Many of us can probably relate to the following situation. When we’re in new or unfamiliar social surroundings, we will often mask our true selves and present a version of our ourselves that we believe others will find more favourable. This can happen online too, particularly on social media sites. For example, an influencer could promote healthy eating online, which contrasts with their offline lifestyle, living off coffee and convenience foods. It could be argued that in both scenarios behaviour is adapted, so it’s more socially desirable to the respective audiences. What is Social Desirability Bias?Social desirability bias refers to our tendency to respond in ways that we feel are more appropriate or socially acceptable to others. Even if untruthful. This can be an issue for some survey takers too, especially when the content is of a sensitive nature. This can result in people answering questions according to how they think their responses will be viewed by others, instead of answering truthfully. The answers they provide, can also be inflated to reflect “good behaviour” or under-inflated to hide “bad behaviour”. For example, think about the following survey question: “How many alcoholic drinks do you consume in a day?”. A respondent might answer 1 or 2, to appear more socially acceptable, hiding the 4 or 5 they really consume. This may not pose a problem if just a few individuals answered in this way. However, if many people answered untruthfully, it could skew your results. In fact, bias is a well-known issue that can easily creep into surveys, if researchers are not vigilant. Sampling bias is a good example. This happens when the methods used unwittingly favour certain outcomes over others, such as when participants are not accurately selected. Social desirability bias in surveys can be exacerbated when they’re delivered to recipients face to face or over the phone. In such scenarios there is often a greater desire among respondents to be viewed more favourably by their interviewer. This can lead to them providing the answers they think their interviewer will want to hear. It’s therefore prudent to consider ways to reduce social desirability bias in your survey. How to avoid Social Desirability BiasWith more careful consideration during survey planning and survey design, you can reduce your chances of experiencing social desirability bias. Here are some of our top tips:
One of the most effective ways to start is with an online survey. One of the key advantages of online surveys, is that they don’t require an interviewer to administer them. Not only does this help to reduce expense, but more importantly there’s no longer any third party involved who could unwittingly influence how a respondent answers your survey – significantly reducing your chances of social desirability bias.
Think about issuing an anonymous survey to your recipients. This allows respondents to answer your questions, without having to leave any identifying details, which also includes their IP address. The great thing about anonymous surveys, is that when people are confident that they can answer all your questions without the risk of identification or potential reprisal, they will be more likely to answer truthfully. It’s why many employee surveys are anonymous, as employers can gather much more valuable information about how staff are feeling.
Depending on the nature of your organisation or your survey’s overall aim, you may want to think about keeping your survey’s purpose relatively vague in order to elicit more honest responses. For example, if your organisation was a supporter of animal rights and you wanted to survey people about their views related to this, but it was obvious from the outset who you were and what you supported, you might find people seriously underestimating or overestimating some of their answers. This could include anything from how much meat they eat, to the number of products they use which are free from animal testing. Think too about the wording you use. Response bias can be an issue with some types of survey questions, such as open-ended ones, if the questions have been badly worded. Poorly constructed questions that end up producing leading questions are a good example of this. They can unwittingly lead respondents down a particular route with their answers. For example, “How amazing was your experience with our customer service team?” By communicating to respondents that you think your customer service team is amazing, you’re already squeezing out the potential for them to provide another answer, which could provide more valuable insight. Another challenge is to avoid wording questions in such a way that they force respondents into providing an absolute categorical response when they might not have one. For example, “Do you always use product X for your cleaning needs” The problem here is that the chances of someone using your product 100% of the time will be very slim. Given that the answer will be mostly no, the response to this question is likely to produce a poor result.
