100+ datasets found
  1. Role of the media in reinforcing stereotypes in the U.S. 2020

    • statista.com
    Updated Mar 9, 2021
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    Statista (2021). Role of the media in reinforcing stereotypes in the U.S. 2020 [Dataset]. https://www.statista.com/statistics/1136068/media-reinforces-stereotypes-in-us/
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    Dataset updated
    Mar 9, 2021
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 2020
    Area covered
    United States
    Description

    A recent survey found that 58 percent of American TV content viewers feel that the media, regarding both news and entertainment, reinforces harmful stereotypes about diverse people in the United States. An even greater proportion felt that the media has a responsibility to undermine these stereotypes, with 68 percent of respondents reporting that it is important for the media to represent the diversity in American communities.

  2. Public opinion on stereotyping of groups in movies/TV in the U.S. 2019

    • statista.com
    Updated Jan 5, 2023
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    Statista (2023). Public opinion on stereotyping of groups in movies/TV in the U.S. 2019 [Dataset]. https://www.statista.com/statistics/815810/public-opinion-stereotyping-film-tv/
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    Dataset updated
    Jan 5, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 11, 2019 - Feb 12, 2019
    Area covered
    United States
    Description

    The statistic shows the public opinion on the stereotyping of selected groups in movies and television in the United States as of February 2019. During the survey, 44 percent of respondents stated that the roles in American TV and movies were often stereotypes for Black people, and 31 percent said the same about the way in which LGBTQ people are represented.

  3. Stereotypes about women among the G7 countries 2024

    • statista.com
    Updated Mar 7, 2025
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    Statista (2025). Stereotypes about women among the G7 countries 2024 [Dataset]. https://www.statista.com/statistics/1219071/stereotypes-about-women-among-the-g7-countries/
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    Dataset updated
    Mar 7, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 23, 2024 - Oct 24, 2024
    Area covered
    United States
    Description

    According to a recent survey, certain stereotypes about women still seem to be widespread among the G7 countries. Indeed, 41 percent of the respondents agreed the female brain is different from the male brain, which supposedly explains why men tend to have more aptitudes in certain subjects and women in others.

  4. d

    Replication Data for: Stereotype threat experiment – high schools -...

    • b2find.dkrz.de
    Updated Oct 22, 2023
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    (2023). Replication Data for: Stereotype threat experiment – high schools - 2016/2017 - Dataset - B2FIND [Dataset]. https://b2find.dkrz.de/dataset/322cc15f-6bc7-5a7e-8d82-b4f79b10398b
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    Dataset updated
    Oct 22, 2023
    Description

    We gathered data from 2.162 Dutch high school students, after (pre-registered) exclusion criteria 2.064 students remained. Responses of those 2.064 students are included in the stored dataset. With this data we test the theory that gender stereotypes can lead to deteriorated math performance for female students, but not for male students. Students participated in an experiment: Students in the experimental condition were exposed to stereotype threat, for students in the control condition stereotype threat was removed. After the manipulation students finished a math test. The main research question is whether the four groups (experimental condition x gender) differ in performance on the math test. DSA proof. - Method: Collected at 21 Dutch high schools in Noord-Brabant, Zuid-Holland, Utrecht, and Overijssel. Data was collected with paper and pencil. - Universe: Dutch high school students from 2HAVO/VWO (age 13-14), in provinces Noord-Brabant, Zuid-Holland, Utrecht, and Overijssel.

  5. Italy: survey on most common stereotypes about women 2018

    • statista.com
    Updated Aug 30, 2024
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    Statista (2024). Italy: survey on most common stereotypes about women 2018 [Dataset]. https://www.statista.com/statistics/861433/most-common-gender-stereotypes-about-women-in-italy/
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    Dataset updated
    Aug 30, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 15, 2018 - Mar 18, 2018
    Area covered
    Italy
    Description

    This statistic illustrates the most widespread stereotypes about women according to a survey among the Italian adult population in 2018. According to data, the most common stereotypes about women were that they were better suited for child-rearing (63 percent of respondents), that they loved shopping (63 percent) and that they should take care of housework (59 percent).

  6. d

    Replication Data for: Shaping adolescents’ gender stereotypes of scientists...

    • b2find.dkrz.de
    Updated Feb 25, 2025
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    (2025). Replication Data for: Shaping adolescents’ gender stereotypes of scientists through role models developing communal goals - Dataset - B2FIND [Dataset]. https://b2find.dkrz.de/dataset/a835131c-7f5c-5c93-8f55-610c9ac81870
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    Dataset updated
    Feb 25, 2025
    Description

    This research consists of two studies conducted after manipulating two basic characteristics of the person working in science (their gender and the opportunity to develop communal tasks). The first study analyzes secondary students’ expression of stereotypes about people working in science and their attitudes towards scientists. The second study examines the effect this manipulation has on students’ positivity towards science careers and the enactment of communal goals. The results suggest the potential role that communal affordances play in the enactment of positive attitudes towards people working in science.

