Development and validation of the Mindfulness Meditation Practice Quality Scales (MMPQS) in a clinical population using Rasch analysis

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Shires, Oleg N. Medvedev, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8218417/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract With mindfulness programs and practice research increasing, measures have been developed to assess mindfulness-related constructs. However, there has not yet been a measure to examine the quality of mindfulness meditation practices. This study aimed to develop and validate the Mindfulness Meditation Practice Quality Scales (MMPQS) in a clinical population. The MMPQS consists of two scales, Concentration ( śamatha ) and Insight ( vipassanā ). Ten items each were initially formulated. Each scale was administered to 368 clinical participants completing Mindfulness-integrated Cognitive Behavior Therapy (MiCBT) at 2 timepoints where scales were completed immediately after practices. Partial Credit Rasch model was applied to investigate psychometric properties of each scale as they represent two distinct dimensions. Best Rasch model fit was achieved after removing misfitting items resulting in six-item versions of Concentration and Insight scales. Both showed high reliability (PSI = 0.9–0.91), invariance across personal factors, and unidimensionality. Construct, divergent, and predictive validity of the six-item scales was supported by correlations with relevant measures in expected directions. This initial validation supported reliability and validity of the MMPQS as measures of Concentration and Insight practice quality, which can be utilized to support research and practice. Future research recommendations include testing the MMPQS in non-clinical populations and across other mindfulness-based programs. Mindfulness Meditation Practice Quality Assessment Rasch Analysis Psychometrics Mindfulness-integrated Cognitive Behavior Therapy (MiCBT) Figures Figure 1 Figure 2 Introduction Mindfulness practice has been defined as the unbiased and non-reactive monitoring of phenomena spontaneously emerging in conscious awareness, with equanimity and clear understanding of their impermanent and impersonal essence (Cayoun, 2017). Research with mindfulness-based programs (MBPs) and practices continues to enjoy rapid growth (Ferreira & Demarzo, 2024). Mindfulness has been found helpful for mental health and wellbeing outcomes, for both clinical (Goldberg et al., 2018), as well as non-clinical (Galante et al., 2021) populations. However, a lot of research has focused on practice quantity (e.g. Strohmaier, 2020; 2021) and less on practice quality , which is arguably equally important (Ribeiro et al., 2018). To meet the needs for research to explore the increasingly diverse applications of mindfulness practice, many different measures of mindfulness have been developed, assessing a variety of mindfulness constructs, such as trait mindfulness, examining a person’s general disposition of mindfulness (e.g. Baer et al., 2006), in-the-moment state mindfulness (e.g. Lau et al., 2006), and mindfulness as a skill to be learned (e.g. Walach et al., 2006). Many mindfulness measures have also been adapted for use with different age groups (e.g. Krägeloh & Strohmaier, 2024; Pallozzi et al., 2017), various clinical populations (e.g. Joos et al., 2025; Winkens et al., 2022), and international contexts (e.g. Henning et al., 2024). More recently, measures have begun to examine factors related to the practice of mindfulness. This includes for example formal and informal mindfulness practice adherence (Hassed et al., 2021), the assessment of a mechanism of mindfulness-based programs, such as equanimity (Rogers et al., 2021), or factors related to the participants’ ability to provide a rating of following practice instructions on a visual analogue scale (Del Re et al., 2013). However, no measure so far has been developed for the quality of different types of mindfulness practices. This is arguably important, since the quality of a practice, rather than only the time spent practicing, is paramount, in particular for novice meditators, to ensure a high quality of practice for those first learning meditation (Ribeiro et al., 2018; Strohmaier & Goldberg, 2024). Additionally, practice quality relating to specific types of mindfulness practices, rather than a generic measure incorporating all different practices, is crucial to ensure a more nuanced assessment. According to Buddhist traditions, meditation includes several different practices, principally concentration ( śamatha ), insight ( vipassanā ), and loving-kindness ( metta ); whereas śamatha and vipassanā are understood as the two central practices (Khantipalo, 1995). In śamatha practices, the focus is on one specific object of concentration. Vipassanā practices involve observing and monitoring thoughts and emotions as they are to understand the true nature of reality—all mental and physical experiences are impermanent and impersonal phenomena—and metta practices cultivate compassion, empathy, and kindness towards oneself and others (Anālayo, 2006a). While loving-kindness is related to mindfulness practice, it serves a different purpose as it focuses on a different construct to mindfulness, and historically is not taught in the satipaṭṭhāna sutta , the formal teaching on the establishment of mindfulness (Anālayo, 2006a; Sedlmeier et al., 2023). Furthermore, Nash and Newberg (2013; 2023), who have developed a universal taxonomy and classification of meditation methods, differentiate between cognitive-directed methods (which includes śamatha and vipassanā ) and affective-directed ones (which includes metta ). Therefore, concentration and insight practices are focused on here first and foremost, since arguably, these are the fundamental practices in Buddhist traditions resulting from Pali sources (Griffiths, 1981; Hart, 1987). Although concentration and insight practices have at times been classed as “contrasting practices” (Griffiths, 1981, p. 606), these are arguably the main practices available to mindfulness meditation practitioners and thus, are practices that need to be completed with a high level of quality. While concentration and insight practices are engaging different processes, they are complimentary practices and become more integrated with increased meditator proficiency and experience over time (Anālayo, 2006a). The interrelationship between concentration and insight practices has been outlined in detail elsewhere (Khantipalo, 1995). The most commonly researched mindfulness meditation practices are those pertaining to concentration and insight (Mehrmann & Rakesh, 2013). In particular, the MBPs with the most research evidence to date are Mindfulness-Based Stress Reduction (MBSR) and Mindfulness-Based Cognitive Therapy (MBCT) (Alsubaie et al., 2017), both of which employ concentration and insight (Kabat-Zinn, 1982; Segal et al., 2002). Research on other types of MBPs have also been comparing the effectiveness of concentration and insight meditation practices in particular, finding that different processes are involved in these practices, but that they are effective for both physiological and mental health. For example, research by Ooishi et al. (2021) found that focused attention practices ( śamatha ) elevated physiological relaxation, whereas open monitoring ( vipassanā ) practices reduced stress and elevated physiological arousal in novice meditators with a history of physical health diagnoses such as diabetes, thyroid dysfunction, or hypertension. Research with śamatha and vipassanā practices have also found to aid in the reduction of psychological distress outcomes in clinical populations, finding a decreased relapse of depressive symptoms (e.g. Kuyken et al., 2016). While traditional psychometric approaches often assume that Likert-scale responses represent interval-level data, this assumption is frequently violated in practice, leading to measurement errors and compromised statistical analyses. The Rasch measurement model offers a robust solution to this fundamental problem by explicitly recognizing the ordinal nature of rating scale responses and providing a principled method for converting these ordinal scores into true interval measurements (Andrich et al., 2009; Siegert et al., 2010). This conversion is particularly important for mindfulness practice quality assessment, where precise measurement differences are crucial for detecting meaningful changes over time and comparing individuals across different practice contexts. The Rasch model's iterative approach allows researchers to identify and remove misfitting items, ensuring that the final scale measures a single underlying construct with equal intervals between scale points. Additionally, the model's ability to test for differential item functioning across demographic groups ensures that the scales perform consistently across different populations, a critical consideration for measures intended for diverse clinical samples (Sutton & Medvedev, 2023). By employing Rasch analysis, researchers can develop measurement tools that meet the stringent requirements necessary for both clinical practice and research applications. Accurately measuring the quality of practice is not only helpful for research purposes, but also important for both teachers and practitioners, in particular novices, to understand both the skills to be cultivated through their meditation and the progress they are making during each practice, while at the same time highlighting any areas of difficulty where the practitioner may need further assistance. Therefore, this study sought to develop a measure of mindfulness meditation practice quality. Due to the close relationship and the most extensive evidence for concentration and insight meditation practices, this paper aimed to develop and validate scales of these meditation practices. One of the MBPs which integrates both concentration and insight meditation practices in a measurable sequential way is Mindfulness-integrated Cognitive Behavior Therapy (MiCBT), a transdiagnostic approach principally used in clinical populations (Francis et al., 2022). Accordingly, examining the psychometric features of these scales through the implementation of MiCBT in a clinical population was both convenient and advantageous for examining their predictive validity across a wide range of moderate to severe mental health conditions. We hypothesized that mindfulness practice quality would improve with continued practice, and greater mindfulness practice quality would predict lower levels of depression, anxiety, and stress and higher satisfaction with life. We expected that mindfulness practice quality would correlate moderately with related concepts. Method Participants In total, 368 adults were included in this study if they were currently seeking treatment in the form of a MiCBT program (Cayoun, 2011 , 2015 ; Cayoun et al., 2019 ) at either of two psychology clinic sites. Participants were either self-referred or referred by a medical professional to both psychology clinics. All were from a clinical population with a variety of moderate to severe mental health disorders, all diagnosed with at least one comorbid condition, as shown in Table 1 . The majority of participants were White (90.4%) Australians (91.15%), who identified as male (59.2%) with an average age of 42.34 (see Table 1 for further details). Table 1 Participant demographics and measure descriptive statistics for Concentration (N = 211) and Insight (N = 157) Scales at Time 1 Demographics / measures Concentration Insight Age: M (SD); % 41.5 (14.6); 18–35: 37.4% 36–48: 31.8% 49–78: 30.8% 43.17 (14.49) 18–35: 33.3% 36–48: 34% 49–78:32.7% Gender (%) Female: 40.8 Male: 58.8 Other: 0.5% Female: 39.7% Male: 59.6% Other: 0.6% Nationality (%) Australian: 91.9% UK: 1.4% Other: 6.6% Australian: 90.4% UK: 1.9% Other: 7.7% Ethnicity (%) White: 89.1% Asian: 5.2% Other: 5.2% White: 91.7% Asian: 4.5% Other: 3.8% Measures (N) Primary Diagnosis N Total Comorbidity N Primary Diagnosis N Total Comorbidity N PTSD OCD Depression Anxiety Personality disorder Adjustment disorder Autism ADHD Alcohol/substance abuse Other 34 18 31 50 9 38 6 16 2 6 4 4 4 4 2 4 3 4 3 3 24 17 27 30 5 25 3 15 2 4 PTSD OCD Depression Anxiety Personality disorder Adjustment disorder Autism ADHD Alcohol/ substance abuse Other 18 13 22 37 7 33 7 14 1 3 4 4 3 4 2 4 3 4 2 2 12 12 20 26 4 22 4 12 1 1 Equanimity: M (SD) 45.96 (11.4) 46.94 (3.8) Depression: M (SD) 7.98 (4.93) 7.82 (5.08) Anxiety: M (SD) 6.3 (3.94) 5.86 (3.8) Stress: M (SD) 10.76 (3.97) 10.42 (3.93) Awareness: M (SD) 36.33 (5.93) 36.16 (6.21) Kindness: M (SD) 4.16 (0.83) 8.34 (1.68) Life satisfaction: M (SD) 19.61 (7.24) 20.01 (7.29) SEMMP: M (SD) 26.48 (6.1) 26.24 (6.13) Note: M = Mean; SD = Standard Deviation; N = Number; PTSD = Post-Traumatic Stress Disorder; OCD = Obsessive-Compulsive Disorder; ADHD = Attention-Deficit/Hyperactivity Disorder; SEMMP = Self-efficacy for mindfulness meditation practice Procedure Participation was voluntary and all participants gave informed consent prior to taking part. They consented to their de-identified data and responses to questionnaires administered at different time points throughout treatment being utilized for the study. The data collection process is outlined in the Supplementary Materials (SM.1). Patients with psychotic or manic symptoms were excluded from the study, given the potential adverse effects that meditating can cause during these mental states (van Dam et al., 2018 ). Throughout the four stages of MiCBT, several mindfulness meditation practices and cognitive-behavioral skills are taught to individuals over a standard period of 10 weeks (Cayoun, 2011 , 2015 ; Cayoun et al., 2019 ), although additional time is often necessary with more severe or chronic conditions (Grabovac & Cayoun, 2025 ). During stage 1, meditation techniques are principally related to intrapersonal regulation. During stage 2, these are integrated with cognitive-behavioral methods, such as imaginal and in vivo exposure to reduce avoidance and promote equanimity and confidence in daily life. During stage 3, meditation techniques are integrated with cognitive-behavioral methods, such as mindful assertiveness, and applied in daily life to promote interpersonal regulation and confidence in tense interpersonal situations. During stage 4, meditation techniques are integrated with a behavioral task to help feel more connected to others and reduce the risk of relapse. Participants in this study were guided through each of these procedures. To learn the meditation techniques, participants were given a set of MP3 audio tracks or The MiCBT Guide smartphone application to guide each practice method. Once the procedure of a method was learned, participants practiced it in silence with a timer if they wished. The MiCBT program integrates meditation skills in a structured and scaffolding manner, starting with a 14-minute progressive muscle relaxation twice daily to motivate daily self-care and reduce stress. During that week, they are also asked to practice mindfulness of the body, remaining aware of postures and movements as often and in as many contexts as possible, as a start to keeping