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Social science research assistants

Also called Research assistant, Research coordinator, Research associate, Project research assistant, Academic research assistant

In brief

In May 2025, BLS counted 30,640 US social science research assistants, with median pay of $61,990 a year. Of the BLS counts since 2021, the highest was 32,940, in May 2024. The earliest figure here is an estimate of 3,000 from 1947, not measured the same way as today's count. The last full technology era for social science research assistants: Qualtrics, Dedoose, ATLAS.ti, MAXQDA, Otter.ai precursors, 2005 to 2020. Online survey platforms and crowdsourcing displaced some RA hours in survey administration and routine coding.

What the sources say

Official outlook

BLS projects employment to grow 5% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Social science research assistants: jobs and pay since 1947 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/19-4061

Chicago

Future History. "Social science research assistants: jobs and pay since 1947." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/19-4061.

BibTeX

@misc{futurehistory_19_4061, author = {{Future History}}, title = {Social science research assistants: jobs and pay since 1947}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/19-4061}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
195019752000now
2026
Known then as Survey Interviewer / Research Clerk / Field Worker (wartime and early postwar survey research era)
Known then as Research Assistant / Research Associate (academic expansion era)
Known then as Research Assistant / Research Coordinator / Project Assistant (think tank and academic computing era)
Known then as Social Science Research Assistants (BLS SOC 19-4061)
Known today as Social Science Research Assistants (BLS SOC 19-4061)
Projected to still be called Social Science Research Assistants (BLS SOC 19-4061)
Data from the May 2025 survey
US Employment
31K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$61,990
Est.

≈ $60,401 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1941

NORC founded by Harry Field in Denver with a $50,000/year Marshall Field Foundation grant and a mission to conduct survey research in the public interest. First survey gauged American attitudes toward entering World War II. Field's national interviewer network was the first scaled deployment of paid social science research support workers in American history.

Beat · 1946

Rensis Likert and six colleagues from the wartime Division of Program Surveys moved to the University of Michigan to found the Survey Research Center. By 1948, the Center had merged with the Research Center for Group Dynamics (relocated from MIT) to form the Institute for Social Research. Under Likert's leadership through 1970, ISR became the world's largest university-based social science research organization and invented modern large-scale academic survey infrastructure -- establishing the institutional template for how university RA labor would be organized for the next half century.

Beat · 1968

The Daddario-Kennedy amendment to the NSF Act formally designated social and behavioral sciences as eligible NSF fields, opening federal grant channels that had previously been informally or partially closed to social scientists. This expanded federal funding for social science research significantly through the 1970s and created sustained institutional demand for RA labor in university-based social science programs.

Beat · 1968

SPSS (Statistical Package for the Social Sciences) was released by Norman Nie, Hadlai Hull, and Dale Bent, initially running on IBM mainframes at Stanford. SPSS put multivariate statistical analysis within reach of any university with mainframe access and an RA who could learn the command syntax. Within a decade it had become the standard tool in academic social science research and redefined what quantitative RA work meant: less card punching, more analytical support.

Beat · 1999

NVivo 1.0 was released, bringing qualitative data analysis software out of the mainframe and DOS command-line era and into a modern Windows interface accessible to any RA. Earlier versions (NUD*IST, 1981) had existed but required significant technical overhead. NVivo made structured qualitative coding -- applying a codebook to interview transcripts, organizing themes, tracking inter-rater reliability -- a routine RA task rather than a specialized technical skill. This standardized qualitative RA workflows much as SPSS had standardized quantitative ones.

