The Cultural Stereotype of Professional Groups: Consensus, Accessibility and Typicality of Stereotypic Contents

Abstract

The purpose of the present work was to measure the stereotypic content of several professional groups in a Portuguese sample, by determining the culturally shared stereotypic attributes, their accessibility and typicality. Study 1 used a spontaneous attribute-generation-task to collect the stereotypic content of 28 professional groups. The frequency of generation was used to measure consensus on the attributes generated. The order of generated attributes was used to determine their accessibility. To further explore the link between attributes and the professional group, a new sample (Study 2) rated how typical each attribute was of the professional group. We map out the usefulness of studying professional stereotype's content.

Full text

Análise Psicológica (2017), 4 (XXXV): 557-568 doi: 10.14417/ap.1385
The cultural stereotype of professional groups: Consensus, accessibility and
typicality of stereotypic contents
Ana Sofia Santos*/ Filipa de Almeida*/ Tomás A. Palma*/ Manuel Oliveira*/ Leonel
Garcia-Marques*
*CICPSI, Faculdade de Psicologia, Universidade de Lisboa
The purpose of the present work was to measure the stereotypic content of several professional groups
in a Portuguese sample, by determining the culturally shared stereotypic attributes, their accessibility
and typicality. Study 1 used a spontaneous attribute-generation-task to collect the stereotypic content
of 28 professional groups. The frequency of generation was used to measure consensus on the
attributes generated. The order of generated attributes was used to determine their accessibility. To
further explore the link between attributes and the professional group, a new sample (Study 2) rated
how typical each attribute was of the professional group. We map out the usefulness of studying
professional stereotype’s content.
Key words: Professional stereotypes, Shared content of stereotypes, Attribute generation task,
Attributes accessibility, Attributes typicality.
Introduction
Knowing the content of stereotypes about different social groups is central to the study of
stereotypes and stereotyping. This information has been either used in simply determining the
stereotypes of social groups (e.g., Devine & Elliot, 1995; Katz & Braly, 1933) or as stimuli to
study the cognitive processes underlying the use of stereotypes (e.g., Devine, 1989; Garcia-
Marques, Santos, & Mackie, 2006; Macrae, Milne, & Bodenhausen, 1994).
The majority of the first empirical studies concerned trait attributions particularly to ethnic groups
(Katz & Braly, 1933); those traits with considerable consensus of endorsement for a particular group
were seen as stereotypic of that group. For instance, in the Katz and Braly study (1933), 75% of the
sample chose “lazy” to describe the Niger group and 78% of the sample chose “scientific” to describe
the German group. Both attributes were considered as stereotypic of the respective groups.
But, in the past few decades, there has been a major change in the cast of the research. Emphasis
has shifted from studying the content of stereotypes through trait ascriptions to studying the cognitive
processes underlying the categorization of individuals with regard to race, gender, sexual orientation,
political affiliation, attractiveness, professional activity and other factors (e.g., Bessenoff & Sherman,
2000; Macrae, Mitchell, & Pendry, 2002; Mather, Johnson, & De Leonardis, 1999; McGarty,
Yzerbyt, & Spears, 2002). As so, understanding the content of cognitions related to social groups is
important because, along with assumptions on how stereotypes are structured in memory, activated
and operated by mental processes, provides the basis for understanding the nature of stereotypes and
stereotyping (Cox & Devine, 2015, for a review, see Hamilton & Sherman, 1994).
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The research was supported in part by Portuguese Science and Technology Foundation under the grant to the first
author (PTDC/PSI-PSO/111992/2009); and with support from the Department of Psychology, University of North
Carolina at Chapel Hill, United States.
Correspondence concerning this article should be addressed to: Ana Sofia Santos, Faculdade de Psicologia,
Universidade de Lisboa, Alameda da Universidade, 1648-013 Lisboa, Portugal. Email: sosantos@fp.ul.pt

One of the challenges facing researchers in studies involving stereotypes is developing an
extensive list of stereotypical items that can be used for additional investigations. It requires a
considerable amount of time and effort generating and pre-testing a large number of personality
traits, and behavior statements representative of stereotypic content, counter-stereotypic
information or even information non-related to the stereotype, before conducting the actual study.
