Measuring Political Participation—Testing Social Desirability Bias in a Web-Survey Experiment

2013 ◽  
Vol 26 (1) ◽  
pp. 98-112 ◽  
Author(s):  
Mikael Persson ◽  
Maria Solevid
Keyword(s):  
Political Participation ◽  
Social Desirability ◽  
Web Survey ◽  
Social Desirability Bias ◽  
Survey Experiment

scholarly journals The Distributional Survey Experiment

2020 ◽  
Author(s):  
Sandra Gilgen
Keyword(s):  
Distributive Justice ◽  
Social Desirability ◽  
Situational Factors ◽  
Social Desirability Bias ◽  
Survey Experiment ◽  
Multidimensional Approach ◽  
Natural Manner ◽  
Justice Research ◽  
Scarce Resources ◽  
Trade Offs

What is a just allocation of goods for whom, when and why? Given that the answer to these questions involve need, merit and equality considerations and call for a multidimensional approach that takes individual, contextual and situational factors into account, we are in need of efficient methods designed to help tackle the complexity. The main aim of this contribution is to introduce the distributional survey experiment (DSE), which was developed precisely for that purpose and captures the nature of the problem of distributional justice by accounting for the trade-offs that individuals are forced to make when allocating scarce resources. The DSE is a new survey experiment that measures people’s justice attitudes in as direct and natural manner as possible, while minimising problems of social desirability bias. This paper focuses on showing and comparing three possible methods for analysing the data from the DSE and discussing its potential for distributive justice research.

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.

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.

scholarly journals Google, Tell Me. Is He Gay? Masculinity, Heterosexuality, and Gendered Anxieties in Google Search Queries about Sexuality

2021 ◽  
pp. 073112142110019
Author(s):  
Emma Mishel ◽  
Tristan Bridges ◽  
Mónica L. Caudillo
Keyword(s):  
Social Desirability ◽  
Google Trends ◽  
Social Desirability Bias ◽  
Same Sex ◽  
Search Queries ◽  
Sexual Identities ◽  
Google Search

It is difficult to gauge people’s acceptance about same-sex sexualities, as responses to questionnaires are prone to social desirability bias. We offer a new proxy for understanding popular concern surrounding same-sex sexualities: prevalence of Google searches demonstrating concern over gay/lesbian sexual identities. Using Google Trends data, we find that Google searches about whether a specific person is gay or lesbian show patterned bias toward masculine searches, in that such searches are much more frequently conducted about boys and men compared with girls and women. We put these findings into context by comparing search frequencies with other popular Google searches about sexuality and otherwise. We put forth that the patterned bias toward masculine searches illustrates support for the enduring relationship between masculinity and heterosexuality and that it does so on a larger scale than previous research has been able to establish.

Exploring the empirical evidence of social desirability bias (SDB) in sport consumer research

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Youngho Park ◽  
Dae Hee Kwak
Keyword(s):  
Social Desirability ◽  
Sport Management ◽  
Marketing Research ◽  
Sport Marketing ◽  
Self Report ◽  
Social Desirability Bias ◽  
Content Type ◽  
Report Data ◽  
Self Report Data ◽  
And Control

PurposeThe current study aims to provide a systematic approach to detecting and identifying social desirability bias (SDB) in survey data using controversial sponsorship as a research context.Design/methodology/approachWe used an experimental approach to manipulate sponsorship situations (e.g. Beer sponsor vs Sports drink sponsor) that could potentially motivate respondents to under-report their perceptions toward the sponsor. By employing both procedural and statistical approaches, our evidence shows that responses toward the controversial sponsor were in fact contaminated by SDB.FindingsThe findings of the study provide methodological and practical implications for how sport marketing scholars and practitioners can identify, detect and control SDB in self-report data.Originality/valueWe argue that some survey research in sport marketing may be prone to SDB, but SDB has not received sufficient attention in sport marketing research. We emphasize the importance of detecting (and avoiding/controlling) SDB in sport management research.

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