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The Relationship Between Adult Attachment Style and Therapeutic Alliance in
Individual Psychotherapy: A Meta-Analytic Review
Article in Psychotherapy Theory Research Practice Training · May 2011
DOI: 10.1037/a0022425 · Source: PubMed
2 authors:
Marc Diener
Joel Monroe
Long Island University
FirstHealth Moore Regional Hospital
Some of the authors of this publication are also working on these related projects:
Psychotherapy Process Research View project
All content following this page was uploaded by Joel Monroe on 01 April 2016.
The user has requested enhancement of the downloaded file.
2011, Vol. 48, No. 3, 237–248
© 2011 American Psychological Association
0033-3204/11/$12.00 DOI: 10.1037/a0022425
The Relationship Between Adult Attachment Style and Therapeutic
Alliance in Individual Psychotherapy: A Meta-Analytic Review
Marc J. Diener and Joel M. Monroe
Argosy University
The present study examined the relationship between adult attachment style and therapeutic alliance in
individual psychotherapy. Search procedures yielded 17 independent samples (total N ⫽ 886, average
n ⫽ 52, standard deviation ⫽ 24) for inclusion in the meta-analysis. Results indicated that greater
attachment security was associated with stronger therapeutic alliances, whereas greater attachment
insecurity was associated with weaker therapeutic alliances, with an overall weighted effect size of r ⫽
.17, p ⬍ .001 (95% confidence interval ⫽ .10 –.23). Publication bias analyses did not indicate any cause
for concern regarding the results. The data were not demonstrably heterogeneous (Q ⫽ 6.10, df ⫽ 16, p ⫽
.99), and all between-study moderator analyses were nonsignificant (p values ⬎ .10) with the exception
of the source of alliance ratings; results indicated that patient-rated alliance demonstrated a significantly
larger relationship with attachment compared with therapist-rated alliance (Qbetween ⫽ 3.95, df ⫽ 1, p ⫽
.047). Implications for clinical practice and future research are discussed.
Keywords: meta-analysis, attachment, alliance, psychotherapy, psychodynamic theory
Supplemental materials:
and alliance into three categories: (a) security and alliance, (b)
avoidance and alliance, and (c) anxiety and alliance. Regarding the
relationship between self-reported adult attachment security and
alliance, Smith et al. (2010) noted that results supported a mediumsized relationship in which greater security predicted greater alliance. Smith et al. (2010) maintained that results for the relationship between self-reported adult attachment avoidance and
alliance were inconsistent, although overall findings did not support such a relationship. They also argued that research on the
relationship between self-reported adult attachment anxiety and
alliance was inconsistent, with overall results failing to support a
significant relationship.
The review by Smith et al. (2010), however, suffers from at least
four important limitations which significantly restrict the validity
of their conclusions. First, although Smith et al. (2010) report
effect sizes from several of the studies they reviewed, no attempt
was made to meta-analytically synthesize the results across studies. Smith et al. (2010) maintained that methodological differences
between the studies precluded direct comparisons between the
findings. However, this issue is an empirical rather than a theoretical one; yet, Smith et al. (2010) do not present sufficient
empirical data to justify their assertion. Second, and related to the
first issue, no moderator analyses were presented to examine the
relationship between methodological or substantive features of
individual studies and their associated effect sizes. Although Smith
et al. (2010) did consider the potential impact of several variables
(e.g., who rated the alliance), their analysis proceeded in a primarily narrative manner without comparison of the various effect sizes
using formal statistical analyses such as categorical subgroup
analyses or metaregression. Third, Smith et al. (2010) included
several studies in their review which detail results of only primary
regression analyses, despite the fact that such multivariate analyses
render the findings incompatible with other studies. Fourth, the
review by Smith et al. (2010) neglected to include at least four
The connection between individual differences in adults’ relatively enduring patterns of interaction in close, personal relationships (Hazan & Shaver, 2007) and the quality of the patient–
therapist relationship in psychotherapy has received considerable
attention in the last several decades of theoretical exposition,
clinical practice, and research (e.g., Ackerman et al., 2001;
Bowlby, 1988; Meyer & Pilkonis, 2001, 2002; Mikulincer &
Shaver, 2007; Smith, Msefti, & Golding, 2010). Nevertheless, the
Division 39 Task Force on Empirically Supported Therapy Relationships concluded that insufficient evidence existed to clearly
support the utility of tailoring the therapy relationship to patient
attachment style (Ackerman et al., 2001). In a more recent systematic review, Smith et al. (2010) divided research findings that
examined the relationship between self-reported adult attachment
This article was published Online First May 23, 2011.
Marc J. Diener and Joel M. Monroe, American School of Professional
Psychology, Argosy University.
Earlier versions of this study were presented at the annual meeting of the
Division of Psychoanalysis (39) of the American Psychological Association, New York, April 2008, and the International Association for Relationship Research, Herzliya, Israel, July, 2010. We thank Dr. Joel Weinberger for his helpful suggestions on earlier versions of this article. We are
grateful to Dr. Rosemarie Vala Stewart for her assistance in reviewing the
literature for relevant studies and to Dr. Michael Borenstein for his guidance on a number of statistical issues. Finally, we thank Drs. Diane
Arnkoff, Gillian Hardy, Dennis Kivlighan, Brent Mallinckrodt, Margaret
Parish, and Eric Sauer for providing additional information on the studies
included in the analyses.