attention in the present moment with an easy object of focus. These two methods are generally achievable in clinical practice, unless patients are severely dissociated. Thereafter, all mindfulness methods are practiced in 30-minute sessions twice daily, starting with concentration training. For this, mindfulness of breath is introduced to teach three essential executive functions: (1) sustaining attention at the entrance of the nostrils while holding in working memory that thoughts are just impermanent and impersonal cognitive events, (2) inhibiting the learned response (to engage with thoughts), and (3) shifting attention back to the breath without attachment to the thought. This skillset is concomitantly applied to regulate attention by cultivating mindfulness of thoughts and mental states in daily activities and shift away from ruminative and other unhelpful thoughts when necessary. Once sufficient ability to concentrate on the breath is acquired during meditation, a set of body scanning techniques—taught in the Burmese vipassana tradition of Ledi Sayadaw, U Ba Khin, and S. N. Goenka—are successively introduced to develop interoceptive awareness and equanimity. The primary aim in the clinical context is to improve emotion regulation through interoceptive desensitization and insight into the impermanence and impersonality of affective valence. Finally, loving-kindness meditation is taught as part of cultivating compassion toward self and others. This is synchronously applied with explicit ethics in daily life, as a behavioral experiment, to cultivate a transpersonal awareness and sense of connectedness with others and the environment at large. When motivated by a compassionate intention, preventing harm to themselves and others helps patients prevent relapsing in a psychological disorder. Measures Mindfulness Meditation Practice Quality Scales (MMPQS) Development The MMPQS consists of two scales and is designed to be administered immediately after different types of meditation practice, to measure the quality of the practice. The scales relate to Concentration and Insight mindfulness practices. The scales were developed by three members of the research team with appropriate and sufficient personal practice and teaching experience of mindfulness methods. Item development for these scales was also informed by the traditional literature on mindfulness of breath (e.g., Anālayo, 2006b ; Hart, 1987 ) and mindfulness of body sensation taught in vipassana meditation (e.g., Anālayo, 2011 , 2020 , 2021 ; Goenka, 1998 , 2000 ; Walshe, 2012 ), as applied in MiCBT (Cayoun, et al., 2019 ; Cayoun, 2017 ). Initially, a pool of items was developed and reduced to 10 items for each scale, based on primary consensus among the authors. Items on each scale were then independently rated on a 1 to 10 scale of endorsement by the authors and discussed for further screening and elaboration. The items were then improved based on these ratings and comments with any disparities discussed until a 90% consensus was reached for each item. The Concentration scale included items such as, “I struggled to maintain my concentration”, and “I was able to refocus immediately after being distracted by mental experiences, such as thoughts and images”. The Insight scale included items such as, “When paying attention to the body, I could feel a range of pleasant, unpleasant, or neutral sensations”, and “I was able to prevent reacting to physical, emotional, or mental experiences”. Participants were asked to indicate how truly representative each of the items on each scale is for them after having completed a practice on a 7-point Likert Scale ranging from “ not at all true” (1) to “ completely true” (7). Each scale was completed twice to observe progress and examine predictive validity (see SM.1 for process of data collection). Concentration and Insight meditation practice quality was determined by summing all items for each scale after reverse coding. Both, the Concentration ( ⍺ = 0.85) and Insight ( ⍺ = 0.86) scales showed good internal consistency in the current sample. The scales were designed as stand-alone assessment tools, and which of the scales to use depends on the specific practice completed. Equanimity Scale 16 (ES-16) The ES-16 (Rogers et al., 2021 ) is a 16-item scale assessing the level of equanimity with two underlying factors, experiential acceptance and non-reactivity, on a 5-point Likert scale, with greater scores relating to greater equanimity. The ES-16 showed good internal consistency ( ⍺ = 0.91) and has good convergent, divergent and predictive validity (Shires et al., 2023 ). Depression Anxiety Stress Scale (DASS-21) The DASS-21 (Henry & Crawford, 2005 ; Lovibond & Lovibond, 1995 ) is a 21-item scale examining general psychopathology over the past week on a 4-point Likert scale with three subscales: depression, anxiety, and stress with greater scores indicating greater symptomatology. The DASS-21 has been found to have good discriminant, convergent and construct validity, and it showed good reliability for each of subscale depression ( ⍺ = 0.96), anxiety ( ⍺ = 0.92), and stress ( ⍺ = 0.95) (Ronk et al., 2013 ). Philadelphia Mindfulness Scale – Awareness (PHLMS-A) The awareness subscale of the PHLMS-A (Cardaciotto et al., 2008 ) consists of ten items, which are scored on a 5-point Likert scale where greater scores demonstrate greater awareness. The awareness subscale of the PHLMS has shown good convergent and discriminant validity, and in this sample, showed good reliability ( ⍺ = 0.82). Compassion Scale (CS) – Kindness subscale The kindness subscale of the CS (Pommier et al., 2019 ) has four items, which are scored on a 5-point Likert scale. Averaged scores are calculated for the kindness subscale, with greater scores indicating greater kindness. The CS and its subscales have shown good discriminant, construct and convergent validity and is reliable in the current sample ( ⍺ = 0.87). Satisfaction With Life Scale (SWLS) The five-item SWLS (Diener et al., 1985 ) measures global cognitive judgements of life satisfaction on a 7-point Likert scale ranging from strongly disagree to strongly agree, with greater scores indicating greater life satisfaction. The SWLS has shown food convergent and discriminant validity and shows internal consistency ( ⍺ = 0.87) (Jovanović et al., 2020 ). Self-Efficacy for Mindfulness Meditation Practice (SEMMP-9) The nine item SEMMP (Birdee et al., 2020 ) is scored on a nine-point Likert scale with greater scores demonstrating greater self-efficacy for mindfulness meditation practice. The SEMMP is reliable ( ⍺ = 0.84) in the current sample and has shown construct and convergent validity with related measures. Data Analyses Descriptive statistics were computed using IBM SPSS v.29 to understand characteristics of the current sample. Rasch analysis was used to validate two scales of the Meditation Practice Quality Scales, pertaining to the most common meditation practices, namely concentration and insight. Rasch analysis was conducted using RUMM2030 (Andrich et al., 2009) iteratively until the Rasch model expectations were met, including both the overall and individual item-fit to the model, no local dependency, scale invariance across demographic factors and evidence of unidimensionality (Siegert et al., 2010). If the likelihood-ratio test were to indicate the presence of significant differences between response options and thresholds across individual items, the unrestricted, partial-credit version of the Rasch model would be chosen (Masters, 1980), and Gustafsson’s (1980) model fit criteria will be adopted as follows. Rasch model fit was assessed primarily through chi-square statistics, where both overall and individual item-trait interaction should be non-significant ( p > 0.05), with Bonferroni adjustment applied by dividing 0.05 by the number of tests conducted (Gustafsson, 1980; Tennant & Conaghan, 2007). Individual item fit residuals must fall within the acceptable range of -2.50 to + 2.50 to demonstrate adequate fit to the model. While not strict requirements, optimal targeting is indicated when person location mean falls between − 0.50 to + 0.50, suggesting good coverage of the sample by the scale, and when item and person fit residuals approximate 0.00 ( SD = 1.00) for excellent overall fit. The item location mean is automatically set to zero as the reference point in RUMM2030. Additionally, no significant differential item functioning (DIF) should be evident across demographic factors such as gender, age, or other personal characteristics to ensure measurement invariance across different groups. Smith’s (2000) method of employing an independent-samples t -test comparison of person estimates to group items with the highest negative and positive loadings on the first principal component when controlling for the principle latent factor. This was used to examine unidimensionality of each scale, which is supported if there are not more than 5% significant t- test comparisons. Local dependency can occur when responses to one item influence the responses to another item resulting in spurious correlations thus escalating measurement error (Baghaei, 2010). Local dependency of items was evaluated employing the residual correlation matrix, which according to Christensen et al. (2016) is present when the magnitude of residual correlations exceeds the mean of all residual correlations by 0.20. Unidimensionality is affected by spurious correlations between locally dependent items, and super-items were created by combining locally dependent items. DIF for personal factors including age, gender, mindfulness practice experience, and time was examined for each scale item. Four age categories of roughly equal size were created (18–35, 36–48, 49–78 years of age). Gender was coded as either female, male, or non-binary. Mindfulness practice experience was divided into either yes or no. Time was coded as Time 1 and Time 2 of completing each scale. Nationality, ethnicity, diagnosis, and practice type, frequency and duration could not be divided into distinct equal overarching categories to calculate DIF. By employing DIF analysis, we can help ensure that this measure is robust across contexts and suitable to be applied for different populations (Sutton & Medvedev, 2023 ). After Rasch analysis, ordinal scores for both scales were converted to interval scores by transforming logit scores. Construct and divergent validity of the MMPQS was examined by running bivariate correlations with several other related measures, namely the Equanimity Scale 16 (ES-16), the Depression, Anxiety, Stress Scale (DASS-21), the Philadelphia Mindfulness Scale – Awareness (PHLMS-A), the Compassion Scale – Kindness (CS), the Satisfaction with Life Scale (SWLS), and the Self-Efficacy for Mindfulness Meditation Practice (SEMMP), with a Pearson’s r < 0.50 or greater generally considered sufficient for divergent validity (Fornell & Larcker, 1981 ). Predictive validity of the MMPQS scales was also completed using regression analyses with Concentration and Insight as predictors and depression, anxiety, stress, and life satisfaction as outcomes to test Hypotheses 3 and 4. Results Descriptive Statistics Table 1 shows participant demographics and descriptive statistics of all measures for each scale. Participants had a variety of comorbid clinical diagnoses, with anxiety disorders being the most common primary diagnosis. Rasch Analyses Concentration Scale Table 2 shows the summary of Rasch model fit statistics for the initial 10-item (A1), second 9-item (A2), third 8-item (A3), fourth 7-item (A4), 6-item (A5), and final 6-item (with the inclusion of two super items) (A6) analyses of the Concentration scale. Initially, in Analysis 1 (A1), Item 7 “I was so immersed in concentration that I had no thoughts, not even being aware that I am focusing on a particular object” was removed, due to significant fit residuals. Upon closer inspection, Item 7 appeared to measure insight more closely than concentration, thus adding a conceptual reason for its exclusion. Analysis two (A2), when Item 7 was removed, identified Item 1 “While I was remaining alert and focused, I felt that time was passing quickly” as problematic since A2 did not have acceptable Rasch model fit statistics. The wording of Item 1 is double-barreled, which may have been confusing for participants to complete, thus adding another reason for its removal. Analysis 3 (A3) with Items 7 and 1 removed, showed a significant item fit residual for Item 3 “I felt motivated and engaged.” and was therefore removed. Additionally, this item appears vague and may be open to misinterpretation. In Analysis 4 (A4), with Items 7, 1 and 3 removed, a significant residual fit still remained. Table 2 Summary of Rasch model fit statistics for the initial (A1; 10-items), second (A2; 9-items), third (A3; 8-items), fourth (A4; 7-items), fifth (A5; 6-items), and final (A6; 6-items including superitems) analyses of the Concentration scale (n = 414) Analyses Item fit residual Person fit residual Goodness of fit PSI a Independent t- test Mean SD Mean SD χ 2 (df) p % A1 0.22 2.62 -0.38 1.37 161.36 (80) < 0.001 0.91 11.19 A2 0.28 2.62 -0.41 1.37 124.47 (72) < 0.001 0.91 12.39 A3 0.26 2.61 -0.43 1.33 121.14 (64) < 0.001 0.91 11.43 A4 0.3 1.87 -0.45 1.26 86.45 (56) 0.006 0.91 12.39 A5 0.32 1.73 -0.45 1.21 56.41 (48) 0.19 0.89 8.77 A6 -0.09 2.98 -0.45 1.03 28.86 (32) 0.63 0.91 2.5 a = Person Separation Index; SD = Standard Deviation; χ 2 = Chi Square; df = Degrees of Freedom. This was followed up with a correlation analysis which indicated a high correlation ( r > 0.8) between Items 6 “When my attention drifted away, I was able to return quickly to my chosen object of concentration.” and 8 “I was able to refocus immediately after being distracted by mental experiences, such as thoughts and images.”. Upon closer inspection, Item 6 was removed as it conceptually measures the same as item 8, and the wording of item 8 is preferable to item 6, as it provides examples. Analysis five (A5) indicated that the item fit residual was no longer significant and thus the six-item version of the Concentration subscale is considered a good fit to the Rasch model showed high reliability and invariance across personal factors. When testing unidimensionality of the 6-item Concentration scale resulting from A5, the independent t -test was above the 5% threshold (A5 = 8.77%). When grouping the two highest positive (items 4 and 9) and two highest negative (items 5 and 9) loadings and running independent-samples t -test comparisons, this resulted in an acceptable t- test comparison of 2.5%, thus supporting strict unidimensionality when Items 4 and 9, and 5 and 8, are combined as super items in A6. Analysis of DIF found no significant difference of the 6-item Concentration scale for age, gender, mindfulness practice experience, or time. Table 3 shows the item-fit statistics for the initial, 10-item version of the Concentration scale, and the item-fit statistics after misfitting items have been removed, resulting in the final 6-item version of the Concentration scale. Figure 1 shows the person-item threshold distribution for Concentration items over time, clearly indicating an increase in scores from Time 1 to Time 2. Table 3 Initial item-fit and final item-fit statistics of the 10-item and 6-item Concentration scale (n = 414) Item Item content Location SE FitResid χ 2 Initial item-fit 1 While I was remaining alert and focused, I felt that time was passing quickly. 