Validity and reliability of the Arabic versions of the General Risk Propensity Scale (GRiPS) and the Risk Proneness Short Scale (R-1)

Validity and reliability of the Arabic versions of the General Risk Propensity Scale (GRiPS) and the Risk Proneness Short Scale (R-1) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validity and reliability of the Arabic versions of the General Risk Propensity Scale (GRiPS) and the Risk Proneness Short Scale (R-1) Feten Fekih-Romdhane, Diana Malaeb, Fouad Sakr, Mariam Dabbous, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3472999/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 Background Until today, only a few studies have been conducted in Arab countries and settings to understand the prevalence and correlates of engaging in domain-specific risky behaviors. However, no or very little research attention has been paid to risk-taking propensity as a predictor of such behaviors. In this study, we propose to validate two short self‐report measures of general risk propensity in Arabic, the General Risk Propensity Scale (GRiPS) and the Risk Proneness Short Scale (R-1) Methods An online survey was carried-out in a sample of native Arabic-speaking adults from Lebanon (n = 748, mean age of 34.48 ± 13.25 years, 66.5% females). The forward-backward translation method was adopted. Results The original one-factor structure of the GRiPS was replicated in this study, except for two items that were removed (item 4 “I would take a risk even if it meant I might get hurt”, and item 5 “Taking risks is an important part of my life”). Analyses showed that the R-1 loaded on the same factor as the GRiPS, and that its scores correlated positively with GRiPS scores. McDonald’s ω and Cronbach’s α values were very good for both the GRiPS (ω and α values of .89) and the R-1 (ω and α values of .87), indicating internal consistency of the scales. The GRiPS exhibited measurement invariance with respect to gender at the configural, metric, and scalar levels. The R-1 was also found to be gender invariant. Finally, medium-sized positive correlations were found between GRiPS/R-1 scores and the impulsiveness dimensions “sensation seeking” and “urgency”. Conclusion The Arabic GRiPS and the R-1 seem to be suitable and applicable as simple, time-efficient and low-cost self-report measures in a wide range of research areas where an individual's general disposition toward risks plays an important role. If conducting the research under no time constraints or limitations, we would recommend using the GRiPS. Risk-taking propensity Psychometric properties Self-report measures validation Arabic 1. INTRODUCTION Risk-taking is part of everyone’s life, albeit differences between individuals exist in their risk-taking propensity. Risk-Taking Propensity (RTP) refers to the cross-situational willingness and inclination of an individual to take a risk that may have potential negative consequences, such as failure, harm, or loss [ 1 , 2 ]. RTP can, therefore, be defined as a stable personality trait [ 3 ] that reflects an individual’s general preference to either avoid or choose a risky behavioral option (e.g., [ 4 , 5 ]), and the way how they estimate the probability, amount, and value of potential losses and gains of a decision under a risk situation. The willingness to take risks varies depending on different factors. With regard to socio-demographics, males tend to actually engage in risky behaviors more than females [ 6 , 7 ], which may be explained by the fact that females have a higher propensity to consider the great probability of negative outcomes and the low possibility of benefits (e.g., enjoyment) from risky behaviors [ 7 ]. In addition, younger individuals seem to be more willing to take risks than older individuals [ 6 ]. Other factors related to RTP include impulsive sensation seeking traits [ 8 – 10 ] and self-esteem [ 9 ]. Risk-taking proneness has also proven to be a predictor for many psychological and behavioral constructs, such as poor subjective health status [ 6 ], lower optimism [ 11 ], unethical behaviors (e.g., cheating) [ 12 ], and a range of actual unhealthy behaviors (e.g., smoking, heavy drinking, risky sexual practices, seat belt non-use) [ 13 ]. Indeed, there is evidence that self-reported risk-taking predisposition is linked to involvement in concrete risky behaviors in different life areas, including career, finance, and health (e.g., [ 6 ]). These are reasons why assessment of the general RTP in daily life situations is a critical component in the prevention and deterrence of the initiation of risky behaviors. Different approaches to assessment of risk-taking have been adopted by researchers. Some measures use computer-based behavioral tasks rather than self-reporting to assess risk proneness, such as the Balloon Analogue Risk Task (BART, [ 14 ]), the Cloudy day Angling Risk Task and the Sunny day Angling Risk Task (C-ART and S-ART, [ 15 ]). These measures consider risk-taking propensity as personality traits like impulsivity and sensation seeking. As for self-report measures, they can be divided into two types. The first type involves domain-specific RTP measures that assess risk taking across one (e.g., Teasing tendency; the Child and Adolescents Teasing Scale [CATS] [ 16 ]) or multiple domains (e.g., Financial, recreational, health/safety, ethical, social; the Multi-Domain Risk Tolerance [MDRT] scale [ 17 ], the Domain-Specific Risk-Taking [DOSPERT] [ 18 ]). The second type involves domain-general RTP measures, such as the Health-Risk Attitude Scale (HRAS-13; [ 19 ]), the General RiskTaking Propensity Scale (GRiPS; [ 2 ]), and the Risk Proneness Short Scale (R-1; [ 9 ]). Among these, one of the most widely used is the DOSPERT Scale. However, the traditional view of risk taking as a domain-specific phenomenon as reflected through this scale was challenged by several researchers over the last years [ 20 , 21 ]. It has been found that, even though domain differences reflect an individual’s risk-taking preferences, a general factor of risk propensity has the potential to account for shared variance across multiple distinct domains [ 22 , 23 ]. Additionally, a general risk factor would better mirror the general disposition towards risk-taking and more efficiently predict RTP across situations [ 23 ]. Besides, emerging evidence advocating that risk-taking shares many features with personality dispositions (e.g., genetic determinants, neurological correlates) further supports a domain-general rather than a domain‐specific RTP [ 3 , 24 ]. For these reasons, we chose in the present paper to translate and validate two domain-general RTP instruments, i.e. the GRiPS and the R-1, into the Arabic language. The GRiPS was developed in 2018 by Zhang et al. [ 2 ] to assess an individual's general propensity to engage in risk-taking, rather than domain-specific risk-taking. It can, therefore, offer insight into the nature of respondents’ RTP across a large spectrum of everyday risky behaviors. The concept of RTP is approached by the GRiPS as a cross-situationally consistent personality trait. The scale is composed of eight items (e.g., “I enjoy taking risks in most aspects of my life”) to whom respondents are asked to specify their degree of agreement or disagreement. The GRiPS was originally validated across five samples of adults and students of different age ranges, and demonstrated to be reliable and valid in measuring RTP through a single-factor structure [ 2 ]. In addition, construct validity was established by testing relationships with other measures of RTP as well as with academic, work, and life-related outcomes. In research practice, the GRiPS was reported to be simple-to-administer, easy-to-understand, convenient to score and interpret [ 25 – 28 ]. The GRiPS has also been validated in Portuguese and yielded adequate psychometric properties, including measurement invariance across gender, in a Brazilian sample aged 16 to 75 years [ 29 ]. The second measure selected for validation in this study is the R-1, which is composed of a single item (i.e. “How do you see yourself – how willing are you in general to take risks?”) scored on a 1 (not at all willing to take risks) to 7 (very willing to take risks) scale. The R-1 was initially developed in the German language and validated in a random and large sample of German adults [ 9 ]. The R-1 was later translated, adapted and validated to English in a sample of adult population from the United Kingdom [ 8 ]. The scale demonstrated good psychometric qualities in both samples, in terms of reliability and validity based on correlations with convergent and discriminant constructs [ 8 , 9 ]. The present study was motivated by lack of a reliable and valid scale to measure the nature of the RTP in general daily life situations among Arabic-speaking populations. To date, research emerging from Arab countries on this topic has only focused on narrow facets of risk-taking and risky behaviors (e.g., health-risk behaviors [ 30 ]). This gap in the literature is critical, because propensity for risk taking has proven to vary substantially between countries and cultures [ 31 ]. Providing economic self-report measures with solid psychometric qualities in the Arabic language can encourage future research in this area in Arab cultural contexts, which might advance understanding of the nature, determinants and cross-country differences of RTP. This study aims to contribute to this field by examining the psychometric properties of the Arabic versions of the GRiPS and the R-1 in a sample of native Arabic-speaking adults from Lebanon. Our hypotheses were the following: (a) the Arabic GRiPS will have a one-factor solution with high internal consistency (McDonald omega and Cronbach’s alpha values exceeding 0.7 [ 32 ]); (b) the GRiPS and the R-1 will show good convergent validity against each other, as well as an adequate concurrent validity (positive correlations with measures of impulsiveness and self-esteem); (c) measurement invariance across gender groups will be established for both scales in our sample. 