Correspondence concerning this article should be addressed to Marc
J. Diener, PhD, American School of Professional Psychology, Clinical
Psychology Program, Argosy University, Washington, DC, 1550 Wilson
Boulevard, Suite 600, Arlington, VA 22209. E-mail: [email protected]
studies (i.e., Dolan, Arnkoff, & Glass, 1993; Hardy, Stiles,
Barkham, & Startup, 1998; Saatsi, Hardy, & Cahill, 2007; Satterfield & Lyddon, 1998), despite the fact that these studies met the
review’s eligibility criteria. In addition, at least two additional
studies (i.e., Marmarosh et al., 2009; Schiff & Levit, 2010) were
published after the deadline set by Smith et al. (2010) in their
A meta-analytic review recently conducted by Diener, Hilsenroth, and Weinberger (2009) examined the relationship between
adult attachment style and patient-reported therapeutic alliance in
individual psychotherapy as a running example in their primer on
meta-analysis of correlation coefficients. Results indicated a positive, statistically significant relationship in which greater attachment security was associated with stronger therapeutic alliance and
in which greater attachment insecurity was associated with weaker
therapeutic alliance (k ⫽ 12, N ⫽ 581, weighted average r ⫽ .17,
p ⬍ .001, 95% confidence interval ⫽ .13–.21). However, the
search for studies ended in 2007, the data analytic procedures
utilized Hunter and Schmidt’s (1990) calculations rather than the
presently more popular method of Hedges et al. (Hedges & Olkin,
1985; Hedges & Vevea, 1998), results were also limited to only
data based on patient self-reported alliance, and moderator analyses were not conducted. The present meta-analysis, therefore, aims
to fill in the gaps in the literature by presenting a more updated and
sophisticated synthesis of the research. First, however, we present
an overview of the relevant theories, concepts, and empirical
findings from the attachment and therapeutic alliance literature.
Adult Attachment Style
Bowlby’s (1969/1982) original articulation of attachment theory
focused on what he termed the “attachment behavioral system,” or
the interconnected series of thoughts, feelings, and behaviors
which serve to maintain the biologically based connection between
infant and caregiver. According to Bowlby (1969/1982), the attachment system serves an evolutionary function of protection and
survival; when frightened, tired, or ill, children seek security and
comfort from a primary caregiver. Bowlby (1988) maintained that
the ability to create and sustain intimate emotional bonds with
others is a primary characteristic of effective personality functioning and overall mental health. The classification system of secure,
anxious, and avoidant attachment put forth by Ainsworth et al.
(initially, these categories were referred to without the descriptive
labels as simply Groups B, C, and A, respectively; Ainsworth,
Blehar, Waters, & Wall, 1978; Mikulincer & Shaver, 2007)—later
expanded to include an additional category of disorganized attachment by Main and Solomon (1990)—was used to describe infant
behavior in the Strange Situation.
The concepts of attachment style were later applied in research
to adult relationships in one of the two general ways: either via use
of (a) quick, self-report measures popular in social psychology, or
(b) narrative-based measures such as the Adult Attachment Interview (AAI; George, Kaplan, & Main, 1985) often used in developmental psychology (Meyer & Pilkonis, 2001; Mikulincer &
Shaver, 2007; Steele, Steele, & Murphy, 2009). Recent metaanalytic data (Roisman et al., 2007) suggest that despite some
similar terminology in their classification systems, data from selfreport measures and the AAI have small, if any, correlations with
each other.
According to attachment theory, interactions with caregivers
over time influence the development of internal working models of
self and other (Meyer & Pilkonis, 2001; Mikulincer & Shaver,
2007). These models, in turn, influence an individual’s enduring
pattern of relationships. Bowlby (1988) outlined the essential task
of psychotherapy as facilitating the exploration and restructuring
of the patient’s attachment representations. He maintained that
these processes resulted from new understanding and relationship
experiences with the therapist. The underlying assumption that the
therapeutic relationship will, in part, reflect the patient’s attachment representations has stimulated numerous research studies.
These studies (e.g., Satterfield & Lyddon, 1995) examined the
hypotheses that secure attachment would predict a stronger therapeutic alliance, whereas insecure attachment would be associated
with a weaker alliance.
Therapeutic Alliance
The concept of the therapeutic alliance appeared in Freud’s
(1912/1958) discussion of the various types of transferences. Freud
defined transference as the template (or templates) that guides an
individual’s erotic life. He specified three aspects of this template,
namely (a) the preconditions necessary for a person to fall in love,
(b) the instincts that this person chooses to satisfy, and (c) the ways
in which this person will go about satisfying them. Freud distinguished between various types of transferences. The broadest
distinction is between the positive and negative transferences, or
between affectionate and hostile feelings, respectively. Freud then
differentiated between positive transference that is accessible to
consciousness versus positive transference that is inaccessible to
consciousness and which derives from sexual instincts. Using
these distinctions, Freud argued that resistance to treatment includes only the negative transference and the unconscious positive
transference. The conscious and positive transference, on the other
hand, constitutes a welcome and useful component of the therapeutic enterprise.
Although Sterba (1934, 1940); Fenichel (1941); Zetzel (1956);
Stone (1961); and Gitelson (1962) all took up these ideas in
various forms, the term working alliance was not coined until it
was first used by Greenson in 1967 (Horvath & Symonds, 1991).
Greenson (1967), who described the working alliance as a “rational relationship between patient and analyst” (p. 46), argued that
this positive collaboration between therapist and patient is crucial
for effective treatment. Subsequent writers have alternated between the terms working alliance and therapeutic alliance.
In his pantheoretical concept of the working alliance, Bordin
(1979) outlined three major components: (a) agreement on goals
for treatment, (b) agreement on tasks to achieve those goals, and
(c) the emotional bond of trust and attachment that develops
between therapist and patient.