0.34 0.05 3.91 14.49 2 I could stay focused on my chosen object of concentration. 0.21 0.06 -3.79*** 34.02 3 I felt motivated and engaged. -0.90 0.05 2.80 13.25 4 r Thoughts were interfering with my ability to focus on my chosen object of concentration. 0.66 0.06 1.70 7.50 5 r I struggled to maintain my concentration. 0.25 0.05 -1.06 10.23 6 When my attention drifted away, I was able to return quickly to my chosen object of concentration. -1.05 0.06 -2.23* 16.10 7 I was so immersed in concentration that I had no thoughts, not even being aware that I am focusing on a particular object. 1.45 0.05 3.42*** 35.97 8 I was able to refocus immediately after being distracted by mental experiences, such as thoughts and images. -0.55 0.05 -1.69 10.71 9 I was able to refocus immediately after being distracted by sensory experiences, such as noise and physical sensations. -0.69 0.05 0.25 9.58 10 I was able to know that I was focused without having to think about it. 0.27 0.05 -1.14 9.50 Final item-fit 2 I could stay focused on my chosen object of concentration. 0.16 0.06 -2.32 14.08 4 r Thoughts were interfering with my ability to focus on my chosen object of concentration. 0.67 0.06 2.17 11.09 5 r I struggled to maintain my concentration. 0.24 0.06 -0.59 7.72 8 I was able to refocus immediately after being distracted by mental experiences, such as thoughts and images. -0.59 0.06 0.13 6.11 9 I was able to refocus immediately after being distracted by sensory experiences, such as noise and physical sensations. -0.77 0.06 2.25 11.23 10 I was able to know that I was focused without having to think about it. 0.29 0.05 0.32 6.18 SE = standard error; FitResid = Fit Residuals; χ 2 = Chi Square; r = reverse-coded item; * = p < 0.05; ** = p < 0.01; *** = p < 0.001. Insight Scale Table 4 shows the summary of Rasch model fit statistics for the initial 10-item (A1), second 9-item (A2), third 7-item (A3), fourth and final 6-item (A4) analysis. The initial analysis (A1) showed acceptable Rasch model fit statistics. However, when checking for unidimensionality, this was not acceptable (> 5%) when comparing highest positive with highest negative item loadings (Items 1, 3, 7 vs. Items 4, 6, 9). Additionally, when examining local dependency of personal factors using DIF, a significant difference in age of participants especially between youngest and oldest groups, and in particular for Item 6 “When I did not experience what I expected, I was able to accept it instead of letting frustration or disappointment overcome me”. Upon closer inspection, the length and wording of this item may also have been unclear for some participants. This item was therefore removed. Table 4 Summary of Rasch model fit statistics for the initial (A1; 10-items), second (A2; 9-items), third (A3; 7-items), and final (A4; 6-items) analyses of the Insight scale (n = 313) Analyses Item fit residual Person fit residual Goodness of fit PSI a Independent t- test Mean SD Mean SD χ 2 (df) p % A1 0.21 1.85 -0.63 1.63 70.92 (50) 0.03 11 A2 0.22 1.74 -0.65 1.61 43.05 (45) 0.55 12.28 A3 0.2 1.54 -0.59 1.42 32.41 (35) 0.59 5.89 A4 0.37 1.88 -0.52 1.29 29.05 (30) 0.51 3.98 a = Person Separation Index; SD = Standard Deviation; χ 2 = Chi Square; df = Degrees of Freedom. However, unidimensionality was still not reached in A2 with Item 6 removed when comparing highest positive with highest negative item loadings (Items 1, 2, 3, 7 vs. items 5, 8, 9). Two items, Item 1 “I was able to feel sensations in the body” and Item 9 “I was able to perceive what I experienced with objectivity and a degree of distance.” were removed, because item 1 had a high correlation (> .80) with item 3 and a high residual correlation with item 9. Similarly, Item 9 had a high correlation with Items 5 and 8. Additionally, the wording of Item 9 may have been confusing for individuals due to being double-barreled. It was therefore decided to remove Items 1 and 9 in A3, however, unidimensionality was still not acceptable (5.89%). Item 7 “While paying attention to the body, I could feel the changing nature of body sensations.” showed a high correlation with Item 3, and appears to be measuring the same as Item 3. Item 7 was therefore removed in A4. The 6-item Insight scale in A4 showed acceptable fit residuals and unidimensionality. After A1, no DIF were observed for any of the person variables, such as age, gender, nationality, ethnicity, mindfulness practice experience, in A2, A3, and A4. Table 5 details the item-fit statistics for the initial, 10-item version, and the item-fit statistics of the final 6-item version of the Insight scale. Figure 2 shows the person-item threshold distribution for Insight items at Time 1 and 2 for A4. The final 6-item Insight scale comprehensively covers the three elements of insight, namely interoceptive awareness (Items 2 and 3), equanimity (Items 4 and 5), and awareness of impermanence and non-self (Items 8 and 10). Table 5 Initial item-fit and final item-fit statistics of the 10-item and 6-item Insight scale (n = 313) Item Item content Location SE FitResid χ 2 Initial item-fit 1 I was able to feel sensations in the body. -0.27 0.06 2.49 8.02 2 I was able to remain attentive to what I felt in the body. 0.31 0.07 -1.72 8.57 3 When paying attention to the body, I could feel a range of pleasant, unpleasant, or neutral sensations. -0.06 0.06 1.03 7.78 4 I was able to prevent reacting to physical, emotional, or mental experiences. 0.04 0.06 2.12 4.96 5 While paying attention to the body, I was able to accept my experiences. -0.63 0.07 -2.02 12.67 6 When I did not experience what I expected, I was able to accept it instead of letting frustration or disappointment overcome me. -0.40 0.06 -1.31 5.23 7 While paying attention to the body, I could feel the changing nature of body sensations. 0.40 0.06 -0.12 5.56 8 While being attentive, I was aware that all of my experiences are impermanent. 0.23 0.06 2.58 5.43 9 I was able to perceive what I experienced with objectivity and a degree of distance. 0.10 0.07 -1.80 10.50 10 I could feel body sensations just as body sensations, without perceiving them as part of who I am. 0.28 0.06 0.90 2.22 Final item-fit 2 I was able to remain attentive to what I felt in the body. 0.27 0.07 -1.51 7.74 3 When paying attention to the body, I could feel a range of pleasant, unpleasant, or neutral sensations. -0.07 0.06 2.45 4.70 4 I was able to prevent reacting to physical, emotional, or mental experiences. -0.01 0.06 1.20 0.82 5 While paying attention to the body, I was able to accept my experiences. -0.65 0.07 -2.24 10.94 8 While being attentive, I was aware that all of my experiences are impermanent. 0.20 0.06 1.88 2.93 10 I could feel body sensations just as body sensations, without perceiving them as part of who I am. 0.25 0.06 0.45 1.93 SE = standard error; FitResid = Fit Residuals; χ 2 = Chi Square; * = p < 0.05; ** = p < 0.01; *** = p < 0.001. Figure 2 Person-item threshold distribution for Insight items at time 1 (blue) and time 2 (red) for Analysis 4 (A4) Conversion of scores for MMPQS Table 6 shows the conversion table and scores for the Concentration and Insight Scales from ordinal to interval scores for both 6-item versions of the Concentration and Insight Scales. Table 6 Conversion scores for Concentration and Insight Scales Ordinal scores Concentration Insight Logits Scale Logits Scale 6 -7.46 6.00 -5.64 6.00 7 -6.50 8.07 -4.65 9.05 8 -5.75 9.72 -3.92 11.29 9 -5.15 11.02 -3.38 12.93 10 -4.62 12.15 -2.95 14.25 11 -4.15 13.16 -2.60 15.34 12 -3.73 14.09 -2.30 16.27 13 -3.34 14.92 -2.03 17.08 14 -2.99 15.69 -1.80 17.81 15 -2.66 16.40 -1.58 18.46 16 -2.35 17.07 -1.39 19.06 17 -2.06 17.70 -1.21 19.61 18 -1.78 18.30 -1.04 20.13 19 -1.52 18.87 -0.88 20.62 20 -1.26 19.43 -0.73 21.09 21 -1.01 19.97 -0.58 21.55 22 -0.76 20.51 -0.43 22.00 23 -0.52 21.04 -0.29 22.44 24 -0.27 21.57 -0.14 22.88 25 -0.03 22.11 0.00 23.33 26 0.23 22.65 0.15 23.79 27 0.49 23.22 0.31 24.27 28 0.76 23.81 0.47 24.77 29 1.05 24.44 0.64 25.30 30 1.36 25.10 0.83 25.87 31 1.68 25.81 1.03 26.49 32 2.03 26.57 1.25 27.17 33 2.41 27.38 1.50 27.93 34 2.82 28.27 1.78 28.79 35 3.26 29.23 2.09 29.75 36 3.74 30.27 2.44 30.82 37 4.27 31.42 2.82 31.99 38 4.87 32.70 3.25 33.30 39 5.53 34.14 3.73 34.77 40 6.32 35.85 4.30 36.52 41 7.47 38.35 5.06 38.86 42 9.16 42.00 6.08 42.00 Validity Analyses Table 7 shows the correlation matrix for the Concentration and Insight Scales with equanimity, depression, anxiety, stress, awareness, kindness, life satisfaction, and self-efficacy for mindfulness meditation practice, demonstrating small to moderate correlations in expected directions with related measures thus indicating construct and divergent validity. As illustrated in Table 8 , Concentration practice quality significantly predicted lower levels of depression, anxiety, and stress, but did not significantly predict life satisfaction. Insight practice quality significantly predicted decreased depression, anxiety, and stress, and increased life satisfaction (Table 8 ). Therefore, the predictive validity of the Concentration and Insight scales is demonstrated in line with expectations. Table 7 Bivariate correlations for Concentration and Insight Scales with related measures Concentration Scale Concentration Equanimity Depression Anxiety Stress Awareness Kindness Life Satisfaction Equanimity 0.43*** Depression -0.22* -0.42*** Anxiety -0.27** -047*** 0.47*** Stress -0.27** -6.53*** 0.52*** 0.56*** Awareness 0.30*** 0.52*** -0.16 -0.14 -0.29** Kindness 0.52 0.10 -0.01 0.08 -0.04 0.25** Life satisfaction 0.15 0.52*** -0.59*** -0.33*** -0.51*** 0.30*** -0.03 SEMMP -0.63*** -0.5*** 0.33*** 0.32*** 0.23* -0.46*** -0.08 -0.23* Insight Scale Insight Equanimity Depression Anxiety Stress Awareness Kindness Life satisfaction Equanimity 0.38*** Depression -0.37*** -0.39*** Anxiety -0.2* -0.44*** 0.45*** Stress -0.22** -0.63*** 0.47*** 0.54*** Awareness 0.39*** -0.49*** -0.16 -0.06 -0.21* Kindness 0.06 0.08 -0.01 0.15 -0.01 0.28** Life satisfaction 0.26** 0.53*** -0.60*** -0.3** -0.49*** 0.30** -0.06 SEMMP -0.62*** -0.38*** 0.22* 0.16 0.14 -0.51*** -0.17 -0.19* SEMMP = self-efficacy for mindfulness meditation practice; c = converted scores; * = p < 0.05; ** = p < 0.01; *** = p < 0.001. Table 8 Regression results for Concentration/Insight practice quality predicting depression, anxiety, stress, and life satisfaction at mid-treatment Predictor: Concentration practice quality Outcome R R 2 Adj. R 2 ∆R 2 F p B SE B β t 95% CI Depression 0.22 0.05 0.04 0.05 6.22 0.01* -0.16 0.07 -0.22 -2.49 [-0.29, -0.03] Anxiety 0.27 0.08 0.07 0.08 9.69 0.002** -0.14 0.04 -0.27 -3.11 [-0.22, -0.05] Stress 0.07 0.07 0.07 0.07 9.29 0.003** -0.17 0.06 -0.27 -3.05 [-0.28, -0.06] Life satisfaction 0.15 0.02 0.01 0.02 2.59 0.11 0.21 0.13 0.15 1.61 [-0.05, 0.47] Predictor: Insight practice quality Outcome R R 2 Adj. R 2 ∆R 2 F p B SE B β t 95% CI Depression 0.37 0.13 0.13 0.13 17.34 < .001*** -0.24 0.06 -0.37 -4.16 [-0.35, -0.12] Anxiety 0.20 0.04 0.03 0.04 4.9 0.03* -0.10 0.04 -0.2 -2.21 [-0.19, -0.01] Stress 0.22 0.05 0.04 0.05 5.98 0.02* -0.13 0.06 -0.22 -2.44 [-0.24, -0.03] Life satisfaction 0.06 0.07 0.06 0.07 8.08 0.005** 0.36 0.13 0.26 2.84 [0.11, 0.6] Depression, Anxiety, Stress measured with Depression Anxiety and Stress Scale (DASS-21); Life satisfaction measured with Satisfaction With Life Scale (SWLS); R = correlation coefficient between predictor & outcome; R 2 = amount of variance accounted for by predictor; Adj. R 2 = adjusted R 2 : generalizability of model; ∆R 2 = adjusted R 2 change; F = model fit of coefficient ratio of improvement; p = significance of ∆R 2 ; B = coefficient of contribution of predictor to model showing direction and size of effect; SE B =Standard Error of coefficient; β = standardized beta coefficient showing standard deviation change of outcome by predictor; t = t-statistic on difference of B to 0; 95% C. I.=95% Confidence Interval. Discussion This study developed and validated the Mindfulness Meditation Practice Quality Scales (MMPQS) including the Concentration and Insight scales in a clinical sample of adult practitioners completing the MiCBT course. Comprehensive psychometric analyses employed the Rasch measurement model and supported six-item versions for the Concentration and Insight Scales and show that both are reliable measures. The Concentration scale consists of items which exclusively measure concentration with the associated cognitive flexibility (refocusing on the breath once distracted) by focusing on one specific object of concentration, having removed double-barreled and highly correlated items from the originally developed items. The six-item Concentration practice quality scale aligns well with Buddhist definitions of śamatha practice (Anālayo, 2006a , b ). Similarly, the Insight scale assesses practice quality in terms of interoceptive awareness, equanimity, and awareness of impermanence and non-self, thus comprehensively incorporating the different aspects of vipassanā practice to foster understanding of the deepest nature of experience (Analayo, 2021; Goenka, 1998 ; Khantipalo, 1995 ). Both the Concentration and Insight scales had small to moderate correlations with measures of equanimity, depression, anxiety, stress, awareness, life satisfaction, and self-efficacy of mindfulness meditation practice, suggesting that the scales are measuring the construct they are proposing to measure, while showing sufficient divergent validity (Fornell & Larcker, 1981 ) in line with expectations. Findings also demonstrated good predictive validity for both scales, with concentration practice quality significantly predicting lower depression, anxiety, and stress, and insight practice quality significantly predicting decreased depression, anxiety and stress, and increased life satisfaction. Thus, these findings are in line with our expectations and previous research, suggesting that concentration and insight mindfulness meditation practices improve mental health and quality of life (Alsubaie et al., 2017 ; Galante et al., 2021 ). Furthermore, both concentration and insight practice quality improved significantly from time one to time two of completing the scales, which is consistent with our predictions and previous research suggesting improvement of mindfulness quality with continued practice (Goldberg et al., 2020 ). It is worth noting that only the Insight scale showed good predictive validity for life satisfaction. Given that mindfulness of breath (concentration) was taught before vipassana (insight) practice, as per the MiCBT model and its corresponding lineage of teachings, it is possible that this difference is simply caused by the serial delivery of these practices—concentration practice starts in week 2 and is typically practiced for one to two weeks, which is followed by insight practice, so it might be too short for patients to feel significant improvement in life satisfaction so early in therapy. While controlling for this was beyond the scope of this psychometric study, future studies could elucidate these differences by lengthening