2. METHODS 2.1. Procedures This cross-sectional study has been conducted from July and August 2023. An anonymous questionnaire has been disseminated to women aged > 18 years, who have recently delivered (4–6 weeks after delivery) in different hospitals in Lebanon. Snowball sampling and respondent-driven sampling techniques were implemented for data collection. A soft copy of the questionnaire was created using Google Forms and sent to participants via hospital’s emails, social media platforms and messaging applications. Prior to participation, study objectives and general instructions were thoroughly explained. The study protocol was approved by the ethics committee of the School of Pharmacy at the Lebanese International University (Reference # 2023RC-023-LIUSOP). Written informed consent was obtained from all subjects for study participation; the online submission of the soft copy was considered equivalent to receiving a written informed consent. 2.2. Participants A total of 748 adults filled the survey, with a mean age of 34.48 ± 13.25 years and 66.5% females. 2.3. Minimum sample size Following the recommendations of Comrey and Lee [ 33 ], a minimum sample of 10 participants per scale’s item are needed to conduct an exploratory factor analysis. Since the GRIPS scale is composed of 8 items, a minimal sample of 80 participants was needed for the exploratory factor analysis (EFA). For the confirmatory factor analysis (CFA), the minimum sample size ranges from 3 to 20 times the number of the scale’s variables [ 34 ]. Therefore, we assumed a minimum sample of 160 participants needed to have enough statistical power based on a ratio of 20 participants per one item of the scale, which was exceeded in our sample. 2.4. Measures The General Risk Propensity Scale (GRiPS; [ 2 ]) and the Risk Proneness Short Scale (R-1; [ 8 , 9 ]) The forward and backward translation method was applied to both scales following international guidelines [ 35 ]. The English version was translated to Arabic by a Lebanese translator who was completely unrelated to the study. Afterwards, a Lebanese psychologist with a full working proficiency in English, translated the Arabic version back to English. The initial and translated English versions were compared to detect and later eliminate any inconsistencies by a committee composed of the research team and the two translators [ 36 , 37 ]. A pilot study was conducted on 30 persons before the start of the official data collection to make sure all questions are well understood; no changes were done consequently. 2.3.2. The Impulsivity Scale (I-8) . The self-administered I-8 scale assessed urgency (e.g., “To make myself feel better, I sometimes do things I regret later.”), premeditation (e.g., “I usually think carefully before I do anything”), perseverance (e.g., “I allocate my time well so that I can complete tasks on time.”), and sensation risk (e.g., “I’m ready to take risks.”). All items are rated from 0 (doesn’t apply at all) to 5 (applies completely), with the premeditation and perseverance subscales having reversed scoring. Higher scores indicating higher impulsivity. In this study, the Cronbach’s α values of the subscales were as follows: urgency: 0.77; premeditation: 0.87; Perseverance: 0.81; and sensation seeking: 0.71 [ 38 ]. The factor structure of the Arabic version was previously verified [ 39 ]. 2.3.3. The Arabic Single-Item Self-Esteem Scale (A-SISE). This is a single item measure (i.e. “I have high self-esteem”) scored on a 5-point Likert scale ranging from 1 (“not at all true of me”) to 5 (“very true of me”) [ 40 ]. The Arabic validated version was used [ 41 ]. 2.5. Analytic Strategy 2.5.1. Data treatment. No missing data was found in our database. We used FACTOR 12.04.01 [ 42 ] to perform the EFA and the SPSS AMOS v.28 program [ 43 ] to carry out the CFA. To examine the internal structure of the test, we randomly divided the sample into two subsamples. We carried out an EFA in the first subsample, made up of 33% (1/3) of the total sample (246 subjects), and a CFA in the second subsample (503 subjects). To check that the data was suitable for EFA we used KMO and Bartlett’s statistic. A preliminary analysis of the items was conducted using the Measure of Sampling Adequacy (MSA) at the item level [ 44 ], and (b) the Anti-Image Correlation (CAI) [ 45 ]. The MSA is a standardized index ranging from 0 to 1, with values below .50 considered unacceptable and leading to item elimination [ 44 ]. On the other hand, the Expected Residual correlation direct Change (EREC) index was used to assess the residual correlation between two items after removing the influence of all definable common factors in the dataset, hence, they should all be approximately 0. Item pairs with high-shared correlation are referred to as doublets [ 40 ]. It is recommended to especially remove items that appear repeatedly in different doublets [ 46 ]. The exploratory factor analysis was carry out with a polychoric correlation matrix given the ordinal nature of the variables [ 47 ]. The method of estimation was Unweighted Least Squares (ULS), following the guidelines in the current literature [ 48 ]. We determined the number of factors using the Optimal Implementation of Parallel Analysis (PA) procedure [ 49 , 50 ]. Confirmatory factor analysis. We aimed at validating the original one-factor structure of the GRIPS scale using CFA, and if divergent do an EFA-CFA strategy as advised by Swami and Barren [ 51 ]. Parameter estimates were obtained using the maximum likelihood method and fit indices. To check if the model was adequate, several fit indices were calculated: the normed model chi-square (χ²/df), the Steiger-Lind root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), the Tucker-Lewis Index (TLI) and the comparative fit index (CFI). Values ≤ 5 for χ²/df, and ≤ .08 for RMSEA, and .95 for CFI and TLI [ 52 ], and ≤ .05 for SRMR [ 53 ] indicate good fit of the model to the data. Multivariate normality was not verified at first (Bollen-Stine bootstrap p = .002); therefore we performed non-parametric bootstrapping procedure (available in AMOS). Evidence of convergent validity was assessed in this subsample using the average variance extracted (AVE), with values of ≥ 0.50 considered adequate [ 54 ]. 2.5.2. Gender invariance. To examine gender invariance of GRiPS scores, we conducted multi-group CFA [ 55 ] using the total sample. Measurement invariance was assessed at the configural, metric, and scalar levels [ 56 ]. We accepted ΔCFI ≤ .010 and ΔRMSEA ≤ .015 or ΔSRMR ≤ .010 (.030 for factorial invariance) as evidence of invariance [ 55 ]. 2.5.3. Reliability and validity analyses. Composite reliability was assessed using McDonald’s ω and Cronbach’s α, with values greater than .70 reflecting adequate composite reliability [ 57 ]. The total GRiPS and R-1 scores followed a normal distribution, with skewness and kurtosis values varying between − 1 and + 1 [ 58 ]. To assess convergent and criterion-related validity, we examined bivariate correlations between total GRiPS and R-1 scores and impulsivity and self-esteem using the Pearson test. Based on Cohen (1992) [ 59 ], values ≤ .10 were considered weak, ~ .30 were considered moderate, and ~ .50 were considered strong correlations. Student t test was used to compare scores between genders. 3. RESULTS 3.1. Psychometric properties of the GRiPS scale The fit indices of the original one-factor structure of the GRiPS (8 items) were poor as follows: χ 2 /df = 336.33/20 = 16.82, RMSEA = 0.145 (90% CI 0.132, 0.159), SRMR = 0.058, CFI = 0.911, TLI = 0.876. Consequently, we conducted an EFA using the first subsample (n = 246). We confirmed the suitability of the data for EFA via the KMO test (KMO = .905) and Bartlett’s test ( p ≤ .001). Next, the relevance of the items was analyzed using the MSA index, which indicated that all items measured the same domain as the rest of the questionnaire, with a value greater than 0.50 for all items. However, 3 doublets were identified through the EREC index, which led to the removal of items 4 and 5, as they were the most frequently repeated in the doublets. Six items remained in the analysis; another factor analysis was then conducted with the final six items. The KMO index (KMO = .890) and Bartlett's test ( p ≤ .001) confirmed the adequacy of the data for the factor analysis. Parallel analysis indicated that a one-factor model would best fit the data. The loading factors are shown in Table 1 . The unidimensional model obtained from the EFA (6 items) was tested via a CFA on the second subsample (n = 503). The fit indices were modest as follows: χ 2 /df = 72.28/9 = 8.03, RMSEA = 0.118 (90% CI 0.094, 0.144), SRMR = 0.046, CFI = 0.958, TLI = 0.929. After correlating residuals of items 7 and 8 because of high modification indices, results improved as follows: χ 2 /df = 24.16/8 = 3.02, RMSEA = 0.063 (90% CI 0.035, 0.093), SRMR = 0.023, CFI = 0.989, TLI = 0.980. The AVE value was adequate (= .55). The standardized loading factors are summarized in Table 1 . The reliability was very good as shown by alpha and omega coefficients (= .89 for both). Table 1 English Items of the GRiPS, loading factors derived from the Exploratory Factor Analysis (EFA) in the first subsample and Standardized Estimates of Factor Loadings from the Confirmatory Factor Analysis (CFA) in the second subsample. Original item number Label EFA CFA 1 Taking risks makes life more fun .89 .83 2 My friends would say that I’m a risk taker .89 .83 3 I enjoy taking risks in most aspects of my life .87 .87 6 I commonly make risky decisions .80 .66 7 I am a believer of taking chances .67 .53 8 I am attracted, rather than scared, by risk .85 .65 Gender invariance of the GRISP scale As reported in Table 2 , we were able to show the invariance across gender at the configural, metric, and scalar levels. No statistically significant difference in terms of GRiPS scores was seen between males and females (16.43 ± 5.06 vs 16.35 ± 4.86, t (747) = .212, p = .832). Table 2 Measurement Invariance of the GRiPS scale across gender in the total sample. Model χ² df CFI RMSEA SRMR Model Comparison Δχ² ΔCFI ΔRMSEA ΔSRMR Δ df p Configural 81.65 16 .973 .074 .040 Metric 92.96 21 .970 .068 .048 Configural vs metric 11.31 .003 .006 .008 5 .045 Scalar 104.37 26 .968 .064 .049 Metric vs scalar 11.41 .002 .004 .001 5 .043 Note. CFI = Comparative fit index; RMSEA = Steiger-Lind root mean square error of approximation; SRMR = Standardised root mean square residual. 