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Dynamic Social Networks in Recovery Homes
Leonard A. Jason
1
, John M. Light
2
, Edward B. Stevens
1
, and Kimberly Beers
1
1
DePaul University
2
Oregon Research Institute
Abstract
Acute treatment aftercare in the form of sober living environments—i.e., recovery houses—
provide an inexpensive and effective medium-term treatment alternative for many with substance
use disorders. Limited evidence suggests that house-situated social relationships and associated
social support are critical determinants of how successful these residential experiences are for their
members, but little is known about the mechanisms underlying these relationships. This study
explored the feasibility of using dynamic social network modeling to understand house-situated
longitudinal associations among individual Alcoholics Anonymous (AA) related recovery
behaviors, length of residence, dyadic interpersonal trust, and dyadic confidant relationship
formation processes. Trust and confidant relationships were measured 3 months apart in U.S.
urban-area recovery houses, all of which were part of a network of substance use recovery homes.
A stochastic actor-based model was successfully estimated from this data set. Results suggest that
confidant relationships are predicted by trust, while trust is affected by recovery behaviors and
length of residence. Conceptualizing recovery houses as a set of independent, evolving social
networks that can be modeled jointly appears to be a promising direction for research.
Ecologically based behavioral theory describes a dynamic transactional interchange
between the individual and his or her social environment (e.g., Bronfenbrenner, 1979; Kelly,
2006; Magnusson, 1987). Individual behavior is constrained by social environments, yet
such behaviors, in the aggregate, also form the social environment. Although this two-way
flow idea underlies and informs the field of community psychology (Jason & Glenwick,
2012), theoretical formulations need to be concrete enough to be empirically evaluated with
quantitative methods. Moreover, methods for studying social systems from a transactional
perspective are still quite limited: even advanced statistical techniques like multilevel
modeling are primarily useful for studying the effect of context on behavior, but not the
reverse (e.g., Todd, Allen, & Javdani, 2012). In contrast, a dynamic network approach
provides a framework for thinking about and describing two-way transactional dynamics, as
well as a methodological approach for studying such systems empirically.
The social network paradigm is distinguished from other behavioral science research by its
focus on relationships, rather than on individual characteristics. In seeing behavior as
fundamentally contextual, social network research shares the transactional tenet with
Correspondence should be sent to Leonard A. Jason, DePaul University, Center for Community Research, Chicago, Il. 60614. The
authors appreciate the help of Rory Murray..
NIH Public Access
Author Manuscript
Am J Community Psychol. Author manuscript; available in PMC 2015 June 01.
Published in final edited form as:
Am J Community Psychol. 2014 June ; 53(0): 324–334. doi:10.1007/s10464-013-9610-6.
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community psychology (Bogat, Zarrett, Peck, & von Eye, 2012). Moreover, recent advances
in dynamic modeling of social networks using well-grounded principles of statistical
inference have provided the capability to estimate fully transactional models from network
and behavioral data.
One of these approaches is the stochastic actor-based model (for details, see Snijders, 2001
and Snijders, van de Bunt, & Steglich, 2010). Briefly put, the stochastic actor-based model
conceptualizes social networks as a set of individuals whose relationships evolve over time
according to an underlying probability structure. This process can depend on a linear
combination of predictors (effects), which are interpretable as hypothesized mechanisms that
jointly predict network evolution. Dependence on predictors (“effects”) is formulated as a
linear log-odds model, similar to a logistic regression, although the underlying model
structure is different.
Model effects include both fixed (e.g., gender, ethnicity) and time-varying (attitudes,
behaviors, etc.) measured characteristics of individuals, but also characteristics of pairs of
individuals, or dyads (e.g., physical distance between them). Such predictors are familiar
from ordinary regression modeling. A major contribution of the network perspective to the
study of social relationships is the idea that structural constraints also matter. Relationship
dynamics, in other words, normally depend not only on individual characteristics, needs, and
preferences, but also on the state of the network. Two important structural effects are
reciprocity (the tendency for reciprocated relationships to be developed, i.e. where each
member of a dyad chooses the other) and transitivity (the tendency for all members of triads
to share the same relationship).
Network approaches in community psychology have been limited largely to studies of
“personal” networks, that is, the personal friendships or other significant relationships
reported by study participants. For example, personal network studies of substance use
recovery have established the relevance of participant-reported associates as facilitators of
treatment entry (Davey, Latkin, Hua, Tobin, & Strathdee, 2007; Kelly et al., 2010) and
mediators of ongoing sobriety (Humphreys & Noke, 1997; Humphreys, Mankowski, &
Moos, 1999; Kaskutas, Bond, & Humphreys, 2002; Longabaugh et al., 1995; Polcin et al.,
2010). Personal networks tend to focus only on the relationships of a focal actor (ego) and
his/her alters rather than on all of the relationships in a system (i.e., whole network data). In
contrast, a whole network provides a structural map of the entire social ecosystem, and
allows models that include the sorts of network-state effects described earlier.
In recent years, whole network studies have opened a new level of insight into the social
dynamics of substance use, especially among youth (e.g. Veenstra, Dijkstra, Steglich & Van
Zalk, 2013), but also in adult populations (Cruz, Emery & Turkheimer, 2012). The whole
network approach has been less commonly applied to treatment and recovery issues,
however, in part because recovering individuals typically are not members of the same
whole network. Residential recovery houses are an important exception. Recovering
individuals may reside in such houses for periods ranging from a few weeks to many
months, and is natural to suspect that the social dynamics within these houses may play an
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important role in residents’ willingness to remain in these supportive environments, and the
level of abstinence-maintaining personal change they are thus able to achieve.
