arxiv: 2603.26930 · v2 · submitted 2026-03-27 · 💻 cs.CY · cs.CL

Recognition: no theorem link

In your own words: computationally identifying interpretable themes in free-text survey data

Jenny S Wang , Aliya Saperstein , Emma Pierson

Authors on Pith no claims yet

Pith reviewed 2026-05-14 22:11 UTC · model grok-4.3

classification 💻 cs.CY cs.CL

keywords free-text analysissurvey datathematic identificationidentity categoriescomputational social scienceheterogeneitymisrecognition

0 comments

The pith

A computational framework identifies structured themes in free-text survey responses about race, gender, and sexual orientation that are more coherent than prior methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces In Your Own Words, a framework that processes free-text survey answers to extract interpretable themes for systematic analysis. On a dataset of 1,004 U.S. participants describing their identities, the resulting themes outperform earlier computational approaches in coherence. These themes support three uses: surfacing overlooked constructs like belonging to guide new structured questions, exposing variation within standard categories that links to health and well-being outcomes, and mapping consistent gaps between self-described and perceived identities.

Core claim

The In Your Own Words framework produces themes from free-text identity descriptions that are more coherent and interpretable than those from past computational methods, directly supporting applications in suggesting new survey questions, revealing heterogeneity within categories, and identifying discordance between self-identified and perceived identities.

What carries the argument

The In Your Own Words computational framework, which automates the extraction of structured, interpretable themes from free-text responses.

If this is right

Themes surface constructs such as belonging and identity fluidity that can guide addition of structured questions to future surveys.
Heterogeneity within standard categories explains additional variation in health, well-being, and identity importance.
Systematic discordance between self-identified and perceived identities highlights mechanisms of misrecognition not captured by existing measures.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The approach could scale to other open-ended survey topics to reduce reliance on manual qualitative coding.
Themes might be combined with structured variables to build stronger predictive models of social outcomes.
Deployment across repeated surveys could track how identity themes shift over time.

Load-bearing premise

The automatically generated themes are genuinely more interpretable and useful for survey research than alternatives without extra human validation or comparison.

What would settle it

Expert ratings comparing coherence and usefulness of themes from this framework versus standard topic models on the same identity dataset, or tests showing whether the themes add predictive power for health outcomes beyond standard categories.

read the original abstract

Free-text survey responses can provide nuance often missed by structured questions, but remain difficult to statistically analyze. To address this, we introduce In Your Own Words, a computational framework for exploratory analyses of free-text survey data that identifies structured, interpretable themes in free-text responses, facilitating systematic analysis. To illustrate the benefits of this approach, we apply it to a new dataset of free-text descriptions of race, gender, and sexual orientation from 1,004 U.S. participants. The themes our approach produces on this dataset are more coherent and interpretable than those produced by past computational methods. The themes have three practical applications in survey research. First, they can suggest structured questions to add to future surveys by surfacing salient constructs - such as belonging and identity fluidity - that existing surveys do not capture. Second, the themes reveal heterogeneity within standardized categories, explaining additional variation in health, well-being, and identity importance. Third, the themes illuminate systematic discordance between self-identified and perceived identities, highlighting mechanisms of misrecognition that existing measures do not reflect. More broadly, our framework can be deployed in a wide range of survey settings to identify interpretable themes from free text, complementing existing qualitative methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper introduces a usable framework for theme extraction from free-text survey responses on a new 1004-person identity dataset, but the superiority claim over prior methods lacks clear quantitative backing or blinded validation.

