1 

The Golden Double Standard: Accounting for Social Desirability in 

Cross-Modal Comparisons 

Steven H. Gittelman, Ph.D. - President, Mktg, Inc.

Victor Lange - Statistical Analyst, Mktg, Inc. 

William Cook – CSO, e-Strategic Advantage 

Susan Frede – VP, Research Methods & Best Practices, Lightspeed Research 

Paul J. Lavrakas, Ph.D. - Independent Consultant, Visiting Scholar Northern Arizona U., and 

2013-2014 AAPOR Past President

Christine Pierce – VP, Watch Methods, Nielsen 

Randall K. Thomas-- VP, Online Research Methods, GfK 

Introduction 

Survey research relies heavily on the accurate capture and analysis of the words of others. Practitioners must 

carefully craft their survey questionnaires to avoid biasing respondents, to avoid confusing them, and to avoid 

boring them, all in the effort to achieve as accurate and pure a response as possible. Among the problems for survey 

researchers to address is response inaccuracy resulting not from the imprecision of our measures, but from the 

individuals we are measuring; i.e., from Respondent-related Measurement Error (cf. Groves, 1989). We often 

confront issues of data quality, where respondents may have to be removed from the final dataset because they were 

clearly not engaged enough with a study to properly consider and respond to its questions. Far more difficult to 

remove or even to detect, however, are those respondents who are diligent in their completion of the questionnaire 

but at times “distort” their answers when they are uncomfortable providing fully accurate responses to questions, 

especially regarding topics of a sensitive nature. 

Surveys have changed little in light of this consideration, but the phenomenon of this “socially desirable” pattern of 

responding has not gone unstudied. As early as 1964, Crowne & Marlowe characterized “the need of subjects to 

respond in culturally sanctioned ways.” Later research showed that social desirability was an attribute that could be 

differentially associated with survey items as well as individuals. That is, whereas certain persons are more likely 

than others to provide socially desirable responses, certain survey items also are more likely to elicit socially 

desirable responses than are other items (Philips & Clancy, 1972). Holding what has since proven to be a minority 

view of this phenomenon, these authors went as far as stating that the problem of social desirability was a threat to 

the entire concept of survey research, where no result could be taken at face value so long as it relied on an 

individual’s self-report. 

Views of social desirability in surveys have become more nuanced since that time. Today it is generally accepted 

that the tendency to respond in socially desirable ways is not uniform across all studies. According to Baker et al. 

(2010) “The social desirability hypothesis proposes that in the presence of an interviewer, some respondents may be 

reluctant to admit embarrassing attributes about themselves and/or may be motivated to exaggerate the extent to 

which they possess admirable attributes.” Conversely, these effects do not emerge as strongly in self-administered 

survey modes that do not have an interviewer present. Topics that have been shown to display significantly less 

social desirability bias in self-administered modes include health (Baker, Zahs & Popa, 2004), donations to charity 

(Taylor, Krane & Thomas, 2005), and racially-motivated political views (Chang & Krosnick, 2009). 

Though these papers may identify the presence of a social desirability bias and also report the degree of difference 

between modes of survey data collection, they did not provide an explicit endorsement for the quality of data 

collected through self-administered mail and online methods. Furthermore, there is a great deal of difference 
2 

between the probability samples employed in these studies and the non-probability samples employed in the surveys 

conducted in market research on a day-to-day basis. For this reason, it is difficult to quantify the degree to which 

the differences between estimates derived from online panels and those collected by phone and in-person methods is 

due to social desirability effects versus the share that is due to sample selection. 

There have been previous attempts to resolve this issue. Frisina and Thomas (2007) attempted to scale item-specific 

social desirability across a set of ten behavioral survey questions by asking respondents how ‘good’ or ‘bad’1 each 

of the behaviors were on a five point scale. Although the method proved effective in predicting the direction of bias 

between phone and online data, it had difficulty predicting the magnitude of those differences. 

Over the past 15 years, an increasing number of surveys have been conducted online and then compared to 

interviewer-administered survey results. To enable more apt comparisons between modes, we believe that we must 

be able to estimate not only the direction of bias but also the degree of bias. While many other differences exist 

between modes of survey administration (e.g. visual versus oral processing), the social desirability bias is probably 

one of the stronger distortions to control for in understanding modal differences, and may even outweigh the impact 

of sample selection bias across modes. 