Substance Use Disorders and Networks
People in recovery from substance use disorders face many obstacles to maintaining
abstinence (Montgomery, Miller, & Tonigan, 1993). For example, many people who finish
substance use treatment relapse over time (Richman & Neumann, 1984; Vaillant, 2003), and
this might be due to the lack of longer-term community-based housing and employment
support (Jason, Olson, & Foli, 2008). A number of self-help organizations provide support
to individuals following treatment, including Alcoholics Anonymous (AA), but such
programs do not provide needed safe and affordable housing, or access to employment. For
these needs, a variety of professionally-run and resident-run residential programs is
available in the US (Polcin, Korcha, Bond, Galloway, & Lapp, 2010). Although such
recovery programs are important sources of housing and employment support, they do not
work for everyone (Longabaugh, Wirtz, Beattie, Noel, & Stout, 1995; Moos & Moos, 2006;
Zywiak, Longabaugh, & Wirtz, 2002). For instance, early dropout from recovery homes
often occurs due to a new resident’s failure to become integrated into the house social
ecology (Moos, 1994; Vaillant, 1983). The dynamics of social integration in recovery
houses may be studied by conceptualzing them as social networks that evolve based on both
structural tendencies and network members’ characteristics.
Research on sobriety-focused social support suggests two mechanisms whereby
participation in self-help groups such as AA may promote more recovery-supportive
personal friendship networks: as a source for recovery-supportive friends, and also a source
of behavioral modeling, advice, and encouragement for staying sober (Kaskutas et al.,
2002). Positive effects of personal network composition have also been associated with
recovery house stays, though the mechanisms underlying this relationship are unknown
(Polcin et al., 2010). Nevertheless, it is plausible that a recovery house stay benefits
residents in the same way as AA involvement, in being a source for alternative friendships,
modeling, advice, and support. Thus, predictors of strong within-house relationships would
be important to investigate. Relevant relationships would be those that promote discussion
of recovery-threatening topics like negative feelings such as stress, anxiety, and loneliness.
Such relationships, which could be called “confidants,” are also important as a source of
interactive problem-solving that are less likely in 12-step meetings.
Regarding formation of confidant relationships, studies have found that trust is a critical
precursor of close relationships (Bonaventura et al., 2006; Horst & Coffé, 2012). Trust tends
to develop in groups in part as a function of time and interindividual exposure (Patulny,
2011), especially when the individuals in the group are dependent on each other for desired
outcomes (Schachter, 1951). Recovery homes might promote interdependence through the
house self-governance structure, as well as norms of mutual support for recovery. Thus, we
hypothesize that if a house member trusts a fellow member, he or she is more likely to
confide in that individual. Moreover, we hypothesize that once a confidant relationship
forms, trust is more likely to be maintained; that is, trust and confidant relationships should
mutually reinforce each other in a positive feedback loop.
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If trust is typically a precondition for a confidant relationship to form, then we would expect
trust to mediate the effects of other predictors of confidant relationships. For instance, 12-
step activities--attending self-help group meetings, reading literature, etc.--are behaviors
readily observable by other house members. Since the recoveries of all members are
threatened by anyone who does not engage in 12-step activities, because such an individual
may appear to not support the norms of recovery (Malloy, 1988), we hypothesize that
residents who exemplify active, behavioral participation in the shared goal of recovery will
be more trusted by others. We also hypothesize that longer-time residents are more likely to
be trusted, due to their experience and demonstrated commitment to recovery.
Summarizing, our objective is to test a simple model of “social integration” dynamics—
formation of trust and confidant relationships—in a sample of recovery houses, as a function
of individuals’ range of 12-step activities and time in residence. Understanding this process
may be a key to explaining the apparent therapeutic value of recovery house residence for
some individuals, and extending it to a broader population.
Method
We collected baseline and 3-month follow-up data on five Oxford House recovery houses
with 31 participants. House size averaged 6.2 residents, somewhat fewer than the national
average (Jason, Davis, Ferrari, & Anderson, 2007). There were four men’s and one women’s
Oxford House that participated in this study, a total of 5 women (16%) and 26 men (84%).
We were able to successfully recruit nearly all house members (only 2 out of 33 did not
participate) and we could follow all but four at a three month follow-up. The present study
excluded 3 individuals who were not present in their house for both waves, bringing the
analysis sample to 28. Each interview was conducted by phone. Any individuals living at a
recruited house at the time the study commenced or entering the participating Oxford House
prior to the second and final assessment were enrolled in the study if they provided informed
consent. Consent was obtained verbally over the phone on an individual basis. No monetary
incentives were used to secure participants’ involvement in the study. Although Oxford
House programs are self-governed, all Oxford Houses operate with similar rules, and these
standardized procedures help limit extraneous variance that might otherwise be present
(Jason et al., 2008).
Measures
The baseline assessment obtained participant’s sex, age, race, length of residency, length of
current sobriety, and referral method to Oxford House.
The Alcoholics Anonymous Affiliation instrument (Humphreys, Kaskutas, & Weisner,
1998) was used to measure 12-step behaviors. It consists of 9 items and operates as a
strength index of an individual’s affiliation with a mutual, self-help group, designed
originally for use with AA members. Items include, for instance, “Do you now have an AA
sponsor?” and “Have you ever called an AA member for help?” The scale is unidimensional
and reliable (current sample alpha = .82).
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Confidant and trust relationships were obtained from what was intended to be a multi-item
measure of “relationship closeness” developed by our research team. The original
instrument (alpha = .81) asked house residents to rate their relationships with fellow
residents on several dimensions including trust and “type of relationship” on 5-point scales.
