Introduction
Clinical supervision is considered one of the most important training methods for psychotherapist development (Rønnestad et al., Reference Rønnestad, Orlinsky, Schröder, Skovholt and Willutzki2018). Within supervision research, there is variance regarding the quality of supervision. For example, some studies report that up to one-third of clinical and counselling psychologists are indeed somewhat or very dissatisfied with their supervision (e.g. McMahon and Errity, Reference McMahon and Errity2013). There is also evidence that marginalized supervisees in particular report emotional difficulties, for example, due to microagressions in supervision (Jendrusina and Martinez, Reference Jendrusina and Martinez2019). In turn, systematic reviews with a focus on cognitive behaviour therapy and related approaches report that supervisees are highly satisfied with supervision, experience positive relationships with their supervisors, indicate higher job satisfaction, and gain feelings of self-efficacy and self-awareness, whereas the effects on supervisees’ skill development remain unclear (Alfonsson et al., Reference Alfonsson, Parling, Spännargård, Andersson and Lundgren2018; Kühne et al., Reference Kühne, Maas, Wiesenthal and Weck2019; Milne and James, Reference Milne and James2000). It is therefore important to regularly monitor the quality of supervision sessions and adjust them if necessary.
Conceptual overlap
When evaluating supervision, researchers use terms such as ‘quality’, ‘effectiveness’, ‘satisfaction’ and ‘supervisory relationship’, whereby satisfaction and relationship are the most frequent variables of interest (Milne, Reference Milne2018). These variables are not always clearly defined and reciprocally influence one another. For example, the terms quality and effectiveness are defined as the successful promotion of skills and techniques and securing quality of treatment but also include the quality of the supervisory relationship (Winstanley and White, Reference Winstanley and White2011; Zarbock et al., Reference Zarbock, Drews, Bodansky and Dahme2009). Corresponding measurements (e.g. the MCSS-26 and STEP-SV) often include relationship subscales regarding feeling understood and supported by supervisors. The supervisory relationship, in turn, often refers to the dyadic relationship, including broader cultural, educational and evaluative aspects (Beinart, Reference Beinart, Watkins and Milne2014). Consequently, some relationship instruments also include subscales regarding skill improvement (e.g. Supervisory Relationship Questionnaire; Palomo et al., Reference Palomo, Beinart and Cooper2010). Similarly, satisfaction with supervision is regarded as ‘the supervisee’s subjective perception of how well or badly supervision progressed’ (Gonsalvez et al., Reference Gonsalvez, Hamid, Savage and Livni2017: p. 95). In this case, it is up to the supervisee to decide on what he or she bases his or her assessment, for example, on skills, methodological factors, trust and respect (e.g. the Supervisee Satisfaction Questionnaire; Ladany et al., Reference Ladany, Hill, Corbett and Nutt1996). Given these intertwined terms and definitions, it is not surprising that the supervisory relationship and supervision effectiveness are strong predictors of supervisee satisfaction (i.e. r=.81 across 27 studies; Park et al., Reference Park, Ha, Lee, Lee and Lee2019).
For these reasons, many researchers advocate for distinguishing between satisfaction and effectiveness (e.g. Binder Reference Binder1993; Gonsalvez et al., Reference Gonsalvez, Hamid, Savage and Livni2017; Milne and James, Reference Milne and James2000; O’Donovan and Kavanagh, Reference O’Donovan, Kavanagh, Watkins and Milne2014). Satisfying supervision might not automatically be effective, just as satisfying doughnuts are not automatically nutritious (Goodyear and Bernard, Reference Goodyear and Bernard1998). In line with this idea is the finding that psychotherapy trainees who did or did not use a feedback system in supervision did not report any significant differences in satisfaction with supervision, although trainees who used that system achieved better client outcomes (Reese et al., Reference Reese, Usher, Bowman, Norsworthy, Halstead, Rowlands and Chisholm2009). Thus, supervision evaluation should be clearly defined and include aspects such as learning aside from satisfaction (Kühne et al., Reference Kühne, Maas, Wiesenthal and Weck2019; Lambert and Ogles, Reference Lambert and Ogles1997). However, only a few questionnaires address the question of whether supervision is effective. In addition, researchers have repeatedly criticized the low validity of measurements and reliance on cross-sectional designs (Alfonsson et al., Reference Alfonsson, Parling, Spännargård, Andersson and Lundgren2018; Kühne et al., Reference Kühne, Maas, Wiesenthal and Weck2019; Watkins et al., Reference Watkins, Vîşcu and Cadariu2021). Furthermore, most of the questionnaires are rather long (e.g. Supervisory Relationship Questionnaire, 67 items; Palomo et al., Reference Palomo, Beinart and Cooper2010) and are not accessible free of charge (e.g. the Manchester Clinical Supervision Scale-26, MCSS-26; Winstanley and White, Reference Winstanley and White2011). Finally, they are not suitable for capturing information about the effectiveness of single sessions in particular (e.g. the Supervisory Working Alliance, SWAI; Efstation et al., Reference Efstation, Patton and Kardash1990; the Evaluation Process within Supervision Inventory, EPSI; Lehrman-Waterman and Ladany, Reference Lehrman-Waterman and Ladany2001).
All in all, considering the previous literature (Gonsalvez et al., Reference Gonsalvez, Hamid, Savage and Livni2017; Maiwald et al., Reference Maiwald, Kühne, Junga, Rudolph, Witthöft, Lüthke, Heid and Weck2019; Milne, Reference Milne2007; O’Donovan and Kavanagh, Reference O’Donovan, Kavanagh, Watkins and Milne2014; Winstanley and White, Reference Winstanley and White2011; Zarbock et al., Reference Zarbock, Drews, Bodansky and Dahme2009), supervision quality can be defined as a combination of satisfaction, relationship and effectiveness. Satisfaction is the subjective evaluation of whether supervisees and supervisors experience a positive emotional response to the session. Relationship is the positive interaction between supervisee and supervisor, including an emotional bond. Effectiveness is the extent to which supervisees and supervisors believe that their expectations are met and the session’s benefits are greater than its costs by achieving intended outcomes such as professional development and improvement of supervisees’ therapeutic competencies.
Supervision measurements that focus on effectiveness and single supervision sessions
Evaluating supervision session-by-session would improve the investigation of variables that are associated with person and context variables contributing to effective or ineffective supervision, supervisory relationship rupture and repair processes, or the transfer of tasks developed in supervision to the therapy room. In addition, it enables an examination of the variation of the relevant variables over time. For example, Ybrandt et al. (Reference Ybrandt, Sundin and Capone2016) showed that the supervisory relationship fluctuated from the perspectives of novice therapists in their group supervision. In the first phase of therapy, the relationship with their supervisors worsened and then improved but decreased again towards the end of therapy. The authors emphasize that especially for a good relationship with new therapists, it is important to respond to their fears and lack of self-confidence in supervision.