the concentration period so that both practice types are measured after the same duration. The iterative Rasch analysis process presented several methodological challenges that were systematically addressed through careful application of advanced psychometric techniques. Initial analyses revealed issues including disordered thresholds, local dependency between items, and violations of unidimensionality, which required strategic interventions such as item removal and creation of super-items to achieve optimal model fit. The precision offered by Rasch methodology proved invaluable in identifying these subtle measurement issues that would remain undetected using traditional approaches. A key advantage of our Rasch-validated scales is the provision of conversion tables that transform ordinal raw scores into interval-level measurements, dramatically improving measurement accuracy. These tables are straightforward to use. Researchers simply locate their participant's raw summed score in the left column and read across to find the corresponding interval score. For example, a participant scoring 25 on the raw Concentration scale converts to an interval score of 22.11, while a score of 30 converts to 25.10—the equal intervals between these converted scores now represent genuine equal units of the underlying construct, unlike the original ordinal scores where the difference between 25–26 may not equal the difference between 29–30. This conversion enables more accurate statistical analyses, precise change detection over time, and meaningful comparison of scores across different populations, ultimately enhancing both research validity and clinical decision-making. Constraints and Generality A Loving-kindness Scale as part of the MMPQS has not yet been validated. This needs to be noted as a limitation. However, arguably, concentration and insight practices are the most common mindfulness meditation practices in keeping with Pali sources (Anālayo, 2006a ; Griffiths, 1981; Khantipalo, 1995 ), and according to the taxonomy by Nash and Newberg (2013; 2023 ), include cognitive-directed meditation methods (which includes śamatha and vipassanā ), whereas affective-directed ones, such as loving-kindness ( metta ) meditation are classified separately. A validation of the Concentration and Insight Meditation Practice Quality Scales (MMPQS) is thus an important first step to evaluating practice quality. The validation of a loving-kindness meditation practice quality scale needs to be completed in future research. Another constraint of this measure is that so far, the MMPQS has only been developed as part of MiCBT and with a clinical population of participants. Future research should therefore validate the MMPQS for use in other MBPs and with other populations, such as non-clinical, general population participants, and longer-term meditators (Kurth et al., 2023 ) as is the usual research strategy with newly developed measures (Krägeloh et al., 2023 ). Similarly, the MMPQS has been examined only with one specific length, practice amount, and face-to-face delivery of practices. Whether the MMPQS is helpful in understanding the quality of other mindfulness practice doses, i.e. practices that differ in length, frequency and amount, needs to be examined next (Strohmaier et al., 2020, 2021 ). Furthermore, whether there is a difference in how mindfulness is learned based on personality aspects (Strohmaier & Medvedev, 2025 ) and how this relates to practice quality would be important to explore in future research. Overall, the MMPQS provides a comprehensive assessment of mindfulness meditation practice quality for the most commonly used and researched mindfulness meditation practices, concentration and insight, to benefit both research and practice, and provides further understanding as to whether practitioners are engaging in practices in a meaningful way and are improving in quality with greater experience. Declarations Author Contribution SS, BC, and CK contributed to the study conception and design. Material preparation was performed by SS, BC, and CK. Data collection was completed by BC and AS. Data analysis was performed by SS, CK, and OM. The first draft of the manuscript was written by SS, and all authors commented on previous versions of the manuscript. 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D., & Tóth-Király, I. (2019). The Development and Validation of the Compassion Scale. Assessment , 27 (1). https://doi.org/10.1177/1073191119874108 Ribeiro, L., Atchley, R. M., & Oken, B. S. (2018). Adherence to practice of mindfulness in novice meditators: practices chosen, amount of time practiced, and long-term effects following a mindfulness-based intervention. Mindfulness , 9 (2), 401–411. https://doi.org/10.1007/s12671-017-0781-3 Rogers, H. T., Shires, A. G., & Cayoun, B. A. (2021). Development and Validation of the Equanimity Scale-16. Mindfulness 12 , 107–120. https://doi.org/10.1007/s12671-020-01503-6 Ronk, F. R., Korman, J. R., Hooke, G. R., & Page, A. C. (2013). Assessing clinical significance of treatment outcomes using the DASS-21. Psychological Assessment , 25 (4), 1103–1110. https://doi.org/10.1037/a0033100 Sauer, S., Walach, H., Schmidt, S., Hinterberger, T., Lynch, S., Büssing, A., & Kohls, N. (2013). Assessment of Mindfulness: Review on State of the Art. Mindfulness , 4 , 3–17. https://doi.org/10.1007/s12671-012-0122-5 Sedlmeier, P., Beckel, A., Conrad, S., Husmann, J., Kullrich, L., Lange, R., Müller, A. L., Neumann, A., Schaaf, T., Schaub, A., Tränkner, A., & Witzel, B. (2023). Mindfulness Meditation According to the Satipatthana Sutta: A Single-Case Study With Participants as Collaborators. Mindfulness 14 , 1636–1649 (2023). https://doi.org/10.1007/s12671-023-02160-1 Segal, Z. V., Williams, M. G., & Teasdale, J. D. (2002). Mindfulness-based cognitive therapy for depression: a new approach to preventing relapse . Guildford. Shires, A., Osborne, S., Cayoun, B. A., Williams, E., & Rogers, K. (2023). Predictive Validity and Response Shift in the Equanimity Scale-16. Mindfulness 14 , 2880–2893. https://doi.org/10.1007/s12671-023-02257-7 Strohmaier, S. (2020). The Relationship Between Doses of Mindfulness-Based Programs and Depression, Anxiety, Stress, and Mindfulness: a Dose-Response Meta-Regression of Randomized Controlled Trials. Mindfulness , 11 (6), 1315–1335. https://doi.org/10.1007/s12671-020-01319-4 Strohmaier, S., Jones, F. W., & Cane, J. E. (2021). Effects of Length of Mindfulness Practice on Mindfulness, Depression, Anxiety, and Stress: a Randomized Controlled Experiment. Mindfulness 12 , 198–214. https://doi.org/10.1007/s12671-020-01512-5 Strohmaier, S., & Goldberg, S. B. (2024). Longitudinal increases in mindfulness practice quality are associated with changes in psychological outcomes and not vice versa – a brief report. Current Psychology , 43 , 18517–18520. https://doi.org/10.1007/s12144-024-05644-y Strohmaier, S., & Medvedev, O. M. (2025). A latent profile analysis of the Big Five personality and mindfulness traits in the general population. Personality and Individual Differences , 245 , 113287. https://doi.org/10.1016/j.paid.2025.113287 Sutton, A., & Medvedev, O. N. (2023). Development and Validation of the Awareness Outcomes Measure (AOM) Using Rasch Approach. Mindfulness , 14 , 473–481. https://doi.org/10.1007/s12671-022-02047-7 Walach, H., Bucheld, N., Buttenmüller, V., Kleinknecht, N., & Schmidt, S. (2006). Measuring mindfulness – The Freiburg mindfulness inventory (FMI). Personality and Individual Differences , 40 , 1543–1555. https://doi.org/10.1016/j.ijchp.2020.03.004 van Dam, N. T., van Vugt, M. K., Vago, D. R., Schmalzl, L., Saron, C. D., Olendzki, A., Meissner, T., Lazar, S. W., Kerr, C. E., Gorchov, J., Fox, K. C. R., Field, B. A., Britton, W. B., Brefczynski-Lewis, J. A., & Meyer, D. E. (2018). Mind the hype: a critical evaluation and prescriptive agenda for research on mindfulness and meditation. Perspectives on Psychological Science , 13 (1), 36–61. https://doi.org/10.1177/1745691617709589 Walshe, M. (2012). The long discourses of the Buddha: A translation of the Digha Nikaya . Wisdom. Winkens, L. H. H. (2022). Mindful Eating Behavior Scale (MEBS). In O. N. Medvedev, C. U. Krägeloh, R. J. Siegert, & N. N. Singh (Eds.), Handbook of Assessment in Mindfulness Research . Springer. https://doi.org/10.1007/978-3-030-77644-2_34-1 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8218417","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561827797,"identity":"ad8a6103-59e2-4edf-b73e-958b9b32a4e7","order_by":0,"name":"Sarah Strohmaier","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie3RMWsCMRTA8SeBc0m5NaJ4X+HJwRWx9LMUCnERepN0OoSCLimu9zF06XwlFJf05hy36OLkUOgi1NKm1yIUGkSnDvlDCBl+5JEAuFz/NPK9UbMQwK8O3mHC9qQxOo58qewACSbPq9cYkuR8cv8028YXN6EeILwMJfjp1Z8EVT9spiBZS+W8EMi7D4bU0lwC0xYCHJoUMsbYINIUJUZKITkbSwALCaZr8kYhqUixww8MhSHvhgQWApp75hZSkZJihlgXSGqGoG0wvfZ6Zp5GSlVUtvAa2WIcP4q8TztqaRmMk5LeJj6ri6jY7C7RvyPz5XbYa7cXlsF+HuF3Gey/yeVyuVyn9AkKj1WekrRYbwAAAABJRU5ErkJggg==","orcid":"","institution":"Victoria University","correspondingAuthor":true,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Strohmaier","suffix":""},{"id":561827798,"identity":"cf32b8b7-720c-48bc-814c-f9dc353732ca","order_by":1,"name":"Bruno Cayoun","email":"","orcid":"","institution":"Mindfulness-integrated CBT Institute","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"","lastName":"Cayoun","suffix":""},{"id":561827799,"identity":"ff06ee9e-acb0-4cd2-b034-12ac12d5aaa2","order_by":2,"name":"Alice G. 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12:30:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePerson-item threshold distribution for Insight items at time 1 (blue) and time 2 (red) for Analysis 4 (A4)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8218417/v1/57f6237cd3f24ee4a3ee646c.png"},{"id":98787481,"identity":"527ee323-f499-437c-8f5c-a65179fb7cff","added_by":"auto","created_at":"2025-12-22 12:43:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1640747,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8218417/v1/02e99efa-dfb0-4a61-97e7-c341f1a90112.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of the Mindfulness Meditation Practice Quality Scales (MMPQS) in a clinical population using Rasch analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMindfulness practice has been defined as the unbiased and non-reactive monitoring of phenomena spontaneously emerging in conscious awareness, with equanimity and clear understanding of their impermanent and impersonal essence (Cayoun, 2017). Research with mindfulness-based programs (MBPs) and practices continues to enjoy rapid growth (Ferreira \u0026amp; Demarzo, 2024). Mindfulness has been found helpful for mental health and wellbeing outcomes, for both clinical (Goldberg et al., 2018), as well as non-clinical (Galante et al., 2021) populations. However, a lot of research has focused on practice \u003cem\u003equantity\u003c/em\u003e (e.g. Strohmaier, 2020; 2021) and less on practice \u003cem\u003equality\u003c/em\u003e, which is arguably equally important (Ribeiro et al., 2018).\u003c/p\u003e\n\u003cp\u003eTo meet the needs for research to explore the increasingly diverse applications of mindfulness practice, many different measures of mindfulness have been developed, assessing a variety of mindfulness constructs, such as trait mindfulness, examining a person\u0026rsquo;s general disposition of mindfulness (e.g. Baer et al., 2006), in-the-moment state mindfulness (e.g. Lau et al., 2006), and mindfulness as a skill to be learned (e.g. Walach et al., 2006). Many mindfulness measures have also been adapted for use with different age groups (e.g. Kr\u0026auml;geloh \u0026amp; Strohmaier, 2024; Pallozzi et al., 2017), various clinical populations (e.g. Joos et al., 2025; Winkens et al., 2022), and international contexts (e.g. Henning et al., 2024). More recently, measures have begun to examine factors related to the practice of mindfulness. This includes for example formal and informal mindfulness practice adherence (Hassed et al., 2021), the assessment of a mechanism of mindfulness-based programs, such as equanimity (Rogers et al., 2021), or factors related to the participants\u0026rsquo; ability to provide a rating of following practice instructions on a visual analogue scale (Del Re et al., 2013). However, no measure so far has been developed for the quality of different types of mindfulness practices. This is arguably important, since the quality of a practice, rather than only the time spent practicing, is paramount, in particular for novice meditators, to ensure a high quality of practice for those first learning meditation (Ribeiro et al., 2018; Strohmaier \u0026amp; Goldberg, 2024). Additionally, practice quality relating to specific types of mindfulness practices, rather than a generic measure incorporating all different practices, is crucial to ensure a more nuanced assessment.\u003c/p\u003e\n\u003cp\u003eAccording to Buddhist traditions, meditation includes several different practices, principally concentration (\u003cem\u003eśamatha\u003c/em\u003e), insight (\u003cem\u003evipassanā\u003c/em\u003e), and loving-kindness (\u003cem\u003emetta\u003c/em\u003e); whereas \u003cem\u003eśamatha\u003c/em\u003e and \u003cem\u003evipassanā\u003c/em\u003e are understood as the two central practices (Khantipalo, 1995). In \u003cem\u003eśamatha\u003c/em\u003e practices, the focus is on one specific object of concentration. \u003cem\u003eVipassanā\u003c/em\u003e practices involve observing and monitoring thoughts and emotions as they are to understand the true nature of reality\u0026mdash;all mental and physical experiences are impermanent and impersonal phenomena\u0026mdash;and \u003cem\u003emetta\u003c/em\u003e practices cultivate compassion, empathy, and kindness towards oneself and others (Anālayo, 2006a). While loving-kindness is related to mindfulness practice, it serves a different purpose as it focuses on a different construct to mindfulness, and historically is not taught in the \u003cem\u003esatipaṭṭhāna sutta\u003c/em\u003e, the formal teaching on the establishment of mindfulness (Anālayo, 2006a; Sedlmeier et al., 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, Nash and Newberg (2013; 2023), who have developed a universal taxonomy and classification of meditation methods, differentiate between cognitive-directed methods (which includes \u003cem\u003eśamatha\u003c/em\u003e and \u003cem\u003evipassanā\u003c/em\u003e) and affective-directed ones (which includes \u003cem\u003emetta\u003c/em\u003e). Therefore, concentration and insight practices are focused on here first and foremost, since arguably, these are the fundamental practices in Buddhist traditions resulting from Pali sources (Griffiths, 1981; Hart, 1987). Although concentration and insight practices have at times been classed as \u0026ldquo;contrasting practices\u0026rdquo; (Griffiths, 1981, p. 606), these are arguably the main practices available to mindfulness meditation practitioners and thus, are practices that need to be completed with a high level of quality. While concentration and insight practices are engaging different processes, they are complimentary practices and become more integrated with increased meditator proficiency and experience over time (Anālayo, 2006a). The interrelationship between concentration and insight practices has been outlined in detail elsewhere (Khantipalo, 1995).