Trust was measured in terms of willingness to loan “alter” different amounts of money. For
nearly all house residents, money is scarce, and “pulling one’s own weight” is a major norm
of Oxford House residence. Willingness to loan money is thus a plausible indicator of trust
in this context. Amounts that could be selected were $0, $10, $20, $50, and $100. Given the
goal of the present analysis to examine temporal dynamics of trust, we dichotomized trust
into willingness to lend $100 to alter (scored as 1) vs. willingness to lend less than that
(scored as 0). In other words, willingness to loan money served as the indicator of trust in
the house context. For “type of relationship”, the categories were confidant, friend,
acquaintance, stranger, or adversary (from most to least close). Relationship types were
classified into dichotomous ratings of “confidant” (scored as 1) vs. any less-close
relationship than confidant (which would include friend, acquaintance, stranger, or
adversary) (scored as 0). We interpreted a rating of confidant as signifying a very close
friendship. About a quarter of potential confidant and trust dyads were scored as 1’s at each
wave, and these relationships are shown by house in Figures 1 and 2.
These single-item-based relationship designations are typical in social network research, in
part because ego’s perception of relationships with alters is considered important, and also
in part to reduce what could be a sizeable response burden if multiple-item ratings had to be
applied to all potential alters (Marsden, 2011). Social network relationships that use ordinal
ratings, as in the current study, have been found to be reliable (Hlebec & Ferligoj, 2002).
Statistical Analyses
A changing house relationship structure may be described at the micro-level in terms of a set
of interdependent network tie changes, that is, dyadic and directed (not necessarily
symmetrical) relationships represented by a changing “multiplex network”, defined as
network linkages of several types among the same individuals. Individual house networks
may be pooled into a single longitudinal network where linkages are constrained to occur
only within houses, provided that the basic dynamics—possibly conditional on measured
covariates—are similar (Ripley, Snijders, & Preciado, 2013). Thus, the individuals in all
houses in the study are thought of as the same population, and as subject to the same social
dynamics, conditional on covariates and initial relationship structures. These assumptions
can be represented with the stochastic actor-based model and estimated using the R package
RSiena (Ripley et al., 2013).
Estimation was carried out using maximum likelihood with a multiplication factor (affecting
the number of simulations) of 50. The model we developed was a multiplex network model,
involving two networks: trust and confidant relationships. Each of these relationships served
as a predictor for the other, to test for a reciprocal relationship. Additionally it is important
to control for so-called “endogenous” network effects (i.e. structural tendency effects) in
these models, as they may otherwise be confounded with substantive predictor effects
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(Steglich, Sinclair, Holliday & Moore, 2012). We specifically included outdegree,
reciprocity, transitivity.
1
As for most models iteratively estimated from a small data set, including all these effects
simultaneously led to convergence issues. The recommended strategy in this cases (Ripley et
al., 2013) is to start with basic endogenous network effects and build up the model, retaining
significant effects and, to the extent possible, insignificant effects as well, provided model
convergence statistics (described later) are still acceptable. Some non-significant effects
could not be retained in our final model for these reasons; they are listed at the bottom of
Table 2.
Results
Figures 1 and 2 show the network linkages for trust and confidant relationships,
respectively, at baseline (Wave 1) and 3-month follow-up (Wave 2). Relationship
designations are directed, with arrows showing direction of choice. Figure 2 shows that a
confidant relationship is usually asymmetrical, confirming its low reciprocity. Of the 28
Wave 1 dyadic confidant links among participants, only four (14%) are symmetrical, shown
by looped pairs of arrows; at Wave 2, three out of 34 are symmetrical (9%). This structure
suggests that a confidant relationship has an element of role specialization to it, where one
confides, the other listens, and not vice-versa. Note that confidant relationships could still
have asymmetrical reciprocity (the strength of the relationship is not equally rated by both
members of the dyad), which would not be captured in this dichotomized measure, but this
level of detail could not be investigated in a small study. On the other hand, trust
relationships were more often symmetric, with 14 of 37 (38%) dyadic linkages mutual at
wave 1, and 16 of 46 (35%) mutual at wave 2. Reciprocity rates for both confidant and trust
relationships varied both over time and across houses, indicating both longitudinal and
cross-sectional variation.
Descriptive statistics by house are shown in Table 1. Network statistics varied somewhat
across houses, but also between waves, indicating a significant amount of change in
relationships over just a 3-month period. Reciprocity was low for confidant relationships,
actually zero for half the observed wave-house combinations. These descriptive results
suggested patterns that might be modeled, so we proceeded to estimate a stochastic actor-
based model with RSiena. Results of the final model are shown in Table 2. Convergence is
measured by t-ratios of simulated compared to observed statistics for each predictor. A value
1
Outdegree captures the probability of ego adding one more tie given how many he/she has already, and it is usually negative for
friendships, meaning that new friendships become less likely the more ego has. According to exchange theory (e.g. Blau, 1964),
reciprocity is predicted in non-hierarchical relationships like close friendships, which we assumed characterized confidant and trust
relationships. Transitivity, meanwhile, often serves as a stand-in for either physical or social proximity effects, which affect the
opportunity to form ties with others. Recovery houses probably provide very similar physical interaction opportunities for all possible
dyads, but social proximity may nevertheless be affected by residents’ preferences for similar recovery-related or leisure activities,
similar schedules (e.g. “night owls” vs. early risers), and so on.
