Genetic underpinnings of survey response

Journal of Organizational Behavior

J. Organiz. Behav. 32, 395–412 (2011)

Published online 7 June 2010 in Wiley Online Library

(wileyonlinelibrary.com) DOI: 10.1002/job.692

* Correspondence to:Raleigh, NC 27695-7

Copyright # 2010

Genetic underpinnings of survey response

LORI FOSTER THOMPSON1*, ZHEN ZHANG2 AND

RICHARD D. ARVEY3

1Department of Psychology, North Carolina State University, Raleigh, North Carolina, U.S.A.2Department of Management, Arizona State University, Tempe, Arizona, U.S.A.3Department of Management and Organization, National University of Singapore, Singapore

Summary This study investigates the influence of genetic factors on survey response behavior. A pool of558 male and 500 female twin pairs from the Minnesota Twin Registry (MTR) was asked tocomplete a paper-and-pencil survey of leadership activities. We used quantitative geneticstechniques to estimate the genetic, shared environmental, and nonshared environmental effectson people’s compliance with the request for survey participation. Results indicated that geneticinfluences explained 45% of the variance in survey response behavior for both women andmen, with little shared environmental effects. Similar estimates were obtained after wepartialled out potential confounds including twin closeness, age, and education. The resultshave important implications for response rates and nonresponse bias in survey-based research.Copyright # 2010 John Wiley & Sons, Ltd.

Introduction

There is little doubt that human behavior is affected by genetic and biological characteristics (e.g.,

Bouchard&McGue,2003;Dick&Rose,2002;Plomin,DeFries,Craig,&McGuffin,2003;Shermanetal.,

1997). Heritable traits, attitudes, values, and interests influence a variety of behaviors, many of which are

demonstrated in theworkplace.To date, research inbehavioral genetics hasadvanced ourunderstandingof

between-individual differences in a number of organizationally relevant domains such as leadership

(Arvey, Zhang, Avolio, & Krueger, 2007; Zhang, Ilies, & Arvey, 2009), vocational interests (Lykken,

Bouchard, McGue, & Tellegen, 1993), entrepreneurship (Zhang, Zyphur, et al., 2009), and job satisfaction

(Arvey, Bouchard, Segal, & Abraham, 1989). Such knowledge is needed to guide the development of

nomological models explaining attitudes and behaviors at work (Ilies, Arvey, & Bouchard, 2006).

Survey response and nonresponse are practically important work behaviors which could benefit from

an expanded research agenda in general, and a behavioral genetic examination in particular. Research

and practice in organizational behavior (OB) often hinges on self-reports of attitudes, beliefs,

behaviors, and personal characteristics. New technologies (e.g., web-based surveys, machine scannable

Lori Foster Thompson, Department of Psychology, North Carolina State University, Campus Box 7650,650, U.S.A. E-mail: [email protected]

John Wiley & Sons, Ltd.

Received 24 October 2008Revised 23 January 2010

Accepted 30 January 2010

396 L. F. THOMPSON ET AL.

paper forms, and text mining software) have enabled notable advances in survey design, distribution,

and analysis (Poncheri, Lindberg, Thompson, & Surface, 2008; Thompson, Surface, Martin, &

Sanders, 2003). As a result, surveys are an increasingly popular data collection tool in organizations

and elsewhere (Church & Waclawski, 1998; Kraut, 1996). Unfortunately, this growing reliance on

surveys appears to be accompanied by declining response rates (Baruch, 1999; Dey, 1997; Schwartz,

Groves, & Schuman, 1998). Poor response rates can lead to a variety of problems (Rogelberg, Luong,

Sederburg, & Cristol, 2000). For example, large numbers of nonrespondents can produce small sample

sizes, resulting in a lack of statistical power needed to perform needed analyses. More importantly,

survey nonresponse raises concerns about nonresponse bias, which occurs when survey requests are

ignored by people who differ from respondents on the study variables of interest. The result is data that

paint an inaccurate picture of the overall population’s standing on the variables studied (Luong &

Rogelberg, 1998; Rogelberg et al., 2000). In short, the increasing reliance on surveys coupled with the

serious consequences of nonresponse creates a pressing need to better understand the tendency to

comply with or ignore requests for survey participation.

Research has uncovered several individual difference variables that help explain why some people

fail to complete surveys when asked to do so. Work in the behavioral genetics domain suggests that

many of these individual differences are heritable. The purpose of the present study is to test whether a

genetic component underlies survey response behavior. Drawing from the behavioral genetics and

survey nonresponse literatures, we synthesize two distinct research streams which have never been

considered in tandem. As a result, this study contributes to both bodies of work by expanding what is

known about the genetic bases of OB-relevant behavior while increasing our understanding of the

underpinnings of survey response.