We have accordingly conducted an original study that gathered information about both the direction and magnitude 

of the social desirability bias that would be anticipated with specific survey items. We anticipate that such responses 

should be effective in predicting actual differences between data collected through different modes. 

Data: 

Measures of difference between phone and online survey modes were obtained from the Foundations of Quality 2 

(FOQ2) survey study conducted by the Advertising Research Foundation in 2013. FOQ2 administered mostly 

identical online questionnaires to 70,377 respondents from 17 of the largest online suppliers in the United States, 

along with a shorter version of the questionnaire asked of 1,008 telephone respondents2. This was an omnibus study 

designed not only to examine responses across a variety of topics, but also across question types, and the survey was 

custom-built to allow for testing the effects of survey design decisions ranging from post-hoc weighting to quality 

control. Central to any comparison of data in this ARF study, however, is knowledge of the “correct” value for any 

question of interest. (Historically, deference has been given to RDD telephone research for greater accuracy, but 

research in the past decade has begun to question the validity of this assumption.) 

To approach the question of socially desirable responding, we followed the example set by Frisina and Thomas by 

creating a questionnaire aimed at generating item-specific measures of sensitivity. However, we reasoned that the 

Frisina and Thomas method confounded respondents’ moral compass with commonly held views by using a “GoodBad” evaluation scale. For example, a socially conservative respondent might feel that homosexuality is a negative 

trait while a more liberal respondent would not, thereby creating a heterogeneous social desirability rating for this 

attribute. Nevertheless, both conservatives and liberals would likely agree that, on average, homosexuality is an 

attribute that survey respondents are more likely to falsely deny (i.e., False Negatives) than falsely claim (i.e. False 

Positives). To attempt to better capture this phenomenon, we asked respondents in our study to predict the 

tendencies of most survey respondents to distort their answers for a set of specific survey questions. 

 

1

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2

 The phone dataset included a representative proportion of cell phone respondents following best practices for 

dual-Frame RDD surveys (cf. AAPOR, 2010). Both datasets received post-stratification weights for both the phone 

and online components to adjust for deviations between the final unweighted samples and the U.S. general 

populatioミげs IhaヴaIteヴistiIs foヴ se┝, age, ヴegioミ, ethミiIit┞ aミd eduIatioミ.
3 

Our respondents were provided the following instructions: 

You are going to be presented with a set of standard survey questions. In surveys like these, we often find 

that some respondents are uncomfortable with the questions asked or wish to make a positive impression 

on the interviewer. Because of this, they provide answers that are not completely accurate in order to 

portray themselves in a better light. Please read each question carefully and tell us, how likely it is that the 

average respondent would misrepresent their opinions and by how much? 

Respondents were presented with a 5-category response format with responses ranging from “Likely to give a much 

higher answer” to “Likely to give a much lower answer”, adjusted as was necessary to conform to the question 

wording.3 Our study questionnaire consisted of 28 items, using each of the benchmark-style questions that were 

contained in the ARF FOQ2 (17 for which it collected both online and phone data and 23 for which only online data 

were collected, but national U.S. benchmark data from external sources were available). Our study was 

administered to 998 online panelists provided by Survey Sampling International, balanced using sampling quotas to 

US proportions of sex, age, income, ethnicity, and region in an online questionnaire with an average duration of 

approximately ten minutes. 

Results: 

 Table 1 displays the relative rankings of the 17 items tested in our study for which both ARF phone and online data 

are available. The left column presents the relative percentage difference between the FOQ2 phone and online mean 

values4 and the right column displays how respondents rated the item relative to the midpoint in our evaluation 

survey. We refer to this measure as the item-specific social desirability rating (ISSDR)5. Negative ISSDR scores 

represent an expected under-reporting of the behavior in question (i.e., a tendency to expect others to falsely deny 

being associated with this behavior). Positive scores represent an expected over-reporting of the behavior in question

(i.e., a tendency to expect others to falsely agree to being associated with this behavior). A score of zero represents 

no expectation of inaccuracy ( i.e., a tendency to expect that others would not exhibit any social desirability in their 

responses about this behavior). 

For example, the relative deviation between FOQ2 phone and online estimates for “Smoking Frequency” is -0.134, 

which constitutes one of the largest negative differences between the items we tested. That value means that 

respondents in the ARF telephone survey reported smoking significantly less frequently than did respondents in the 

ARF online survey. This corresponds with the expectations of the respondents in our survey – the fact that 

“Smoking Frequency” received the second-most negative ISSDR score (-0.46 as shown in the right column of Table 

1) of items tested indicates the belief of respondents that it had a high likelihood of underreporting. The strong 

similarity in order between the two comparisons is compelling evidence that the magnitude of social desirability bias 

was successfully captured by the method. The correlation between the paired scores in the two columns of Table 1 

is R=0.88. Thus there is a very high correspondence between what respondents in our experiment thought would 

happen and what was actually observed in the ARF surveys. 

