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International Journal of Offender Therapy and Comparative Criminology http://ijo.sagepub.com/

Shifting Perspectives: A Confirmatory Factor Analysis of the Working Alliance Inventory (Short Form) With High-Risk Violent Offenders Elizabeth C. Ross, Devon L.L. Polaschek and Marc Wilson Int J Offender Ther Comp Criminol published online 2 December 2010 DOI: 10.1177/0306624X10384948 The online version of this article can be found at: http://ijo.sagepub.com/content/early/2010/12/02/0306624X10384948 Published by: http://www.sagepublications.com

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IJO384948

IJ OXXX10.1177/0306624X10384948Ross et al.International Journal of Offender Therapy and Comparative Criminology

Shifting Perspectives: A Confirmatory Factor Analysis of the Working Alliance Inventory (Short Form) With High-Risk Violent Offenders

International Journal of Offender Therapy and Comparative Criminology XX(X) 1–16 © The Author(s) 2010 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0306624X10384948 http://ijo.sagepub.com

Elizabeth C. Ross1, Devon L. L. Polaschek2, and Marc Wilson2

Abstract The Working Alliance Inventory (WAI) is the most widely used measure of the therapeutic alliance. However, previous studies of the factor structure of the WAI have obtained disparate results. This study examined ratings from three rater perspectives (therapists, clients, and observers) in a rehabilitation program for high-risk violent prisoners with high PCL-psychopathy scores. The authors used confirmatory factor analysis with a short version of the WAI and examined one-, two-, and three-factor models. It was hypothesized that the context and client characteristics could alter the factor structure of the ratings. However, no differences were found in factor structure by perspective; the results from all rater perspectives suggested that a two-factor structure was the best fit for the data. Thus, we concluded that therapists, observers, and prisoners all saw the goals and task aspects of the therapeutic alliance as distinct from the bond component. Keywords therapy alliance, Working Alliance Inventory, psychopathic prisoners, factor structure, offender rehabilitation

1

Wellington Psychological Services, Wellington, New Zealand Victoria University of Wellington, New Zealand

2

Corresponding Author: Devon L. L. Polaschek, School of Psychology, Victoria University of Wellington, P.O. Box 600, Wellington 6140, New Zealand Email: [email protected]


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International Journal of Offender Therapy and Comparative Criminology XX(X)

Process issues, as their name suggests, are those variables in psychological treatment relating to the process of therapy (Marshall, Fernandez, et al., 2003). In essence, they describe how psychological treatment unfolds beyond just the content involved. Common aspects of process include the therapist’s style, the client’s perceptions of the therapist, and the client–therapist collaboration or therapeutic alliance (Marshall & Serran, 2004). The therapeutic alliance (TA) is now seen as one of the most important aspects of the process; it accounts for a sizeable portion of variance in therapy outcome, often about 25% in meta-analyses (e.g., Horvath & Luborsky, 1993). The past two decades have seen significant progress in clarifying the best content for offender rehabilitation programs (i.e., criminogenic needs; Andrews & Bonta, 2006). Empirical research into the potential importance of process issues in increasing responsivity and improving outcomes has been slower to develop, though several recent studies have examined therapist behavior in sex offenders (Marshall, Serran, et al., 2003), and the quality of the therapeutic alliance with partner-violent men (Taft, Murphy, Musser, & Remington, 2004), and with probationers with mental illnesses (Skeem, Eno Louden, Polaschek, & Camp, 2007). To our knowledge, no published studies have examined the TA in general violence treatment programs. The few research studies with partner-violent men have linked the TA to positive treatment outcome. Brown and O’Leary (2000) examined the role of the TA between client and therapist in group treatment outcome in 70 husband-towife violent couples. They found that the strength of husbands’ alliances with their therapists in Session 1 was positively associated with treatment outcome, as measured by decreased husband-to-wife mild and severe psychological and physical aggression. However, strength of wives’ alliance was unrelated to treatment outcome, and although husbands’ alliance was related to treatment outcome, it was unrelated to treatment completion (Brown & O’Leary, 2000). Taft, Murphy, King, Musser, and DeDeyn (2003) also found that alliance predicted outcome in partner-violent men. Taft and colleagues used multilevel modeling to examine the role of process and treatment adherence factors as predictors of partner reports of abuse following participation in a CBT group for partner-violent men. They found that therapist ratings of TA predicted lower levels of physical and psychological abuse at the 6-month follow-up and were the strongest predictors of outcome. These studies illustrate that the TA in violent offender treatment shows promise as a predictor of treatment outcome. Because of the significant role that the TA plays in treatment, and its relationship to treatment outcome, it is important to fully understand this concept, and the measures we use to assess it. The history of the therapeutic alliance started with Freud, who proposed the idea of positive transference, which was thought to “clothe” the therapist in authority and help the client to believe in the therapist’s work (Freud, 1913, p. 122). The concept of the therapeutic alliance remained firmly psychodynamic until the seminal work of E. S. Bordin. Bordin (1979) conceptualized the working