Despite the importance of professional stereotyping, very few recent publications appear
associated with search terms such as professional stereotypes, workers stereotypes, labor or
occupational stereotypes (for an exception, see, Moreira, Garcia-Marques, & Santos, 2008). This
is because most research using professional stereotypes is usually interested in studying the effect
of these stereotypes in some outcome variable and not in exploring professional stereotypes per
se (e.g., Garcia-Marques et al., 2006; Santos et al., 2012; but see, Cox & Devine, 2015).
Thus, the primary goal of the present work is to assess what are the culturally shared stereotypic
attributes of various professions. In previous work, we (Moreira et al., 2008) focused on the
stereotypic content of 32 professional groups, in the Portuguese context. Although the present
work resembles in purpose and method our previous work, it extends its findings as it includes 22
new professional groups that were not studied in our previous work (the six professional groups
assessed by both work are signalized with an asterisk, see Table 1). The current work further
extends previous work by assessing the accessibility of generated stereotypic attributes and by
providing typicality judgments of the generated attributes provided by an independent sample.
Table 1
Summarized data for most generated attributes by professional group (Study 1)
N attributes generated by at Total number of Consensus for the most
least 20% of the sample attributes generated generated attribute %
Actors* 8 16 37.8
Lawyers* 6 12 500.
Nannies 5 07 94.6
Salesmen 7 10 58.1
Athletes 5 15 60.8
Doctors* 7 15 58.1
Hair stylists 5 15 47.3
Librarians 9 14 63.5
Computer programmers 5 14 770.
Nurses 4 13 51.4
Politicians 4 15 56.8
Secretaries 5 13 43.2
Bar bouncers 3 09 85.7
Mechanics* 6 10 48.6
Soldiers 4 11 51.4
Artists 3 08600.
Environmental activists 3 14 25.7
Detectives 5 11 61.4
Professors 7 15 45.9
Fitness trainers 5 17 37.8
Police officers 4 19 33.8
Writers* 3 12 67.1
Social workers 4 09 28.6
Interior designers* 3 12 65.7
Farmers 5 12 67.1
Chefs 4 16 32.9
Plastic surgeons 5 12 44.3
Photographers 4 13 51.4
We believe that the current study will contribute to the stereotypes literature in some ways: (1)
it provides the systematic generated stereotypic content and association data for several
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professional groups; (2) it informs on the consensual ‘cultural’ nature of representations about
professions, that is, on the shared ideas about the attributes associated to those professions; and
(3) it creates an available resource and tool for other researchers to explore questions related to
professional stereotypes and stereotyping.
The present work
Study 1 mapped the stereotypic content, and its consensus, of 28 professional groups, using a
spontaneous generation task (Katz & Braly, 1933). This task further allowed us to identify that
certain attributes are highly accessible and therefore occur early in participants’ protocols, as the
order in which the attributes were generated can be used as a measure of their accessibility.
Literature of nonsocial categorization has been referring to attributes that are context-independent
(Barsalou, 1989) and that, rather than being definitional, are properties that simply have been
processed frequently with the category. In a similar vein, the literature of social categorization
suggests that the more high central is a attribute for an individual the more earlier it will be
spontaneously generated by him and the earlier will be its ordinal output position (Garcia-Marques
et al., 2006). Following this reasoning, we assumed that these highly accessible attributes have
been processed frequently within the professional category and therefore occur early in participants’
protocols (their ordinal output position), informing about their centrality for the category.
In Study 2, an independent sample provided typicality judgments of the previously generated
attributes, as a second measure of stereotypic content.
Study 1
In Study 1 we explored the stereotypic content of 28 professional groups, its consensus and the
attributes’ level of accessibility.
In previous research (e.g., Garcia-Marques et al., 2006; Moreira et al., 2008) professional groups
(medical doctors, computer programmers, professors, bar bouncers, salesmen, librarians and so
on) were used as targets. They were consensually identified by pretest participants as familiar,
and clear-cut professional groups in Portuguese society and therefore provided an appropriate test
of the shared stereotypic beliefs hypothesis. From these, we randomly selected the following 28
professional groups: actors, lawyers, nannies, salesmen, athletes, doctors, hair stylists, professors,
librarians, computer programmers, nurses, fitness trainers, politicians, secretaries, police officers,
bar bouncers, mechanics, soldiers, writers, social workers, artists, interior designers, environmental
activists, farmers, chefs, detectives, plastic surgeons and photographers. Given the time necessary
to complete the task, we limited the professional groups to a feasible number and we divided the
set of 28 professional groups in two halves, which allowed us to minimize the task’s completion
time as well as its predictable dropout rate.