  7. o

    Data from: Stereotypes as Energy Savers

    • osf.io
    url
    Updated Apr 24, 2024
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    Andrew R. Smith; Abigail Branco (2024). Stereotypes as Energy Savers [Dataset]. http://doi.org/10.17605/OSF.IO/H8DEJ
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    urlAvailable download formats
    Dataset updated
    Apr 24, 2024
    Dataset provided by
    Center For Open Science
    Authors
    Andrew R. Smith; Abigail Branco
    Description

    Stereotypes are generalizations made about people based on their group membership. There is a wealth of literature examining the ways stereotypes contribute to the demonization of marginalized communities, impede communication, and permeate social structures. However, insufficient attention is directed towards the potential evolutionary benefit of stereotyping. Macrae et al. (1994) found that stereotypes may “free up” cognitive resources, which enables attention to be directed towards other essential functions. This paper has been cited over 1,700 times despite potential limitations including the small and homogeneous sample size, study and stereotype labels’ age, and failure to consider an alternative explanation; namely that labels, stereotypes or not, provide additional cognitive clues. Our research aims to replicate and extend Macrae et al. (1994)’s study to test whether stereotyping frees cognitive resources for diversion towards alternative tasks.

  8. H

    Replication Data for: Opposition to Women Political Leaders: Gender Bias and...

    • dataverse.harvard.edu
    • search.dataone.org
    Updated Dec 31, 2022
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    Yuya Endo (2022). Replication Data for: Opposition to Women Political Leaders: Gender Bias and Stereotypes of Politicians among Japanese Voters [Dataset]. http://doi.org/10.7910/DVN/VHFYWX
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 31, 2022
    Dataset provided by
    Harvard Dataverse
    Authors
    Yuya Endo
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Women are extremely underrepresented in Japanese political life. One possible reason for this is that voters are biased against women. Compared to American voters, to what extent are Japanese voters opposed to women political leaders? What kinds of stereotypes do they have about women politicians? To answer these questions and probe the external validity of American studies on this topic, we conducted a survey that elicits respondents’ attitudes toward women political leaders. First, our list experiment outcomes revealed that approximately 10% of Japanese, and 20% of the Liberal Democratic Party (LDP) supporters, oppose a woman becoming prime minister. Second, we also identified respondents’ gender stereotypes by asking them directly about their impressions of politicians, which revealed that Japanese voters have strong stereotypes for men and women politicians regarding their policy areas of expertise and personal characteristics. These stereotypes are strongest among men and older voters as well as voters who support the LDP. Our findings have broad implications for the literature on gender and politics beyond the study of Japanese politics.

  9. Data and Code for: "Hacking Gender Stereotypes: Girls’ Participation in...

    • openicpsr.org
    Updated Apr 27, 2022
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    Michela Carlana; Margherita Fort (2022). Data and Code for: "Hacking Gender Stereotypes: Girls’ Participation in Coding Clubs" [Dataset]. http://doi.org/10.3886/E168821V1
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    Dataset updated
    Apr 27, 2022
    Dataset provided by
    American Economic Associationhttp://www.aeaweb.org/
    Authors
    Michela Carlana; Margherita Fort
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Italy
    Description

    We analyze gender gaps in academic interests and perception of barriers to achieve own career goals and how girls applying to the coding clubs differ from those that decide not to apply.

  10. e

    Special Eurobarometer SP545 : Gender stereotypes

    • data.europa.eu
    excel xlsx, zip
    Updated Dec 17, 2024
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    Directorate-General for Communication (2024). Special Eurobarometer SP545 : Gender stereotypes [Dataset]. https://data.europa.eu/data/datasets/s2974_100_3_sp545_eng?locale=fi
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    excel xlsx, zipAvailable download formats
    Dataset updated
    Dec 17, 2024
    Dataset authored and provided by
    Directorate-General for Communication
    License

    http://data.europa.eu/eli/dec/2011/833/ojhttp://data.europa.eu/eli/dec/2011/833/oj

    Description

    This survey provides an overview of the perceptions of gender stereotypes in various contexts: household and work, politics and leadership positions, and perceptions of different treatment by gender in everyday situations. EU citizens generally support gender equality as beneficial for all. However, certain gender stereotypes persist in different areas, with differences detected between Member States and age groups.