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alliance as a pantheoretical construct that could thus apply not only to psychodynamic therapy but also to interventions derived from other theoretical foundations, such as cognitive–behavioral therapy. Bordin’s construct had three components: goals, tasks, and bond. He proposed that a strong working alliance forms if a therapist and client agree on the goals of therapy, agree on the tasks needed to meet those goals, and have a bond between them that will facilitate this process. Bordin’s clear operationalization of the construct greatly facilitated the measurement of the therapeutic alliance. Horvath and Greenberg (1989) developed the Working Alliance Inventory (WAI) with the express purpose of measuring Bordin’s three factors; it consists of three corresponding subscales. Goals evaluates whether the therapist and client mutually endorse and value the aims that are the target of therapy (Horvath & Greenberg, 1989). Tasks measures whether both parties perceive in-therapy activities and assignments as relevant and effective and accept responsibility for their completion (Horvath & Greenberg, 1989). Lastly Bond measures the complex network of positive attachments between client and therapist, including the values of trust, acceptance, and confidence (Horvath & Greenberg, 1989). The WAI has parallel therapist, client, and observer versions, meaning multiple perspectives of the relationship can be measured and compared. The observer version in particular is of interest as it is thought to provide more of an “objective” view of the relationship (Fenton, Cecero, Nich, Frankforter, & Carroll, 2001). Since its development, the WAI has become the most widely used and well-known measure of therapeutic alliance and as such its psychometric structure has been explored (Andrusyna, Tang, DeRubeis, & Luborsky, 2001; Corbiere, Bisson, Lauzon, & Ricard, 2006; Hatcher & Barends, 1996; Hatcher & Gillaspy, 2006; Tracey & Kotovic, 1989). In an early study, Tracey and Kotovic had 84 clients and 15 therapists complete the WAI after the first treatment session. They then used confirmatory factor analysis (CFA) to test whether a model with one general factor, a model with three specific factors, or a bi-level model was the best fit. They found that the bi-level factor structure, with a “general alliance” factor as its primary factor and three secondary specific factors, fitted the data best (Tracey & Kotovic, 1989). By selecting the items most indicative of the three specific factors, they also developed from these data the 12-item short form of the WAI (WAI-S) used in the present study. In a similar study with 231 patients completing the WAI and using principal components analysis (PCA), Hatcher and Barends (1996) found—in contrast—support for a model with two independent factors: Goals and Task items grouped on one factor and Bond items loaded on the other. It is notable that both of the previous studies were of psychodynamic therapies, and ratings were made from inside the therapeutic alliance (i.e., by therapists or patients). Andrusyna et al. (2001) broadened the empirical base on both fronts by using an observer-rated WAI-S with a sample of 70 therapist–client dyads in CBT for depression. Using PCA, they found support for Hatcher and Barends’s (1996) results: A two-factor model fitted best. However, one of the bond items loaded onto the Goals and Tasks factor—labeled Agreement/Confidence—whereas the remaining three


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loaded onto a general Bond factor, which they labeled Relationship (Andrusyna et al., 2001). Interestingly, the bond item that loaded onto the first factor addressed clients’ confidence in their therapist, which could arguably be seen as relating to therapist competence and thus more connected to goals and tasks than to the relationship. More recently, other variations of the WAI have been constructed and tested using exploratory and confirmatory factor analysis. Hatcher and Gillaspy’s (2006) evaluation of both the WAI and WAI-S failed to derive a satisfactory model fit. However, Goals and Tasks were found to be highly correlated. They then created a revised shortened version—the WAI-SR—and found that a three-factor structure had adequate fit. Corbiere, Bisson, Lauzon, and Ricard (2006) recently also tested a French version of the WAI-S, using therapist and client ratings. They found that a unidimensional structure was the best fit for the data, though only after statistical adjustment, and a twofactor structure was not tested. In summary, research on the WAI factor structure has supported one, two, and three factor models. It is difficult to interpret this disparity given that several different forms of the WAI have been used, with different theoretical orientations and with ratings from varying perspectives. Therefore, the present study aimed to test all three models. However, given that the Andrusyna et al. (2001) study, although focusing on treatment of depression, was the most similar to the present study in terms of treatment modality, sample size and the WAI version used, we hypothesized that a two-factor structure would be the best fit for our data. In addition to investigating the best factor model for the WAI-S, a second purpose of the current study was to examine the factor structure for data from each of the three rater perspectives: therapist, client, and observer. Most previous studies examined only one rater perspective each (e.g., Andrusyna et al., 2001; Hatcher & Barends, 1996; Tracey & Kotovic, 1989), and those that looked at both client and therapist ratings found no clear differences between raters (e.g., Corbiere et al., 2006; Hatcher & Gillaspy, 2006). None has looked at ratings from three perspectives on the same therapeutic relationships, and therefore this research is unique in this respect. We speculated that a number of distinctive aspects of the therapy context, and client characteristics, could yield a distinctive internal factor structure to the WAI-S ratings obtained in this study. First, the therapy program took place in a medium-security prison unit. The therapy clients were violent men with high levels of psychopathic and DSM-IV Cluster B personality characteristics, who were nearing parole, and at very high risk of future criminal and violent offending (Polaschek, 2008). In a recent review (E. C. Ross, Polaschek, & Ward, 2008), we suggested that foreknowledge of such characteristics as well as widespread suspicion about the veracity of offender selfpresentation in therapy can fuel therapists’ suspicion about the genuineness of client behavior, which may lead therapists to avoid developing a bond with the client and instead focus on tasks and goals in a more detached manner. Indeed such an approach has been advocated when working with clients with these types of serious personality disorders (Galloway & Brodsky, 2003).


