But What Will People Think?: Getting beyond Social Desirability Bias by Increasing Cognitive Load

2015 ◽  
Vol 57 (2) ◽  
pp. 313-322 ◽  
Author(s):  
Megan Stodel
Keyword(s):  
Cognitive Load ◽  
Data Quality ◽  
Social Desirability ◽  
Social Research ◽  
Quality Data ◽  
Social Desirability Bias ◽  
Cognitive Loading ◽  
Notable Effect ◽  
Survey Questions ◽  
As If

Social desirability bias reduces data quality when respondents adjust how they answer questions, leading to responses that less accurately reflect reality. Cognitive loading could mitigate this. By setting respondents a task to do alongside answering survey questions, this technique occupies the respondent, which could mean that they will be less concerned with social desirability. Previous research indicates that people who have been cognitively loaded are more honest and less strategic, so theoretically it is possible this would have a notable effect. It would be useful to test this as, if it is effective, it would be beneficial for market and social research, and further to this could have gamification applications, leading to surveys that produce higher quality data alongside being more engaging.

scholarly journals Can you repeat the question? Paradata as a lens to understand respondent experience answering cognitively demanding, sensitive questions

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252512
Author(s):  
Suzanne O. Bell ◽  
David Bishai
Keyword(s):  
Social Desirability ◽  
Response Times ◽  
Primary Objective ◽  
Reproductive Age ◽  
Quality Data ◽  
Social Desirability Bias ◽  
Response Latencies ◽  
Final Model ◽  
Survey Questions ◽  
List Experiment

Survey researchers hope that respondents will provide high-quality data, but evidence suggests that social desirability bias may be commonplace. Social desirability can lead to significant underreporting or overreporting of sensitive behaviors. With better understanding of the cognitive processes that respondents use to prepare and deliver their responses, survey designers could hope to minimize social desirability bias or at least detect settings that lessen its impact. The primary objective of this study was to use survey paradata to understand the psychology of responding to certain types of survey questions. More specifically, we sought to determine how emotional triggering can alter response latencies to cognitively demanding and sensitive survey questions on induced abortion, which is underreported. We hypothesize that having had a prior abortion might lengthen response times to an indirect question about abortion among respondents who have experienced this sensitive reproductive outcome as they hesitate in deciding whether and how to respond to the question. Data come from a representative survey of 6,035 reproductive age women in Rajasthan, India. We used list experiment question active screen time paradata in conjunction with responses from direct questions on abortion to assess our hypothesis. Our final model was a multivariate linear regression with random effects at the level of the interviewer, including adjustments for respondent, community, and interviewer characteristics to estimate within-respondent effects. Results suggest that women who reported an abortion on the direct abortion questions took 5.11 (95% CI 0.21, 10.00) seconds longer to respond to the list experiment treatment list compared to the control list in comparison to women who did not report an abortion on the direct abortion questions. This study demonstrates the additional insights gained when focusing on response latencies to cognitively demanding questions involved in the measurement of sensitive behaviors.

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

scholarly journals Comparison of Data Quality between PAPI and CAPI Modes of Data Collection in Longitudinal Household Surveys: RLMS–HSE Experiment Results

2020 ◽  
Vol 26 (4) ◽  
pp. 8-30
Author(s):  
Aigul Klimova ◽  
Evgeniy Terentev
Keyword(s):  
Experimental Study ◽  
Data Collection ◽  
Data Quality ◽  
Social Desirability ◽  
Social Desirability Bias ◽  
Household Surveys ◽  
Sensitive Questions ◽  
Improve Data Quality ◽  
Monitoring Survey

This article presents the results of an experimental study on how the transition from PAPI to CAPI modes affected data quality in longitudinal household surveys. The study was conducted in 2018–2019 within the Russian Longitudinal Monitoring Survey (RLMS–HSE). In the previous paper, which was based on data from the 26th wave of the RLMS HSE, it was shown that the use of CAPI leads to a significant decrease in the rate of non-substantive responses (“Don’t know”), as well as significant differences in sensitive questions. This paper was aimed at verifying these findings using new data collected during the 27th wave of the RLMS–HSE. The results show that the use of CAPI leads to a decrease in the rate of non-substantive responses, which helps to improve data quality. However, it was shown that the use of CAPI could lead to an increase in social desirability bias.

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

Tax compliance and social desirability bias of taxpayers: experimental evidence from Indonesia

2021 ◽  
pp. 1-18
Author(s):  
Endra Iraman ◽  
Yoshikuni Ono ◽  
Makoto Kakinaka
Keyword(s):  
Experimental Evidence ◽  
Social Desirability ◽  
Tax Evasion ◽  
Tax Compliance ◽  
Computer Assisted ◽  
Social Desirability Bias ◽  
Lower Income ◽  
Tax Returns ◽  
List Experiment ◽  
Experiment Technique

Abstract Identifying taxpayers who engage in noncompliant behaviour is crucial for tax authorities to determine appropriate taxation schemes. However, because taxpayers have an incentive to conceal their true income, it is difficult for tax authorities to uncover such behaviour (social desirability bias). Our study mitigates the bias in responses to sensitive questions by employing the list experiment technique, which allows us to identify the characteristics of taxpayers who engage in tax evasion. Using a dataset obtained from a tax office in Jakarta, Indonesia, we conducted a computer-assisted telephone interviewing survey in 2019. Our results revealed that 13% of the taxpayers, old, male, corporate employees, and members of a certain ethnic group had reported lower income than their true income on their tax returns. These findings suggest that our research design can be a useful tool for understanding tax evasion and for developing effective taxation schemes that promote tax compliance.

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