    Processed data

    Processed data files for the Eurobarometer surveys are published in .xlsx format.

    • Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
    • Volume AP "Previous survey trends" The file compares to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
    • Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
    • Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
    • Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
    • Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
    • Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).
    • Volume D "Trends"" The file compares to previous polls in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results. _

    For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

  11. m

    Gender Stereotypes in Impression Formation Qualtrics Data

    • data.mendeley.com
    Updated Apr 8, 2020
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    Regina Fairfax (2020). Gender Stereotypes in Impression Formation Qualtrics Data [Dataset]. http://doi.org/10.17632/58cmn4bccx.2
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    Dataset updated
    Apr 8, 2020
    Authors
    Regina Fairfax
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Dataset for research study "Gender Stereotypes in Impression Formation." Participants were randomly assigned to one of three conditions: stereotype-contradicting, stereotype-confirming, and neutral. All groups completed a mental imagery task that either contradicted, confirmed, or was neutral to gender stereotypes about physicians. The participants then completed a first impressions task, in which they chose between headshots of a man and a woman and decided who was most likely to be the physician. Participants’ judgements and response latency were recorded. Comparisons were made between the responses and response latency both across age and within the conditions across groups, as well as overall responses and response latency among the three groups. In addition, the descriptive data from the mental imagery task were analyzed.

  12. f

    Data_Sheet_1_Computational Modeling of Stereotype Content in Text.zip

    • frontiersin.figshare.com
    zip
    Updated Jun 6, 2023
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    Kathleen C. Fraser; Svetlana Kiritchenko; Isar Nejadgholi (2023). Data_Sheet_1_Computational Modeling of Stereotype Content in Text.zip [Dataset]. http://doi.org/10.3389/frai.2022.826207.s001
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    zipAvailable download formats
    Dataset updated
    Jun 6, 2023
    Dataset provided by
    Frontiers
    Authors
    Kathleen C. Fraser; Svetlana Kiritchenko; Isar Nejadgholi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Stereotypes are encountered every day, in interpersonal communication as well as in entertainment, news stories, and on social media. In this study, we present a computational method to mine large, naturally occurring datasets of text for sentences that express perceptions of a social group of interest, and then map these sentences to the two-dimensional plane of perceived warmth and competence for comparison and interpretation. This framework is grounded in established social psychological theory, and validated against both expert annotation and crowd-sourced stereotype data. Additionally, we present two case studies of how the model might be used to answer questions using data “in-the-wild,” by collecting Twitter data about women and older adults. Using the data about women, we are able to observe how sub-categories of women (e.g., Black women and white women) are described similarly and differently from each other, and from the superordinate group of women in general. Using the data about older adults, we show evidence that the terms people use to label a group (e.g., old people vs. senior citizens) are associated with different stereotype content. We propose that this model can be used by other researchers to explore questions of how stereotypes are expressed in various large text corpora.

  13. c

    Establishing How Intergroup Bias Influences the Formation and Evolution of...

    • datacatalogue.cessda.eu
    • beta.ukdataservice.ac.uk
    Updated Mar 25, 2025
    + more versions
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    Martin, D (2025). Establishing How Intergroup Bias Influences the Formation and Evolution of Stereotypes - Experimental Data, 2017-2020 [Dataset]. http://doi.org/10.5255/UKDA-SN-855439
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    Dataset updated
    Mar 25, 2025
    Dataset provided by
    University of Aberdeen
    Authors
    Martin, D
    Time period covered
    Jan 2, 2017 - Dec 1, 2020
    Area covered
    United Kingdom
    Variables measured
    Individual
    Measurement technique
    Lab-based experimental psychology using a diffusion chain methodology. The data were collected on personal computers. A sample of undergraduate students was recruited to take part in the experiments.
    Description

    These are data from the primary dependent measures from my ESRC grant examining how intergoup bias influences the formation of novel lab-based stereotypes via a process of cumulative cultural evolution. The data were collected from undergraduate participants who were tested individually. Each participant was a single 'generation' within a 'diffusion chain' of four generations. Participants were asked to try and learn some information about some novel social targets. The responses each participant produced during a test phase were used as the learning materials for the next participant (i.e., generation). The dependent measures of interest were the accuracy with which people completed the task and the amount of structure that was present in their responses. We were interested in whether a stereotype-like categorical structure would develop as information was passed down the chains and whether this would result in associated increases in accuracy.