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Second, both therapists’ and offender-clients’ ability to form a bond with each other may be hampered by the current strong emphasis on treatment integrity in correctional programs (McMurran, 2006). At its most rigid, highly prescriptive manuals coupled with very tight treatment integrity monitoring can significantly depersonalize the interactions between treatment deliverers and offender clients, create the impression of imposing goals and tasks, and thus hamper the ability to form a therapeutic relationship. Elsewhere, high technical adherence has been demonstrated to damage the therapeutic process (Henry, Strupp, Butler, Schacht, & Binder, 1993). Several factors may alter offender-client responses on the WAI-S. The WAI-S is a self-report instrument and there are substantial grounds for concern about the veracity of self-report measures with offenders (Mills, Loza, & Kroner, 2003), especially in this context where they are nearing parole and they are aware that their progress will be relayed to the parole board (Polaschek & Bell, 2010). Furthermore, offenders often view the world in black-and-white terms (R. R. Ross & Fabiano, 1985), which may lead to their making globalized reports of the therapeutic relationship. And high-risk correctional clients differ in many obvious ways from the stereotype of the typical psychotherapy client. Previous New Zealand research has suggested a preponderance of dismissive attachment styles among violent men (Hudson & Ward, 1997), suggesting that they may undervalue all aspects of the therapeutic alliance, and pay it relatively little attention (Howells & Day, 2006). Lastly, offender-clients in this study were poorly educated, mainly non-European young men with a markedly non-selfreflective personal style. In contrast, given therapists’ and observers’ training and personal experience, they may have a more nuanced view of the relationship, particularly observers, since they are outside of the relationship and therefore would seem to be best placed to integrate information from both parties. Taken together, these factors support the value of examining the possible internal psychometric structure of WAI data across differing perspectives.

Method The Therapeutic Program The research was conducted in Te Whare Manaakitanga,1 a 30-bed purpose-built lowmedium security unit at Rimutaka Prison near Wellington. Since its opening in 1998, the unit has been the home of an intensive group-based 28-week cognitive–behavioral therapy program designed to reduce the risk of violent recidivism in violent male prisoners nearing parole. The program has been found to be effective in reducing both general and violent recidivism (Polaschek, Wilson, Townsend, & Daly, 2005). Three closed treatment groups run at any one time, each with 10 offenders and two cotherapists: a psychologist and a second therapist with previous experience in human service delivery. Each therapy group meets for four 3-hour sessions each week. A clinical supervisor observes one session each week for the entire length of the program, to


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monitor treatment integrity and provide feedback for the therapists. Clinical supervisors made the WAI-S observer ratings in this study.

Sample Data were collected from seven treatment groups over 3.5 years. Those who consented to take part in the research included 70 prisoners, 11 therapists, and 2 supervisors. About 70% of the prisoners were of Maori or Pacific Island ethnicity, with an average treatment age of 31 years. All were nearing the end of a prison sentence for a violent offense. Their average risk of returning to prison on release (i.e., committing another serious offense) was actuarially estimated to be 65% over the next 5 years, and their mean score on the Psychopathy Checklist: Screening Version was 19.5; 52% were at or above the cut-off score used for the diagnosis of psychopathy (i.e., 20; Cooke, Michie, Hart, & Hare, 1999). The therapists were five clinically or forensically trained psychologists and six others with diverse backgrounds in rehabilitation work. Three were male and eight female; three were of Maori ethnicity and the rest New Zealand or Australian European. The average age of therapists was 35 years. The average level of education in years was 18.5 and the average level of years in practice was 5.2 years. The clinical supervisors were both senior clinical psychologists with extensive experience in correctional practice and at Te Whare Manaakitanga.

Measures The WAI-S (Tracey & Kotovic, 1989) was used to measure the therapeutic alliance. Tichenor and Hill (1989) developed an observer-rated version of the WAI by altering the pronouns for each WAI item, and we followed this procedure to construct an observer version of the WAI-S. The reliability and validity of the Working Alliance Inventory in both the long and short form has been repeatedly established (Horvath, 1994a; Tracey & Kotovic, 1989). The short form was chosen for this study because the simple language and short length was most suited to a correctional setting where there are high demands on time for therapists, and offenders often have limited literacy and short attention spans.

