Psychometric Properties of Multidimensional Dispositional Greed Assessment (MDGA); and investigate the role of culture and sociodemographic characteristics on greed

Psychometric Properties of Multidimensional Dispositional Greed Assessment (MDGA); and investigate the role of culture and sociodemographic characteristics on greed | 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 Article Psychometric Properties of Multidimensional Dispositional Greed Assessment (MDGA); and investigate the role of culture and sociodemographic characteristics on greed Jaber Alizadehgoradel, Malak Karimova, Nikzad Ghanbari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7603666/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 This article analyzes the psychometric properties of the Multidimensional Dispositional Greed Assessment (MDGA), which is designed to measure dispositional greed in adults. Dispositional greed is recognized as a key personality trait in social psychology and can have profound effects on individuals’ behaviors and decision-making processes. In this study, we examined the factor structure of the 20-item MDGA scores using Confirmatory Factor Analysis (CFA). This analysis was conducted on a sample of adults from two countries, Iran and Azerbaijan, comprising 607 individuals. The aim of this research was to assess the validity and reliability of this tool in measuring dispositional greed across different cultures. The results of the confirmatory factor analysis indicated that a three-factor structure fits the data well and accounts for a significant portion of the variance in the scores (sbX2 = 426.18 (p < 0.01); SRMR = 0.048; RMR = 0.061, GFI = 0.93, AGFI = 0.92, RFI = 0.96, IFI = 0.98, PNFI = 0.83, NNFI = 0.97, NFI = 0.97; CFI = 0.98; RMSEA = 0.051). The Cronbach’s alpha values for the three factors Insatiable (0.873), Desire (0.875), and Retention (0.924) were obtained. Significant differences in the levels of greed were also observed among the cultural and sociodemographic variables. These findings help us gain a better understanding of the various dimensions of dispositional greed and suggest that MDGA can serve as an effective self-report tool for researchers in this field. Additionally, we emphasize the importance of this tool for future research and provide recommendations for various research domains. In particular, examining the cultural and social influences on dispositional greed, as well as its relationship with economic and social behaviors, can be vital topics that warrant further investigation. Ultimately, this study can contribute to the development of theories and models related to dispositional greed and enhance our understanding of this personality trait across different societies. Health sciences/Health care Biological sciences/Psychology Social science/Psychology Dispositional Greed. Psychometric properties. Confirmatory Factor Analysis Figures Figure 1 Figure 2 1. Introduction While greed is an age-old concept in society, it has gained significant attention as a prominent topic in contemporary scientific discourse. Particularly following economic crises, scholars have begun to explore and analyze the various dimensions of greed across multiple fields and contexts ( 1 ). Researchers argue that greed can be perceived in two distinct ways: on one hand, as a positive motivator that drives productivity (such as economic growth), and on the other hand, as a destructive force that hinders progress and damages interpersonal relationships ( 2 ). For instance, from an economic perspective, greed can serve as a driving force for individual and societal growth, development, and innovation. However, many people also view greed as a negative trait that can lead to significant personal and social repercussions, including scandals, bankruptcy, fraud, and harm to others ( 3 ). Moreover, excessive greed may be linked to an unfulfilled desire to satisfy one’s emotional and social needs ( 4 ). Research in psychology has historically examined greed primarily as a situational emotion ( 5 ). More recently, scholars have shifted their focus to the dispositional aspects of greed as a stable trait or motivation ( 3 ). While specific situations can trigger greed, individuals exhibit variability in how greed influences their motivation (motivational trait; 2). Given the emphasis on dispositional greed, there is a need for further exploration of its development and manifestation as a stable trait, distinct from the investigation of situational triggers. Despite the growing interest in dispositional greed, a consistent definition among researchers remains elusive, partly due to the complexity of the construct itself ( 6 ). Greed is often regarded as a negative personality trait. From various religious viewpoints (such as Christianity and Islam), greed is seen as a vice and is associated with sinful behaviors ( 7 ). Christian texts refer to greed as the “love of money,” which is considered the “root of all evil” (1 Timothy 6:10). Psychology researchers suggest that greed should be recognized as a fourth dimension of the current dark triad, which includes narcissism, machiavellianism, and psychopathy ( 8 ). The levels of individuals’ dispositional greed have been positively correlated with their levels of narcissism ( 9 ). Dispositional greed is conceptually linked to constructs such as envy, self-interest, miserliness, and materialism; however, it is distinct from these constructs in terms of motivation, focus, and level of desire ( 10 ). In response to the lack of definitional clarity, Lambie and Stickl Haugen ( 10 ) conducted a comprehensive review of the existing literature and proposed a practical definition of dispositional greed. This definition encompasses the desire to acquire more than what one possesses, the urge to retain what one has at all costs, and a persistent sense of dissatisfaction. Additionally, it includes individuals’ desires for anything they find valuable, which may extend beyond monetary or materialistic interests. Overall, the definition of greed consists of several dimensions, including (a) the desire for anything of value (including both material and non-material goods); (b) acquisition motivations (i.e., the desire to obtain more); (c) retention motivations (the desire to hold onto what one has); (d) insatiability; and (e) disregard for the costs associated with fulfilling one’s desires. Furthermore, Lambie and et al ( 11 ) developed a tool that provides a complex and multidimensional assessment of greed to examine its manifestations in individuals. Although there were multiple tools for measuring dispositional greed before the multidimensional dispositional greed assessment (MDGA) developed by Lambie et al. ( 11 ), these tools have slight differences in their focus and conceptual framework. As Zeelenberg et al. ( 12 ) pointed out, while existing scales agree on the fundamental characteristics of greed, they differ in their conceptual backgrounds. For instance, three out of five scales incorporate the idea that greed may come at the expense of others or involve manipulating others (GR€€D Scale, 13; Greed Trait Measure; 14), while other scales do not include this concept. Moreover, the existing scales do not provide a comprehensive concept of greed. Specifically, greed may encompass the following components: (a) an excessive desire to acquire more material possessions and resources, (b) an excessive desire for non-material things, (c) a disregard for the costs associated with obtaining one’s desires, (d) insatiability, (e) acquisition motivations (for example, the desire to acquire more), and (f) retention motivations (for example, the desire to hold on to what one already has; see Lambie & Stickl Haugen, 10). However, none of the existing scales encompass all the aspects of greed mentioned above. Prior to the MDGA, most greed assessments were developed as unidimensional, one-factor scales that focused on specific aspects of greed (GR€€D Scale, 13; Greed Trait Measure; Mussel et al., 14), thereby providing a limited definition. Another limitation of the scales prior to the MDGA is the absence of a measure for the retention component. Researchers have posited that retention may be a part of greed ( 2 ), and there is preliminary empirical evidence suggesting that retention may indeed be an aspect of greed (see Krekels, 15). Nevertheless, the existing greed scales primarily focus on the acquisition of desired goods and do not include items that measure retention ( 11 ). Considering the limitations of the greed measurement scales prior to the MDGA, as well as the absence of a comprehensive tool for assessing greed in both Iran and Azerbaijan, we decided to evaluate the psychometric properties of the Multidimensional Dispositional Greed Assessment (MDGA) questionnaire within these two cultures. MDGA is recognized as the first multidimensional scale that includes more than one factor based on a broader definition of greed (such as considering retention motivations). The normalization of this scale, which can comprehensively measure different levels of greed, may help us better understand individual differences in this area. This understanding could, in turn, lay the groundwork for applied counseling research that examines the factors influencing the development of greed in individuals. 