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Egocentric Social Network Structure, Health, and Pro- Social Behaviors in a National Panel Study of Americans A. James O’Malley1*., Samuel Arbesman2*., Darby Miller Steiger3, James H. Fowler4,
Nicholas A. Christakis1,5
1 Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, United States of America, 2 Ewing Marion Kauffman Foundation, Kansas City,
Missouri, United States of America, 3 Gallup Government, Cleveland, Ohio, United States of America, 4 Departments of Medical Genetics and Political Science, University of
California San Diego, La Jolla, California, United States of America, 5 Department of Sociology, Harvard University, Cambridge, Massachusetts, United States of America
Abstract
Using a population-based, panel survey, we study how egocentric social networks change over time, and the relationship between egocentric network properties and health and pro-social behaviors. We find that the number of prosocial activities is strongly positively associated with having more friends, or an increase in degree, with approximately 0.04 more prosocial behaviors expected for every friend added. Moreover, having more friends is associated with an improvement in health, while being healthy and prosocial is associated with closer relationships. Specifically, a unit increase in health is associated with an expected 0.45 percentage-point increase in average closeness, while adding a prosocial activity is associated with a 0.46 percentage-point increase in the closeness of one’s relationships. Furthermore, a tradeoff between degree and closeness of social contacts was observed. As the number of close social contacts increases by one, the estimated average closeness of each individual contact decreases by approximately three percentage-points. The increased awareness of the importance of spillover effects in health and health care makes the ascertainment of egocentric social networks a valuable complement to investigations of the relationship between socioeconomic factors and health.
Citation: O’Malley AJ, Arbesman S, Steiger DM, Fowler JH, Christakis NA (2012) Egocentric Social Network Structure, Health, and Pro-Social Behaviors in a National Panel Study of Americans. PLoS ONE 7(5): e36250. doi:10.1371/journal.pone.0036250
Editor: Matjaž Perc, University of Maribor, Slovenia
Received March 15, 2012; Accepted April 3, 2012; Published May 15, 2012
Copyright: � 2012 O’Malley et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Research for this paper was supported by National Institutes of Health (NIH) Grants R01 AG024448 and P01 AG031093 and by Robert Wood Johnson Foundation Award #58729. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: [email protected] (AJO); [email protected]) (SA)
. These authors contributed equally to this work.
Introduction
Although egocentric network studies – wherein a subject is
asked to identify his or her social contacts and their relationships –
have a long history in sociology, their use in health surveys is rare.
But increased attention to the role of social networks in medicine
suggests that a basic understanding of the structure of American
social networks and how they change may be important [1]. We
collected egocentric social network data from a nationally
representative sample of Americans in order to study the
relationship between individuals social networks and their health
and related social behaviors. The norm in national population
surveys is to sample new individuals each year [2,3,4]. While this
can allow statistical understanding of population-level, egocentric
social networks structure at a fixed time, it does not allow us to
study the change in individual networks over time. Therefore, here,
we also sought to characterize how individuals egocentric social
networks change over time, the extent to which close social
contacts are gained or lost, and the extent to which an ego’s social
contacts (their ‘‘alters’’) come to know each other over time.
Obtaining social network information that is population
representative is challenging. To sample individuals who are
actually connected to each other, non-random methods are
needed, or else a set of null relationships in which no two
individuals know each other is likely [5]. On the other hand, the
necessity of sampling pairs of individuals who are connected is at
odds with the requirements of population representativeness that
are fundamental to most surveys, for which random or probability-
weighted random sampling is desirable. Therefore, we imple-
mented a compromise between a population-representative survey
and a full sociocentric network ascertainment, in order to obtain
population representative estimates of quantities related to
networks and various behaviors.