Also ego, alter, and similarity effects of 12-step activities and time in residence were examined as potential predictors of both trust and
confidant ties. An ego effect captures the effect of an individual characteristic X on the number of ties ego tries to create; for example,
newer residents may initially attempt to form more ties than longer-time residents. An alter effect is the reverse: it captures the effect
of and alter being chosen, depending on X. Similarity, as the name suggests, indicates whether the choosing individual is more likely
to direct a relationship to another if he or she is more similar on X, whereas “higher X” captures tendencies to select alters for whom
ego has a higher value on X.
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of .10 or less is preferred for each effect, and it is evident that this criterion was met. Also,
the maximum autocorrelation of successive simulation effect statistics, measuring the
independence of successive simulations, was .22, within the recommended upper limit of
0.30 (Ripley et al., 2013). Thus, even with a small data set, we found we could estimate a
simple, reasonable-quality stochastic actor-based model in RSiena.
We hypothesized that if one individual trusts another, there is a greater probability the other
will be named as a confidant, and vice-versa. The parameter showing the effect of trust on
confidant is 2.52 (p <.01), but the converse effect of confidant on trust was not found (not
shown). Trust was not predicted by alter’s 12-step activity, but we did find a significant
positive ”12-step ego” effect (4.48, p<.01), implying that individuals who engaged in more
12-step activity were generally more likely to form trust relationships.
We also found weak evidence that length of house residence made an individual more likely
to be trusted (“house time alter”), but when we substituted an effect of relative house time
for ego compared to alter (“higher house time ego v. alter”), the effect was substantial and
significant (−3.21, p=.05), corresponding to a 25-fold increase in the odds of ego trusting
alter per month of additional alter house time, compared to ego.
In addition, because of the relative novelty of this type of analysis, we examined variations
of hypothesized effects, on an exploratory basis. These are listed at the bottom of Table 2.
None was statistically significant, but including any in the final model led to convergence
issues. Given the limited sample, we express no certainty that any of these effects are null; a
larger study will be needed to draw such conclusions.
Discussion
Moos (2008) has described the hypothesized “active ingredients” present in successful
recovery house programs—the presumed causal mechanisms—based upon the well-
established theoretical traditions of social control, social learning (Bandura, 1971),
behavioral economics (Bickel & Vuchinich, 2000), and stress/coping theory (Kaplan, 1996).
Although perhaps the clearest attempt to link recovery house social processes with
outcomes, Moos’ framework says very little about the group processes thought to generate
successful outcomes. Our study’s general approach and findings thus bring a social
dynamics perspective to Moos’ framework.
Two of our hypothesized dynamic mechanisms were confirmed. First, trust predicts
formation of confidant relationships: the odds of ego establishing a confidant relationship
with alter are over 11 times larger (exp (2.52)) when ego trusts alter. However, confidant
relationships do not predict greater trust, suggesting that trust precedes formation of
confidant relationships. If having a confidant should turn out to be supportive of recovery--
not investigated here but consistent with other studies, e.g. Kelly and Urbanoski (2012),
Rynes and Tonigan, (2012)-- such a finding would focus attention on the importance trust-
formation processes in recovery house settings. In that regard, we found a pattern of newer
residents coming to trust longer-time residents. This finding suggests that one feature of a
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successful recovery house may be a mix of residents in which new arrivals are balanced by
longer-tenured veterans. .
Our thinking about confidants had originally conceived this relationship from the
perspective of the social support literature, as something like a very close friendship, which
would in most cases be a source of mutual support (Carley & Krackhardt, 1996). We did
indeed find that confidant relationships, like friendships, tended to be relatively exclusive, in
that the more alters nominated by ego, the increasingly less likely they were to nominate
another alter. Combined with the substantial (though not significant at a .05 level) anti-
reciprocity effect, it follows that a single nonreciprocated confidant is typical, since the odds
of a second confidant are only 10% the size of the first, and a third is just 1% of the first
(exp(−2.2) ≈ 0.10). This was further reflected in the low incidence of reciprocated confidant
relationships. Such evidence is consistent not so much with friendship as it is with hierarchy.
It appears that longer-time residents (or those possessing other residence tenure-related
attributes, e.g. length of abstinence, perceived stability, experience, and knowledge) hold the
higher-status positions, possibly supplying mentoring and support for recovery in exchange
for respect and gratitude. According to social exchange theory (e.g. Blau, 1964), the
asymmetric exchange of dissimilar goods or services is characteristic of hierarchical social
relationships.
The question of the role of true and symmetrical friendships vs. the apparently hierarchical
confidant-like relationships in supporting substance abuse recovery remains. It is doubtful
that our sample was large and diverse enough to tease apart separate dynamics of these
relationships, but probably both exist; e.g. some of the confidant relationships were mutual,
but not enough to establish a clear picture of what predicts their formation. This distinction
could be investigated in a larger study. Moreover, although use of multi-item instruments is
not typical in social network studies (Marsden, 2011), future studies will be more clearly
interpretable if such instruments can be developed and fielded.
The model also suggested several trust-related dynamic mechanisms. The positive ego effect
of 12-Step activity means that individuals with higher levels of 12-Step activity are more
likely to form trust relationships. However, level of 12-Step activity had no effect on the
trustworthiness of alters, nor was there any tendency for ego-alter similarity on level of 12-
Step activity as a basis for trust (not shown). Furthermore, the negative higher house time
effect indicates that newer residents are more likely to trust longer-time residents. Taken
together, these results suggest that newer members are most likely to trust other residents
based on their tenure, rather than their behavioral investment in 12-step activities.