Table 1 

Phone-Online Proportional Deviations and Ratings of FOQ2 Items 

Item 

Phone-Online 

Deviation ISSDR 

 

3

 A second version of this questionnaire was tested that separated out the concepts of direction and degree. The 

results were discouraging and this approach did not reach full fielding. 

4

 Due to the diverse scaling of the 17 items, deviation was calculated as a relative proportion difference: 

x(Phone) x(Online) / x(Phone)

5

 Raw responses were on a 1-5 scale; values are reported as differences from the midpoint of 3. 
4 

Drinks in Past Year -0.152 -0.59 

Smoking Frequency -0.134 -0.46 

100 Cigarettes in Life -0.087 -0.36 

12 Drinks in Life -0.029 -0.09 

Have Cell Phone -0.018 0.16 

Hours of sleep/night 0.000 0.04 

Days under-slept 0.001 0.09 

Married 0.019 0.03 

Driver's License 0.021 0.20 

Proportion of calls on cell 0.052 0.09 

Valid Passport 0.083 0.10 

Self-reported health 0.090 0.52 

Religiosity 0.117 0.44 

Church Attendance 0.122 0.57 

Hours working/week 0.127 0.26 

Strengthening Phys. Activity 0.429 0.70 

Vigorous Phys. Activity 0.454 0.63 

In order to convert this correlation into an actual predicted correction, we used a univariate regression of deviation 

on ISSDR with R2 = 0.71, such that each of the 17 survey items tested was treated as an observation (results are 

reported in Table 2). The predicted values generated by this regression can be interpreted as the expected social 

desirability bias for each item. After removing this social desirability component from our phone estimates, the 

residual deviations should theoretically represent remaining bias due to other factors (i.e., ones other than social 

desirability, such as sample selection). 

Table 2 

Univariate Regression Results 

Phone-Online Deviation β Std. Err. t P>t 

ISSDR -0.3780664 0.0590392 -6.4 <0.001 

Constant 1.146786 0.1703642 6.73 <0.001 

Adjusted R2=0.71 

The magnitude of this “correction” for each of the 17 questionnaire items we tested is displayed in Figure 1. If 

social desirability is the dominant driver of difference between the modes, we would expect these “corrected” values 

to be much closer to the online mode than they were originally. While the model manages to reduce much of the 

bias from our original estimates in an absolute sense, it has no shortage of problems, largely resulting from 

overcorrections to measures near the top of the ISSDR scale. In the case of self-reported health, for example, 

applying our derived social desirability “correction” resulted in a mean value that was further from the original 

online mean, but in the opposite direction, going from a significantly higher to a significantly lower value. 

Figure 1 

Bias Corrections using univariate regression 
5 

However, this univariate method is only the simplest application of the data at our disposal. It is logical to 

compensate for respondents’ nonlinear usage of the ISSDR scale by applying a quadratic transformation that 

emphasizes extreme responses. Additionally, by creating an interaction between the standard deviation of each item 

(obtained within the FOQ2 data) with ISSDR, we can moderate our corrections to conform to the manner in which 

the various question scales in the FOQ2 items were used by employing each item’s standard deviation in an 

interaction (results are reported in Table 3). This new model achieves an R2 of 0.96 and manages to remove almost 

all differences between our phone and online estimates, to a degree where only one measure retains a statistically 

significant difference between the two modes at α = 0.05 (Figure 2). In this case, respondents in our survey 

indicated that they believed that cell phone ownership was likely to be overestimated by a moderate amount, while 

actual modal differences in the ARF data were very small. After applying our social desirability correction factor, 

the resulting prediction shows ownership among ARF online panelists to be significantly higher than among ARF 

phone respondents. It seems plausible that this may represent a true difference in sample characteristics when we 

consider that a set of respondents who all own computers are likely to own other technology at higher rates as well. 