The final area to think about is using a consumer panel. A consumer panel is ideal when you need to find survey participant’s fast, or you don’t have a sufficient volume of the correct audience among your existing contacts. Consumer panels typically offer instant access to millions of respondents around the world. This makes them one of the most effective ways of reaching the exact demographic or niche audience group you require. Other benefits include the careful vetting of audiences to ensure they’re right for your survey. This includes the use of disqualification questions. These eliminate respondents from completing your survey, if they are unable to answer these questions correctly at the beginning. This helps to maintain the quality of your survey data. ^Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health Find articles by C. A. Latkin ^Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health Find articles by C. Edwards ^Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health Find articles by M.A. Davey-Rothwell ^Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health Find articles by K. E. Tobin Author information Copyright and License information Disclaimer Social desirability response bias may lead to inaccurate self-reports and erroneous study conclusions. The present study examined the relationship between social desirability response bias and self-reports of mental health, substance use, and social network factors among a community sample of inner-city substance users. The study was conducted in a sample of 591 opiate and cocaine users in Baltimore, Maryland from 2009–2013. Modified items from the Marlowe-Crowne Social Desirability Scale were included in the survey, which was conducted face-to-face and using Audio Computer Self Administering Interview (ACASI) methods. There were highly statistically significant differences in levels of social desirability response bias by levels of depressive symptoms, drug use stigma, physical health status, recent opiate and cocaine use, Alcohol Use Disorders Identification Test (AUDIT) scores, and size of social networks. There were no associations between health service utilization measures and social desirability bias. In multiple logistic regression models, even after including the Center for Epidemiologic Studies Depression Scale (CES-D) as a measure of depressive symptomology, social desirability bias was associated with recent drug use and drug user stigma. Social desirability bias was not associated with enrollment in prior research studies. These findings suggest that social desirability bias is associated with key health measures and that the associations are not primarily due to depressive symptoms. Methods are needed to reduce social desirability bias. Such methods may include the wording and prefacing of questions, clearly defining the role of “study participant,” and assessing and addressing motivations for socially desirable responses. Keywords: opiates, cocaine, heroin, social desirability bias, mental health, self-reports Social desirability bias is the tendency to underreport socially undesirable attitudes and behaviors and to over report more desirable attributes. One major theory of social desirability bias by Paulhus (1984) suggests two components. One is impression management, which is the purposeful presentation of self to fit into a situation or please an audience. A second component is self-deception, which may be unconscious, and is a based on the motivation to maintain a positive self-concept. Tourangeau and Yan (2007), based on an extensive literature review, conclude that socially desirable response bias is often motivated by the desire to avoid embarrassment and repercussions from disclosing sensitive information, which is impression management. In a systematic review of social desirability Perinelli and Gremigni, (2016) noted that most studies had been conducted with college students. In one drug treatment sample, Zemore (2012) found that social desirability bias was associated with the alcohol and drug severity subscales of the Addiction Severity Index. In another non-student sample, Davis and colleagues (2014) reported that among male offenders in Canada who completed substance use treatment, there was an increase in social desirability among those who reported the greatest change in drug and alcohol attitudes. Stephens (1991) suggested that the role of “street addict” was a central attribute of substance users’ relationships. In the role of street addict, providing misinformation and hustling are viewed as appropriate. Consequently, the role of street addict may lead to problematic self-reports if research is seen as a hustle. In the fields of health, role theory has been primarily applied to the patient-provider relationship, with an emphasis on improving the traditional patient or “sick” role as passively following providers’ requests to active patient roles in which patients become partners in their health care (Parsons, 1975; Armstrong, 2014). The traditional patient role can be viewed as similar to the role of research subject who passively complies with the instruction of the researcher. Few studies have examined how social roles may influence the accuracy of self-report data; however, when developing social desirability scales, participants are asked to play the role of “faking good” or “bad” to develop items that can differentiate when individuals are not giving accurate responses. In the current study, we were interested in examining the relationships between social desirability and mental and physical health and health care utilization among out of treatment inner-city heroin and cocaine users. We were