Heritable determinants of survey response

Beyond the situational determinants of survey response (e.g., Rogelberg & Stanton, 2007; Yammarino,

Skinner, & Childers, 1991), several dispositional traits, attitudes, and perceptions have been shown to

influence compliance with survey requests. Because these personal characteristics are to some extent

genetically influenced, they may carry these influences through to survey response behavior. With regard

to traits, research has suggested that high achievers are especially inclined to respond to voluntary surveys

in an academic setting (Dey, 1997; Sax, Gilmartin, & Bryant, 2003). Other personality variables have been

shown to account for both passive and active forms of nonresponse (Rogelberg et al., 2003). Passive

nonresponse occurs due to happenstance, such as when survey recipients misplace or forget to complete

surveys they may have otherwise intended to fill out. Rogelberg et al. (2003) have shown that passive

nonrespondents are less conscientious than those who complete surveys upon request.

Active nonrespondents make an overt, conscious, a priori decision to withhold their participation at

the time in which they receive a survey (Rogelberg et al., 2003). Compared to those who complete

surveys, active nonrespondents are more ‘‘reciprocation wary’’—that is, they are more likely to have a

personality disposition which prompts them to feel exploited in social exchange relationships

(Spitzmuller, Glenn, Barr, Rogelberg, & Daniel, 2006). Furthermore, active nonrespondents tend to be

less conscientious than survey respondents, and some evidence suggests they are also less agreeable

(Rogelberg et al., 2003; Rogelberg, Spitzmuller, Little, & Reeve, 2006). Personality characteristics,

including conscientiousness and agreeableness, have been shown to be substantially heritable (Loehlin,

1992). A genetic component to active and passive survey nonresponse therefore appears likely.

Another factor driving active nonresponse is attitudes toward the survey sponsor. Active

nonrespondents tend to be less satisfied than respondents with the institution or organization sponsoring

the survey (Rogelberg et al., 2003). Such satisfaction may have genetic underpinnings because

Copyright # 2010 John Wiley & Sons, Ltd. J. Organiz. Behav. 32, 395–412 (2011)

DOI: 10.1002/job

GENETICS AND SURVEY RESPONSE 397

personality, which is heritable, is thought to predispose individuals to particular interpretations of

events (Judge, Heller, & Mount, 2002). In effect, genetically influenced interpretations of previous

encounters with the survey sponsor may impact satisfaction which can in turn affect survey

participation. People also have a predisposition toward evaluating their environment (e.g., interactions

with the survey sponsor) in ways that are consistent with their affective disposition (Hershberger,

Lichtenstein, & Knox, 1994). Affectivity, which has been shown to be heritable (Finkel & McGue,

1997; Tellegen, Lykken, Bouchard, Wilcox, Segal, & Rich, 1988), may thus shape perceptions of the

survey sponsor’s shortcomings and subsequently discourage survey response behavior.

Similarly, job satisfaction has been linked to the willingness to respond to OB surveys. Research

addressing this point has focused on the attitudes of employees who indicate they would refuse to

complete a work-related survey if asked to do so. These ‘‘noncompliants’’ hold negative attitudes—not

only toward their organizations, but also toward their jobs (Rogelberg et al., 2000). Meanwhile, data

from several samples have indicted that genetic factors may explain as much as 30% of the variance in

job satisfaction (Arvey et al., 1989; Arvey, McCall, Bouchard, Taubman & Cavanaugh, 1994).

In general, OB researchers have successfully argued that survey participation is a form of helping

behavior (e.g., Rogelberg et al., 2006; Spitzmuller, Glenn, Sutton, Barr, & Rogelberg, 2007,

Spitzmuller et al., 2006). Often, applied OB surveys are initiated by a prospective respondent’s

employer and specifically designed for the good of the organization. In such cases, survey response can

be considered a form of organizational citizenship behavior (e.g., Youssefnia, 2000). Other times,

surveys are initiated by OB researchers who are external to the organization for the purpose of scientific

inquiry (e.g., Allen, 2003; Major, Fletcher, Davis, & Germano, 2008). Responses to such surveys may

be considered a more general form of prosocial or helping behavior which contributes to the well-being

of the researcher, science, and society. Behavioral genetics research has found that genetic factors

influence the propensity of people to help (e.g., Knafo & Plomin, 2006; Matthews, Batson, Horn, &

Rosenman, 1981; Rushton, Fulker, Neale, Nias, & Eysenck, 1986). As a form of helping behavior,

responses to OB surveys conducted for research and/or practice should thus be genetically influenced.

In summary, many of the traits and attitudes that have been empirically linked to survey participation

have been shown to be heritable. Genetics could influence survey response through factors such as

personality (e.g., conscientiousness, agreeableness), affectivity, attitudes toward the sponsoring

organization, and job satisfaction. Moreover, voluntary survey participation can be conceptualized as a

helping/prosocial behavior, and prosocial behavioral tendencies have been shown to be heritable. Thus,

genetics are expected to affect survey response. The present study tests the hypothesis that survey

response behavior is genetically influenced.

Method

Sample and procedure

The pool of potential participants for this study was obtained from the Minnesota Twin Registry

(MTR), a birth-record-based registry of intact identical (i.e., monozygotic or MZ) and fraternal (i.e.,

dizygotic or DZ) twin pairs born within the state of Minnesota. The database from which our sample

was drawn documented each participant’s gender and indicated whether each twin pair was identical or

fraternal. The twins in our study were reared together.

