Introductión/aim: Considering the increasing use of digital surveys and the risks associated with information quality, it is important to understand how completion time affects data quality. This research aims to analyze the parabolic relationship between completion time and response quality in digital questionnaires.

Methodology: This research employs a quantitative methodology with a non-experimental and cross-sectional design. A 32-item questionnaire based on the MLQ was utilized. The survey was administered to 224 participants in Ecuador and Peru, selected through non-probability sampling. Response quality was evaluated using criteria such as standard deviation, incomplete surveys, straightlining, and inconsistencies, while completion time was recorded automatically.

Results: The regression confirms the existence of an inverse parabolic behavior of response quality as a function of completion time, implying the existence of an optimal range for quality. Perceived burden, female gender, and age showed positive coefficients on response quality.

Conclusions: The findings confirm a parabolic relationship between survey completion time and response quality, identifying an optimal duration that maximizes data reliability.

---

Keywords:
Response quality,
data quality,
completion time,
digital surveys,
linear regression,
questionnaire length.

RSN_17(37)_59_optimo

Alqudah, A., & Al-Shraifin, N. (2020). The effect of the length of a questionnaire on the accuracy estimations of the ability and the psychometric properties of the item and scale in the light of item response theory. An-Najah University Journal for Research – B (Humanities), 34(6), 953-982. https://doi.org/10.35552/0247-034-006-002

Asensio, M., & Revilla, M. (2022). Number of answer categories for bipolar item specific scales in face-to-face surveys: Does more mean better? Quality and Quantity, 56(3), 1413-1433. https://doi.org/10.1007/s11135-021-01183-x

Avolio, B., & Bass, B. (2004). Multifactor leadership questionnaire. Instrument (Leader and Rater Form) and Scoring Guide (Form 5X-Short). English and Spanish versions. Mind Garden, Inc.

Bauer, I., Kunz, T., & Gummer, T. (2025). Plain language in web questionnaires: Effects on data quality and questionnaire evaluation. International Journal of Social Research Methodology, 28(1), 57-69. https://doi.org/10.1080/13645579.2023.2294880

Castro-Atanes, A., & Loureiro, M. L. (2026). Using paradata to improve data quality: A case study on post-wildfire restoration. Forest Policy and Economics, 188, artículo 103816. https://doi.org/10.1016/j.forpol.2026.103816

Cernat, A., Sakshaug, J., Christmann, P., & Gummer, T. (2022). The impact of survey mode design and questionnaire length on measurement quality. Sociological Methods and Research, 53(4). https://doi.org/10.1177/00491241221140139

Cornesse, C., & Blom, A. G. (2023). Response quality in nonprobability and probability-based online panels. Sociological Methods & Research, 52(2), 879-908. https://doi.org/10.1177/0049124120914940

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5^td ed.). SAGE Publications.

Cuskley, C., & Sulik, J. (2024). The burden for high-quality online data collection lies with researchers, not recruitment platforms. Perspectives on Psychological Science, 19(6), 891-899. https://doi.org/10.1177/17456916241242734

Damrongpanit, S. (2019). Factor structure and measurement invariance of the self-discipline model using the different-length questionnaires: Application of multiple matrix sampling. Universal Journal of Educational Research, 7(1), 133-145. https://doi.org/10.13189/ujer.2019.070118

De Rada, V. D. (2022). Strategies to improve response rates to online surveys. Papers: Revista de Sociología, 107(4), e3073. https://doi.org/10.5565/rev/papers.3073

Décieux, J. P. (2022). Sequential on-device multitasking within online surveys: A data quality and response behavior perspective. Sociological Methods and Research. (publicado en línea primero: https://doi.org/10.1177/00491241221082593)

Décieux, J. P. (2024). Sequential on-device multitasking within online surveys: A data quality and response behavior perspective. Impreso posteriormente en Sociological Methods & Research, 53(3), 1384-1411. https://doi.org/10.1177/00491241221082593

Décieux, J. P., & Sischka, P. E. (2024). Comparing data quality and response behavior between smartphone, tablet, and computer devices in responsive design online surveys. SAGE Open, 14(2). https://doi.org/10.1177/21582440241252116