read the letter

The core contribution is a computational pipeline called In Your Own Words that turns open-ended survey answers into structured, readable themes. They run it on fresh data from 1004 U.S. respondents describing their race, gender, and sexual orientation, then show three downstream uses: surfacing constructs like belonging or identity fluidity that could become new closed questions, documenting variation inside standard categories that predicts health and well-being outcomes, and flagging systematic gaps between self-reported and perceived identities. That last part is the most concrete; it gives a measurable handle on misrecognition that existing scales miss. The dataset itself is a plus—large enough for exploratory work and tied to real survey questions. The writing is straightforward and the applications feel grounded in how survey researchers actually work. The weak point is the headline comparison. The abstract states the new themes are more coherent and interpretable than earlier computational approaches, yet the provided text gives no topic coherence scores, no blinded human ratings with reliability numbers, and no explicit protocol for how the authors chose which themes counted as better. If the full paper relies only on post-hoc qualitative judgment, that claim stays untested rather than demonstrated. Minor issues include the usual risk that any unsupervised method can overfit to the sample, but nothing here looks load-bearing. This is worth a serious referee for anyone building or analyzing identity or health surveys; the dataset and use cases are specific enough that reviewers can check the method directly. I would send it out rather than desk-reject, with the expectation that the validation section gets tightened.

Referee Report

1 major / 0 minor

Summary. The manuscript introduces 'In Your Own Words,' a computational framework for identifying structured, interpretable themes in free-text survey responses. It applies the framework to a new dataset of 1,004 U.S. participants' free-text descriptions of race, gender, and sexual orientation, claiming the resulting themes are more coherent and interpretable than those from prior computational methods. The themes are shown to support three applications in survey research: suggesting new questions by surfacing constructs like belonging and identity fluidity, revealing heterogeneity within standardized categories that explains variation in health and well-being, and illuminating discordance between self-identified and perceived identities.

Significance. If the superiority claim holds under rigorous validation, the framework would offer a scalable computational tool that complements qualitative coding in survey analysis, enabling systematic exploration of free-text data across social science domains and potentially improving question design and measurement of identity-related constructs.

major comments (1)

[Abstract and evaluation sections] Abstract and evaluation sections: the headline claim that the themes are 'more coherent and interpretable than those produced by past computational methods' is unsupported by any reported quantitative metrics (e.g., NPMI coherence scores), blinded human rater studies with inter-rater reliability, or explicit comparison protocol against baselines such as LDA. The comparison therefore reduces to unblinded qualitative judgment, which is insufficient to substantiate the central superiority assertion.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights an important opportunity to strengthen the empirical support for our central claims. We address the major comment below and commit to revisions that will make the comparison more rigorous while preserving the manuscript's focus on interpretability in applied survey contexts.

read point-by-point responses

Referee: Abstract and evaluation sections: the headline claim that the themes are 'more coherent and interpretable than those produced by past computational methods' is unsupported by any reported quantitative metrics (e.g., NPMI coherence scores), blinded human rater studies with inter-rater reliability, or explicit comparison protocol against baselines such as LDA. The comparison therefore reduces to unblinded qualitative judgment, which is insufficient to substantiate the central superiority assertion.

Authors: We acknowledge that the current version relies primarily on qualitative demonstration of coherence and interpretability without accompanying quantitative metrics or a fully specified blinded protocol. In the revised manuscript we will add NPMI coherence scores comparing our framework against LDA and at least one additional baseline on the same dataset, include an explicit description of the qualitative comparison protocol (including how themes were selected and presented), and report inter-rater reliability for any human judgments used. These additions will directly address the concern while retaining the applied focus on survey-research utility. revision: yes

Circularity Check

0 steps flagged

No circularity; new framework applied to independent dataset

full rationale

The paper introduces a computational framework for theme identification in free-text survey responses and applies it to a new dataset of 1,004 participants. The central claims rest on this external application and qualitative comparison to prior methods rather than any self-referential equations, fitted parameters renamed as predictions, or load-bearing self-citations that reduce the output to the input by construction. No derivation steps are shown to be tautological or equivalent to the framework's own definitions. The analysis is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no information on specific parameters, axioms, or new entities introduced by the method.

pith-pipeline@v0.9.0 · 5517 in / 1016 out tokens · 48075 ms · 2026-05-14T22:11:05.005451+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

116 extracted references · 116 canonical work pages · 5 internal anchors

[1]

Methods, data, analyses: a journal for quantitative methods and survey methodology (mda)11(2), 115–134 (2017)

Singer, E., Couper, M.P.: Some methodological uses of responses to open questions and other verbatim comments in quantitative surveys. Methods, data, analyses: a journal for quantitative methods and survey methodology (mda)11(2), 115–134 (2017)

work page 2017
[2]