A 16-page paper-and-pencil survey of leadership activities was sent to 558 male twin pairs (half

identical, half same-sex fraternal) and 500 female twin pairs (half identical, half same-sex fraternal).


DOI: 10.1002/job


The survey instruments sent to the male and female samples were highly similar. They included

identical demographic items as well as identical questions about the kinds and types of leadership

positions each twin held at different times (e.g., leadership roles at work). In addition, the male survey

asked about decisions to buy or sell stocks in several situations (i.e., financial risk-taking). The female

survey included measures of transformational leadership and dispositional hope in lieu of the financial

risk-taking items. Overall, 90% of the questions/items were the same, and the total work load for

respondents across the two surveys was quite similar.

The cover letters accompanying the two surveys were identical. They were sent by the faculty

members directing the leadership study and appeared on university letterhead. They promised

confidentiality, explained that the survey was being conducted for research purposes, and indicated that

participation is important for knowledge accumulation and beneficial to society. Each packet included a

$5 bill and a pre-addressed, postage-paid return envelope with the survey. Those who did not return the

survey still collected the $5; as such, surveys were not completed for personal financial gain.

Like many people, the individuals examined in this study had prior experience answering paper-and-

pencil surveys. However, they had not received any MTR-related surveys for at least 6 years before the

leadership survey was administered. The leadership survey was administered to the male sample in

1999 and to the female sample in 2004. The 558 male twin pairs selected for this survey represented an

entire cohort of twins born between 1961 and 1964. The 500 female twin pairs were randomly selected

from a larger cohort of female twins born between 1936 and 1955. Table 1 reports the details of the pool

and sample characteristics. The fraternal twins in a pair have the same gender. With regard to race, all

sample members were Caucasian.

Although there were 5 years between the male and female leadership survey administrations, we did not

find significant differences in the individual-level response rate between the two gender groups. As Table 1

shows, 646 men and 581 women completed and returned the survey, yielding individual-level response

rates of 57.9% and 58.1%, respectively. Excluding individuals towhom surveys were undeliverable due to

wrong mailing addresses, the effective pool size for the analyses of response behavior included 1100 men

and 988 women. Table 1 reports the breakdown of cases omitted due to incorrect mailing addresses.

Excluding undeliverable surveys, the individual level response ratewas 58.8% for the overall sample. This

rather high participation rate by both twin types is not uncommon in twin research, where participants tend

to be relatively cooperative—perhaps recognizing the uniqueness and value of their data. Table 1 also

reports the sample sizes for the quantitative genetic analyses before controlling for potential confounds,

i.e., 550 pairs of male twins and 494 pairs of female twins.

Measures

Survey response

Survey response was coded as 0 if an individual did not complete and return the questionnaire (for

reasons other than delivery failure). It was coded as 1 if he/she completed and returned the survey. This

0/1 variable was analyzed using an underlying latent variable approach described later in detail. An

underlying continuous variable is assumed to account for the response or nonresponse to the survey.

Previous research has used the same approach to study 0/1 variables using behavioral genetics models

(e.g., entrepreneurial status, Nicolaou, Shane, Cherkas, Hunkin, & Spector, 2008).

Zygosity

The twins’ zygosity was determined by their response to a background questionnaire administered 6

years prior to the male leadership survey and 20 years prior to the female leadership survey.

Approximately 78 and 80% of the male and female twins who were contacted completed this zygosity


DOI: 10.1002/job

Tab

le1

.C

har

acte

rist

ics

of

the

stu

dy

sam

ple

Mal

etw

ins

Fem

ale

twin

s

x2

b/w

mal

esan

dfe

mal

esM

ZD

ZT

ota

lx

2b

/wM

Z/D

ZM

ZD

ZT

ota

lx

2b

/wM

Z/D

Z

Qu

esti

on

nai

res

sen

t5

58

55

81

11

65

00

50

01

00

0A

dm

inis

trat

ion

yea

r1

99

92

00

4C

ases

excl

ud

edfr

om

anal

yse

sU

nd

eliv

ered

/wro

ng

add

ress

61

01

68

41

2S

amp

lesi

zefo

rin

div

idu

al-l

evel

anal

ysi

sN

um

ber

of

ind

ivid

ual

s5

52

54

81

10

04

92

49

69

88

Nu

mb

ero

fp

airs

27

62

74

55

02

46

24

84

94

Su

rvey

sre

ceiv

ed3

31

31

56

46

30

12

80

58

1In

div

idu

alle

vel

resp

on

sera

te5

9.3

%5

6.5

%5

7.9

%1

.50

60

.2%

56

.0%

58

.1%

3.4

00

.01

No

te:

No

ne

of

the

x2

test

sin

this

tab

lew

ere

sign

ifica

nt.

All

par

tici

pan

tsar

eC

auca

sian

/Wh

ite.

MZ

stan

ds

for

mon

ozy

go

tic

or

iden

tica

ltw

ins

and

DZ

stan

ds

for

diz

yg

oti

cor

frat

ernal

twin

s.