Consistent with attachment theory (Bowlby, 1988) and hypotheses articulated in prior research (e.g., Eames & Roth, 2000; Reis
& Grenyer, 2004; Satterfield & Lyddon, 1995), we predicted for
the present study that secure attachment would be positively correlated with alliance, whereas insecure attachment would be negatively correlated with alliance. In addition, as a result of shared
method variance, we predicted that the relationship between selfreported alliance and self-reported attachment would be more
robust than the relationship between therapist-rated alliance and
patient self-reported attachment. We also sought to examine the
potential impact of attrition on the relationship between attachment
and alliance. Specifically, increased levels of attrition could lead to
biased results in which patients with lower levels of alliance drop
out of research studies, leaving only patients with stronger alliances (Smith et al., 2010). This potential bias could restrict the
variance and thus attenuate the true attachment-alliance relationship. As a result, we predicted that studies with higher attrition
rates would yield smaller effect sizes. Finally, results from Smith
et al. (2010) suggested that effect sizes based on data from the
secure global attachment category would be more consistent (i.e.,
statistically significant, larger, or both) than effect sizes based on
data from the anxious and avoidant global attachment category.
However, these conclusions were based on a limited review of the
research and they were not drawn from any theoretical developments within the attachment literature. As a result, in the present
study we predicted that effect sizes based on data from all three
global attachment categories (i.e., secure, anxious, and avoidant)
would be similar in direction, magnitude, and statistical significance.
Literature Search
The following procedures were used in our search of the literature to find individual studies for inclusion in the meta-analysis.
We conducted a series of PsycINFO searches, using the terms
“attachment AND alliance” through 2010; the final search was
performed on July 6, 2010. Each abstract was reviewed. Potentially relevant publications were retrieved in full and examined.
Second, we also used several review articles/chapters (e.g., Meyer
& Pilkonis, 2001, 2002; Mikulincer & Shaver, 2007) to locate
relevant references. Third, we checked the reference sections of
relevant publications retrieved in the first two steps to locate
additional references.
The following criteria were used to determine eligibility for
inclusion in the present study: (a) Only published articles (rather
than books, book chapters, or unpublished studies) were included
in our analysis; (b) articles had to be published in English; (c)
articles had to present data relevant to our research hypotheses; (d)
studies had to examine the relationship between adult attachment
style and therapeutic alliance in individual psychotherapy; studies
of marital and group treatments were excluded; (e) attachment
measures had to assess attachment style in adults’ close interpersonal relationships; that is, they needed to measure relatively
enduring patterns of thought, feeling, and behavior which cut
across significant relationships and which reflect the individual’s
interpersonal trust, concern for rejection, and desire for closeness
(Meyer & Pilkonis, 2001); methods such as scoring systems applied to the AAI were excluded given that they appear to measure
a different construct, namely the individual’s representation of
parental behavior (Crowell, Fraley, & Shaver, 1999) and the
“adult’s sense of the way these relationships and events had
affected adult personality” (Main, Kaplan, & Cassidy, 1985. p.
90); (f) studies using only measures of patient attachment to the
therapist were excluded as the underlying construct is conceptually very close to therapeutic alliance, potentially leading to artifactual inflation of effect sizes; (g) studies had to provide sufficient
data to permit calculation of effect sizes. All abstracts or full-text
publications were reviewed by either the first or second author for
potential inclusion in the meta-analysis.
Data Abstraction
Effect sizes were calculated by the first author for each study
included in the meta-analysis. Primary studies often presented
correlational results which were used as the effect size metric. In
a number of cases, though, means, standard deviations, and sample
sizes were presented and these were transformed into r. In the case
of one study (i.e., Hardy et al., 1998), only group means and an
overall sample size were presented; however, group sample sizes
and standard deviations were not available from the first author of
the publication (G. Hardy, personal communication, January 20,
2010). To obtain the number of participants in each group, the
overall sample size (N ⫽ 79) was subdivided into three groups
(Overinvolved, Underinvolved, and Balanced), using a prorating
procedure based on percentages of each group from the larger
sample reported in the study. The means for the intervention arms
receiving 8 and 16 sessions reported in Table 5 of Hardy et al.
(1998) were aggregated to yield an overall mean for the Openness
subscale of the Agnew Relationship Measure (ARM; AgnewDavies, Stiles, Hardy, Barkham & Shapiro, 1998). To estimate the
relevant standard deviations, the full sample standard deviations
for client or therapist ratings reported in the study by AgnewDavies et al. (1998) was used, following procedures outlined by
Lipsey and Wilson (2001). The means, standard deviations, and
sample sizes for the ARM Openness data were then used to
calculate Cohen’s d which was transformed into r. Next, post hoc
tests appeared to have been conducted comparing the three interpersonal styles on the ARM ratings for the Partnership (patientand therapist-rated data) and Initiative (therapist-rated data) subscales. Although the number of post hoc tests reported in the study
did not seem to fit the number of relevant comparisons, the
following effect sizes were coded as r ⫽ .00 in order to be
conservative: (a) comparisons between Balanced versus Overinvolved and between Balanced and Underinvolved clients, using
both patient and therapist ratings from the Partnership subscale
(four comparisons in total); (b) comparisons between Balanced
versus Overinvolved and Balanced versus Underinvolved, using
therapist ratings from the Initiative subscale (two comparisons in
All effect sizes were assigned a positive value if they were
consistent with our a priori predictions, or a negative value if they
were inconsistent with our a priori predictions. Given the skewed
distribution of correlation coefficients, effect sizes were first transformed into Fisher’s Z of r, weighted by their inverse variances,
averaged and then transformed back into r following standard
meta-analytic procedures (e.g., Borenstein, Hedges, Higgins, &
Rothstein, 2009; Lipsey & Wilson, 2001). If a study reported
relevant analyses but stated only that the results were nonsignificant without providing data to permit calculation of an effect size,
the effect size was entered as r ⫽ .00 to be conservative following
standard meta-analytic convention (Horvath & Symonds, 1991;
Martin, Garske, & Davis, 2000). Only one effect size and p value
was calculated for each study in order to maintain the assumption
of independence that is necessary for a meta-analysis. When studies reported multiple effect sizes, these effect sizes were averaged,
again following standard meta-analytic convention (Horvath &
Symonds, 1991; Martin et al., 2000). All calculations were conducted using Fisher’s Zr transformation. Results were then transformed back into r.