Sociology Compass19(1), 70031 (2025)

Pao, C., Donnelly Moran, K., Compton, D.L., Kaufman, G., Dowling, J.A.: The case for “other”: Measuring gender and sexual identity in survey research. Sociology Compass19(1), 70031 (2025)

work page 2025
[3]

other, describe

Wong, J.S., Valentino, L., Pao, C., Donnelly Moran, K., Compton, D., Kaufman, G.: What to do with “other, describe”. Sociological Methodology55(2), 244–268 (2025)

work page 2025
[4]

International Journal for Quality in Health Care24(5), 509–516 (2012)

Riiskjær, E., Ammentorp, J., Kofoed, P.-E.: The value of open-ended questions in surveys on patient experience: number of comments and perceived usefulness from a hospital perspective. International Journal for Quality in Health Care24(5), 509–516 (2012)

work page 2012
[5]

any other comments?

O’Cathain, A., Thomas, K.J.: “any other comments?” open questions on questionnaires–a bane or a bonus to research? BMC medical research methodology4(1), 25 (2004)

work page 2004
[6]

Prewitt, K.: Racial classification in america: where do we go from here? Daedalus134(1), 5–17 (2005)

work page 2005
[7]

Aspinall, P.J.: Answer formats in british census and survey ethnicity questions: does open response better capture ‘superdiversity’? Sociology46(2), 354–364 (2012)

work page 2012
[8]

Socius, 2, 1–11 (2016)

Magliozzi, D., Saperstein, A., Westbrook, L.: Scaling up: Representing gender diversity in survey research. Socius, 2, 1–11 (2016)

work page 2016
[9]

Routledge, New York, NY (2017)

Glaser, B., Strauss, A.: Discovery of Grounded Theory: Strategies for Qualitative Research. Routledge, New York, NY (2017)

work page 2017
[10]

MIS quarterly, 21–54 (2013)

Venkatesh, V., Brown, S.A., Bala, H.: Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS quarterly, 21–54 (2013)

work page 2013
[11]

International Journal of Qualitative Methods21(2022)

Beresford, M., Wutich, A., Bray, M.V., Ruth, A., Stotts, R., SturtzSreetharan, C., Brewis, A.: Coding qualitative data at scale: Guidance for large coder teams based on 18 studies. International Journal of Qualitative Methods21(2022)

work page 2022
[12]

Proceedings of the 42nd International Conference on Machine Learning267, 44997– 45023 (2025)

Movva, R., Peng, K., Garg, N., Kleinberg, J., Pierson, E.: Sparse autoencoders for hypothesis generation. Proceedings of the 42nd International Conference on Machine Learning267, 44997– 45023 (2025)

work page 2025
[13]

ICLR (2026) 57

Movva, R., Milli, S., Min, S., Pierson, E.: What’s in my human feedback? learning interpretable descriptions of preference data. ICLR (2026) 57

work page 2026
[14]

arXiv preprint 2512.10092 (2025)

Jiang, N., Sun, X., Dunlap, L., Smith, L., Nanda, N.: Interpretable embeddings with sparse autoencoders: A data analysis toolkit. arXiv preprint 2512.10092 (2025)

work page arXiv 2025
[15]

Journal of Machine Learning research3(Jan), 993–1022 (2003)

Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning research3(Jan), 993–1022 (2003)

work page 2003
[16]

BERTopic: Neural topic modeling with a class-based TF-IDF procedure

Grootendorst, M.: Bertopic: Neural topic modeling with a class-based tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022)

work page internal anchor Pith review Pith/arXiv arXiv 2022
[17]

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics, 2956–2984 (2024)

Pham, C.M., Hoyle, A., Sun, S., Resnik, P., Iyyer, M.: TopicGPT: A prompt-based topic modeling framework. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics, 2956–2984 (2024)

work page 2024
[18]

In: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp

Zhong, M., Wang, P., Field, A.: Hicode: Hierarchical inductive coding with llms. In: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 31048–31066 (2025)

work page 2025
[19]