Beat · 2024

The American Association of University Professors (AAUP) published a major report in May 2025 on artificial intelligence and academic professions, documenting that AI poses risks of work intensification and potential job losses across academic staff categories including research support roles. The report found that 90% of US colleges and universities had introduced AI initiatives but only 20% had published policies governing AI use in research contexts -- leaving RA-employing research labs to navigate AI adoption without institutional guidance.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace social science research assistants? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+5%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 19-4061 (Social science research assistants): 38.4 thousand jobs in 2025, projected 40.4 thousand in 2035, a change of +1.9 thousand (+5.0%). Matrix employment counts all jobs in the occupation, including self-employed workers (7.7% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
62.1%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 19-4061.00 Social Science Research Assistants. Shown: human-rated β = 0.621, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.394 and γ (all LLM-assisted) 0.848; GPT-4-rated β 0.667. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for social science research assistants

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereTranscribe and process interview and focus group audio for qualitative analysis, submitting recordings immediately after data collection to Sonix or Otter.ai for AI transcription (99%+ accuracy, FERPA-compliant, NVivo/ATLAS.ti export-ready), spot-checking a 10-minute passage per recording against the audio for speaker attribution accuracy (AI diarization degrades in multi-participant focus groups), correcting speaker identification errors before importing into the qualitative analysis platform, and maintaining the IRB-mandated data security protocol for all participant recordings.

Transcribe and process interview and focus group audio for qualitative analysis, submitting recordings immediately after data collection to Sonix or Otter.ai for AI transcription (99%+ accuracy, FERPA-compliant, NVivo/ATLAS.ti export-ready), spot-checking a 10-minute passage per recording against the audio for speaker attribution accuracy (AI diarization degrades in multi-participant focus groups), correcting speaker identification errors before importing into the qualitative analysis platform, and maintaining the IRB-mandated data security protocol for all participant recordings.[9],[10],[1]

Tools picking this up
Where your edge is

Manual interview transcription is now fully automatable for standard one-on-one interviews in quiet settings: Sonix produces an analysis-ready transcript in minutes, reclaiming the single most time-consuming task in the traditional RA workload. The one accuracy check that remains essential is speaker diarization in focus groups: AI speaker identification degrades significantly when three or more participants speak over each other, when participants have heavy accents, or when background noise is present. Build a routine of spot-checking several minutes of each recording against the transcript before uploading to the coding platform. Errors in speaker attribution are invisible to a reader of the final paper but corrosive to the qualitative analysis, catching them is precision work that cements your value to the PI beyond what any automated system provides.

AI is sitting alongside you hereConduct literature searches and systematic review screening, building search strings for PubMed, Web of Science, PsycINFO, and SSRN

Conduct literature searches and systematic review screening, building search strings for PubMed, Web of Science, PsycINFO, and SSRN; uploading abstract sets to Elicit for AI-assisted relevance screening and structured data extraction (sample size, effect size, methodology); validating AI-extracted fields against a 10% spot-check sample to measure extraction accuracy; and importing the final included-studies table into the reference manager (Zotero, Mendeley) with annotation of each paper's methodological design for the PI.[3],[8],[1]

Where your edge is

Elicit now performs structured data extraction at ~81% accuracy vs. human reviewers (Hilkenmeier et al. 2025), meaning the volume RA work of abstract screening and field extraction is largely automatable. What remains is methodological judgment that Elicit cannot supply: defining inclusion/exclusion criteria with disciplinary precision, recognizing domain-specific coding errors (a paper that uses "sample size" loosely to mean analytic sample vs. recruited sample), and deciding when AI extraction confidence is low enough to require full human review. Build proficiency in validation workflows: the RA who can tell the PI "Elicit got 94 of 100 right; here are the 6 errors and their pattern" is doing irreplaceable quality-control work. This is the task where AI fluency translates most directly into RA career security in the near term.

AI is sitting alongside you herePrepare, manipulate, and manage research databases: using Julius AI or GitHub Copilot to generate R/Python data-cleaning scripts from plain-English task descriptions (recode missing-value conventions, standardize variable naming against the codebook, merge datasets on participant ID with integrity checks), running AI-generated cleaning scripts against the raw dataset and reviewing output for domain-specific errors the AI cannot catch without codebook knowledge (survey response codes, study-specific variable conventions), verifying database integrity against source documents for a spot-check sample, and maintaining version-controlled data files per the IRB data management plan.