3.2. Psychometric properties of the R-1 scale 3.2.1. Exploratory Factor Analysis on the total sample. Bartlett’s test of sphericity, χ 2 (15) = 3025, p < .001, and KMO (.888) indicated that the GRIPS items had adequate common variance for factor analysis. The results of the EFA revealed one factor, which explained 69.10% of the common variance. When adding the R-1, Bartlett’s test of sphericity, χ 2 (21) = 3342.5, p < .001, and KMO (.907) remained adequate. The one-factor solution obtained explained 64.92% of the variance. 3.2.2. Exploratory Factor Analysis on with males. Similar results were seen in men; Bartlett’s test of sphericity, χ 2 (15) = 930.5, p < .001, and KMO (.846) again indicated that the GRiPS scales’ items had adequate common variance for factor analysis among men. A one-factor solution was obtained explaining 67.26% of the variance. When adding the R-1, Bartlett’s test of sphericity, χ 2 (21) = 1008.4, p < .001, and KMO (.868) remained adequate. The one-factor solution obtained explained 62.27% of the variance. 3.2.3. Exploratory Factor Analysis with females. For women, Bartlett’s test of sphericity, χ 2 (15) = 1562, p < .001, and KMO (.881) again indicated that the GRIPS items had adequate common variance for factor analysis. The results of the EFA revealed two factors, which explained 63.45% of the common variance. When adding the R-1, Bartlett’s test of sphericity, χ 2 (21) = 1780.5, p < .001, and KMO (.868) remained adequate. The one-factor solution obtained explained 60.13% of the variance. 3.2.4. Factor structure congruence and composite reliability. The factor loadings reported in Table 3 for the total sample, women and men separately suggest strong similarity across factor structures. McDonald’s ω and Cronbach’s α were very good for the GRiPS and for the R-1 in the total sample, men and women respectively. Table 3 Loading factors obtained from the Exploratory Factor Analysis (EFA) in the total sample and in males and females separately. Model 1: Total sample Model 2: Males only Model 3: Females only GRIPS GRIPS + R-1 GRIPS GRIPS + R-1 GRIPS GRIPS + R-1 1 .87 .87 .86 .86 .82 .82 2 .86 .86 .82 .81 .83 .83 3 .88 .88 .86 .85 .84 .85 6 .74 .74 .73 .72 .68 .69 7 .63 .62 .68 .69 .56 .55 8 .77 .77 .73 .73 .75 .74 R-1 .61 .54 .62 McDonald’s ω .89 .87 .90 .88 .88 .87 Cronbach’s α .89 .87 .90 .88 .88 .87 GRiPS = General Risk-Taking Propensity Scale; R-1 = Risk Proneness Short Scale. Numbers in bold indicate the highest loading of the item on its respective factor. 3.3. Construct and concurrent validity Both GRiPS and R-1 correlated significantly and positively with each other. They were also significantly and positively correlated with urgency, lack of premeditation, lack of perseverance and sensation seeking in the total sample. In males, premeditation that showed no significance. Furthermore, higher GRiPS were significantly associated with higher self-esteem. In females, higher GRiPS scores were significantly associated with lower self-esteem (Table 4 ). Table 4 Correlation of the GRiPS and the Arabic version of the R-1 with other continuous variables. 1 2 3 4 5 6 7 1. GRIPS 1 2. R-1 .55*** 1 3. Urgency .42*** .26*** 1 4. Lack of premeditation .14*** − .01 − .11** 1 5. Lack of perseverance .16*** − .08* − .12** .72*** 1 6. Sensation seeking .45*** .31*** .41*** − .53*** − .60*** 1 7. Self-esteem .05 .07* − .11** − .27*** − .23*** .17*** 1 *p < .05; **p < .01; ***p < .001. GRiPS = General Risk-Taking Propensity Scale; R-1 = Risk Proneness Short Scale. DISCUSSION Until today, only a few studies have been conducted in Arab countries and settings to understand the prevalence and correlates of engaging in domain-specific risky behaviors (e.g., Tobacco smoking, alcohol and substance misuse, unprotected sex [ 30 ]). However, no or very little research attention has been paid to RTP as a predictor of such behaviors. In this study, we propose to validate two short self‐report measures of general risk propensity in Arabic, the GRiPS and the R-1. Both scales assess risk-taking as a general disposition and a broad construct, thus enabling to predict broad outcomes. Findings demonstrated that both scales were valid and reliable. In addition, measurement invariance of the GRiPS and the R-1 across participants by gender was established, indicating that general risk propensity can be validly measured across Arabic-speaking male and female respondents. Both GRiPS and R-1 correlated positively and significantly with each other, as well as with impulsiveness dimensions and self-esteem. Therefore, the GRiPS and the R-1 seem to be suitable and applicable as simple, time-efficient and low-cost self-report measures in a wide range of research areas where an individual's general disposition toward risks plays an important role. If conducting the research under no time constraints or limitations, we would recommend using the GRiPS. In terms of factorial validity, the EFA-to-CFA approach to explore the best-fitting model of the GRiSP suggested that the original one-factor structure was replicated in this study, except for two items that were removed (item 4 “I would take a risk even if it meant I might get hurt”, and item 5 “Taking risks is an important part of my life”). In contrast to our findings, Porfírio et al. [ 29 ] were able to replicate the unidimensional model with all eight items loading on a single latent variable in a Brazilian sample of adults from the general population. These variations in factor structure might be explained by cultural variations in people’s perceptions of, and attitudes toward, engaging in risky behaviors [ 31 ]. Analyses showed that the R-1 loaded on the same factor as the GRiPS, and that its scores correlated positively with GRiPS scores, which suggests that the R-1 is relevant and informative to measure the RTP construct to the same extent as the GRiPS. Furthermore, McDonald’s ω and Cronbach’s α values were very good for both the GRiPS (ω and α values of .89) and the R-1 (ω and α values of .87), indicating internal consistency of the scales. Using multi-group factor analysis, the study showed that the GRiPS exhibited measurement invariance with respect to gender at the configural, metric, and scalar levels. The R-1 was also found to be gender invariant. These findings indicate that both scales measure the RTP construct in the same way across males and females, and that inferences based on gender differences on the variances or means of GRiPS and R-1 latent scores are likely not confounded by measurement bias [ 60 , 61 ]. Consistently, measurement invariance of the GRiPS was demonstrated in the Brazilian sample [ 29 ]. Establishing this psychometric property is of high relevance, given that gender comparisons represent a key research interest in the area of risk-propensity [ 62 ]. Finally, correlation analyses found that the GRiPS and the R-1 showed good convergent validity against each other, which reflects the similar construct of general risk-taking propensity they assess. Medium-sized positive correlations were found between GRiPS/R-1 scores and the impulsiveness dimensions “sensation seeking” and “urgency”. This is in line with previous research which reported an overlap between risk proneness and both sensation seeking [ 8 – 10 , 63 ] and urgency [ 8 – 10 ], whereas weak correlations with other impulsiveness dimensions possibly reflect their connection to psychological, rather than physiological, disorders [ 63 ], and that they are clearly distinct from risk proneness [ 8 , 9 ]. As for self-esteem, non-significant correlation with GRiPS scores and only a small positive correlation with R-1 scores were observed with risk proneness. These findings are consistent with those of previous studies that were mixed, showing either non-significant [ 8 ] or small positive [ 9 ] relation of self-esteem to domain-general RTP. Study limitations This study has some limitations that need to be acknowledged. First, data was gathered using a web-based survey design and convenience sampling, which may limit the representativeness of our sample. Second, the generalization of our findings to the broad Arabic-speaking population may be hindered by the inclusion of participants from a single country and culture. Future studies may extend the sample to include participants from Arab countries and cultural backgrounds other than Lebanon. Finally, other relevant psychometric characteristics, including test-retest reliability and predictive validity, remain to be explored in further research. Implications These limitations aside, the Arabic versions of the GRiPS and R-1 demonstrated good psychometric qualities, and are recommended for use to assess general disposition to engaging in risky behaviors among Arabic-speaking adults from the general population. Findings hold practical implications for policymakers and practitioners seeking to early detect and timely manage people prone to risk-taking behaviors. Routine use of valid and reliable general RTP measures may allow practitioners to determine whether a particular risky behavior is rooted in a general disposition to risk-taking, so that they can inform and adapt their own therapeutic approach. To this end, the GRiPS and R-1 appear to be more advantageous than domain-specific RTP measures, as they have strong predictive power in explaining variations in domain-specific risk-taking behavior (e.g., [ 6 ]). However, it is of note that when seeking to predict specific risky behaviors in particular situations (e.g., Financial, Ethical, Recreation), domain-specific measures such as the CATS [ 16 ], the MDRT scale [ 17 ], or the DOSPERT [ 18 ] could be more appropriate. Hopefully, the availability of these two sound self-report instruments would lead Arab researchers to investigate this research area and provide new insight into the nature and clinical significance of RTP in Arabic-speaking populations. CONCLUSION The present study presents the validation process