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe most commonly researched mindfulness meditation practices are those pertaining to concentration and insight (Mehrmann \u0026amp; Rakesh, 2013). In particular, the MBPs with the most research evidence to date are Mindfulness-Based Stress Reduction (MBSR) and Mindfulness-Based Cognitive Therapy (MBCT) (Alsubaie et al., 2017), both of which employ concentration and insight (Kabat-Zinn, 1982; Segal et al., 2002). Research on other types of MBPs have also been comparing the effectiveness of concentration and insight meditation practices in particular, finding that different processes are involved in these practices, but that they are effective for both physiological and mental health. For example, research by Ooishi et al. (2021) found that focused attention practices (\u003cem\u003eśamatha\u003c/em\u003e) elevated physiological relaxation, whereas open monitoring (\u003cem\u003evipassanā\u003c/em\u003e) practices reduced stress and elevated physiological arousal in novice meditators with a history of physical health diagnoses such as diabetes, thyroid dysfunction, or hypertension. Research with \u003cem\u003eśamatha\u003c/em\u003e and \u003cem\u003evipassanā\u003c/em\u003e practices have also found to aid in the reduction of psychological distress outcomes in clinical populations, finding a decreased relapse of depressive symptoms (e.g. Kuyken et al., 2016).\u003c/p\u003e\n\u003cp\u003eWhile traditional psychometric approaches often assume that Likert-scale responses represent interval-level data, this assumption is frequently violated in practice, leading to measurement errors and compromised statistical analyses. The Rasch measurement model offers a robust solution to this fundamental problem by explicitly recognizing the ordinal nature of rating scale responses and providing a principled method for converting these ordinal scores into true interval measurements (Andrich et al., 2009; Siegert et al., 2010). This conversion is particularly important for mindfulness practice quality assessment, where precise measurement differences are crucial for detecting meaningful changes over time and comparing individuals across different practice contexts. The Rasch model\u0026apos;s iterative approach allows researchers to identify and remove misfitting items, ensuring that the final scale measures a single underlying construct with equal intervals between scale points. Additionally, the model\u0026apos;s ability to test for differential item functioning across demographic groups ensures that the scales perform consistently across different populations, a critical consideration for measures intended for diverse clinical samples (Sutton \u0026amp; Medvedev, 2023). By employing Rasch analysis, researchers can develop measurement tools that meet the stringent requirements necessary for both clinical practice and research applications.\u003c/p\u003e\n\u003cp\u003eAccurately measuring the quality of practice is not only helpful for research purposes, but also important for both teachers and practitioners, in particular novices, to understand both the skills to be cultivated through their meditation and the progress they are making during each practice, while at the same time highlighting any areas of difficulty where the practitioner may need further assistance. Therefore, this study sought to develop a measure of mindfulness meditation practice quality. Due to the close relationship and the most extensive evidence for concentration and insight meditation practices, this paper aimed to develop and validate scales of these meditation practices.\u003c/p\u003e\n\u003cp\u003eOne of the MBPs which integrates both concentration and insight meditation practices in a measurable sequential way is Mindfulness-integrated Cognitive Behavior Therapy (MiCBT), a transdiagnostic approach principally used in clinical populations (Francis et al., 2022). Accordingly, examining the psychometric features of these scales through the implementation of MiCBT in a clinical population was both convenient and advantageous for examining their predictive validity across a wide range of moderate to severe mental health conditions. We hypothesized that mindfulness practice quality would improve with continued practice, and greater mindfulness practice quality would predict lower levels of depression, anxiety, and stress and higher satisfaction with life. We expected that mindfulness practice quality would correlate moderately with related concepts.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eIn total, 368 adults were included in this study if they were currently seeking treatment in the form of a MiCBT program (Cayoun, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cayoun et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) at either of two psychology clinic sites. Participants were either self-referred or referred by a medical professional to both psychology clinics. All were from a clinical population with a variety of moderate to severe mental health disorders, all diagnosed with at least one comorbid condition, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The majority of participants were White (90.4%) Australians (91.15%), who identified as male (59.2%) with an average age of 42.34 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for further details).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eParticipant demographics and measure descriptive statistics for Concentration (N\u0026thinsp;=\u0026thinsp;211) and Insight (N\u0026thinsp;=\u0026thinsp;157) Scales at Time 1\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics / measures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eConcentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eInsight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge: M (SD); %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e41.5 (14.6);\u003c/p\u003e \u003cp\u003e18\u0026ndash;35: 37.4%\u003c/p\u003e \u003cp\u003e36\u0026ndash;48: 31.8%\u003c/p\u003e \u003cp\u003e49\u0026ndash;78: 30.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e43.17 (14.49)\u003c/p\u003e \u003cp\u003e18\u0026ndash;35: 33.3%\u003c/p\u003e \u003cp\u003e36\u0026ndash;48: 34%\u003c/p\u003e \u003cp\u003e49\u0026ndash;78:32.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eFemale: 40.8\u003c/p\u003e \u003cp\u003eMale: 58.8\u003c/p\u003e \u003cp\u003eOther: 0.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eFemale: 39.7%\u003c/p\u003e \u003cp\u003eMale: 59.6%\u003c/p\u003e \u003cp\u003eOther: 0.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNationality (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAustralian: 91.9%\u003c/p\u003e \u003cp\u003eUK: 1.4%\u003c/p\u003e \u003cp\u003eOther: 6.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eAustralian: 90.4%\u003c/p\u003e \u003cp\u003eUK: 1.9%\u003c/p\u003e \u003cp\u003eOther: 7.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eWhite: 89.1%\u003c/p\u003e \u003cp\u003eAsian: 5.2%\u003c/p\u003e \u003cp\u003eOther: 5.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eWhite: 91.7%\u003c/p\u003e \u003cp\u003eAsian: 4.5%\u003c/p\u003e \u003cp\u003eOther: 3.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasures (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrimary Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal Comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTSD\u003c/p\u003e \u003cp\u003eOCD\u003c/p\u003e \u003cp\u003eDepression\u003c/p\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003cp\u003ePersonality disorder\u003c/p\u003e \u003cp\u003eAdjustment disorder\u003c/p\u003e \u003cp\u003eAutism\u003c/p\u003e \u003cp\u003eADHD\u003c/p\u003e \u003cp\u003eAlcohol/substance abuse\u003c/p\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e31\u003c/p\u003e \u003cp\u003e50\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e38\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePTSD\u003c/p\u003e \u003cp\u003eOCD\u003c/p\u003e \u003cp\u003eDepression\u003c/p\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003cp\u003ePersonality disorder\u003c/p\u003e \u003cp\u003eAdjustment disorder\u003c/p\u003e \u003cp\u003eAutism\u003c/p\u003e \u003cp\u003eADHD\u003c/p\u003e \u003cp\u003eAlcohol/ substance abuse\u003c/p\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e22\u003c/p\u003e \u003cp\u003e37\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e33\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e26\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e22\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquanimity: M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e45.96 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e46.94 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression: M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e7.98 (4.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e7.82 (5.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety: M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e6.3 (3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e5.86 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress: M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e10.76 (3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e10.42 (3.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness: M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e36.33 (5.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e36.16 (6.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKindness: M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e4.16 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e8.34 (1.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife satisfaction: M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e19.61 (7.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e20.01 (7.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEMMP: M (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e26.48 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003e26.24 (6.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: M\u0026thinsp;=\u0026thinsp;Mean; SD\u0026thinsp;=\u0026thinsp;Standard Deviation; N\u0026thinsp;=\u0026thinsp;Number; PTSD\u0026thinsp;=\u0026thinsp;Post-Traumatic Stress Disorder; OCD\u0026thinsp;=\u0026thinsp;Obsessive-Compulsive Disorder; ADHD\u0026thinsp;=\u0026thinsp;Attention-Deficit/Hyperactivity Disorder; SEMMP\u0026thinsp;=\u0026thinsp;Self-efficacy for mindfulness meditation practice\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003e Participation was voluntary and all participants gave informed consent prior to taking part. They consented to their de-identified data and responses to questionnaires administered at different time points throughout treatment being utilized for the study. The data collection process is outlined in the Supplementary Materials (SM.1). Patients with psychotic or manic symptoms were excluded from the study, given the potential adverse effects that meditating can cause during these mental states (van Dam et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThroughout the four stages of MiCBT, several mindfulness meditation practices and cognitive-behavioral skills are taught to individuals over a standard period of 10 weeks (Cayoun, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cayoun et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), although additional time is often necessary with more severe or chronic conditions (Grabovac \u0026amp; Cayoun, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). During stage 1, meditation techniques are principally related to intrapersonal regulation. During stage 2, these are integrated with cognitive-behavioral methods, such as imaginal and \u003cem\u003ein vivo\u003c/em\u003e exposure to reduce avoidance and promote equanimity and confidence in daily life. During stage 3, meditation techniques are integrated with cognitive-behavioral methods, such as mindful assertiveness, and applied in daily life to promote interpersonal regulation and confidence in tense interpersonal situations. During stage 4, meditation techniques are integrated with a behavioral task to help feel more connected to others and reduce the risk of relapse. Participants in this study were guided through each of these procedures.\u003c/p\u003e \u003cp\u003eTo learn the meditation techniques, participants were given a set of MP3 audio tracks or \u003cem\u003eThe MiCBT Guide\u003c/em\u003e smartphone application to guide each practice method. Once the procedure of a method was learned, participants practiced it in silence with a timer if they wished.\u003c/p\u003e \u003cp\u003eThe MiCBT program integrates meditation skills in a structured and scaffolding manner, starting with a 14-minute progressive muscle relaxation twice daily to motivate daily self-care and reduce stress. During that week, they are also asked to practice mindfulness of the body, remaining aware of postures and movements as often and in as many contexts as possible, as a start to keeping attention in the present moment with an easy object of focus. These two methods are generally achievable in clinical practice, unless patients are severely dissociated.\u003c/p\u003e \u003cp\u003eThereafter, all mindfulness methods are practiced in 30-minute sessions twice daily, starting with concentration training. For this, mindfulness of breath is introduced to teach three essential executive functions: (1) sustaining attention at the entrance of the nostrils while holding in working memory that thoughts are just impermanent and impersonal cognitive events, (2) inhibiting the learned response (to engage with thoughts), and (3) shifting attention back to the breath without attachment to the thought. This skillset is concomitantly applied to regulate attention by cultivating mindfulness of thoughts and mental states in daily activities and shift away from ruminative and other unhelpful thoughts when necessary.\u003c/p\u003e \u003cp\u003eOnce sufficient ability to concentrate on the breath is acquired during meditation, a set of body scanning techniques\u0026mdash;taught in the Burmese vipassana tradition of Ledi Sayadaw, U Ba Khin, and S. N. Goenka\u0026mdash;are successively introduced to develop interoceptive awareness and equanimity. The primary aim in the clinical context is to improve emotion regulation through interoceptive desensitization and insight into the impermanence and impersonality of affective valence.\u003c/p\u003e \u003cp\u003eFinally, loving-kindness meditation is taught as part of cultivating compassion toward self and others. This is synchronously applied with explicit ethics in daily life, as a behavioral experiment, to cultivate a transpersonal awareness and sense of connectedness with others and the environment at large. When motivated by a compassionate intention, preventing harm to themselves and others helps patients prevent relapsing in a psychological disorder.