DOI: 10.1002/job



measure. This measure has demonstrated a 95% accuracy rate when compared with elaborate

serological analysis (e.g., Lykken, Bouchard, McGue, & Tellegen, 1990; Sarna, Kaprio, Sistonen, &

Koskenvuo, 1978). The zygosity measure was used as a grouping variable in the two-group structural

equation modeling analysis discussed below.

Control variables

Gender, age, and education

Participants’ gender (female¼ 1, male¼ 0) and age (in years) were derived from their birth records.

Educational level was measured in the background questionnaire as the number of years of education.

Twin closeness

Twin closeness could be a potential confound in the estimation of genetic influences on survey

response. As shown in the analysis section, genetic relatedness is assumed to be the only explanation

for twins’ concordance in survey response behavior. If MZ twins are closer to their co-twins (e.g., talk

more frequently with each other) than DZ twins are, MZ twins may have answered the survey in a more

coordinated manner for reasons other than genetic relatedness. Thus, there is a need to control for the

potential confounding effects of twin closeness. Twin closeness was assessed in the background

questionnaire used to measure zygosity by asking each individual to indicate his/her contact frequency

with the twin partner. Participants were asked how often they talk to their twin using a seven-point

Likert scale (1¼ never, 2¼ seldom, 3¼ on holidays, 4¼monthly, 5¼weekly, 6¼ daily, and 7¼we

live together). The intraclass correlation (ICC[1]) for this measure is 0.74, showing a high level of

within-pair agreement. We averaged the two scores of a twin pair to represent the closeness of the pair.

Analyses

We used behavioral genetics methodology to estimate the genetic influences on survey response

behavior (Plomin, DeFries, McClearn, & McGuffin, 2008). This methodology utilizes the difference in

genetic relatedness between MZ twins (who share all of their genetic material) and DZ twins (who

share on average 50% of their genes) to estimate the relative genetic and environmental contributions to

the observed variance of a phenotype (in this case, survey response behavior).

A series of two-group structural equation models (SEM) were estimated with a set of constraints on path

coefficients and latent factor correlations. In both the MZ group and DZ group, the variance of survey

response behavior is parsed into three components: Additive genetic variance, shared environmental

variance, and nonshared environmental variance plus measurement error. Additive genetic effects (i.e.,

latent variable A) refer to the effects of the summation of genes across loci, while shared (i.e., latent

variable C) and nonshared (i.e., latent variable E) environmental effects refer to environmental effects that

contribute to twin similarity and differences, respectively. Measurement error also contributes to

nonshared environmental variance. The three latent variables (i.e., A, C, and E) are standardized variables

so that their corresponding path coefficients represent the strength of their influences. Figure 1 shows the

path diagram for the model for one group in the two-group SEM analysis.

According to behavioral genetics theory, greater similarity between the two members of a MZ twin

pair relative to those in a DZ twin pair is indicative of additive genetic contributions. In particular, the

structural relationships represented by Figure 1 can be written as the following structural equations

(control variables not shown):

Pij ¼ aAij þ cCij þ eEij (1)

Vp ¼ a2 þ c2 þ e2 (2)


DOI: 10.1002/job

c

1 for both MZ and DZ

groups

a

Response

Twin 1

Response

Twin 2

A1 A2

E1 E2

a

e e

C2C1

c

1 for MZ group; 0.5 for DZ group

Control Variables

Figure 1. Quantitative genetics model for survey response behavior. Note: A, C, E are standardized latent variablesrepresenting additive genetic, shared-environmental, and nonshared environmental influences, respectively. Thesubscripted numbers (1 and 2) refer to the first and second twin within a pair. a, c, and e are the path coefficients to

be estimated; they are constrained to be equal between the MZ and DZ groups


where Pij is the measure of survey response behavior of the ith individual in the jth pair (i¼ 1, 2;

j¼ 1. . ..n), Aij, Cij, and Eij are standardized latent variables, and their coefficients represent the additive

genetic influence (a), shared environmental influence (c), and nonshared environmental influence (e).

Vp is the total variance of survey response behavior and is typically standardized as having a value of 1.

Heritability is estimated as h2¼ a2/Vp. Because A, C, and E are assumed to be independent with each

other, Vp can be decomposed to the additive genetic variance (a2), shared environmental influence (c2),

and nonshared environmental influence (e2).

In the two-group SEM, the path coefficients a, c, and e in the MZ group were held to be equal to the

corresponding path coefficients in the DZ group. As Figure 1 indicates, the cross-twin correlations of

the genetic factors are fixed at 1.0 for MZ group and 0.5 for DZ group. This is because behavioral

genetics methods and theories show that MZ twins share all of their genetic material and DZ twins

share on average 50% of their segregating genes (Plomin et al., 2008). The cross-twin correlations of

the shared-environmental factors are both fixed at 1.0 for MZ and DZ groups because by definition, they

are shared by the two members in a twin pair. Based on the tracing rules for path diagrams (Kline,

1998), the predicted variance-covariance matrices for the full ACE models are as follows:

Twin 1 Twin 2

MZ-twin group: Twin 1

Twin 2

a2 þ c2 þ e2 a2 þ c2

a2 þ c2 a2 þ c2 þ e2

� �

Twin 1 Twin 2

DZ-twin group: Twin 1

Twin 2a2 þ c2 þ e2 0:5� a2 þ c2

0:5� a2 þ c2 a2 þ c2 þ e2

� �


DOI: 10.1002/job


The path coefficients a, c, and e were estimated in the SEM models using a latent variable approach

to present survey nonresponse. In other words, an underlying continuous variable was assumed for

response/nonresponse. When this variable exceeds a threshold value, survey response is manifested as

1. Models were fit to the cross-twin variance–covariance matrices using asymptotically distribution

free weighted least squares (Browne, 1984; Neale, 2004).