The first author also coded each study for the following features
in order to examine the relationship between potential methodological or substantive moderators and the resulting effect sizes:
average age of participants; gender; ethnicity; education; average
treatment length; attrition rate, mean of Working Alliance Inventory (WAI; Horvath & Greenberg, 1989) scores (12 out of 17
studies included in the meta-analysis provided this data); primary
diagnosis; alliance rater; attachment rater; country; primary treatment type; treatment setting; alliance measure; attachment measure; and global attachment category for alliance measure used
(e.g., security, anxiety, avoidance, as per Smith et al., 2010; the
full coding forms, adapted in part from Sharf, Primavera, &
Diener, 2010, are found in Appendix A). Given that the metaanalysis included only 17 independent samples, a number of the
levels of the moderator variables were collapsed in order to yield
greater statistical power for the analyses.
Quantitative Data Synthesis
Effect sizes were aggregated across studies using the random
effects method of Hedges and colleagues (Hedges & Olkin, 1985;
Hedges & Vevea, 1998). Random effects methods are considered
to be more representative of real-world data (National Research
Council, 1992) and yield results that are more generalizable than
their fixed-effect counterparts (Hedges & Vevea, 1998). These
calculations, performed using the Comprehensive Meta-Analysis
(Borenstein, Hedges, Higgins, & Rothstein, 2005) software, involve weighting each effect size by the inverse of its variance
(Borenstein et al., 2009). The variance used in the weighting
procedure has two components: a within-study variance and a
between-study variance.
Continuous moderator analyses were conducted using mixed
effects (method of moments) meta-regression analyses, with the
average effect size for each study serving as the dependent variable
and each continuous moderator variable serving as a covariate.
Because current meta-analytic software will not conduct a multiple
meta-regression analysis, each covariate was examined using a
separate meta-regression.
Most of the categorical moderator analyses were conducted
using simple subgroup random effects meta-analyses. However,
two of the moderator variables (i.e., global attachment category,
alliance rater) required a different approach. Given the fact that
current meta-analytic software cannot calculate exact values of the
study variances for these subgroup analyses with the type of
complex data structure of the present meta-analysis (i.e., some
studies provided only patient-rated alliance data, some provided
both therapist-rated data and patient-rated data, etc.), we opted to
treat each level of the moderator variable (i.e., patient-rated data,
therapist-rated data, etc.) as independent of the others in order to
be conservative (M. Borenstein, personal communication, January
1, 2010). For the global attachment category moderator variable,
subgroup random effects meta-analyses were conducted using the
following levels of the grouping variable: (a) security, (b) anxiety,
(c) avoidance, (d) security and anxiety (Hardy et al., 1998, and
Saatsi et al., 2007, each presented data comparing alliance scores
for secure vs. anxious patients), and (e) security and avoidance
(Hardy et al., 1998, and Saatsi et al., 2007, each presented data
comparing alliance scores for secure vs. avoidant patients). For the
alliance moderator variable, subgroup random effects metaanalyses were conducted using the following levels of the grouping variable: (a) patient-rated alliance data and (b) therapist-rated
alliance data. For all categorical moderator analyses, Q tests,
analogous to analysis of variance in primary research (Borenstein
et al., 2009; Lipsey & Wilson, 2001), were calculated to determine
whether the various levels of the moderator variable differed
significantly from each other. When all subgroups in a particular
analysis had at least six studies, estimates of the variance of true
effect sizes were not pooled; however, when at least one subgroup
had fewer than six studies, estimates of the variance of true effect
sizes were pooled because the accuracy yielded by pooling is
likely to be greater than any real differences between subgroups
(Borenstein et al., 2009).
Publication Bias
Potential publication bias of the overall meta-analysis was assessed in multiple ways, including (1) a funnel plot display, (2)
Duval and Tweedie’s (2000a, 2000b) trim and fill procedure, (3)
Begg and Mazumdar’s (1994) rank correlation, and (4) Egger’s
regression intercept (Egger, Davey Smith, Schneider, & Minder,
Study Flow
Figure 1 contains a chart detailing the flow of studies through
the present meta-analysis. The PsycINFO searches identified 167
abstracts for potential inclusion. Examination of review articles/
chapters, back-checking of reference sections, and fortuity (e.g.,
finding additional articles in the same issue as one containing a
previously identified publication) yielded another 159 publications
for potential inclusion. In all, 326 records were screened. One
hundred thirty-seven did not meet criteria for inclusion and were
immediately excluded. The remaining 189 articles were retrieved
in full and examined further. Of these, 172 were excluded; see
Appendix B for details of these exclusions at the online website for
the journal’s supplementary materials at
a0022425.supp. Seventeen studies remained for inclusion in the
present meta-analysis. Several individual effect sizes were excluded from several of the publications that were otherwise included in the meta-analysis; details of these exclusions are also
listed in Appendix B.