That is, we conducted a national, egocentric study. We fielded a
network survey instrument in a nationally representative sample in
order to study the relationship between individuals social networks
and their health and behaviors. The instrument extracts informa-
tion from respondents (egos) on their relationships to the peers
they spend the most time with or discuss important issues with
(alters) and also on the relationships between all pairs of named
peers. Moreover, we collected this information repeatedly across
time. Thus a respondent’s social network is (partially) revealed in a
traditional random sample without having to ascertain the full
population network.
Using such a survey instrument, we can examine the change in
the number of close social contacts (‘‘degree’’), tie strength
(‘‘closeness’’), and the number of interconnections between
contacts (‘‘transitivity’’), how they are related to each other, and
how they relate to measures of health and prosocial behavior. We
focused primarily on prosocial behavior, broadly defined as
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altruism and generosity, or any activity that promotes the general
welfare of society, e.g., participation in community enrichment,
contributions to charity, and volunteering [6].
Methods
Survey Instrument We developed a social network survey instrument for deploy-
ment with Gallup’s ongoing, longitudinal, probability-based panel
of American households. We constructed the survey by modifying
the social network instrument used previously by the GSS [7,8,9]
and by the National Health and Social Life Survey [10,11]. The
survey consists of items seeking information on ego characteristics,
alter characteristics, ego-alter relationships, and the relationship
between each pair of alters. Ego health and behavioral traits are
also measured. Surveys were completed online by the egos.
The network component of the survey asked egos to name (up
to) four adults with whom they spent the most free time in the past
12 months and (up to) four adults with whom they most often
discussed important issues. Alters could be family members,
friends, work/school colleagues, and so forth. Thus, each ego
named up to eight distinct alters yielding a maximum of 28 alter-
alter relationships for which they also provided information.
Individual characteristics included gender, age, race, ethnicity,
education, employment status, income, marital status, religious
preference, political affiliation, health, health behaviors, and
prosocial behavior. Ego-alter variables included the type of
relationship and frequency of contact, and alter-alter variables
consisted of type and strength of the relationship. The key alter-
alter variable is the ego’s assessment of the strength of their
relationship.
The health and behavioral traits for the ego consisted of a series
of items that, for analytic purposes, were combined into scales.
Prosocial behavior measured whether the ego donated blood,
donated money, donated clothing, financially supported a political
candidate, or volunteered to help prepare for a major public
emergency. Health behaviors included smoking status, BMI
(weight/height 2 ), whether they wanted to gain or lose weight,
whether they took active steps to improve their health (e.g.,
adhered to a diet, quit smoking, cut back alcohol). The questions
on health included physical health, mental health, and missed
work due to sickness.
The network measures included the number of alters with
whom the ego named as having a close relationship (degree), the
average strength of these relationships (closeness), and the
connectedness of the named alters to each other according to
the ego (transitivity).
Study Design The Gallup Panel is a nationally representative, multi-mode
panel recruited through random digit dialing methods. Only the
web-based portion of the Panel was eligible for participation in this
study. Data was collected on randomly chosen and nationally
representative Americans in June 2009, December 2009, and July
2010.
A total of 6,000 randomly selected web-based members of the
Gallup panel were sent an email invitation that asked them to
respond to a survey about ‘‘the various people that you spend
your free time with and have important conversations with.’’
Invitations for waves 2 and 3 explained that this was a
continuation of the earlier research in which they had
participated. A reminder email was sent for each wave in order
to boost completion rates. The first wave collected data from a
sample of 3,232 respondents (out of 6,000– a 53.9% completion
rate). Of those 3,232 respondents, 2,305 responded to wave 2 in
December 2009 (71.3%) and 2,114 responded to wave 3 in July
2010 (65.4% of wave 1).
The analysis sample consists of all individuals that responded at
wave 1 and, therefore, includes some who have missing data in
waves 2 and 3.
Network Measures The degree of each ego is the number of alters they name, a
quantity ranging from 0 to the maximum 8. The strength of each
ego-to-alter and alter-to-alter relationship is rated from 0 = no-
relationship to 10 = very strong relationship; 0 strength only
occurs in alter-to-alter relationships. We scaled degree and
strength to obtain quantities that range from 0 to 1; the closeness
of an ego’s relationships is, therefore, the average strengths of
their reported ties (see Figure 1). We hypothesized that closeness
would have greater discriminatory power than degree.
