Using Mindful Attention Awareness Scale on male prisoners: Confirmatory factor analysis and Rasch models

Ali Poorebrahim; Chung-Ying Lin; Vida Imani; Shapour Soltankhah Kolvani; Seyed Abbas Alaviyoun; Narges Ehsani; Amir H Pakpour

doi:10.1371/journal.pone.0254333

. 2021 Jul 13;16(7):e0254333. doi: 10.1371/journal.pone.0254333

Using Mindful Attention Awareness Scale on male prisoners: Confirmatory factor analysis and Rasch models

Ali Poorebrahim ¹, Chung-Ying Lin ^2,^*, Vida Imani ³, Shapour Soltankhah Kolvani ⁴, Seyed Abbas Alaviyoun ⁴, Narges Ehsani ⁵, Amir H Pakpour ^5,^6,^*

Editor: Gilles van Luijtelaar⁷

PMCID: PMC8277060 PMID: 34255773

Abstract

Aim

This study tested the construct validity (i.e., factor structure) of the Persian Mindful Attention Awareness Scale (MAAS) on a sample of male prisoners.

Methods

All the participants (mean±SD age = 39.44±7.94 years) completed three scales—the Persian MAAS, the Insomnia Severity Index (ISI), and the 12-item General Health Questionnaire (GHQ-12). Confirmatory factor analysis (CFA) and Rasch analysis with differential item functioning (DIF) were applied to examine the construct validity of the MAAS. Specifically, the DIF was tested across different insomnia status (using ISI with a cutoff of 15), psychiatric well-being status (using GHQ-12 with a cutoff of 12), and age (using mean age of 39.44 as the cutoff).

Results

The CFA results showed a single factor solution for the Persian MAAS. The Rasch results showed all MAAS items fit in the construct (infit mean square [MnSq] = 0.72 to 1.41; outfit MnSq = 0.74 to 1.39) without displaying DIF items (DIF contrast = -0.34 to 0.31 for insomnia condition; -0.22 to 0.25 for psychiatric well-being; -0.26 to 0.29 for age).

Conclusions

The Persian version of the MAAS is, therefore, a valid instrument to measure mindfulness among Iranian male prisoners.

Introduction

Studies investigating mindfulness have been increased and keep growing in the recent years [1–4]. Specifically, researchers tried to identify how attentional present-centeredness; i.e., a central facet of “mindfulness” [5], or in converse, how inattentiveness or “mind-wandering” [6, 7], may relate to psychological health among different populations. Based on the empirical findings from correlational, interventional, and laboratory research, a conclusion has been made that mindfulness can be beneficial toward psychological well-being, including increased well-being, reduced symptoms, and improved behavioral regulation [8]. Therefore, mindfulness-based interventions (MBIs) have been designed and developed with promising and positive results across different conditions, including sexual functions [9], generalized anxiety disorder [10], and diabetes [11]. Moreover, a review summarized the effectiveness of MBIs through evaluating randomized controlled trials (RCTs) and found that the analyzed rigorous RCTs showing the effectiveness of MBIs on different outcomes, including addition, chronic pain, and depression relapse [12].

In order to maximize the benefits of MBIs, using a validated instrument to understand mindfulness is essential. The concept of “mindfulness” has been argued for its operational definition [13] because of the arguments on whether mindfulness is a single factor [1, 14, 15] or a multifactor [5, 16] construct. However, current evidence on mindfulness models is prone to multifactor [17, 18]. Nevertheless, a single factor (i.e., attention and awareness of the present) can be used as the basis to assess mindfulness before expanding the concept of mindfulness to pose various dimensionalities, considering that a single-factor structure is easier to be studied and understood than a multi-factor structure. Therefore, the self-reported Mindful Attention Awareness Scale (MAAS), which has a one-factor structure, is one of the potential instruments for healthcare providers to assess mindfulness [1]. Although the MAAS suffers from some problems in its content validity (i.e., only assessing part of the mindfulness via mindlessness facet) and becomes outdated in the Western countries, it deserves a breaking point for the countries without appropriate instruments assessing mindfulness.

The MAAS was developed with a name of “trait scale” because the developers hypothesized that inattentiveness is a psychological trait that can be independently measured in the general population [1]. More specifically, the MAAS assesses the tendency to be attentive and aware of present moment experience in daily living for people who do not have any meditation experience [1]. Given that the promising psychometric properties of the MAAS have been reported across different populations (e.g., [1, 4, 19, 20]) and different languages (e.g., [21–23]), the MAAS has been widely used in both research and clinical settings [3]. Additionally, several brief versions of the MAAS have been developed and validated (e.g., [24, 25]). In addition, the MAAS has been integrated into other mindfulness instruments to assess the nature of multifactor structure in mindfulness (e.g., the Five Facet Mindfulness Questionnaire) [26].

However, it is unclear whether the MAAS can be validly applied to prisoners because to the best of our knowledge, no psychometric information has been reported for this specific population. A sound instrument needs to have accumulated evidence on its psychometric testing across different populations to fulfill the scientific inquiry [27]. More specifically, the evidence of an instrument’s psychometric properties is highly dependent on the tested populations; therefore, the satisfactory reliability and validity found in one population (e.g., adolescents) cannot be simply generalized to another population (e.g., elders). Hence, it is crucial for the MAAS to be tested for its psychometric properties in a population that has never been examined (i.e., the prisoners used in the present study).

Prisoners are proposed to have high risk of developing mental illness or to have high chance to worsen their mental health problems prior to imprison during imprisonment [27]. Specifically, the development of or worsened psychological disorder may be contributed by several factors during imprisonment, such as overcrowding or lack of privacy, meaningless activities or imposed loneliness, behavioral issues of employees and fellow prisons, or insecurity about future [28]. In this regard, the imprisonment factors may impact the prisoners to rate their mindfulness or to interpret the content of the MAAS items. Therefore, assessing the construct validity of the MAAS among this specific population is warranted.

The aim of this study was to use two theories of psychometric testing to examine the construct validity of the Persian MAAS among male prisoners. Confirmatory factor analysis from the classical test theory was used to examine whether the Persian MAAS has a one-factor structure in the male prisoners. Then, Rasch analysis from the item response theory was applied to evaluate (1) whether all the Persian MAAS items are embedded in the same construct (i.e., mindfulness); (2) whether the Likert-type scale used in the Persian MAAS follow its difficulty order (i.e., score 1 should be easier than score 2); and (3) whether the Persian MAAS was interpreted similarly across different conditions (insomnia, psychiatric well-being, and age) in the male prisoners.

Methods

Participants and procedure

The cross-sectional study was conducted in the main prison in Guilan province (Lakan Prison) between February 2019 and December 2019. The prison was created in 1990 with 23,000 square meters of infrastructure and about 7 hectares of enclosed area. It is located 13 km from Rasht city center. During the data collection period, the total number of prisoners was 3200 with majority of the inmates were males. The target participants of the present study were drug-abusing prisoners in Lakan prison and 600 of 1400 male prisoners were randomly selected for assessing their eligibility. Eligible participants should achieve the inclusion criteria of (1) male offenders currently serving a prison sentence with problem drug abuse, (2) aged 18 years of order, and (3) were able and willing to provide informed consent. Prisoners were excluded if having cognitive impairment from conditions such as severe illness or injury.