Study Characteristics
Relevant information from the 17 articles is briefly summarized
in Table 1. More detailed information can be found in Supplementary Table 1 in the journal’s online supplementary materials Web
site. The meta-analytic sample consisted of a total of 886 participants (when discussing numbers of participants in the current
meta-analysis, these numbers are rounded to the nearest whole
number to facilitate ease of comprehension), with a mean sample
Abstracts identified through
Additional records identified through
PsycINFO database searches
other sources (e.g., backwards
(n = 167)
reference search, literature reviews;
(n = 159)
Records screened
Records excluded
(n = 326)
(n = 137)
Full-text articles
Full-text articles
assessed for
excluded, with reasons
(see Appendix B for
(n = 189)
(n = 172)
Studies included in
(n = 17)
Figure 1. Flow diagram for attachment-alliance meta-analysis. Adapted from flow diagram in Moher, D., Liberati,
A., Tetzlaff, J., Altman, D. G., The PRISMA Group (2009). Preferred reporting items for systematic reviews and
meta-analyses: The PRISMA Statement. PLoS Med, 6, e1000097. doi:10.1371/journal.pmed1000097
size of 52 and a standard deviation of 24. Table 2 contains the
moderator codes for each independent sample.
Quantitative Data Synthesis
Overall effect size. As predicted, greater attachment security
predicted stronger alliance scores, whereas weaker attachment
security predicted weaker alliance scores with an overall weighted
effect size of r ⫽ .17, p ⬍ .001 (95% confidence interval ⫽
.10 –.23). The size of this association falls in between a small and
medium effect size (Cohen, 1988); it is in the middle third of the
treatment effects (although the lower end of the 95% confidence interval dips below this benchmark), and the lower third of
the overall effect sizes, found in Hemphill’s (2003) review. Several supplementary analyses were conducted to explore whether
the decision to include two studies (i.e., Romano et al., 2008;
Schiff & Levit, 2010) in the meta-analysis may have biased the
results. Romano et al.’s (2008) study investigated the relationship between attachment and alliance for volunteer rather than
for “real” clients, thus distinguishing its sample from those of
the remaining studies in the meta-analysis. Schiff and Levit’s
(2010) study explored the relationship between attachment and
alliance in treatment offered by social workers to their female
patients in a methadone clinic; only one-fourth of the clients
met weekly with their social workers and more than 50% of
clients reported meeting “seldom” or “never,” thus distinguish-
ing the intervention provided from the remaining studies in the
meta-analysis. As a result of the unique characteristics of each
of these studies, the analyses were conducted excluding each
one as well as excluding both of them. Results of all analyses
were essentially identical to those of the main analyses presented earlier.
Results of the homogeneity test (Q ⫽ 6.10, df ⫽ 16, p ⫽ .99)
indicate no demonstrable evidence for potential moderators. Nevertheless, following the recommendations of Rosenthal and DiMatteo (2001), we present the results of several moderator analyses later to aid future researchers in further clarifying the results.
Publication bias. Figure 2 presents a funnel plot with data
from the meta-analysis together with imputed data to correct for
potential publication bias using the Trim and Fill procedure outlined by Duval and Tweedie (2000a, 2000b). An “eye-ball” test of
the funnel plot does not indicate any potential for publication bias
because the studies with the larger effect sizes at the bottom of the
plot do not tend to fall on the right side of the graph. Even if
publication bias did exist, the impact is likely negligible, given the
fact that the Trim and Fill procedure did not trim a single effect,
leaving the adjusted and observed overall effects identical. Results
of Begg and Mazumdar’s (1994) rank correlation (␶ ⫽ ⫺0.02, p
[one-tailed] ⫽ .45) and Egger’s (Egger et al., 1997) regression
intercept (intercept ⫽ ⫺0.08, p [one-tailed] ⫽ .45) are not demonstrably consistent with the conclusion that publication bias exists,
Table 1
Overall Random Effects Meta-Analysis of Attachment
and Alliance
Hypothesis 1: Relationship Between Attachment
and Alliance
Study name
Bruck et al., 2006
Dolan et al., 1993
Eames & Roth, 2000
Goldman & Anderson, 2007
Hardy et al., 1998
Kivlighan, Patton, & Foote,
Mallinckrodt, Coble, &
Gantt, 1995
Mallinckrodt, Porter, &
Kivlighan, 2005
Marmarosh et al., 2009
Parish & Eagle, 2003
Reis & Grenyer, 2004
Romano, Fitzpatrick, &
Jansen, 2008
Saatsi et al., 2007
Satterfield & Lyddon, 1995
Satterfield & Lyddon, 1998
Sauer, Lopez, & Gormley,
Schiff & Levit, 2010
Weighted Mean r
although these results may be affected by the low power of the
tests (Borenstein et al., 2009).
Moderator analyses. For a number of analyses, moderator
data could not be coded from the overwhelming number of studies
and the analyses were therefore not conducted; these included the
metaregression for attrition rate (only five studies out of 17 provided codeable data), the metaregression for education (only three
studies out of 17 provided codeable data), and the categorical
subgroup analysis for primary diagnosis (only five studies out of
17 provided codeable data).
None of the continuous moderator analyses were statistically
significant. These results included the metaregressions for age
(slope ⫽ ⫺.01, p ⫽ .23), gender (slope ⫽ ⫺.002, p ⫽ .46),
ethnicity (slope ⫽ .000, p ⫽ .92), and mean WAI (slope ⫽ .16,
p ⫽ .22).
For the categorical subgroup analyses, patient-rated alliance
demonstrated a statistically significant stronger correlation with
attachment style (average weighted r ⫽ .17, 95% confidence
interval ⫽ .10 –.24, p ⬍ .001) than therapist-rated alliance (average weighted r ⫽ .01, 95% confidence interval ⫽ ⫺.14 - .16, p ⫽
.89; Qbetween ⫽ 3.95, df ⫽ 1, p ⫽ .047). The remaining categorical
subgroup analyses were nonsignificant, including the moderator
variables for average treatment length (Qbetween ⫽ .03, p ⫽ .86,
df ⫽ 1, k ⫽ 8), country (Qbetween ⫽ .05, p ⫽ .82, df ⫽ 1, k ⫽ 17),
primary treatment type (Q between ⫽ 2.13, p ⫽ .55,
df ⫽ 3, k ⫽ 11), treatment setting (Qbetween ⫽ .19, p ⫽ .66, df ⫽
1, k ⫽ 17), alliance measure (Qbetween ⫽ .80, p ⫽ .67, df ⫽ 2, k ⫽
17), attachment measure (Qbetween ⫽ .06, p ⫽ .97, df ⫽ 2, k ⫽ 17),
and global attachment category (Qbetween ⫽ .88, p ⫽ .93, df ⫽ 4).