2. Methods 2.1. Participants To norm and conduct a confirmatory factor analysis of the MDGA, a link to the MDGA questionnaire was distributed to a sample of adults in Iran and Azerbaijan. A total of 609 non-clinical participants, including 353 Iranians (58.2%) and 254 Azerbaijanis (41.8%), completed the questionnaire in its entirety. The minimum criteria for participation in this study were as follows: (a) a minimum age of 18 years and (b) the ability to read and write. The majority of participants were female, with 439 females (72.3%) and 168 males (27.7%). Table 1 presents the complete demographic characteristics. 2.3. Procedure The design of this study followed the guidelines and regulations of the Declaration of Helsinki and was approved by the Ethics Committee of Zanjan University (IR.ZNU.REC.1404.004). The recruitment and data collection process were conducted online through Google Forms. The questionnaires designed in Google Forms included the following: (1) an overview of the research and an informed consent form, (2) a general demographic questionnaire (including questions related to ethnicity and gender), (3) the 20-item MDGA questionnaire, (4) the GR€€D scale, (5) the Short Dark Triad (SD3) scale, and (6) The Iowa–Netherlands Comparison Orientation Measure (INCOM). All participants indicated informed consent to participate in the research. Measures Demographic questionnaire A demographic questionnaire was included in the survey to gather information about participants’ characteristics, including age, gender, marital status, and more. Multidimensional Dispositional Greed Assessment (MDGA) The MDGA (11) is a 20-item instrument designed to assess individuals’ levels of multidimensional dispositional greed across three distinct domains: (a) Insatiable Pursuit for More at Any Cost (9 items), (b) Desire for More (7 items), and (c) Retention Motivation (4 items). The MDGA items are formulated as statements such as “I will get what I want at all costs, even if I have to lie.” For each item, participants indicated their level of agreement or disagreement with each statement on a 5-point Likert scale, ranging from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”). Lambie et al. (11) demonstrated adequate internal consistency, with reliability coefficients ranging from (α = .943 to .956). GR€€D scale The GR€€D scale (13) is a self-report instrument designed to measure levels of dispositional greed in individuals and is unidimensional in nature. This scale consists of 12 items related to participants’ personal attitudes and behaviors, such as “My primary goal is to earn a lot of money.” Each item features a seven-point response scale ranging from 1 (does not apply at all) to 7 (fully applies). The overall scores of this scale exhibited a Cronbach’s alpha of .89, indicating evidence of construct and criterion-related validity (14, 16). In Lambie et al., (11) study, the GR€€D scale scores recorded an acceptable Cronbach’s alpha of .948. The GR€€D scale scores had an acceptable Cronbach’s alpha of .845 in our study. Short Dark Triad (SD3) The SD3 (17) has been specifically designed to measure the three dimensions of the dark triad (Machiavellianism, narcissism, and psychopathy). The scale consists of 27 items (9 for each dimension), for which participants must indicate their level of agreement using five response options rated on a Likert-type scale, ranging from “totally disagree” to “totally agree.” These items include statements indicative of psychopathic traits (e.g., “People who mess with me always regret it”), narcissistic traits (e.g., “People see me as a natural leader”), and Machiavellian traits (e.g., “It is not wise to tell your secrets”). Out of the 27 items, 5 statements were phrased in the opposite direction (items 11, 15, 17, 20, and 25) and needed to be recoded to obtain the final score. Higher scores on the scale indicate higher levels of dark triad traits. In the current study, the SD3 scores had an acceptable Cronbach’s alpha (Machiavellianism (.662), Narcissism (.654) and psychopathy (.647). The Iowa–Netherlands Comparison Orientation Measure ( INCOM ) The core instrument of the INCOM scale (18) consists of 11 items designed to measure individuals’ tendency to make social comparisons. This scale includes statements such as: “I always want to know what others in a similar situation would do.” The response options in this scale are rated from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicate a greater tendency to engage in social comparisons. The scale has shown Cronbach’s alpha values ranging from .78 to .85 in 10 American samples and from .78 to .84 in 12 Dutch samples. The construct validity and criterion-related validity of this scale have been well documented and confirmed through multiple tests. The INCOM scale scores had an acceptable Cronbach’s alpha of .810 in our study. Statistical analysis This research consisted of several distinct stages. In the first stage, the normality of the variables was tested using the Kolmogorov-Smirnov test and Skew-ness and Kurtosis. The Kolmogorov-Smirnov test demonstrated that the variables followed a normal distribution (P > 0.05), and the Skew-ness and Kurtosis Values calculated were within ±3, indicating normality of the data (see Table 1). The Multidimensional Dispositional Greed Assessment (MDGA) was analyzed in the second stage by descriptive statistics, such as mean, standard deviation (SD), factor loading, and range. In the third step, Confirmatory Factor Analysis (CFA) was conducted to investigate the factor structure of the MDGA scale. To estimate model fitness indexes, Maximum Likelihood was used and Fitness indexes with a 90% confidence interval were assessed, including the Root Mean Square Error of Approximation (RMSEA); Comparative Fit Index (CFI), Normed Fit Index (NFI), Non-Normed Fit Index (NNFI), Parsimony Normed Fit Index (PNFI), Incremental Fit Index (IFI), Relative Fit Index (RFI), Adjusted Goodness of Fit Index (AGFI), Goodness of Fit Index (GFI), Root Mean Square Residual (RMR), and Standardized Root Mean Square Residual (SRMR). In the fourth step, Internal consistency reliability of MDGA was investigated using data from the main study and based on Cronbach's alpha, McDonald’s omega, and Guttman’s lambda coefficient test. And Finally, Pearson correlations were calculated between the MDGA and the GR€€D, Short Dark Triad (SD3), and The Iowa–Netherlands Comparison Orientation Measure (INCOM) in order to evaluate validity. The data analysis was performed using statistical software, including SPSS-26 and LISREL-8.0. 2.4. Data analyses This study is a cross-sectional study carried out on a convenience sampling method, and 609 non-clinical Iranian (n=353, 58.2%) and Azerbaijanian (n=254, 41.8%) people participated in the present study. Of these, 2 participants were omitted because of incomplete data, so the final sample was 607 participants. The subjects comprised 439 females (72.3%) and 168 males (27.7%), Table 1 shows the detailed sociodemographic characteristics of the participants. Table 1 Participant sociodemographic characteristics (n=607) Variable n ( % ) or (Mean ± SD) Nationality Iran 353(58.2) Azerbaijan 254(41.8) Gender female 439 (72.3) male 168 (27.7) Marital Status single 322 (53.0) married 275 (45.3) divorce 10 (1.6) Age 30.60 (9.91) Time online (hours/days) 5.95 (4.04) Educational Status under DIPLOMA 9 (1.5) DIPLOMA 70 (11.5) Bachelor’s degree 239(39.4) Master’s degree 24340.0() PHD 46(7.6) Time on social Media (hours/days) 3.95 (1.78) Number of followers 675.01 (2164.75) Most used social media platforms Facebook 20 (3.3) Instagram 309 (50.9) Telegram 95 (15.7) WhatsApp 96 (15.8) YouTube 54 (8.9) Twitter 33 (5.4) financial situation weak 121(19.9) moderate 452(74.5) good 34(5.6) financial satisfaction yes 194(32.0) no 413(68.0) 3. Result Factor structure Factor loadings for all items were statistically significant (P<0.001). Findings demonstrated the standardized estimates for all items of TIAS were over 0.50, except item 18; the factor loadings of items 18 were 0.39 (Table 2). Investigating the fitness of the present model demonstrated that the model has a good fit with the data, and findings support the three-factor model (Table 2 and Figure 1) . Table 2. descriptive statistic indices for the items of the Multidimensional Dispositional Greed Assessment (MDGA) Item-Total statistics Items statistics Components Kurtosis Skewness C.D. I.T. V F. L SD Mean Items 5.561 2.331 .878 .486 144.160 0.72 .848 1.44 Insatiable Item1 .802 -1.141 .878 .463 142.091 0.75 1.049 3.97 Desire Item2 -1.080 -.125 .879 .454 139.851 0.87 1.244 3.10 Retention Item3 1.259 1.464 .876 .514 140.342 0.75 1.088 1.76 Insatiable Item4 .868 -1.151 .878 .478 141.546 0.78 1.064 3.93 Desire Item5 -1.051 -.243 .877 .497 138.720 0.89 1.240 3.14 Retention Item6 .929 1.281 .875 .570 139.213 0.83 1.072 1.85 Insatiable Item7 -.085 -.796 .875 .548 139.117 0.78 1.115 3.71 Desire Item8 -1.128 .043 .876 .518 137.699 0.86 1.272 2.85 Retention Item9 1.599 1.446 .876 .545 140.669 0.76 1.013 1.76 Insatiable Item10 -.696 -.441 .874 .573 137.476 0.76 1.183 3.35 Desire Item11 -1.122 -.190 .875 .544 137.160 0.82 1.258 3.05 Retention Item12 .304 1.015 .875 .563 138.694 0.59 1.119 2.10 Insatiable Item13 .170 -.972 .876 .522 139.524 0.77 1.132 3.78 Desire Item14 .923 1.337 .879 .420 143.052 0.52 1.057 1.78 Insatiable Item15 .030 -.946 .877 .494 139.840 0.71 1.163 3.72 Desire Item16 .744 1.172 .877 .494 140.976 0.54 1.077 1.98 Insatiable Item17 1.249 -1.206 .883 .275 147.153 0.39 .996 3.95 Desire Item18 .475 1.162 .878 .469 141.347 0.53 1.097 1.93 Insatiable Item19 -.603 2.331 .881 .390 142.340 0.57 1.186 2.13 Insatiable Item20 