Method
Participants
One hundred and forty four undergraduate students (86,1% females, Mage=20.6, SD=5.10,
Range=18-51) from the Department of Psychology (University of Lisbon, Portugal) participated
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voluntarily for partial course credit. Two blocks of 14 professional groups were created and
subjects were randomly assigned to one of them. Seventy-four subjects received block 1.
Procedure
Participants were tested in small group sessions of up to 10 people in individual workstations.
They received the task instructions through Qualtrics Online Survey Software. On each screen,
participants saw a single label for each professional group and were asked to list typical attributes
of that group without restriction in number and type. It was explicitly told that those attributes
could be like short descriptions of typical behaviors by the group, feelings or personality traits
they think “people in general” attributes to those groups. Professional groups by block were
randomly assigned to the participants. Instructions directed participants to provide their gut
responses and not to censor themselves, and assured the anonymity of responses, attempting to
minimize social desirability bias. Further, instructions told respondents to consider what “people
in general” think rather than asking about individual commitment to the stereotype (see Garcia-
Marques, et al., 2006). Data was collected in Portuguese language.
Results and discussion
Coding
One pair of independent coders coded the attributes generated for each professional group and
sorted the extensive list into clusters of direct synonyms. The coders then identified the item that
was the most representative of each cluster. They also eliminated any synonyms generated by the
same participant for the same group. This procedure brought a more intelligible and clear meaning
to the lists of attributes for each professional group. The list initially composed of 1264 attributes
generated, resulted in two different groupings for the different coders (350 clusters for coder A
and 348 clusters for coder B). Each cluster was constituted by a variable number of exclusive
synonymous attributes and was represented by only one synonymous attribute (also exclusive)
for each coder. We evaluated the agreement between coders in two indexes: (1) agreement between
the content of the clusters, calculated by counting the number of clusters between coders that
matched in at least 90% of the attributes included; (2) agreement between attributes chosen as
representative of each cluster.
The results revealed coders agreement in 320 clusters, a high index of accordance at the level
of 91.4%. The interrater reliability for the attributes representative of each cluster was calculated
on those remaining 320 clusters and revealed an accordance of 89.4%.
Most generated attributes, by professional group
The coding yields data documenting how often each professional group name brought to mind
each of the attributes. For example, in the Nannies professional group, 60.8% of participants
generated caring, 21.6% of participants generated nice, and 94.6% of participants generated sweet.
To avoid an exhaustive description, Appendix 1 lists the attributes generated by at least 20% of
the sample. We dropped the characteristics rarely mentioned to avoid placing undue emphasis on
idiosyncratic responses and to make the presentation more readable.
Percentages were used to address the level of consensus (see compiled data in Table 1, column
3). It evidenced that only three professional groups – nannies, computer programmers and bar
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bouncers – showed a high consensus (up to 70%) for the attribute most generated. Eleven
professional groups showed a consensus slightly below the 50% for the attribute most generated.
But four of those professional groups showed a much lower consensus, environmental activists
(25.7%), social workers (28.6%), chefs (32.9%), but also, police officers (33.8%).
Table 1 provides also (see second column) information about the number of attributes generated
by at least 20% of the sample. For five of the professional groups (bar bouncers, artists,
environmental activists, writers, and interior designers), the sample agrees on two to three
attributes to describe the group, and the consensus for the most generated attribute was always up
to 60%, except for the environmental activists. The agreement on seven or more attributes also
occurs for five of the professional groups.
The case of stereotypic content shared by professional groups
One aspect of the data worth mentioning is that several professional groups share the same
stereotypic content. For example, actors made accessible creative, but hair stylists, artists, writers,
and chefs have also generated that same attribute. In those cases, we advise to consider other
important dimensions of the information that might distinguish those professional groups. On one
hand, the level of consensus for each attribute can be different. See, for instance, that the level of
consensus (see Appendix 1, third column) is quite different for chefs (32.9%) in comparison with
writers (67.1%). But even when it is similar (hair stylists and chefs), the other attributes most
generated substantially differ (creative, chatty, gossipy and friendly versus creative, professional,
fat and talented).