Further insight into the nature of trust relationships in a recovery house context is gained
from endogenous network effects. First, the marginally positive outdegree effect for trust
indicates that trust relationships are definitely not exclusive, and there may even be a
tendency for trusting others to “cascade”, in that doing so becomes easier the more others
the individual already trusts. Confidants (and, as we know from other studies, friendship)
carry with them an obligation to commit scarce resources (time and energy) to maintain
them. Trust does not, although a willingness to trust seems to grow as a result of initial
experiences. Interestingly and a bit surprisingly, trust is not typically reciprocal, however,
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which could indicate that trust develops from ego’s experiences with alter, rather than as a
reaction to alter’s opinion of (willingness to trust) ego.
The major weakness of this study is its small size and short duration. Indeed, our data
collection was intended as more a proof of concept from which we might also glean a few
useful descriptive facts, for example, whether social dynamics in recovery houses actually
vary over time in a way that suggests they could be modeled. Results are suggestive and
promising, but should be interpreted cautiously. The very simple model presented here could
not control for many alternatives. For instance, our inability to separately estimate
relationship change rates and number of out-choices by house, despite evident heterogeneity
in these statistics, suggests that some of the effects we are interpreting as within-house
change mechanisms could actually be more about between-house differences. This
ecological correlation problem (cf. Snijders & Bosker, 2012) could be overcome simply
with more data.
Another side effect of study design was our inability to determine the key predictors of the
outcomes of ultimate interest: likelihood of relapse, re-establishment of work and family
relationships, and improved mental health, etc. Had they been measured, the 3-month time
window and small sample would have limited the likelihood of any significant findings.
Nevertheless, components of the house dynamic social network system may predict
important proximal and distal recovery-related outcomes. This theoretical expectation rests
on studies that have linked social support, self-help group attitudes (e.g., spirituality,
personal responsibility), behaviors (e.g., having a sponsor, being a sponsor, reading
literature) with successful abstinence. For example, Jason, Stevens, Ferrari, Thompson, and
Legler (2012) found that early friendships in an Oxford House reduce the likelihood of
future relapse. Additionally, there is reason to expect that these recovery house constructs
will positively affect mediators of abstinence, such as a minimal 6-month stay, a more
supportive personal network outside the house, and recovery-specific self-efficacy. Stevens,
Jason, and Ferrari (2011) found a significant relationship between in-house sense of
community and likelihood of leaving, and a minimum six-month Oxford House stay is
related to increased self-efficacy and continued abstinence. DiClemente, Fairhurst, and
Piotrowski (1995) also found that efficacy expectations stabilize after six months of
abstinence. In this study, we primarily found evidence of individuals selecting others for
support, but many questions remain. Does success at establishing these apparently
asymmetrical relationships suffice to improve recovery prospects? Is there a value-added
effect of being chosen as a source of support, instead or in addition? And what about
symmetrical friendship relationships—are they important? More generally, as suggested
above, there is much yet to be learned about which dynamics lead to positive, supportive,
and protective house social environments, and who fits in well, and who has difficulty, and
why.
Inspired by Moos (2007) and Vaillant (2003), our perspective in this investigation was trans-
theoretical. We suggest that recovery from addiction can be advantageously viewed as a
transactional process of interaction between the individual and his or her social environment.
More specifically, the effectiveness of a recovery house stay in promoting recovery can be
viewed in a similarly transactional framework. This type of process is clearly a complex
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system, involving a variety of mutually interacting variables. Moos and Vaillant proposed a
variety of individually simple subprocesses that are likely to explain the beneficial effects of
self-help group involvement, most of which would extend to recovery house residence, that
is, social bonding, monitoring, goal direction, modeling, positive reinforcement, rewarding
alternatives to using, and advice and outlets for dealing with negative emotions and stress.
Such complex, transactional systems are difficult to model, and all too often, simplified
representations adopted for the sake of tractability end up omitting the very mechanisms of
interest; thus, for example, a regression model that shows a correlation between two
variables over time ignores the fact that such a correlation could come about through many
different mechanisms. All models are simplifications, of course, but the dynamic network
approach modeled by stochastic actor- based model represents a clear advance, in our view,
in regards both to accurately representing the transactional system of interest, as well as
providing a tractable link to data.
In summary, our study takes a unique approach to understanding recovery house social
dynamics that may support abstinence and mediators of abstinence. The social network
design and accompanying stochastic actor-based model analysis enabled us to measure
house-specific multiplex networks, and pool data across houses to model relationship
dynamics as longitudinal network development These relationships could ultimately be
predictors of key recovery outcomes. Though this is a preliminary study with a relatively
small sample, it suggests that a dynamic network framework is a promising approach. In
larger studies, it could help answer long-standing questions of how and why community-
based recovery houses support sobriety, perhaps moving them more into the mainstream of
substance use disorders treatment protocols.
Acknowledgments
The first author appreciates the financial support from the National Institute on Alcohol Abuse and Alcoholism
(NIAAA grant numbers AA12218 and AA16973), the National Institute on Drug Abuse (NIDA grant numbers
DA13231 and DA19935), and the National Center on Minority Health and Health Disparities (grant MD002748).
Dr. Light was supported by grant number HD052887 from the National Institute for Child Health and
Development.
References
Bandura, A. Social learning theory. New York, NY: General Learning Press; 1971.
Bickel, WK.; Vuchinich, RE., editors. Reframing health behavior change with behavior economics.
Mahwah, NJ: Erlbaum; 2000.
Blau, PM. Exchange and Power in Social Life. New York: Wiley; 1964.
Bogat, GA.; Zarrett, N.; Peck, SC.; von Eye, A. The person-oriented approach and community
research. In: Jason, LA.; Glenwick, DS., editors. Methodological approaches to community-based
research. Washington, DC: American Psychological Association; 2012. p. 89-110.