of two short self-report measures of general RTP in the Arabic language and culture, namely the GRiPS (six items) and the R-1 (a single item). Results indicate both measures are reliable and valid to assess RTP in Arabic-speaking male and female adults. Offering such scales in the Arabic language and cultural context would allow future cross-cultural comparisons among populations from different parts of the world. DECLARATIONS Ethics Approval and Consent to Participate The Ethics Committee of the School of Pharmacy at the Lebanese International University approved this study protocol. An informed consent was obtained from all participants when submitting the online form. All methods were performed in accordance with the relevant guidelines and regulations. Consent for publication: Not Applicable. Availability of data and materials All data generated or analyzed during this study are not publicly available due to restrictions imposed by the ethics committee. The dataset supporting the conclusions is available upon request to the corresponding author (SH). Competing interests The authors have nothing to disclose. Funding None. Author contributions FFR, SO and SH designed the study; FFR wrote the paper; SH carried out the analysis and interpreted the results; DM, MD and FS collected the data; all authors reviewed the paper for intellectual content; all authors reviewed the final manuscript and gave their consent. Acknowledgements We would like to thank all participants. REFERENCES Weber EU, Blais AR, Betz NE. A domain‐specific risk‐attitude scale: Measuring risk perceptions and risk behaviors. Journal of behavioral decision making. 2002;15(4):263-90. Zhang DC, Highhouse S, Nye CD. Development and validation of the general risk propensity scale (GRiPS). 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Journal of Risk Research. 2020;23(4):490-503. Kornilova TV, Pavlova EM. Risk Intelligence Scale and its Relationship with Risk Readiness and Emotional Intelligence. И ПСИХОТЕРАПИЯ. 2020:60. Lamb TL, Winter SR, Rice S, Ruskin KJ, Vaughn A. Factors that predict passengers willingness to fly during and after the COVID-19 pandemic. Journal of air transport management. 2020;89:101897. Zhang DC, Renshaw TL. Personality and college student subjective wellbeing: A domain-specific approach. Journal of Happiness Studies. 2020;21:997-1014. Porfírio JCC, de Moraes YL, Richardson G. Psychometric properties of the General Risk Propensity Scale (GRiPS) in a Brazilian sample. 2022. Barakat C, Yousufzai S. Health-Risk Behaviors of Adolescents from Arab Nations. In: Laher I, editor. Handbook of Healthcare in the Arab World. Cham: Springer International Publishing; 2020. p. 1-26. Mata R, Josef AK, Hertwig R. Propensity for Risk Taking Across the Life Span and Around the Globe. 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Validation of the Arabic Version of the Freiburg Mindfulness Inventory (FMI-Ar) Among a Sample of Lebanese University Students. 2022. Kovaleva A, Beierlein C, Kemper C, Rammstedt B. Eine kurzskala zur messung von impulsivität nach dem UPPS-ansatz: Die skala impulsives-verhalten-8 (I-8). Gesis, 2012. Awad E, Salameh P, Sacre H, Malaeb D, Hallit S, Obeid S. Association between impulsivity and orthorexia nervosa / healthy orthorexia: any mediating effect of depression, anxiety, and stress? BMC Psychiatry. 2021;21(1):604. doi: 10.1186/s12888-021-03594-4. PubMed PMID: 34861836; PubMed Central PMCID: PMCPMC8640965. Brailovskaia J, Margraf J. How to measure self-esteem with one item? Validation of the German Single-Item Self-Esteem Scale (G-SISE). Current Psychology: A Journal for Diverse Perspectives on Diverse Psychological Issues. 2020;39:2192-202. doi: 10.1007/s12144-018-9911-x. Fekih-Romdhane F, Bitar Z, Rogoza R, Sarray El Dine A, Malaeb D, Rashid T, et al. Validity and reliability of the arabic version of the self-report single-item self-esteem scale (A-SISE). BMC psychiatry. 2023;23(1):351. Lorenzo-Seva U, Ferrando PJ. FACTOR: A computer program to fit the exploratory factor analysis model. Behavior research methods. 2006;38(1):88-91. Muthén B, Muthén L. Mplus. Handbook of item response theory: Chapman and Hall/CRC; 2017. p. 507-18. Kaiser HF, Rice J. Little jiffy, mark IV. Educational and psychological measurement. 1974;34(1):111-7. Mulaik SA. Foundations of factor analysis: CRC press; 2009. Ferrando PJ, Lorenzo-Seva U, Hernández-Dorado A, Muñiz J. Decalogue for the factor analysis of test items. Psicothema. 2022;34(1):7. Muthén B, Kaplan D. A comparison of some methodologies for the factor analysis of non‐normal Likert variables. British Journal of Mathematical and Statistical Psychology. 1985;38(2):171-89. Lloret-Segura S, Ferreres-Traver A, Hernández-Baeza A, Tomás-Marco I. El análisis factorial exploratorio de los ítems: una guía práctica, revisada y actualizada. Anales de psicología/annals of psychology. 2014;30(3):1151-69. Calderón Garrido C, Navarro González D, Lorenzo Seva U, Ferrando Piera PJ. Multidimensional or essentially unidimensional? A multi-faceted factoranalytic approach for assessing the dimensionality of tests and items. Psicothema. 2019. Timmerman ME, Lorenzo-Seva U. Dimensionality assessment of ordered polytomous items with parallel analysis. Psychological methods. 2011;16(2):209. Swami V, Barron D. Translation and validation of body image instruments: Challenges, good practice guidelines, and reporting recommendations for test adaptation. Body Image. 2019;31:204-20. doi: 10.1016/j.bodyim.2018.08.014. PubMed PMID: 30220631. Hu Lt, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal. 1999;6(1):1-55. Byrne BM. Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming: psychology press; 2013. Malhotra N, Hall J, Shaw M, Oppenheim P. Marketing research: An applied orientation: Deakin University; 2006. Chen FF. Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural equation modeling: a multidisciplinary journal. 2007;14(3):464-504. Vadenberg R, Lance C. A review and synthesis of the measurement in variance literature: Suggestions, practices, and recommendations for organizational research. Organ Res Methods. 2000;3:4-70. Dunn TJ, Baguley T, Brunsden V. From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British journal of psychology. 2014;105(3):399-412. Hair Jr JF, Sarstedt M, Ringle CM, Gudergan SP. Advanced issues in partial least squares structural equation modeling: saGe publications; 2017. Cohen J, editor Quantitative methods in psychology: A power primer. Psychological bulletin; 1992: Citeseer. Kline RB. Principles and practice of structural equation modeling: Guilford publications; 2023. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis: Pearson new international edition. Essex: Pearson Education Limited. 2014;1(2). Mishra S. Decision-making under risk: Integrating perspectives from biology, economics, and psychology. Personality and Social Psychology Review. 2014;18(3):280-307. Groskurth K, Nießen D, Rammstedt B, Lechner CM. The impulsive behavior short scale–8 (I-8): A comprehensive validation of the English-language adaptation. Plos one. 2022;17(9):e0273801. Additional Declarations No competing interests reported. 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INTRODUCTION","content":"\u003cp\u003eRisk-taking is part of everyone\u0026rsquo;s life, albeit differences between individuals exist in their risk-taking propensity. Risk-Taking Propensity (RTP) refers to the cross-situational willingness and inclination of an individual to take a risk that may have potential negative consequences, such as failure, harm, or loss [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. RTP can, therefore, be defined as a stable personality trait [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] that reflects an individual\u0026rsquo;s general preference to either avoid or choose a risky behavioral option (e.g., [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]), and the way how they estimate the probability, amount, and value of potential losses and gains of a decision under a risk situation. The willingness to take risks varies depending on different factors. With regard to socio-demographics, males tend to actually engage in risky behaviors more than females [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which may be explained by the fact that females have a higher propensity to consider the great probability of negative outcomes and the low possibility of benefits (e.g., enjoyment) from risky behaviors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, younger individuals seem to be more willing to take risks than older individuals [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Other factors related to RTP include impulsive sensation seeking traits [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and self-esteem [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Risk-taking proneness has also proven to be a predictor for many psychological and behavioral constructs, such as poor subjective health status [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], lower optimism [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], unethical behaviors (e.g., cheating) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and a range of actual unhealthy behaviors (e.g., smoking, heavy drinking, risky sexual practices, seat belt non-use) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Indeed, there is evidence that self-reported risk-taking predisposition is linked to involvement in concrete risky behaviors in different life areas, including career, finance, and health (e.g., [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]). These are reasons why assessment of the general RTP in daily life situations is a critical component in the prevention and deterrence of the initiation of risky behaviors.