805305
research-article2018
MRE0010.1177/1470785318805305International Journal of Market ResearchLarson
Article
Controlling social desirability bias
Ronald B. Larson
International Journal of
Market Research
2019, Vol. 61(5) 534­–547
© The Author(s) 2018
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https://doi.org/10.1177/1470785318805305
DOI: 10.1177/1470785318805305
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Mid-America Consultants International, USA
Abstract
Social desirability bias can change the results from marketing experiments and surveys. However,
there are few illustrations that show how serious social desirability bias can be. This research
starts by reviewing the options for identifying and reducing social desirability bias in experiments
and surveys and for controlling its effects. Then two examples that use a social desirability bias
scale or a transformation of it (that may improve its utility) as control variables are described.
Data from a national panel survey in the United States is used to show that controlling social
desirability bias can change the set of demographic variables that are judged to be statistically
significant and can have important effects on coefficient sizes. These illustrations will hopefully
stimulate more consideration of social desirability bias, more use of bias measures in marketing
studies, and more research on the control options.
Keywords
bias control, environmental concern, religious attendance, scales, social desirability bias
measurement, social norms
When marketing researchers survey consumers, they may discover that some respondents provide
answers that differ from their actual attitudes, values, or behaviors. If subjects change their answers
for impression management (to look better to others), self-deception (to feel good about themselves), or identity definition, social desirability bias (SDB) occurs. Impression management can
occur when researchers interact with subjects (e.g., face-to-face surveys) while the other sources of
SDB can occur in all types of surveys. This bias can result from social norms that suggest positive
or negative answers to questions are socially preferred. It has affected experiments and surveys for
many years (Gittelman et al., 2015). Tourangeau and Yan (2007) referenced studies that suggested
respondents may underreport illicit drug use, alcohol consumption, smoking, abortion, bankruptcy,
energy consumption, criminal behavior, and racist attitudes. Overreporting was found for voting,
exercise, seat belt use, having a library card, and energy conservation. More recent examples of
SDB effects include the overstatement of incomes by some Danish respondents, the inflation of
interest in buying organic food, and a contribution toward the survey errors in the 2016 U.S. presidential election (Brownback & Novotny, 2018; Hariri & Lassen, 2017; Larson, 2018).
Corresponding author:
Ronald B. Larson, Mid-America Consultants International, 811 2nd Avenue North, Suite 284, Fargo, ND 58102, USA.
Email: ron.larson@live.com
Larson
535
Impression management can occur when others could learn about a respondent’s choices.
Therefore, anonymous, self-administered surveys should have less SDB than telephone or
face-to-face surveys (Dodou & De Winter, 2014; Heerwegh & Loosveldt, 2007; Kreuter,
Presser, & Tourangeau, 2008; Nederhof, 1985; Richman, Kiesler, Weisband, & Drasgow, 1999).
Even if impression management effects are reduced, self-deception and identity definition can
still bias findings. Brenner and DeLamater (2016) argued that differences between face-to-face
and anonymous surveys tend to be small and suggested the SDB exists in anonymous surveys
because people may be answering questions to define their identities. Therefore, other steps
may be needed to reduce this bias.
Few illustrations show how serious this bias can be. This study provides two examples. The
article starts by reviewing options to reduce or control SDB. Next, two examples are shown that
illustrate how SDB can change both the variables statistically significant and the magnitude of
coefficients. The article concludes with recommendations for future studies. Hopefully, more
researchers will encourage honesty in their surveys, disguise their research focus (to reduce social
norm pressures), and attempt to control any SDB that remains in their data.
Options to address SDB
Most researchers do not try to control SDB in their data and many list this bias as a potential limitation of their work. Those who are more concerned about SDB usually choose at least one of four
actions: try to directly reduce the bias, try to understand its causes and indirectly reduce it, try to
prove it is not a significant problem, or try to control its effects.