Ethics approvals for this study were granted by the Regional Research Ethics Committee at Qazvin University of Medical Sciences (IR.QUMS.REC.1397.294) and Prisons and Security and Corrective Measures Organization (70/31/13). The participation to the study was voluntary and anonymous. The study was performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from participants before enrollment.

Instruments

Mindful Attention Awareness Scale (MAAS)

The MAAS contains 15 items to assess individual differences in the level of mindfulness and it is one of the commonly used instruments in assessing mindfulness [1, 3]. All the items are rated on a 6-point Likert-type scale and a summated score can be computed for the MAAS total score, where a higher score indicates a higher level of mindfulness. The MAAS has been translated into different languages with promising psychometric properties [19, 22, 23, 29, 30], including Persian versions [31, 32]. Moreover, the internal consistency of the MAAS was adequate (Cronbach’s α = 0.878; McDonalds’ ω = 0.879).

Insomnia Severity Index (ISI)

The ISI contains 7 items to assess severity of the insomnia. All the items are rated on a 5-point Likert-type scale and a summated score can be computed for the ISI total score (ranged between 0 and 28), where a higher score indicates a higher level of insomnia. Moreover, the total score of ISI ranges between 0 and 28, where 0–7 indicates absence of insomnia, 8–14 indicates sub-threshold insomnia, 15–21 indicates moderate insomnia, and 22–28 indicates severe insomnia [33]. The ISI has been translated into Persian version with promising psychometric properties (e.g., Cronbach’s α = 0.82 to 0.87) [34, 35]. Moreover, the internal consistency of the ISI was adequate (Cronbach’s α = 0.900; McDonalds’ ω = 0.902).

12-item General Health Questionnaire (GHQ-12)

The GHQ-12 contains 12 items to assess health, especially psychiatric well-being, of an individual. All the items are rated on a 4-point Likert-type scale and a summated score can be computed for the GHQ-12 total score, where a higher score indicates worse psychiatric well-being. Moreover, the total score of GHQ-12 ranges between 0 and 36, where a score equal to and greater than 12 indicates having psychiatric well-being problem [36]. The GHQ-12 has been translated into Persian version with promising psychometric properties (e.g., Cronbach’s α = 0.78 to 0.84) [37, 38]. Moreover, the internal consistency of the GHQ-12 was adequate (Cronbach’s α = 0.714; McDonalds’ ω = 0.653).

Data analysis

Apart from the descriptive statistics (mean and SD for continuous variables; frequency and percentage for categorical variables), confirmatory factor analysis (CFA) using lavaan package in the R software and Rasch analysis using WINSTEPS 4.3.0 (winsteps.com) were used to test the construct validity of the Persian MAAS among male prisoners.

In the CFA, a one-factor structure was tested for the MAAS using the diagonally weighted least squares (DWLS) estimator. Several fit indices, including comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR), were applied to examine whether the one-factor structure is supported. Specifically, both CFI and TLI > 0.9 together with both RMSEA and SRMR < 0.08 support the one-factor structure of the MAAS [39]. Factor loadings of the MAAS items were evaluated using the cutoff of 0.4; that is, a factor loading > 0.4 indicates the need of that item [40].

In the Rasch, a rating scale model (RSM) was used to examine the MAAS. We considered using Rasch model with RSM instead of other item-response theory models (e.g., the two parameter or the three logistic parameter model with partial credit model or graded response model) because Rasch model with RSM can provide simpler estimation in modeling (i.e., Rasch model with RSM needs not to estimate other parameter like discrimination and the category difference for every two responses). Thus, the benefit of using such a model than other types of item-response theory model is it fits better with the principle of parsimony.

In the Rasch modeling with RSM, global test on type I error rates across all item fits was performed first, and a nonsignificant test indicates all items embedded in the same construct. Then, mean square (MnSq) of information-weighted fit statistics (infit) and that of outlier-sensitive fit statistics (outfit) were applied to understand whether an item fit in the MAAS construct. Specifically, both infit and outfit MnSq range between 0.5 and 1.5 indicate the fit of an item [39]. Moreover, the 6-point Likert-type of the MAAS was examined whether the score increased monotonically; that is, the difficulty of a lower score is not greater than a higher score. Average measure of the six scores and step measure of every two scores were used. Additionally, both infit and outfit MnSq were used to examined whether the monotonic increase was supported. Specifically, both infit and outfit MnSq range between 0.4 and 1.6 indicate the monotonic increase [41]. Local independence was tested to understand whether residual correlations exist among items. Specifically, we tested the Rasch residual for every item and used the correlations between the Rasch residuals to examine the local independence, where an absolute correlation coefficient > 0.4 indicating substantial dependence. Lastly, differential item functioning (DIF) across for the thresholds were used to examine whether the male prisoners under different conditions (i.e., different types of health problems and different age) interpret the MAAS items similarly. Specifically, the DIF was examined across insomnia condition (using the cutoff of 15 in the ISI), psychiatric well-being (using the cutoff of 12 in the GHQ-12), and age group (using the mean age of 39.44). A DIF contrast < 0.05 indicates that different groups interpret a MAAS item similarly [42, 43].

Apart from the CFA and Rasch, Spearman’s rho correlations were carried to understand the associations between the MAAS, the ISI, and the GHQ-12 scores.

Results

Table 1 demonstrates the characteristics of the sample. In brief, the mean (SD) age of the male prisoners was 39.44 (7.94) years. They received, on average, 9.51 (3.05) years of education. More than half of the participants were married (58.4%). The majority of the participants had a history of drug abuse (78.9%) and more than half of the participants had a history of alcohol abuse (59.3%). On average, the MAAS mean score was 4.21 (0.91).

Table 1. Socio-demographic characteristics of prisoners in Rasht Lakan correctional institution.

	Mean or n	SD or %
Age (year)	39.44	7.94
Educational year	9.51	3.01
Marital status
Single	88	24.4%
Married	211	58.4%
Divorced	62	17.2%
Having children
Yes	199	55.1%
No	162	44.9%
Occupational status
Jobless	10	2.8%
Daily labor	137	37.9%
Self-employed	200	55.4%
Employed	14	3.9%
History of drug abuse
Yes	285	78.9%
No	76	21.1%
History of alcohol abuse
Yes	214	59.3%
No	147	40.7%
MAAS mean score	4.21	0.91

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MAAS = Mindful Attention Awareness Scale

The one-factor structure of the MAAS is supported by the fit indices of the CFA, including CFI (0.982), TLI (0.979), RMSEA (0.044), 95% CI of RMSEA (0.032, 0.055), and SRMR (0.064), except for the significant χ² test (Table 2). Moreover, all the factor loadings in the MAAS were strong (0.478 to 0.679) and significant (Table 3). Rasch analysis further shows that the global test on type I error rates associated with the Rasch item fit statistics was nonsignificant (p = 0.48), indicating the satisfactory item fit across all the items. Indeed, all the items fit in the same construct (infit MnSq = 0.72 to 1.41; outfit MnSq = 0.74 to 1.39) with all items were mutually correlated (bi-point serial correlations = 0.51 to 0.60) (Table 3). Additionally, the ordering of the six-point Likert scale was monotonically increased in the difficulty (average measure from -0.46 at threshold 1 to 1.09 at threshold 6; step measure was -0.97 at threshold 2 and 0.80 at threshold 6; infit MnSq = 0.89 to 1.06 and outfit MnSq = 0.81 to 1.14 for the thresholds) (Table 4). Tests on local response dependence indicate that there were no substantial residual correlations among the items (r = -0.33 to 0.27).