2.534 1.362 - - - - - 6.754 16.72 - Insatiable .592 -.779 - - - - - 5.831 26.42 - Desire -.952 -.089 - - - - - 4.524 12.13 - Retention 1.167 .356 - - - - - 12.440 55.28 - MDGA Note: V = scale variance if item deleted, F.L. = factor loading, I.T. = corrected item-total correlations, C.D. = Cronbach’s alpha if item deleted, MDGA = Multidimensional Dispositional Greed Assessment Table 2 shows descriptive statistics of the twenty MDGA -related items. The mean score obtained was 2.35 (SD = 1.11), and all 20 items exhibited means in the 0.711–1.37 range. All the corrected item-total correlations surpassed 0.40 (see details in Table 2). Table 3. Confirmatory Factor Analysis (CFA) and Fit indexes Goodness of Fit index three-factor BM three-factor AM three-factor Iran three-factor Azerbaijan Recommended Value Decision RMSEA (CI 90%) .078 (.072-.083) .051 (.045 -.057) .081 (.073-.088 .078 (.068-.087) ≥ 0.08 Good Fit sb X 2 780.35 426.18 549.09 420.90 - - CFI 0.95 0.98 0.90 0.91 ≤ 0.90 Excellent Fit NFI 0.94 0.97 0.86 0.86 ≤ 0.90 Excellent Fit NNFI 0.94 0.97 0.86 0.86 ≤ 0.90 Excellent Fit PNFI 0.82 0.83 0.76 0.76 ≤ 0.50 Excellent Fit IFI 0.95 0.98 0.90 0.91 ≤ 0.90 Excellent Fit RFI 0.93 0.96 0.84 0.85 ≤ 0.90 Excellent Fit AGFI 0.86 0.92 0.83 0.83 ≤ 0.80 Excellent Fit GFI 0.89 0.93 0.86 0.87 ≤ 0.90 Good Fit RMR 0.067 0.061 0.060 0.095 ≥ 0.08 Good Fit SRMR 0.056 0.048 0.048 0.079 ≥ 0.08 Excellent Fit Legend: RMSEA: Root Mean Square Error of Approximation; CFI: Comparative Fit Index; NFI: Normed Fit Index; NNFI: Non-Normed Fit Index; PNFI: Parsimony Normed Fit Index; IFI: Incremental Fit Index; RFI: Relative Fit Index; AGFI: Adjusted Goodness of Fit Index; GFI: Goodness of Fit Index; RMR: Root Mean Square Residual; SRMR: Standardized Root Mean Square Residual: (BM); Model fit indexes of MDGA before modification, (AM); Model fit after modification Based on the literature, three-factor model was exanimated through Confirmatory factor analysis (CFA), model provided marginal fit to data. The Confirmatory Factor Analysis (CFA) and Fitness indexes results for a three-dimension structure are illustrated in Table 3. Confirmatory factor analysis displayed that three-factor structure provided a good fit to the data: sb X 2 = 426.18 ( p < 0.01); SRMR= 0.048; RMR= 0.061, GFI= 0.93, AGFI= 0.92, RFI=0.96, IFI=0.98, PNFI=0.83, NNFI=0.97, NFI=0.97; CFI= 0.98; RMSEA =0.051. These results demonstrated all standardized factor loadings for all items were statistically significant (p<0.01), that supporting each item as adequately each component (Table 3). As shown in Tables 2 and Figure 1, all items of loads show a significant factor, and standardized factor loading for all items over 0.50 except item 18. 1.1 Internal consistency reliability Internal consistency reliability was investigated using data from the main study and based on the Cronbach's alpha, McDonald’s omega, and Guttman’s lambda coefficient test, in which Cronbach’s alpha, McDonald’s omega, and Guttman’s lambda coefficient for Multidimensional Dispositional Greed Assessment (MDGA) was measured 0.88, 0.88 and 0.78 that indicate excellent internal reliability. Table 4. Internal consistency reliability of the Multidimensional Dispositional Greed Assessment (MDGA) Variable α ω λ6 Insatiable 0.876 0.873 0.812 Desire 0.875 0.881 0.825 Retention 0.924 0.924 0.872 The Multidimensional Dispositional Greed Assessment (MDGA) 0.883 0.881 0.782 Note : α= Cronbach’s alpha; ω= McDonald’s omega; λ6= Guttman’s lambda-6 1.2 Convergent, divergent, and discriminant validity We used indexes, including Average variance Extracted (AVE), Maximum shared Squared Variance (MSV), and Average shared Squared Variance (ASV) for investigation validity of MDGA. As shown in Table 5, for all components, CR was higher than 0.50, which indicates acceptable construct reliability. In all components, CR was higher than AVE and AVE > 0.50 for all factors except desire (AVE=0.444), that indicates acceptable convergent validity. Also, findings demonstrated AVE were higher than MSV and ASV in all components, which illustrates that the discriminant validity of the Multidimensional Dispositional Greed Assessment (MDGA) was acceptable. the person correlations acquired between the MDGA with GR€€D scale, INCOM and Short Dark Triad (SD3) indicate good convergent validity (Table 5). Table 5. Descriptive statistics, Validity and correlations between GR€€D scale, INCOM and Short Dark Triad (SD3) with the MDGA CR AVE MSV ASV GR€€D scale INCOM Machiavellianism Narcissism Psychopathy Insatiable 0.878 0.515 0.105 0.094 .306** .301** .193** .112** .328** Desire 0.876 0.444 0.105 0.104 .617** .323** .162** .199** .107** Retention 0.924 0.752 0.102 0.093 .134** .341** .184** .079 .166** MDGA - - - - .504** .439** .248** .183** .289** M 49.78 35.07 27.75 27.61 21.19 SD 12.440 6.81 5.07 4.65 4.83 Note : M=Man; SD=Standard deviation; CR=Construct Reliability; AVE= Average variance Extracted; MSV= Maximum shared Squared Variance; ASV= Average shared Squared Variance; N= 607; all P values are significant at the 0.01 level The findings of Table 5 show the relationship among MDGA with other psychological variables in non-clinical population, which there was significant positive relationship between the total score of MDGA with GR€€D scale (r=0.50, P<0.01), INCOM (r=0.43, P<0.01), Machiavellianism (r=0.24, P<0.01), Narcissism (r=0.183, P<0.01) and Psychopathy (r=0.289, P<0.01). in other word, Findings demonstrated good convergent validity for the Multidimensional Dispositional Greed Assessment (MDGA) (see more details in Table 5). We investigate relationship sociodemographic characteristics with greed, MANOVA and independent t-test was conducted to compare Iranian and Azerbaijan participants on subscales and total mean score of MDGA. finding of MANOVA showed significant difference between Iranian and Azerbaijan [F (1,605) = 20.63, P = 0.001, partial η2=0.033], and mean score of insatiable in Iranian participants (M=17.76, SD=6.10) was significantly higher than Azerbaijan (M=15.28, SD=7.33), but in mean score of retention [F (1,605) = 3.79, P = 0.026, partial η2=0.006] Iranian participants (M=11.83, SD=4.26) lower than Azerbaijan(M=12.55, SD=4.83), in subscale of desire retention [F (1,605) = 0.004, P = 0.95, partial η2=0.001] there was no significant difference between Iranian (M=26.40, SD=5.42) and Azerbaijan (M=26.43, SD=6.36) participants. Independent t-test for comparing Iranian (M=56.011, SD=11.43) and Azerbaijan (M=54.27, SD=13.67) participants on total score MDGA indicated no significant difference [t(605) =1.69,P=0.091] between two groups. A One-way ANOVA was conducted to compare the effect of age on the total score of MDGA. An analysis of variance showed that the effect of age on MDGA was significant, [F (5,601) = 6.63, P = 0.001, partial η2=0.052]. post hoc comparisons using Bonferroni test indicated that the mean score for under 24 years old (M=57.44, SD=11.09) was significantly higher than mean score for 40-49 years (M=50.72, SD=12.89) and over 50 years (M=47.20, SD=14.35) age groups. mean score for over 50 age group was significantly lower than mean score of 25-29 (M=55.91, SD=13.25), 30-34 (M=56.12, SD=10.88), and 35-39 years age groups (M=55.05, SD=13.18) see graph 2. For comparing marital state on the total score of MDGA, finding ANOVA showed significant effect of marital status on greed score [F (2,604) = 21.43, P = 0.001, partial η2=0.066]. post hoc comparisons indicated that the mean score for single participants (58.14, SD=11.68) was significantly higher than mean score for married people (M=51.77, SD=12.47). however, there was not significant difference between single and divorce participant (M=59.28,11,39) on the total score of greed. Finding of ANOVA demonstrated no significant effect of educational status on greed score [F (4,602) = 1.97, P = 0.098, partial η2=0.013]. however, the mean score of greed in higher educated groups include a (M=53.69, SD=14.40) and master’s degree (M=53.82, SD=12.91) years old (M=57.44, SD=11.09) was significantly higher than slightly higher than bachelor’s degree (M=56.62, SD=), Diploma (M=56.60, SD=11.70) and under diploma participants (M=57.11, SD=7.57). finding of ANOVA showed significant difference among socioeconomical classes on total greed score [F (2,604) = 5.45, P = 0.005, partial η2=0.018]. post hoc comparisons indicated that the mean score of greed for high socioeconomic participants (54.35, SD=14.01) was significantly lower than low socioeconomic participants (M=58.60, SD=12.59) than mean score for married people (M=51.77, SD=12.47), and there was not significant difference between socioeconomic middle class participants (M=54.46, 12.15) and high socioeconomic on the total score of greed. Finding of ANOVA showed significant difference among the type of social media used on greed score [F (5,601) = 2.84, P = 0.015, partial η2=0.023]. post hoc comparisons indicated that the mean score of greed for Instagram users (56.85, SD=11.70) was significantly higher than WhatsApp users (M=52.36, SD=11.20). there was no significant difference between other types social media users on total score of greed. Finally, independent t test was conducted to compare male and female on the mean score of MDGA. Finding demonstrated that there was significant difference between male and female score [t (605) = 2.97, P = 0.003], and mean score of greed in men (n=168, M=57.69, SD=13.85) was significantly higher than mean score in women (n=439, M=54.36, SD=11.74). 