\u003c/p\u003e \u003cp\u003eDifferent approaches to assessment of risk-taking have been adopted by researchers. Some measures use computer-based behavioral tasks rather than self-reporting to assess risk proneness, such as the Balloon Analogue Risk Task (BART, [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]), the Cloudy day Angling Risk Task and the Sunny day Angling Risk Task (C-ART and S-ART, [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]). These measures consider risk-taking propensity as personality traits like impulsivity and sensation seeking. As for self-report measures, they can be divided into two types. The first type involves domain-specific RTP measures that assess risk taking across one (e.g., Teasing tendency; the Child and Adolescents Teasing Scale [CATS] [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]) or multiple domains (e.g., Financial, recreational, health/safety, ethical, social; the Multi-Domain Risk Tolerance [MDRT] scale [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the Domain-Specific Risk-Taking [DOSPERT] [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]). The second type involves domain-general RTP measures, such as the Health-Risk Attitude Scale (HRAS-13; [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]), the General RiskTaking Propensity Scale (GRiPS; [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]), and the Risk Proneness Short Scale (R-1; [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]). Among these, one of the most widely used is the DOSPERT Scale. However, the traditional view of risk taking as a domain-specific phenomenon as reflected through this scale was challenged by several researchers over the last years [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It has been found that, even though domain differences reflect an individual\u0026rsquo;s risk-taking preferences, a general factor of risk propensity has the potential to account for shared variance across multiple distinct domains [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, a general risk factor would better mirror the general disposition towards risk-taking and more efficiently predict RTP across situations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Besides, emerging evidence advocating that risk-taking shares many features with personality dispositions (e.g., genetic determinants, neurological correlates) further supports a domain-general rather than a domain‐specific RTP [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For these reasons, we chose in the present paper to translate and validate two domain-general RTP instruments, i.e. the GRiPS and the R-1, into the Arabic language.\u003c/p\u003e \u003cp\u003eThe GRiPS was developed in 2018 by Zhang et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] to assess an individual's general propensity to engage in risk-taking, rather than domain-specific risk-taking. It can, therefore, offer insight into the nature of respondents\u0026rsquo; RTP across a large spectrum of everyday risky behaviors. The concept of RTP is approached by the GRiPS as a cross-situationally consistent personality trait. The scale is composed of eight items (e.g., \u0026ldquo;I enjoy taking risks in most aspects of my life\u0026rdquo;) to whom respondents are asked to specify their degree of agreement or disagreement. The GRiPS was originally validated across five samples of adults and students of different age ranges, and demonstrated to be reliable and valid in measuring RTP through a single-factor structure [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition, construct validity was established by testing relationships with other measures of RTP as well as with academic, work, and life-related outcomes. In research practice, the GRiPS was reported to be simple-to-administer, easy-to-understand, convenient to score and interpret [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The GRiPS has also been validated in Portuguese and yielded adequate psychometric properties, including measurement invariance across gender, in a Brazilian sample aged 16 to 75 years [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The second measure selected for validation in this study is the R-1, which is composed of a single item (i.e. \u0026ldquo;How do you see yourself \u0026ndash; how willing are you in general to take risks?\u0026rdquo;) scored on a 1 (not at all willing to take risks) to 7 (very willing to take risks) scale. The R-1 was initially developed in the German language and validated in a random and large sample of German adults [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The R-1 was later translated, adapted and validated to English in a sample of adult population from the United Kingdom [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The scale demonstrated good psychometric qualities in both samples, in terms of reliability and validity based on correlations with convergent and discriminant constructs [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study was motivated by lack of a reliable and valid scale to measure the nature of the RTP in general daily life situations among Arabic-speaking populations. To date, research emerging from Arab countries on this topic has only focused on narrow facets of risk-taking and risky behaviors (e.g., health-risk behaviors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]). This gap in the literature is critical, because propensity for risk taking has proven to vary substantially between countries and cultures [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Providing economic self-report measures with solid psychometric qualities in the Arabic language can encourage future research in this area in Arab cultural contexts, which might advance understanding of the nature, determinants and cross-country differences of RTP. This study aims to contribute to this field by examining the psychometric properties of the Arabic versions of the GRiPS and the R-1 in a sample of native Arabic-speaking adults from Lebanon. Our hypotheses were the following: (a) the Arabic GRiPS will have a one-factor solution with high internal consistency (McDonald omega and Cronbach\u0026rsquo;s alpha values exceeding 0.7 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]); (b) the GRiPS and the R-1 will show good convergent validity against each other, as well as an adequate concurrent validity (positive correlations with measures of impulsiveness and self-esteem); (c) measurement invariance across gender groups will be established for both scales in our sample.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. Procedures\u003c/h2\u003e\n\u003cp\u003eThis cross-sectional study has been conducted from July and August 2023. An anonymous questionnaire has been disseminated to women aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years, who have recently delivered (4\u0026ndash;6 weeks after delivery) in different hospitals in Lebanon. Snowball sampling and respondent-driven sampling techniques were implemented for data collection. A soft copy of the questionnaire was created using Google Forms and sent to participants via hospital\u0026rsquo;s emails, social media platforms and messaging applications. Prior to participation, study objectives and general instructions were thoroughly explained. The study protocol was approved by the ethics committee of the School of Pharmacy at the Lebanese International University (Reference # 2023RC-023-LIUSOP). Written informed consent was obtained from all subjects for study participation; the online submission of the soft copy was considered equivalent to receiving a written informed consent.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2. Participants\u003c/h2\u003e\n\u003cp\u003eA total of 748 adults filled the survey, with a mean age of 34.48\u0026thinsp;\u0026plusmn;\u0026thinsp;13.25 years and 66.5% females.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3. Minimum sample size\u003c/h2\u003e\n\u003cp\u003eFollowing the recommendations of Comrey and Lee [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e], a minimum sample of 10 participants per scale\u0026rsquo;s item are needed to conduct an exploratory factor analysis. Since the GRIPS scale is composed of 8 items, a minimal sample of 80 participants was needed for the exploratory factor analysis (EFA). For the confirmatory factor analysis (CFA), the minimum sample size ranges from 3 to 20 times the number of the scale\u0026rsquo;s variables [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. Therefore, we assumed a minimum sample of 160 participants needed to have enough statistical power based on a ratio of 20 participants per one item of the scale, which was exceeded in our sample.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4. Measures\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eThe General Risk Propensity Scale (GRiPS; [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]) and the Risk Proneness Short Scale (R-1; [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e])\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe forward and backward translation method was applied to both scales following international guidelines [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. The English version was translated to Arabic by a Lebanese translator who was completely unrelated to the study. Afterwards, a Lebanese psychologist with a full working proficiency in English, translated the Arabic version back to English. The initial and translated English versions were compared to detect and later eliminate any inconsistencies by a committee composed of the research team and the two translators [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. A pilot study was conducted on 30 persons before the start of the official data collection to make sure all questions are well understood; no changes were done consequently.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.2. The Impulsivity Scale (I-8)\u003c/strong\u003e. The self-administered I-8 scale assessed urgency (e.g., \u0026ldquo;To make myself feel better, I sometimes do things I regret later.\u0026rdquo;), premeditation (e.g., \u0026ldquo;I usually think carefully before I do anything\u0026rdquo;), perseverance (e.g., \u0026ldquo;I allocate my time well so that I can complete tasks on time.\u0026rdquo;), and sensation risk (e.g., \u0026ldquo;I\u0026rsquo;m ready to take risks.