To our knowledge, there are only two promising instruments for examining single supervision sessions, the Short Scale to Evaluate Supervision and Supervisor Competence (SE-SC8; Gonsalvez, Reference Gonsalvez2021) and the Questionnaire to Evaluate Supervision (STEP-SV; Zarbock et al., Reference Zarbock, Drews, Bodansky and Dahme2009): Gonsalvez and colleagues (Gonsalvez, Reference Gonsalvez2021; Gonsalvez et al., Reference Gonsalvez, Hamid, Savage and Livni2017) developed several self-report scales to monitor (group) supervisor competence based on the competence framework of supervision (American Psychological Association, 2014), including normative (e.g. demonstrating expertise), formative (enhancing reflective skills), and restorative (e.g. being supportive) skills. The 8-item short scale (SE-SC8; Gonsalvez, Reference Gonsalvez2021) demonstrated adequate convergent and discriminant validity and good internal reliability in a sample of n=122 supervisees. However, the scale primarily assesses supervisor competence and is therefore of limited use for a general assessment of supervision quality from the perspectives of supervisees and supervisors.
The STEP-SV is a 12-item questionnaire for supervision effectiveness, with both supervisee and supervisor versions. It showed good internal consistency in a sample of n=90 trainees and n=37 supervisors. Although convergent or discriminant validities are still unknown, they demonstrated concurrent validity insofar as the Relationship and Clarifying subscales positively predicted satisfaction with supervision. However, the authors indicated that some items from the supervisor version did not load on the expected factors in an exploratory factor analysis (EFA). Together with the low or non-significant correlations between supervisee and supervisor perspectives, this could mean that the items were understood differently by each group. Thus, an examination of the equivalence of ‘supervision effectiveness’ across both groups, for example, by testing measurement invariance (Putnick and Bornstein, Reference Putnick and Bornstein2016), has yet to be conducted. Finally, the authors also state that their items might be too generic and ‘confounded with consumer and provider satisfaction and does not only assess the quality of supervision’ (Zarbock et al., Reference Zarbock, Drews, Bodansky and Dahme2009: p. 202). For these reasons, psychometrically sound self-report measures are needed to study the processes of effective supervision sessions in more detail (Knox and Hill, Reference Knox and Hill2021; Watkins et al., Reference Watkins, Vîşcu and Cadariu2021).
Variables contributing to effective supervision sessions
Regardless of the exact definition of the constructs, satisfaction with and the effectiveness of supervision depend on many variables that are associated with either the supervisees/supervisors or the context of supervision. For example, at the person level, fear of negative evaluation can lead to non-disclosure of errors in clinical supervision (Gray et al., Reference Gray, Ladany, Walker and Ancis2001), which, in turn, is associated with lower satisfaction with supervision. In contrast, there is a positive correlation between supervision satisfaction and supervisee self-efficacy (Kühne et al., Reference Kühne, Maas, Wiesenthal and Weck2019). At the contextual level, the applied techniques and the setting may also influence supervision quality. For example, the most frequently used technique in supervision is case discussion, followed by information brokering and recommending literature (Weck et al., Reference Weck, Kaufmann and Witthöft2017). However, providing competence feedback in particular and using role-playing are positively correlated with an improved supervisory relationship and satisfaction with supervision (Lehrman-Waterman and Ladany, Reference Lehrman-Waterman and Ladany2001; Maiwald et al., Reference Maiwald, Kühne, Junga, Rudolph, Witthöft, Lüthke, Heid and Weck2019; Reiser and Milne, Reference Reiser and Milne2016; Weck et al., Reference Weck, Kaufmann and Witthöft2017). Furthermore, group supervision is one of the most common supervision modes and has numerous advantages over individual sessions (Ögren et al., Reference Ögren, Boalt Boëthius, Sundin, Watkins and Milne2014). These include the opportunity to learn from peers and receive feedback. However, group supervision also poses particular challenges in terms of group dynamics, time management, and supervisory relationships (Hedegaard, Reference Hedegaard2020; Ögren et al., Reference Ögren, Boalt Boëthius, Sundin, Watkins and Milne2014). The extent to which the quality of supervision differs in group or individual sessions is still an open question. In addition, concepts of supervision differ between theoretical orientations (Nelson, Reference Nelson2014; Weck et al., Reference Weck, Kaufmann and Witthöft2017). Two of the most discussed orientations are psychodynamic therapy (PDT) and cognitive behaviour therapy (CBT). While PDT focuses mainly on the transference and counter-transference of supervisees, CBT supervision is more goal-oriented and addresses supervisees’ assumptions that hinder the therapeutic process. However, Weck et al. (Reference Weck, Kaufmann and Witthöft2017) found rather small differences between CBT and PDT. As only a few studies have investigated the relationships between contextual and personal variables and the effectiveness of single supervision sessions, this study aims to contribute to answering these research questions.
Objectives
The present study has two goals. First, we aimed to develop a brief self-report questionnaire (i.e. the Quality of Supervision Questionnaire; QSQ) that (a) focuses on effectiveness instead of satisfaction, (b) is suitable for evaluating the quality of single supervision sessions, and (c) can be used by both supervisees and supervisors to closely monitor the supervision process from both perspectives. The second goal of this study was to better understand which variables are associated with more effective supervision sessions. On the one hand, we examined the relationships between the QSQ and person variables, such as being a supervisee or supervisor, and supervisee fear of negative evaluation and self-efficacy. On the other hand, we studied context variables, such as the setting (e.g. individual vs group supervision, CBT vs PDT) and specific techniques (e.g. case discussion, competence feedback). We expected positive associations between the QSQ and self-efficacy and competence feedback but negative correlations with fear of negative evaluation. However, the analyses with respect to the supervisee–supervisor differences and the setting were exploratory without directed hypotheses.
Method
Participants, recruitment, and cligibility criteria
Between March and August 2022, psychotherapy trainees (supervisees) and supervisors were recruited by contacting 254 training institutes across Germany via email. All institutes offer state-approved training in one of four treatments (i.e. CBT, psychodynamic therapy, psychoanalysis, and systemic therapy) that are recognized under social law. The survey was viewed by 724 people, 526 of whom responded to the questions. The inclusion criteria were (a) participation in psychotherapy training or work as a licensed supervisor and (b) provided informed consent (n=16 did not provide consent and were excluded). The final sample consisted of N=510 participants (n=374 supervisees, n=136 supervisors). Table 1 shows the sample characteristics for a subset of the data (n=354), namely, for those participants who also provided information on their demographic variables at the end of the survey. On average, the supervisees based their answers on a supervision session that occurred primarily in an individual (n=150, 60.0%) rather than a group-based setting (n=100, 40.0%). It was scheduled on average 9.46 days after the actual treatment session (SD=11.63; range 0–115) and lasted approximately 79.99 minutes (SD=44.31, range: 22–240; note that group-based supervision must be conducted every fourth therapy session). At the time of recruitment, the supervisees were currently treating 7.36 patients (SD=3.82, range: 0–27) in in-patient and/or out-patient settings on average, but most of whom were in out-patient settings (n=218, 86.5%; in-patient: n=34, 13.5%). On average, the supervisors had been working as supervisors for 12.4 years (SD=10.34, < 1 year to 49 years). They based their answers on individual supervision rather than group supervision (n=86, 85.1% vs n=15 group supervision, 14.9%), and sessions lasted approximately 68.76 minutes (SD=38.37, range: 45–240).