    The proposed research will establish how the membership and status of social groups influences how cultural stereotypes form and change. Cultural stereotypes are template-like depictions of social categories whereby group membership is associated with the possession of certain attributes (e.g., scientists are geeky, Scottish people are miserly, men like the colour blue). Stereotypes exert substantial influence on us as individuals and on our society: when people endorse stereotypes it leads to prejudice and discrimination towards members of minority groups; even when people refute stereotypes the mere knowledge of their content can lead to unconscious bias in thoughts and behaviour. Yet, in the face of an infinitely complex social environment stereotypes play a vital social cognitive role by efficiently organising and structuring social information. Given their ubiquity and influence it is perhaps surprising that relatively little is known about how cultural stereotypes form and change.

    We propose that stereotypes form and change via a process of cumulative cultural evolution. Because people possess shared biases that influence how information is remembered and communicated, when knowledge is repeatedly passed from person to person these biases accumulate causing the content of information to change in predictable ways. Research has shown that when information is passed down chains of individuals - a bit like the children's game often called 'Chinese whispers' or 'telephone' - it becomes increasingly simplified and structured. For example, we recently demonstrated that as novel social information passes from person to person it develops a stereotype-like structure that was not previously present. Thus, through the process of cumulative cultural evolution, even very small amounts of bias at the level of individual people can translate into much bigger societal biases like cultural stereotypes.

    The proposed research will establish whether individual biases associated with a person's membership of social groups influences the formation and evolution of cultural stereotypes. Whether we perceive others as belonging to the same social group as ourselves (the in-group) or a different social group (the out-group) has profound implications for our thoughts and behaviours. Group membership tends to lead to intergroup bias, with people more likely to favour in-group members and discriminate against out-group members. The proposed research will determine whether repeatedly communicating social information about in-group and out-group members results in the formation of relatively positive in-group stereotypes and negative out-group stereotypes. In addition, the proposed research will also establish whether it is possible to predict how the content of stereotypes will evolve based on the perceived status of different out-groups (e.g., whether they are perceived to be high status or low status).

    The proposed research will therefore help establish whether cumulative cultural evolution leads to the unintentional but inevitable formation of stereotypes, whose content is largely determined by the shared biases of perceivers rather than the actual properties of the groups themselves.

  14. H

    Replication Data for: Gender Differences in Political Knowledge: Bringing...

    • dataverse.harvard.edu
    Updated Oct 18, 2017
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    Harvard Dataverse (2017). Replication Data for: Gender Differences in Political Knowledge: Bringing Situation Back In [Dataset]. http://doi.org/10.7910/DVN/OZRQIQ
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    text/plain; charset=us-ascii(1038), application/x-spss-syntax(7044), text/x-fixed-field(20227), docx(16655), tsv(80295), bin(152934)Available download formats
    Dataset updated
    Oct 18, 2017
    Dataset provided by
    Harvard Dataverse
    License

    https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/OZRQIQhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/OZRQIQ

    Description

    One of the best-known empirical findings in the political sciences is the gender difference in political knowledge: women show less political knowledge than men. Conventional research argues that this difference is mainly a product of socialization, structural factors, and biology. Our paper brings a new perspective to the explanation of the gender gap in political knowledge. Based on an online survey and an experiment, we emphasize the relevance of gender stereotypes as a situational pressure that reduces the performance of women in a political knowledge test. Two conclusions emerge from the analysis: First, our results indicate the existence of a negative stereotype related to the political knowledge of women. Second, the activation of gender stereotypes affects performance on a political knowledge test. Consistent with previous research on stereotype threat, our results indicate that the performance of men on a political knowledge test is affected by gender stereotypes.

  15. m

    Data for: Aging Stereotypes Influence False Memories in the Social Contagion...

    • data.mendeley.com
    • narcis.nl
    Updated Nov 2, 2017
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    Katya Numbers (2017). Data for: Aging Stereotypes Influence False Memories in the Social Contagion Paradigm [Dataset]. http://doi.org/10.17632/rz48s5z2c9.1
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    Dataset updated
    Nov 2, 2017
    Authors
    Katya Numbers
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Raw data from Studies 1 & 2 from Numbers et al. JARMAC submission (under review). Data files contain false and veridical recall and recognition data, as well as post experimental questionnaire data. Accuracy proportions for Study 1 are 0%, 33% and 100% false, and confederate age conditions are young and old. Accuracy proportions for Study 2 are 0% and 100% false, and age stereotype conditions are positive and negative.