In egocentric networks, an ego’s transitivity is the average
value of the relationship between all pairs of alters, herein
assumed to be mutual (i.e., the relationship from j to k is the
same as that from k to j). Denote the degree of ego i by ni and
the strength of the relationship between alter j and alter k by zjk: When relationships are binary-valued (e.g., corresponding to the
presence of any relationship at all), transitivity is given by
transi~2n {1 i (ni{1)
{1 X
jvk I (zjkw0), ð1Þ
and is interpreted as the proportion of pairs of alters for which
some form of relationship exists. If relationships are quantified in
terms of their strengths, transitivity is given by
transi~2n {1 i (ni{1)
{1 X
jvk zjk: ð2Þ
Because (2) is our preferred measure, from here on by
‘‘transitivity’’ we mean the weighted form in (2). Egocentrically,
transitivity is a local measure specific to an ego, not a network
property.
In general, egocentric network measures are more loosely
representative of sociological constructs than their sociocentric
counterparts [12]. For example, egocentric degree does not
account for directionality and therefore cannot characterize
reciprocity (if A is a friend of B then B is more likely to be a
friend of A). Moreover, because the egocentric design used here
does not differentiate between different types of triads, it
confounds transitivity (A and B are more likely to be friends if C
is a friend of both of them) with three-cycles (A is a friend of B who
is a friend of C who is a friend of A) and other triadic structures.
Nonetheless, egocentric network measures based on survey data
are still useful for describing how individual’s networks evolve.
Because closeness is undefined if the ego has no alters (i.e.,
degree = 0) and transitivity is undefined if they have one or no
alters (i.e., degree#1), we define them to have value 0 if
no:peeri~I (ni~0)~1 and one:peeri~I (niƒ1)~1 respectively. We include no:peeri in any model in which closeness is a predictor and no:peeri in any model in which transitivity is a predictor. We exclude from the analysis of closeness observations with
no:peeri~1 and for the analysis of transitivity we discard observations with one:peeri ~1:
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Statistical Models We fit a series of models where the ego’s health and behaviors
are regressed on the network measures, adjusting for personal
characteristics of the ego such as gender and age. In addition, we
adjusted for the value of the dependent variable at the previous
wave, personal characteristics of the ego (e.g., gender, age), and
survey wave. We also fit similar models in which the network
measures are regressed on the health and behavioral (HB)
measures.
Let yit, wit, and xit be the vectors of HB (BMI, health, health behavior, prosocial behavior), their network variables (degree,
closeness, transitivity), and exogenous control variables (gender,
age, survey wave) for individual i at survey wave t (t~1,2,3): Element h of yit and wit is denoted by yhit and whit respectively. When applicable, no:peerit and one:peerit are included in wit: The model for health and behavioral trait h regressed on the
network measures is
E½yhitjyhit{1,wit,wit{1,xit�~b0zb1yhit{1zb2wit zb3wit{1zb4xit
ð3Þ
and the model for network measure k regressed on the HB is
E½wkitjwkit{1,yit,yit{1,xit�~h0zh1wkit{1zh2yit zh3yit{1zh4xit
ð4Þ
Both (3) and (4) allow the effects of current and lagged predictors
to be separated and are jointly useful for detecting directionality of
effects. For example, the scenario when b2 is not significant whileb3 and h2 are significant is consistent with network measures having a causal effect on HB but not the converse. In general, if an
element of b3 or h3 is significant, there is stronger evidence of a causal relationship than if the corresponding element of b2 or h2 is significant.
In the event that the effects of current values of the key
predictors (network measures or HB) are nearly all non-
significant, we consider a reduced model in which they are
excluded. In general, lagged models have the advantage over
cross-sectional models of being more causally defensible. How-
ever, they can lack power.