Table 3 

Multivariate Nonlinear Regression 

Phone-Online Deviation β Std. Err. t P>t 

ISSDR 0.9676106 0.5989531 1.62 0.134 

ISSDR26 -0.1971139 0.1011118 -1.95 0.077 

Standard Deviation 0.7996123 0.1172743 6.82 <0.001 

ISSDR X Standard Deviation -0.5074258 0.0763459 -6.65 <0.001 

ISSDR2 X Standard Deviation 0.0793256 0.012191 6.51 <0.001 

 

6

 Though ISSDR is reported elsewhere in the paper as being zero-centered, it is actually a positive value centered 

aヴouミd さ3,ざ HeIause it is Hased oミ a 1-5 sIale ┘ith the ┗alue さ3ざ Heiミg the sIale マidpoint and meaning no social 

desirability expected. This avoids any problems of sign inherent in a quadratic transformation. 

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0 

0.2 

0.4 

0.6 

0.8 

-25

-20

-15

-10

-5 

0 

5 

10

15

20

25

Drinks in Past Year 

Smoking Frequency 

100 Cigarettes in Life 

12 Drinks in Life 

Married 

Hours of sleep/night 

Days underslept 

Proportion of calls on cell 

Valid Passport 

Have Cell Phone 

Driver's License 

Hours working/week 

Religiosity 

Self-reported health 

Church Attendance 

Vigorous Phys. Activity 

Strengthening Phys. Activity 

ISSDR 

Standard Errors from Original Internet Mean 

Original 

Difference 

Between Modes 

Difference After 

ISSDR 

Correction 

ISSDR 
6 

Constant -1.1154 0.8804368 -1.27 0.231 

Adjusted R2=0.96 

Figure 2 

BiasCorrections using multivariate non-linear regression

The larger implication of the scale and apparent accuracy of these corrections is that the lion’s share of the 

differences we observe between modes seems attributable to social desirability bias and not other factors (e.g. 

sample selection differences). 

We cannot expect this finding to hold beyond the set of questions we tested in our experiment. For example, the set 

of items used include virtually nothing pertaining directly to internet usage, which is the area where we would 

expect authentic differences between respondents surveyed through the two modes to be most prevalent. 

Nevertheless, the topics that we tested do represent a fairly broad range of lifestyle measures and our results suggest 

that there are fewer authentic differences than has been argued in the past. 

Benchmarks

In addition to establishing a substantial amount of social desirability bias within the FOQ2 telephone data, these 

results also have implications for data collected in the context of a more rigorously executed Dual Frame RDD 

survey. Theoretically, it may be the case that a telephone survey conducted to more exacting standards, with 

response rates upwards of 40%, may be far more defensible as a national standard than that of a commercially 

generated random sampling of 1,000 phone respondents with a single-digit response rate. We would not expect a 

more representative sample frame to impact response error, however. 

-0.8 

-0.6 

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0 

0.2 

0.4 

0.6 

0.8 

-25

-20

-15

-10

-5 

0 

5 

10

15

20

25

Drinks in Past Year 

Smoking Frequency 

100 Cigarettes in Life 

12 Drinks in Life 

Married 

Hours of sleep/night 

Days underslept 

Proportion of calls on cell 

Valid Passport 

Have Cell Phone 

Driver's License 

Hours working/week 

Religiosity 

Self-reported health 

Church Attendance 

Vigorous Phys. Activity 

Strengthening Phys. Activity 

Sensitivity Rating of Question 

Standard Errors from Internet Mean 

Original Error 

ISSDR-Corrected Error 

ISSDR 
7 

In order to test whether the influence of ISSDR extends to data collected by more rigorous means, we made use of 

the same set of 28 benchmarks that were contained in the FOQ2 survey. We drew mean estimates from four major 

surveys: the Behavioral Risk Factor Surveillance System (BRFSS), the National Health Interview Survey (NHIS), 

the American Community Survey (ACS), and the General Social Survey (GSS)7. Though these questions covered 

many topics contained within these studies, the question wording and response scales used were not all identical. 

These differences make for a far less ideal testing ground for our theory than the FOQ2 data, which was not only 

identically worded and scaled8, but also collected at identical time periods, while some benchmark data was as many 

as three years old. 

In order to evaluate the similarity of the FOQ2’s chosen question format to those of available benchmark surveys, 

six experienced industry professionals from FOQ2’s analysis group were asked to rate each item on a 1-9 scale9. 

Using the combination of these six independent evaluations, the total set of 28 eligible questions was broken into 

two sets. Ten questions received average scores of greater than 7 and were treated as functionally identical. The 

remaining 18 questions varied from receiving very low comparability scores to being very near this arbitrary cut-off 

point, but this cut-off was strictly enforced to avoid the problem of justifying our results in the context of any 

substantial differences in question wording. 