Technical report, U.S

Ennis, S., Tiv, M., Fernandez, L., Bhaskar, R., Porter, S.: Examining racial identity responses among people with middle eastern and north african ancestry in the american community survey. Technical report, U.S. Census Bureau, Center for Economic Studies (2024)

work page 2024
[20]

Legal Studies38(4), 587–606 (2018)

Garland, F., Travis, M.: Legislating intersex equality: Building the resilience of intersex people through law. Legal Studies38(4), 587–606 (2018)

work page 2018
[21]

Education Trust (2022)

Morgan, I.: Equal is not good enough: An analysis of school funding equity across the us and within each state. Education Trust (2022)

work page 2022
[22]

American Journal of Public Health (0), 1–13 (2025)

Weideman, B.C., Ecklund, A.M., Alley, R., Rosser, B.S., Rider, G.N.: Research funded by national institutes of health concerning sexual and gender minoritized populations: A tracking update for 2012 to 2022. American Journal of Public Health (0), 1–13 (2025)

work page 2012
[23]

American journal of public health100(3), 468–475 (2010)

Bostwick, W.B., Boyd, C.J., Hughes, T.L., McCabe, S.E.: Dimensions of sexual orientation and the prevalence of mood and anxiety disorders in the united states. American journal of public health100(3), 468–475 (2010)

work page 2010
[24]

Journal of Neurology269(11), 5963–5972 (2022)

G¨ ottgens, I., Darweesh, S.K., Bloem, B.R., Oertelt-Prigione, S.: The impact of multiple gender dimensions on health-related quality of life in persons with parkinson’s disease: an exploratory study. Journal of Neurology269(11), 5963–5972 (2022)

work page 2022
[25]

Population research and policy review40(1), 9–31 (2021)

Read, J.G., Lynch, S.M., West, J.S.: Disaggregating heterogeneity among non-hispanic whites: evidence and implications for us racial/ethnic health disparities. Population research and policy review40(1), 9–31 (2021)

work page 2021
[26]

In: Machine Learning for Healthcare Conference, pp

Movva, R., Shanmugam, D., Hou, K., Pathak, P., Guttag, J., Garg, N., Pierson, E.: Coarse race data conceals disparities in clinical risk score performance. In: Machine Learning for Healthcare Conference, pp. 443–472 (2023). PMLR

work page 2023
[27]

In: Cohen, A.B

Gentile, B., Campbell, W.K., Twenge, J.M.: Generational cultures. In: Cohen, A.B. (ed.) Cul- ture Reexamined: Broadening Our Understanding of Social and Evolutionary Influences, pp. 31–48. American Psychological Association, Washington, DC (2014)

work page 2014
[28]

American Economic Review107(4), 967–1004 (2017)

Shiller, R.J.: Narrative economics. American Economic Review107(4), 967–1004 (2017)

work page 2017
[29]

In: AEA Papers and Proceedings, vol

Ferrario, B., Stantcheva, S.: Eliciting people’s first-order concerns: Text analysis of open-ended survey questions. In: AEA Papers and Proceedings, vol. 112, pp. 163–169 (2022). American Economic Association 2014 Broadway, Suite 305, Nashville, TN 37203

work page 2022
[30]

EPJ Data Science14(1), 28 (2025)

Dunivin, Z.O.: Scaling hermeneutics: a guide to qualitative coding with llms for reflexive content analysis. EPJ Data Science14(1), 28 (2025)

work page 2025
[31]

https://www.prolific.com

Prolific: Prolific. https://www.prolific.com. Online platform. Version: Dec 2024-Jan 2025. 58 London, UK (2025)

work page 2024
[32]

Vaughan, R.: Oversampling in health surveys: Why, when, and how? American Public Health Association (2017)

work page 2017
[33]

Scandinavian Journal of Public Health45(6), 637–646 (2017)

Anderssen, N., Malterud, K.: Oversampling as a methodological strategy for the study of self- reported health among lesbian, gay and bisexual populations. Scandinavian Journal of Public Health45(6), 637–646 (2017)

work page 2017
[34]

Sociology of Race and Ethnicity5(1), 55–69 (2019)