Direct reduction of the bias
The first option, try to reduce faking and boost honesty, includes maintaining subject anonymity or
adding confidentiality assurances—but the benefit may be small (Fernandes & Randall, 1992). A
researcher might claim to be using a lie detector (which is fake—called a “bogus pipeline”). Bogus
pipelines produced measurable effects in many analyses (Roese & Jamieson, 1993). Jones and
Elliott (2017) used this method to show how SDB can influence constructs such as religious orientation and spirituality. The introduction to a survey could include statements that encourage honesty (i.e., similar to “cheap talk”). Using a noun in an honesty appeal (e.g., “Please don’t be a
cheater”) may reduce cheating more than using a verb (e.g., “Please don’t cheat”) (Bryan, Adams,
& Monin, 2013). People could be (falsely) warned that faking would be identified with a “lie” scale
or could be told that their answers will be audited (Bryan et al., 2013; McFarland & Ryan, 2006).
Brenner and DeLamater (2016) suggested that disguising a survey’s purpose may reduce the bias
related to identity definition.
Indirect reduction of the bias
Many options can help shrink this bias. Using a survey mode with respondent anonymity and
modifying questions to neutralize answers that appear socially acceptable may help. For example,
Backstrom and Bjorklund (2014) repeated a personality survey after neutralizing some questions
and found that response changes were linked with SDB scales. Face-saving alternatives also could
be added as answer options (Duff, Hanmer, Park, & White, 2007; Persson & Solevid, 2014).
Surveys can keep interviewers in the dark about which questions are being answered (i.e., “randomized response”). A card-sorting process may be included in a study (e.g., “Q-sort”; Fluckinger,
2014). Another option involves varying a list of sensitive items by subject. Each subject states how
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International Journal of Market Research 61(5)
many items in their list are true (i.e., “item count”; Glynn, 2013; Lippitt, Masterson, Sierra, Davis,
& White, 2014). Option attributes can be evaluated in various combinations to reduce SDB
(Tomassetti, Dalal, & Kaplan, 2016). This technique helped reduce overreporting of voting (Comsa
& Postelnicu, 2013; Holbrook & Krosnick, 2010). Playing background music can add environmental complexity and reduce SDB (Lalwani, 2009). Increasing the cognitive load of subjects (e.g.,
asking them to remember a set of numbers) may also reduce bias (Stodel, 2015).
Measurement of the socially desirable responding
Psychologists have examined socially desirable responding, developed scales to identify and measure it, and constructed subscales to classify subjects according to the reasons behind their responses.
For many years, studies used the Marlowe–Crowne Scale (Crowne & Marlowe, 1960) that included
33 true–false questions such as “Before voting, I thoroughly investigate the qualifications of all the
candidates” and “I never hesitate to go out of my way to help someone in trouble.” Some researchers were concerned about adding so many items to a survey and others suggested that some questions had become dated. Fischer and Fick (1993) compared the Marlowe–Crowne scale with eight
shortened versions that used a subset of the original questions. They identified two shortened
scales that seemed to perform better than the others. Form X1 was the best, a 10-item scale proposed by Strahan and Gerbasi (1972).
Other “lie” scales have been developed. A particularly popular one, the Balanced Inventory of
Desirable Responding or BIDR, asks people to rate the truth of 40 statements (Paulhus, 1984).
Questions included “My first impressions of people usually turn out to be right” and “I never regret
my decisions.” This scale is often assumed to have two subcomponents, impression management
and self-deception. Support for these subcomponents is mixed (Kovacic, Galic, & Jerneic, 2014;
Lanyon & Carle, 2007; Li & Bagger, 2007) and additional subgroups have been formed. Impression
management has been split into agentic image and communal image and self-deception has been
divided into asset exaggeration and deviance denial (Blasberg, Rogers, & Paulhus, 2014; Paulhus
& John, 1998; Paulhus & Reid, 1991; Paulhus & Trapnell, 2008). In some contexts, even more
dimensions may exist. Lee and Sargeant (2011) considered why donors may overstate their charitable giving and suggested that SDB had six dimensions: (a) impression management, (b) selfdeception, (c) level of involvement, (d) extrinsic benefits, (e) intrinsic benefits, and (f) social
norms. When researchers want to understand the reasons for socially desirable responses, having a
scale with multiple dimensions may give them new insights and may allow them to address the
problem indirectly.