\u0026rdquo;). All items are rated from 0 (doesn\u0026rsquo;t apply at all) to 5 (applies completely), with the premeditation and perseverance subscales having reversed scoring. Higher scores indicating higher impulsivity. In this study, the Cronbach\u0026rsquo;s \u0026alpha; values of the subscales were as follows: urgency: 0.77; premeditation: 0.87; Perseverance: 0.81; and sensation seeking: 0.71 [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. The factor structure of the Arabic version was previously verified [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.3. The Arabic Single-Item Self-Esteem Scale (A-SISE).\u003c/strong\u003e This is a single item measure (i.e. \u0026ldquo;I have high self-esteem\u0026rdquo;) scored on a 5-point Likert scale ranging from 1 (\u0026ldquo;not at all true of me\u0026rdquo;) to 5 (\u0026ldquo;very true of me\u0026rdquo;) [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. The Arabic validated version was used [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e2.5. Analytic Strategy\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.1. Data treatment.\u003c/strong\u003e No missing data was found in our database. We used FACTOR 12.04.01 [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] to perform the EFA and the SPSS AMOS v.28 program [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] to carry out the CFA. To examine the internal structure of the test, we randomly divided the sample into two subsamples. We carried out an EFA in the first subsample, made up of 33% (1/3) of the total sample (246 subjects), and a CFA in the second subsample (503 subjects). To check that the data was suitable for EFA we used KMO and Bartlett\u0026rsquo;s statistic. A preliminary analysis of the items was conducted using the Measure of Sampling Adequacy (MSA) at the item level [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e], and (b) the Anti-Image Correlation (CAI) [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. The MSA is a standardized index ranging from 0 to 1, with values below .50 considered unacceptable and leading to item elimination [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. On the other hand, the Expected Residual correlation direct Change (EREC) index was used to assess the residual correlation between two items after removing the influence of all definable common factors in the dataset, hence, they should all be approximately 0. Item pairs with high-shared correlation are referred to as doublets [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. It is recommended to especially remove items that appear repeatedly in different doublets [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. The exploratory factor analysis was carry out with a polychoric correlation matrix given the ordinal nature of the variables [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. The method of estimation was Unweighted Least Squares (ULS), following the guidelines in the current literature [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. We determined the number of factors using the Optimal Implementation of Parallel Analysis (PA) procedure [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfirmatory factor analysis.\u003c/strong\u003e We aimed at validating the original one-factor structure of the GRIPS scale using CFA, and if divergent do an EFA-CFA strategy as advised by Swami and Barren [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. Parameter estimates were obtained using the maximum likelihood method and fit indices. To check if the model was adequate, several fit indices were calculated: the normed model chi-square (\u0026chi;\u0026sup2;/df), the Steiger-Lind root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), the Tucker-Lewis Index (TLI) and the comparative fit index (CFI). Values\u0026thinsp;\u0026le;\u0026thinsp;5 for \u0026chi;\u0026sup2;/df, and \u0026le;\u0026thinsp;.08 for RMSEA, and .95 for CFI and TLI [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e], and \u0026le;\u0026thinsp;.05 for SRMR [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e] indicate good fit of the model to the data. Multivariate normality was not verified at first (Bollen-Stine bootstrap p\u0026thinsp;=\u0026thinsp;.002); therefore we performed non-parametric bootstrapping procedure (available in AMOS). Evidence of convergent validity was assessed in this subsample using the average variance extracted (AVE), with values of \u0026ge;\u0026thinsp;0.50 considered adequate [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.2. Gender invariance.\u003c/strong\u003e To examine gender invariance of GRiPS scores, we conducted multi-group CFA [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e] using the total sample. Measurement invariance was assessed at the configural, metric, and scalar levels [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]. We accepted \u0026Delta;CFI\u0026thinsp;\u0026le;\u0026thinsp;.010 and \u0026Delta;RMSEA\u0026thinsp;\u0026le;\u0026thinsp;.015 or \u0026Delta;SRMR\u0026thinsp;\u0026le;\u0026thinsp;.010 (.030 for factorial invariance) as evidence of invariance [\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.3. Reliability and validity analyses.\u003c/strong\u003e Composite reliability was assessed using McDonald\u0026rsquo;s \u0026omega; and Cronbach\u0026rsquo;s \u0026alpha;, with values greater than .70 reflecting adequate composite reliability [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. The total GRiPS and R-1 scores followed a normal distribution, with skewness and kurtosis values varying between \u0026minus;\u0026thinsp;1 and +\u0026thinsp;1 [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. To assess convergent and criterion-related validity, we examined bivariate correlations between total GRiPS and R-1 scores and impulsivity and self-esteem using the Pearson test. Based on Cohen (1992) [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e], values\u0026thinsp;\u0026le;\u0026thinsp;.10 were considered weak, ~ .30 were considered moderate, and ~\u0026thinsp;.50 were considered strong correlations. Student t test was used to compare scores between genders.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Psychometric properties of the GRiPS scale\u003c/h2\u003e\n \u003cp\u003eThe fit indices of the original one-factor structure of the GRiPS (8 items) were poor as follows: \u0026chi;\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;336.33/20\u0026thinsp;=\u0026thinsp;16.82, RMSEA\u0026thinsp;=\u0026thinsp;0.145 (90% CI 0.132, 0.159), SRMR\u0026thinsp;=\u0026thinsp;0.058, CFI\u0026thinsp;=\u0026thinsp;0.911, TLI\u0026thinsp;=\u0026thinsp;0.876. Consequently, we conducted an EFA using the first subsample (n\u0026thinsp;=\u0026thinsp;246). We confirmed the suitability of the data for EFA via the KMO test (KMO\u0026thinsp;=\u0026thinsp;.905) and Bartlett\u0026rsquo;s test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;.001). Next, the relevance of the items was analyzed using the MSA index, which indicated that all items measured the same domain as the rest of the questionnaire, with a value greater than 0.50 for all items. However, 3 doublets were identified through the EREC index, which led to the removal of items 4 and 5, as they were the most frequently repeated in the doublets. Six items remained in the analysis; another factor analysis was then conducted with the final six items. The KMO index (KMO\u0026thinsp;=\u0026thinsp;.890) and Bartlett\u0026apos;s test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;.001) confirmed the adequacy of the data for the factor analysis. Parallel analysis indicated that a one-factor model would best fit the data. The loading factors are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe unidimensional model obtained from the EFA (6 items) was tested via a CFA on the second subsample (n\u0026thinsp;=\u0026thinsp;503). The fit indices were modest as follows: \u0026chi;\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;72.28/9\u0026thinsp;=\u0026thinsp;8.03, RMSEA\u0026thinsp;=\u0026thinsp;0.118 (90% CI 0.094, 0.144), SRMR\u0026thinsp;=\u0026thinsp;0.046, CFI\u0026thinsp;=\u0026thinsp;0.958, TLI\u0026thinsp;=\u0026thinsp;0.929. After correlating residuals of items 7 and 8 because of high modification indices, results improved as follows: \u0026chi;\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;24.16/8\u0026thinsp;=\u0026thinsp;3.02, RMSEA\u0026thinsp;=\u0026thinsp;0.063 (90% CI 0.035, 0.093), SRMR\u0026thinsp;=\u0026thinsp;0.023, CFI\u0026thinsp;=\u0026thinsp;0.989, TLI\u0026thinsp;=\u0026thinsp;0.980. The AVE value was adequate (=\u0026thinsp;.55). The standardized loading factors are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The reliability was very good as shown by alpha and omega coefficients (=\u0026thinsp;.89 for both).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEnglish Items of the GRiPS, loading factors derived from the Exploratory Factor Analysis (EFA) in the first subsample and Standardized Estimates of Factor Loadings from the Confirmatory Factor Analysis (CFA) in the second subsample.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOriginal item number\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLabel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEFA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaking risks makes life more fun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMy friends would say that I\u0026rsquo;m a risk taker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI enjoy taking risks in most aspects of my life\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI commonly make risky decisions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI am a believer of taking chances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI am attracted, rather than scared, by risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eGender invariance of the GRISP scale\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAs reported in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, we were able to show the invariance across gender at the configural, metric, and scalar levels. No statistically significant difference in terms of GRiPS scores was seen between males and females (16.