Bonaventura M, Ames K, Brumpton R, Garratt R, Hall K, Wilson N. Human friendship favours
cooperation in the Iterated Prisoner's Dilemma. Behaviour. 2006; 143:1383–1395.
Bronfenbrenner, U. The ecology of human development. Cambridge, MA: Harvard University Press;
1979.
Butts, CT. sna: Tools for social network analysis. R package version 2.2-0 . 2010. Retrieved from
http://CRAN.R-project.org/package=sna
Carley KM, Krackhardt D. Cognitive inconsistencies and non-symmetric friendship. Social Networks.
1996; 18:1–27. http://dx.doi.org/10.1016/0378-8733(95)00252-9.
Jason et al.
Page 10
Am J Community Psychol. Author manuscript; available in PMC 2015 June 01.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
Cowles M, Davis C. On the origins of the .05 level of statistical significance. American Psychologist.
1982; 37:553–558.
Cruz JE, Emery RE, Turkheimer E. Peer network drinking predicts increased alcohol use from
adolescence to early adulthood after controlling for genetic and shared environmental selection.
Developmental Psychology. 2012; 48(5):1390–1402. [PubMed: 22390657]
Davey MA, Latkin CA, Hua W, Tobin KE, Strathdee S. Individual and social network factors that
predict entry to drug treatment. American Journal on Addictions. 2007; 16(1):38–45. http://
dx.doi.org/10.1080/10601330601080057. [PubMed: 17364420]
DiClemente, CC.; Fairhurst, S.; Piotrowski, N. The role of self-efficacy in the addictive behaviors. In:
Maddux, J., editor. Self-efficacy, adaption and adjustment: Theory, research and application. New
York, NY: Plenum Press; 1995. p. 109-141.
Hlebec V, Ferligoj A. Reliability of social network measurement instruments. Field Methods. 2002;
14(3):288–306.
Humphreys K, Mankowski ES, Moos RH, Finney JW. Do enhanced friendship networks and active
coping mediate the effect of self-help groups on substance abuse? Annals of Behavioral Medicine.
1999; 21:54–60. http://dx.doi.org/10.1007/BF02895034. [PubMed: 18425655]
Humphreys K, Kaskutas LA, Weisner C. The Alcoholics Anonymous Affiliation Scale: Development,
reliability, and norms for diverse treated and untreated populations. Alcoholism: Clinical and
Experimental Research. 1998; 22:974–978. http://dx.doi.org/10.1111/j.1530-0277.1998.tb03691.x.
Humphreys K, Noke J. The influence of post-treatment mutual help group participation on the
friendship networks of substance abuse patients. American Journal of Community Psychology.
1997; 25(1):1–16. [PubMed: 9231993]
Horst M, Coffé H. How friendship network characteristics influence subjective well-being. Social
Indicators Research. 2012; 107:509–529. [PubMed: 22707845]
Jason LA, Davis MI, Ferrari JR, Anderson E. The need for substance abuse after-care: Longitudinal
analysis of Oxford House. Addictive Behaviors. 2007; 32:803–818. http://dx.doi.org/10.1016/
j.addbeh.2006.06.014. [PubMed: 16843612]
Jason, LA.; Glenwick, DS. Introduction: An overview of methodological innovations in community
research. In: Jason, LA.; Glenwick, DS., editors. Methodological approaches to community-based
research. Washington, DC: American Psychological Association; 2012. p. 3-10.
Jason LA, Olson BD, Ferrari JR, Majer JM, Alvarez J, Stout J. An examination of main and interactive
effects of substance abuse recovery housing on multiple indicators of adjustment. Addiction. 2007;
102:1114–1121. http://dx.doi.org/10.1111/j.1360-0443.2007.01846.x. [PubMed: 17567399]
Jason, LA.; Olson, BD.; Foli, K. Rescued lives: The Oxford House approach to substance abuse. New
York, NY: Routledge; 2008.
Jason LA, Stevens E, Ferrari JR, Thompson E, Legler R. Social networks among residents in recovery
homes. Advances in Psychology Study. 2012; 1:4–12. [PubMed: 23956954]
Kaplan, HB. Psychosocial stress from the perspective of self theory. In: Kaplan, HB., editor.
Psychosocial stress: Perspectives on structure, theory, life-course and methods. San Diego, CA:
Academic Press; 1996. p. 175-244.
Kaskutas LA, Bond J, Humphreys K. Social networks as mediators of the effect of Alcoholics
Anonymous. Addiction. 2002; 97:891–900. http://dx.doi.org/10.1046/j.1360-0443.2002.00118.x.
[PubMed: 12133128]
Kelly, JG. Becoming ecological: An expedition into community psychology. New York, NY: Oxford
University Press; 2006.
Kelly JF, Stout RL, Magill M, Tonigan JS, Pagano ME. Mechanisms of behavior change in Alcoholics
Anonymous: Does AA lead to better alcohol use outcomes by reducing depression symptoms?
Addiction. 2010; 105:626–636. doi:10.1111/j.1360-0443.2009.02820.x. [PubMed: 20102345]
Kelly JF, Urbanoski K. Youth recovery contexts: The incremental effects of 12-step attendance and
involvement on adolescent outpatient outcomes. Alcoholism: Clinical and Experimental Research.
2012; 36(7):1219–1229.
Knecht AB, Burk WJ, Weesie J, Steglich CEG. Friendship and alcohol use in early adolescence: A
multilevel social network approach. Journal of Research on Adolescence. 2012; 21:475–487.
Jason et al.