Table 2. Confirmatory factor analysis (CFA) results.

Fit indices	One factor
χ² (df)/ p-value	151.313 (90)/ < 0.001
Normed χ²	1.68
CFI	0.982
TLI	0.979
RMSEA (90%CI)	0.044 (0.031, 0.055)
SRMR	0.064

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Normed χ² = χ² divided by df.

CFI = comparative fit index; TLI = Tucker-Lewis index; RMSEA = root mean square of error approximation; SRMR = standardized root mean square residual; CI = confidence interval.

Table 3. Standardized factor loadings in confirmatory factor analysis (CFA) and Rasch difficulties and fit statistics for each item.

MAAS	CFA	Difficulties	Item discrimination	Rasch	Outfit MnSq	Model SE	Correlation
Items description	Loadings^a	Difficulties	Item discrimination	Infit MnSq	Outfit MnSq	Model SE	Correlation
1. I could be experiencing some emotion and not be conscious of it until sometime later	0.517	-0.36	1.09	0.72	0.80	0.05	0.51
2. I break or spill things because of carelessness, not paying attention, or thinking of something else.	0.600	-0.66	1.04	1.11	0.99	0.06	0.54
3. I find it difficult to stay focused on what’s happening in the present.	0.598	0.20	1.07	0.97	1.08	0.04	0.56
4. I tend to walk quickly to where I’m going without paying attention along the way.	0.491	0.17	0.77	1.14	1.18	0.04	0.52
5. I tend not to notice feelings of physical tension or discomfort until they really grab my attention	0.478	0.36	0.69	1.19	1.21	0.04	0.52
6. I forget a person’s name almost as soon as I’ve been told it for the first time.	0.504	0.21	0.71	1.41	1.39	0.04	0.51
7. It seems I’m “running on automatic” without much awareness of what I’m doing.	0.563	0.05	1.05	1.02	1.01	0.04	0.57
8. I rush through activities without being really attentive to them.	0.627	0.01	1.18	0.81	0.74	0.04	0.60
9. I get so focused on the goal I want to achieve that I lost touch with what I am doing right now to get there	0.546	0.11	0.97	0.93	0.97	0.04	0.55
10. I do jobs or tasks automatically, without being aware of what I’m doing	0.598	0.09	0.97	1.05	1.08	0.04	0.58
11. I find myself listening to someone with one ear, doing something else at the same time.	0.545	0.12	0.85	0.93	1.00	0.04	0.53
12. I drive places on “automatic pilot” and then wonder why I went there.	0.679	-0.38	1.12	1.00	0.90	0.05	0.59
13. I find myself preoccupied with the future or the past	0.643	0.42	1.29	0.86	0.83	0.04	0.62
14. I find myself doing things without paying attention.	0.617	-0.06	1.15	0.94	0.87	0.04	0.58
15. I snack without being aware that I’m eating.	0.511	-0.27	0.89	1.15	1.09	0.05	0.52

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^a Loadings are derived from single-factor model.

Infit = information-weighted fit statistic; Outfit = outlier-sensitive fit statistics; MnSq = mean square.

MAAS = Mindful Attention Awareness Scale; CFA = confirmatory factor analysis

Table 4. Response disordering tests on Mindful Attention Awareness Scale.

	Average measure	Step measure	Infit MnSq	Outfit MnSq
Score 1	-0.46	--	1.06	1.14
Score 2	-0.15	-0.97	1.01	1.03
Score 3	0.05	-0.60	0.99	0.99
Score 4	0.30	0.30	0.89	0.81
Score 5	0.62	0.48	0.94	0.92
Score 6	1.09	0.80	1.09	1.06

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1: Almost always; 2: Very frequently; 3: Somewhat frequently; 4: Somewhat infrequently; 5: Very infrequently; 6: Almost never.

Infit = information-weighted fit statistic; Outfit = outlier-sensitive fit statistics; MnSq = mean square.

No DIF items were displayed for the MAAS across insomnia condition (DIF contrast = -0.34 to 0.31), across psychiatric well-being (DIF contrast = -0.22 to 0.25), and across age (DIF = -0.26 to 0.29) (Table 5). Moreover, the MAAS total score was significantly correlated with the ISI (r = -0.601; p<0.001) and the GHQ-12 (r = -0.384; p<0.001) scores.

Table 5. Differential item functioning (DIF) across different subgroups in the Mindful Attention Awareness Scale.

Item #	DIF across insomnia condition^a	DIF across psychiatric disorder^b	DIF across age^c
1. I could be experiencing some emotion and not be conscious of it until sometime later	0.08	0.13	0.01
2. I break or spill things because of carelessness, not paying attention, or thinking of something else.	-0.22	0.25	0.01
3. I find it difficult to stay focused on what’s happening in the present.	-0.15	0.01	0.15
4. I tend to walk quickly to where I’m going without paying attention along the way.	0.16	0.09	-0.12
5. I tend not to notice feelings of physical tension or discomfort until they really grab my attention	0.31	0.17	-0.08
6. I forget a person’s name almost as soon as I’ve been told it for the first time.	-0.14	0.25	-0.12
7. It seems I’m “running on automatic” without much awareness of what I’m doing.	-0.11	0.05	-0.08
8. I rush through activities without being really attentive to them.	0.13	-0.02	-0.12
9. I get so focused on the goal I want to achieve that I lost touch with what I am doing right now to get there	0.01	-0.10	-0.12
10. I do jobs or tasks automatically, without being aware of what I’m doing	0.01	0.06	-0.26
11. I find myself listening to someone with one ear, doing something else at the same time.	0.14	0.33	0.13
12. I drive places on “automatic pilot” and then wonder why I went there.	-0.34	-0.22	0.29
13. I find myself preoccupied with the future or the past	-0.24	-0.21	0.07
14. I find myself doing things without paying attention.	0.14	-0.14	0.08
15. I snack without being aware that I’m eating.	0.11	-0.08	0.13

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DIF contrasts = Difficulty in subgroup 1 –difficulty in subgroup 2, and all DIF contrasts were nonsignificant.