Once a dataset of comparable questions was created, the multivariate model from the previous section was applied 

to this subset of identical questions in an attempt at cross-validation. Among these ten items, the predictions made 

in the initial phone versus online model continue to explain a significant share of the deviation from FOQ2 

estimates, as is shown in Figure 3. The correlation between the predictions of the model and observed deviations is 

R=0.77, with the most notable over-correction being Religiosity. An additional and more problematic shortcoming 

was that a few measures, especially those not used in modeling the FOQ 2 internal comparison, were substantially 

under-corrected by the model. 

Figure 3 

Predictions of the ISSDR model versus deviations from benchmark values

 

7

 The BRFSS is a 500,000 interview survey conducted annually by telephone. The NHIS is a 35,000 household 

survey conducted annually in person. The ACS is an interview of 2 million conducted by mixed mode (see 

http://www.census.gov/acs/www/Downloads/ACS_Information_Guide.pdf for more details). The GSS is 5,000 

interview study conducted in person. For specific breakdowns of the variables and benchmark equivalents used,

see appendix Table A1. 

8

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9

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simply cannot be Ioマpaヴed due to sIaliミg diffeヴeミIesざ aミd aミ iteマ ヴated 9 ┘as a Ioマpaヴisoミ さ“uffiIieミtl┞ siマilaヴ 

to pヴoduIe effeIti┗el┞ ideミtiIal ヴesultsざ
8 

*- Note that the error scale along the y-axis has changed, as the standard errors of benchmark estimates aretoo 

sensitive to form a manageable scale 

A sample of ten observations is sufficient to support the initial finding that observed deviations from interviewerassisted (in this case phone) modes of research are correlated with expectations of bias. Ideally, a model could be 

formulated that better matched the scale of deviations across modes and across questions not originally included in 

its formulation. The seventeen cross-modal items we used are probably too few to build such a broadly applicable 

model. Additionally, many items with the greatest ISSDR in the survey set were excluded from the FOQ2 phone 

study to reduce the length of the interview. Consequently, when those items are reintroduced to the model, it 

provides a much smaller magnitude of correction than observed in the pure phone versus online model. 

Further research may also approach the specific question wordings used in the ISSDR survey. Although we believe 

that the results we obtained are compelling, little experimentation went into identifying the most effective language 

for prompting the desired results. Increased study of potential metrics could identify a superior question format that 

would increase respondent comprehension of the task, perhaps leading to less reliance on neutral responses and a 

more dynamic social desirability scale. 

Discussion 

It may be a foregone conclusion to many readers that online research is destined to replace traditional phone 

research in all but the most exceptional circumstances. In large part, this transition has already occurred. 

Nevertheless, the specter of phone research always looms over any attempt at attaining population or political data 

online. Practitioners and analysts remember a time when data were rooted in probability theory and when inferences 

about the population were made from it without caveats. On average, the differences between the modes are 

negligible, but whenever a large discrepancy emerges, it casts doubt (perhaps unfairly) on online research as a 

whole. 

Our findings should not be interpreted as a claim that online research has somehow overcome its shortcomings. In 

many ways, the average online study is no more scientific in its execution than it was when the mode was in its 

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0 

0.2 

0.4 

0.6 

-70%

-50%

-30%

-10%

10%

30%

50%

70%

Mental Health 

Smoking Frequency 

100 Cigarettes in Life 

12 Drinks in Life 

Non-English at Home 

Hours Sleep/Night 

Days underslept 

Rent Stress 

Religiosity 

Life Satisfaction 

ISSDR 

Relative Percent Error* 

Original Difference 

Between Modes 

Difference After ISSDR 

Correction 

ISSDR 
9 

infancy. What may need to change is the reverence often given to research collected via telephone (and possibly 

face-to-face) by comparison. Whether a small study conducted to the standards of market research or a flagship 

study conducted by a government agency, the social desirability effects of interviewer-assisted modes may play at 

least as large a result in modal discrepancies as any form of selection bias. What may be more concerning is that 

unlike telephone’s more recent issues with expanding non-coverage, the effects of social desirability most likely 

reach far back into past decades of interviewer-assisted survey research. This makes parsing out the extent to which 

the differences we obtain may be due to social desirability versus sample differences is an important endeavor. 