Prepare, manipulate, and manage research databases: using Julius AI or GitHub Copilot to generate R/Python data-cleaning scripts from plain-English task descriptions (recode missing-value conventions, standardize variable naming against the codebook, merge datasets on participant ID with integrity checks), running AI-generated cleaning scripts against the raw dataset and reviewing output for domain-specific errors the AI cannot catch without codebook knowledge (survey response codes, study-specific variable conventions), verifying database integrity against source documents for a spot-check sample, and maintaining version-controlled data files per the IRB data management plan.[11],[1],[7]

Where your edge is

AI code generation (Julius AI, GitHub Copilot, ChatGPT ADA) has automated the syntax layer of data cleaning: generating a script to remove duplicates, recode missing values, merge datasets, and check referential integrity now takes minutes from a plain-English description. What the AI cannot supply is domain knowledge of the dataset: knowing that response code "9" means "don't know" vs. "refused" in this survey codebook, knowing that participant IDs in the wave-2 file use a different format convention than wave-1, knowing that the "date" column has three different entry formats because three different RAs collected it. This context knowledge is what turns an AI-generated cleaning script from a dangerous approximation into a reliable pipeline. Document the cleaning logic, not just the code, so the PI can audit every recoding decision.

Where social science research assistants go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

Social Science Research Assistants who develop survey design, qualitative coding, and data analysis skills have direct transferable value in commercial market research. Market research firms, brand strategy consultancies, UX research teams at tech companies, and consumer insights groups at corporations actively recruit bachelor's and master's-level researchers who can conduct interviews, manage survey platforms (Qualtrics, SurveyMonkey), apply qualitative coding workflows, and produce findings summaries. The job market is substantially larger and faster-growing than the academic RA pipeline, with faster career progression (research coordinator → senior analyst → manager within several years is standard in commercial research vs. the decade-long PhD track). Compensation is higher than academic RA positions at comparable experience levels. The cultural shift is from methodological rigor for its own sake to decision-speed: commercial research clients need directional findings on a turnaround, not methodologically defensible estimates with confidence intervals. RAs who can translate their academic research skills into commercial language, framing an interview study as "customer discovery" rather than "qualitative data collection", will find this pivot accessible.

What you'd add
  • · Commercial research platforms: Qualtrics advanced features (conjoint analysis, MaxDiff, panel management), SurveyMonkey, and equivalent tools with AI-assisted analysis features now required in most analyst job descriptions
  • · UX research methods: usability testing protocols, journey mapping, Jobs-to-be-Done interviewing framework, research synthesis with tools like Dovetail or Aurelius, the applied methods most in demand at tech companies recruiting from social science backgrounds
  • · Business communication: translating research findings from academic language ("the data suggest a weak association with p=0.08") into directional recommendations ("participants strongly prefer X; recommend prioritizing for next quarter"), a rhetorical register shift that academic training works against
  • · AI tool fluency for research operations: Caplena or Yabble for open-end coding at commercial turnaround speed, Otter.ai for remote interview synthesis, Julius AI for rapid descriptive analytics, the tools commercial clients expect RAs to use independently
  • · Research operations basics: managing vendor relationships (panel providers, recruitment agencies), writing a research proposal with a timeline and cost estimate, presenting findings to a non-researcher audience with appropriate confidence calibration
What it takesMost of your skills carry over
Tools of the era

How has the work of social science research assistants changed since 1941?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Paper survey schedules and mechanical tabulating machines (Hollerith cards)

    The RA of the wartime and early postwar era worked with pen, clipboard, and paper. Field interviewers read questions from a printed schedule and recorded responses in handwriting. Back at the office, coders transferred responses to punched Hollerith cards using card-punch machines; tabulating machines counted the columns and sorted the decks. The RA's job was largely physical and procedural: knock on doors, read from the script, record the answers, code the card. Statistical analysis meant counting and percentage tables. The mechanical tabulator was the first technology to reshape RA work: it moved quantification from hand counting to machine counting, enabling surveys of a scale that would have been impossible otherwise.