DeKeseredy, W. S., Stoneberg, D. M., Nolan, J., & Lory, G. L. (2021). Improving the quality of survey data on college campus woman abuse: The contribution of a supplementary open-ended question. Violence Against Women, 27(12-13), 2477-2490. https://doi.org/10.1177/1077801220975496

Douglas, B. D., Ewell, P. J., & Brauer, M. (2023). Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA. PLoS ONE, 18(3), e0279720. https://doi.org/10.1371/journal.pone.0279720

Eisele, G., Vachon, H., Lafit, G., Kuppens, P., Houben, M., Myin-Germeys, I., & Viechtbauer, W. (2022). The effects of sampling frequency and questionnaire length on perceived burden, compliance, and careless responding in experience sampling data in a student population. Assessment, 29(2), 136-151. https://doi.org/10.1177/1073191120957102

Ganassali, S. (2008). The influence of the design of web survey questionnaires on the quality of responses. Survey Research Methods, 2(1), 21-32. https://doi.org/10.18148/srm/2008.v2i1.598

Garbarski, D., Dykema, J., Yonker, J. A., Bae, R. E., & Rosenfeld, R. A. (2025). Improving the measurement 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 Methodology, 13(1), 18-38. https://doi.org/10.1093/jssam/smae043

Gibson, A. M., & Bowling, N. A. (2020). The effects of questionnaire length and behavioral consequences on careless responding. European Journal of Psychological Assessment, 36(2), 410-420. https://doi.org/10.1027/1015-5759/a000526

Goerres, A., & Höhne, J. K. (2023). Evaluating the response effort and data quality of established political solidarity measures: A pre-registered experimental test in an online survey of the German adult resident population in 2021. Quality and Quantity, 57(6), 5431-5447. https://doi.org/10.1007/s11135-022-01594-4

Hernández, R., Fernández, C., & Baptista, M. (2014). Metodología de la investigación. McGraw-Hill Education.

Ito, D., & Todoroki, M. (2021). Evaluating the quality of online survey data collected in 2018 in the USA: Univariate, bivariate, and multivariate analyses. International Journal of Japanese Sociology, 30(1), 140-162. https://doi.org/10.1111/ijjs.12117

Izaguirre, J. A., Jordán, D. P., & Palacios, T. Y. (2025). Eficiencia en el tiempo de respuesta y calidad de los datos en encuestas en línea. Revista Venezolana de Gerencia, 30(Especial 13), 407-419. https://doi.org/10.52080/rvgluz.30.especial13.27

Korytnikova, N. V. (2021). Paradata as indicators of online survey data quality: Classification experience. Sotsiologicheskie Issledovaniya, 3, 111-120. https://doi.org/10.31857/S013216250010298-0

Kunz, T., & Gummer, T. (2024). Effects of objective and perceived burden on response quality in web surveys. International Journal of Social Research Methodology, 28(4), 385-395. https://doi.org/10.1080/13645579.2024.2393795

Lau, K. H. C., & Kasneci, E. (2026). What shapes participant data quality? A scoping review and case study of crowdsourced webcam eye tracking in AI interviews. Proceedings of the ACM on Human-Computer Interaction, 10(3), artículo 3806017. https://doi.org/10.1145/3806017

Malhotra, N. (2016). Investigación de mercados (5ª ed.). Pearson Educación.

Meitinger, K., Behr, D., & Braun, M. (2021). Using apples and oranges to judge quality? Selection of appropriate cross-national indicators of response quality in open-ended questions. Social Science Computer Review, 39(3), 434-455. https://doi.org/10.1177/0894439319859848

Myles, P., Axson, E. L., & Mitchell, C. (2026). Data quality, provenance and transparency in real-world data: Aligning quality standards with data governance legal frameworks. Journal of Data Protection & Privacy, 8(2), 131-143. https://doi.org/10.69554/PGGW3813

Nur, A. A., Leibbrand, C., Curran, S. R., Votruba-Drzal, E., & Gibson-Davis, C. (2023). Managing and minimizing online survey questionnaire fraud: Lessons from the Triple C project. International Journal of Social Research Methodology, 27(5), 613-619. https://doi.org/10.1080/13645579.2023.2229651

Paleardi, F., & Novello, M. (2026). From words to categories: Data quality and the methodological value of open-ended questions in classifying economic activities. Quality & Quantity, 60(1), 2385-2405. https://doi.org/10.1007/s11135-025-02348-8