4. Discussion We conducted a study to investigate the psychometric properties, factor structure, and evidence of validity of MDGA scores among samples of adults from two countries, Iran and Azerbaijan. The results of the confirmatory factor analysis (CFA) indicated that the three-factor structure provided a good fit to the data ( P < 0.01). These findings demonstrated that all standardized factor loadings for each item were statistically significant ( P < 0.01), supporting the adequacy of each item in representing its respective component. These results are consistent with the findings from the CFA study by Lambie et al. (2022), which was conducted on a validating sample of adults in the United States. The Cronbach’s alpha values for the three factors Insatiable (0.873), Desire (0.875), and Retention (0.924) were obtained that indicated excellent internal validity of MDGA Our results also provided evidence for the concurrent validity of MDGA scores. The findings indicated a strong positive relationship between the total MDGA scores and the GR€€D scale scores (13), which supports the concurrent validity of the MDGA. These results are consistent with previous research and provide further evidence for the concurrent validity of MDGA scores (11). Additionally, we examined the relationship between the individual MDGA subscale scores and the GR€€D scores. Our findings revealed strong positive correlations between the GR€€D scores and three MDGA subscales. This strong relationship aligns with the multidimensional definition of greed proposed by Lambie and Stickl Haugen (10). Furthermore, the aspect of retention motivation measured within the MDGA is unique. The MDGA is recognized as the only known greed scale that includes an individual retention motivation component, highlighting the need for further research to explore the retention aspect of dispositional greed and its relationship to the overall construct. There was also a significant positive correlation between MDGA and three subscales related to the dark triad (Machiavellianism, narcissism, and psychopathy) as well as social comparisons. There are conceptual similarities between greed and the current dark triad. Firstly, both phenomena have similar developmental origins linked to childhood uncertainty. For example, Chen (19), using an evolutionary life history approach, noted that childhood unpredictability may lead to the emergence of greed. Additionally, Jonason et al (20) have examined the connections between childhood unpredictability and dark traits in their research. Secondly, greed and the traits of the dark triad share many central characteristics. For instance, both are associated with conspicuous consumption (21), diminished self-control, and a tendency towards unethical behavior (22), such that greed shows a relationship with psychopathy across various games (13). Therefore, these traits should be examined as a dark trait. Some distinctive features make the MDGA unique. This three-factor model presents greed as a multidimensional construct, whereas previous research has primarily identified a unidimensional structure. The MDGA contributes to the literature by offering a multidimensional assessment of greed. Lambie and Stickl Haugen (10) proposed a multidimensional definition of greed that includes six characteristics: (1) excessive desire for material things; (2) excessive desire for non-material things; (3) disregard for the costs associated with obtaining one’s desires; (4) insatiability; (5) acquisition motivation; and (6) retention motivation. Although the MDGA reveals only three factors, its items combine multiple theoretical dimensions and support the conceptual foundations of a multidimensional definition of greed. The MDGA is notable for incorporating the motivation for retention (the desire to hold onto what one possesses). While research findings in this area are varied, initial evidence suggests that retention may be a component of greedy behavior (15). This aspect has rarely been explored in the existing literature, highlighting the need for further empirical research on the retention dimension of greed (10,2). The MDGA serves as a suitable tool for conducting this additional research. Psychologists and mental health professionals can utilize the MDGA to explore the concept of greed and enhance their understanding of clients. By considering greed as an inherent motivation, the MDGA provides a tool for assessing motivations and behaviors related to greed. For instance, MDGA Factor 1, titled “Insatiable Pursuit for More at All Costs,” may indicate individuals’ disregard for the needs of others and highlight social and relational issues. Greed can lead to serious consequences, such as financial ruin and damage to interpersonal relationships (3), which may contribute to clients’ concerns. Therefore, evaluating levels of greed with high MDGA scores (i.e., <80) can be effective in setting appropriate treatment goals. Additionally, the MDGA can be used to examine the relationship between childhood experiences and the emergence of greed. For example, Liu et al. (23) found that higher socioeconomic status in childhood was associated with greed in adolescence, while Chen (19) explored the connection between childhood unpredictability and levels of greed in adulthood, indicating that attachment may mediate this relationship. Thus, psychologists can adopt a developmental approach to assess and explore greed. In the present study, the results of the demographic variables indicated that there are differences between the two countries in two subcomponents of greed. Specifically, the Iranian population exhibited higher levels of Insatiable greed, while the Azerbaijani population demonstrated higher levels of Retention. These findings necessitate an examination of cultural differences and their impact on the extent and type of greed exhibited by individuals. It appears that in certain cultures, one subtype of greed may be more predominant, which could contribute to a better understanding of human behaviors related to greed. In terms of gender differences, the results also showed that men scored higher than women in the Insatiable component and the total score, and this difference is significant. In studies that have examined greed as a personality trait, it has been observed that men generally score higher than women (23). Additionally, research indicates that men are more likely than women to engage in unethical behavior, which may be related to greed (24). In terms of differences in greed across various age groups, the results indicated that individuals under 24 years of age and those aged 25 to 35 exhibited higher levels of greed compared to participants over 50 years old. In other words, younger participants displayed greater levels of greed relative to their older counterparts. This finding is consistent with the research conducted by Hoyer et al. (25), which demonstrated that greed was negatively correlated with age. Additionally, in terms of marital status, the results indicated that single individuals exhibited higher levels of greed compared to married individuals. The claim that single individuals exhibit higher levels of greed compared to married individuals is a complex issue. Some studies suggest that marriage may lead to a focus on family and a decrease in social connections. Reasons for the higher greed of single individuals include: (1) Focus on individual wealth: Greater attention to career advancement and financial security (26). (2) Fewer social obligations: Lack of family responsibilities that allows for a focus on personal gain (27). (3) Potential for social isolation: Feelings of isolation that can lead to greater reliance on financial resources (28). No differences in greed were observed in terms of educational level. Individuals who spent more time online showed higher levels of greed compared to those who spent less time online. Research indicates that increased time spent online is associated with higher levels of materialism and potentially greed. One study has shown that individuals who spend more time on social media exhibit greater materialism compared to those who read newspapers. These findings suggest that online environments may enhance attitudes or behaviors related to materialism, which could be linked to greed (29). In terms of economic status, individuals with a poor economic situation exhibited higher levels of greed compared to those with a moderate financial status. Research on the relationship between economic status and greed shows mixed results. Some studies suggest that individuals with lower socioeconomic status may exhibit more greed, while other research indicates that those with higher economic status may be more prone to unethical behaviors. Overall, the impact of economic status on greed may vary depending on the circumstances (30). Ultimately, among social media users, Instagram users exhibited higher levels of greed compared to WhatsApp users. While some studies indicate that Instagram users may exhibit certain personality traits more than WhatsApp users or show a greater tendency toward specific behaviors, there is no direct connection between the use of this platform and a general trait like “greed.” However, high levels of “neuroticism” and low levels of “agreeableness” have been identified as predictors of excessive Instagram use (31), which may indirectly relate to greed. Limitations Although the present study offers evidence for the validity of MDGA scores, it also has significant limitations. Examining