Procedure These data were collected as part of a larger longitudinal study of therapeutic alliances in this program. All potential prisoner, therapist, and supervisor participants were approached prior to the start of their therapy program and invited to take part in the study. Prisoners were informed that this research was independent from the Department of Corrections, and their data were confidential. Offenders were instructed that their decision about participation in research would have no effect on their treatment or their applications for parole. Therapists were advised that their ratings would not be made


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available to their employer or clinical supervisor. Supervisors were advised that their ratings were similarly confidential to the study. WAI-S ratings were collected from therapists, offenders, and observers at four points in each cohort of the program, but only data from the first administration— which occurred during the second week of therapy—are reported in this study. The 1st week of the program is introductory, so the 2nd week was selected to approximate the third session of individual therapy, which previous research has suggested is the ideal point for assessing early alliance (Horvath, 1994b). Each prisoner rated each of his cotherapists separately, and the cotherapists worked separately to complete a WAI-S for each of their 10 clients. Observers completed WAI-S forms—one for each client–therapist dyad—immediately after they had observed a session in the 2nd week of therapy. Altogether 49 client ratings, 68 therapist ratings, and 68 observer ratings of the WAI-S were collected for data analysis.

Results The main purposes of this study were to test—using CFA—whether a one-factor, twofactor, or three-factor model was the best fit for the WAI-S data, and to establish whether model fit was equivalent across all three rater perspectives. The one-factor model as found by Corbiere et al. (2006) was made up of the overall WAI-S scale. The twofactor model supported by Andrusyna et al. (2001) places the goals and tasks subscales on one factor and the bond subscale on the other, and the three-factor model mirrors the original three subscales (goals, tasks, bond) developed by Tracey and Kotovic (1989). Before undertaking the CFAs, we planned to average ratings to avoid violating the assumption of independence. We averaged each client’s ratings of his two therapists, each therapist’s rating of the same client, and each observer’s rating of the same client. Preliminary analyses indicated adequate correlations between rating pairs across each perspective (therapist-raters’ r .42, p .01; client-raters’ r .68, p .01; observerraters’ r .69, p .01). Preliminary analyses of the models—treating the raters as independent—reduced model fit: further supporting combined ratings. We then used the AMOS 4 structural equation modeling software (Arbuckle & Wothke, 1999) to run nine CFAs as we tested each of the three hypothesized structures using data from each of the three rater perspectives. In line with general CFA guidelines for thorough testing of model fit, each model was evaluated based on a combination of fit indices; the chi-square goodness of fit, the chi-square/degrees of freedom ratio (χ2/df), the goodness-of-fit index (GFI), the comparative fit index (CFI), the root mean square residual (RMR), and the root mean square error of approximation (RMSEA; Thompson, 2004). Used in this context, desirable chi-square values are low and nonsignificant, indicating that the null hypothesis—that the model is not a good fit—can be rejected. However, as the chi-square statistic is sensitive to sample size (small samples diminish the power of the statistic, whereas large samples frequently produce a significant statistic even when the residual matrix


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Table 1. Results From the CFA of the One-Factor, Two-Factor, and Three-Factor Model for Client (n 49), Including and Excluding Item 4 One-factor Fit indices 2

F df p F2/df GFI CFI RMR RMSEA

Two-factor

Three-factor

Item 4

Item 4

Item 4

Item 4

Item 4

Item 4

171.69 54 .01 3.18 .62 .66 .53 .24

111.85 44 .01 2.54 .67 .77 .22 .20

156.15 53 .01 2.94 .68 .80 .17 .19

96.87 43 .01 2.25 .75 .87 .12 .16

153.35 51 .01 3.01 .67 .70 .50 .23

93.53 41 .01 2.28 .73 .82 .14 .18

Note: CFA confirmatory factor analysis; GFI goodness-of-fit index; CFI comparative fit index; RMR root mean square residual; RMSEA root mean square error of approximation.

is small; Kline, 1994), it is also recommended that its value should be no more than double the degrees of freedom (i.e., the χ2/df ratio should not exceed 2; Byrne, 1989), and one should not rely solely on the chi-square as the only index of fit. The GFI measures the amount of variance and covariance accounted for by the model; good fit is indicated when the value approaches one. The CFI compares the existing model fit with a null uncorrelated model; a value greater than .9 indicates a very good fit (Mueller, 1996). Lastly, RMR and RMSEA measure the size of the residuals in the model and the discrepancy between observed and estimated covariance divided by the degrees of freedom; the results should be lower than .15 and between .05 and .10, respectively (Mueller, 1996). We examined factor reliability using Cronbach’s alpha, interitem correlation coefficients, and factor loadings. Possible modification indices for the models were also tested. Tables 1, 2, and 3 illustrate the fit indices for each model, in the order of rater perspective. Analyses of interitem correlation coefficients indicated that Item 4 (“My therapist/client does not understand what I am trying to accomplish in therapy”) had a very low interitem correlation coefficient and so the tables illustrate the model fit including and excluding Item 4. Overall, the results clearly indicate that removing Item 4 improves model fit across all perspectives, and the models produce acceptable factor loadings. For example, when we examined the factor loadings of the two-factor observer-rated model without Item 4, all but one variable produced factor loadings exceeding .70. The exception was Item 10 (Goals and Tasks loading was .57). Removing Item 10 would have reduced both the model chi-square and the degrees of freedom with negligible improvement on the fit indices, so only Item 4 was removed. Across all three rater perspectives, a one-factor structure was clearly the poorest fit, with high chi-square, χ2/df ratio, RMR, and RMSEA values and low CFI and GFI values across all perspectives. The two-factor structure gave an equally acceptable fit