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMindfulness Meditation Practice Quality Scales (MMPQS) Development\u003c/h2\u003e \u003cp\u003eThe MMPQS consists of two scales and is designed to be administered immediately after different types of meditation practice, to measure the quality of the practice. The scales relate to Concentration and Insight mindfulness practices. The scales were developed by three members of the research team with appropriate and sufficient personal practice and teaching experience of mindfulness methods. Item development for these scales was also informed by the traditional literature on mindfulness of breath (e.g., Anālayo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006b\u003c/span\u003e; Hart, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) and mindfulness of body sensation taught in vipassana meditation (e.g., Anālayo, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Goenka, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Walshe, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), as applied in MiCBT (Cayoun, et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cayoun, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Initially, a pool of items was developed and reduced to 10 items for each scale, based on primary consensus among the authors. Items on each scale were then independently rated on a 1 to 10 scale of endorsement by the authors and discussed for further screening and elaboration. The items were then improved based on these ratings and comments with any disparities discussed until a 90% consensus was reached for each item.\u003c/p\u003e \u003cp\u003eThe Concentration scale included items such as, \u0026ldquo;I struggled to maintain my concentration\u0026rdquo;, and \u0026ldquo;I was able to refocus immediately after being distracted by mental experiences, such as thoughts and images\u0026rdquo;. The Insight scale included items such as, \u0026ldquo;When paying attention to the body, I could feel a range of pleasant, unpleasant, or neutral sensations\u0026rdquo;, and \u0026ldquo;I was able to prevent reacting to physical, emotional, or mental experiences\u0026rdquo;.\u003c/p\u003e \u003cp\u003eParticipants were asked to indicate how truly representative each of the items on each scale is for them after having completed a practice on a 7-point Likert Scale ranging from \u0026ldquo;\u003cem\u003enot at all true\u0026rdquo;\u003c/em\u003e (1) to \u0026ldquo;\u003cem\u003ecompletely true\u0026rdquo;\u003c/em\u003e (7). Each scale was completed twice to observe progress and examine predictive validity (see SM.1 for process of data collection). Concentration and Insight meditation practice quality was determined by summing all items for each scale after reverse coding. Both, the Concentration (\u003cem\u003e⍺\u003c/em\u003e = 0.85) and Insight (\u003cem\u003e⍺\u003c/em\u003e = 0.86) scales showed good internal consistency in the current sample. The scales were designed as stand-alone assessment tools, and which of the scales to use depends on the specific practice completed.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEquanimity Scale 16 (ES-16)\u003c/h3\u003e\n\u003cp\u003eThe ES-16 (Rogers et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) is a 16-item scale assessing the level of equanimity with two underlying factors, experiential acceptance and non-reactivity, on a 5-point Likert scale, with greater scores relating to greater equanimity. The ES-16 showed good internal consistency (\u003cem\u003e⍺\u003c/em\u003e = 0.91) and has good convergent, divergent and predictive validity (Shires et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDepression Anxiety Stress Scale (DASS-21)\u003c/h3\u003e\n\u003cp\u003eThe DASS-21 (Henry \u0026amp; Crawford, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lovibond \u0026amp; Lovibond, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) is a 21-item scale examining general psychopathology over the past week on a 4-point Likert scale with three subscales: depression, anxiety, and stress with greater scores indicating greater symptomatology. The DASS-21 has been found to have good discriminant, convergent and construct validity, and it showed good reliability for each of subscale depression (\u003cem\u003e⍺\u003c/em\u003e = 0.96), anxiety (\u003cem\u003e⍺\u003c/em\u003e = 0.92), and stress (\u003cem\u003e⍺\u003c/em\u003e = 0.95) (Ronk et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhiladelphia Mindfulness Scale \u0026ndash; Awareness (PHLMS-A)\u003c/h2\u003e \u003cp\u003eThe awareness subscale of the PHLMS-A (Cardaciotto et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) consists of ten items, which are scored on a 5-point Likert scale where greater scores demonstrate greater awareness. The awareness subscale of the PHLMS has shown good convergent and discriminant validity, and in this sample, showed good reliability (\u003cem\u003e⍺\u003c/em\u003e = 0.82).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCompassion Scale (CS) – Kindness subscale\u003c/h3\u003e\n\u003cp\u003eThe kindness subscale of the CS (Pommier et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) has four items, which are scored on a 5-point Likert scale. Averaged scores are calculated for the kindness subscale, with greater scores indicating greater kindness. The CS and its subscales have shown good discriminant, construct and convergent validity and is reliable in the current sample (\u003cem\u003e⍺\u003c/em\u003e = 0.87).\u003c/p\u003e\n\u003ch3\u003eSatisfaction With Life Scale (SWLS)\u003c/h3\u003e\n\u003cp\u003eThe five-item SWLS (Diener et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) measures global cognitive judgements of life satisfaction on a 7-point Likert scale ranging from strongly disagree to strongly agree, with greater scores indicating greater life satisfaction. The SWLS has shown food convergent and discriminant validity and shows internal consistency (\u003cem\u003e⍺\u003c/em\u003e = 0.87) (Jovanović et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSelf-Efficacy for Mindfulness Meditation Practice (SEMMP-9)\u003c/h2\u003e \u003cp\u003eThe nine item SEMMP (Birdee et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) is scored on a nine-point Likert scale with greater scores demonstrating greater self-efficacy for mindfulness meditation practice. The SEMMP is reliable (\u003cem\u003e⍺\u003c/em\u003e = 0.84) in the current sample and has shown construct and convergent validity with related measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Analyses\u003c/h2\u003e \u003cp\u003eDescriptive statistics were computed using IBM SPSS v.29 to understand characteristics of the current sample. Rasch analysis was used to validate two scales of the Meditation Practice Quality Scales, pertaining to the most common meditation practices, namely concentration and insight. Rasch analysis was conducted using RUMM2030 (Andrich et al., 2009) iteratively until the Rasch model expectations were met, including both the overall and individual item-fit to the model, no local dependency, scale invariance across demographic factors and evidence of unidimensionality (Siegert et al., 2010). If the likelihood-ratio test were to indicate the presence of significant differences between response options and thresholds across individual items, the unrestricted, partial-credit version of the Rasch model would be chosen (Masters, 1980), and Gustafsson\u0026rsquo;s (1980) model fit criteria will be adopted as follows. Rasch model fit was assessed primarily through chi-square statistics, where both overall and individual item-trait interaction should be non-significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), with Bonferroni adjustment applied by dividing 0.05 by the number of tests conducted (Gustafsson, 1980; Tennant \u0026amp; Conaghan, 2007). Individual item fit residuals must fall within the acceptable range of -2.50 to +\u0026thinsp;2.50 to demonstrate adequate fit to the model. While not strict requirements, optimal targeting is indicated when person location mean falls between \u0026minus;\u0026thinsp;0.50 to +\u0026thinsp;0.50, suggesting good coverage of the sample by the scale, and when item and person fit residuals approximate 0.00 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00) for excellent overall fit. The item location mean is automatically set to zero as the reference point in RUMM2030. Additionally, no significant differential item functioning (DIF) should be evident across demographic factors such as gender, age, or other personal characteristics to ensure measurement invariance across different groups.\u003c/p\u003e \u003cp\u003eSmith\u0026rsquo;s (2000) method of employing an independent-samples \u003cem\u003et\u003c/em\u003e-test comparison of person estimates to group items with the highest negative and positive loadings on the first principal component when controlling for the principle latent factor. This was used to examine unidimensionality of each scale, which is supported if there are not more than 5% significant \u003cem\u003et-\u003c/em\u003etest comparisons.\u003c/p\u003e \u003cp\u003eLocal dependency can occur when responses to one item influence the responses to another item resulting in spurious correlations thus escalating measurement error (Baghaei, 2010). Local dependency of items was evaluated employing the residual correlation matrix, which according to Christensen et al. (2016) is present when the magnitude of residual correlations exceeds the mean of all residual correlations by 0.20. Unidimensionality is affected by spurious correlations between locally dependent items, and super-items were created by combining locally dependent items.\u003c/p\u003e \u003cp\u003eDIF for personal factors including age, gender, mindfulness practice experience, and time was examined for each scale item. Four age categories of roughly equal size were created (18\u0026ndash;35, 36\u0026ndash;48, 49\u0026ndash;78 years of age). Gender was coded as either female, male, or non-binary. Mindfulness practice experience was divided into either yes or no. Time was coded as Time 1 and Time 2 of completing each scale. Nationality, ethnicity, diagnosis, and practice type, frequency and duration could not be divided into distinct equal overarching categories to calculate DIF. By employing DIF analysis, we can help ensure that this measure is robust across contexts and suitable to be applied for different populations (Sutton \u0026amp; Medvedev, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter Rasch analysis, ordinal scores for both scales were converted to interval scores by transforming logit scores. Construct and divergent validity of the MMPQS was examined by running bivariate correlations with several other related measures, namely the Equanimity Scale 16 (ES-16), the Depression, Anxiety, Stress Scale (DASS-21), the Philadelphia Mindfulness Scale \u0026ndash; Awareness (PHLMS-A), the Compassion Scale \u0026ndash; Kindness (CS), the Satisfaction with Life Scale (SWLS), and the Self-Efficacy for Mindfulness Meditation Practice (SEMMP), with a Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.50 or greater generally considered sufficient for divergent validity (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Predictive validity of the MMPQS scales was also completed using regression analyses with Concentration and Insight as predictors and depression, anxiety, stress, and life satisfaction as outcomes to test Hypotheses 3 and 4.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows participant demographics and descriptive statistics of all measures for each scale. Participants had a variety of comorbid clinical diagnoses, with anxiety disorders being the most common primary diagnosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRasch Analyses\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eConcentration Scale\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the summary of Rasch model fit statistics for the initial 10-item (A1), second 9-item (A2), third 8-item (A3), fourth 7-item (A4), 6-item (A5), and final 6-item (with the inclusion of two super items) (A6) analyses of the Concentration scale. Initially, in Analysis 1 (A1), Item 7 \u0026ldquo;I was so immersed in concentration that I had no thoughts, not even being aware that I am focusing on a particular object\u0026rdquo; was removed, due to significant fit residuals. Upon closer inspection, Item 7 appeared to measure insight more closely than concentration, thus adding a conceptual reason for its exclusion. Analysis two (A2), when Item 7 was removed, identified Item 1 \u0026ldquo;While I was remaining alert and focused, I felt that time was passing quickly\u0026rdquo; as problematic since A2 did not have acceptable Rasch model fit statistics. The wording of Item 1 is double-barreled, which may have been confusing for participants to complete, thus adding another reason for its removal. Analysis 3 (A3) with Items 7 and 1 removed, showed a significant item fit residual for Item 3 \u0026ldquo;I felt motivated and engaged.\u0026rdquo; and was therefore removed. Additionally, this item appears vague and may be open to misinterpretation. In Analysis 4 (A4), with Items 7, 1 and 3 removed, a significant residual fit still remained.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSummary of Rasch model fit statistics for the initial (A1; 10-items), second (A2; 9-items), third (A3; 8-items), fourth (A4; 7-items), fifth (A5; 6-items), and final (A6; 6-items including superitems) analyses of the Concentration scale (n\u0026thinsp;=\u0026thinsp;414)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalyses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eItem fit residual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePerson fit residual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGoodness of fit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePSI\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIndependent \u003cem\u003et-\u003c/em\u003etest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e \u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e(df)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e161.36 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124.47 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e121.14 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.45 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.41 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.86 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e = Person Separation Index; SD\u0026thinsp;=\u0026thinsp;Standard Deviation; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Chi Square; df\u0026thinsp;=\u0026thinsp;Degrees of Freedom.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis was followed up with a correlation analysis which indicated a high correlation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.8) between Items 6 \u0026ldquo;When my attention drifted away, I was able to return quickly to my chosen object of concentration.\u0026rdquo; and 8 \u0026ldquo;I was able to refocus immediately after being distracted by mental experiences, such as thoughts and images.