further evidence of test–retest reliability and concurrent validity with broader national samples could enhance the robustness of the findings. Moreover, there are limitations associated with self-report measures, especially when assessing negatively perceived traits like greed. Future researchers may explore levels of dispositional greed among participants through observational methods, such as examining the stability of greed-related behaviors across different conditions and utilizing a behavioral coding system for more accurate analysis. Overall, our objective was to examine the factor structure of MDGA scores in adult samples from Iran and Azerbaijan. The results indicated that the three-factor structure fits well with the data and aligns with the CFA study conducted by Lambie et al. (11) in a validated sample of American adults. The findings also provided additional evidence for the concurrent validity of MDGA scores through positive correlations with the GR€€D scale scores (13). Consequently, clinicians and researchers should consider these results and the utility of the MDGA in assessing individuals’ levels of multidimensional dispositional greed. Declarations Funding This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution JA, Conceptualization, supervision, editing and review, writingMK, Data gathering, data analysis, visualizationNG, Methodology, data analysis, visualization Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Helzer, E. G. & Rosenzweig, E. Examining the role of harm-to-others in lay perceptions of greed. Organ. Behav. Hum Decis. Process. 160 , 106–114 (2020). Seuntjens, T. G. The psychology of greed. Seuntjens, T. G., Zeelenberg, M., Van de Ven, N. & Breugelmans, S. M. Dispositional greed. J. Personal. Soc. Psychol. 108 (6), 917 (2015). Wang, L. & Murnighan, J. K. On greed. Acad. Manag. Ann. 5 (1), 279–316 (2011). Bruins, J. J., Liebrand, W. B. & Wilke, H. A. About the saliency of fear and greed in social dilemmas. Eur. J. Social Psychol. 19 (2), 155–161 (1989). Jin, H. & Zhou, X. Y. Greed, leverage, and potential losses: A prospect theory perspective. Math. Finance: Int. J. Math. Stat. Financial Econ. 23 (1), 122–142 (2013). Aimran, W., Setia, A. & Basri, A. ENGAGING STRUCTURAL GREED TODAY: CHRISTIANS AND MUSLIMS IN DIALOGUE. Islamic Sci. ; 12 (1). (2014). Marcus, D. K. & Zeigler-Hill, V. A big tent of dark personality traits. Soc. Pers. Psychol. Compass . 9 (8), 434–446 (2015). Sekhar, S., Uppal, N. & Shukla, A. Dispositional greed and its dark allies: An investigation among prospective managers. Pers. Indiv. Differ. 162 , 110005 (2020). Lambie, G. W. & Haugen, J. S. Understanding greed as a unified construct. Pers. Indiv. Differ. 141 , 31–39 (2019). Lambie, G. W., Stickl Haugen, J. & Tabet, S. M. Development and initial validation of the multidimensional dispositional greed assessment (MDGA) with adults. Cogent Psychol. 9 (1), 2019654 (2022). Zeelenberg, M. & Breugelmans, S. M. The good, bad and ugly of dispositional greed. Curr. Opin. Psychol. 46 , 101323 (2022). Mussel, P. & Hewig, J. The life and times of individuals scoring high and low on dispositional greed. J. Res. Pers. 64 , 52–60 (2016). Mussel, P., Reiter, A. M., Osinsky, R. & Hewig, J. State-and trait-greed, its impact on risky decision-making and underlying neural mechanisms. Soc. Neurosci. 10 (2), 126–134 (2015). Krekels, G. Essays on dispositional greed: The effect of insatiability on consumer behavior (Doctoral dissertation, Ghent University). Mussel, P., Rodrigues, J., Krumm, S. & Hewig, J. The convergent validity of five dispositional greed scales. Pers. Indiv. Differ. 131 , 249–253 (2018). Jones, D. N. & Paulhus, D. L. Introducing the short dark triad (SD3) a brief measure of dark personality traits. Assessment 21 (1), 28–41 (2014). Gibbons, F. X. & Buunk, B. P. Individual differences in social comparison: development of a scale of social comparison orientation. J. Personal. Soc. Psychol. 76 (1), 129 (1999). Chen, B. B. An evolutionary life history approach to understanding greed. Pers. Indiv. Differ. 127 , 74–78 (2018). Jonason, P. K., Icho, A. & Ireland, K. Resources, harshness, and unpredictability: The socioeconomic conditions associated with the Dark Triad traits. Evolutionary Psychol. 14 (1), 1474704915623699 (2016). Seuntjens, T. G., Zeelenberg, M., van de Ven, N. & Breugelmans, S. M. Greedy bastards: Testing the relationship between wanting more and unethical behavior. Pers. Indiv. Differ. 138 , 147–156 (2019). Muris, P., Merckelbach, H., Otgaar, H. & Meijer, E. The malevolent side of human nature: A meta-analysis and critical review of the literature on the dark triad (narcissism, Machiavellianism, and psychopathy). Perspect. Psychol. Sci. 12 (2), 183–204 (2017). Liu, Z., Sun, X. & Tsydypov, L. Scarcity or luxury: Which leads to adolescent greed? Evidence from a large-scale Chinese adolescent sample. J. Adolesc. 77 , 32–40 (2019). Elaad, E. & Gonen-Gal, Y. E. Face-to-face lying: Gender and motivation to deceive. Front. Psychol. 13 , 820923 (2022). Hoyer, K., Zeelenberg, M. & Breugelmans, S. M. Further tests of the scarcity and luxury hypotheses in dispositional greed: Evidence from two large-scale Dutch and American samples. Curr. Psychol. 42 (14), 12045–12054 (2023). Gerstel, N., Sarkisian, N. & Marriage The good, the bad, and the greedy. Contexts 5 (4), 16–21 (2006). Puciato, D., Rozpara, M., Bugdol, M. & Mróz-Gorgoń, B. Socio-economic correlates of quality of life in single and married urban individuals: a Polish case study. Health Qual. Life Outcomes . 20 (1), 58 (2022). Adamczyk, K. & Segrin, C. Perceived social support and mental health among single vs. partnered Polish young adults. Curr. Psychol. 34 (1), 82–96 (2015). Pellegrino, A., Abe, M. & Shannon, R. The dark side of social media: content effects on the relationship between materialism and consumption behaviors. Front. Psychol. 13 , 870614 (2022). Piff, P. K., Stancato, D. M., Côté, S., Mendoza-Denton, R. & Keltner, D. Higher social class predicts increased unethical behavior. Proceedings of the National Academy of Sciences. ;109(11):4086-91. (2012). Balta, S., Emirtekin, E., Kircaburun, K. & Griffiths, M. D. Neuroticism, trait fear of missing out, and phubbing: The mediating role of state fear of missing out and problematic Instagram use. Int. J. Mental Health Addict. 18 (3), 628–639 (2020). Additional Declarations No competing interests reported. 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13:09:00","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140859,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7603666/v1/a7d9eda97ba415bd7bc1a58d.html"},{"id":93403485,"identity":"ff4d8e99-efa8-4e4a-8a31-063fb3746830","added_by":"auto","created_at":"2025-10-13 13:09:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71738,"visible":true,"origin":"","legend":"\u003cp\u003eModel fit indexes of The Multidimensional Dispositional Greed Assessment (MDGA) in total sample (n=609), (A); Model fit indexes of MDGA before modification, (B); Model fit after modification\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7603666/v1/40a5b886afbe1350b8bbd706.png"},{"id":93403477,"identity":"127da858-21e8-48a7-8e9a-897912816b85","added_by":"auto","created_at":"2025-10-13 13:08:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":22272,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 1. Sociodemographic and MDGA\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: MDGA= Multidimensional Dispositional Greed Assessment\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7603666/v1/ef18b0e3716a98780b6f2acf.png"},{"id":93405119,"identity":"98714ff2-2164-400e-963e-610fb2339b1e","added_by":"auto","created_at":"2025-10-13 13:33:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1096898,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7603666/v1/cfcd2244-e1df-45af-9d30-ba63cb9a3622.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychometric Properties of Multidimensional Dispositional Greed Assessment (MDGA); and investigate the role of culture and sociodemographic characteristics on greed","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWhile greed is an age-old concept in society, it has gained significant attention as a prominent topic in contemporary scientific discourse. Particularly following economic crises, scholars have begun to explore and analyze the various dimensions of greed across multiple fields and contexts (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Researchers argue that greed can be perceived in two distinct ways: on one hand, as a positive motivator that drives productivity (such as economic growth), and on the other hand, as a destructive force that hinders progress and damages interpersonal relationships (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). For instance, from an economic perspective, greed can serve as a driving force for individual and societal growth, development, and innovation. However, many people also view greed as a negative trait that can lead to significant personal and social repercussions, including scandals, bankruptcy, fraud, and harm to others (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Moreover, excessive greed may be linked to an unfulfilled desire to satisfy one’s emotional and social needs (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Research in psychology has historically examined greed primarily as a situational emotion (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). More recently, scholars have