also interested in how social desirability bias may be linked to reports of drug user stigma, drug use, and social networks. It has been documented that people who are not depressed tend to rate themselves better than others rate them (Lewinsohn et al., 1980). This phenomenon, which has been called an “illusory glow,” may lead individuals who do not exhibit depression to have an overly positive, as compared to the perceptions of others, view of themselves. Consequently, we also examined whether including a measure of depression in the analytic models would diminish associations between social desirability response bias and health and drug measures. The study was conducted from July 2009 to July 2013 in Baltimore, Maryland. Recruitment was conducted by street-based outreach, word-of-mouth, flyers, advertisements in local papers, and referrals. Inclusion criteria for enrollment into the study were: aged 18–55, willingness to attend intervention sessions, at least one drug-related HIV risk behavior, and at least one sexual risk behavior. Study details have been reported elsewhere (Latkin et al., 2013). The survey was conducted face-to-face and using Audio Computer Self Administering Interview. For the current analyses, which were from the first follow-up visit 6-months post-enrollment, 596 out of 657 participants reported a history of heroin and/or cocaine use. Five participants were excluded due to missed data for a total of 591 participants. The social desirability (SD) scale was 10 items (1 for “yes” and 0 for “no”) based on the Marlowe-Crowne Social Desirability Scale (Andrews and Meyer, 2003). Using the median as a cut point the SD scale was dichotomized to high and low. The 10-item Alcohol Use Disorders Identification Test (AUDIT) was included (Babor et al., 2001). A 10 point scale assessed the last time a participant reported using 7 types of drugs, which included snorting heroin and cocaine; injecting heroin, cocaine, and speedball; smoking crack, and prescription opiate use. The drug user stigma scale was comprised of 17-items (Latkin et al., 2013), which were asked of participants who reported using heroin, cocaine, or crack in the past 6 months (N=410). The Cronbach’s alpha for scale was .91. The 20-item Centers for Epidemiological Studies Depression Scale (CES-D) assessed level of depressive symptoms (Radloff, 1977). The size of the social network was assessed with a modified version of the Personal Network Inventory (Latkin et al., 1996). Measures of health care utilization were assessed by reporting any use of a hospital or emergency room in the past 6 months. Participants were asked if they had participated in research studies in the past 6 months. Age, gender, homelessness, educational, subjective health, and employment status were also assessed. T-tests and chi-square models examined the association between levels of social desirability and continuous and dichotomous variables. Adjusted logistic regression models were then used to examine the associations between the variables of drug use stigma, depression, AUDIT score, substance use, network size, and subjective health status with levels of social desirability. The scales for each of these variables were converted to z-scores in order to standardize the distribution and facilitate interpretation of odds ratios. Among 591 participants with complete data on the SD scale, 331 were male (56%), and the median age was 45 years. On the SD scale, the range was 0–10, 5.42 (mean), and 5 (median). As seen in Table 1, there was a significant difference in reports of having a main sexual partner and subject reports of health status. Marginal differences were found in reports of any heroin or cocaine use in the prior 6 months (χ2 = 3.11, p<0.10). For the t-test models, there were highly significant differences in levels of depressive symptoms, drug user stigma, recent drug use frequency, and size of social networks. Multivariate logistic models indicated highly significant associations between levels of SD and depressive symptoms, drug user stigma, recent drug use frequency, and subjective health status. Level of SD was also associated with participants’ AUDIT score and social network size (Table 2). Results of chi-square and t-tests examining the association between levels of social desirability bias and participant characteristics
Logistic regression models of the association between social desirability bias (dichotomous) and participant characteristics b
One plausible explanation for these findings is that they are artifacts of depressive cognitions. To test this interpretation, we added the CES-D measure to the logistic models. These models indicated that the association between SD and participants’ recent drug use (OR=0.773, 95%CI:0.649–0.921, p<0.01) and drug user stigma (OR=0.772, 95%CI:0.610–0.977, p<0.05) remained significant after controlling for depressive symptoms. However, the associations between SD and participants’ AUDIT score, social network size, and subjective health status were attenuated after controlling for depression. Social desirability response bias was not found to be associated with enrollment in prior research studies, indicating that “professional subjects” do not appear to provide more socially desirable responses. However, we did find that those who have high levels of socially desirable responding report significantly fewer symptoms of depression, lower frequency of recent drug use, smaller social networks, lower drug user stigma, and lower AUDIT scores. It was also associated with subject health status and reports of having a main partner. We did not find that adjusting for levels of depressive symptoms eliminated the association