    Effect on the work

    Mechanical tabulation made large-scale social surveys economically viable for the first time, which expanded rather than contracted RA employment: larger surveys required more interviewers, more coders, and more card-punch operators. The principal survey organizations of this era -- NORC, the Michigan SRC, Roper, Gallup -- staffed up on RA-equivalent workers throughout the late 1940s and 1950s.

  • Statistical computing: IBM mainframes, SPSS (1968), SAS (1976)

    SPSS -- the Statistical Package for the Social Sciences -- was released in 1968 by Norman Nie, Hadlai Hull, and Dale Bent, originally running on IBM mainframes at Stanford. SAS followed in 1976 from North Carolina State. Both systems transformed what a research assistant could do with quantitative data: instead of hand tabulating or writing Fortran programs from scratch, an RA could learn a command syntax, submit a job to the university mainframe, and receive printed output within hours. Crosstabulations that once required days of mechanical card sorting took minutes. Factor analyses that had been feasible only for a few research groups with direct compute access became routine. The RA's technical skills shifted: mastery of punch cards and mechanical tabulator protocol gave way to knowledge of SPSS card decks and later terminal commands. This era also saw the professionalization of the RA role in academic settings -- universities began treating graduate RAs as grant-funded workers rather than apprentices, with formalized stipend and tuition-remission packages.

    Effect on the work

    Statistical computing allowed smaller research teams to handle larger datasets, modestly compressing the number of dedicated data-coding and card-punching positions. However, the expansion of federal social science funding through the 1970s more than offset any productivity-driven displacement: demand for RA labor grew as the number of funded social science research projects grew.

  • Personal computers, desktop SPSS/Stata/R, Qualtrics precursors, NVivo (1999)

    The shift from mainframe batch computing to desktop personal computers fundamentally changed how RA work was organized. SPSS for DOS (1984), SPSS for Windows (1992), Stata (1985), and eventually R (1993, widely adopted in social science by the late 1990s) put a full statistical environment on the RA's own desk. NVivo (originally NUD*IST, 1981; NVivo 1.0 released 1999) did the same for qualitative coding: instead of paper-and-scissors codebooks applied to printed transcripts, RAs could import interview text and apply codes in a dedicated software environment. Web-based surveys arrived with early Zoomerang (1999) and SurveyMonkey (1999) precursors to Qualtrics (2002). Manual transcription of interview audio -- one of the most time-consuming RA tasks -- remained entirely manual through this era, requiring several hours of RA time per interview hour.

    Effect on the work

    Desktop computing reduced the per-RA overhead for data entry and basic analysis but did not eliminate RA positions -- it changed what RA hours were spent on, with less time on mechanical coding and more time on analysis support and data quality review. The growth of online survey platforms in the early 2000s began to reduce demand for in-person survey interviewers while maintaining demand for RAs in data management and analysis.

  • Qualtrics (2002), Dedoose (2011), ATLAS.ti, MAXQDA, Otter.ai precursors (survey research platform era)

    The 2000s and 2010s consolidated the RA's digital toolkit into a recognizable modern form. Qualtrics (founded 2002, widely adopted in academic research by 2008) standardized online survey deployment and removed most of the administrative overhead of paper survey logistics. ATLAS.ti (commercially available from 1993, widely adopted in the 2000s) and MAXQDA gave qualitative researchers a professional-grade coding environment. Dedoose (2011) served mixed-methods teams linking qualitative excerpts to quantitative participant data. This era also saw the rise of Amazon Mechanical Turk (2005) and Prolific (2014) as RA-displacing tools for specific tasks: online crowdworkers could complete tasks that previously required hired RA labor (transcribing short audio clips, labeling images, categorizing text). The RA's role narrowed somewhat toward higher-skill analytical and coordination functions, with the routine volume work increasingly distributed to crowdwork platforms or automated by survey software.

    Effect on the work

    Online survey platforms and crowdsourcing displaced some RA hours in survey administration and routine coding. The net effect on RA employment was ambiguous: survey platforms reduced labor input per study, but the number of studies grew as data collection costs fell.