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.06 vs 16.35\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86, \u003cem\u003et\u003c/em\u003e(747)\u0026thinsp;=\u0026thinsp;.212, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.832).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eMeasurement Invariance of the GRiPS scale across gender in the total sample.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel Comparison\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u0026chi;\u0026sup2;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;CFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;RMSEA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;SRMR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConfigural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConfigural vs metric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScalar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetric vs scalar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e CFI\u0026thinsp;=\u0026thinsp;Comparative fit index; RMSEA\u0026thinsp;=\u0026thinsp;Steiger-Lind root mean square error of approximation; SRMR\u0026thinsp;=\u0026thinsp;Standardised root mean square residual.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Psychometric properties of the R-1 scale\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.1. Exploratory Factor Analysis on the total sample.\u003c/strong\u003e Bartlett\u0026rsquo;s test of sphericity, \u0026chi;\u003csup\u003e2\u003c/sup\u003e(15)\u0026thinsp;=\u0026thinsp;3025, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and KMO (.888) indicated that the GRIPS items had adequate common variance for factor analysis. The results of the EFA revealed one factor, which explained 69.10% of the common variance.\u003c/p\u003e\n \u003cp\u003eWhen adding the R-1, Bartlett\u0026rsquo;s test of sphericity, \u0026chi;\u003csup\u003e2\u003c/sup\u003e(21)\u0026thinsp;=\u0026thinsp;3342.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and KMO (.907) remained adequate. The one-factor solution obtained explained 64.92% of the variance.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.2. Exploratory Factor Analysis on with males.\u003c/strong\u003e Similar results were seen in men; Bartlett\u0026rsquo;s test of sphericity, \u0026chi;\u003csup\u003e2\u003c/sup\u003e(15)\u0026thinsp;=\u0026thinsp;930.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and KMO (.846) again indicated that the GRiPS scales\u0026rsquo; items had adequate common variance for factor analysis among men. A one-factor solution was obtained explaining 67.26% of the variance.\u003c/p\u003e\n \u003cp\u003eWhen adding the R-1, Bartlett\u0026rsquo;s test of sphericity, \u0026chi;\u003csup\u003e2\u003c/sup\u003e(21)\u0026thinsp;=\u0026thinsp;1008.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and KMO (.868) remained adequate. The one-factor solution obtained explained 62.27% of the variance.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.3. Exploratory Factor Analysis with females.\u003c/strong\u003e For women, Bartlett\u0026rsquo;s test of sphericity, \u0026chi;\u003csup\u003e2\u003c/sup\u003e(15)\u0026thinsp;=\u0026thinsp;1562, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and KMO (.881) again indicated that the GRIPS items had adequate common variance for factor analysis. The results of the EFA revealed two factors, which explained 63.45% of the common variance.\u003c/p\u003e\n \u003cp\u003eWhen adding the R-1, Bartlett\u0026rsquo;s test of sphericity, \u0026chi;\u003csup\u003e2\u003c/sup\u003e(21)\u0026thinsp;=\u0026thinsp;1780.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, and KMO (.868) remained adequate. The one-factor solution obtained explained 60.13% of the variance.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.4. Factor structure congruence and composite reliability.\u003c/strong\u003e The factor loadings reported in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e for the total sample, women and men separately suggest strong similarity across factor structures. McDonald\u0026rsquo;s \u0026omega; and Cronbach\u0026rsquo;s \u0026alpha; were very good for the GRiPS and for the R-1 in the total sample, men and women respectively.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLoading factors obtained from the Exploratory Factor Analysis (EFA) in the total sample and in males and females separately.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 1: Total sample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 2: Males only\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eModel 3: Females only\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGRIPS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGRIPS\u0026thinsp;+\u0026thinsp;R-1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGRIPS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGRIPS\u0026thinsp;+\u0026thinsp;R-1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGRIPS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGRIPS\u0026thinsp;+\u0026thinsp;R-1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMcDonald\u0026rsquo;s \u0026omega;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCronbach\u0026rsquo;s \u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eGRiPS\u0026thinsp;=\u0026thinsp;General Risk-Taking Propensity Scale; R-1\u0026thinsp;=\u0026thinsp;Risk Proneness Short Scale. Numbers in bold indicate the highest loading of the item on its respective factor.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Construct and concurrent validity\u003c/h2\u003e\n \u003cp\u003eBoth GRiPS and R-1 correlated significantly and positively with each other. They were also significantly and positively correlated with urgency, lack of premeditation, lack of perseverance and sensation seeking in the total sample. In males, premeditation that showed no significance. Furthermore, higher GRiPS were significantly associated with higher self-esteem. In females, higher GRiPS scores were significantly associated with lower self-esteem (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation of the GRiPS and the Arabic version of the R-1 with other continuous variables.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. GRIPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. R-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.55***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Urgency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.42***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.26***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. Lack of premeditation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.14***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.11**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5. Lack of perseverance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.16***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.08*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.72***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6. Sensation seeking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.45***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.31***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.41***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.53***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.60***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7. Self-esteem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.11**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.23***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.17***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001. GRiPS\u0026thinsp;=\u0026thinsp;General Risk-Taking Propensity Scale; R-1\u0026thinsp;=\u0026thinsp;Risk Proneness Short Scale.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eUntil today, only a few studies have been conducted in Arab countries and settings to understand\u0026nbsp;the prevalence and correlates of engaging in domain-specific risky behaviors (e.g., Tobacco smoking, alcohol and substance misuse, unprotected sex [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]). However, no or very little research attention has been paid to RTP as a predictor of such behaviors. In this study, we propose to validate two short self‐report measures of general risk propensity in Arabic, the GRiPS and the R-1. Both scales assess risk-taking as a general disposition and a broad construct, thus enabling to predict broad outcomes. Findings demonstrated that both scales were valid and reliable. In addition, measurement invariance of the GRiPS and the R-1 across participants by gender was established, indicating that general risk propensity can be validly measured across Arabic-speaking male and female respondents. Both GRiPS and R-1 correlated positively and significantly with each other, as well as with impulsiveness dimensions and self-esteem. Therefore, the GRiPS and the R-1 seem to be suitable and applicable as simple, time-efficient and low-cost self-report measures in a wide range of research areas where an individual's general disposition toward risks plays an important role. If conducting the research under no time constraints or limitations, we would recommend using the GRiPS.\u003c/p\u003e\n\u003cp\u003eIn terms of factorial validity, the EFA-to-CFA approach to explore the best-fitting model of the GRiSP suggested that the original one-factor structure was replicated in this study, except for two items that were removed (item 4 \u0026ldquo;I would take a risk even if it meant I might get hurt\u0026rdquo;, and item 5 \u0026ldquo;Taking risks is an important part of my life\u0026rdquo;). In contrast to our findings, Porf\u0026iacute;rio et al. [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] were able to replicate the unidimensional model with all eight items loading on a single latent variable in a Brazilian sample of adults from the general population. These variations in factor structure might be explained by cultural variations in people\u0026rsquo;s perceptions of, and attitudes toward, engaging in risky behaviors [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. Analyses showed that the R-1 loaded on the same factor as the GRiPS, and that its scores correlated positively with GRiPS scores, which suggests that the R-1 is relevant and informative to measure the RTP construct to the same extent as the GRiPS. Furthermore, McDonald\u0026rsquo;s \u0026omega; and Cronbach\u0026rsquo;s \u0026alpha; values were very good for both the GRiPS (\u0026omega; and \u0026alpha; values of .89) and the R-1 (\u0026omega; and \u0026alpha; values of .87), indicating internal consistency of the scales.