Page 11
Am J Community Psychol. Author manuscript; available in PMC 2015 June 01.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
Liggett, TM. Continuous time Markov processes: An introduction. Providence, RI: American
Mathematical Society; 2010.
Longabaugh R, Wirtz PW, Beattie MC, Noel N, Stout R. Matching treatment focus to patient social
investment and support: 18-month follow-up results. Journal of Consulting and Clinical
Psychology. 1995; 63:296–307. http://dx.doi.org/10.1037/0022-006X.63.2.296. [PubMed:
7751491]
Magnusson, D. Individual development from an interactional perspective. Hillsdale, NJ: Lawrence
Erlbaum; 1987.
Majer JM, Jason LA, Aase DM, Droege JR, Ferrari JR. Categorical 12-step involvement and
continuous abstinence at two years. Journal of Substance Abuse Treatment. 2013; 44:46–51.
[PubMed: 22498012]
Majer JM, Jason LA, Ferrari JR, Miller SA. Twelve-step involvement among a U.S. national sample of
Oxford House residents. Journal of Substance Abuse Treatment. 2011; 41:37–44. [PubMed:
21353447]
Malloy, JP. Oxford House Manual 2. Silver Spring, MD: Oxford House, Inc; 1988.
Marsden, PV. Survey Methods for Network Data. In: Scott, J.; Carrington, PJ., editors. The SAGE
Handbook of Social Network Analysis. Thousand Oaks, CA: Sage; 2011. p. 370-388.
Montgomery HA, Miller WR, Tonigan JS. Differences among AA groups: Implications for research.
Journal of Studies on Alcohol. 1993; 54:502–504. [PubMed: 8341051]
Moos, RH. The Social Climate Scales: A user's guide. 3rd ed.. Palo Alto, CA: Mind Garden, Inc.;
1994.
Moos RH. Active ingredients of substance use-focused self-help groups. Addiction. 2007; 103:387–
396. http://dx.doi.org/10.1111/j.1360-0443.2007.02111.x. [PubMed: 18269361]
Moos RH, Moos BS. Rates and predictors of relapse after natural and treated remission from alcohol
use disorders. Addiction. 2006; 101:212–222. http://dx.doi.org/10.1111/j.
1360-0443.2006.01310.x. [PubMed: 16445550]
Morgenstern J, Labouvie E, McCrady BS, Kahler CW, Frey RM. Affiliation with Alcoholics
Anonymous after treatment: a study of its therapeutic effects and mechanisms of action. Journal of
Consulting and Clinical Psychology. 1997; 65(5):768–777. [PubMed: 9337496]
NIAAA. 2012 Congressional Budget Justification. 2011. See http://www.niaaa.nih.gov/
ABOUTNIAAA/CONGRESSIONALINFORMATION/BUDGET/Pages/FY12CJ.aspx
Patulny R. Social trust, social partner time and television time. Social Indicators Research. 2011;
101:289–293.
Polcin DL, Korcha R, Bond J, Galloway G, Lapp W. Recovery from addiction in two types of sober
living houses: 12-month outcomes. Addiction Research and Theory. 2010; 18:442–455. http://
dx.doi.org/10.3109/16066350903398460.
Richman A, Neumann B. Breaking the 'detox loop' for alcoholics with social detoxification. Drug and
Alcohol Dependence. 1984; 13:65–73. http://dx.doi.org/10.1016/0376-8716(84)90033-4.
[PubMed: 6428845]
Ripley, RM.; Snijders, TAB.; Preciado, P. Manual for RSiena. Oxford, UK: Department of Statistics,
Nuffield College; 2013.
Rynes KN, Tonigan JS. Do social networks explain 12-step sponsorship effects? A prospective lagged
mediation analysis. Psychology of Addictive Behaviors. 2012; 26(3):432–439. [PubMed:
21895349]
Schachter S. Deviation, rejection, and communication. Journal of Abnormal and Social Psychology.
1951; 46:190–207. http://dx.doi.org/10.1037/h0062326.
Snijders TAB. The statistical evaluation of social network dynamics. Sociological Methodology. 2001;
31:361–395. http://dx.doi.org/10.1111/0081-1750.00099.
Snijders, TAB. Models for longitudinal network data. In: Carrington, P.; Scott, J.; Wasserman, S.,
editors. Models and methods in social network analysis. New York, NY: Cambridge University
Press; 2005. p. 215-247.
Snijders TAB, Baerveldt CA. A multilevel network study of the effects of delinquent behavior on
friendship evolution. Journal of Mathematical Sociology. 2003; 27:123–151. http://dx.doi.org/
10.1080/00222500305892.
Jason et al.
Page 12
Am J Community Psychol. Author manuscript; available in PMC 2015 June 01.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
Snijders, TAB.; Bosker, RJ. Multilevel analysis: An introduction to basic and advanced multilevel
modeling. 2nd ed. Thousand Oaks, CA: Sage; 2012.
Snijders TAB, van de Bunt GG, Steglich CEG. Introduction to stochastic actor-based models for
network dynamics. Social Networks. 2010; 32:44–60. http://dx.doi.org/10.1016/j.socnet.
2009.02.004.
Snijders, TAB.; Steglich, C.; Schweinberger, M. Modeling the co-evolution of networks and behavior.
In: van Monfort, K.; Oud, H.; Satorra, A., editors. Longitudinal models in the behavioral and
related sciences. Mahwah, NJ: Lawrence Earlbaum; 2007. p. 41-71.
Steglich C, Sinclair P, Holliday J, Moore L. Actor-based analysis of peer influence in a Stop Smoking
In Schools Trial (ASSIST). Social Networks. 2012; 34:359–369.