We also find it quite important to consider the information related to the output position of
those shared attributes to fully understand the whole picture of the stereotype generated (see the
Output position of attributes generated, by professional group section, for more details), because
its level of accessibility may diverge for the several groups.
Output position of attributes generated, by professional group
The order of attributes generated by professional group was determined by first calculating a
median order position for each attribute generated by the total sample. Based on those medians
for each attribute, attributes were ordered from the ones listed temporally earlier to those appearing
later (see the Appendix, fourth column).
With this measure, we expected to identify that certain attributes are highly accessible and
therefore occur early in participants’ protocols. The rationale behind this is that some attributes
are simply properties that have been processed frequently within a group, being highly accessible.
Those attributes promptly coming to participants’ minds may fairly be considered the attributes
that strongly validate, sustain and perpetuate the stereotypic beliefs, as they can be considered
more context-independent stereotypic content (but see, Santos et al., 2012).
Study 2
Determining that certain attributes describe professional groups among individuals sharing the
same culture is important to establish the existence of stereotypes and their content. Typicality
rating scales can be usefully added to this procedure. Theoretically, such measures seem to probe
fairly directly the associations between groups and features that are the hallmarks of stereotypes.
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We cannot be exactly sure of how people make these judgments. Possibly subjects use some sort
of salience criterion — what most easily springs to mind — as a rough guide to likelihood. They
might use a sort of exemplar availability (Tversky & Kanheman, 1974): if they can easily think
of several smart lawyers, they rate lawyers as being likely to be smart. Or perhaps they are making
an implicit probability judgment: “I think 60% of lawyers are smart, and therefore I will rate
lawyers as 5 points smart on a 7-point scale.” Krueger (1996) took a measure of typicality of the
attribute for the group as his standard measure and then investigated the predictive power of
various percentage measures. Generally, the attribute typicality ratings were well predicted by the
percentage of trait attributions (r=.68). So, typicality ratings as a stereotype measure may fairly
be, in any event, another way of asking what percentage of a group has a particular feature.
We asked a new sample of participants to rate the typicality of the attributes generated in Study
1, for each professional group. As conceptually sustained, results should provide convergent evidence
for the attributes stereotypic of a group, expected to be rated as the most typical of that group in
Study 2, further validating the stereotypes provided by the attributes generated in Study 1.
Method
Participants
Forty-eight undergraduate students (77% females, Mage=25.5, SD=14.48, Range=18-62) from
the Department of Psychology (University of Lisbon, Portugal) participated voluntarily. Two
blocks of 14 professional groups were created and subjects were randomly assigned to one of
them. Twenty-four participants received block 1, the other 24 participants received the 14
professional groups of block 2.
Procedure
Instructions were given through Qualtrics Online Survey Software, in a data collection context
similar to the one from Study 1. Participants evaluated each professional group on several attributes
(generated by at least 10% of the total sample from Study 1), using a 7-point rating scale ranging
from 1 (extremely atypical of the group) to 7 (extremely typical of the group). Each pair of attribute
– professional group appeared on each page and were randomly ordered. The total number of
attributes evaluated by each participant surrounded the 137 attributes.
Results
Mean typicality of attributes generated, by professional group
Mean (and SD) typicality judgments for each attribute by professional group are presented in
Appendix 1. Again, to avoid an exhaustive description, the attributes listed are the ones generated
by at least 20% of the sample.
A superficial analysis of both percentages for generated traits (Study 1) and the trait’s typicality
judgments (Study 2) seems to suggest that in most of the professional groups (with the exception
of actors, secretaries and environmental activists) traits most generated are consistently judged as
most typical by an independent sample.
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In a similar vein, a comparison of both trait’s typicality judgments (Study 2) and the order in
which traits were generated in Study 1 seems to suggest that in most professional groups highly
typical traits are generated earlier.
General discussion
Study 1 used a spontaneous attribute generation task to collect the culturally shared stereotypes
of 28 professional groups in a Portuguese sample. The inference percentages of each attribute by
professional group were used to measure consensus for the stereotypic attributes. We further
looked at the order of generated attributes to help determine their accessibility.