Croll, P.R., Gerteis, J.: Race as an open field: Exploring identity beyond fixed choices. Sociology of Race and Ethnicity5(1), 55–69 (2019)

work page 2019
[35]

Journal of Survey Statistics and Methodology13(1), 18–38 (2025)

Garbarski, D., Dykema, J., Yonker, J.A., Bae, R.E., Rosenfeld, R.A.: Improving the mea- surement of gender in surveys: Effects of categorical versus open-ended response formats on measurement and data quality among college students. Journal of Survey Statistics and Methodology13(1), 18–38 (2025)

work page 2025
[36]

15: standards for maintaining, collecting, and presenting federal data on race and ethnicity

Revesz, R.: Revisions to omb’s statistical policy directive no. 15: standards for maintaining, collecting, and presenting federal data on race and ethnicity. Federal Register. Published March 29(2024)

work page 2024
[37]

Technical report, Pew Research Center (2020)

Amaya, A., Vogels, E.A., Brown, A.: Adapting how we ask about the gender of our survey respondents. Technical report, Pew Research Center (2020). https://www.pewresearch.org/ decoded/2020/09/11/adapting-how-we-ask-about-the-gender-of-our-survey-respondents/

work page 2020
[38]

Psi Chi Journal of Psychological Research27(4), 232–255 (2022)

Hughes, J.L., Camden, A.A., Yangchen, T., Smith, G.P., Domenech Rodr´ ıguez, M.M., Rouse, S.V., McDonald, C.P., Lopez, S.: Guidance for researchers when using inclusive demographic questions for surveys: Improved and updated questions. Psi Chi Journal of Psychological Research27(4), 232–255 (2022)

work page 2022
[39]

Archives of sexual behavior 49(7), 2301–2318 (2020)

Suen, L.W., Lunn, M.R., Katuzny, K., Finn, S., Duncan, L., Sevelius, J., Obedin-Maliver, J.,et al.: What sexual and gender minority people want researchers to know about sexual orientation and gender identity questions: A qualitative study. Archives of sexual behavior 49(7), 2301–2318 (2020)

work page 2020
[40]

Journal of adolescence13(2), 171–183 (1990)

Phinney, J.S., Alipuria, L.L.: Ethnic identity in college students from four ethnic groups. Journal of adolescence13(2), 171–183 (1990)

work page 1990
[41]

Annual review of sociology 19(1), 139–161 (1993)

Porter, J.R., Washington, R.E.: Minority identity and self-esteem. Annual review of sociology 19(1), 139–161 (1993)

work page 1993
[42]

Technical report, Pew Research Center, Washing- ton, D.C

Menasce Horowitz, J., Brown, A., Cox, K.: The role of race and ethnicity in americans’ personal lives. Technical report, Pew Research Center, Washing- ton, D.C. (April 2019). https://www.pewresearch.org/social-trends/2019/04/09/ the-role-of-race-and-ethnicity-in-americans-personal-lives/

work page 2019
[43]

Proceedings of the National Academy of Sciences116(49), 24480–24485 (2019)

G¨ ulg¨ oz, S., Glazier, J.J., Enright, E.A., Alonso, D.J., Durwood, L.J., Fast, A.A., Lowe, R., Ji, C., Heer, J., Martin, C.L.,et al.: Similarity in transgender and cisgender children’s gender development. Proceedings of the National Academy of Sciences116(49), 24480–24485 (2019)

work page 2019
[44]

Journal of Bisexuality14(3-4), 433–456 (2014)

Galupo, M.P., Davis, K.S., Grynkiewicz, A.L., Mitchell, R.C.: Conceptualization of sexual ori- entation identity among sexual minorities: Patterns across sexual and gender identity. Journal of Bisexuality14(3-4), 433–456 (2014)

work page 2014
[45]

https://platform.openai.com/docs/guides/embeddings

OpenAI: OpenAI text embeddings. https://platform.openai.com/docs/guides/embeddings. Accessed August 2025

work page 2025
[46]

Arseniev-Koehler, A.: Theoretical foundations and limits of word embeddings: what types of meaning can they capture? Sociological Methods & Research53(4), 1753–1793 (2024) 59

work page 2024
[47]

Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (2025)

Wang, J.S., Haider, S., Tohidi, A., Gupta, A., Zhang, Y., Callison-Burch, C., Rothschild, D., Watts, D.J.: Media bias detector: Designing and implementing a tool for real-time selection and framing bias analysis in news coverage. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (2025)

work page 2025
[48]

International Journal of Cognitive Computing in Engineering6, 100–108 (2025)

Petukhova, A., Matos-Carvalho, J.P., Fachada, N.: Text clustering with large language model embeddings. International Journal of Cognitive Computing in Engineering6, 100–108 (2025)

work page 2025
[49]

Toy Models of Superposition

Elhage, N., Hume, T., Olsson, C., Schiefer, N., Henighan, T., Kravec, S., Hatfield-Dodds, Z., Lasenby, R., Drain, D., Chen, C., Grosse, R., McCandlish, S., Kaplan, J., Amodei, D., Wattenberg, M., Olah, C.: Toy models of superposition. arXiv preprint 2209.10652 (2022)

work page internal anchor Pith review Pith/arXiv arXiv 2022
[50]

arXiv preprint arXiv:2408.00657 (2024)

O’Neill, C., Ye, C., Iyer, K., Wu, J.F.: Disentangling dense embeddings with sparse autoen- coders. arXiv preprint arXiv:2408.00657 (2024)

work page arXiv 2024
[51]

Crenshaw, K.: Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory and Antiracist Politics, pp. 23–51. Routledge, New York, NY (1997)

work page 1997
[52]

Sociological methods & research49(1), 3–42 (2020)

Nelson, L.K.: Computational grounded theory: A methodological framework. Sociological methods & research49(1), 3–42 (2020)

work page 2020
[53]

Journal of Machine Learning Technologies2(1), 37–63 (2011)

Powers, D.M.W.: Evaluation: From precision, recall and f-measure to roc., informedness, markedness & correlation. Journal of Machine Learning Technologies2(1), 37–63 (2011)

work page 2011
[54]

biometrics, 159–174 (1977)

Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. biometrics, 159–174 (1977)

work page 1977
[55]

Journal of Behavioral Education10(4), 205–212 (2000)

Watkins, M.W., Pacheco, M.: Interobserver agreement in behavioral research: Importance and calculation. Journal of Behavioral Education10(4), 205–212 (2000)

work page 2000
[56]

Technical report, National Bureau of Economic Research (2026)

Asirvatham, H., Mokski, E., Shleifer, A.: Gpt as a measurement tool. Technical report, National Bureau of Economic Research (2026)

work page 2026
[57]

Available at SSRN (2024)

Rothschild, D.M., Brand, J., Schroeder, H., Wang, J.: Opportunities and risks of llms in survey research. Available at SSRN (2024)

work page 2024
[58]

Politics & society27(1), 105–138 (1999)

Kim, C.J.: The racial triangulation of asian americans. Politics & society27(1), 105–138 (1999)

work page 1999
[59]

Social Forces100(2), 506–539 (2021)

Schachter, A.: Intersecting boundaries: Comparing stereotypes of native-and foreign-born members of ethnoracial groups. Social Forces100(2), 506–539 (2021)

work page 2021
[60]

Journal of consulting and clinical psychology42(2), 155 (1974)

Bem, S.L.: The measurement of psychological androgyny. Journal of consulting and clinical psychology42(2), 155 (1974)

work page 1974
[61]

Sex roles63(3), 264–276 (2010)

Wylie, S.A., Corliss, H.L., Boulanger, V., Prokop, L.A., Austin, S.B.: Socially assigned gender nonconformity: A brief measure for use in surveillance and investigation of health disparities. Sex roles63(3), 264–276 (2010)

work page 2010
[62]

Journal of Official Statistics 35(4), 859–884 (2019)

Mishel, E.: Intersections between sexual identity, sexual attraction, and sexual behavior among a nationally representative sample of american men and women. Journal of Official Statistics 35(4), 859–884 (2019)

work page 2019
[63]

Science331(6023), 1447–1451 (2011)