shifted their focus to the dispositional aspects of greed as a stable trait or motivation (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). While specific situations can trigger greed, individuals exhibit variability in how greed influences their motivation (motivational trait; 2). Given the emphasis on dispositional greed, there is a need for further exploration of its development and manifestation as a stable trait, distinct from the investigation of situational triggers. Despite the growing interest in dispositional greed, a consistent definition among researchers remains elusive, partly due to the complexity of the construct itself (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGreed is often regarded as a negative personality trait. From various religious viewpoints (such as Christianity and Islam), greed is seen as a vice and is associated with sinful behaviors (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Christian texts refer to greed as the “love of money,” which is considered the “root of all evil” (1 Timothy 6:10). Psychology researchers suggest that greed should be recognized as a fourth dimension of the current dark triad, which includes narcissism, machiavellianism, and psychopathy (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The levels of individuals’ dispositional greed have been positively correlated with their levels of narcissism (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Dispositional greed is conceptually linked to constructs such as envy, self-interest, miserliness, and materialism; however, it is distinct from these constructs in terms of motivation, focus, and level of desire (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn response to the lack of definitional clarity, Lambie and Stickl Haugen (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) conducted a comprehensive review of the existing literature and proposed a practical definition of dispositional greed. This definition encompasses the desire to acquire more than what one possesses, the urge to retain what one has at all costs, and a persistent sense of dissatisfaction. Additionally, it includes individuals’ desires for anything they find valuable, which may extend beyond monetary or materialistic interests. Overall, the definition of greed consists of several dimensions, including (a) the desire for anything of value (including both material and non-material goods); (b) acquisition motivations (i.e., the desire to obtain more); (c) retention motivations (the desire to hold onto what one has); (d) insatiability; and (e) disregard for the costs associated with fulfilling one’s desires. Furthermore, Lambie and et al (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) developed a tool that provides a complex and multidimensional assessment of greed to examine its manifestations in individuals.\u003c/p\u003e\u003cp\u003eAlthough there were multiple tools for measuring dispositional greed before the multidimensional dispositional greed assessment (MDGA) developed by Lambie et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), these tools have slight differences in their focus and conceptual framework. As Zeelenberg et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) pointed out, while existing scales agree on the fundamental characteristics of greed, they differ in their conceptual backgrounds. For instance, three out of five scales incorporate the idea that greed may come at the expense of others or involve manipulating others (GR€€D Scale, 13; Greed Trait Measure; 14), while other scales do not include this concept. Moreover, the existing scales do not provide a comprehensive concept of greed. Specifically, greed may encompass the following components: (a) an excessive desire to acquire more material possessions and resources, (b) an excessive desire for non-material things, (c) a disregard for the costs associated with obtaining one’s desires, (d) insatiability, (e) acquisition motivations (for example, the desire to acquire more), and (f) retention motivations (for example, the desire to hold on to what one already has; see Lambie \u0026amp; Stickl Haugen, 10). However, none of the existing scales encompass all the aspects of greed mentioned above. Prior to the MDGA, most greed assessments were developed as unidimensional, one-factor scales that focused on specific aspects of greed (GR€€D Scale, 13; Greed Trait Measure; Mussel et al., 14), thereby providing a limited definition. Another limitation of the scales prior to the MDGA is the absence of a measure for the retention component. Researchers have posited that retention may be a part of greed (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), and there is preliminary empirical evidence suggesting that retention may indeed be an aspect of greed (see Krekels, 15). Nevertheless, the existing greed scales primarily focus on the acquisition of desired goods and do not include items that measure retention (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConsidering the limitations of the greed measurement scales prior to the MDGA, as well as the absence of a comprehensive tool for assessing greed in both Iran and Azerbaijan, we decided to evaluate the psychometric properties of the Multidimensional Dispositional Greed Assessment (MDGA) questionnaire within these two cultures. MDGA is recognized as the first multidimensional scale that includes more than one factor based on a broader definition of greed (such as considering retention motivations). The normalization of this scale, which can comprehensively measure different levels of greed, may help us better understand individual differences in this area. This understanding could, in turn, lay the groundwork for applied counseling research that examines the factors influencing the development of greed in individuals.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo norm and conduct a confirmatory factor analysis of the MDGA, a link to the MDGA questionnaire was distributed to a sample of adults in Iran and Azerbaijan. A total of 609 non-clinical participants, including 353 Iranians (58.2%) and 254 Azerbaijanis (41.8%), completed the questionnaire in its entirety. The minimum criteria for participation in this study were as follows: (a) a minimum age of 18 years and (b) the ability to read and write. The majority of participants were female, with 439 females (72.3%) and 168 males (27.7%). Table 1 presents the complete demographic characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003e2.3. Procedure\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe design of this study followed the guidelines and regulations of the Declaration of Helsinki and was approved by the Ethics Committee of Zanjan University (IR.ZNU.REC.1404.004).\u0026nbsp;The recruitment and data collection process were conducted online through Google Forms. The questionnaires designed in Google Forms included the following: (1) an overview of the research and an informed consent form, (2) a general demographic questionnaire (including questions related to ethnicity and gender), (3) the 20-item MDGA questionnaire, (4) the GR\u0026euro;\u0026euro;D scale, (5) the Short Dark Triad (SD3) scale, and (6) The Iowa\u0026ndash;Netherlands Comparison Orientation Measure (INCOM).\u0026nbsp;All participants indicated informed consent to participate in the research.\u003c/p\u003e\n\u003cp dir=\"\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eMeasures\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic questionnaire\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;A demographic questionnaire was included in the survey to gather information about participants\u0026rsquo; characteristics, including age, gender, marital status, and more.\u003c/p\u003e\n\u003cp dir=\"\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eMultidimensional Dispositional Greed Assessment (MDGA)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MDGA (11) is a 20-item instrument designed to assess individuals\u0026rsquo; levels of multidimensional dispositional greed across three distinct domains: (a) Insatiable Pursuit for More at Any Cost (9 items), (b) Desire for More (7 items), and (c) Retention Motivation (4 items). The MDGA items are formulated as statements such as \u0026ldquo;I will get what I want at all costs, even if I have to lie.\u0026rdquo; For each item, participants indicated their level of agreement or disagreement with each statement on a 5-point Likert scale, ranging from 1 (\u0026ldquo;Strongly Disagree\u0026rdquo;) to 5 (\u0026ldquo;Strongly Agree\u0026rdquo;). Lambie et al. (11) demonstrated adequate internal consistency, with reliability coefficients ranging from (\u0026alpha; = .943 to .956).\u003c/p\u003e\n\u003cp dir=\"\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eGR\u0026euro;\u0026euro;D scale\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GR\u0026euro;\u0026euro;D scale (13) is a self-report instrument designed to measure levels of dispositional greed in individuals and is unidimensional in nature. This scale consists of 12 items related to participants\u0026rsquo; personal attitudes and behaviors, such as \u0026ldquo;My primary goal is to earn a lot of money.\u0026rdquo; Each item features a seven-point response scale ranging from 1 (does not apply at all) to 7 (fully applies). The overall scores of this scale exhibited a Cronbach\u0026rsquo;s alpha of .89, indicating evidence of construct and criterion-related validity (14, 16). In Lambie et al., (11) study, the GR\u0026euro;\u0026euro;D scale scores recorded an acceptable Cronbach\u0026rsquo;s alpha of .948. The GR\u0026euro;\u0026euro;D scale scores had an acceptable Cronbach\u0026rsquo;s alpha of .845 in our study.