\u0026rdquo;. Upon closer inspection, Item 6 was removed as it conceptually measures the same as item 8, and the wording of item 8 is preferable to item 6, as it provides examples. Analysis five (A5) indicated that the item fit residual was no longer significant and thus the six-item version of the Concentration subscale is considered a good fit to the Rasch model showed high reliability and invariance across personal factors.\u003c/p\u003e \u003cp\u003eWhen testing unidimensionality of the 6-item Concentration scale resulting from A5, the independent \u003cem\u003et\u003c/em\u003e-test was above the 5% threshold (A5\u0026thinsp;=\u0026thinsp;8.77%). When grouping the two highest positive (items 4 and 9) and two highest negative (items 5 and 9) loadings and running independent-samples \u003cem\u003et\u003c/em\u003e-test comparisons, this resulted in an acceptable \u003cem\u003et-\u003c/em\u003etest comparison of 2.5%, thus supporting strict unidimensionality when Items 4 and 9, and 5 and 8, are combined as super items in A6.\u003c/p\u003e \u003cp\u003eAnalysis of DIF found no significant difference of the 6-item Concentration scale for age, gender, mindfulness practice experience, or time. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the item-fit statistics for the initial, 10-item version of the Concentration scale, and the item-fit statistics after misfitting items have been removed, resulting in the final 6-item version of the Concentration scale. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the person-item threshold distribution for Concentration items over time, clearly indicating an increase in scores from Time 1 to Time 2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eInitial item-fit and final item-fit statistics of the 10-item and 6-item Concentration scale (n\u0026thinsp;=\u0026thinsp;414)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFitResid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eInitial item-fit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhile I was remaining alert and focused, I felt that time was passing quickly.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI could stay focused on my chosen object of concentration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.79***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI felt motivated and engaged.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003csup\u003er\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThoughts were interfering with my ability to focus on my chosen object of concentration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003csup\u003er\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI struggled to maintain my concentration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhen my attention drifted away, I was able to return quickly to my chosen object of concentration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.23*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was so immersed in concentration that I had no thoughts, not even being aware that I am focusing on a particular object.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to refocus immediately after being distracted by mental experiences, such as thoughts and images.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to refocus immediately after being distracted by sensory experiences, such as noise and physical sensations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to know that I was focused without having to think about it.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinal item-fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI could stay focused on my chosen object of concentration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003csup\u003er\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThoughts were interfering with my ability to focus on my chosen object of concentration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003csup\u003er\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI struggled to maintain my concentration.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to refocus immediately after being distracted by mental experiences, such as thoughts and images.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to refocus immediately after being distracted by sensory experiences, such as noise and physical sensations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to know that I was focused without having to think about it.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;standard error; FitResid\u0026thinsp;=\u0026thinsp;Fit Residuals; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Chi Square; \u003csup\u003er\u003c/sup\u003e = reverse-coded item; * = \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** = \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** = \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eInsight Scale\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the summary of Rasch model fit statistics for the initial 10-item (A1), second 9-item (A2), third 7-item (A3), fourth and final 6-item (A4) analysis. The initial analysis (A1) showed acceptable Rasch model fit statistics. However, when checking for unidimensionality, this was not acceptable (\u0026gt;\u0026thinsp;5%) when comparing highest positive with highest negative item loadings (Items 1, 3, 7 vs. Items 4, 6, 9). Additionally, when examining local dependency of personal factors using DIF, a significant difference in age of participants especially between youngest and oldest groups, and in particular for Item 6 \u0026ldquo;When I did not experience what I expected, I was able to accept it instead of letting frustration or disappointment overcome me\u0026rdquo;. Upon closer inspection, the length and wording of this item may also have been unclear for some participants. This item was therefore removed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSummary of Rasch model fit statistics for the initial (A1; 10-items), second (A2; 9-items), third (A3; 7-items), and final (A4; 6-items) analyses of the Insight scale (n\u0026thinsp;=\u0026thinsp;313)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalyses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eItem fit residual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePerson fit residual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGoodness of fit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePSI\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIndependent \u003cem\u003et-\u003c/em\u003etest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e \u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e(df)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.92 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.05 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.41 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.05 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e = Person Separation Index; SD\u0026thinsp;=\u0026thinsp;Standard Deviation; \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Chi Square; df\u0026thinsp;=\u0026thinsp;Degrees of Freedom.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHowever, unidimensionality was still not reached in A2 with Item 6 removed when comparing highest positive with highest negative item loadings (Items 1, 2, 3, 7 vs. items 5, 8, 9). Two items, Item 1 \u0026ldquo;I was able to feel sensations in the body\u0026rdquo; and Item 9 \u0026ldquo;I was able to perceive what I experienced with objectivity and a degree of distance.\u0026rdquo; were removed, because item 1 had a high correlation (\u0026gt;\u0026thinsp;.80) with item 3 and a high residual correlation with item 9. Similarly, Item 9 had a high correlation with Items 5 and 8. Additionally, the wording of Item 9 may have been confusing for individuals due to being double-barreled.\u003c/p\u003e \u003cp\u003eIt was therefore decided to remove Items 1 and 9 in A3, however, unidimensionality was still not acceptable (5.89%). Item 7 \u0026ldquo;While paying attention to the body, I could feel the changing nature of body sensations.\u0026rdquo; showed a high correlation with Item 3, and appears to be measuring the same as Item 3. Item 7 was therefore removed in A4. The 6-item Insight scale in A4 showed acceptable fit residuals and unidimensionality.\u003c/p\u003e \u003cp\u003eAfter A1, no DIF were observed for any of the person variables, such as age, gender, nationality, ethnicity, mindfulness practice experience, in A2, A3, and A4. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e details the item-fit statistics for the initial, 10-item version, and the item-fit statistics of the final 6-item version of the Insight scale. Figure\u0026nbsp;2 shows the person-item threshold distribution for Insight items at Time 1 and 2 for A4. The final 6-item Insight scale comprehensively covers the three elements of insight, namely interoceptive awareness (Items 2 and 3), equanimity (Items 4 and 5), and awareness of impermanence and non-self (Items 8 and 10).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eInitial item-fit and final item-fit statistics of the 10-item and 6-item Insight scale (n\u0026thinsp;=\u0026thinsp;313)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFitResid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eInitial item-fit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to feel sensations in the body.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to remain attentive to what I felt in the body.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhen paying attention to the body, I could feel a range of pleasant, unpleasant, or neutral sensations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to prevent reacting to physical, emotional, or mental experiences.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhile paying attention to the body, I was able to accept my experiences.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhen I did not experience what I expected, I was able to accept it instead of letting frustration or disappointment overcome me.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhile paying attention to the body, I could feel the changing nature of body sensations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhile being attentive, I was aware that all of my experiences are impermanent.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to perceive what I experienced with objectivity and a degree of distance.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI could feel body sensations just as body sensations, without perceiving them as part of who I am.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinal item-fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to remain attentive to what I felt in the body.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhen paying attention to the body, I could feel a range of pleasant, unpleasant, or neutral sensations.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI was able to prevent reacting to physical, emotional, or mental experiences.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhile paying attention to the body, I was able to accept my experiences.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhile being attentive, I was aware that all of my experiences are impermanent.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI could feel body sensations just as body sensations, without perceiving them as part of who I am.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;standard error; FitResid\u0026thinsp;=\u0026thinsp;Fit Residuals; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Chi Square; * = \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** = \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** = \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2\u003c/b\u003e \u003cem\u003ePerson-item threshold distribution for Insight items at time 1 (blue) and time 2 (red) for Analysis 4 (A4)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eConversion of scores for MMPQS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the conversion table and scores for the Concentration and Insight Scales from ordinal to interval scores for both 6-item versions of the Concentration and Insight Scales.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eConversion scores for Concentration and Insight Scales\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrdinal scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eConcentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eInsight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLogits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eValidity Analyses\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the correlation matrix for the Concentration and Insight Scales with equanimity, depression, anxiety, stress, awareness, kindness, life satisfaction, and self-efficacy for mindfulness meditation practice, demonstrating small to moderate correlations in expected directions with related measures thus indicating construct and divergent validity. As illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, Concentration practice quality significantly predicted lower levels of depression, anxiety, and stress, but did not significantly predict life satisfaction. Insight practice quality significantly predicted decreased depression, anxiety, and stress, and increased life satisfaction (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Therefore, the predictive validity of the Concentration and Insight scales is demonstrated in line with expectations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eBivariate correlations for Concentration and Insight Scales with related measures\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eConcentration Scale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEquanimity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAwareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKindness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLife Satisfaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquanimity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-047***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.53***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.30***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.29**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKindness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.59***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEMMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.63***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.46***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.23*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsight Scale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEquanimity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAwareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKindness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLife satisfaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquanimity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.37***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.39***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.44***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.63***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.21*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKindness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.60***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.3**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.49***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEMMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.62***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.19*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eSEMMP\u0026thinsp;=\u0026thinsp;self-efficacy for mindfulness meditation practice; \u003csup\u003ec\u003c/sup\u003e = converted scores; * = \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** = \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** = \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eRegression results for Concentration/Insight practice quality predicting depression, anxiety, stress, and life satisfaction at mid-treatment\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003ePredictor: Concentration practice quality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e∆R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003csub\u003e\u003cem\u003eB\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[-0.29, -0.