  • AI research tools: Elicit, Consensus, Otter.ai, Sonix, ATLAS.ti AI, MAXQDA AI Assist, Julius AI, ChatGPT (the automation frontier)

    The 2020s brought the sharpest tool transition in the occupation's eighty-year history. Elicit (2021) and Consensus (2023) automated systematic literature review and structured data extraction from research papers. Otter.ai and Sonix reduced interview transcription -- historically several hours of RA labor per interview hour -- to minutes. ATLAS.ti Intentional AI Coding and MAXQDA AI Assist scaffolded qualitative codebooks onto transcripts in hours. Julius AI and GitHub Copilot generated R/Python analysis scripts from plain-English descriptions. ChatGPT's Advanced Data Analysis executed Python directly on uploaded datasets. By 2025, every core task category that had defined RA work since the 1940s -- literature search, coding, transcription, data cleaning, basic statistical analysis -- was substantially automatable. Hilkenmeier et al. (2025) documented Elicit achieving 81% accuracy on structured data extraction vs. human reviewers for social science systematic reviews. PS: Political Science and Politics (2024) documented GPT-4 achieving 94% accuracy on archival extraction tasks previously assigned to RAs. The Eloundou et al. (2023, published Science 2024) study assigned Social Science Research Assistants one of the highest LLM exposure scores (alpha=0.91, beta=0.85) in the entire dataset of US occupations.

    Effect on the work

    Inside Higher Ed (2025) and AAUP (2025) documented faculty actively reducing RA hiring as AI tools absorbed routine research tasks. BLS projects employment growth 2024-34 -- "as fast as average" -- suggesting the occupation survives the AI wave rather than collapsing, but the composition of surviving RA work shifts decisively toward IRB-regulated fieldwork, participant interaction, and AI-toolstack management rather than the volume processing tasks that dominated before 2020.

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Social Science Research Assistants employment and pay by year

On record since1941
Latest US employment30,640 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$61,990 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+5% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 27 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
19473,000n/aEst., NORC at the University of Chicago Researched contextReport
196012,000$3,900Est., NSF Brief History (employment) Researched contextEst., NACE Salary Survey historical… (pay) Researched contextReport
197028,000n/aEst., Federal Funding for Academic Research Researched contextReport
1980n/a$14,500Est., NACE Salary Survey historical… Researched contextReport
199032,000n/aEst., IPUMS USA Researched contextReport
200415,710$34,360BLS OEWS May 2004 Official statisticReport
200516,320$33,950BLS OEWS May 2005 Official statisticReport
200615,840$33,860BLS OEWS May 2006 Official statisticReport
200716,070$35,870BLS OEWS May 2007 Official statisticReport
200818,120$35,650BLS OEWS May 2008 Official statisticReport
200921,720$37,500BLS OEWS May 2009 Official statisticReport
201025,830$37,230BLS OEWS May 2010 Official statisticReport
201126,200$38,800BLS OEWS May 2011 Official statisticReport
201226,370$37,140BLS OEWS May 2012 Official statisticReport
201326,830$38,310BLS OEWS May 2013 Official statisticReport
201427,780$39,460BLS OEWS May 2014 Official statisticReport
201528,060$42,480BLS OEWS May 2015 Official statisticReport
201630,030$43,190BLS OEWS May 2016 Official statisticReport
201731,500$46,000BLS OEWS May 2017 Official statisticReport
201834,550$46,640BLS OEWS May 2018 Official statisticReport
201935,580$47,510BLS OEWS May 2019 Official statisticReport
202035,330$49,210BLS OEWS May 2020 Official statisticReport
202128,690$49,720BLS OEWS May 2021 Official statisticReport
202228,720$50,470BLS OEWS May 2022 Official statisticReport
202330,890$56,400BLS OEWS May 2023 Official statisticReport
202432,940$58,040BLS OEWS May 2024 Official statisticReport
202530,640$61,990BLS OEWS May 2025 Official statisticReport
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