Table 1. Sample characteristics
The sample size was n=101 for supervisors and n=250–253 for supervisees. aEither the number of current supervisors with whom a supervisee is working, or the number of supervisees a supervisor is currently supervising.
Sample size and power
The sample size was guided by the analysis requiring the largest sample (i.e. EFA). Recommendations vary between authors and reach from at least n=300 to 400 (Goretzko et al., Reference Goretzko, Pham and Bühner2021). Thus, we aimed for a sample size of at least n=300.
Item generation
Items were developed using top-down and bottom-up procedures: (1) reviewing the literature and deriving a definition for quality of supervision, (2) considering existing supervision measures, (3) interviewing six supervisees and supervisors about their experiences, and (4) revising the first items. In accordance with the definitions of ‘supervision quality’ described above, including its elements satisfaction, relationship, and effectiveness, we formulated items (e.g. satisfaction: ‘All my concerns were addressed satisfactorily’; relationship: ‘We were a good team’; and effectiveness: ‘The session was effective’). Items of existing supervision measures (Efstation et al., Reference Efstation, Patton and Kardash1990; Gonsalvez et al., Reference Gonsalvez, Hamid, Savage and Livni2017; Ladany et al., Reference Ladany, Hill, Corbett and Nutt1996; Lehrman-Waterman and Ladany, Reference Lehrman-Waterman and Ladany2001; Palomo et al., Reference Palomo, Beinart and Cooper2010; Winstanley and White, Reference Winstanley and White2011; Zarbock et al., Reference Zarbock, Drews, Bodansky and Dahme2009) were considered when they matched our definitions and were adapted for assessing the quality of single supervision sessions (e.g. ‘Now it is easier for me to understand my patient(s)’, based on the Supervisory Working Alliance Inventory (SWAI); Efstation et al., Reference Efstation, Patton and Kardash1990).
In addition, the third author (A.S.) conducted six independent expert interviews. Three supervisees (women, age: M=30, SD=2.2, two CBT, one psychodynamic; all treating out-patients) and three supervisors (one woman, two men, age: M=46, SD=7.7; supervision experience: M=9 years, all CBT) were asked to describe details of an individual supervision session that they perceived as particularly effective or ineffective. All interviews were audio-recorded, transcribed, and categorized. The content analysis yielded four main themes of effective supervision sessions (productive structure, positive relationship, focus on specific situations rather than global topics, and reflection on and connection between theory and practice), which were used to create additional items (e.g. ‘We linked theoretical knowledge to therapeutic practice’). A first pool of 81 items was developed. All co-authors provided feedback regarding content validity, redundancy, and formulations, and the pool was reduced to 38 items.
Measures
Measures for validity analyses
Successful supervision
The questionnaire to evaluate supervision (STEP-SV; Zarbock et al., Reference Zarbock, Drews, Bodansky and Dahme2009) assesses successful supervision with three subscales on a rating scale from 1 (not at all true) to 7 (totally true): Clarifying (5 items), Relationship (3 items), and Problem Coping (4 items). The internal consistencies were α=.94 (total scale), α=.88 (clarifying), α=.83 (problem solving) and α=.85 (relationships) for the supervisees, and α=.87 (total scale), α=.78 (clarifying), α=.76 (problem solving) and α=.60 (relationships) for the supervisors.
Supervision alliance
We translated the Supervisory Working Alliance Inventory (SWAI; Efstation et al., Reference Efstation, Patton and Kardash1990) into German using forward and back translation through a native English speaker. It is one of the most widely used instruments for the assessment of supervisory relationships and consists of 19 items for supervisees (e.g. ‘My supervisor makes the effort to understand me’) and 23 items for supervisors (e.g. ‘I help my trainee stay on track during our meetings’), and it uses a rating scale from 1 (almost never) to 7 (almost always). The internal consistencies were α=.93 (supervisees) and α=.72 (supervisors).
Measures of person and context variables
Fear of negative evaluation
The German version of the revised Brief Fear of Negative Evaluation Scale (Reichenberger et al., Reference Reichenberger, Schwarz, König, Wilhelm, Voderholzer, Hillert and Blechert2016) assessed supervisees’ anxiety of being negatively evaluated by others in social situations with 12 items (e.g. ‘I worry about what other people will think of me even when I know it doesn’t make any difference’; Cronbach α=.94) and a rating scale from 1 (not at all characteristic for me) to 5 (absolutely characteristic for me).
Counselling self-efficacy
Supervisee self-efficacy was measured with two subscales of the German Counselor Activity Self-Efficacy Scales (CASES-R; Hahn et al., Reference Hahn, Weck, Witthöft and Kühne2021): Confidence in Applying Basic Therapy Skills (15 items) and Managing a Session (9 items). The items used a 10-point Likert scale (0 = ‘no confidence’ to 9 = ‘complete confidence’; Cronbach’s α=.89).
Supervision techniques
We asked the participants to what extent 11 methods (Maiwald et al., Reference Maiwald, Kühne, Junga, Rudolph, Witthöft, Lüthke, Heid and Weck2019; Weck et al., Reference Weck, Kaufmann and Witthöft2017) were applied in their last supervision session (e.g case discussion, role play). They described the extent on a rating scale from 1 (not at all) to 4 (very much).
Competence feedback and adherence to feedback rules
We asked participants if they received explicit feedback on their therapeutic competences (yes/no). There are general guidelines for providing feedback to learners to increase its effectiveness. For example, feedback should be behaviour-based and concrete, provided as soon after the exercise as possible, and given with permission from the learner (Freeman, Reference Freeman1985; e.g. Hattie and Timperley, Reference Hattie and Timperley2007; Heckman-Stone, Reference Heckman-Stone2004). Thus, we also assessed the extent to which those typical ‘feedback rules’ were met (e.g. ‘The feedback referred to a specific behaviour or task’, ‘I had the chance to decide whether I would like to receive feedback’, ‘The feedback was personally hurtful’; Cronbach’s α=.82 and α=.67 for supervisees and supervisors, respectively). The rating scale ranged from 1 (do not agree at all) to 5 (agree completely).
Data analysis
The analyses were performed using RStudio 1.1.456 (RStudio Team, 2015).