To additionally explore the connection between each attribute and the professional group it was
linked to, a new sample (Study 2) rated how typical each attribute was for the respective
professional group.
These data constitute essential information for any examination of stereotypes and stereotyping.
The compound evidence provided by these several measures can be further use as an indicator of
how valid these items are for the explored professional groups1. Besides, it informs about some
properties of the link between each item and the professional group it was attach to. In what
follows, we exemplify some ways in which these data can be useful to inform empirical, theoretical
and practical questions about stereotypes.
By looking at the order of generated attributes, it is possible to test more specific hypotheses
about how the activation of an attribute follows immediately the activation of the group name or,
instead, implies the activation of a previous attribute. For example, participants in the salesmen
condition only generate annoying as a response after they generate liar and talkative, implying
that the path of activation may be salesmen →liar →talkative →annoying. This is especially
relevant in the case of professional groups that seem to overlap in the same most consensual
attributes, but that might differentiate in the paths of activation among those attributes. Knowing,
for instance, how often participants responded with strong for the bar bouncers and that, in the
output position order, intimidating only appeared after mean being generated, gives one some
indication of how bar bouncers may be indirectly linked to intimidating through mean. So, this
research can provide a tool that researchers in various fields might use to further our knowledge
on stereotypes.
Also, some stereotypes are more salient than others. That potentially increases the chances for
the stereotypes to be activated and reinforced, and consequently harder to change. The use, in the
present work, of a measure as the spontaneous attribute generation task informed us that
professional groups vary in the extent they have a salient consensual stereotype, that is to say, a
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1Note however that, results from typicality ratings (Study 2) and inference percentages (Study 1) among
professional groups may differ as the nature of the two measures is, in some ways rather distinct. According
to some authors (Cox et al., 2015) for some stereotypes the probability that an attribute brings to mind the
group can be higher than the probability of the group bringing to mind the attribute. In fact, some stereotypes
develop to link group membership to visible attributes that can serve as categorization cues. Measures such
as the spontaneous generation task (Study 1), in which participants report the associates that come to mind by
the name of a stereotyped group, don’t capture the stereotypes that have high probabilities of being activated
in the Attribute ® Group direction, because it is the Group ® Attribute direction that is always being evaluated.
Nonetheless, in the attribute typicality measure (Study 2), all the attributes generated are available to be rated,
in spite of the level of consensus obtained; and such attributes might be rated as highly typical on the basis of
being a categorization cue.

reportable shared stereotype. At the limit, such a direct measure may well be taken as a measure
of the existence of a reportable-shared stereotype. The more a stereotype is salient, the more prone
people are to report information that is associated with the stereotype. And it can also inform us
of professional groups with less salient consensual stereotypes.
Furthermore, the level of consensus shared by some attributes and their accessibility may be a
particularly useful cue when trying to change stereotype concepts. If we conceive an intervention
as a way of trying to change Group →Attribute associations, or to focus in regulating the inferences
people draw from those associations, knowing a priori the attributes with higher levels of
consensus that simultaneously are the most accessible, as informed by the output position analysis,
would predictably constitute an advantage. Presumably, these two parameters (consensus and
order of output position) will focus stereotype change programs more directly on the best directions
for increasing efficacy. Nonetheless, even if, for example, people start to dismiss as inadequate
the idea that most lawyers are liars, they may continue to believe that most lawyers are intelligent
and hardworking, because it will likely not change those links and inferences (see Cox & Devine,
2015). But those are the associations, one could argue, that would be beneficial to maintain.
Last but not least, the data provided about professional stereotype content could also be used
in other areas. Think, for instance, of the study by Bogart, Bird, Walt, Delahanty e Figler (2004),
which showed that the stereotype people hold of physicians is affecting people’s health behavior
in a negative way. Looking at our data, one could wonder whether part of the content of some of
the stereotypes assessed could help explain some societal issues. For example, the group of
politicians is described as deceptive, liar and corrupts. Could it be that such a stereotype
contributes towards the relatively low voter turnout of Portugal?