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Ross et al. Table 2. Results From the CFA of the One-Factor, Two-Factor, and Three-Factor Model for Therapist (n 68), Including and Excluding Item 4 One-factor Fit indices 2


Two-factor

Three-factor

Item 4

Item 4

Item 4

Item 4

Item 4

Item 4

167.28 54 .01 3.10 .64 .76 .16 .21

120.95 44 .01 2.75 .69 .81 .12 .19

145.62 53 .01 2.75 .68 .80 .17 .19

96.87 43 .01 2.25 .75 .87 .12 .16

145.54 51 .01 2.85 .68 .80 .16 .19

96.79 41 .01 2.36 .75 .87 .12 .17


Table 3. Results From the CFA of the One-Factor, Two-Factor, and Three-Factor Model for Observer (n 68) One-factor Fit indices 2


Two-factor

Three-factor

Item 4

Item 4

Item 4

Item 4

Item 4

Item 4

225.61 54 .01 4.18 .47 .65 .10 .29

198.10 44 .01 4.50 .46 .67 .10 .30

96.63 53 .01 1.82 .72 .91 .05 .15

69.12 43 .01 1.61 .76 .94 .04 .13

92.15 51 .01 1.81 .72 .92 .05 .15

65.55 41 .01 1.60 .77 .95 .04 .13


for both observers’ and clients’ alliance ratings, with χ2/df ratios less than or just greater than 2, relatively high GFI, and good CFI values. The RMSEA and RMR values were acceptable for observers but the results from the client perspective suggest a lack of fit in some part of that model. The therapist-rated two-factor structure was a poor fit overall. As Tables 1, 2, and 3 illustrate, the performance of the three-factor structure was similar to the two-factor model both in terms of the values of the fit indices, and the superiority of the observer- and client-rated models over the therapists’ perspective. Overall, the observer data produced the best model fits, yielding approximately equal, but not entirely satisfactory, empirical support for the two- and


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Table 4. Modification of Observer Rated Two-Factor Model Using Error Correlations Allowing Items 6 and 8, and 2 and 12, to Correlate Fit indices F2 df p F2/df GFI CFI RMR RMSEA

Observer two-factor (minus Item 4) 57.95 41 .04 1.41 .78 .96 .04 .10


three-factor structures (i.e., similar respective fit indices). Specifically, the CFI and GFI are within .01 for the two models, with both CFI values being relatively poor, the RMR and RMSEA are identical, the χ2/df ratio is less than 2 for both, and both suffer from a lack of fit according to the chi-square statistic. Therefore, model modifications were carried out to investigate whether the model could be improved. Table 4 displays the results for modifying analyses, allowing for correlation between four error terms identified by AMOS as likely to improve model fit for the observer-rated two-factor structure. Allowing these modifications resulted only in a marginal improvement in model fit (GFI .78 vs. .76, CFI .96 vs. .94), and though the model chi-square is significantly better, ∆χ2(2) 11.17, p .001, it is still significant, suggesting overall minimal improvement over the unmodified model. The reliability analyses displayed in Table 5 complement the CFA results for each model. Using conventional criteria for alpha interpretation (George & Mallery, 2003), individual factor coefficients ranged from acceptable to excellent, with one exception. The reliability of the client-rated goal factor was unacceptably low (α .46). The need to reduce the scale to three items will have contributed to this problem but the average item–total alpha of .53 further suggests that the scale is not psychometrically viable. When these three items were combined with the Task scale items, reliability was good (α .86). Overall reliability was highest for the global factor, reflecting its relatively large number of items. The observer perspective demonstrated the highest reliability across all models and factors (Table 5), followed by therapists’ ratings, and then clients’. This ordering is in contrast to the CFA findings, where the therapist model performed the most poorly. Table 6 illustrates the correlations between factors in the two- and three-factor models using the observer perspective. Goals and Tasks correlate highly (r .90), suggesting they work well as one factor, and also correlate less highly (r .69) with Bond, suggesting they are separate from this factor. Clients’ and therapists’ ratings followed the same pattern, but with lower correlations for therapists, consistent with the generally poor model fits demonstrated for this rater perspective.