Handling of missing data
The data included missing values (supervisees: 0.57%, supervisors: 0.65%). Visual inspection of the response pattern indicated that missing values most likely arose because participants dropped out of the survey and not for any other reason related to the observed data. Thus, the percentage of missing values in the QSQ items (supervisees: 8.78%; supervisors: 5.63%) was less than in the following questionnaires (supervisees: 32.20% for CASES-R; supervisors: 25.27% for SWAI), and Little’s test per questionnaire indicated that most data were missing at random (p ≥ .132; see ESM2 in the Supplementary material for details). Thus, we conducted multiple imputation by chained equations (MICE; imputation was conducted per questionnaire in combination with some demographic variables with 20 iterations). All results from the following analyses (i.e. descriptive analyses, EFA, group comparisons, correlations) were pooled across all 20 datasets according to Rubin’s rules (Heymans and Eekhout, Reference Heymans and Eekhout2019), including Fishers’ Z transformation of correlations. In the measurement invariance, missing values were addressed using full-information maximum likelihood estimation (FIML), as it produces results comparable to those of multiple imputation (e.g. Liu and Sriutaisuk, Reference Liu and Sriutaisuk2021) and is a straightforward method built into R as a standard procedure.
Exploratory factor analysis and item selection
The EFA was performed on the supervisee data, and the obtained solution was applied to the supervisor data as part of the measurement invariance analysis (see below). Consistent with our definition of supervision quality (see ‘Item generation’ section above), we included the entire item pool in the EFA, including items on satisfaction, relationship, and effectiveness. With the help of EFA, we wanted to gain information about the differentiability between these aspects. Pre-requisites for the EFA were tested with Bartlett’s test and Shapiro’s test of multivariate normality. To determine the number of factors, we combined parallel analysis and the empirical Kaiser criterion. In line with current recommendations in the literature (Goretzko et al., Reference Goretzko, Pham and Bühner2021), we calculated and compared multiple EFAs with different rotation methods (i.e. promax vs oblimin as oblique rotations), different estimations depending on the data distribution (i.e. maximum likelihood (ML) estimation for normal distributions or weighted least squares (WLS) estimation with polychoric correlations for non-normal data), and different factor solutions (e.g. three vs four factors). In addition, we added bootstrapping with 2000 iterations. The best fitting factor solution was chosen in accordance with the following factor retention criteria: (1) at least four items per factor (Goretzko et al., Reference Goretzko, Pham and Bühner2021), (2) high explained variance, (3) sufficient content validity, (4) good item communalities (h 2>.60), and (5) good item statistics, preferring items with moderate item difficulties, normal distribution, good item discrimination (r>.30), and at least moderate correlations with all other items (r>.30). Finally, before the factor structure was further examined using measurement invariance (see next section), we conducted an additional EFA on the combined dataset (supervisees and supervisors) with the final 12 items to ensure that the factor solution did not change with fewer items.
Measurement invariance
Before comparing and interpreting the mean differences between supervisees and supervisors, we tested for measurement invariance (MI), which assesses the equivalence of a construct (i.e. interpretation of items) across groups (Byrne and Watkins, Reference Byrne and Watkins2003; Putnick and Bornstein, Reference Putnick and Bornstein2016). Using multi-group confirmatory analysis (lavaan; Rosseel, Reference Rosseel2012), we tested for configural (equivalence of factorial structure), metric (equivalence of factor loadings), scalar (equivalence of item intercepts), and strict (equivalence of item residuals) MIs. Configural model fit was regarded as acceptable when the root mean square error of approximation was RMSEA < .08 and the standardized root mean square residual was SRMR < .08, and the comparative fit index was CFI >.90 (Byrne and Watkins, Reference Byrne and Watkins2003). Three additional models with increasing restrictions (metric, scalar and strict) were specified and compared with each other. A significant worsening of model fit (i.e. non-invariance) was indicated by a ∆CFI ≥ –.005, ∆RMSEA ≥ .010, and ∆SRMR ≥ .025 (metric MI) or ∆SRMR ≥ .005 (scalar MI; Chen, Reference Chen2007). These cut-off criteria are suggested when sample sizes are unequal.
Reliability and validity
Internal consistency was determined using Cronbach’s alpha. We determined the convergent validity by calculating the correlations between the QSQ and the STEP-SV or SWAI. We expected the total scale of the QSQ to have a high correlation with the STEP-SV and the SWAI (with r ≥ .50).
Associations with person and context variables
To examine person variables, we compared all the outcome scores of supervisees and supervisors (i.e. the means of the QSQ and its subscales, the STEP-SV and its subscales, the SWAI, and the adherence to feedback rules). To this end, we used a series of independent t-tests with Bonferroni–Holm adjusted significance levels for 10 tests and calculated Cohen’s d as an effect size measure. In addition, we calculated the correlations between the QSQ and supervisees’ fear of negative evaluation and their self-efficacy.
To examine context variables in both datasets (i.e. supervisee and supervisor), we compared differences in the QSQ for the following variables: (a) supervision mode (individual vs group supervision), (b) theoretical orientation (CBT vs PDT), and (c) competence feedback (feedback vs no feedback). To this end, we used a series of independent t-tests for each variable, applying Bonferroni–Holm adjusted significance levels for four tests (total scale and three subscales) each and calculating Cohen’s d as an effect size measure. Furthermore, we calculated correlations in each dataset (i.e. supervisee and supervisor) between the QSQ and the variables: (a) techniques in supervision and (b) adherence to feedback rules.
Results
Item statistics
Overall, item difficulty was rather high (most items P ≥ .75 and M ≥ 3.73; Table 2), and item discrimination was good (r i(t-i) ≥ .38; Table 2). Based on inter-item correlations, we excluded two items (24, 27) with low associations with all other items (r < .30) from all further analyses because they targeted quite situation-specific aspects that did not apply to all supervision sessions (i.e. feedback based on video recordings, discussion of therapy documents).
Table 2. Item statistics and results of exploratory factor analysis with three Factors and oblimin rotation (imputed data set with n=374 supervisees
F1, Factor 1 (Effectiveness); F2, Factor 2 (Relationship); F3, Factor 3 (Procedural Knowledge); h 2, communality; P, item difficulty; r i(t-i), item discrimination. Final items of QSQ are printed in bold italics.
Exploratory factor analysis and item selection
Pre-requisites
Shapiro’s test of normality was significant (p < .001); thus, WLS estimation with polychoric correlations was used in the EFA. Bartlett’s test of sphericity was significant (combined χ 2 , F(630, 302.59)=18.086, p < .001), indicating that the data were suitable for EFA. Parallel analyses and the empirical Kaiser criterion suggested 5 and 3 factors, respectively.