  16. d

    Replication Data for: \"Gender Stereotypes in the Classroom and Effects on...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 22, 2023
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    Alan, Sule; Ertac, Seda; Mumcu, Ipek (2023). Replication Data for: \"Gender Stereotypes in the Classroom and Effects on Achievement\" [Dataset]. https://search.dataone.org/view/sha256%3A727c029864463f92a4b8eb5e13e564e7c9bf16b908299000eeca8bda85fe31bb
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Alan, Sule; Ertac, Seda; Mumcu, Ipek
    Description

    Replication Data for: "Gender Stereotypes in the Classroom and Effects on Achievement"

  17. Data and Code for: Interaction, Stereotypes and Performance. Evidence from...

    • openicpsr.org
    Updated Jul 6, 2022
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    Lucia Corno; Eliana La Ferrara; Justine Burns (2022). Data and Code for: Interaction, Stereotypes and Performance. Evidence from South Africa [Dataset]. http://doi.org/10.3886/E174501V1
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    Dataset updated
    Jul 6, 2022
    Dataset provided by
    American Economic Associationhttp://www.aeaweb.org/
    Authors
    Lucia Corno; Eliana La Ferrara; Justine Burns
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    South Africa
    Description

    Edit 0_main.do to adjust the default path to the base replication folder.Add all ado files to the relevant STATA folderRun 0_main.do to replicate the main and appendix tables and figures of the paper.

  18. o

    Replication data for: How Stereotypes Impair Women's Careers in Science

    • openicpsr.org
    stata
    Updated Feb 22, 2021
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    Ernesto Reuben; Paola Sapienza; Luigi Zingales (2021). Replication data for: How Stereotypes Impair Women's Careers in Science [Dataset]. http://doi.org/10.3886/E133061V1
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    stataAvailable download formats
    Dataset updated
    Feb 22, 2021
    Authors
    Ernesto Reuben; Paola Sapienza; Luigi Zingales
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Women outnumber men in undergraduate enrollments, but they are much less likely than men to major in mathematics or science or to choose a profession in these fields. This outcome often is attributed to the effects of negative sex-based stereotypes. We studied the effect of such stereotypes in an experimental market, where subjects were hired to perform an arithmetic task that, on average, both genders perform equally well. We find that without any information other than a candidate's appearance (which makes sex clear), both male and female subjects are twice more likely to hire a man than a woman. The discrimination survives if performance on the arithmetic task is self-reported, because men tend to boast about their performance, whereas women generally underreport it. The discrimination is reduced, but not eliminated, by providing full information about previous performance on the task. By using the Implicit Association Test, we show that implicit stereotypes are responsible for the initial average bias in sex-related beliefs and for a bias in updating expectations when performance information is self-reported. That is, employers biased against women are less likely to take into account the fact that men, on average, boast more than women about their future performance, leading to suboptimal hiring choices that remain biased in favor of men.

  19. Stereotyping of ethnic minorities in Hollywood movies 2016, by ethnicity

    • statista.com
    Updated Feb 8, 2016
    + more versions
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    Statista (2016). Stereotyping of ethnic minorities in Hollywood movies 2016, by ethnicity [Dataset]. https://www.statista.com/statistics/548694/stereotyping-minorities-hollywood-ethnicity/
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    Dataset updated
    Feb 8, 2016
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 4, 2016 - Feb 7, 2016
    Area covered
    United States
    Description

    The survey shows result of survey on stereotyping of racial minorities in Hollywood movies in the United States as of February 2016. The results were split my respondents' ethnicity. During the survey, 16 of Afrian American respondents stated Hollywood movies did a good job of potraying racial minorities.

  20. Share of individuals adhering to gender stereotypes Japan 2023, by gender...

    • statista.com
    Updated Nov 6, 2024
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    Statista (2024). Share of individuals adhering to gender stereotypes Japan 2023, by gender and age [Dataset]. https://www.statista.com/statistics/1535002/japan-share-follow-traditional-gender-stereotypes-by-gender-and-age/
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    Dataset updated
    Nov 6, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Sep 11, 2023 - Sep 12, 2023
    Area covered
    Japan
    Description

    According to a survey conducted in September 2023, 70 percent of female respondents aged 15 to 19 years old stated that they preferred to be feminine, the highest share among women in Japan. For men, the share who desired to act masculine was highest among the oldest age group.

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Statista (2021). Role of the media in reinforcing stereotypes in the U.S. 2020 [Dataset]. https://www.statista.com/statistics/1136068/media-reinforces-stereotypes-in-us/
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Role of the media in reinforcing stereotypes in the U.S. 2020

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Dataset updated
Mar 9, 2021
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Mar 2020
Area covered
United States
Description

A recent survey found that 58 percent of American TV content viewers feel that the media, regarding both news and entertainment, reinforces harmful stereotypes about diverse people in the United States. An even greater proportion felt that the media has a responsibility to undermine these stereotypes, with 68 percent of respondents reporting that it is important for the media to represent the diversity in American communities.

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