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Ross et al. Table 5. Results From the Reliability Analyses of the Proposed Models for Client (n Therapist (n 68), and Observer (n 68) Factors Goals Tasks Bond Goals or Tasks Global

49),

Client

Therapist

Observer

.46 .91 .78 .86 .89

.71 .86 .88 .89 .91

.89 .93 .96 .95 .96

Table 6. Factor Correlations for the Observer Perspective Observer Goals Tasks Bond Goals and Task

Goals

Tasks

– .90 .78

– .87

Bond

– .69

Discussion The aim of this study was to investigate the factor structure of early-treatment WAI-S ratings in a prison rehabilitation sample of high-risk violent offenders, across three rater perspectives. These results appear to be the first reporting ratings from a prisoner sample in group treatment. As we noted earlier, the psychometric performance of the WAI-S with this population is of interest for several reasons. First, correctional environments are challenging places in which to establish a therapeutic alliance (E. C. Ross et al., 2008). Second, there appear to be no published studies in which data have been analyzed from all three rater perspectives. Third, the treatment was delivered in a group setting, which is also distinctive in contrast with much of the previous WAI research. The CFA results support equally a two- or three-factor structure as the best fit for these WAI-S data, but reliability was substantially improved—especially for client ratings—by combining the Goals and Tasks subscales (with Item 4 removed) into a single factor. The correlation between the Goals and Tasks subscales was large and the correlation between the Bond subscale and the Goals/Tasks subscale was moderate, further supporting the utility of a two-factor structure. Therefore, our results best support the use of a two-factor structure, differentiating the relationship-oriented bond factor from the more technically oriented goals and tasks factors. Our conclusions are in line with two studies that found a similar two-factor model was superior or at least the most viable structure (Andrusyna et al., 2001; Hatcher & Barends, 1996).


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The finding that the therapist perspective had the poorest overall model fits may be a result of averaging the therapist raters before fitting the models, given the modest correlation between therapists when rating the same client. But as noted, model fit deteriorated when individual therapists’ data were analyzed independently. Alternatively, the finding may indicate that therapists view the therapeutic alliance in a manner distinct from observers and clients. Perhaps they have a more nuanced view that the WAI-S captured inadequately. However, the relatively lower correlations between therapists’ ratings indicated possible underlying differences between individual therapists, an issue that could be fruitful to explore in further studies. Therapists are often observed to be vulnerable to “capture” by the therapeutic process; it has been argued that good therapists who form strong alliances naturally lose objectivity, which may lead to more globalization of ratings by some therapists, compared to observers (Sullivan, Skovholt, & Jennings, 2005). Interestingly, despite the therapists’ ratings yielding unacceptable factor structures, their overall internal reliability was high, suggesting that they were rating the scale items somewhat consistently. Observers’ ratings produced the strongest factor structure, and the highest factor loadings and factor correlations, perhaps in part because they were also the most reliable raters—a hypothesis that is backed up by previous research (Andrusyna et al., 2001; Brown & O’Leary, 2000; Fenton et al., 2001). The superior factor structure derived from their ratings supports the contention that they may be able to make the most objective and differentiated ratings. Although we had hypothesized that clients would favor a global structure of the alliance, the data did not support this view. Rather, the results suggest that violent men did make a distinction between emotional evaluations of their therapists and their ability to work together on tasks to achieve mutual goals. One research-relevant implication of these results is that with violent offenders it may be important to include the men’s perspectives of the therapy process and not assume they will be biased by personality characteristics or a tendency to fake good. A shared view of the TA is also in contrast to a raft of research pointing to differences between therapists’ and clients’ views of the alliance (Bachelor & Salamé, 2000; Hersoug, Hoglend, Monsen, & Havik, 2001; Taft et al., 2003). This surprising finding requires replication with other offender rehabilitation samples. Although our results were conclusive in their lack of support for the one-factor model, none of the model fits could be described as excellent, perhaps in part because of the small sample size. Most authorities on factor analysis suggest a sample size of at least 100 to create a viable model (Hau & Marsh, 2004). Testing with a larger population could examine whether a clearer difference arose between two and three factor structures, and whether therapist-rated data could produce an adequate model fit. There are differences in quality of fit between rater perspectives, but the structure of the WAI-S does not differ between raters as we hypothesized. Taken together with the finding of poor fit for the one-factor model, this result has interesting clinical implications; it suggests that even given the characteristics of this client population, in a therapeutic relationship all parties distinguished the therapy “work” from the therapeutic


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bond. This is an encouraging result in a rehabilitation program for exceptionally challenging offenders; clients and therapists may be able to concentrate on the goals and tasks of therapy even if they find it difficult to form a bond with each other. From an offender’s perspective, the result may also reflect the face validity of cognitive–behavioral approaches: that is, that the underlying therapy theory—reflected in the links between goals and tasks—is readily understood or accepted by the client early in the therapy socialization process. As we noted above, it has been argued that when working with psychopathic populations, therapists may be more effective if they do not form a bond but endeavour instead to remain therapeutically detached, and to produce change in the client through a collaborative working relationship focusing primarily on goals and tasks (Galloway & Brodsky, 2003). This study did find that—to varying extents—data from all rater perspectives supported Galloway and Brodsky’s distinction between bond and “therapy work.” However, our results, including those from other recent analyses of this data set, do not support the clinical stance proposed by Galloway and Brodsky. We found that early-in-therapy alliance scores—including bond scores—were in the range typical of general psychotherapy samples, and did not predict the amount of change over the course of the program. However, therapy alliance increased over the months of treatment, and alliance increases were related to greater change (Polaschek & Ross, 2010). Taken together, these two studies’ results argue that if bond development initially appears difficult, therapy with high-risk violent psychopaths should begin by concentrating on goals and tasks; improvements in bond may then follow. In terms of future research, empirical support for viewing the alliance as a two-part construct supports the continued separate measurement and analysis of bond and working aspects of the alliance. First, further research is needed to establish whether both factors of the therapeutic alliance are equally necessary in achieving therapeutic gain (Galloway & Brodsky, 2003). Second, we can investigate whether certain aspects of clients’ and therapists’ personality are differentially related to the two parts of the alliance. For example, it would be interesting to see whether client and therapist attachment affects the bond more than goals and tasks, or whether in fact psychopaths score higher on goals and tasks than bond. The current study is a valuable preliminary step in the investigation of the psychometric structure of the therapeutic relationship in high-risk offender rehabilitation. Further research is needed with larger samples to address some of the limitations of this study. In addition, the relationship between the therapeutic alliance and client, therapist, and program factors remains to be investigated, alongside the course of the alliance over the entire length of a program, and its association with changes in treatment that reduce the risk of further violent offending. Declaration of Conflicting Interests The author(s) declared no conflicts of interests with respect to the authorship and/or publication of this article.