Although we believe the current research lays important groundwork for further study of
professional stereotypes, there are some limitations to our methodology. The measures applied do
not easily measure the intensity of the association of features for individual perceivers, as they
never capture the strength of association of the attribute to the group (Schneider, 2003), and they
fail to measure the speed with which attributes come to the individuals’ minds (e.g. Fazio, Jackson,
Dunton, & Williams1995). For example, if we find that 188 of 259 subjects describe Professors
as intelligent, we have some sense that this attribute is collectively a part of the stereotype.
However, we probably cannot easily make assertions about whether this really represents strong
associations for individual subjects. Does the fact that more people mention intelligence as an
attribute-describing Professors than any other attribute, mean that this is the strongest feature in
most individuals’ stereotypes? One corollary is that, within attributes with limited consensus, there
might be attributes strongly associated to the group that, because of some contextual factor, were
not more frequently generated in the spontaneous generation task (Schneider, 2003). Still, in our
favor we can say that salience of stereotypes should affect not only the speed of accessing
information, but also what people can report, in that when stereotypes are salient, people are more
prone to report information that is associated with the stereotype. And such a direct measure can
be taken as a measure of the existence of stereotypes.
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Este trabalho teve como objetivo medir o conteúdo estereotípico de vários grupos profissionais numa
amostra Portuguesa, através da determinação dos atributos estereotípicos partilhados culturalmente,
da sua acessibilidade e tipicidade. No Estudo 1 o conteúdo estereotípico de 28 grupos profissionais
foi medido através duma tarefa de geração espontânea de atributos. A frequência de geração dos
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atributos foi usada para medir o consenso sobre os atributos gerados. A ordem em que os atributos
foram gerados foi usada para determinar a sua acessibilidade. Adicionalmente, para explorar ainda
mais a ligação entre os atributos gerados e o grupo profissional, uma nova amostra (Estudo 2) avaliou
a tipicidade de cada atributo para o grupo profissional. Discute-se em detalhe a utilidade de estudar os
conteúdos estereotípicos de grupos profissionais.
Palavras-chave:Estereótipos profissionais, Consenso social sobre conteúdos estereotípicos, Tarefa
de geração de atributos, Acessibilidade de atributos, Tipicidade de atributos.
Appendix 1
Percentage of attributes generated, their output position and mean typicality, by professional
group (Study 1 and Study 2)
Output Mean
Group Attributes % position Typicality (SD)
Actors Friendly (simpáticos) 37.8 3 4.67 (1.52)
(Actores) Arrogant (convencidos) 29.7 3 5.04 (1.51)
Hardworking (trabalhadores) 27.0 3 4.67 (1.25)
Funny (divertidos) 25.7 2 4.92 (1.22)
Attractive (bonitos) 25.7 2 5.58 (1.26)