To investigate the possibility of nonlinearity, we tested whether
transitivity-squared was predictive of ego’s outcomes. However, no
such effects were found and so we omitted transitivity-squared as a
predictor. We also tested whether the turnover in the ego’s peers,
defined as
turnoverit~f riendsadditzf riendsloseit{jnit{nit{1j,
where jxj~x if xw0 and 0 otherwise, had an impact beyond degree and change of degree. In all instances, estimated effects for
turnoverit were not significant.
To improve scalability of the estimated regression coefficients,
the network measures were scaled to the unit interval when
functioning as predictors (as for models of HB) and to
percentages when functioning as outcomes. In addition, age
was scaled to units of 10 years.
Results
Of the 6,000 subjects in the sampling frame, a total of 3,232
responded to the first survey. Of these, 2,305 subjects responded
to the second survey, and of these 1,809 responded to all three
surveys. In response to the name generators (‘‘Who do you spend
free time with’’ and ‘‘Who do you discuss important issues
with’’), we found that Americans identify an average of 4.461.8
close social contacts (the average respondent lists 2.2 friends, 0.76
spouses, 0.28 siblings, 0.44 coworkers, and 0.30 neighbors). This
corresponds to past work with the GSS [9].
Figure 1. Egocentric network involving an ego with N = 8 alters labeled A, B, …, H. For clarity, the left panel shows only the ego-alter ties while the right panel shows only alter-alter ties. Closeness is computed as the average strength of the ego-alter ties (left panel) and dividing by 10 to make the range 0 to 1. Analogously, transitivity is computed as the average strength of the alter-alter ties (right-panel, including the 0 strength null ties that are not depicted) and dividing by 10. doi:10.1371/journal.pone.0036250.g001
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Figure 2. Changes and transitions in network measures between consecutive waves. doi:10.1371/journal.pone.0036250.g002
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Figure 3. Relationships between changes in network measures. doi:10.1371/journal.pone.0036250.g003
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Change in Network Measures Between the six-month waves, an ego’s degree increased 31% of
the time, stayed the same 27% of the time, and decreased 42% of
the time. The transition matrix for ego’s degree (not presented)
revealed that unchanged degree occurred most commonly and
that there was a clear tendency towards lower degree, especially
among egos that began with modest degree (2 to 4 alters).
The distributions of closeness and transitivity measures were
very similar between waves with almost equal numbers of
increasing and decreasing transitions (Figure 2). Closeness
increased 46%, remained the same 8%, and decreased 46% of
the time, while transitivity increased 50%, stayed the same 5%,
and decreased 45% of the time. All network measures regressed
towards the mean (higher values were more likely to decline while
lower values were more likely to increase); degree and transitivity
were most and least affected, respectively.
Relationships Among Network Measures Change in degree and change in closeness have a distinctly
negative relationship (Figure 3), and people seem to trade of the
number of contacts they have with the closeness of those contacts.
Furthermore, increased degree is associated with reduced transi-
tivity, while closeness and transitivity are positively correlated.
Thus, as an individual accumulates more alters, the average
closeness of their own relationships and of the relationships
between the alters in their egocentric network decline. These
observations illustrate that correlations often arise in social
networks due to natural constraints, even when there is no
mathematical constraint. For example, individuals that name
more alters may on average have ties of lower average strength
due to a limit on the number of very close relationships an
individual can maintain. Likewise, across all pairs of alters, average
relationship strength is likely to be lower if degree is higher as the
relative frequency of pairs with a non-close relationship increases.
Table 1. Regressions of individual outcomes on current and lagged network measures.