^a assessed by Insomnia Severity Index with a cutoff score of 15 (suggesting moderate to severe insomnia)

^b assessed by 12-item general health questionnaire (GHQ-12) with scores of 12

^c patients were classified into older (higher than mean age >39.44) and younger (lower than mean age ≤39.44)

Discussion

The present study, to the best of our knowledge, is the first one that assessed the psychometric properties of the MAAS on a specific population: prisoners. Our findings supported the construct validity of the Persian MAAS; that is, the Persian MAAS is interpreted as a one-factor structure among male prisoners. Furthermore, prisoners under different conditions (i.e., different age, different psychiatric well-being, and different sleep problems) all interpret the Persian MAAS similarly. Therefore, the use of MAAS for prisoners is verified to be valid, including combing and comparing the level of mindfulness across different conditions for prisoners. Moreover, the MAAS mean score found in the present sample of prisoners was slightly higher than that of college students (MAAS mean score = 3.89 for Bangla students [19]; 3.88 for Greek students [44]; and 3.72 to 4.01 for American students [1, 30, 45]) but lower than that of general population (MAAS mean score = 4.86 for Italians [23] and 4.45 for Canadians [46]).

As comparing with prior studies on MAAS psychometric properties [19, 22, 23, 29, 30], our results share similar CFA results, which indicate that the MAAS is valid in a one-factor structure. However, a previous Iran study showed that the Persian MAAS has a two-factor structure [32]. A possible reason for the different structure findings may be due to the studied samples. Mohsenabadi et al. [32] examined the psychometric properties of the MAAS on a sample of adolescents aged between 12 and 18 years. On the other hand, prior studies showing the one-factor structure of MAAS examined its psychometric properties on young adults (e.g., university students) [19, 22, 23, 29, 30]. Because the cognition developments are different between adolescents and young adults, it is possible that different structures of MAAS are interpreted between adolescents and young adults. Given that the mean age of our participants was 39.44; our participants might have similar interpretation of the MAAS structure to the structure interpretation from young adults instead of that from adolescents.

Some studies used Rasch analysis to examine the MAAS found that the MAAS has some misfit items [20, 47]. In contrast, our findings indicated that all the MAAS items were fit in the construct of mindfulness. A possible reason for the different findings may be due to the different language versions: Goh et al. [47] examined the English MAAS and found 5 misfit items; Inchausti et al. [20] examined the Spanish MAAS and found 2 misfit items; we examined the Persian MAAS and found no misfit items. Another possible reason for the different findings may be due to the different models used in the Rasch: Goh et al. [47] used partial credit model; Inchausti et al. [20] and we used rating scale model. However, future studies are warranted to accumulate evidence of Rasch analysis results on the MASS because such evidence is little among current literature.

Because prisoners usually encounter substantial distress due to several imprisonment factors [27], they may need to receive appropriate interventions to prevent or to treat their distress. In this regard, MBI is a potential approach for healthcare providers to help the prisoners. With the verified psychometric properties of the MAAS, healthcare providers can use the MAAS to monitor the effectiveness of the MBI designed for prisoners. However, cautious should be paid attention to because the content validity of the MAAS only contributes to the mindlessness facet of mindfulness. Therefore, healthcare providers may want to integrate the MAAS with other mindfulness instrument to evaluate the effects of mindful programs.

There are some limitations in this study. First, only male prisoners were recruited in this study. Therefore, it is unclear whether the promising psychometric properties of MAAS found in this study can be generalized to female prisoners given that males and females have different responses to psychological distress. Second, the male prisoners were recruited from the same institution; therefore, the generalizability of our findings may not extend to other settings. Third, only construct validity of the MAAS was assessed in the present study; therefore, other important psychometric properties such as reproducibility (i.e., test-retest reliability) and responsiveness (i.e., whether the MAAS can effectively detect mindfulness improvement) remain unknown. Future studies may thus want to assess other properties of the MAAS among prisoners. Fourth, all the instruments were self-reported; therefore, the commonly encountered biases (e.g., recall bias and social desirability) were hard to control in this study. However, given that all the self-reported measures used in the present study have good psychometric properties, the bias problems might not be serious.

Conclusion

In conclusion, our findings indicated that the Persian MAAS had robust properties in its construct validity when assessing male prisoners. Specifically, two test theories have been applied and both theories supported the construct validity of the Persian MAAS. The scale may be used for prisoners to detect and monitor their mindfulness condition. For example, the Persian MAAS can be used to examine whether any MBIs designed for prisoners have intervention effects.

Supporting information

S1 Data

(XLSX)

Click here for additional data file.^{(64.8KB, xlsx)}

Abbreviations

CFI: Confirmatory factor analysis
infit: Information-weighted fit statistic
MnSq: Mean square
outfit: Outlier-sensitive fit statistic
RMSEA: Root mean square error of approximation
SEM: Structural equation model
SRMR: Standardized root mean square residual
TLI: Tucker-Lewis index

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

The authors received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0254333.r001

Decision Letter 0

Karl Bang Christensen

6 Apr 2020

PONE-D-20-09274

Using Mindful Attention Awareness Scale on male prisoners: confirmatory factor analysis and Rasch models

PLOS ONE

Dear Dr. Pakpour,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we have decided that your manuscript does not meet our criteria for publication and must therefore be rejected.

Specifically:

The methods are unclear. Did you use CFA for oridnal indicators (items) as implemented in Mplus or the R-package lavaan ?

The results are lacking. You should report chi-square, df and P-value for the CFA fit. You should also report the RMSEA and corresponding confidence interval. I would be appropritae to cite literature indicating the type I error rates associated with the Rasch item fit statistics. You do not report on local response dependence - this is unacceptable.

How can you state that 'CFA results showed a single factor solution for the Persian MAAS', when CFI=0.928 and TLI=0.908 ?

I am sorry that we cannot be more positive on this occasion, but hope that you appreciate the reasons for this decision. I also hope that my comments can help you revise the manuscript if you decide to submit it elsewhere.

Yours sincerely,

Karl Bang Christensen, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

The methods are unclear. Did you use CFA for oridnal indicators (items) as implemented in Mplus or the R-package lavaan ?

How can you state that 'CFA results showed a single factor solution for the Persian MAAS', when CFI=0.928 and TLI=0.908 ?

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

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PLoS One. 2021 Jul 13;16(7):e0254333. doi: 10.1371/journal.pone.0254333.r002

Author response to Decision Letter 0

21 Apr 2020

Dear Editor-in-Chief,

Thank you for inviting us to revise our manuscript entitled “Using Mindful Attention Awareness Scale on male prisoners: confirmatory factor analysis and Rasch models” (PONE-D-20-09274).

Below we have provided our point-by-point reply to the comments made by the previous academic editor.

We look forward to your further comments.

Sincerely yours,

Amir H. Pakpour, PhD

Chung-Ying Lin, PhD

Response to Comments:

1. The methods are unclear. Did you use CFA for ordinal indicators (items) as implemented in Mplus or the R-package lavaan ?

Reply: We have now provided the software information in the revised.

“Apart from the descriptive statistics (mean and SD for continuous variables; frequency and percentage for categorical variables), confirmatory factor analysis (CFA) using AMOS 24.0 and Rasch analysis using WINSTEPS 4.3.0 (winsteps.com) were used to test the construct validity of the Persian MAAS among male prisoners.”

2. The results are lacking. You should report chi-square, df and P-value for the CFA fit. You should also report the RMSEA and corresponding confidence interval. I would be appropriate to cite literature indicating the type I error rates associated with the Rasch item fit statistics. You do not report on local response dependence - this is unacceptable.

Reply: We have now clearly stated the information.