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[-0.22, -0.05]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[-0.28, -0.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[-0.05, 0.47]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredictor: Insight practice quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAdj. R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e∆R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSE\u003c/b\u003e\u003csub\u003e\u003cb\u003eB\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[-0.35, -0.12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[-0.19, -0.01]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[-0.24, -0.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[0.11, 0.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eDepression, Anxiety, Stress measured with Depression Anxiety and Stress Scale (DASS-21); Life satisfaction measured with Satisfaction With Life Scale (SWLS); R\u0026thinsp;=\u0026thinsp;correlation coefficient between predictor \u0026amp; outcome; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;amount of variance accounted for by predictor; Adj. R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;adjusted R\u003csup\u003e2\u003c/sup\u003e: generalizability of model; ∆R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;adjusted R\u003csup\u003e2\u003c/sup\u003e change; F\u0026thinsp;=\u0026thinsp;model fit of coefficient ratio of improvement; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;significance of ∆R\u003csup\u003e2\u003c/sup\u003e; B\u0026thinsp;=\u0026thinsp;coefficient of contribution of predictor to model showing direction and size of effect; SE\u003csub\u003eB\u003c/sub\u003e=Standard Error of coefficient; \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;standardized beta coefficient showing standard deviation change of outcome by predictor; t\u0026thinsp;=\u0026thinsp;t-statistic on difference of B to 0; 95% C. I.=95% Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study developed and validated the Mindfulness Meditation Practice Quality Scales (MMPQS) including the Concentration and Insight scales in a clinical sample of adult practitioners completing the MiCBT course. Comprehensive psychometric analyses employed the Rasch measurement model and supported six-item versions for the Concentration and Insight Scales and show that both are reliable measures. The Concentration scale consists of items which exclusively measure concentration with the associated cognitive flexibility (refocusing on the breath once distracted) by focusing on one specific object of concentration, having removed double-barreled and highly correlated items from the originally developed items. The six-item Concentration practice quality scale aligns well with Buddhist definitions of \u003cem\u003eśamatha\u003c/em\u003e practice (Anālayo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006a\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003eb\u003c/span\u003e). Similarly, the Insight scale assesses practice quality in terms of interoceptive awareness, equanimity, and awareness of impermanence and non-self, thus comprehensively incorporating the different aspects of \u003cem\u003evipassanā\u003c/em\u003e practice to foster understanding of the deepest nature of experience (Analayo, 2021; Goenka, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Khantipalo, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth the Concentration and Insight scales had small to moderate correlations with measures of equanimity, depression, anxiety, stress, awareness, life satisfaction, and self-efficacy of mindfulness meditation practice, suggesting that the scales are measuring the construct they are proposing to measure, while showing sufficient divergent validity (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) in line with expectations. Findings also demonstrated good predictive validity for both scales, with concentration practice quality significantly predicting lower depression, anxiety, and stress, and insight practice quality significantly predicting decreased depression, anxiety and stress, and increased life satisfaction. Thus, these findings are in line with our expectations and previous research, suggesting that concentration and insight mindfulness meditation practices improve mental health and quality of life (Alsubaie et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Galante et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, both concentration and insight practice quality improved significantly from time one to time two of completing the scales, which is consistent with our predictions and previous research suggesting improvement of mindfulness quality with continued practice (Goldberg et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is worth noting that only the Insight scale showed good predictive validity for life satisfaction. Given that mindfulness of breath (concentration) was taught before vipassana (insight) practice, as per the MiCBT model and its corresponding lineage of teachings, it is possible that this difference is simply caused by the serial delivery of these practices\u0026mdash;concentration practice starts in week 2 and is typically practiced for one to two weeks, which is followed by insight practice, so it might be too short for patients to feel significant improvement in life satisfaction so early in therapy. While controlling for this was beyond the scope of this psychometric study, future studies could elucidate these differences by lengthening the concentration period so that both practice types are measured after the same duration.\u003c/p\u003e \u003cp\u003eThe iterative Rasch analysis process presented several methodological challenges that were systematically addressed through careful application of advanced psychometric techniques. Initial analyses revealed issues including disordered thresholds, local dependency between items, and violations of unidimensionality, which required strategic interventions such as item removal and creation of super-items to achieve optimal model fit. The precision offered by Rasch methodology proved invaluable in identifying these subtle measurement issues that would remain undetected using traditional approaches. A key advantage of our Rasch-validated scales is the provision of conversion tables that transform ordinal raw scores into interval-level measurements, dramatically improving measurement accuracy. These tables are straightforward to use. Researchers simply locate their participant's raw summed score in the left column and read across to find the corresponding interval score. For example, a participant scoring 25 on the raw Concentration scale converts to an interval score of 22.11, while a score of 30 converts to 25.10\u0026mdash;the equal intervals between these converted scores now represent genuine equal units of the underlying construct, unlike the original ordinal scores where the difference between 25\u0026ndash;26 may not equal the difference between 29\u0026ndash;30. This conversion enables more accurate statistical analyses, precise change detection over time, and meaningful comparison of scores across different populations, ultimately enhancing both research validity and clinical decision-making.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eConstraints and Generality\u003c/h2\u003e \u003cp\u003eA Loving-kindness Scale as part of the MMPQS has not yet been validated. This needs to be noted as a limitation. However, arguably, concentration and insight practices are the most common mindfulness meditation practices in keeping with Pali sources (Anālayo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006a\u003c/span\u003e; Griffiths, 1981; Khantipalo, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), and according to the taxonomy by Nash and Newberg (2013; \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), include cognitive-directed meditation methods (which includes \u003cem\u003eśamatha\u003c/em\u003e and \u003cem\u003evipassanā\u003c/em\u003e), whereas affective-directed ones, such as loving-kindness (\u003cem\u003emetta\u003c/em\u003e) meditation are classified separately. A validation of the Concentration and Insight Meditation Practice Quality Scales (MMPQS) is thus an important first step to evaluating practice quality. The validation of a loving-kindness meditation practice quality scale needs to be completed in future research.\u003c/p\u003e \u003cp\u003eAnother constraint of this measure is that so far, the MMPQS has only been developed as part of MiCBT and with a clinical population of participants. Future research should therefore validate the MMPQS for use in other MBPs and with other populations, such as non-clinical, general population participants, and longer-term meditators (Kurth et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) as is the usual research strategy with newly developed measures (Kr\u0026auml;geloh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, the MMPQS has been examined only with one specific length, practice amount, and face-to-face delivery of practices. Whether the MMPQS is helpful in understanding the quality of other mindfulness practice doses, i.e. practices that differ in length, frequency and amount, needs to be examined next (Strohmaier et al., 2020, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, whether there is a difference in how mindfulness is learned based on personality aspects (Strohmaier \u0026amp; Medvedev, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and how this relates to practice quality would be important to explore in future research.\u003c/p\u003e \u003cp\u003eOverall, the MMPQS provides a comprehensive assessment of mindfulness meditation practice quality for the most commonly used and researched mindfulness meditation practices, concentration and insight, to benefit both research and practice, and provides further understanding as to whether practitioners are engaging in practices in a meaningful way and are improving in quality with greater experience.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSS, BC, and CK contributed to the study conception and design. Material preparation was performed by SS, BC, and CK. Data collection was completed by BC and AS. Data analysis was performed by SS, CK, and OM. The first draft of the manuscript was written by SS, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to extend our gratitude to the participants for taking part in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eMaterials used in this study are fully referenced. Participant permission was not sought to make raw data available.This study was reviewed and received ethical approval from the Canterbury Christ Church University ethics panel (ID: ETH2122-0142). All participants gave informed consent.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlsubaie, M., Abbott, R., Dunn, B., Dickens, C., Keil, T. F., Henley, W., \u0026amp; Kuyken, W. (2017). 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Springer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-77644-2_34-1\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-77644-2_34-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mindfulness Meditation Practice Quality, Assessment, Rasch Analysis, Psychometrics, Mindfulness-integrated Cognitive Behavior Therapy (MiCBT)","lastPublishedDoi":"10.21203/rs.3.rs-8218417/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8218417/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith mindfulness programs and practice research increasing, measures have been developed to assess mindfulness-related constructs. However, there has not yet been a measure to examine the quality of mindfulness meditation practices. This study aimed to develop and validate the Mindfulness Meditation Practice Quality Scales (MMPQS) in a clinical population. The MMPQS consists of two scales, Concentration (\u003cem\u003eśamatha\u003c/em\u003e) and Insight (\u003cem\u003evipassanā\u003c/em\u003e). Ten items each were initially formulated. Each scale was administered to 368 clinical participants completing Mindfulness-integrated Cognitive Behavior Therapy (MiCBT) at 2 timepoints where scales were completed immediately after practices. Partial Credit Rasch model was applied to investigate psychometric properties of each scale as they represent two distinct dimensions. Best Rasch model fit was achieved after removing misfitting items resulting in six-item versions of Concentration and Insight scales. Both showed high reliability (PSI\u0026thinsp;=\u0026thinsp;0.9\u0026ndash;0.91), invariance across personal factors, and unidimensionality. Construct, divergent, and predictive validity of the six-item scales was supported by correlations with relevant measures in expected directions. This initial validation supported reliability and validity of the MMPQS as measures of Concentration and Insight practice quality, which can be utilized to support research and practice. Future research recommendations include testing the MMPQS in non-clinical populations and across other mindfulness-based programs.\u003c/p\u003e","manuscriptTitle":"Development and validation of the Mindfulness Meditation Practice Quality Scales (MMPQS) in a clinical population using Rasch analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:02:16","doi":"10.21203/rs.3.rs-8218417/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ec12e67-d447-4e04-8831-40eb20694922","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"87904917708139017847218447479945995311","date":"2026-05-13T12:42:42+00:00","index":55,"fulltext":""},{"type":"reviewerAgreed","content":"22800531729956057990129922310488525429","date":"2026-05-13T11:59:03+00:00","index":54,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T12:01:05+00:00","index":48,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T10:02:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:02:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8218417","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8218417","identity":"rs-8218417","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}