between social desirability response bias and several of the key outcomes. The results from the current study suggest that individuals who tend to give more socially desirable responses may underreport depressive symptoms as mental health issues are stigmatized in similar populations (Rusch et al., 2008). This interpretation is cause for concern as self-reports are the primary method of assessing depressive symptoms. We also found that more objective measures of health, such as hospitalization and emergency room use, did not correlate with socially desirable responding. These behaviors may be seen as normative and less influenced by social desirability bias. The magnitude of the association between SD and subjective health, depression, recent drug use frequency, and drug user stigma suggest that this bias may have a significant impact on self-reports of certain health attributes and behaviors. These findings beg the question of how investigators can reduce socially desirable responding. The role of research participant is often ambiguous, and it is incumbent on investigators to shape this role so that participants perceive that it is socially acceptable to provide accurate information about their mental health and substance use. In order not to promote the role of “drug user,” it may be useful to begin an interview with items that are counter to the sick or drug user role, such as asking what the participant enjoys about drug use as well as the problems caused by drug use. Providing accurate information on one’s health behaviors, especially mental health, may threaten the self-concept of participants and reveal behaviors, such as drug use, which are contrary to common medical and public health advice. One way to address this dilemma is to make reporting the behavior appear to be normative. A pitfall of this approach is that participants may over report behaviors. Another potential approach to obtaining more accurate mental health data is to present the assessment as potentially useful to participants by stating that some participants find it helpful to talk about the interview topics since the interview provides an opportunity to “get things off their chest.” Framing the interview in this vein is not without its pitfalls as it is important to not portray the interview as a therapeutic encounter. However, for populations that have few opportunities to talk about mental and physical health issues such interviews have potential benefits. It may also behoove investigators to ask the question of why participants should provide accurate data and what motivations participants have to provide accurate information. There is a pressing need to research this issue, especially among populations that may have been treated poorly by social services or experienced discrimination due to their economic status, race, and substance use. The focus of this study was on substance users, specifically opioids and cocaine users. We do not have evidence that substance users are more likely than non-users to provide socially desirable responses. There may be some settings, such as drug treatment and paid research, where there are strong pressures to provide socially desirable responses to meet perceived or actual enrollment criteria. In such settings, it is incumbent on investigators to structure interviews to reduce under or over-reporting of certain behaviors as well as motivate participants to provide accurate information. Although biological assays may augment self-reports, another strategy is to mask the enrollment criteria by asking questions on a screener that are not actually related to the criteria. Alternatively, one could expand enrollment criteria for a baseline to include participants who may not be eligible for a full trial. This approach may reduce incentives to report behaviors that may increase eligibility. Tourangeau et al., (1997) found that the bogus pipeline procedure increased self-reports of drinking and illicit substance use in a community sample. Although this approach may be useful for assessing substance use, it is less likely to be effective for measures of mental health and stigma. There are several limitations to this study. The sample was not random and design cross-sectional. The study findings suggest several lines of research to increase the validity of self-report data. This includes studies that modify participants’ role to encourage more accurate self-reports, systematically test whether providing information that may indicate that the behavior is more socially acceptable improves self-reports, and explore whether enhancing motivations to provide accurate information results in greater reports of socially undesirable behaviors. Research should also examine whether it is possible to construct research experiences that lead to participants scoring lower on measures of social desirability. HighlightsAmong substance users, social desirability response bias was associated with self-reports of physical and mental health, substance use, and social network factors. Even after adjusting for depression social desirability was associated with self-reported health and substance use. Social desirability was not, however, associated with more objective health measures nor was it related to being a “professional study subject”. Study participants, NIH grants DA022961 & DA032217 supported this research. Role of Funding Sources The funding was by NIH. The funders had no role in the study design, analyses, interpretation, or writing. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Conflict of Interest None of the authors have financial conflicts of interests.
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