EFA
We tested 3- to 5-factor solutions (see Table 2 for the 3-factor solution and ESM3 and 4 (see Supplementary material) for the 4- and 5-factor solutions). The solutions explained 55% (three factors), 58% (four factors) and 60% (five factors) of the variance. All solutions included three factors that targeted topics of effectiveness (Factor 1), knowledge (Factor 2), and relationship (Factor 3). However, the 4- and 5-factor solutions contained additional factors that were difficult to interpret. The fourth factor in both solutions was broadly related to time management but contained only three items, including one item with a low factor loading and a low item communality (Item 15, λ=0.30, h 2=0.35). The fifth factor referred to receiving feedback but included only one item with only moderate factor loading and item communality (Item 21, λ=0.57, h 2=0.49). The 4- to 5-factor solutions therefore did not meet our factor retention criteria, and we chose the 3-factor solution (Table 2). We labelled the three factors Effectiveness, Procedural Knowledge, and Relationship.
Item selection
In the next step, we eliminated additional items from the questionnaire. In selecting the items, we were guided by our factor retention criteria and selected the items based on the factor loadings, thereby ensuring that they sharpened the content validity of each factor and displayed good item statistics. In line with our study objectives, we also focused on effectiveness rather than satisfaction with supervision. For example, we excluded Item 7 (‘The session was a waste of time’), which was strongly skewed (M=4.39, P=.88) and did not add much information to the factor Effectiveness in comparison with, for example, Item 6 (‘The session was effective’), which was somewhat less skewed but represented the construct better and yielded comparable statistics (λ=0.92, h 2=.73, P=.81, r i(t-i)=.78). Furthermore, Item 30 (‘I perceived a learning progress’) displayed only a moderate factor loading on Effectiveness and communality (λ=0.54, h 2=.58). However, we included this item because its difficulty and discrimination were good (P=.75, r i(t-i)=.76), and we considered it an essential part of supervision effectiveness (Kühne et al., Reference Kühne, Maas, Wiesenthal and Weck2019). Finally, care was taken to ensure that at least four items with good item statistics formed one factor.
The final questionnaire contained three factors with four items each (Table 2). Factor 1, Effectiveness, assesses the supervisees’ general perceptions that the supervision session met their expectations (Item 3), was effective (Item 6), was associated with learning progress (Item 30), and that they benefited from professional exchange (Item 32). Factor 2, Procedural Knowledge, measures the supervisees’ knowledge of what to do in the next therapy session (Item 8) and how to do it (Item 9) and the impression that supervisory advice was specific (Item 10) and doable (Item 12). Factor 3, Relationship, represents the emotional bond and cooperation between supervisees and supervisors, including the feeling that errors are permitted (Item 34), the feeling of being a good team (Item 35), constructive handling of disagreements (Item 36), and handling of embarrassing situations (Item 37).
Before the factor structure was further examined using measurement invariance (see next paragraph), we conducted an additional EFA on the combined dataset (supervisees and supervisors) with the final 12 items to ensure that the factor solution did not change with fewer items (see ESM5 and 6 of the Supplementary material for detailed results). The corresponding parallel analyses and the empirical Kaiser criterion suggested three and two factors, respectively. While the 3-factor solution (ESM5 in the Supplementary material) matched the original solution, the 2-factor solution (ESM6 in the Supplementary material) explained less variance (56 vs 63% in the 3-factor solution), had more items with low communalities (eight vs five items), and did not sufficiently separate effectiveness and relationship. For this reason, we chose the 3-factor QSQ as described above. The item statistics for the final QSQ for both the supervisee and supervisor versions are displayed in ESM7 of the Supplementary material.
Measurement invariance
The 12-item QSQ was tested for MI between the supervisee and supervisor datasets. The basic model without any restrictions showed a good model fit, CFI=.955, RMSEA=.074, SRMR=.054, χ2(102)=218.76, p < .001. Both configural MI and metric MI were reached (configural MI: ∆CFI < .001, ∆RMSEA < .001, ∆SRMR < .001; metric MI: ∆CFI=–.003, ∆RMSEA=.001, ∆SRMR=.009). However, the scalar model showed worse model fit (∆CFI=–.010, ∆RMSEA=.004, and ∆SRMR=.003). To analyse problematic items, we followed an exploratory approach by sequentially releasing the intercept constraints of all items (Putnick and Bornstein, Reference Putnick and Bornstein2016). Partial scalar MI was reached by releasing the intercept of Item 10 (proposing concrete ideas), with ∆CFI=–.004, ∆RMSEA=.00, and ∆SRMR=.003, indicating that the QSQ was partially equivalent in terms of the interpretation of item means in both groups (see ESM6 of the Supplementary material for detailed results).
Reliability and validity
The internal consistency of the supervisee version of the QSQ was satisfactory, with α=.91 (total scale), α=.88 (Effectiveness), α=.83 (Procedural Knowledge), and α=.87 (Relationship). As Table 3 shows, the convergent validity was satisfactory, with strong correlations between the QSQ and the STEP-SV (r=.68, p < .001) and between the QSQ and the SWAI (r=.58, p < .001). Notably, the correlations between the STEP-SV and the SWAI were similarly high (r=.47 to .61, p < .001).
Table 3. Convergent validity (supervisees (n=374) are in lower half, and supervisors (n=136) are in upper half)
All correlations are significant at least at p < 0.05. QSQ, Quality of Supervision Questionnaire; QSQ-E, Effectiveness (QSQ); QSQ-P, Procedural Knowledge (QSQ); QSQ-R, Relationship (QSQ); STEP, Questionnaire to Evaluate Supervision (STEP-SV); STEP-C, clarifying (STEP-SV); STEP-P, problem coping (STEP-SV); STEP-R, relationship (STEP-SV); SWAI, Supervisory Working Alliance Inventory.
The internal consistencies for the supervisor version of the QSQ were satisfactory for the total scale (α=.81) and Procedural Knowledge (α=.83) but weaker for Effectiveness (α=.66) and Relationship (α=.60). The convergent validity was satisfactory for the total scale for the STEP-SV (r=.51, p < .001) but lower than expected for the SWAI (r=.43, p < .001). Overall, the correlational patterns were comparable between supervisees and supervisors but lower in absolute values for supervisors.
Associations with person variables
Figure 1 shows the results of the independent t-tests for the differences between supervisees and supervisors. The results showed that supervisors evaluated supervision sessions generally better than supervisees, with small to moderate effect sizes (d=0.33 to 0.58; QSQ total and subscales). The effect sizes for the differences between supervisees and supervisors regarding the STEP-SV and the SWAI were comparable to the results for the QSQ (d=0.21 to 0.54). In addition, supervisors were more likely than supervisees to report that the feedback rules were largely adhered to when providing competency feedback (d=.63). Furthermore, supervisee self-efficacy was positively but marginally correlated with the QSQ (QSQ: r=.16, p < .001; Effectiveness: r=.09, p < .001; Procedural Knowledge: r=.18, p < .001; Relationship: r=.14, p < .001). The supervisees’ level of fear of negative evaluation was negatively but only marginally associated with the QSQ (QSQ: r=–.04, p < .001; Effectiveness: r=–.02, p=.051, Procedural Knowledge: r=–.07, p < .001; and Relationship: r=–.01, p=.203).