“The one-factor structure of the MAAS is verified by the fit indices of the CFA, including CFI (0.928), TLI (0.908), RMSEA (0.063), 95% CI of RMSEA (0.052, 0.074), and SRMR (0.049), except for the significant χ2 test (Table 2).”

“Rasch analysis further shows that The global test on type I error rates associated with the Rasch item fit statistics was nonsignificant (p = 0.48), indicating the satisfactory item fit across all the items.”

“Tests on local response dependence indicate that there were no substantial residual correlations among the items (r = -0.33 to 0.27).”

3. How can you state that 'CFA results showed a single factor solution for the Persian MAAS', when CFI=0.928 and TLI=0.908 ?

Reply: The practice of CFI and TLI > 0.9 to indicate data-model fit is widely used in the literature, including those published recently. Also, statistics textbook also recommends the cutoff at 0.9. Therefore, we are confident that using CFI = 0.928 and TLI = 0.908 (together with other CFA fit indices) can support the decision of a single factor solution for the Persian MAAS.

Ref:

Hoyle, R. H., & Panter, A. T. (1995). Writing about structural equation modeling. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and application. Thousand Oaks, CA: Sage.

Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford Press.

Leung, H., Pakpour, A. H., Strong, C., Lin, Y.-C., Tsai, M.-C., Griffiths, M. D., Lin, C.-Y., Chen, I.-H. (2020). Measurement invariance across young adults from Hong Kong and Taiwan among three internet-related addiction scales: Bergen Social Media Addiction Scale (BSMAS), Smartphone Application-Based Addiction Scale (SABAS), and Internet Gaming Disorder Scale-Short Form (IGDS-SF9)(Study Part A). Addictive Behaviors, 101, 105969.

Chen, I.-H., Strong, C., Lin, Y.-C., Tsai, M.-C., Leung, H., Lin, C.-Y., Pakpour, A. H., Griffiths, M. D. (2020). Time invariance of three ultra-brief internet-related instruments: Smartphone Application-Based Addiction Scale (SABAS), Bergen Social Media Addiction Scale (BSMAS), and the nine-item Internet Gaming Disorder Scale- Short Form (IGDS-SF9) (Study Part B). Addictive Behaviors, 101, 105960.

Pakpour, A. H., Tsai, M.-C., Lin, Y.-C., Strong, C., Latner, J. D., Fung, X. C. C., Lin, C.-Y., & Tsang, H. W. H. (2019). Psychometric properties and measurement invariance of the Weight Self-Stigma Questionnaire and Weight Bias Internalization Scale in Hongkongese children and adolescents. International Journal of Clinical and Health Psychology, 19, 150-159.

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PLoS One. doi: 10.1371/journal.pone.0254333.r003

Decision Letter 1

George Vousden

4 Feb 2021

PONE-D-20-09274R1

Using Mindful Attention Awareness Scale on male prisoners: confirmatory factor analysis and Rasch models

PLOS ONE

Dear Dr. Pakpour,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please accept our sincere apologies for the time it has taken to process the appeal decision on your manuscript. Unfortunately, we were not able to secure an academic editor to assess your manuscript and this has resulted in delays in the processing of your manuscript.

Your manuscript has been evaluated by 3 expert reviewers and you will find their comments below. The reviews offer their praise for several elements of the manuscript, including the importance of studying the sample population and the sample size employed.

Please note that as per our publication criteria, PLOS ONE requires that all experiments, statistics and other analyses are performed to a high technical standard, described in sufficient detail and adhere to appropriate reporting guidelines and community standards. Conclusions must be presented in an appropriate fashion and be supported by the data (Please see http://journals.plos.org/plosone/s/criteria-for-publication). The reviewers’ comments concern the statistical analysis performed, including the justification for the methods employed and the ability of the results to support the conclusions.

One of the reviewers has also provided comments encouraging you to engage in open science practices. All PLOS journals require authors to make all data necessary to replicate their study’s findings publicly available without restriction at the time of publication. When specific legal or ethical restrictions prohibit public sharing of a data set, authors must indicate how others may obtain access to the data. Authors must share the “minimal data set” for their submission. PLOS defines the minimal data set to consist of the data required to replicate all study findings reported in the article, as well as related metadata and methods. (Please see here: https://journals.plos.org/plosone/s/data-availability).

I note that you have indicated that all data are fully available without restriction all relevant data are within the manuscript and its Supporting Information files. However, no Supporting Information files have been included. In your revised manuscript please ensure that you provide details of where your data are deposited and how these can be accessed. More information on data deposition methods is provided in the link provided in the previous paragraph. Whilst only data sharing is a requirement for publication, I do encourage to make your scripts available to further improve the reproducibility of your work. If you provide any materials used in your study as Supporting Information files please ensure that you have permission to reproduce these with a CC BY license (please see here for more details: https://journals.plos.org/plosone/s/licenses-and-copyright).

We look forward to receiving your revised manuscript. Please accept my apologies again for the delay in processing your submission.

Please submit your revised manuscript by Mar 20 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

George Vousden

Senior Staff Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please describe what considerations were made for the prisoners included in this study. For instance, were participants able to opt out of the study? Did individuals who did not participate receive the same treatment offered to participants?

3. Please amend your list of authors on the manuscript to ensure that each author is linked to an affiliation. Authors’ affiliations should reflect the institution where the work was done (if authors moved subsequently, you can also list the new affiliation stating “current affiliation:….” as necessary).

4. Thank you for updating your data availability statement. You note that your data are available within the Supporting Information files, but no such files have been included with your submission. At this time we ask that you please upload your minimal data set as a Supporting Information file, or to a public repository such as Figshare or Dryad.

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As soon as you confirm the location of the data underlying your findings, we will be able to proceed with the review of your submission.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

**********

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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: After reviewing the new manuscript and the responses to the reviewers, I consider that this version meets all the requirements to be published in Plos One as an original article.

I have no further comment to make.

Reviewer #2: Overall, I see added value on this paper. However, there are several necessary changes (especially regarding the introduction and theoretical sections) that need to occur prior to acceptance. I provide a list below. Perhaps the best added value of this contribution is the access to a big hard-to-reach sample of prisoners from a non-WEIRD population. A focus on this would improve the paper significantly.

Abstract:

-Fit indices are not required in the abstract, consider removing them.

Introduction:

-The whole introduction needs restructuring. While I see added value on validating the MAAS in this population and I see worthy work in the authors' efforts, the core messages of the introduction are misguided. The MAAS is right now an outdated instrument for Western scenarios (e.g. integration in other instruments, and content validity issues since it measures Mindlessness or Automatic Pilot, not mindfulness), used in laboratory settings due to its simplicity. However, it is important to provide validated instruments world-wide, so I see value in this paper. A restructured introduction in this direction would be a great (and necessary) plus for the paper (and accordingly, changes in discussion). Some other inquiries are provided below.

-MBIs count with heavier evidence than those provided. Consider adding reivews or meta-analyses when picturing evidence on MBIs (e.g. Creswell, 2017, Annual review of Psychology).

-Current evidence on mindfulness models rely strongly on multidimensional definitions (e.g. Lindsay & Creswell, 2017; Hölzel et al, 2011).