As predicted, the results indicate that individuals with more
secure attachment styles demonstrated stronger alliances, whereas
individuals with more insecure attachment styles demonstrated
weaker alliances. Although not directly comparable, the overall
effect size in the present study resembles the magnitude of the
relationship between alliance and outcome (average weighted r ⫽
.22, r ⫽ .28; respectively, Martin et al., 2000; Horvath, Del Re,
Flückiger, & Symond, 2011), a relationship considered reflective
of the power of alliance as “the most robust predictor of treatment
success” (Safran & Muran, 2000; p. 1). Results also indicated no
demonstrable evidence for publication bias and further suggested
that its effect would likely be negligible even if it did exist.
These findings are consistent with attachment theory’s emphasis
on the importance of attachment representations in influencing
enduring relationship patterns. Although none of the studies used
methodology that would permit causal inferences, the findings do
suggest the contiguity between general patterns of relational functioning outside of the consulting room and the quality of the
therapeutic alliance in individual psychotherapy.
Attachment theory would point to the centrality of internal
working models in explaining this overlap across the different
types of interpersonal relationships. Internal working models contain implicit, unconscious assumptions about self and others,
which are used to make sense of the inherently ambiguous interpersonal world (Main et al., 1985; cf. Wachtel, 1993). For individuals with more secure attachment styles, the assumptions connected to their internal working models reflect a trust in the
benevolence of others, the adequacy and essential goodness of the
self, and the desire for interpersonal connection. In working together with their therapists, therefore, these individuals are more
likely to be able to form an emotional bond, to agree on goals for
treatment, and to agree on tasks to achieve those goals (Bordin,
In contrast, for individuals with more insecure attachment
styles, the assumptions connected to their internal working models
reflect a distrust of the motives and intentions of others, a more
negative self representation, a wariness to engage intimately with
others, a pressing need to be reassured of the love of others, or
some combination of that. Therefore, while working together with
their therapist, these individuals have a more difficult time cultivating an emotional bond, agreeing with their therapist on goals for
treatment and on tasks to achieve those goals.
In psychotherapy, therefore, clinicians are encouraged to pay
particular attention to the quality of the therapeutic alliance
when working with individuals with a history of more insecure
attachment. These types of attachment histories could serve as
“red flags,” allowing the therapist to predict the potential for
ruptures in the alliance and intervene proactively to minimize
their deleterious effects while also capitalizing on the therapeutic opportunities inherent in working through them. With such
patients, therapists would do well to carefully monitor the
relationship for signs of distance or discontent. When therapists
spot these signs, they can use relationally based interventions to
repair the alliance (e.g., Crits-Christoph et al., 2006). As an
University-counseling center
Outpatient clinics
University-based Tx center
Research clinic
University counseling centers
University counseling center
University counseling center
Outpatient university clinic
Tx setting for students serving as
volunteer clients
Research clinic
University-based counseling
University-based counseling
service center
Methadone clinics
Dolan et al., 1993
Eames & Roth, 2000
Goldman & Anderson,
Hardy et al., 1998
Kivlighan et al., 1998
Mallinckrodt et al., 1995
Mallinckrodt et al., 2005
Marmarosh et al., 2009
Parish & Eagle, 2003
Reis & Grenyer, 2004
Romano et al., 2008
Satterfield & Lyddon,
Satterfield & Lyddon,
Sauer et al., 2003
Schiff & Levit, 2010
Equivalent # received 2
types of Tx
Cognitive therapy
Equivalent # received 2
types of Tx
Primary Tx type
% HS or
Short-term (fewer
than eight
Medium (8–16
Medium (8–16
Longer (17 or
more sessions)
Medium (8–16
Medium (8–16
Medium (8–16
Longer (17 or
more sessions)
Mean Tx lengthb
Note. Dx ⫽ diagnosis; CBT ⫽ cognitive-behavioral therapy; HS ⫽ high school; Tx ⫽ treatment; — ⫽ study did not provide this data. Moderator codings for the effect size level variables as well
as the study level variables of alliance measure and attachment measure (data for the latter two variables are available in Supplementary Table 1) are not presented in this table due to space considerations
but are available upon request from the first author.
For purposes of the analysis, all data from this variable were collapsed into one of two categories: (a) university-based outpatient treatment center, (b) other. b For purposes of the analysis, all data
from this variable were collapsed into one of two categories: (a) medium (8 –16 sessions), (b) other (longer [17 or more sessions] or short-term [fewer than eight sessions]). c For purposes of the
analysis, all data from this variable were collapsed into one of two categories: (a) USA, (b) non-USA. d Some studies report the sum of all items averaged across participants, whereas others report
only the average WAI item score. In addition, some studies utilized the 36 item version of the WAI, whereas others used the 12 item version. To make the results comparable across studies, therefore,
data were transformed when necessary into the average WAI item score. e This value was obtained by averaging the Working Alliance Inventory (WAI) scores from both patient (5.13) and therapist
(4.74) ratings. f This value was obtained by averaging the WAI scores across the three sessions for which data were reported. g The WAI was not used as the alliance measure in this study. h This
value was obtained by averaging the scores from patient ratings (5.54) and therapist ratings (5.16). i In this study, patients dropped out of treatment at several different points; given the heterogeneity
in the literature about how to define dropouts (Sharf et al., 2010), we decided to take the average of number of participants who dropped out across each of the three different time periods. This number
was then used in a ratio to the total number of participants to yield the attrition rate. j This value was obtained by taking the mean of the client and therapist WAI ratings across all three time points
for which data were presented in the study.