Figure 1. Differences between supervisees and supervisors. All effects are significant (Bonferroni-Holm adjusted significance levels, **p<.01, *p<.05, †p=.05). QSQ, Quality of Supervision Questionnaire; QSQ-E, effectiveness (QSQ); QSQ-P, procedural knowledge (QSQ); QSQ-R, relationship (QSQ); STEP, Questionnaire to Evaluate Supervision (STEP-SV); STEP-C, clarifying (STEP-SV); STEP-P, problem coping (STEP-SV); STEP-R, relationship (STEP-SV); SWAI, Supervisory Working Alliance Inventory; FeedRules, adherence to feedback rules when providing competence feedback; d, Cohen’s d.
Associations with context variables
Table 4 shows the results of the independent t tests for (a) supervision mode, (b) theoretical orientation, and (c) competence feedback. There were no significant differences between individual and group supervision sessions, either from the perspectives of the supervisees or the supervisors, although two effect sizes indicated a slight tendency for supervisees to regard individual sessions as more effective (QSQ-total scale, d=–0.22; QSQ-Effectiveness, d=–0.29). Furthermore, although theoretical orientation (CBT vs PDT) did not have a significant impact on supervisees’ supervision quality, compared with PDT supervisors, CBT supervisors reported that Procedural Knowledge was much more prominent in their sessions (d=–.86). Finally, the supervisees found those sessions that included explicit competence feedback to be better (compared with sessions without competence feedback; all QSQ subscales, d=0.43 to 0.53). Similarly, the supervisors reported that more procedural knowledge was achieved if competence feedback was explicitly provided (vs no competence feedback; d=–0.68).
Table 4. Differences in QSQ depending on mode, theoretical orientation, and competence feedback
Significant effects are in bold (Bonferroni–Holm adjusted significance levels). QSQ, Quality of Supervision Questionnaire; QSQ-E, Effectiveness (QSQ); QSQ-P, Procedural Knowledge (QSQ); QSQ-R, Relationship (QSQ); CBT, cognitive behaviour therapy; PDT, psychodynamic therapy and psychoanalysis; CF, competence feedback; noCF, no competence feedback.
Table 5 shows the correlations between the QSQ and supervision techniques. Almost all associations were significant, although the effects were mostly marginal or small. Moderate correlations were found only for supervisors between personal goal setting and the QSQ total scale (r=.33, p < .001) or Procedural Knowledge (r=.39, p < .001). In addition, there were differential effects between supervisees and supervisors. For example, while case discussion was generally much more important for supervisees than for supervisors (e.g. r=.29 vs .02 for the QSQ total score), personal goal setting, in particular, was more strongly associated with session quality for supervisors than for supervisees (e.g. r=.33 vs .22 for the QSQ total score). In addition, information brokering and self-reflection had one of the strongest correlations with the QSQ for both supervisees and supervisors, whereby self-reflection was associated primarily with Relationship for supervisors (r=.27, p < .001). These four techniques (case discussion, information brokering, personal goal setting, and self-reflection) were also used most frequently overall (i.e. in ≥ 71.1% of sessions). However, the supervisors indicated that they had used any of the supervision techniques to a greater extent than supervisees (e.g. homework: 57.2 vs 25.9%). In addition, the following correlations show that adhering to feedback rules was generally associated with better sessions in both the supervisee dataset (QSQ: r=.23, p < .001; Effectiveness: r=.20, p < .001; Procedural Knowledge: r=.18, p < .001; Relationship: r=.20, p < .001) and the supervisor dataset (QSQ: r=.41, p < .001; Effectiveness: r=.39, p < .001; Procedural Knowledge: r=.28, p < .001; Relationship: r=.30, p < .001).
Table 5. Correlations between QSQ and techniques used within the supervision session
All correlations are significant at least at p<0.05. QSQ. Quality of Supervision Questionnaire; QSQ-E, Effectiveness (QSQ); QSQ-P, Procedural Knowledge (QSQ); QSQ-R, Relationship (QSQ). aValues refer to those subjects who indicated that this method did occur in session (2 = ‘little’, 4 = ‘very much’); bnot significant with p>0.05.
Discussion
The first objective of this study was to develop a questionnaire to measure the quality of individual supervision sessions with a particular focus on more clearly separating the constructs of supervision effectiveness, satisfaction, and relationship. The final 12-item Quality of Supervision Questionnaire (QSQ) consists of three factors (Effectiveness, Procedural Knowledge, and Relationship). The internal consistency and convergent validity from the supervisee perspective were largely satisfactory. However, the correlations between all supervision measures require some thoughts on the construct ‘supervision quality’ and its measurement. In addition, the mixed internal consistencies and low convergent validities in the supervisor dataset indicate that the reliability of the supervisor version is somewhat limited.
Measurement of supervision quality
The QSQ is a valid method for monitoring the progress of supervision from session to session in a time-efficient manner. In contrast to other questionnaires (Ladany et al., Reference Ladany, Hill, Corbett and Nutt1996; Palomo et al., Reference Palomo, Beinart and Cooper2010; Winstanley and White, Reference Winstanley and White2011; Zarbock et al., Reference Zarbock, Drews, Bodansky and Dahme2009) and in line with the requirement of considering aspects other than satisfaction (Kühne et al., Reference Kühne, Maas, Wiesenthal and Weck2019; Milne, Reference Milne2018), the QSQ subscales do not contain any general satisfaction items and are defined briefly and succinctly. This made it possible for the Relationship subscale to focus exclusively on the constructive collaboration between supervisee and supervisor and their mutual problem solving, as described in the definition of supervision (Falender, Reference Falender2014). Aspects of skills evaluation or education are not included here, in contrast to other procedures (e.g. SWAI). Due to its good convergent validity, one advantage of the Relationship subscale is that it can capture the supervisory relationship in a time-efficient manner. By using it from session to session, it is also possible to detect and react to relationship ruptures promptly.
The other two QSQ subscales relate primarily to concrete learning outcomes (i.e. general learning progress and specific knowledge), which is also a central element of competency-based supervision (Falender, Reference Falender2014). Falender (Reference Falender2014) notes, among other things, that the acquisition of knowledge and the promotion of self-efficacy are central components of effective supervision. The QSQ is aimed precisely at these aspects, particularly the Procedural Knowledge subscale, and assesses the what and how of the therapeutic approach as well as the confidence to be able to implement it. Against this background, it is not surprising that the convergent validity of Procedural Knowledge was partly lower than that of the other subscales. For example, it was moderately correlated with the alliance questionnaire SWAI (r=.36) and only weakly correlated with the QSQ Relationship subscale (r=.25). This underscores that a good relationship contributes to a good session, but it does not automatically lead to knowledge of what and how to do it in the future (Goodyear and Bernard, Reference Goodyear and Bernard1998). Thus, this scale seems to be a particularly interesting aspect of session quality, as knowledge is often considered a pre-requisite for demonstrated skills (Heinze et al., Reference Heinze, Weck, Maaß and Kühnein press; Muse and McManus, Reference Muse and McManus2013).