The magnitude to which modal differences are predicted by the very simple ratings of the concept we identify as 

item-specific social desirability suggests it plays a dominant role in the results of phone surveys. This does not 

prove that online (or any self-administered mode) will provide an unbiased measurement of any sensitive concept. 

It does, however, indicate problems with the practice of using standard phone surveys in the pursuit of a 

representative standard without taking the concept of social desirability into account. In this climate of uncertainty, 

establishing reliable benchmarks will require the experience of an expert practitioner, capable of applying 

corrections of the sort suggested either in this research or that which follows it. 

The presence of a viable procedure for correcting estimates of social desirability should have far-reaching 

ramifications. With the organizations behind a number of venerable phone and in-person studies (such as the 

Behavioral Risk Factor Surveillance System and American National Election Study) examining the consequences of 

switching to more cost-effective modes (often self-administered and online), it is important that the issue of making 

proper comparisons does not go overlooked. Understanding the magnitude of bias present in interviewer-assisted 

modes and the degree of correction required for sensitive questions may improve the accuracy of measurement in all 

modes of interviewing. 

Appendix: 

Table A1

Comparison of Survey Items to Benchmark Values 

Identical Questions 

FOQ Question Scale Reference 

Source 

Reference Question Reference 

Scale 

During the past 30 

days, for about 

how many days 

have you felt you 

did not get enough 

rest or sleep? 

0-30 days BRFSS 2011 -

QLREST2 

During the past 30 

days, for about how 

many days have you 

felt you did not get 

enough rest or sleep? 

0-30 days 

How often in the 

past 12 months 

would you say 

you were worried 

or stressed about 

having enough 

money to pay your 

rent/mortgage? 

Would you say 

that you were 

worried or 

stressed...?

Always->Never (5 point 

scale) 

BRFSS 2011 - 

SCNTMONY 

How often in the past 

12 months would you 

say you were worried 

or stressed about 

having enough money 

to pay your 

rent/mortgage? Would 

you say you were 

worried or stressed--- 

Always-> 

Never (5 point 

scale) 

Now thinking 

about your mental 

health, which 

0-30 days BRFSS 2011 - 

MENTHLTH 

Now thinking about 

your mental health, 

which includes stress, 

0-30 days 
10

includes stress, 

depression, and 

problems with 

emotions, for how 

many days during 

the past 30 days 

was your mental 

health not good? 

depression, and 

problems with 

emotions, for how 

many days during the 

past 30 days was your 

mental health not 

good? 

Next, we’d like to 

ask you about 

some of your 

general feelings 

and perceptions. 

<p>In general, 

how satisfied are 

you with your 

life? 

[TELEPHONE 

ADD: Are you 

very satisfied, 

satisfied, 

dissatisfied, or 

very dissatisfied? 

Very satisfied-> Very 

dissatisfied (4 point scale) 

BRFSS 2010 - 

LSATISFY 

In general, how 

satisfied are you with 

your life? 

Very satisfied-

> Very 

dissatisfied (4 

point scale) 

Have you smoked 

at least 100 

cigarettes in your 

ENTIRE LIFE? 

Yes/No BRFSS 2011- 

smokek100 

Have you smoked at 

least 100 cigarettes in 

your entire life? 

[Note: 5 packs = 100 

cigarettes] 

Yes/No 

Do you smoke 

cigarettes every 

day, some days, or 

not at all? 

Every day-> Not at all (3 

point scale) 

BRFSS 2011 - 

SMOKDAY2 

Do you now smoke 

cigarettes every day, 

some days, or not at 

all? 

Every day-> 

Not at all (3 

point scale) 

Have you had at 

least 12 alcoholic 

drinks over your 

ENTIRE LIFE? 

Yes/No NHIS 2011 - 

ALCLIFE 

In your ENTIRE 

LIFE, have you had at 

least 12 drinks of any 

type of alcoholic 

beverage? 

Yes/No 

Do you speak a 

language other 

than English at 

home? 

Yes or No ACS Q14 Does this person 

speak a language 

other than English at 

home 

Yes/No 

On average, how 

many hours of 

sleep do you get in 

a 24-hour period? 

Hours/Minutes per 24-

hour period 

NHIS - 

ACISLEEP 

On average, how 

many hours of sleep 

do you get in a 24-

hour period? 

1-24 hours, 

round up 

minutes 

To what extent do 

you consider 

yourself to be a 

religious person? 

Very-> Not at all (4 point 

scale) 

GSSRELPERSON To what extent do you 

consider yourself a 

religious person? Are 

you . . 