Pan, Y., Sun, Q., Ashoori, M., Zhao, G., & Darzi, A. (2026). Passively collected location data shopping guide: A literature review on data quality evaluation. Journal of Transportation Engineering, Part A: Systems, 152(8). https://doi.org/10.1061/JTEPBS.TEENG-9802

Peytchev, A., & Peytcheva, E. (2017). Reduction of measurement error due to survey length: Evaluation of the split questionnaire design approach. Survey Research Methods, 11(4), 361-368. https://doi.org/10.18148/srm/2017.v11i4.7145

Rhim, J., Kwak, M., Gong, Y., & Gweon, G. (2022). Application of humanization to survey chatbots: Change in chatbot perception, interaction experience, and survey data quality. Computers in Human Behavior, 126, artículo 107034. https://doi.org/10.1016/j.chb.2021.107034

Robb, K. A., Gatting, L., & Wardle, J. (2017). What impact do questionnaire length and monetary incentives have on mailed health psychology survey response? British Journal of Health Psychology, 22(4), 671-685. https://doi.org/10.1111/bjhp.12239

Rodd, J. M. (2024). Moving experimental psychology online: How to obtain high quality data when we can’t see our participants. Journal of Memory and Language, 134, artículo 104472. https://doi.org/10.1016/j.jml.2023.104472

Rong, G., Chen, Y., Koch, T., & Honda, K. (2026). Assessing data quality in citation analysis: A case study of Web of Science and Crossref. Journal of Informetrics, 20, artículo 101775. https://doi.org/10.1016/j.joi.2026.101775

Schneider, S., Lee, P.-J., Hernández, R., Junghaenel, D. U., Stone, A. A., Meijer, E., Jin, H., Kapteyn, A., Orriens, B., & Zelinski, E. M. (2024). Cognitive functioning and the quality of survey responses: An individual participant data meta-analysis of 10 epidemiological studies of aging. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 79(5), gbae030. https://doi.org/10.1093/geronb/gbae030

Smallwood, E., Burke, R., Barnes, A. P., & Ivarson, M. (2026). The DaTUM framework: A cross-sector thematic analysis of data quality dimensions and their impacting factors. International Journal of Information Management Data Insights, 6(1), artículo 100407. https://doi.org/10.1016/j.jjimei.2026.100407

St. George, S., & Arhin, E. O. (2026). Comparing the convenience, data quality, generalizability, and outcomes of student- and MTurk-generated data in an experimental vignette rape perception study. Behavioral Sciences & the Law, 44, artículo 70039. https://doi.org/10.1002/bsl.70039

Traisathit, P., Worasuwannarak, W., Aramrattana, A., Angkurawaranon, C., Jiraporncharoen, W., Buawangpong, N.,
Intui, K., Rankantha, A., Thanuta, Y., & Srikummoon, P. (2026). Quality assessment of the forensic surveillance system: A data-quality monitoring system for substance-abuse detection in unnatural deaths in Thailand. Forensic Science International: Synergy, 13, artículo 100708. https://doi.org/10.1016/j.fsisyn.2026.100708

Van Laar, S., & Braeken, J. (2024). Prevalence of random responders as a function of scale position and questionnaire length in the TIMSS 2015 eighth-grade student questionnaire. International Journal of Testing, 24(1), 24-52. https://doi.org/10.1080/15305058.2023.2263206

Vehovar, V., Couper, M. P., & Čehovin, G. (2023). Alternative layouts for grid questions in PC and mobile web surveys: An experimental evaluation using response quality indicators and survey estimates. Social Science Computer Review, 41(6), 2122-2144. https://doi.org/10.1177/08944393221132644

Vehovar, V., Berzelak, N., & Čehovin, G. (2024). Identifying a set of key paradata indicators in web surveys based on the relationship with response quality, respondent characteristics and survey estimates. Survey Research Methods, 18(3), 225-249. https://doi.org/10.18148/srm/2024.v18i3.8304

Wind, S. A., & Lugu, B. (2024). Combining nonparametric and parametric item response theory to explore data quality: Illustrations and a simulation study. Applied Measurement in Education, 37(2), 109-131. https://doi.org/10.1080/08957347.2024.2345592

Dimensions

PlumX