-Psychometric developments on mindfulness state clearly that the MAAS has been already integrated in other multidimensional instruments (e.g. FFMQ).

Methods:

- I see of great value to provide such a big sample for a hard-to-reach scneario such as prisons. My congratulations.

- Instruments are provided with Cronbach's alpha. Although this is widely used, is flawed (see McNeish, 2018, Viladrich, Angulo-Brunet & Doval, 2017, and Trizano-Hermosilla & Alvarado, 2016, for references). Given all self-reports are ordinal and probably with normality or tau-equivalence issues, I recommend to remove alpha and provide mcdonald's omega, guttman's lambda6, GLB or other similar indices. The JASP software is a free and effective tool to obtain these indices (download at: https://jasp-stats.org/download/)

- CFA was used with maximum likelihood when ordinal items were implemented. This is open to method bias since ML assumes items to be continuous and normal. Current estimation methods for ordinal items are (1) Weighted Least Squares Mean-and-Variance Adjusted (WLSMV) or Maximum Likelihood Robust (MLR), and (2) polychoric correlations. If the authors encounter software limitations, MPlus and lavaan package of R provide all these requirements.

- Why Rasch models? There is no justification on why these models are better than other IRT models (e.g. Likelihood Ratio Tests for 1 (Rasch), 2, 3 or 4 paremeter logistics models). Finally, why rating scale model? Why not, for example, the graded response model? These choices need to be justified. In addition, local independence check methods need to be specified in the Data Analysis section.

- It would be interesting to test external validity of the MAAS in this sample beyond DIF. For example, correlation analysis for the MAAS scores with the ISI and GHQ scores.

- Psychology is right now facing a reproducibility crisis (see Munafó et al, 2017, for details). Is there a possibility for the authors engaging in open science practices? For example, sharing openly in the Open Science Framework the data, scipts, and other materials to ensure reproduction of results. Since the data may encounter privacy issues due to prison environment, this is not a request but a suggestion for general improvement of psychological science. In any case, I did not see any statement on this matter in the manuscript.

Discussion:

- "Our Rasch analysis results corresponded to our CFA results; that is, both psychometric

testing supported the one-factor structure of the MAAS and no items were misfit". This is relatively obvious since the same sample was applied. Consider removing.

- The practical implications need to consider the content validity issues of the MAAS (measuring the mindlessness facet of mindfulness).

Reviewer #3: Introduction:

Page 3: Reference to mediation in second paragraph. I'm not clear on why the general population's experience of meditation relates to the factor structure of mindfulness as a construct.

Page 4, first paragraph: "brief state" versions - do you mean brief versions, or versions of the MAAS intended to measure state vs trait mindfulness?

Results:

The term "verified" is used to describe a one-factor structure, this wording is too strong. First, though many of the fit statistics are acceptable, the RMSEA is outside of the good fit range, and these statistics are often most useful in comparison to other models (i.e., there could be a 3 factors structure with even better fit statistics). Are there are factor structures of the MAAS that have been proposed or found (such as the Iranian study cited in the Discussion)? If so, please test in comparison. If not, please just change wording to "one-factor structure is supported by the fit indices"

Also, I think it would be valuable for this study to report descriptive statistics of the MAAS and compare the values in this population to others in other samples.

**********

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Reviewer #1: No

Reviewer #2: Yes: Oscar Lecuona

Reviewer #3: No

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2021 Jul 13;16(7):e0254333. doi: 10.1371/journal.pone.0254333.r004

Author response to Decision Letter 1

10 Feb 2021

Dear Dr. Vousden,

Thank you for inviting us to revise our manuscript entitled “Using Mindful Attention Awareness Scale on male prisoners: confirmatory factor analysis and Rasch models” (PONE-D-20-09274R1).

Below we have provided our point-by-point reply to the comments made by the three reviewers. All the revisions are presented using red fonts in the revised manuscript. We deeply appreciate their comments, which help us substantially improve our work.

We look forward to your further comments.

Sincerely yours,

Amir H. Pakpour, PhD

Chung-Ying Lin, PhD

Response to Editor:

1. Please note that as per our publication criteria, PLOS ONE requires that all experiments, statistics and other analyses are performed to a high technical standard, described in sufficient detail and adhere to appropriate reporting guidelines and community standards. Conclusions must be presented in an appropriate fashion and be supported by the data (Please see http://journals.plos.org/plosone/s/criteria-for-publication). The reviewers’ comments concern the statistical analysis performed, including the justification for the methods employed and the ability of the results to support the conclusions.

Reply: We have now clearly responded to the reviewers’ comments regarding the justifications of choosing specific statistical methods. Please see our detailed response to Reviewer #1’s comment 4.

2. One of the reviewers has also provided comments encouraging you to engage in open science practices. All PLOS journals require authors to make all data necessary to replicate their study’s findings publicly available without restriction at the time of publication. When specific legal or ethical restrictions prohibit public sharing of a data set, authors must indicate how others may obtain access to the data. Authors must share the “minimal data set” for their submission. PLOS defines the minimal data set to consist of the data required to replicate all study findings reported in the article, as well as related metadata and methods. (Please see here: https://journals.plos.org/plosone/s/data-availability).

Reply: We have now added the dataset but we believe that our data is partly sensitive because they are prisoners. We need to make sure from our institution if we can publish our whole dataset.

3. Please describe what considerations were made for the prisoners included in this study. For instance, were participants able to opt out of the study? Did individuals who did not participate receive the same treatment offered to participants?

Reply: Thanks for your comment. The participation to the study was voluntary and anonymous. There were no any interventional materials (or treatment) as this study was a cross-sectional study. The participants were only asked to complete the study measure after describing study aims and checking eligibility criteria.

Response to Reviewer #1:

1. After reviewing the new manuscript and the responses to the reviewers, I consider that this version meets all the requirements to be published in Plos One as an original article. I have no further comment to make.

Reply: Thank you for the positive comment.

Response to Reviewer #2:

1. Overall, I see added value on this paper. However, there are several necessary changes (especially regarding the introduction and theoretical sections) that need to occur prior to acceptance. I provide a list below. Perhaps the best added value of this contribution is the access to a big hard-to-reach sample of prisoners from a non-WEIRD population. A focus on this would improve the paper significantly.

Reply: Thank you for the positive comment. We also appreciate your following comments, which help us substantially improve this work.

2. Abstract:

-Fit indices are not required in the abstract, consider removing them.

Reply: We have now removed the fit indices from the Abstract.

“The CFA results showed a single factor solution for the Persian MAAS.”

3. Introduction:

-MBIs count with heavier evidence than those provided. Consider adding reviews or meta-analyses when picturing evidence on MBIs (e.g. Creswell, 2017, Annual review of Psychology).

-Current evidence on mindfulness models rely strongly on multidimensional definitions (e.g. Lindsay & Creswell, 2017; Hölzel et al, 2011).

-Psychometric developments on mindfulness state clearly that the MAAS has been already integrated in other multidimensional instruments (e.g. FFMQ).

Reply: Thank you for the guidance in the Introduction and also the valuable references. We have now restructured the Introduction with the incorporation of your suggested references.