Term BMI Health Health behaviors Pro-social behavior
Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI
Network measures
Degree 0.02 (20.83, 0.88) 0.08 (20.11, 0.27) 20.02 (20.25, 0.21) 0.33 (0.19, 0.48)
Closeness 20.14 (21.37, 1.10) 0.34 (20.03, 0.70) 0.38 (20.09, 0.86) 0.22 (20.09, 0.54)
Transitivity 20.04 (20.86, 0.77) 20.06 (20.24, 0.12) 20.19 (20.38, 0.01) 20.09 (20.23, 0.06)
Lag network measures
Degree 0.08 (20.71, 0.88) 0.13 (20.06, 0.32) 0.00 (20.22, 0.22) 20.08 (20.22, 0.07)
Closeness 0.14 (21.24, 1.53) 0.08 (20.32, 0.47) 20.23 (20.69, 0.24) 20.22 (20.51, 0.07)
Transitivity 0.22 (20.44, 0.87) 0.07 (20.12, 0.25) 0.20 (0.01, 0.40) 0.11 (20.04, 0.26)
Other predictors
Wave 0.26 (20.07, 0.60) 0.02 (20.05, 0.09) 0.09 (0.01, 0.17) 20.05 (20.11, 0.00)
Female 20.11 (20.32, 0.10) 0.00 (20.06, 0.05) 0.04 (20.03, 0.10) 0.00 (20.04, 0.04)
Age (10s of years) 0.03 (20.08, 0.15) 0.01 (20.01, 0.03) 0.03 (0.01, 0.05) 0.03 (0.02, 0.05)
Lag dependent variable 0.89 (0.81, 0.96) 0.78 (0.76, 0.81) 0.56 (0.53, 0.60) 0.74 (0.72, 0.77)
doi:10.1371/journal.pone.0036250.t001
Table 2. Regressions of network measures on lagged health and behavioral traits.
Term Degree (%) Closeness (%) Transitivity (%)
Estimate 95% CI Estimate 95% CI Estimate 95% CI
Lagged health behaviors
BMI 0.15 (0.02, 0.27) 0.02 (20.05, 0.08) 0.00 (20.10, 0.10)
Health 0.17 (20.52, 0.85) 0.45 (0.09, 0.81) 0.11 (20.46, 0.68)
Health behavior 0.54 (20.21, 1.28) 20.26 (20.63, 0.11) 0.06 (20.57, 0.68)
Pro-social behavior 0.38 (20.52, 1.27) 0.46 (0.02, 0.91) 0.48 (20.25, 1.20)
Other predictors
Wave 20.88 (22.59, 0.83) 1.65 (0.81, 2.50) 1.83 (0.20, 3.45)
Female 5.98 (4.38, 7.59) 0.86 (0.08, 1.63) 22.18 (23.44, 20.91)
Age (10 s of years) 0.59 (0.02, 1.16) 0.11 (20.16, 0.37) 0.89 (0.42, 1.36)
Lag dependent variable 0.31 (0.26, 0.35) 0.46 (0.41, 0.51) 0.63 (0.59, 0.67)
Note: When the full model in Equation 5 was fit, (current) pro-social behavior was highly predictive of degree (2.54 percentage-points, 1.39–3.67). doi:10.1371/journal.pone.0036250.t002
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Effects between Egocentric Network and Individual Outcomes
Across the four HB we found that current and lagged network
measures were significant predictors in various models (Table 1),
suggesting that the model in Equation (4) was appropriate.
However, in the models of the three network measures (Equation
5), significant effects were more common for the lagged than the
current values of the HB (the strong association of prosocial
behavior with degree is the lone exception). Therefore, to simplify
interpretation, in these models we excluded the current HB
predictor variables and refit them (Table 2).
Table 1 shows that individuals who had more friends (higher
degree) were likely to behave more prosocially (estimate: 0.33; CI:
(0.19, 0.48)). Because we re-scaled degree to the unit interval to
improve the scalability of estimates, the interpretation of the
coefficient is that 0.33 more prosocial behaviors are expected for
every 8 friends added (approximately 0.04 more behaviors per
friend added). Furthermore, when the roles of degree and
prosocial behavior were reversed, we also found a strong effect
(2.54 percentage-points, 1.39–3.67), the only instance of a network
measure having a significant effect under the full model (Equation
5). In both models, the lagged effects are weak, suggesting that the
change in the predictor is more important than its level. These
results imply that the relationship between degree and prosocial
behavior is bidirectional, fast-acting, and substantial.