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Funding The author(s) received no financial support for the research and/or authorship of this article.

Note 1.

Formerly known as the Rimutaka Violence Prevention Unit.

References Andrews, D. A., & Bonta, J. (2006). The psychology of criminal conduct (4th ed.). Cincinnati OH: Anderson. Andrusyna, T. P., Tang, T. Z., DeRubeis, R. J., & Luborsky, L. (2001). The factor structure of the Working Alliance Inventory in cognitive-behavioral therapy. Journal of Psychotherapy Practice and Research, 10, 173-178. Arbuckle J. L., & Wothke, W. (1999). AMOS 4.0 user’s guide. Chicago, IL: Smallwaters. Bachelor, A., & Salamé, R. (2000). Participants’ perceptions of dimensions of the therapeutic alliance over the course of therapy. Journal of Psychotherapy Practice and Research, 9, 39-53. Bordin, E. S. (1979). The generalizability of the psychoanalytic concept of the working alliance. Psychotherapy: Theory, Research and Practice, 16, 252-260. Brown, P. D., & O’Leary, K. D. (2000). Therapeutic alliance: Predicting continuance and success in group treatment for spouse abuse. Journal of Consulting and Clinical Psychology, 68, 340-345. Byrne, B. M. (1989). A primer of LISREL: Basic applications and programming for confirmatory factor analytic models. New York, NY: Springer. Cooke, D. J., Michie, C., Hart, S. D., & Hare, R. D. (1999). Evaluating the screening version of the Hare Psychopathy Checklist–Revised (PCL:SV): An item response theory analysis. Psychological Assessment, 11, 3-13. Corbiere, M., Bisson, J., Lauzon, S., & Ricard, N. (2006). Factorial validation of a French shortform of the Working Alliance Inventory. International Journal of Methods in Psychiatric Research, 15, 36-45. Fenton, L. R., Cecero, J. J., Nich, C., Frankforter, T. L., & Carroll, K. M. (2001). Perspective is everything: The predictive validity of six working alliance instruments. Journal of Psychotherapy Practice and Research, 10, 262-268. Freud, S. (1913). On the beginning of treatment: Further recommendations on the technique of psychoanalysis. In J. Strachey (Ed.), The standard edition of the complete psychological works of Sigmund Freud (pp. 122-144). London, UK: Hogarth Press. Galloway, V. A., & Brodsky, S. L. (2003). Caring less, doing more: The role of therapeutic detachment with volatile and unmotivated clients. American Journal of Psychotherapy, 57, 32-38. George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference (11.0 update, 4th ed.). Boston, MA: Allyn & Bacon.


15

Ross et al.