Although the QSQ has several advantages, we believe that each supervision instrument investigated in this study has added value. For example, the SWAI measures supervision quality in terms of ‘working alliance’, including collaboration, understanding, feedback, structure, goals, technique, and patient focus. For this reason, it correlates approximately equally with all other instruments and their subscales in this sample. Therefore, the SWAI seems to be well suited for the assessment of a general picture of supervision quality. However, for a differentiated analysis and use at the session level, it seems somewhat too global. The STEP-SV, on the other hand, makes it possible to capture quality in a more differentiated way. In doing so, it follows an established theory in which Relationship measures understanding by the supervisor, Clarification measures (self-) reflection and patient focus, and Problem Coping measures productivity in future sessions. We advocate that researchers clearly describe which definition of quality they wish to examine (i.e. a combination of several aspects vs a specific focus) and that they be aware of potential conceptual overlap when using scales that assess quality rather globally (e.g. working alliance) as well as psychometric properties. Researchers who wish to specifically evaluate single CBT supervision sessions and are primarily interested in variables other than satisfaction, for example, general productivity, process knowledge, and relationship, can rely on the QSQ as a valid and reliable self-report measure.
The supervisor version of the QSQ
Even if the Effectiveness and Relationship subscales had lower internal consistencies in the supervisor version, this does not automatically call into question the reliability of short scales, especially when short scales assess a broad construct (Ziegler et al., Reference Ziegler, Kemper and Kruyen2014). However, considering the lower correlations with the STEP-SV, the overall conclusion about the supervisor version must be formulated somewhat cautiously. On the one hand, the results might be due to a smaller sample size than in the supervisee sample (n=136) or unclear factor structure of the supervisors’ versions of the STEP-SV (Zarbock et al., Reference Zarbock, Drews, Bodansky and Dahme2009). On the other hand, the results of the MI test (i.e. partial scalar MI) indicated that supervisors interpreted some items differently than supervisees did. It should be borne in mind that scalar MI is almost never achieved in practice and that some authors are convinced that a violation of MI does not substantially reduce the validity of the scale and that group differences can still be calculated and interpreted (Robitzsch and Lüdtke, Reference Robitzsch and Lüdtke2023). In our case, the MI results indicate that supervisors and supervisees might use other benchmarks and quality standards, perhaps due to different levels of expertise and opportunities for comparison. This idea also fits well with the finding that supervisors use somewhat different techniques to be more effective than supervisees (e.g. find case discussions less effective than supervisees). Other authors also report psychometric problems in their supervisor versions or using partly different items (Efstation et al., Reference Efstation, Patton and Kardash1990; Zarbock et al., Reference Zarbock, Drews, Bodansky and Dahme2009). To our knowledge, no other instrument has been previously tested for measurement invariance. Consequently, future researchers should replicate the findings in larger samples and perhaps develop items exclusively from the supervisor perspective rather than simply adapting the wording of the same items to fit both perspectives.
Associations with person and context variables
Although this study focuses on a randomly selected supervision session, many of the results are consistent with previous findings on the entire supervision process. These include, for example, the relationships between quality and self-efficacy (Kühne et al., Reference Kühne, Maas, Wiesenthal and Weck2019), fear of negative evaluation (e.g. Gray et al., Reference Gray, Ladany, Walker and Ancis2001), giving competence feedback (Lehrman-Waterman and Ladany, Reference Lehrman-Waterman and Ladany2001; Maiwald et al., Reference Maiwald, Kühne, Junga, Rudolph, Witthöft, Lüthke, Heid and Weck2019), and differences between supervisee and supervisor perspectives (Mathieson et al., Reference Mathieson, Barnfield and Beaumont2009; O’Donovan and Kavanagh, Reference O’Donovan, Kavanagh, Watkins and Milne2014; Zarbock et al., Reference Zarbock, Drews, Bodansky and Dahme2009). However, the effect sizes for self-efficacy and fear of negative evaluations were only marginal and may not reflect meaningful relationships. To study the relationships in more detail, future studies should examine the situational component of these variables (i.e. state self-efficacy, state fear of negative evaluations) when using the QSQ rather than focusing on trait measures, as in the present study. Furthermore, we found that video analysis and role-playing are still rarely used in sessions, while case discussions make up a large proportion of sessions (Milne and Dunkerley, Reference Milne and Dunkerley2010; Weck et al., Reference Weck, Kaufmann and Witthöft2017). In addition, there are some differential findings worth discussing.
Person variables
The extent of session quality depends largely on whether you are a supervisor or a supervisee. Supervisors tend to rate session quality and supervisory alliance as significantly better than supervisees. This finding is similar to research findings on psychotherapy, in which therapists tend to over-estimate their effectiveness relative to their patients’ outcomes (Constantino et al., Reference Constantino, Boswell, Coyne, Muir, Gaines and Kraus2023). At worst, this over-estimation can lead to negative experiences for supervisees if the supervisor continues potential harmful behaviour, such as ignoring the supervisees’ cultural background or behaving cold and distant (Ellis et al., Reference Ellis, Berger, Hanus, Ayala, Swords and Siembor2014). However, considering that quality was rated as very high in both groups, this likely did not occur in our sample.
Context variables
It is promising that the supervision mode and theoretical orientation did not have a major impact on supervision quality. The only specific finding that CBT supervisors (vs PDT supervisors) perceived that their sessions promoted significantly more procedural knowledge is generally in line with PDT sessions being less behaviour- or goal-oriented (Weck et al., Reference Weck, Kaufmann and Witthöft2017). Nonetheless, one should keep in mind that the sample consisted of only 21 supervisors for PDT, which limits the generalizability of the results. Although the relationship between the QSQ and supervision mode (i.e. individual vs group sessions) was not significant, it might be worth examining potential differences regarding expectations and professional exchange group supervision in future studies, given the small effects for QSQ Effectiveness (Nelson, Reference Nelson2014).