Stober (2001) proposed a 16-item SDB scale, called SDS-17. This scale was shorter than
Marlowe–Crowne and BIDR and was highly correlated with them (Musch, Ostapczuk, & Klaiber,
2012; Tatman & Kreamer, 2014). Blake, Valdiserri, Neuendorf, and Nemeth (2006) reported that
this scale was closely associated with impression management and that it was not linked with six
demographic measures (age, gender, education, employment, income, and marital status). Tatman
and Kreamer (2014) found that the SDS-17 had strong internal consistency and reliability. This
scale was not designed to be divided into subdimensions.
Testing for the bias
The third option for researchers is to test for SDB in a study. A scale-based measure is often used.
A few studies have used a scale to identify respondents with high SDB scores and dropped them
from their sample (e.g., Goetzke, Nitzko, & Spiller, 2014). Choosing the level that constitutes a
“high” SDB score seems arbitrary. Dropping respondents is unnecessary if it is possible to control
Larson
537
for the SDB effects. When the SDB scale is correlated with a key research variable, the results may
be too high or too low. Some studies assumed that when this correlation was considered small (but
statistically significant), SDB was not a problem (e.g., Antonetti & Maklan, 2016; Kaiser, Wolfing
& Fuhrer (1999); Polonsky, Vocino, Grimmer, & Miles, 2014). However, SDB could still influence
the results. Including the scale in the full model and examine the coefficients seems like a better
approach. Norwood and Lusk (2011) argued that examining the correlation is not appropriate for
laboratory experiments where measures such as willingness-to-pay are estimated because hypothetical bias also may be a problem and these two biases can be intertwined. A method to identify
hypothetical bias has also been proposed to identify SDB, comparing the results from direct and
indirect questions. When people are asked indirect questions (e.g., how would the average person
respond), their responses tend to have less SDB (Fisher & Tellis, 1998; Jo, Nelson, & Kiecker,
1997). In theory, the larger the gap between the two question types, the more hypothetical and SDB
exists. For example, Gallardo and Wang (2013) compared direct and indirect valuations for a fruit
with environmental features and concluded there was no SDB because valuations were not significantly different.
Several other techniques have been developed for the third option, trying to prove that SDB is
not a problem for a study. One technique is to include fictitious questions that appear to have some
socially desirable answers to learn if many respondents will provide inaccurate responses. This
approach has not always been effective (Kam, Risavy, & Perunovic, 2015). Another method is to
include questions that can be verified to identify overclaiming. Caskie, Sutton, and Eckhardt (2014)
asked undergraduates their grade point averages (GPAs) and checked their answers. Lower achieving students were more likely to be inaccurate and gender differences were found (i.e., women
tended to overreport while men tended to underreport). Some researchers have used observers or
proxy subjects (e.g., friends or coworkers) to describe the attitudes and behaviors of individuals,
which could help to identify and correct for the bias (e.g., Connolly, Kavanagh, & Viswesvaran,
2007). Experts could judge the social desirability of specific answers to questions and develop an
index of each subject’s socially desirable responses (Konstabel, Aavik, & Allik, 2006). Because of
potential gender differences in socially desirable responding, items should be assessed from the
perspective of both genders (Paunonen, 2016). Measuring the time used to answer each question
may help identify biased responses because when people give socially desirable responses, they
tend to spend less time and effort (Gamberini et al., 2014; Kaminska & Foulsham, 2013, 2016).
Structural equation modeling can be used to identify SDB (e.g., Ferrando, Lorenzo-Seva, & Chico,
2009; Heerwegh & Loosveldt, 2011; Ziegler & Buehner, 2009). Factor mixture models may also
help identify which respondents are most likely to provide biased responses, identify which items
are likely to have biased responses, and identify which variables may predict SDB (Leite & Cooper,
2010).
Controlling for the bias
The fourth option, the focus of this article, involves including a measure for SDB in the analysis to
control the effects of the bias. Researchers could use several of the previous techniques to reduce
SDB and still need to control the bias in the analysis. The scales discussed earlier could be incorporated into the research and turned into an index for each respondent. This SDB index could be
added as an independent variable in the statistical analysis.