Very-> Not at 

all (4 point 

scale) 

Non-Identical Questions 
11

On how many 

days during the 

PAST 30 DAYS, 

how often did you 

do the following 

kinds of physical 

activity? A 

VIGOROUS 

leisure-time 

physical activities 

for AT LEAST 10 

MINUTES that 

cause HEAVY 

sweating or 

LARGE increases 

in breathing or 

heart rate? 

0-30 days NHIS 2011 

[Adult File] - 

vigfreqw 

 How often do you do 

VIGOROUS leisuretime physical 

activities for AT 

LEAST 10 

MINUTES that cause 

HEAVY SWEATING 

or LARGE increases 

in breathing or heart 

rate? 

Respondent 

chooses time 

period with 

which to 

phrase their 

response 

Physical activities 

specifically 

designed to 

STRENGTHEN 

your muscles 

(such as lifting 

weights, doing 

calisthenics, etc.)? 

0-30 days NHIS 2011 

[Adult File] - 

strfreqw 

How often do you do 

LEISURE-TIME 

physical activities 

specifically designed 

to STRENGTHEN 

your muscles such as 

lifting weights or 

doing calisthenics? 

(Include all such 

activities even if you 

have mentioned them 

before.) 

Varies: See 

above 

In the PAST 12 

MONTHS (365 

days), on how 

many different 

DAYS would you 

estimate that you 

drank any type of 

alcoholic 

beverage? Your 

best guess is fine. 

0-365 days NHIS 2011 - 

ALC12MYR 

In the PAST YEAR, 

how often did you 

drink any type of 

alcoholic beverage? 

Varies: See 

Above 

About how many 

minutes or hours 

in a Typical Day 

do you do each of 

the following? 

Watch Television 

Do not do this at all -> 

More than 5 hours a day 

(8 points scale) 

NHANES - 

PAQ.710 

Over the past 30 days, 

on average how many 

hours per day did 

{SP} sit and watch 

TV or videos? Would 

you say...

less than 1 

hour; 1 hour, 2 

hours; 3 hours, 

4 hours; 5+ 

hours; none, do 

not watch TV 

or Videos 

In the past 7 

DAYS, about how 

long in total did 

you spend 

working or doing 

any work-related 

activities for pay? 

Please include 

Hours and Minutes GSS - HRS1, 

HRS 2 

How many hours did 

you work last week, at 

all jobs?; How many 

hours a week do you 

usually work, at all 

jobs? 

0-89 Hours 
12

total time for all 

the jobs you work 

for pay. 

Do you own or 

rent your home? 

Own/Rent/Other 

Arrrangement 

GSSDWELOWN Do you/Does your 

family) own your 

(home/apartment), 

pay rent, or what? 

Own/Buying, 

Rent, Other 

Are you currently 

married? 

Yes or No ACS - Q20 What is this person’s 

marital status? 

Now married, 

Widowed, 

Divorced, 

Seperated, 

Never Married 

Do you, or does 

anyone else in the 

household, have 

any of the 

following 

conditions? 

Blindness or 

serious difficulty 

seeing even when 

wearing glasses 

Yes or No ACS Q17b Is this person blind or 

does he/she have 

serious difficulty 

seeing even when 

wearing glasses? 

Yes/No 

Do you, or does 

anyone else in the 

household, have 

any of the 

following 

conditions? 

Deafness or 

serious difficulty 

hearing 

Yes, I have this; Yes, 

another member of the 

household has this; Yes, 

both I and another 

member of the household 

have this; No, no one in 

the household has this 

ACS Q17a Is this person deaf or 

does he/she have 

serious difficulty 

hearing? 

Yes/No 

Do you, or does 

anyone else in the 

household, have 

any of the 

following 

conditions? A 

serious difficulty 

in walking or 

climbing stairs 

Yes, I have this; Yes, 

another member of the 

household has this; Yes, 

both I and another 

member of the household 

have this; No, no one in 

the household has this 

ACS Q18b Does this person have 

serious difficulty 

walking or climbing 

stairs 

Yes/No 

Do you, or does 

anyone else in the 

household, have 

Yes, I have this; Yes, 

another member of the 

household has this; Yes, 

ACS Q18a Because of a physical, 

mental, or emotional 

condition, does this 

Yes/No 
13

any of the 

following 

conditions? A 

serious difficulty 

in concentrating, 

remembering, or 

making decisions 

because of a 

physical, mental, 

or emotional 

condition 

both I and another 

member of the household 

have this; No, no one in 

the household has this 

person have serious 

difficulty 

concentrating, 

remembering or 

making decisions? 