We first conducted the analyses without controlling for potential confounds. We then re-ran the analyses

after partialling out the influence of twin closeness, age, and education. The logic of partialling out control

variables in twin models is the same logicas in regressionanalysis. In models without control variables, the

total variance in the dependent variable was decomposed into the A, C, and E components. In models with

control variables, the variance after partialling out the control variables’ contribution was then

decomposed into A, C, and E components. Previous research has utilized similar methods for partialling

out potential confounds (e.g., Kohler & Rodgers, 1999; Nicolaou et al., 2008). Due to missing data in the

control variables, the sample size was smaller for the second set of analyses with control variables. Model

fit in all analyses was evaluated using the chi-squared (x2) fit statistic and a variety of model fit indices. A

series of nested models were compared and the best-fitting model was chosen to calculate heritability of

survey response. In the nested models, parameters (a orc or both)were dropped (i.e., fixed to zero) from the

full ACE model to test if their removal resulted in a significant decline in model fit. In addition, we

examined gender as a potential moderator by conducting and comparing separate analyses on female and

male samples.

Results

Table 2 provides the individual-level means, standard deviations, and correlations among the variables

examined in this study for the total sample and for each gender group separately. In the total sample,

MZ twins are slightly more likely to respond to the survey than DZ twins (r¼ 0.07, p< 0.05). Age and

gender are almost perfectly correlated because, as indicated earlier, the female and male twins belong

to two different age cohorts. The male and female samples showed similar patterns of relationships

among the majority of variables.

Our hypothesis predicted that response behavior is genetically influenced. Table 3 provides the

results for model fitting before partialling out potential confounds. As shown in Table 3, the shared-

environment components in the ACE model failed to exert significant influence. The estimated c2

values were not significantly different from zero (0.00 for male twins, 0.10 for female twins, and 0.02

for the total sample; the 95% confidence intervals all include zero). After restricting the corresponding

paths to zero, the AE models showed better fit than CE models for both the male and female samples as

well as for the total sample as a whole. For example, the AE model for the male sample has satisfactory

fit indexes (CFI¼ 0.98, TLI¼0 .99, and RMSEA¼ 0.02), whereas the CE model has much worse fit

(CFI¼ 0.67, TLI¼ 0.75, and RMSEA¼ 0.13). The AE models are more parsimonious than their

corresponding ACE models, and did not exhibit worse fit than the full ACE models (i.e., nonsignificant

Dx2 with Ddf¼ 1). Consequently, the AE models were chosen as the best fitting model.

Table 4 provides the results after partialling out the influence of twin closeness, age, and education.

The results are highly similar to those in Table 3 but the sample size is smaller due to missing values in

the control variables. Based on x2 difference tests, the AE models were again the best-fitting models for

the male, female, and the total sample. The heritability estimates are similar to those in Table 3 before

controlling for the confounds, and the males and females have similar heritability estimates (i.e., 0.46

and 0.49, respectively).


DOI: 10.1002/job

Table 2. Means, standard deviations, and correlations of the variables

Variable Mean SD 1 2 3 4 5

Combined sample1. Survey response 0.58 0.49 —2. Zygosity (0¼DZ, 1¼MZ) 0.50 0.50 0.07� —3. Gender (0¼male,1¼ female) 0.47 0.50 0.01 0.01 —4. Age 53.30 9.31 0.03 �0.01 0.92�� —5. Education (years) 13.85 2.57 0.00 0.07� �0.11�� 0.15�� —6. Twin closeness 4.55 1.14 �0.02 0.25�� 0.13�� 0.18�� 0.03Male sample1. Survey response 0.58 0.49 —2. Zygosity (MZ¼ 1, DZ¼ 0) 0.50 0.50 0.08 —3. Age 45.55 1.01 0.00 0.11�� —4. Education (years) 14.03 2.72 0.05 0.09� �0.04 —5. Twin closeness 4.65 1.11 0.07 0.29�� 0.03 �0.06 —Female sample1. Survey response 0.58 0.49 —2. Zygosity (MZ¼ 1, DZ¼ 0) 0.50 0.50 0.06 —3. Age 61.95 6.36 0.09� �0.08 —4. Education (years) 13.58 2.29 0.05 0.05 �0.09� —5. Twin closeness 4.41 1.18 0.00 0.21�� 0.30�� 0.02 —

Note: For the whole sample, N varies from 1733 to 2088 due to missing data on education and twin closeness. For the male sample,N varies from 957 to 1100; for the female sample, N varies from 776 to 988. MZ refers to monozygotic twins and DZ refers todizygotic twins. Tetrachoric or polyserial correlations are reported for dichotomous variables.�p< .05; ��p< .01; ��p< .001.