\u003c/p\u003e\n\u003cp dir=\"\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eShort Dark Triad (SD3)\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SD3 (17) has been specifically designed to measure the three dimensions of the dark triad (Machiavellianism, narcissism, and psychopathy). The scale consists of 27 items (9 for each dimension), for which participants must indicate their level of agreement using five response options rated on a Likert-type scale, ranging from \u0026ldquo;totally disagree\u0026rdquo; to \u0026ldquo;totally agree.\u0026rdquo; These items include statements indicative of psychopathic traits (e.g., \u0026ldquo;People who mess with me always regret it\u0026rdquo;), narcissistic traits (e.g., \u0026ldquo;People see me as a natural leader\u0026rdquo;), and Machiavellian traits (e.g., \u0026ldquo;It is not wise to tell your secrets\u0026rdquo;). Out of the 27 items, 5 statements were phrased in the opposite direction (items 11, 15, 17, 20, and 25) and needed to be recoded to obtain the final score. Higher scores on the scale indicate higher levels of dark triad traits. In the current study, the SD3 scores had an acceptable Cronbach\u0026rsquo;s alpha (Machiavellianism (.662), Narcissism (.654) and psychopathy (.647).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Iowa\u0026ndash;Netherlands Comparison Orientation Measure (\u003c/strong\u003e\u003cstrong\u003eINCOM\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe core instrument of the INCOM scale (18) consists of 11 items designed to measure individuals\u0026rsquo; tendency to make social comparisons. This scale includes statements such as: \u0026ldquo;I always want to know what others in a similar situation would do.\u0026rdquo; The response options in this scale are rated from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicate a greater tendency to engage in social comparisons. The scale has shown Cronbach\u0026rsquo;s alpha values ranging from .78 to .85 in 10 American samples and from .78 to .84 in 12 Dutch samples. The construct validity and criterion-related validity of this scale have been well documented and confirmed through multiple tests.\u0026nbsp;The INCOM scale scores had an acceptable Cronbach\u0026rsquo;s alpha of .810 in our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research consisted of several distinct stages. In the first stage, the normality of the variables was tested using the Kolmogorov-Smirnov test and Skew-ness and Kurtosis. The Kolmogorov-Smirnov test demonstrated that the variables followed a normal distribution (P \u0026gt; 0.05), and the Skew-ness and Kurtosis Values calculated were within \u0026plusmn;3, indicating normality of the data (see Table 1). The Multidimensional Dispositional Greed Assessment (MDGA) was analyzed in the second stage by descriptive statistics, such as mean, standard deviation (SD), factor loading, and range. In the third step, Confirmatory Factor Analysis (CFA) was conducted to investigate the factor structure of the MDGA scale. To estimate model fitness indexes, Maximum Likelihood was used and Fitness indexes with a 90% confidence interval were assessed, including the Root Mean Square Error of Approximation (RMSEA); Comparative Fit Index (CFI), Normed Fit Index (NFI), Non-Normed Fit Index (NNFI), Parsimony Normed Fit Index (PNFI), Incremental Fit Index (IFI), Relative Fit Index (RFI), \u0026nbsp;Adjusted Goodness of Fit Index (AGFI), Goodness of Fit Index (GFI), Root Mean Square Residual (RMR), and Standardized Root Mean Square Residual (SRMR). In the fourth step, Internal consistency reliability of MDGA was investigated using data from the main study and based on Cronbach\u0026apos;s alpha, McDonald\u0026rsquo;s omega, and Guttman\u0026rsquo;s lambda coefficient test. And Finally, Pearson correlations were calculated between the MDGA and the GR\u0026euro;\u0026euro;D, Short Dark Triad (SD3), and The Iowa\u0026ndash;Netherlands Comparison Orientation Measure (INCOM) in order to evaluate validity. The data analysis was performed using statistical software, including SPSS-26 and LISREL-8.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Data analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a cross-sectional study carried out on a convenience sampling method, and 609 non-clinical Iranian (n=353, 58.2%) and Azerbaijanian (n=254, 41.8%) people participated in the present study. Of these, 2 participants were omitted because of incomplete data, so the final sample was 607 participants. The subjects comprised 439 females (72.3%) and 168 males (27.7%), Table 1 shows the detailed sociodemographic characteristics of the participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 Participant sociodemographic characteristics (n=607)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (\u003cem\u003e%\u003c/em\u003e) or (Mean \u0026plusmn; \u003cem\u003eSD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eNationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eIran\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e353(58.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eAzerbaijan\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e254(41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003efemale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e439 (72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003emale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e168 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003esingle\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e322 (53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003emarried\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e275 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003edivorce\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e10 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e30.60 (9.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eTime online (hours/days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e5.95 (4.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eEducational Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eunder DIPLOMA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e9 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eDIPLOMA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e70 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eBachelor\u0026rsquo;s degree\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e239(39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eMaster\u0026rsquo;s degree\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e24340.0()\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003ePHD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e46(7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eTime on social Media (hours/days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e3.95 (1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eNumber of followers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e675.01 (2164.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003eMost used social media platforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFacebook\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e20 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eInstagram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e309 (50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eTelegram\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e95 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eWhatsApp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e96 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eYouTube\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e54 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eTwitter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e33 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003efinancial situation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eweak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e121(19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e452(74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003egood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e34(5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003efinancial satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e194(32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 182px;\"\u003e\n \u003cp\u003e413(68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"3. Result","content":"\u003cp\u003e\u003cstrong\u003eFactor structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFactor loadings for all items were statistically significant (P\u0026lt;0.001). Findings demonstrated the standardized estimates for all items of TIAS were over 0.50, except item 18; the factor loadings of items 18 were 0.39 (Table 2). Investigating the fitness of the present model demonstrated that the model has a good fit with the data, and findings support the three-factor model (Table 2 and Figure 1)\u003cspan dir=\"RTL\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e\u0026nbsp; descriptive statistic indices for the items of the Multidimensional Dispositional Greed Assessment (MDGA)\u003c/p\u003e\n\u003ctable dir=\"rtl\" border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eItem-Total statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 153px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eItems statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eComponents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eKurtosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eSkewness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eC.D.