Survey Respondents

This dataset provides detailed records of survey respondents, including demographic information, completion rates, segmentation labels, and response quality metrics. It enables in-depth analysis of participant behavior, demographic trends, and survey effectiveness, making it ideal for market research, academic studies, and customer insights.

  • last updated 29 Oct 2025
  • by GoMask
The brief that made it

Demographic segmentation and profiling for targeted marketing

Sample rows

preview · 8 of 200 rows · all 17 columns
respondent_idstringcompletion_statusstringcompletion_ratefloatattention_check_passedbooleansegment_labelstringsurvey_idstringresponse_datedatetimeresponse_quality_scorefloatageintegergenderstringethnicitystringeducation_levelstringincome_bracketstringcountrystringregionstringcitystringresponse_time_secondsinteger
RSP0001completed100trueyoung professionalsSURV2024A2024-04-28T09:32:14Z92.529femaleWhitebachelor's degree40k-60kUSACaliforniaSan Francisco775
RSP0002partial85.6falsecollege studentsSURV2024B2024-04-28T16:45:01Z67.119maleHispanicassociate degree20k-40kCanadaOntarioToronto420
RSP0003completed100trueurban mid-careerSURV2024C2024-04-29T13:24:20Z80.237non-binaryBlackmaster's degree60k-80kUKGreater LondonLondon1012
RSP0004abandoned0falseteenagersSURV2024D2024-04-29T19:15:10Zblank16femaleEast Asianhigh school<20kAustraliaVictoriaMelbourne10
RSP0005completed100trueretireesSURV2024E2024-04-30T08:41:55Z97.966maleWhitedoctorate100k+GermanyBavariaMunich1345
RSP0006partial54.7falseuniversity studentsSURV2024A2024-04-30T14:03:28Z45.622maleSouth Asianbachelor's degree20k-40kIndiaMaharashtraMumbai312
RSP0007completed100truemid-career professionalsSURV2024B2024-05-01T11:24:55Z75.845femaleArabmaster's degree80k-100kUAEDubaiDubai889
RSP0008abandoned2falselow engagementSURV2024C2024-05-01T16:36:22Z3.28otherNative Americanother<20kUSAArizonaPhoenix22

What the 200 rows show

from the 200-row sample

Abandoned (completion status) stands out: mean completion_rate is 1.8, against 87.5 for the rest.

  • 56%attention_check_passed = true
  • 99.5median completion_rate
  • 5surveys
  • 5genders
  • 7education levels
  • 7income brackets
Mean completion_rate by completion_status200 rows
050100100.0completed100 rows69.7partial70 rows1.8abandoned30 rows
completion_rate200 rows, in bands of 10
05511030014314131214109050100completion_rate →

Median 99.5, from 0.0 to 100.0.

segment_label200 rows · top 10 of 25 values
  1. young professionals39
  2. retirees23
  3. urban mid-career21
  4. students21
  5. low engagement12
  6. super seniors12
  7. inactive10
  8. teenagers8
  9. graduate students8
  10. university students6
17 columns by typefrom the column list below
  • string 11
  • integer 2
  • float 2
  • datetime 1
  • boolean 1

Columns

17 columns in four groups
blueprint · 17 columns
columntypedescriptionexample
Text 11 columns
respondent_idstringUnique identifier for each survey respondentuniqueRSP0001
survey_idstringIdentifier for the specific survey completed by the respondent5 surveysSURV2024A
completion_statusstringIndicates whether the survey was completed, partially completed, or abandonedcompleted · partial · abandonedcompleted
genderstringGender of the respondentmale · female · non-binary · prefer not to say · other · optionalfemale
ethnicitystringEthnic background of the respondentoptionalWhite
education_levelstringHighest level of education attained by the respondent7 values · optionalbachelor's degree
income_bracketstringIncome bracket of the respondent7 values · optional40k-60k
countrystringCountry of residence of the respondentoptionalUSA
regionstringRegion or state/province within the countryoptionalCalifornia
citystringCity of residenceoptionalSan Francisco
segment_labelstringDemographic segment or group assigned to the respondent (e.g., 'young professionals', 'retirees')optionalyoung professionals
Numbers 4 columns
completion_ratefloatPercentage of survey questions answered by the respondent (0-100)0 to 100100
response_quality_scorefloatQuality metric for the respondent's answers (e.g., based on consistency, attention checks; 0-100)0 to 100 · optional92.5
ageintegerAge of the respondent in years0 to 120 · optional29
response_time_secondsintegerTotal time taken by the respondent to complete the survey in seconds0 or more · optional775
Dates and times 1 column
response_datedatetimeTimestamp when the respondent submitted the survey2024-04-28T09:32:14Z
True or false 1 column
attention_check_passedbooleanIndicates if the respondent passed embedded attention checksoptionaltrue

Use it for

  • attention chec…56%112 of 200 rowsmean completion rate …100.0comple…69.7partial1.8abando…

    A marketing dashboard

    The attention_check_passed rate, completion_rate by completion_status and a breakdown of segment_label. Excel, Power BI or Tableau.

  • Why do the 30 abandoned rows have a mean completion_rate of 1.8?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable respondents with survey_id, response_date and completion_status to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 17 columns, your size and your rules. See 20 rows before you pay.

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This dataset200 rows17 columns
Yours1,000 rows17 columnscountry: UK only

blueprint · survey-respondents

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 17 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Survey start before completion timestamp
  • Completion rate percentage 0-100
  • Demographic categories for segmentation
  • Response quality score based on completeness
  • Incentive status for participation tracking
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
survey-respondents

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