Given the fact that the two sets of analyses yielded similar results, and following the practice of

previous research (e.g., Nicolaou et al., 2008), we rely upon the model estimation results without

control variables (a larger sample) for interpretation. As Table 3 shows, based on the whole sample,

45% of the variance in survey response behavior was explained by genetic influences, whereas 55% of

the variance was explained by nonshared environmental factors as well as measurement error. Thus, the

study hypothesis was supported. This estimate of genetic influence remains similar for the male and

female samples, indicating that gender does not moderate the strength of genetic influence on survey

response. Because we can safely assume that the genetic influence on survey response behavior is

exogenous (i.e., genetic factors influence survey response behavior but not vice versa) we can conclude

a somewhat strong causal relationship based on the results.

Discussion

Surveys are a popular data collection tool for OB research and practice, yet relatively little is known

about the factors driving compliance with requests for survey participation (Spitzmuller et al., 2007).

Our knowledge of how to design, deliver, and analyze surveys has outpaced our understanding of the

factors that encourage prospective respondents to complete questionnaires. There is a clear need for

research of this nature due to its implications for sample sizes and nonresponse bias in surveys

conducted for OB research and practice. Drawing upon the behavioral genetics and survey nonresponse


DOI: 10.1002/job

Tab

le3

.M

od

elfi

tre

sult

sfo

rg

enet

icin

flu

ence

so

nsu

rvey

resp

on

seb

ehav

ior

(wit

ho

ut

con

tro

lvar

iab

les)

#o

fp

airs

Mo

del

Var

ian

ceco

mp

on

ents

(95

%C

I)M

od

elF

itin

dex

es

MZ

DZ

a2

c2e2

x2

df

p-v

alu

eC

FI

TL

IR

MS

EA

Mal

esa

mp

le2

76

27

4A

CE

0.4

7(0

.12

,0

.61

)0

.00

(0.0

0,

0.0

7)

0.5

3(0

.32

,0

.85

)4

.13

0.2

50

.98

0.9

80

.02

CE

—0

.17

(0.0

5,

0.3

6)

0.8

3(0

.64

,0

.95

)4

0.2

40

.00

0.6

70

.75

0.1

3A

Ea

0.4

7(0

.28

,0

.58

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DOI: 10.1002/job


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DOI: 10.1002/job



literatures, this study demonstrates that survey response behavior is substantially heritable

(h2¼ 0.45). The current study is the first to examine the genetic components of survey participation.

As Ilies et al. (2006) suggested, findings from behavioral genetics research might have profound

implications for examining constructs central to the study of behavior in organizations. This

study not only helps illuminate the underpinnings of survey response tendencies, but it also expands

what is known about the genetic influences driving helping behavior since survey response is

considered one type of helping behavior (Rogelberg et al., 2006; Spitzmuller et al., 2007; Spitzmuller

et al., 2006).

The magnitude of the genetic influence on survey response behavior is worth considering. Results

showed that genetic influences explained 45% of the variance in survey response after partialling out

potential confounds. Taking the square root of this value indicates a 0.67 correlation with survey

response behavior. This is considerably larger than the effects that have been found for other

antecedents of survey response (e.g., response facilitation techniques such as preliminary notification,

incentives, and so forth). To put this in context, we could compare it to the correlations resulting

from Yammarino et al.’s (1991) meta-analysis of survey response predictors. According to this meta-

analysis, the two most powerful predictors of survey response (preliminary notification and $0.50

incentives) yielded average correlations of only 0.176 and 0.184, respectively. Admittedly, the studies

comprising Yammarino et al.’s (1991) meta-analysis showed some variability in the individual

effect sizes obtained. Even so, 95% of the 184 correlations meta-analyzed were at or below 0.30.

Studies examining personality predictors of passive and active survey nonresponse offer additional

points of comparison. Using the formulas provided by Becker (2000) to convert published means and

standard deviations to correlation coefficients, results show correlations between conscientiousness

and passive nonresponse ranging from r¼�0.08 to �0.15 (Rogelberg et al., 2003). The influence of

conscientiousness on active nonresponse is characterized by correlation coefficients from �0.22

to �0.27 (Rogelberg et al., 2003). By comparison, the genetic influence shown in the current

study (r¼ 0.67) substantially exceeds the influence of other antecedents typically examined in the

literature.

Limitations

While the results of this research are noteworthy, they should be interpreted in the context of several

limitations. It is important to acknowledge that this study was not conducted on a completely random

sample. Instead, an initial survey, used to obtain zygosity information 6–20 years prior to the present

study, determined the population whose response behaviors were investigated. As such, we essentially

selected out those who had not previously participated in a survey. The impact of this limitation is likely

minimized by the high rate of response to the initial zygosity measures administered prior to this study.

