin); tomate-aguacate (tomato-avocado); espinaca-bróculi
(spinach-broccoli); malanga-yuca (malanga-yucca); lechuga-perejil
(lettuce-parsley); yuca-bonato (yucca-bonato).
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Clothing
1. Subcategories:
a. Accessories & Head: jewelry such as aretes (earrings), pulsera
(bracelet), reloj (watch) etc., bufanda (scarf), cinturón/correa
(belt), corbata (tie), tirantes, pañuelo (handkerchief), guantes
(gloves) and different head accessories such as sombrero (hat),
cachucha (cap), etc.
b. Footwear: botas (boots), medias (socks), sandalias (sandals),
zapatos (shoes) etc.
c. Underwear, sleeping and swimming wear: calzón (pants/panties),
calzoncillo (boxers), camisones (nightgown), combinación (slip),
corpiño (sleeveless blouse/bra), enaguas (underskirt), faja
(strip), pijama (pajama), traje de bano/trusa (bathing suit), sostén/brassier (bra), etc.
d. Outerwear: abrigo (coat), estola, chaleco (vest), gabán/gabardina (overcoat/raincoat), poncho (poncho/cape), pulover
(pullover/jersey), saco (jacket), suéter (sweater), capa (cloak),
chubasquero (raincoat), chal (shawl), etc.
e. Upper Body: bata (robe), vestido (dress), blusa (blouse), camisa
(shirt), etc.
f. Lower body: Bermuda (Bermuda shorts), falda/saya/pollera
(skirt), pantalón corto (shorts), pantalones (pants), etc.
2. Strong pairs:
zapatos-medias (shoes-socks); vestido-blusa (dress-blouse); calzoncillosmedias (briefs-socks); suéter-abrigo (sweater-coat); vestido-saya
(dress-skirt); saya-pantalón (skirt-pants); camisa-camiseta (shirt-Tshirt); camisa-pulóver (shirt-pullover); vestido-pantalón (dress-pants);
sostén-bragas (bra-panties); pantalón-falda (pants-skirt); blusapulóver (blouse-pullover).