Creative (criativos) 24.3 1 5.46 (1.29)
Famous (famosos) 24.3 2 0.06 (1.08)
Extroverted (extrovertidos) 23.0 2 5.67 (1.11)
Lawyers Liars (mentirosos) 50.0 1 5.38 (1.49)
(Advogados) Intelligent (inteligentes) 36.5 3 5.17 (1.25)
Hardworking (empenhados) 33.8 2 5.42 (1.47)
Persuasive (persuasivos) 31.1 3 6.13 (.83)
Sneaky (manipuladores) 23.0 4 4.29 (.73)
Cultured (cultos) 21.6 4 4.58 (1.12)
Nannies Sweet (carinhosas) 94.6 1 5.46 (1.04)
(Amas) Caring (dedicadas) 60.8 2 5.63 (1.11)
Patient (pacientes) 39.2 3 5.25 (1.33)
Nice (boas) 21.6 3 5.25 (1.05)
Responsible (responsáveis) 20.3 3 5.33 (1.03)
Salesmen Annoying (chatos) 58.1 3 6.13 (.93)
(Vendedores) Convincing (insistentes) 51.4 3 6.17 (1.07)
Outgoing (sociáveis) 41.9 4 05.5 (1.19)
Liar (mentirosos) 36.5 2 05.5 (1.32)
Talkative (faladores) 31.1 2 5.13 (1.48)
Sly (manipuladores) 25.7 3 5.75 (1.30)
Hardworking (empenhados) 21.6 4 4.25 (1.05)
Athletes Hardworking (esforçados) 60.8 2 6.5 (.58)
(Atletas) Strong (fortes) 59.5 3 6.75 (.52)0
Healthy (saudáveis) 29.7 0.1.5 6.54 (.64)0
Dedicated (disciplinados) 21.6 0.3.5 6.17 (.85)0
Persistent (persistentes) 24.3 3 6.04 (.98)0
Doctors Intelligent (inteligentes) 58.1 1 6.29 (.79)0
(Médicos) Hardworking (trabalhadores) 50.0 0.2.5 5.54 (1.35)
Dedicated (empenhados) 37.8 2 4.54 (1.00)
Arrogant (arrogantes) 21.6 0.2.5 4.54 (1.26)
Responsible (responsáveis) 25.7 3 5.83 (.90)0
Cold (frios) 23.0 404.5 (1.17)
Friendly (simpáticos) 33.8 3 04.5 (1.15)
Hair Stylists Chatty (faladores) 47.3 2 6.33 (.85)0
(Cabeleireiros) Friendly (simpáticos) 47.3 2 5.46 (.95)0
Gossipy (coscuvilheiros) 47.3 2 5.96 (1.10)
Creative (engenhosos) 31.1 1 5.63 (.97)0
Cheerful (alegres) 28.4 3 4.88 (1.12)
566

Librarians Bookish (cultos) 63.5 2 5.04 (1.65)
(Bibliotecários) Quiet (calmos) 56.8 1 5.63 (1.25)
Introverted (introvertidos) 41.9 2 0.05 (1.61)
Organized (metódicos) 32.4 3 5.83 (.99)0
Intelligent (inteligentes) 32.4 1 4.63 (1.60)
Responsible (responsáveis) 25.7 4 5.29 (1.34)
Friendly (simpáticos) 23.0 0.3.5 3.63 (1.35)
Nerdy (cromos) 23.0 3 4.79 (1.38)
Helpful (prestáveis) 21.6 3 4.21 (1.29)
Computer Programmers Smart (inteligentes) 77.0 2 5.71 (1.51)
(Programadores Computador) Anti social (anti sociais) 44.6 0.2.5 4.67 (1.65)
Introverted (introvertidos) 43.2 0.3.5 5.38 (1.38)
Nerdy (cromos) 41.9 2 5.83 (1.49)
Hardworking (trabalhadores) 25.7 0.4.5 4.96 (1.27)
Nurses Thoughtful (cuidadosos) 51.4 1 5.46 (1.15)
(Enfermeiros) Nice (simpáticos) 41.9 2 4.79 (1.11)
Compassionate (atenciosos) 36.5 3 4.71 (1.40)
Helpful (prestáveis) 36.5 2 5.46 (.91)0
Politicians Deceptive (hipócritas) 56.8 2 6.67 (.55)0
(Políticos) Liar (mentirosos) 36.5 2 6.67 (.55)0
Corrupt (corruptos) 32.4 1 6.67 (.70)0
Selfish (egoistas) 25.7 4 6.08 (1.11)
Secretaries Organized (organizados) 43.2 2 5.71 (1.06)
(Secretários) Hardworking (trabalhadores) 41.8 3 4.21 (1.29)
Helpful (prestáveis) 28.3 4 4.13 (1.33)
Responsible (responsáveis) 24.3 0.2.5 5.29 (1.24)
Bar Bouncers Strong (fortes) 85.7 1 6.5 (.76)
(Seg. Discoteca) Mean (antipáticos) 40.0 2 5.75 (1.53)
Intimidating (intimidantes) 25.7 3 5.29 (1.46)
Mechanics Greasy (sujos) 48.6 2 4.96 (1.27)