Although the association of lagged transitivity with health
behaviors is just significant at the 0.05 level (Table 1), it is of
similar magnitude and opposite in sign to that of transitivity.
Therefore, the overall effect of transitivity is almost entirely
accounted for by change in it, not its level.
In the model of health, the coefficients of the degree terms are
positive (0.08 and 0.13) and the confidence interval of lagged
degree ({0:06, 0.32) overlaps 0 by a small amount (Table 1). After excluding degree, lagged degree had a significant association with
health (0.18, 0.03–0.34) suggesting that the collinearity of the
degree terms reduced the level of lagged degree in the original
model. However, the association of lagged health on degree is not
significant (Table 2) suggesting that the relationship of degree to
health is directional (i.e., not reciprocated) and perhaps varying
over time.
We discovered several other directional effects of modest
significance (Table 2), including lagged health on closeness (0.45,
0.09–0.81) and lagged prosocial behavior on closeness (0.46, 0.02–
0.91). Therefore, a unit increase in health is associated with an
expected 0.45 percentage-point increase in average closeness,
while adding a prosocial activity leads to a 0.46 percentage-point
increase in the closeness of one’s relationships.
The results for the other predictors reveal that age has the
strongest association with the outcomes (positive effects on health
behaviors and prosocial behavior) and network measures (positive
effects on degree and transitivity). As revealed in the exploratory
analyses, degree decreased over time while both closeness and
transitivity increased. It is interesting to note that females named
nearly 6% more friends on average and had stronger relationships,
but the relationships among their alters were weaker.
Discussion
In addition to providing a quantitative description of the
changes in individuals’ social networks over time in a national
sample, we uncovered interesting relationships among the
egocentric network measures, including the negative dependence
between degree and either closeness of ties or transitivity among
alters. Such dependencies would seem to be a consequence of
individuals’ limited capacity to maintain large numbers of close-
ties: as our networks become larger, each tie we have to others is
expected to weaken. To our knowledge, the phenomena that the
greater the number of ties, the less well ones alters know each
other, has not been reported previously.
Just as there are certain cognitive limits to the number of
individuals one can have as part of one’s social network, [13,14] it
also appears that there are cognitive and temporal considerations
for how humans manage their interactions. In particular, we find
that the reported average closeness to all friends decreases as the
number of one’s friends increases, suggesting an invariant total
expenditure on social interaction. An increase of one in the
number of close social contacts was associated with a decrease of
0.03 in the average closeness of each individual contact on a scale
where 0 = do not know and 1 = extremely close. An increase of
two close contacts was associated with a decrease in closeness of
nearly 0.06 (a substantial reduction on this scale). Because, in prior
research, ties are typically modeled as either present or absent,
with no strength information, these findings are some of the first of
their kind.
In addition, we evaluated a series of regression models relating
individuals’ egocentric network measures to their health and
behavioral traits. The strongest result we found is the bidirectional
association between a respondent’s degree and their prosocial
behavior (Figure 4). Moreover, when current network measures
were excluded, degree was associated with health, while the
reverse is not true (i.e., health was not associated with a subsequent
change in the number of friends); this result is consistent with prior
work [15]. In addition, being healthier and more prosocial was
associated with the development of closer relationships, but the
converse was not observed.
Although our principal finding that degree and prosocial
behavior are highly predictive of each other is a contemporaneous
effect, and thus open to more scrutiny from a causal standpoint
than a lagged association of the same significance, we nonetheless
raise the possibility that this provides evidence of some effect.