Saatsi et al., 2007
Outpatient psychiatry department
Tx settinga
Bruck et al., 2006
Table 2
Moderator Codes for Each Independent Sample
Funnel Plot of Standard Error by Fisher's Z
Standard Error
Fisher's Z
Figure 2. Graphical representation of potential publication bias. The white circles represent each of the studies
that were actually included plotted by the size of the effect (in Fisher’s Z) on the x-axis and the standard error
on the y-axis. If there was publication bias, we would expect that the largest effects would have the largest
standard error (yielding void in the lower left quadrant; Borenstein et al., 2009). Studies toward the tip of the
triangle have the smallest standard error. The white diamond represents the weighted average effect size of the
actual studies included in the meta-analysis. The following iterative procedure is used for the trim and fill:
studies at the extreme positive side of the graph are removed, the weighted average effect is recalculated, and
this process of trimming the plot continues until the distribution of studies is symmetric around the weighted
average effect. Next, each removed study is added back in and a mirror image of the study is imputed to correct
for reduction in the variance of effects as a result of the trimming procedure. The black diamond represents the
weighted average effect size calculated using the studies actually included in the meta-analysis as well as
the imputed studies. In the present meta-analysis, there were no studies that needed to be removed and therefore
the observed and imputed overall effect size is identical.
example, therapists can point out instances in which patient
behaviors reflect problems in the alliance, discuss their meaning, explore the connection to the ongoing therapeutic work,
and draw parallels to interpersonal expectations and emotional
reactions in patients’ attachment relationships outside of therapy. In addition, explicit discussion of therapeutic goals and
tasks to achieve those goals as well as attention to the overall
emotional climate can facilitate improvements in the alliance
while simultaneously opening up avenues of fruitful exploration
of repetitive interpersonal patterns.
Nevertheless, the magnitude of the relationship between attachment
and alliance suggests that much of the variance in alliance remains to
be accounted for even after taking into consideration patients’ attachment styles. In this respect, the findings suggest that an individual’s
enduring relationship pattern outside of therapy does not automatically map onto the therapeutic relationship. Instead, individuals with
more insecure attachment styles can still develop positive working
alliances with their therapists. This possibility is supported by the
finding that the mean of WAI scores across 12 studies providing
sufficient data was 5.74 on a 7-point scale (both the unweighted and
weighted means were identical in this instance), suggesting generally
strong alliances across the different individual studies. Perhaps therapists in the original studies tailored their interpersonal stance to the
specific attachment styles of their patients, allowing for increased
engagement in the therapeutic relationship. Alternatively, the unique
nature of the therapeutic relationship may itself permit a more adaptive and collaborative interpersonal approach, allowing patients to
diverge from their well-trodden paths in relationships.
Hypothesis 2: Patient-Reported Alliance Versus
Therapist-Reported Alliance in Relation to Attachment
Consistent with a priori predictions, there was a significant
difference in the magnitude of the relationship between patientreported alliance and patient-reported attachment on the one hand,
and the relationship between therapist-reported alliance and patient
self-reported attachment on the other hand. These results suggest
that the similarity in relational patterns between relationships in
general and the therapeutic relationship may be more robust when
viewed from the patient’s perspective. That is, patients may perceive the working alliance in ways that are more similar to their
general attachment style than do their therapists. However, given
the number of studies included in the present meta-analysis relative to the number of moderator analyses conducted, these results
should be considered exploratory and interpreted with caution
(Borenstein et al., 2009).
Hypothesis 3: The Impact of Attrition on the
Relationship Between Attachment and Alliance
Given the small number of studies that provided data on attrition
rates (i.e., five out of 17 studies), the analyses to test this particular
hypothesis could not be conducted. Researchers are enjoined to
regularly provide these data for readers and future researchers.
Given the positive and statistically significant relationship between
alliance and psychotherapy dropouts (Sharf et al., 2010), it is likely
that individuals who dropped out of treatment prematurely had
weaker alliances than those who remained in psychotherapy. As a
result, the effect size found in the present study may have been
attenuated due to restrictions on the range of alliance scores.
However, this possibility cannot be explored without additional
Hypothesis 4: Effect Sizes and Global Attachment
As predicted, no differences in magnitude, direction, or statistical significance were found between effect sizes based on data
from the secure, anxious, or avoidant global attachment categories
(average weighted r values ⫽ .19, ⫺.14, and ⫺.15, respectively;
all p values ⬍ .001). Effect sizes based on data from both secure
and anxious or secure and avoidant global attachment categories,
however, were both nonsignificant (p values ⬎ .10). Nevertheless,
they were in the same direction (i.e., they reflect correlations with
alliance in the predicted directions, albeit nonsignificantly) and
general range as results from the aforementioned three global
attachment categories (average weighted r values ⫽ .14 for security and anxiety, and .14 for security and avoidance). The average
weighted effect sizes for these two global attachment categories
(i.e., secure and anxious; secure and avoidant) are reported in the
present study with a positive valence as they reflect comparisons in
the original studies between secure versus anxious and secure
versus avoidant patients that were in the predicted direction,
namely secure patients had higher mean alliance scores than either
anxious or avoidant patients.1 Their lack of statistical significance
likely stemmed from the fact that each of these global attachment
categories had data from only two studies.