How well can you 

read a newspaper 

or book in...? A. 

English B. 

Spanish 

Not at all -> Very Well (4 

Point Scale) 

Pew Would you say you 

can read a newspaper 

or book in English: 

very well ->not 

at all (4 Point 

Scale) 

How much do you 

weigh without 

shoes on? 

Pounds: NHANES - 

WHQ.025 

How much {do 

you/does SP} weigh 

without clothes or 

shoes? [If {you 

are/she is} currently 

pregnant, how much 

did {you/she} weigh 

before your 

pregnancy? 

Pounds or 

Kilograms 

Of all the 

telephone calls 

that you or other 

members of your 

home receive, 

how many are 

received on a cell 

phone or 

smartphone? 

All recieved on cell 

phones/smartphones; 

Almost all calls are 

recieved on cell 

phones/smartphones; 

Some recieved on cell 

phones/smart phones and 

some on regular phones; 

Very few are recieved on 

cell phones/smartphones; 

None on cell 

phones/smartphones 

NHIS - 

PHONEUSE 

Of all the telephone 

calls that you or your 

family receives, are... 

All or almost 

all calls 

received on 

cell phones; 

Some received 

on cell phones 

and some on 

regular phones, 

Very few or 

none on cell 

phones 

EXCLUDING 

YOU, how many 

other adults (age 

18 or over) live in 

your household? 

0-25 Adults ACS 2011 How many people are 

living or staying at 

this address? 

Open Ended 

Response 
14

How many 

bedrooms are in 

your house, 

apartment, or 

mobile home? 

That is, how many 

bedrooms would 

you list if your 

house, apartment, 

or mobile home 

were on the 

market for sale or 

rent? 

1 -> 5+ Bedrooms (5 

points scale) 

ACS 2011 How many of these 

rooms are bedrooms? 

Count as bedrooms 

those rooms you 

would 

list if this house, 

apartment, or mobile 

home 

were for sale or rent. 

If this is an 

efficiency/studio 

apartment, print "0" 

Open Ended 

Response 

How many 

automobiles, vans, 

and trucks of oneton capacity or 

less are kept at 

home for use by 

members of your 

household? 

0-20 ACS 2011 How many 

automobiles, vans, 

and trucks of one-ton 

capacity or less are 

kept at home for use 

by members of this 

household? 

None -> 6 or 

more (7 point 

scale) 

Works Cited: 

Baker, R., Zahs, D., &Popa, G. (2004). ‘Health Surveys in the 21st Century: Telephone vs. Web. In Eighth 

Conference on Health Survey Research Methods, Peachtree City, GA (Vol. 34). 

Baker, R., Blumberg, S., Brick, J.M., Couper, M., Courtright, M., Dennis, M., Dillman, D., Frankel, M., Garland, P., 

Groves, R., Kennedy, C., Krosnick., J., Lee, S., Lavrakas, P.J., Link, M., Piekarski, L., Rao, K., Rivers, D., 

Thomas, R.K. &Zahs, D. (2010). AAPOR Report on Online Panels. Retrieved from the American 

Association of Political Opinion Research 

website:http://www.aapor.org/AAPOR_Releases_Report_on_Online_Survey_Panels1.htm

Chang, L., &Krosnick, J. A. (2009). National surveys via RDD telephone interviewing versus the internet 

comparing sample representativeness and response quality. Public Opinion Quarterly, 73(4), 641-678.

Crowne, D.P. & Marlowe, D. (1964).The Approval Motive: Studies in Evaluative Dependence. New York: Wiley. 

Frisinia, L., Thomas, R.K., Krane, D. & Taylor, H. (2007) Scaling Social Desirability: Establishing Its Influence 

Across Modes. American Association of Public Opinion Research Annual Conference, Anaheim, CA. 

Groves, R. M. (1989). Survey Errors and Survey Costs. New York: John Wiley and Sons. 

Phillips, D.L., and Clancy, K.J. Some effects of"social desirability" in survey studies. American Journal of 

Sociology (1972): 921-940. 
15

Taylor, H., Krane, D., & Thomas, R.K. (2005). “Best Foot Forward: Social Desirability in Telephone vs. Online 

Surveys.” Public Opinion Pros, Feb. Retrieved from 

http://www.publicopinionpros.com/from_field/2005/feb/taylor.asp.