Nevertheless, it is important to consider its implications. Using the zygosity measure as a ‘‘prescreen’’

may have caused us to achieve a higher response rate to the leadership survey than would have been

obtained if we had sent the leadership survey to the entire population that received the initial zygosity

measure. To help alleviate concerns about this, we should point out that the present study was not

designed for the purpose of estimating survey response rates among populations of twins. Rather, it was

designed to look at the relationships between genetics and survey response. Because the analyses used

to test our hypothesis were based on the comparison of pair concordance between identical and

fraternal twins, this prescreening issue should not result in serious bias on the heritability estimates

obtained.

Looking at this limitation from another angle, the fact that everyone in our study population (whether

they completed the leadership survey or not) had completed at least one earlier survey (i.e., the zygosity


DOI: 10.1002/job


measure) limits our ability to confidently generalize to people with no prior survey experience. We

presume that many members of the working population to which we wish to generalize have completed

one or more surveys (e.g., organizational climate surveys re-administered annually) at various points in

their lives. For this reason, we do not expect prior survey experience to pose a major threat to the

external validity of our findings.

It should be noted that this study was restricted to Caucasian twins born in Minnesota between 1936

and 1955 as well as those born between 1961 and 1964. The degree to which our findings characterize

people who are not twins as well as individuals from other races, regions, countries, and generations is

simply unknown. Cross-cultural and cross-generational replication would help increase confidence in

the external validity of the findings.

Twin closeness, which was examined as a control variable, was measured 6–20 years prior to the

administration of the leadership survey. This is another limitation. To gauge the seriousness of this

concern, we examined the stability of twin closeness via archival data. The twin registry included

closeness ratings provided by 276 female twins whose initial rating was followed by a second rating

provided 8 years later. The correlation between the two measures of closeness conducted 8 years apart

was r¼ 0.82 (N¼ 276, p< 0.001). This high correlation may reduce some concerns regarding the

accuracy of our closeness measure. The fact that the conclusions of this study remain the same

regardless of whether the closeness control variable was included may also help alleviate concerns

about potential inaccuracies in the closeness measure.

Research in the social and organizational sciences is often limited by cross sectional designs which

do not examine behavior over time. For example, studies of the antecedents of prosocial behavior in

general (e.g., Batson, Bolen, Cross, & Neuringer-Benefiel, 1986; Bierhoff & Rohmann, 2004) and

survey response behavior in particular (e.g., Sax et al., 2003) commonly evaluate the relationship

between participants’ scores on an initial personality inventory and their subsequent responses to a

single helping or survey opportunity. Our research suffers from this limitation as well. Although

practical constraints precluded an examination of responses to multiple survey administrations, a

longitudinal design would have strengthened this study by enabling us to examine research questions

pertaining to patterns of survey response over time.

Finally, we should also point out that this study did not include potential mediators (e.g., individual

differences, affect, etc.) which might have helped clarify why survey response is heritable. Hopefully,

the new discovery uncovered in this study will serve as both a catalyst and a compass which stimulates

and guides follow-up research that digs deeper to address this issue.

Implications

Despite its limitations, this study contributes to the emerging body of knowledge pertaining to

individual differences in survey response behavior (e.g., Rogelberg et al., 2003; Rogelberg et al., 2006;

Sax et al., 2003). If attitudes/traits and survey response are heritable (potentially due to common

genetic influences), and surveys are used to assess employee attitudes/traits, researchers should be

concerned about whether data obtained from a sample that opted to submit a given survey generalize to

the broader population of interest. Using a survey on conscientiousness as an example, if the study’s

goal is to estimate the overall level of conscientiousness within a given population, the results obtained

from those who volunteer to complete the measure may reveal an artificially high mean level of the trait

due to the nonresponse of those low in conscientiousness. If the survey’s goal is to examine the impact

of conscientiousness on an outcome of interest, the predictor data obtained may suffer from a restriction

of range, which can also bias study results. Depending on the nature of the data, range restriction can


DOI: 10.1002/job


distort (i.e., decrease or increase) the correlation between a predictor and an outcome measured by a

survey (Zimmerman & Williams, 2000). Beyond bivariate correlations, more complex statistics (e.g.,

regression, SEM) are also affected by the type of nonresponse bias suggested above, but in less

straightforward ways (Dey, 1997).1

Thus, this study suggests that there is a biological basis for nonresponse to result in systematic bias in

certain studies unless carefully controlled for. Dealing with nonresponse is henceforth not something

that scholars can easily dismiss. The current study strongly reinforces the need for researchers to deal

with this problem in a constructive fashion. Under certain circumstances, this may entail collecting data

through means other than voluntary surveys. Recognizing that in practice surveys are often the only

viable data collection option, this study also underscores the need to identify and implement creative

methods for encouraging participation from those predisposed to nonresponse.

Future research directions

As the role and nature of surveys in OB research and practice continue to evolve, there is no reason to

believe that we have conceived and tested all possible ideas for improving response rates. In all

likelihood, a host of useful techniques awaits empirical discovery. Hopefully, this study will encourage

future research aimed at developing and testing new response facilitation techniques. Studies designed

to determine what kind of incentives or interventions best ‘‘overcome’’ the predisposition to not return

surveys would be of particular value.