There is some question about how to ask the SDB scale questions. Many scales originally were
introduced with dichotomous, true–false questions. The bias measure was the total number of
answers to the questions that suggested a bias. Kam (2013) recommended using Likert-type scales
and totaling the top-two-box scores (or bottom-two-box scores). Others prefer the dichotomous
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International Journal of Market Research 61(5)
response scale (either two options or three options—true, neutral, and false; for example, Gignac,
2013). Because Stober, Dette, and Musch (2002) concluded that 7-point Likert-type scales could
be better for identifying SDB, all the SDS-17 questions in this survey were answered with a 7-point
Likert-type scale (e.g., 1 indicated strongly disagree and 7 indicated strongly agree). Other studies
have used the “top-two-box” scoring system for SDB (e.g., Sosik, Avolio, & Jung, 2002).
Some have criticized employing SDB measure as a control variable. Most of the concern
appears to be with the use of older SDB scales (e.g., Marlowe–Crowne and BIDR) in personality
studies. These scales tend to be linked with personality traits such as emotional stability, conscientiousness, and honesty-humility (De Vries, Zettler, & Hilbig, 2014; McCrae & Costa, 1983; Ones,
Viswesvaran, & Reiss, 1996; Zettler, Hilbig, Moshagen, & De Vries, 2015). Uziel (2014) reported
that impression management subscales may be linked with personality while self-deception subscales appeared to measure SDB. Perinelli and Gremigni (2016) recommended using both general
personality scales and SDB scales in surveys to improve the bias control. However, multicollinearity may make it difficult to quantify SDB when both the bias scale and personality measures are
independent variables (Connelly & Chang, 2016). Paunonen and LeBel (2012) conducted Monte
Carlo simulations with personality traits and found that social desirability effects can be elusive. If
specific personality traits result in some subjects adjusting their responses (e.g., overestimating
new product purchases), substituting the personality measures that are linked with SDB for the
SDB measure might compensate for these tendencies.
There are other concerns about SDB scales. Instead of implicitly assuming the SDB controls are
necessary, Tracey (2016) argued that researchers should justify why SDB controls would improve
research findings. Fisher and Katz (2000) suggested that correlations between SDB and value
questions may be measuring cultural information and not bias. Uziel (2010) suggested that using
self-reported measures with unknown statistical properties to adjust data with an unknown amount
of bias could do more harm than good. More research is needed on the various SDB scales and
subscales to understand their properties.
Method
To illustrate how the SDS-17 scale can help control SDB, an anonymous, web-based survey of
U.S. adults was distributed in January 2015 by Qualtrics. A total of 895 adults started the survey
and some did not finish. An attention check was included to improve response quality (Abbey &
Meloy, 2017). Panelists were asked questions about their height and weight. If a response was not
feasible (e.g., a person claimed to be extremely tall), the respondent was deleted. The sample frame
was adults aged 25 to 65 (a few respondents outside of that age range were dropped). Complete,
usable responses totaled 725. Qualtrics reported that at least 100 responses came from each of the
four Census regions, suggesting some geographic diversity in the sample. The sample has good
demographic diversity. About 67% of the sample were women, 22% were aged 35 to 44, 24% were
aged 45 to 54, and 29% were aged 55 to 65. About 35% were single, divorced, or widowed and
41% had children present in their homes. For education, 77% had some college experience and
36% completed a 4-year degree (or more). About 37% had household incomes between US$40,000
and US$79,999, 15% had incomes between US$80,000 and US$119,999, and 11% had incomes of
at least US$120,000. Like in many other panel surveys, non-Whites were underrepresented (16%
of respondents).
After examining the SDS-17 questions (Table 1), some researchers may realize that they might
answer several in socially desirable ways. Therefore, honest respondents may have scores above
zero. The SDB score distribution ranged from 0 to 16 with a mean of 6.3 (Table 2). The SDS-17
scale has produced a variety of average scores. Studies of German, Swiss, and American subjects
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Table 1. Statements from the SDS-17 scale by Stober (2001).