It also seems relatively irrelevant which technique is used to a particularly large extent within a session as all methods showed positive but only small correlations with the QSQ. This relatively small influence of one specific method is comparable to the finding in the psychotherapy literature that specific interventions/techniques alone explain only a small proportion of the variance in therapy outcomes (Wampold and Imel, Reference Wampold and Imel2015). However, technique and interpersonal factors (i.e. how the technique is used) are often intertwined (De Felice et al., Reference De Felice, Giuliani, Halfon, Andreassi, Paoloni and Orsucci2019). This idea fits well with the result that adherence to feedback rules when providing competence feedback was associated with more effective sessions (Freeman, Reference Freeman1985). Adherence to feedback rules included: praise, value-free communication that does not hurt supervisees personally, feedback that is based on behaviours, tasks, or the last therapy session, addressing changeable aspects and making suggestions for improvement; and the opportunity to decide whether competence feedback is welcomed. Thus, the present study once again emphasizes the role of competence feedback (Freeman, Reference Freeman1985; Heckman-Stone, Reference Heckman-Stone2004; Weck et al., Reference Weck, Junga, Kliegl, Hahn, Brucker and Witthöft2021), which was one of the strongest factors influencing the quality of supervision sessions, with medium effect sizes, even though only approximately one-third of supervisees in our sample received such feedback.
Implications for supervision practice
Many researchers recommend that supervisors monitor their supervision quality regularly (cf. Milne and Reiser, Reference Milne and Reiser2017). Against this background, the QSQ might be particularly important for quickly and inexpensively observing supervision quality and identifying (perhaps unexpectedly) unhelpful supervision sessions, especially from the perspective of the supervisees (see ESM7 of the Supplementary material for the final questionnaires). We believe that the QSQ Relationship subscale could replace longer supervisory alliance questionnaires if time resources are limited. Although the content of the scale differs somewhat from the typical definition of the supervisory relationship (emotional bond, agreement on tasks and goals), it correlates sufficiently strongly with the SWAI to serve as an indicator of a cooperative relationship. The results can also serve as a basis for discussion in supervision, on the basis of which mutual expectations can be aligned. However, we advise combining the QSQ with objective measures of supervisor competence (e.g. Reiser et al., Reference Reiser, Cliffe and Milne2018), supervisee learning outcomes or competency, and patient outcomes. These multiple perspectives (supervisors, supervisees, and raters) enable a more realistic picture of the quality of supervision (Knox and Hill, Reference Knox and Hill2021; Muse and McManus, Reference Muse and McManus2013; Watkins et al., Reference Watkins, Vîşcu and Cadariu2021).
Limitations and future research
First, it should be mentioned that this study validated the German version of the QSQ. The English translation must therefore also be investigated in an English-speaking sample. Although the QSQ has overall good psychometric properties, its factor structure needs to be replicated with confirmatory factor analyses in further studies, preferably with a longitudinal design. It is particularly important that supervisors and supervisees evaluate the same sessions to obtain a differentiated picture of the supervision process. There is also a lack of information on discriminant validity. However, the low correlations of the QSQ with self-efficacy (CASES) already provide initial indications that the QSQ captures supervision-related aspects and is not merely a measure of supervisee self-efficacy. Nevertheless, future studies should explicitly examine discriminant validity. In addition, the skewness of the data is striking. However, most supervision questionnaires also have skewed data (Gonsalvez et al., Reference Gonsalvez, Hamid, Savage and Livni2017; Zarbock et al., Reference Zarbock, Drews, Bodansky and Dahme2009), which indicates high satisfaction with supervision. Moreover, it is quite possible that in longitudinal studies, there is greater variance in the QSQ across supervisors and sessions. Nonetheless, future revisions of the instrument should consider ways to measure ‘quality’ in a more differentiated way, e.g. by items that make a high degree of agreement more difficult (e.g. ‘My expectations of the session are met 100%’) or by using a 7-point rating scale. Furthermore, while the analyses for supervisees were adequately powered, the analyses for supervisors were partly under-powered, should be interpreted with caution, and should be replicated in larger samples. It should also be noted that numerous moderator variables can have an additional influence on the results, such as the duration of the supervision session (which, for example, showed a high variance in our sample) or the general level of competence of the trainees (and therefore their experience with supervision sessions). Future studies could specifically control for such influences. Finally, it must be mentioned that the authors are cognitive behavioural therapists, and despite efforts to include other supervision concepts (cf. expert interviews with PDT therapists), the QSQ may not be equally appropriate for all theoretical orientations. In addition, aspects of culture and diversity were not sufficiently considered in either item development or sample recruitment, limiting its generalizability to marginalized groups.
In conclusion, the QSQ, especially its supervisee version, is a reliable and valid instrument for examining the quality of an individual supervision session, is potentially suitable for longitudinal study designs, and is applicable to individual and group sessions.
Key practice points
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(1) The QSQ measures the quality of a supervision session from the supervisee’s and supervisor’s perspectives with 12 items. It measures how effective a supervision session was (e.g. in terms of learning progress), how much it promoted the acquisition of procedural knowledge (e.g. knowing exactly what to do in the next therapy session), and how cooperative and constructive the relationship between supervisor and supervisee was.
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(2) Supervisors and training institutes can use the QSQ to record the quality of single supervision sessions and thus monitor the supervision process. This is relevant because in the present sample, supervisors generally rated the quality of their sessions higher than supervisees.
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(3) Higher quality supervision sessions are associated with receiving competence feedback and adherence to ‘feedback rules’, but only marginally with generally higher self-efficacy levels. There were no significant differences between individual or group sessions. Cognitive behaviour supervisors perceive greater procedural knowledge in their sessions than psychodynamic supervisors.
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(4) The QSQ Relationship subscale could replace longer supervisory alliance questionnaires if time resources are limited.
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(5) The reliability of the subscales from the supervisor perspective is currently still limited. Therefore, the overall scale should be interpreted with caution.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S1754470X24000321
Data availability statement
We pre-registered the methods and statistical analyses on the Open Science Framework where the original data sets and scripts of analysis will be available: https://doi.org/10.17605/OSF.IO/BF3RE
Acknowledgements
None.
Author contributions
Ulrike Maaß: Conceptualization (lead), Data curation (lead), Formal analysis (lead), Funding acquisition (equal), Investigation (equal), Methodology (lead), Project administration (lead), Software (lead), Validation (lead), Visualization (lead), Writing – original draft (lead), Writing – review & editing (lead); Franziska Kühne: Conceptualization (supporting), Writing – original draft (supporting); Alexandra Schöttler: Conceptualization (supporting), Investigation (lead), Methodology (equal), Project administration (equal); Florian Weck: Conceptualization (supporting), Resources (lead), Supervision (lead).
Financial support
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Competing interests
The authors declare none.
Ethical standards
This study was pre-registered at the Open Science Framework (28 May 2022), and the study protocol was approved by the ethics committee of the University of Potsdam (reference number 8/2022). The study conformed to the Declaration of Helsinki, and informed consent was obtained from all participants. All data, analysis codes, and research materials are available at: https://doi.org/10.17605/OSF.IO/BF3RE. Deviations from the pre-registration are described in the electronic Supplementary material online (ESM1).
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