There is also a need for basic research designed to identify the specific components that make up the

environmental influence on response behavior. By capturing and modeling these environmental

influences, researchers can disentangle them from measurement error, identify their unique

contribution in explaining survey response behavior, and then use this information to inform the

development of response facilitation techniques.

As Ilies et al. (2006) point out, the field of OB would benefit from additional research on how

genotype-environment interactions affect outcomes of interest. Future research should seek to identify

environmental factors that moderate the extent to which survey response is genetically based. Perhaps

organizational factors (e.g., the degree to which prospective respondents feel their employers have

followed up on past survey results) reduce the strength of the genetic influence on survey response in

applied settings.

One important aspect of this study is its potential to stimulate research designed to pinpoint the trait

and attitudinal variables that mediate the effect of the genetic influence on survey response. It is

possible that genetics predispose people to particular attitudes, cognitions, and affective states that

inhibit survey response. Potential attitudinal mediators include attitudes toward surveys in general

(Rogelberg et al., 2006), attitudes toward the survey sponsor (Rogelberg et al., 2003), and trust that the

survey sponsor will act on the data provided (Thompson & Surface, 2007, 2009). Another possibility

involves perceptions of oversurveying. Currently, it is not uncommon for individuals to receive requests

for survey participation from a host of organizations (e.g., employers, churches, clubs, marketers,

political organizations). This may cause people to feel oversurveyed. Perhaps certain personality traits

(e.g., reciprocation wariness; Spitzmuller et al., 2006) lower the threshold (i.e., the number of surveys)

required before an individual begins to feel oversurveyed. Advanced modeling techniques which allow

for the examination of individual differences in growth trajectories over time would be particularly

useful in research designed to examine this issue. Overall, studies investigating perceptions of surveys

1While it is difficult to know the precise impact of nonresponse bias in practice, strategies for assessing the likely influence ofnonresponse on survey results have been offered in the literature (Viswesvaran, Barrick, & Ones, 1993).


DOI: 10.1002/job


and oversurveying, as well as other attitudinal mediators of the effect uncovered in this study, could

begin to inform the development of targeted interventions encouraging participation from those prone

to nonresponse. The caveat, however, is that these potential mediators (e.g., traits, attitudes, and

perceptions) may need to be measured using methods other than voluntary self-reported surveys. To

this end, some personality instruments provide a format for other-ratings. Furthermore, independent

observers in assessment centers could provide measures on focal individuals’ attitudes.

Finally, research refining the outcome examined in this study would be informative. Rogelberg et al.

(2003) maintain that nonresponse can be active or passive in nature. These two forms of nonresponse

may exhibit different degrees of heritability because they may be influenced by distinct sets of traits and

attitudes. This possibility awaits empirical investigation.

Conclusion

Surveys play a critical role in OB research and practice alike. Practitioners use them to accomplish a

variety of objectives, such as diagnosing organizational problems, assessing climate, and measuring the

impact of change initiatives. Meanwhile, researchers rely on surveys, which are often administered

outside of the workplace, to gather data for studies designed to generate new knowledge in the field of

OB. In both research and applied contexts, problems stemming from low response rates and

nonresponse bias create a need to better understand the decision to comply with or ignore appeals for

survey participation. The dearth of research addressing this need has provoked the criticism that

‘‘survey nonresponse is a rather neglected stepchild in OB research’’ (Spitzmuller et al., 2006: p. 19).

However, it has also stimulated studies on the antecedents of response behavior, which have appeared

in the OB literature in recent years (e.g., Rogelberg et al., 2003; Rogelberg et al., 2006; Spitzmuller

et al., 2006; Spitzmuller et al., 2007). The current study is the first to consider the role genetics plays in

survey response behavior. Hopefully, future studies will build off of this one to increase what is known

about the underpinnings of survey response. Ultimately, such work can be used to improve response

rates as well as the accuracy of the conclusions drawn from the survey data collected from voluntary

respondents.

Author biographies

Lori Foster Thompson, is an associate professor in the Industrial/Organizational Psychology program

at North Carolina State University. Her research, teaching, and consulting pertain to employee

reactions to emerging technologies, organizational surveys, and humanitarian work psychology.

She has co-authored a book, book chapters, and various articles on these topics and currently serves

on the editorial board of The Industrial-Organizational Psychologist (TIP), the Journal of Organiz-

ational Behavior, and Ergometrika, where she is associate editor.

Zhen Zhang, is an Assistant Professor of Management at Arizona State University. His research

focuses on leadership process and development, the biological basis of organizational behavior, and

research methods. His work has appeared in several journals, including Journal of Applied Psychology,

Organizational Behavior and Human Decision Processes, the Leadership Quarterly, and Organiz-

ational Research Methods.


DOI: 10.1002/job


Richard Arvey, is currently the Head of the Department of Management and Organization, National

University of Singapore. He received his PhD from the University of Minnesota and has taught and

conducted research at the Universities of Tennessee, Houston, and California-Berkeley. He conducts

research on issues pertaining to job satisfaction, leadership, motivation, as well as recruitment and

staffing areas.

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Genetic underpinnings of survey response