1. I sometimes litter.
2. I always admit my mistakes openly and face the potential negative consequences.
3. In traffic I am always polite and considerate of others.
4. I always accept others’ opinions, even when they don’t agree with my own.
5. I take out my bad moods on others now and then.
6. There has been an occasion when I took advantage of someone else.
7. In conversations I always listen attentively and let others finish their sentences.
8. I never hesitate to help someone in case of emergency.
9. When I have made a promise, I keep it—no ifs, ands, or buts.
10. I occasionally speak badly of others behind their back.
11. I would never live off other people.
12. I always stay friendly and courteous with other people, even when I am stressed out.
13. During arguments I always stay objective and matter-of-fact.
14. There has been at least one occasion when I failed to return an item that I borrowed.
15. I always eat a healthy diet.
16. Sometimes I only help because I expect something in return
SDS: social desirability scale.
The 17th statement about illegal drug use is typically not included in the scale.
and dichotomous scoring had means of 10.34, 10.30, and 9.18 (Durrani & Rajagopal, 2016; Englert
& Rummel, 2016; Scholz et al., 2013) while other studies of German students and American adults
and dichotomous scoring had means of 4.8 and 5.9 (Cote, Gyurak, & Levenson, 2010; Malesza &
Ostaszewski, 2016).
Another issue involves how a change in the SDB sum measure will influence the dependent
variable. If an individual’s score moved from 1 to 5, there might be a small amount of concern
about the change. Given that the mean was 6.3, moving from 7 to 11 should raise more bias concerns. However, if the score changed from 12 to 16, the incremental effect probably should be low
because the subject was already identified as a provider of socially desirable responses. The linear
nature of the sum score suggests that 4-point changes (i.e., 1 to 5 and 7 to 11) would have an equal
impact on the dependent variable. Because a change from 7 to 11 should have a larger impact and
a change from 12 to 16 should have a smaller impact, a nonlinear transformation on the summated
variable may improve the analysis. The option suggested by this article is a logistic transformation.
This creates a continuous measure with some known properties (e.g., bounded between 0 and 1).
Table 2 shows that, for this sample, a move from 1 to 5 would raise the transformed measure by
about 0.15, a move from 7 to 11 would raise it by about 0.40, and a move from 12 to 16 would raise
it by about 0.005. Other transformations could be tested to learn which one is best.
The survey included several questions, answered with 7-point scales that serve as dependent
variables in binary logistic regressions. Although the models used are not necessarily the definitive
models for explaining these dependent variables, they illustrate how a researcher conducting a
demographic segmentation may be misled if SDB is not considered. The dependent variable in the
first regression indicated whether a respondent reported at least monthly attendance at organized
religious activities. About 36.7% of the sample said they attended at least once per month. Studies
from the United States and Canada suggest that gender, ethnicity, age, marital status, the presence
of children, education, and income may all be linked with attendance (Azzi & Ehrenberg, 1975;
Clark, 2000; Eagle, 2011; Ehrenberg, 1977; Gruber, 2004; Long & Settle, 1977).
Religious attendance responses are affected by SDB (Brenner, 2011a, 2011b, 2012; Hadaway,
Marler, & Chaves, 1998; Rossi & Scappini, 2014). Presser and Stinson (1998) found that religious
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International Journal of Market Research 61(5)
Table 2. Distribution of social desirability bias scores based on the SDS-17 scale.
Count for SDS-17
Frequency of score
Percentage distribution
Transformed score
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Total
37
39
67
50
57
60
52
56
56
58
48
44
35
31
19
13
3
725
5.1
5.4
9.2
6.9
7.9
8.3
7.2
7.7
7.7
8
6.6
6.1
4.8
4.3
2.6
1.8
0.4
100
0.00129279
0.00350638
0.00947423
0.02534110
0.06600997
0.16115491
0.34306616
0.58670003
0.79418521
0.91296125
0.96611597
0.98726193
0.99527589
0.99825689
0.99935804
0.99976374
0.99991307
SDS: social desirability scale.
attendance had declined over time while reported attendance in surveys had remained steady and
concluded that SDB had increased. Brenner (2017) conducted face-to-face interviews and found
about half of the subjects lowered their estimate of religious attendance during the interview, which
suggests their first answer was inflated.