“Moreover, a review summarized the effectiveness of MBIs through evaluating randomized controlled trials (RCTs) and found that the analyzed rigorous RCTs showing the effectiveness of MBIs on different outcomes, including addition, chronic pain, and depression relapse [12].”

“The concept of “mindfulness” has been argued for its operational definition [13] because of the arguments on whether mindfulness is a single factor [1,14,15] or a multifactor [5,16] construct. However, current evidence on mindfulness models is prone to multifactor [17,18].”

“Although the MAAS suffers from some problems in its content validity (i.e., only assessing part of the mindfulness via mindlessness facet) and becomes outdated in the Western country, it deserves a breaking point for the countries without appropriate instruments assessing mindfulness.”

“In addition, the MAAS has been integrated into other mindfulness instruments to assess the nature of multifactor structure in mindfulness (e.g., the Five Facet Mindfulness Questionnaire) [26].”

References:

12. Creswell JD. Mindfulness interventions. Annu Rev Psychol. 2017;68(1):491-516.

17. Hölzel BK, Carmody J, Vangel M, et al. Mindfulness practice leads to increases in regional brain gray matter density. Psychiatry Res. 2011;191(1):36-43. doi:10.1016/j.pscychresns.2010.08.006

18. Lindsay EK, Creswell JD. Mechanisms of mindfulness training: Monitor and Acceptance Theory (MAT). Clin Psychol Rev. 2017;51:48-59. doi:10.1016/j.cpr.2016.10.011

26. Baer RA, Smith GT, Hopkins J, Krietemeyer J, Toney L. Using self-report assessment methods to explore facets of mindfulness. Assessment. 2006;13(1):27-45. doi:10.1177/1073191105283504

4. Methods:

- I see of great value to provide such a big sample for a hard-to-reach scenario such as prisons. My congratulations.

- It would be interesting to test external validity of the MAAS in this sample beyond DIF. For example, correlation analysis for the MAAS scores with the ISI and GHQ scores.

Reply: Thank you. We have now made the following revisions in the Methods and Results.

(1) Both Cronbach’s alpha and McDonald’s omega are presented for the used instruments.

“Moreover, the internal consistency of the MAAS was adequate (Cronbach’s α=0.878; McDonalds’ ω=0.879).”

“Moreover, the internal consistency of the ISI was adequate (Cronbach’s α=0.900; McDonalds’ ω=0.902).”

“Moreover, the internal consistency of the GHQ-12 was adequate (Cronbach’s α=0.714; McDonalds’ ω=0.653).”

(2) We have now used weighted least squares mean-and-variance adjusted (WLSMV) estimator in the CFA. The results are revised accordingly (Please refer to Tables 2 and 3).

“In the CFA, a one-factor structure was tested for the MAAS using the diagonally weighted least squares (DWLS) estimator.”

“The one-factor structure of the MAAS is supported by the fit indices of the CFA, including CFI (0.982), TLI (0.979), RMSEA (0.044), 95% CI of RMSEA (0.032, 0.055), and SRMR (0.064), except for the significant χ2 test (Table 2). Moreover, all the factor loadings in the MAAS were strong (0.478 to 0.679) and significant (Table 3).”

(3) We have now provided the justifications regarding the use of Rasch model and rating scale model. The local independence check methods are clearly mentioned in the Data Analysis section.

“We considered using Rasch model with RSM instead of other item-response theory models (e.g., the two parameter or the three logistic parameter model with partial credit model or graded response model) because Rasch model with RSM can provide simpler estimation in modeling (i.e., Rasch model with RSM needs not to estimate other parameter like discrimination and the category difference for every two responses). Thus, the benefit of using such a model than other types of item-response theory model is it fits better with the principle of parsimony.”

“Local independence was tested to understand whether residual correlations exist among items. Specifically, we tested the Rasch residual for every item and used the correlations between the Rasch residuals to examine the local independence, where an absolute correlation coefficient > 0.4 indicating substantial dependence.”

(4) The correlations between the MAAS, ISI, and GHQ scores are conducted and presented.

“Apart from the CFA and Rasch, Pearson correlations were carried to understand the associations between the MAAS, the ISI, and the GHQ-12 scores.”

“Moreover, the MAAS total score was significantly correlated with the ISI (r=-0.60; p<0.001) and the GHQ-12 (r=-0.44; p<0.001) scores.”

(5) We have now added the dataset but we believe that our data is partly sensitive because they are prisoners. We need to make sure from our institution if we can publish our whole dataset.

5. Discussion:

- "Our Rasch analysis results corresponded to our CFA results; that is, both psychometric testing supported the one-factor structure of the MAAS and no items were misfit". This is relatively obvious since the same sample was applied. Consider removing.

- The practical implications need to consider the content validity issues of the MAAS (measuring the mindlessness facet of mindfulness).

Reply: We have now removed the sentence “Our Rasch analysis results corresponded to our CFA results; that is, both psychometric testing supported the one-factor structure of the MAAS and no items were misfit”. Also, we have mentioned the content validity issues of the MAAS in the practical implications.

“However, cautious should be paid attention to because the content validity of the MAAS only contributes to the mindlessness facet of mindfulness. Therefore, healthcare providers may want to integrate the MAAS with other mindfulness instrument to evaluate the effects of mindful programs.”

Response to Reviewer #3:

1. Page 3: Reference to mediation in second paragraph. I'm not clear on why the general population's experience of meditation relates to the factor structure of mindfulness as a construct.

Reply: We have now revised the descriptions here.

“Nevertheless, a single factor (i.e., attention and awareness of the present) can be used as the basis to assess mindfulness before expanding the concept of mindfulness to pose various dimensionalities, considering that a single-factor structure is easier to be studied and understood than a multi-factor structure.”

2. Page 4, first paragraph: "brief state" versions - do you mean brief versions, or versions of the MAAS intended to measure state vs trait mindfulness?

Reply: They are brief versions. We have now corrected the descriptions.

“Additionally, several brief versions of the MAAS have been developed and validated (e.g., [21,22])”

3. Results:

Reply: Thank you for the guidance. We have now revised the wording to “supported”.

“The one-factor structure of the MAAS is supported by the fit indices of the CFA, including CFI (0.928), TLI (0.908), RMSEA (0.063), 95% CI of RMSEA (0.052, 0.074), and SRMR (0.049), except for the significant χ2 test (Table 2)”

4. Also, I think it would be valuable for this study to report descriptive statistics of the MAAS and compare the values in this population to others in other samples.

Reply: We have now described the descriptive statistics and compared with other studies.

“On average, the MAAS mean score was 4.21 (0.91).”

“Moreover, the MAAS mean score found in the present sample of prisoners was slightly higher than that of college students (MAAS mean score=3.89 for Bangla students [19]; 3.88 for Greek students [44]; and 3.72 to 4.01 for American students [1,30,45]) but lower than that of general population (MAAS mean score=4.86 for Italians [23] and 4.45 for Canadians [46]).”

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PLoS One. doi: 10.1371/journal.pone.0254333.r005

Decision Letter 2

Gilles van Luijtelaar

4 May 2021

PONE-D-20-09274R2

Using Mindful Attention Awareness Scale on male prisoners: confirmatory factor analysis and Rasch models

PLOS ONE

Dear Dr. Pakpour,

Thank you for submitting your manuscript to PLOS ONE. The revision was adequate, there is a minor issue left. PLease see the comments of a single reviewer. We invite you to submit a revised version of the manuscript that addresses the single point raised during the review process.