\u003c/p\u003e\n\u003cp\u003eUsing multi-group factor analysis, the study showed that the GRiPS exhibited measurement invariance with respect to gender at the configural, metric, and scalar levels. The R-1 was also found to be gender invariant. These findings indicate that both scales measure the RTP construct in the same way across males and females, and that inferences based on gender differences on the variances or means of GRiPS and R-1 latent scores are likely not confounded by measurement bias [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]. Consistently, measurement invariance of the GRiPS was demonstrated in the Brazilian sample [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. Establishing this psychometric property is of high relevance, given that gender comparisons represent a key research interest in the area of risk-propensity [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]. Finally, correlation analyses found that the GRiPS and the R-1 showed good convergent validity against each other, which reflects the similar construct of general risk-taking propensity they assess. Medium-sized positive correlations were found between GRiPS/R-1 scores and the impulsiveness dimensions \u0026ldquo;sensation seeking\u0026rdquo; and \u0026ldquo;urgency\u0026rdquo;. This is in line with previous research which reported an overlap between risk proneness and both sensation seeking [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e] and urgency [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e], whereas weak correlations with other impulsiveness dimensions possibly reflect their connection to psychological, rather than physiological, disorders [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e], and that they are clearly distinct from risk proneness [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. As for self-esteem, non-significant correlation with GRiPS scores and only a small positive correlation with R-1 scores were observed with risk proneness. These findings are consistent with those of previous studies that were mixed, showing either non-significant [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] or small positive [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] relation of self-esteem to domain-general RTP.\u003c/p\u003e\n\u003cp\u003eStudy limitations\u003c/p\u003e\n\u003cp\u003eThis study has some limitations that need to be acknowledged. First, data was gathered using a web-based survey design and convenience sampling, which may limit the representativeness of our sample. Second, the generalization of our findings to the broad Arabic-speaking population may be hindered by the inclusion of participants from a single country and culture. Future studies may extend the sample to include participants from Arab countries and cultural backgrounds other than Lebanon. Finally, other relevant psychometric characteristics, including test-retest reliability and predictive validity, remain to be explored in further research.\u003c/p\u003e\n\u003cp\u003eImplications\u003c/p\u003e\n\u003cp\u003eThese limitations aside, the Arabic versions of the GRiPS and R-1 demonstrated good psychometric qualities, and are recommended for use to assess general disposition to engaging in risky behaviors among Arabic-speaking adults from the general population. Findings hold practical implications for policymakers and practitioners seeking to early detect and timely manage people prone to risk-taking behaviors. Routine use of valid and reliable general RTP measures may allow practitioners to determine whether a particular risky behavior is rooted in a general disposition to risk-taking, so that they can inform and adapt their own therapeutic approach. To this end, the GRiPS and R-1 appear to be more advantageous than domain-specific RTP measures, as they have strong predictive power in explaining variations in domain-specific risk-taking behavior (e.g., [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]). However, it is of note that when seeking to predict specific risky behaviors in particular situations (e.g., Financial, Ethical, Recreation), domain-specific measures such as the CATS [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], the MDRT scale [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], or the DOSPERT [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] could be more appropriate. Hopefully, the availability of these two sound self-report instruments would lead Arab researchers to investigate this research area and provide new insight into the nature and clinical significance of RTP in Arabic-speaking populations.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe present study presents the validation process of two short self-report measures of general RTP in the Arabic language and culture, namely the GRiPS (six items) and the R-1 (a single item). Results indicate both measures are reliable and valid to assess RTP in Arabic-speaking male and female adults. Offering such scales in the Arabic language and cultural context would allow future cross-cultural comparisons among populations from different parts of the world.\u003c/p\u003e"},{"header":"DECLARATIONS","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of the School of Pharmacy at the Lebanese International University approved this study protocol. An informed consent was obtained from all participants when submitting the online form. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are not publicly available due to restrictions imposed by the ethics committee. The dataset supporting the conclusions is available upon request to the corresponding author (SH).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFFR, SO and SH designed the study; FFR wrote the paper; SH carried out the analysis and interpreted the results; DM, MD and FS collected the data; all authors reviewed the paper for intellectual content; all authors reviewed the final manuscript and gave their consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all participants.\u003c/p\u003e"},{"header":"REFERENCES","content":"\u003col\u003e\n\u003cli\u003eWeber EU, Blais AR, Betz NE. 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The impulsive behavior short scale\u0026ndash;8 (I-8): A comprehensive validation of the English-language adaptation. Plos one. 2022;17(9):e0273801.\u003c/li\u003e\n\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":"Risk-taking propensity, Psychometric properties, Self-report measures, validation, Arabic","lastPublishedDoi":"10.21203/rs.3.rs-3472999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3472999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUntil today, only a few studies have been conducted in Arab countries and settings to understand the prevalence and correlates of engaging in domain-specific risky behaviors. However, no or very little research attention has been paid to risk-taking propensity as a predictor of such behaviors. In this study, we propose to validate two short self‐report measures of general risk propensity in Arabic, the General Risk Propensity Scale (GRiPS) and the Risk Proneness Short Scale (R-1)\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn online survey was carried-out in a sample of native Arabic-speaking adults from Lebanon (n\u0026thinsp;=\u0026thinsp;748, mean age of 34.48\u0026thinsp;\u0026plusmn;\u0026thinsp;13.25 years, 66.5% females). The forward-backward translation method was adopted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe original one-factor structure of the GRiPS was replicated in this study, except for two items that were removed (item 4 \u0026ldquo;I would take a risk even if it meant I might get hurt\u0026rdquo;, and item 5 \u0026ldquo;Taking risks is an important part of my life\u0026rdquo;). Analyses showed that the R-1 loaded on the same factor as the GRiPS, and that its scores correlated positively with GRiPS scores. McDonald\u0026rsquo;s ω and Cronbach\u0026rsquo;s α values were very good for both the GRiPS (ω and α values of .89) and the R-1 (ω and α values of .87), indicating internal consistency of the scales. The GRiPS exhibited measurement invariance with respect to gender at the configural, metric, and scalar levels. The R-1 was also found to be gender invariant. Finally, medium-sized positive correlations were found between GRiPS/R-1 scores and the impulsiveness dimensions \u0026ldquo;sensation seeking\u0026rdquo; and \u0026ldquo;urgency\u0026rdquo;.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe Arabic GRiPS and the R-1 seem to be suitable and applicable as simple, time-efficient and low-cost self-report measures in a wide range of research areas where an individual's general disposition toward risks plays an important role. If conducting the research under no time constraints or limitations, we would recommend using the GRiPS.\u003c/p\u003e","manuscriptTitle":"Validity and reliability of the Arabic versions of the General Risk Propensity Scale (GRiPS) and the Risk Proneness Short Scale (R-1)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-12 16:06:04","doi":"10.21203/rs.3.rs-3472999/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":"aa2f9133-802a-4bac-ad6d-1e308070676f","owner":[],"postedDate":"March 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T06:24:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-12 16:06:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3472999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3472999","identity":"rs-3472999","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}