The second example used as a dependent variable the top-two-box Likert-type scale responses
to the statement “I consider the potential environment impact of my actions when making many of
my decisions.” About 34.7% of subjects agreed or strongly agreed with the statement. Unlike
religiosity, green attitudes have weak associations with demographics (Diamantopoulos,
Schlegelmilch, Sinkovics, & Bohlen, 2003; Martin & Schouten, 2012; Roberts, 1996; Straughan &
Roberts, 1999). Both regressions used demographics as the independent variables (gender, race,
age [four classes, three included], marital status, presence of children, education [three classes, two
included], and income [four classes, three included]). The models tried to identify which variables
significantly increased or decreased the probability of a positive response.
Results
The first regression examined self-reported religious attendance. The left columns in Table 3 show
that three of the seven demographic variables were statistically significant at the 95% level (starred)
without the SDB variable. Respondents who were older, had children in the household, or had a
4-year college degree were significantly more likely to attend religious activities at least once per
month. With the sum SDB variable, the results in the middle columns, the coefficient on an age
class fell by 17% and it moved out of statistical significance (at the 5% level). The coefficient on
marital status increased in magnitude by 28% and it became significant. Therefore, older people
are probably not significantly more likely to be frequent attendees and those who are single,
541
Larson
Table 3. Analyses of those claiming they attended religious activities at least once per month.
Constant
Female
Non-White
Age of 35 to 44 years
Age of 45 to 54 years
Age of 55 to 65 years
Single, divorced, or
widowed
Children present
Some college including
2-year degree
Four-year college degree
or more
Incomes of US$40,000
to US$79,000
Incomes of US$80,000
to US$119,000
Income of US$120,000+
SDS sum total
SDS transformed
Without a SDB bias
measure
With the sum SDB
bias measure
With the transformed
SDB bias measure
Coefficient
p value
Coefficient
p value
Coefficient
p value
−1.285*
0.054
0.321
−0.117
0.277
0.477*
−0.360
.000
.757
.153
.456
.229
.044
.054
−1.962*
−0.020
0.246
−0.124
0.217
0.395
−0.460*
.000
.911
.283
.607
.356
.103
.016
−1.737*
0.002
0.242
−0.117
0.205
0.390
−0.433*
.000
.989
.290
.628
.382
.106
.022
0.625*
0.070
.001
.774
0.617*
0.163
.001
.461
0.620*
0.143
.001
.515
0.557*
.016
0.774*
.001
0.745*
.002
0.162
.405
0.114
.566
0.129
.514
0.120
.647
0.040
.882
0.070
.793
0.175
.547
−0.025
0.108*
.932
.000
0.059
.842
0.962*
.000
SDS: social desirability scale.
*Significant at the 95% level.
divorced, or widowed are less likely to be frequent attendees. Note that the coefficient on the
children-present variable remained stable and the coefficient on the 4-year-college-degree-or-more
variable increased by 39%. It is important to acknowledge that although overstatement of religious
attendance is well documented and the SDB measure appears to adjust for some of this overstatement, the true relationships between the variables are not known.
Table 4 shows the second regression results for environmental concern. Those who were 35 to
44 years of age were significantly less likely to agree with the statement than those who were 25 to
34. In this analysis, high-income individuals were also significantly more likely to agree. However,
when the SDB indicator was included in the model, the middle columns of results, the coefficient
on the high-income variable fell by 41% and was no longer significant. The coefficient on the
4-year-college-degree-or-more variable increased by 157% and became significant. The coefficient on the age class that was significant remained stable. Both these examples had a change in the
mix of significant demographics when the SDB measure was added.
Another example with some social norm content confirms that using the SDB control can
change the results. Respondents were also asked if they agreed with the following “Given the
choice, I would buy humanely raised meat even if it cost a little more.” The top-two-box score was
the dependent variables and the same independent variables were used. Without the SDB sum variable, female and high-income measures were significant and positive and the two older age classes
were significant and negative. With the SDB variable, the same four variables remained significant
and both education variables became significant and positive. In this case, adding the SDB sum
measure increased the number of variables that were statistically significant.