Limitations and Conclusion
Limitations of the present study include the fact that only
published studies were included in the meta-analysis. This inclusion criterion may have biased the results given that stronger, more
significant findings tend to find their way into the published
literature more often than weaker, less significant ones (Borenstein
et al., 2009). Nevertheless, results of the publication bias analyses
did not provide any demonstrable evidence for bias, and they
indicated that the potential impact of any such bias is likely
In addition, the statistical power for the moderator analyses may
have been hampered by the relatively small number of studies
included in the meta-analysis. Future research, therefore, would
benefit from a larger pool of studies (both published and nonpublished, as explained earlier) to permit identification of additional
potential moderator variables. Finally, the study is limited by the
fact that ratings were completed by a single judge. This limitation
is partially mitigated by the fact this rater has demonstrated reliability in coding effect sizes and other relevant data in a recent
meta-analytic investigation; at the study level, the codes of the
rater demonstrated an average intraclass correlation coefficient
(ICC; results were identical within rounding error for all three
models of ICC) of .84 with an independent rater (Diener, Hilsenroth, Shaffer, & Sexton, in press) which is considered to be
indicative of excellent reliability (Cicchetti, 1994). There was only
a single variable coded at the level of the effect size, and this rater
demonstrated good reliability with an ICC of .60 when compared
with the independent rater (Cicchetti, 1994). In addition, the data
for the present study are clearly detailed both in this publication
and in the online supplementary materials, allowing researchers to
form their own conclusions.
Overall, though, results suggested the convergence between
adult attachment style and the quality of the therapeutic alliance.
Individuals with more secure attachment styles tend to develop
stronger alliances, whereas individuals with less secure attachment
styles tend to develop weaker alliances. These findings highlight
the relational consistencies across different interpersonal arenas
and suggest the potential utility of attending to the therapeutic
implications of patient attachment histories. These findings may
also be robust to the challenge of publication bias, further strengthening their evidentiary basis. The relationship between attachment
and alliance did not appear to differ, depending on whether secure,
anxious, or avoidant attachment was assessed. Finally, patients
may perceive the therapeutic alliance in a manner more consistent
with their general attachment style when compared with their
therapists’ perception of the alliance. Taken together, these results
support attachment theory’s emphasis on the clinical significance
of patient attachment representations, and suggest the potential
therapeutic utility of integrating attachment-specific relational interventions.
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(Appendix follows)
Appendix A
Coding Criteria for Moderator Analysesa
Study Level Variables
Treatment setting
1 ⫽ University-based treatment center
2 ⫽ Outpatient psychiatry department
3 ⫽ Outpatient clinic
4 ⫽ Mixture of two or more of the following: university counseling center, community counseling agency, independent practice,
mental health center, or other
5 ⫽ Research clinic
9 ⫽ Cannot tell
Primary treatment type (code for primary treatment type, e.g.,
most patients received CBT or therapists primarily identified with
CBT relative to other orientations)
1 ⫽ Cognitive– behavioral therapy
2 ⫽ Psychodynamic/psychoanalytic
3 ⫽ Eclectic/integrative
4 ⫽ Equivalent number of patients received two types of treatments (e.g., eclectic tied with CBT)
9 ⫽ Cannot tell
Primary diagnosis
1 ⫽ Psychotic disorder
2 ⫽ Mood disorder
3 ⫽ Anxiety disorder
4 ⫽ Eating disorder
5 ⫽ Personality disorder
6 ⫽ Other
7 ⫽ Mixed diagnoses
99 ⫽ Cannot tell
Average age of sample participants (“99” if cannot tell): ______
Gender [% Male (“999” if cannot tell)]: ______
Education [% Completed High School or Higher (“999” if
cannot tell)]: ______
Ethnicity [% White (“999” if cannot tell)]: ______
Average treatment length
1 ⫽ short-term (⬍8 sessions)
2 ⫽ Medium (8 –16 sessions)
3 ⫽ Longer (17 or more sessions)
9 ⫽ Cannot tell
Attrition rate (“999” if cannot tell): ______
1 ⫽ U.S.A.
2 ⫽ U.K.
3 ⫽ Canada
4 ⫽ Europe
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5 ⫽ Australia
9 ⫽ Other (write in :________________)
99 ⫽ Cannot tell
Mean of WAI scores: ______
Effect Size Level Variables
Alliance rater
1 ⫽ Patient
2 ⫽ Therapist
3 ⫽ External rater
Alliance measure
1 ⫽ WAI- 36 items
2 ⫽ WAI- 12 items
3 ⫽ Other (specify:____________________________)
Attachment rater
1 ⫽ Patient
2 ⫽ Therapist
3 ⫽ External rater
Attachment measure
1 ⫽ Adult Attachment Scale
2 ⫽ Attachment History Questionnaire
3 ⫽ Experiences in Close Relationships Scale
4 ⫽ Relationship Styles Questionnaire
5 ⫽ Relationship Questionnaire
6 ⫽ Other (specify: ____________________________)
Global attachment category
1 ⫽ Security (e.g., Secure/security; Depend; Close)
2 ⫽ Avoidance (e.g., Avoidance; Dismissive; Fearful; Relationships as secondary; Discomfort with closeness)
3 ⫽ Anxiety (e.g., Anxiety; Preoccupied; Need for approval)
4 ⫽ Secure and Anxious (e.g., study reports contrast between
secure and anxious participants)
5 ⫽ Secure and Avoidant (e.g., study reports contrast between
secure and avoidant participants)
Adapted from J. Sharf, L. H. Primavera, & M.J. Diener 2010. Dropout
and therapeutic alliance: A meta-analysis of adult individual psychotherapy. Psychotherapy: Theory, Research, Practice and Training, 47, 637645.
Received November 19, 2010
Accepted November 22, 2010 䡲