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We look forward to receiving your revised manuscript.

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Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

There is a minor issue left, as can be seen from the comments of a single reviewer concerning the use of pearson's correlation coefficient and whether the assumptions for the usage of this paramentric statistic is allowed.

Next two typo's. Otherwise it is fine.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #2: Thanks to the authors for addressing all the comments. I have a relevant comment and some minor corrections. Apart from that, I have no further comment on the manuscript.

- There is no justification on why estimating correlations between instrument scores with Pearson correlations. Assumption checks for normality and outliers is necessary to ensure Pearson's r estimates the correlations adequately, and if not, use alternative estimators (e.g. Spearman's rho or Kendall's tau-b).

- "Western country" instead of "Western countries" (p. 5).

- "Modern test theory" instead of "item response theory" (p. 6).

Reviewer #3: All issues addressed, thank you! I have no further issues and am recommending publication of this manuscript.

**********

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Reviewer #2: Yes: Oscar Lecuona

Reviewer #3: No

PLoS One. 2021 Jul 13;16(7):e0254333. doi: 10.1371/journal.pone.0254333.r006

Author response to Decision Letter 2

10 May 2021

Dear Dr. Vousden,

Thank you for inviting us to revise our manuscript entitled “Using Mindful Attention Awareness Scale on male prisoners: confirmatory factor analysis and Rasch models” (PONE-D-20-09274R2).

We look forward to your further comments.

Sincerely yours,

Amir H. Pakpour, PhD

Chung-Ying Lin, PhD

Response to Reviewer #2:

1. - There is no justification on why estimating correlations between instrument scores with Pearson correlations. Assumption checks for normality and outliers is necessary to ensure Pearson's r estimates the correlations adequately, and if not, use alternative estimators (e.g. Spearman's rho or Kendall's tau-b).

Reply: We have conducted Spearman's rho correlations for the above mentioned associations.

2. - "Western country" instead of "Western countries" (p. 5).

- "Modern test theory" instead of "item response theory" (p. 6).

Reply: We have now edited these. Thanks

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PLoS One. doi: 10.1371/journal.pone.0254333.r007

Decision Letter 3

Gilles van Luijtelaar

26 May 2021

PONE-D-20-09274R3

Using Mindful Attention Awareness Scale on male prisoners: confirmatory factor analysis and Rasch models

PLOS ONE

Dear Dr. Pakpour,

Thank you for submitting your manuscript to PLOS ONE. A very small issue remains. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jul 10 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Gilles van Luijtelaar, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Additional Editor Comments (if provided):

Please add a rationale, after checking for outliers and normality of the distributions, for using a parametric correlation coefficient, or use a non-parametric one to be on the safe side.

The reviewer was happy with all the changes made by you. A minor issue that the reviewer noticed was that there was no justification on why estimating correlations between instrument scores with Pearson correlations. Assumption checks for normality and outliers are necessary to ensure Pearson's r estimates the correlations adequately, and if not, use alternative estimators (e.g. Spearman's rho or Kendall's tau-b).

- "Western country" instead of "Western countries" (p. 5).

- "Modern test theory" instead of "item response theory" (p. 6).

[Note: HTML markup is below. Please do not edit.]

PLoS One. 2021 Jul 13;16(7):e0254333. doi: 10.1371/journal.pone.0254333.r008

Author response to Decision Letter 3

8 Jun 2021

Dear Prof. Dr. van Luijtelaar,

Thank you for inviting us to revise our manuscript entitled “Using Mindful Attention Awareness Scale on male prisoners: confirmatory factor analysis and Rasch models” (PONE-D-20-09274R3).

We look forward to your further comments.

Sincerely yours,

Amir H. Pakpour, PhD

Chung-Ying Lin, PhD

Response to editor:

1. - Please add a rationale, after checking for outliers and normality of the distributions, for using a parametric correlation coefficient, or use a non-parametric one to be on the safe side.

Reply: Thank you for the positive feedback. We agree that using nonparametric estimator for the correlations is better. Therefore, we have now conducted Spearman's rho correlations for the above mentioned associations.

“Apart from the CFA and Rasch, Spearman’s rho correlations were carried to understand the associations between the MAAS, the ISI, and the GHQ-12 scores.”

“Moreover, the MAAS total score was significantly correlated with the ISI (r=-0.601; p<0.001) and the GHQ-12 (r=-0.384; p<0.001) scores.”

2. - "Western country" instead of "Western countries" (p. 5).

- "Modern test theory" instead of "item response theory" (p. 6).

Reply: We have now edited these. Thanks

“and becomes outdated in the Western countries,”

“Then, Rasch analysis from the item response theory was applied to evaluate”

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PLoS One. doi: 10.1371/journal.pone.0254333.r009

Decision Letter 4

Gilles van Luijtelaar

28 Jun 2021

Using Mindful Attention Awareness Scale on male prisoners: confirmatory factor analysis and Rasch models

PONE-D-20-09274R4

Dear Dr. Pakpour,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Gilles van Luijtelaar, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0254333.r010

Acceptance letter

Gilles van Luijtelaar

5 Jul 2021

PONE-D-20-09274R4

Using Mindful Attention Awareness Scale on male prisoners: confirmatory factor analysis and Rasch models

Dear Dr. Pakpour:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Gilles van Luijtelaar

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Data

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Data Availability Statement

All relevant data are within the paper and its Supporting information files.

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PERMALINK

Using Mindful Attention Awareness Scale on male prisoners: Confirmatory factor analysis and Rasch models

Ali Poorebrahim

Chung-Ying Lin

Vida Imani

Shapour Soltankhah Kolvani

Seyed Abbas Alaviyoun

Narges Ehsani

Amir H Pakpour

Roles

Abstract

Aim

Methods

Results

Conclusions

Introduction

Methods

Participants and procedure

Instruments

Mindful Attention Awareness Scale (MAAS)

Insomnia Severity Index (ISI)

12-item General Health Questionnaire (GHQ-12)

Data analysis

Results

Table 1. Socio-demographic characteristics of prisoners in Rasht Lakan correctional institution.

Table 2. Confirmatory factor analysis (CFA) results.

Table 3. Standardized factor loadings in confirmatory factor analysis (CFA) and Rasch difficulties and fit statistics for each item.

Table 4. Response disordering tests on Mindful Attention Awareness Scale.

Table 5. Differential item functioning (DIF) across different subgroups in the Mindful Attention Awareness Scale.

Discussion

Conclusion

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Karl Bang Christensen

Roles

Author response to Decision Letter 0

Decision Letter 1

George Vousden

Roles

Author response to Decision Letter 1

Decision Letter 2

Gilles van Luijtelaar

Roles

Author response to Decision Letter 2

Decision Letter 3

Gilles van Luijtelaar

Roles

Author response to Decision Letter 3

Decision Letter 4

Gilles van Luijtelaar

Roles

Acceptance letter

Gilles van Luijtelaar

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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