Steglich CEG, Snijders TAB, Pearson M. Dynamic networks and behavior: Separating selection from
influence. Sociological Methodology. 2010; 40:329–392. http://dx.doi.org/10.1111/j.
1467-9531.2010.01225.x.
Stevens EB, Jason LA, Ferrari JR. Measurement performance of the Sense of Community Index in
substance abuse recovery communal housing. Australian Community Psychologist. 2011; 23:135–
147.
Todd, NR.; Allen, NE.; Javdani, S. Multilevel modeling: Method and application for community-based
research. In: Jason, LA.; Glenwick, DS., editors. Methodological approaches to community-based
research. Washington, DC: American Psychological Association; 2012. p. 167-186.
Vaillant, GE. The natural history of alcoholism. Cambridge, MA: Harvard University Press; 1983.
Vaillant GE. A 60-year follow-up of alcoholic men. Addiction. 2003; 98:1043–1051. http://dx.doi.org/
10.1046/j.1360-0443.2003.00422.x. [PubMed: 12873238]
Vaillant GE. Alcoholics anonymous: Cult or cure? Australian and New Zealand Journal of Psychiatry.
2005; 39:431–436. [PubMed: 15943643]
Veenstra R, Dijkstra JK, Steglich C, Van Zalk MHW. Network–behavior dynamics. Journal of
Research on Adolescence. 2103; 23(3):399–412.
Zywiak WH, Longabaugh R, Wirtz PW. Decomposing the relationships between pretreatment social
network characteristics and alcohol treatment outcome. Journal of Studies on Alcohol. 2002;
63(1):114–121. [PubMed: 11925053]
Jason et al. Page 13
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Figure 1.
Trust ($100) Relationshipsa
a. Includes 28 residents who participated in both waves
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Figure 2.
Confidant Relationshipsa
a. Includes 28 residents who participated in both waves
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Table 1
Descriptive Statistics of Recovery House Networks by Wave
House: 1 2 3 4 5
Wave: 1 2 1 2 1 2 1 2 1 2
Number of Residents
a
4 5 6 7 5
Percent female 100% 0% 0% 0% 0%
Mean Age 46.5 53.0 47.8 46.4 47.8
Percent non-White 100% 100% 0% 0% 0%
Percent HS Grad/GED 75% 80% 67% 86% 100%
Confidant Outdegree
d
1.00 1.25 0.60 1.00 0.33 1.33 1.14 1.71 2.50 1.67
Confidant Reciprocity 0.33 0.25 0.00 0.00 0.00 0.14 0.00 0.09 0.25 0.00
Confidant Transitivity
c
1.00 1.00 0.00 1.00 0.00 0.43 1.00 0.78 0.83 0.50
Trust $100 Outdegree
d
2.00 2.00 0.20 1.00 2.33 2.17 3.43 4.00 0.67 1.33
Trust $100 Reciprocity 0.33 0.33 0.00 0.00 0.27 0.18 0.50 0.56 0.33 0.33
Trust $100 Transitivity
c
0.70 0.70 0.00 0.00 0.74 0.88 0.78 0.93 0.00 0.13
a
Includes residents present for both waves
b
Data are provided for residents who completed both waves of the study (n=27).
c
Proportion of “weakly transitive” triads, i.e. triads {i,j,k} such that if i->j and j->k, then i->k. Defined to be zero when there are no triads {i,j,k} such that i->j and j->k.
d
Average outdegrees (out-choices) per participant
e
Outdegree, reciprocity, and transitivity were calculated using the R package ‘sna’ (Butts, 2010), v. 2.2-0.
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Table 2
Stochastic Actor-Based Model Results
Parameter
Estimate
SE p-Value
95%
Confidence
Interval
Convergence
t-ratio
a
Final Model
Trust $100: Rate
1.71 0.35
<.01
(1.0,2.4) 0.01
Trust $100: Outdegree 1.49 1.14 .19 (−0.7,3.7) 0.02
Trust $100: Reciprocity −0.40 0.86 .64 (−2.1,1.3) 0.00
Trust $100: Transitivity −0.10 0.30 .74 (−0.7,0.5) 0.04
Trust $100: Higher house Time vs. Alter −3.21 1.65
.05
(−6.4,0.0) 0.01
Trust $100: 12-Step Ego 4.48 1.49
<.01
(1.6,7.4) 0.06
Confidant: Rate 2.32 0.61
<.01
(1.1,3.5) 0.02
Confidant: Outdegree −1.49 0.64
.02
(−2.7,−0.2) 0.01
Confidant: Reciprocity −0.92 0.69 .18 (−2.3, 0.4) 0.07
Confidant: Transitivity 0.18 0.44 .68 (−0.7,1.0) 0.05
Confidant: 12-Step Ego −0.27 0.18 .13 (−0.6,0.1) 0.03
Confidant: Trust $100 2.52 0.87
<.01
(0.8,4.2) 0.07
Other non-significant effects
b
Trust $100:12-Step alter, Trust $100:12-Step similarity, Trust $100:12-step similarity×reciprocity,
Trust $100:house time alter, Trust$100:house time ego, Trust $100:house time similarity,
Confidant:12-Step alter, Confidant:12-Step similarity, Confidant:house time alter, Confidant:house time ego,
Confidant:house time similarity,
a
Ratio of deviations of simulated vs. observed statistics for each effect, calculated in Phase 3 of the RSiena model estimation procedure. Conventionally, a value of less than 0.10 indicates good
convergence (Ripley et al., 2013).
b
Nonsignificant effects that could not be included in the final model without affecting model quality (t-ratios, autocorrelations, standard error estimates)
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