(Mecânicos) Hardworking (trabalhadores) 45.7 2 4.21 (1.58)
Ill-mannered (rudes) 32.9 2 4.58 (1.27)
Dumb (burros) 31.4 3 5.33 (.90)0
Skillful (competentes) 30.0 4 3.79 (1.50)
Thief (ladrões) 28.6 0.4.5 3.17 (1.18)
Soldiers Brave (corajosos) 51.4 2 6.17 (1.18)
(Soldados) Strong (fortes) 45.7 2 5.88 (1.01)
Inflexible (rígidos) 21.4 4 5.92 (.95)0
Loyal (patriotas) 20.0 3 6.04 (.98)0
Artists Creative (criativos) 60.0 1 6.21 (1.04)
(Artistas) Unique (diferentes) 40.0 2 6.04 (.98)0
Intelligent (inteligentes) 21.4 4 4.5 (1.29)
Environmental Activists Protester (reinvidicadores) 25.7 2 5.83 (1.07)
(Ambientalistas) Hippies (hippies) 20.0 3 4.58 (1.41)
Tree huggers? (ecológicos) 20.0 3 6.38 (.86)0
Detectives Smart (perspicazes) 61.4 1 5.88 (1.01)
(Detectives) Alert (atentos) 32.9 3 5.83 (1.50)
Curious (curiosos) 30.0 3 5.71 (1.02)
Sneaky (discretos) 28.6 3 5.42 (1.15)
Mysterious (misteriosos) 25.7 4 5.21 (1.38)
Professors Knowledgeable (cultos) 45.9 2 5.71 (1.06)
(Professores) Hardworking (trabalhadores) 39.2 3 0.05 (1.19)
Thoughtful (profundos) 37.8 2 5.46 (.96)0
Demanding (exigentes) 31.1 3 5.17 (.99)0
Friendly (simpáticos) 29.7 3 4.58 (.99)0
Worried (preocupados) 29.7 3 4.71 (.89)0
Patient (pacientes) 24.3 3 4.25 (1.64)
Fitness Trainers Fit (atléticos) 37.8 1 6.33 (.69)0
(Treinadores de ginásio) Friendly (simpáticos) 31.1 3 5.98 (1.19)
Sculptural (esculturais) 29.7 3 5.92 (1.11)
Dedicated (empenhados) 29.7 4 4.71 (1.51)
Healthy (saudáveis) 24.3 3 5.38 (.70)0
567

Police Officers Forceful (autoritários) 33.8 1 6.08 (.76)0
(Polícias) Serious (sérios) 27.0 1 5.08 (1.32)
Arrogant (arrogantes) 25.7 3 5.21 (1.26)
Strong (fortes) 20.3 3 4.96 (1.10)
Writers Creative (imaginativos) 67.1 2 6.21 (.87)0
(Escritores) Intelligent (inteligentes) 38.6 3 5.83 (.99)0
Cultured (cultos) 22.9 4 5.96 (1.06)
Social Workers Kind (simpáticos) 28.6 0.2.5 4.96 (1.51)
(Assistentes Sociais) Committed (dedicados) 27.1 0.3.5 5.21 (1.55)
Helpful (solidários) 22.9 0.1.5 5.21 (1.55)
Hardworking (trabalhadores) 20.0 5 4.88 (1.33)
Interior Designers Imaginative (inovadores) 65.7 2 5.58 (1.00)
(Decoradores) Personable (agradáveis) 22.9 3 4.21 (1.26)
Fashionable (extravagantes) 18.6 3 0.04 (1.28)
Farmers Hardworking (trabalhadores) 67.1 2 6.08 (1.11)
(Agricultores) Simple (simplórios) 37.1 0.1.5 5.83 (1.34)
Poor (pobres) 28.6 2 4.67 (1.62)
Uncultured (incultos) 28.6 3 4.71 (1.49)
Old-fashioned (antiquados) 24.3 4 4.96 (1.54)
Chefs Creative (imaginativos) 32.9 2 5.21 (1.22)
(Chefes) Talented (talentosos) 28.6 2 4.96 (1.02)
Fat (gordos) 20.0 2 4.38 (1.11)
Professional (profissionais) 20.0 3 4.33 (1.11)
Plastic Surgeons Smart (inteligentes) 44.3 3 6.42 (.70)0
(Cirurgiões Plásticos) Wealthy (ricos) 44.3 2 6.13 (1.05)
Vain (vaidosos) 28.3 3 2.83 (1.86)
Careful (cuidadosos) 22.9 3 5.67 (1.25)
Learners (estudiosos) 20.0 3 6.13 (.78)0
Photographers Different (únicos) 51.4 1 5.71 (1.06)
(Fotógrafos) Artistic (artisticos) 21.4 3 5.46 (1.32)
Observers (observadores) 20.0 0.2.5 5.54 (1.26)
Intelligent (inteligentes) 20.0 3 3.79 (1.35)
Submitted: 09/02/2017 Accepted: 17/04/2017
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