Hatcher, R. L., & Barends, A. W. (1996). Patients’ view of the alliance in psychotherapy: Exploratory factor analysis of three alliance measures. Journal of Consulting and Clinical Psychology, 64, 1326-1336. Hatcher, R. L., & Gillaspy, J. A. (2006). Development and validation of a revised short version of the Working Alliance Inventory. Psychotherapy Research, 16, 12-25. Hau, K.-T., & Marsh, H. W. (2004). The use of item parcels in structural equation modelling: Non-normal data and small sample sizes. British Journal of Mathematical & Statistical Psychology, 57, 327-352. Henry, W. P., Strupp, H. H., Butler, S. F., Schacht, T. E., & Binder, J. L. (1993). Effects of training in time-limited dynamic psychotherapy: Changes in therapist behavior. Journal of Consulting and Clinical Psychology, 61, 434-440. Hersoug, A. G., Hoglend, P., Monsen, J. T., & Havik, O. E. (2001). Quality of working alliance in psychotherapy: Therapist variables and patient/therapist similarity as predictors. Journal of Psychotherapy Practice and Research, 10, 205-216. Horvath, A. O. (1994a). Empirical validation of Bordin’s pantheoretical model of the alliance: The Working Alliance Inventory perspective. In A. O. Horvath & L. S. Greenberg (Eds.), The working alliance: Theory, research, and practice (pp. 109-128). New York, NY: John Wiley. Horvath, A. O. (1994b). Research on the alliance. In A. O. Horvath & L. S. Greenberg (Eds.), The working alliance: Theory, research, and practice (pp. 259-286). New York, NY: John Wiley. Horvath, A. O., & Greenberg, L. S. (1989). The development and validation of the Working Alliance Inventory. Journal of Counseling Psychology, 56, 223-233. Horvath, A. O., & Luborsky, L. (1993). The role of the therapeutic alliance in psychotherapy. Journal of Consulting and Clinical Psychology, 61, 561-573. Howells, K., & Day, A. (2006). Affective determinants of treatment engagement in violent offenders. International Journal of Offender Therapy and Comparative Criminology, 50, 174-186. Hudson, S. M., & Ward, T. (1997). Intimacy, loneliness, and attachment style in sexual offenders. Journal of Interpersonal Violence, 12, 323-339. Kline, P. (1994). An easy guide to factor analysis. London, UK: Routledge. Marshall, W. L., Fernandez, Y. M., Serran, G. A., Mulloy, R., Thornton, D., Mann, R. E., & Anderson, D. (2003). Process variables in the treatment of sexual offenders: A review of the relevant literature. Aggression and Violent Behavior, 8, 205-234. Marshall, W. L., & Serran, G. A. (2004). The role of the therapist in offender treatment. Psychology, Crime and Law, 1, 309-320. Marshall, W. L., Serran, G. A., Fernandez, Y. M., Mulloy, R., Mann, R. E., & Thornton, D. (2003). Therapist characteristics in the treatment of sexual offenders: Tentative data on their relationship with indices of behaviour change. Journal of Sexual Aggression, 9, 25-30. McMurran, M. (2006). Drug and alcohol programmes: Concept, theory, and practice. In C. R. Hollin & E. J. Palmer (Eds.), Offending behaviour programmes: Development, application, and controversies (pp. 179-207). Chichester, UK: Wiley.


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Mills, J. F., Loza, W., & Kroner, D. G. (2003). Predictive validity despite social desirability: Evidence for the robustness of self-report among offenders. Criminal Behaviour and Mental Health, 13, 140-150. Mueller, R. O. (1996). Basic principles of structural equation modeling: An introduction to LISREL and EQS. New York, NY: Springer. Polaschek, D. L. L. (2008). Rimutaka Violence Prevention Unit evaluation report V: Interim/ progress report on prospective evaluation: VRS pilot project, psychometric measures of treatment change. Unpublished report to the New Zealand Department of Corrections, Wellington, New Zealand. Polaschek, D. L. L., & Bell, R. K. (2010). Using psychometric self-report scales inside and outside rehabilitation: Is offender self-report affected by the context? Manuscript in preparation. Polaschek, D. L. L., & Ross, E. C. (2010). Do early therapeutic alliance, motivation, and change readiness predict therapy outcomes for high risk violent prisoners? Criminal Behaviour and Mental Health, 20, 100-111. Polaschek, D. L. L., Wilson, N. J., Townsend, M. R., & Daly, L. R. (2005). Cognitive-behavioral rehabilitation for high-risk violent offenders: An outcome evaluation of the Violence Prevention Unit. Journal of Interpersonal Violence, 20, 1-17. Ross, E. C., Polaschek, D. L. L., & Ward, T. (2008). The therapeutic alliance: A theoretical revision for offender rehabilitation. Aggression and Violent Behavior, 13, 462-480. Ross, R. R., & Fabiano, E. A. (1985). Time to think: A cognitive model of delinquency prevention and offender rehabilitation. Johnson City, TN: Institute of Social Sciences and Arts. Skeem, J. L., Eno Louden, J., Polaschek, D. L. L., & Camp, J. (2007). Assessing relationship quality in mandated community treatment: Blending care with control. Psychological Assessment, 19, 397-410. Sullivan, M. F., Skovholt, T. M., & Jennings, L. (2005). Master therapists’ construction of the therapy relationship. Journal of Mental Health Counseling, 27, 48-71. Taft, C. T., Murphy, C. M., King, D. W., Musser, P. H., & DeDeyn, J. M. (2003). Process and treatment adherence factors in group cognitive-behavioral therapy for partner violent men. Journal of Consulting and Clinical Psychology, 71, 812-820. Taft, C. T., Murphy, C. M., Musser, P. H., & Remington, N. A. (2004). Personality, interpersonal, and motivational predictors of the working alliance in group cognitive-behavioral therapy for partner violent men. Journal of Consulting and Clinical Psychology, 72, 349-354. Thompson, B. (2004). Exploratory and confirmatory factor analysis. Washington, DC: American Psychological Association. Tichenor, V., & Hill, C. E. (1989). A comparison of six measures of working alliance. Psychotherapy, 26, 195-199. Tracey, T. J., & Kotovic, A. M. (1989). Factor structure of the Working Alliance Inventory. Psychological Assessment, 37, 369-375.