Whether degree affects prosocial behavior or the converse, or
Figure 4. Flow diagram illustrating the primary effects found between network measures and HB. The strongest effect is the contemporaneous bidirectional effect between number of friends (degree) and prosocial behavior (solid line) while the lagged directional effects (dashed lines) were weaker but still statistically significant. Relationships within network measures and within HB are not depicted. doi:10.1371/journal.pone.0036250.g004
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whether both occur simultaneously, is a question that may only be
answerable by a study in which subjects are even more closely
followed over time. Across the pairs of individual outcomes and
network measures, there were no other bidirectional associations
with such a high level of significance.
Considering all results together, the only network measure that
consistently predicted health status and also one’s health and
prosocial behavior is degree, while both health and prosocial
behavior predicted network measures (closeness and degree
respectively). These results are consistent with a circular evolution
or feedback mechanism among one’s network and one’s health or
behavior, possibly reflecting a process that for some or all
individuals may tend to an equilibrium position.
Although an egocentric study provides a feasible way of
obtaining network information on a large-scale (e.g., by augment-
ing an existing national survey), it has several limitations compared
to a full sociocentric study. First, unlike sociocentric studies, we do
not observe the status of the relationship from the perspective of
alters. Egocentric data is based purely upon the knowledge,
reflection, and recall of the ego, which may be inaccurate –
especially when describing the relationship between two alters.
Because we do not observe alter’s view of relationships, we are not
able to validate the ego self-report, nor are we able to ascertain
directionality of relationships [16]. This prevents us from utilizing
directionality to distinguish the effect of induction from other
phenomena, such as homophilly [17,18,19]. However, the analysis
is still relevant if we consider the fact that an ego’s perception of
relationships may be more important than whether or not the
perceived relationship is validated via reciprocation by the alter.
Second, without some means of validating the relationship
between pairs of alters, relationships elicited via surveys are prone
to confound true relationship status with recall – it is likely that
relationships with whom an individual has recently been in contact
are the most likely to be reported. Therefore, changes in the size or
composition of a network may be observed due to changes in the
set of alters an individual recently interacted with, even if their true
egocentric network is unchanged [20,21,22]. If measurement error
is random, estimated effects will be attenuated (biased towards 0).
Thus, we are more likely to not report a true effect (type II error)
than to claim a significant effect when the true effect is 0 (type I
error). Such problems with self-reported relationships also apply to
sociocentric designs if tie-ascertainment is through self-report. An
alternative to recall is to evaluate relationships by monitoring
human interactions [23]. Although monitoring might be a more
precise way of measuring relationships, or at least to generate a list
of potential alters to use in an interview with the ego, in practice
such monitoring is likely to involve enormous cost. Clearly, an
important area for future work is to study the properties of
egocentric networks when relationships are not completely
ascertained due to incomplete recollection of ties.
Another limitation is that online surveys have been found to
yield less accurate results than in-person interviews, as individuals
may answer more mechanically, particularly in completing the
response matrix of the relationships between their alters [24].
However, an advantage of performing the survey online is that the
expense is much lower and there is no risk of bias from interviewer
effects.
Egocentric network studies embedded within population surveys
enable population-representative data to be obtained on individ-
uals’ relationships. It is clear that, despite the advantages that
sociocentric studies offer, they are not practical on a national scale
(except, perhaps, if phone data for a whole nation were used). An
understanding of social network structure and its relationship with
health and health behaviors can improve understanding of health
phenomena such as collateral effects [25], design of healthcare
interventions, and evaluation of healthcare policy studies [1].
Egocentric studies, such as that described here, can also provide
information about social networks and how they change in ways
relevant to health at the national level.
Acknowledgments
We thank Donald Beck, formerly at Gallup and now at Booz Allen, for
help with the initial study execution.
Author Contributions
Conceived and designed the experiments: SA NAC JF. Performed the
experiments: DMS. Analyzed the data: AJO. Contributed reagents/
materials/analysis tools: AJO SA. Wrote the paper: AJO SA NAC.
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