Walton, G.M., Cohen, G.L.: A brief social-belonging intervention improves academic and health outcomes of minority students. Science331(6023), 1447–1451 (2011)

work page 2011
[64]

Journal of Counseling Psychology71(3), 139 (2024)

Lee, B.A., Neville, H.A.: The ibelong scale: Construction and validation of a measure of racial– ethnic–cultural belonging. Journal of Counseling Psychology71(3), 139 (2024)

work page 2024
[65]

Population and Devel- opment Review51(1), 519–538 (2025) 60

Saperstein, A.: Recognizing identity fluidity in demographic research. Population and Devel- opment Review51(1), 519–538 (2025) 60

work page 2025
[66]

Technical report, Pew Research Center, Washington, DC (2015)

Pew Research Center: Appendix c: Multiracial survey topline. Technical report, Pew Research Center, Washington, DC (2015). https://www.pewresearch.org/social-trends/wp-content/ uploads/sites/3/2015/06/2015-06-11 multiracial-in-america final-updated.pdf

work page 2015
[67]

post-gay

Russell, S.T., Clarke, T.J., Clary, J.: Are teens “post-gay”? contemporary adolescents’ sexual identity labels. Journal of Youth and Adolescence38(7), 884–890 (2009)

work page 2009
[68]

American journal of public health100(10), 1953–1960 (2010)

Conron, K.J., Mimiaga, M.J., Landers, S.J.: A population-based study of sexual orientation identity and gender differences in adult health. American journal of public health100(10), 1953–1960 (2010)

work page 1953
[69]

Psychological Reports65(2), 577–578 (1989)

Verkuyten, M.: Happiness among adolescents in the netherlands: Ethnic and sex differences. Psychological Reports65(2), 577–578 (1989)

work page 1989
[70]

Social Indicators Research53, 189–222 (2001)

Michalos, A.C., Zumbo, B.D.: Ethnicity, modern prejudice and the quality of life. Social Indicators Research53, 189–222 (2001)

work page 2001
[71]

Current directions in psychological science27(3), 170–175 (2018)

Yip, T.: Ethnic/racial identity-a double-edged sword? associations with discrimination and psychological outcomes. Current directions in psychological science27(3), 170–175 (2018)

work page 2018
[72]

Self and Identity9(4), 383–402 (2010)

Burrow, A.L., Ong, A.D.: Racial identity as a moderator of daily exposure and reactivity to racial discrimination. Self and Identity9(4), 383–402 (2010)

work page 2010
[73]

Understanding regression analysis, 113–117 (1997)

Allen, M.P.: Testing hypotheses in nested regression models. Understanding regression analysis, 113–117 (1997)

work page 1997
[74]

Pearson Education, Upper Saddle River, NJ (2018)

Greene, W.H.: Econometric Analysis. Pearson Education, Upper Saddle River, NJ (2018)

work page 2018
[75]

Journal of the Royal statistical society: series B (Methodological) 57(1), 289–300 (1995)

Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological) 57(1), 289–300 (1995)

work page 1995
[76]

Science241(4865), 540–545 (1988)

House, J.S., Landis, K.R., Umberson, D.: Social relationships and health. Science241(4865), 540–545 (1988)

work page 1988
[77]

American journal of Epidemiology109(2), 186–204 (1979)

Berkman, L.F., Syme, S.L.: Social networks, host resistance, and mortality: a nine-year follow- up study of alameda county residents. American journal of Epidemiology109(2), 186–204 (1979)

work page 1979
[78]

Ethnicity & health13(4), 321–334 (2008)

Pickett, K.E., Wilkinson, R.G.: People like us: ethnic group density effects on health. Ethnicity & health13(4), 321–334 (2008)

work page 2008
[79]

Antecol, H., Bedard, K.: Unhealthy assimilation: why do immigrants converge to american health status levels? Demography43(2), 337–360 (2006)

work page 2006
[80]

American Economic Review106(5), 461–466 (2016)

Garc´ ıa-P´ erez, M.: Converging to american: Healthy immigrant effect in children of immigrants. American Economic Review106(5), 461–466 (2016)

work page 2016

Showing first 80 references.