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International Journal of Market Research 61(5)
Table 4. Analyses of those who stated that they considered potential environment impacts.
Constant
Female
Non-White
Age of 35 to 44 years
Age of 45 to 54 years
Age of 55 to 65 years
Single, divorced, or
widowed
Children present
Some college including
2-year degree
Four-year college degree
or more
Incomes of US$40,000
to US$79,000
Incomes of US$80,000
to US$119,000
Income of US$120,000+
SDS sum total
SDS transformed
Without a SDB bias
measure
With the sum SDB
bias measure
With the transformed
SDB bias measure
Coefficient
p value
Coefficient
p value
Coefficient
p value
−1.057*
0.088
0.205
−0.657*
−0.225
−0.147
0.322
.001
.613
.352
.006
.316
.525
.079
−2.178*
−0.020
0.083
−0.643*
−0.382
−0.342
0.210
.000
.913
.723
.010
.109
.163
.277
−1.781*
0.012
0.085
−0.631*
−0.389
−0.333
0.246
.000
.950
.714
.011
.100
.170
.197
0.226
0.199
.234
.358
0.195
0.365
.330
.109
0.205
0.320
.301
.156
0.220
.346
0.565*
.024
0.510*
.038
0.123
.528
0.040
.845
0.060
.767
0.421
.106
0.312
.257
0.353
.196
0.687*
.016
0.406
0.178*
.181
.000
0.531
.075
1.556*
.000
SDS: social desirability scale.
*Significant at the 95% level.
All three models were also run with the transformed SDB variable. With this variable, there
were some slight coefficient changes (see the right-hand columns in Tables 3 and 4). However,
there were no changes in which variables were significant compared with the models with the sum
SDB measure. The logic behind the transformed SDB variable is fairly strong and it probably
should be tested in future studies.
Discussion and conclusion
SDB can influence the interpretations of consumer surveys and experiments. Perinelli and Gremigni
(2016) believed clinical psychologists should consider SDB when investigating self-reported
behaviors. Kuokkanen and Sun (2016) concluded that neglecting a quantitative assessment of SDB
reduces the value of marketing studies. SDB can be particularly important in cross-country research
because some cultures have much higher levels of bias (Steenkamp, DeJong, & Baumgartner,
2010; Tellis & Chandrasekaran, 2010).
Several options are available to address SDB concerns that include attempting to reduce the bias
directly or indirectly and striving to minimize its effects. In this research, steps were taken to
reduce the bias (e.g., neutralized questions, an anonymous, online survey). Instead of focusing on
whether SDB was present, the measure was included in the regression to control the effects. The
SDB measure was significant in all the regressions. Adding the measure may cause some variables
to gain or lose statistical significance, may change coefficient sizes as much as 100% or more, and
may improve research accuracy. The examples illustrated in this study confirmed the benefits of
Larson
543
SDB control. Considering SDB could change the demographic profile of individuals with a particular attitude or behavior, which is often particularly important for marketers. As more people
become familiar with SDB, it would become another control variable, explained in the same way
as controls for age, ethnicity, and gender differences.
Researchers who choose to use SDB measures should be aware of the options and the controversy with these measures. The Marlowe–Crowne Scale, the BIDR scale, SDS-17 scale, and shortened versions of them are probably the most prevalent SDB scales. The statistical properties of
various scales and subscales need further analysis. It is important that the normal relationship
between the scale questions and the dependent variable is limited. Another issue involves how to
incorporate the scale into questionnaires. This survey scattered the SDS-17 scale questions to minimize the carryover between questions. Other researchers prefer keeping questions on the same
issue together. This could also be examined in future research. Most of the controversy about using
an SDS scale as a control variable dealt with personality research. Other studies might test an SDS
scale on different marketing topics to learn about the importance for each subject. The logic behind
the transformation of the SDS measure seems fairly sound, but more testing of this and other possible transformations would be helpful. The overall lessons from this research are that many
options exist to reduce and control SDB and not controlling for SDB could lead to inaccurate
analyses of marketing survey and experiment results.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
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