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eI.T.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eF. L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e5.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e144.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eInsatiable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-1.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e142.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eDesire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-1.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e139.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e140.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eInsatiable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-1.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e141.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eDesire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-1.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e138.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e139.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eInsatiable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e139.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eDesire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-1.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e137.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e140.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eInsatiable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e137.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eDesire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-1.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e137.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e138.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eInsatiable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e139.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eDesire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e143.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eInsatiable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e139.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eDesire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e140.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eInsatiable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-1.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e147.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eDesire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e141.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eInsatiable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e142.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003eInsatiable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eItem20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e2.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e6.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e16.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eInsatiable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e5.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e26.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eDesire\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e4.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e12.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp dir=\"LTR\"\u003e1.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp dir=\"LTR\"\u003e12.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp dir=\"LTR\"\u003e55.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp dir=\"LTR\"\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp dir=\"LTR\"\u003eMDGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u0026nbsp;\u003cstrong\u003eV\u003c/strong\u003e = scale variance if item deleted, \u003cstrong\u003eF.L.\u0026nbsp;\u003c/strong\u003e= factor loading, \u003cstrong\u003eI.T.\u003c/strong\u003e = corrected item-total correlations, \u003cstrong\u003eC.D.\u003c/strong\u003e = Cronbach\u0026rsquo;s alpha if item deleted, \u003cstrong\u003eMDGA\u0026nbsp;\u003c/strong\u003e= Multidimensional Dispositional Greed Assessment\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc315851865\"\u003eTable 2 shows descriptive statistics of the twenty MDGA -related items. The mean score obtained was 2.35 (SD = 1.11), and all 20 items exhibited means in the 0.711\u0026ndash;1.37 range. All the corrected item-total correlations surpassed 0.40 (see details in Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"\"\u003e\u003cstrong\u003e\u003cspan dir=\"LTR\"\u003eTable 3. \u003c/span\u003e\u003c/strong\u003e\u003cspan dir=\"LTR\"\u003eConfirmatory Factor Analysis (CFA) and Fit indexes\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eGoodness of Fit index\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ethree-factor BM\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ethree-factor AM\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ethree-factor Iran\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ethree-factor Azerbaijan\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eRecommended Value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eDecision\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eRMSEA (CI 90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e.078\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e(.072-.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e.051 (.045 -.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e.081 (.073-.088\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e.078 (.068-.087)\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e\u0026ge; 0.08\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eGood Fit\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003csub\u003esb\u003c/sub\u003eX\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e780.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e426.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e549.09\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e420.90\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e-\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.90\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.91\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e\u0026le; 0.90\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eExcellent Fit\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.86\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.86\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e\u0026le; 0.90\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eExcellent Fit\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eNNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.86\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.86\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e\u0026le; 0.90\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eExcellent Fit\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003ePNFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.76\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.76\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e\u0026le; 0.50\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eExcellent Fit\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eIFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.90\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.91\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e\u0026le; 0.90\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eExcellent Fit\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eRFI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.93\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.96\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.84\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.85\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e\u0026le; 0.90\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eExcellent Fit\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eAGFI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.86\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.92\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.83\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.83\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e\u0026le; 0.80\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eExcellent Fit\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eGFI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.89\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.93\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.86\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.87\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e\u0026le; 0.90\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eGood Fit\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eRMR\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.067\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.061\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.060\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.095\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e\u0026ge; 0.08\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eGood Fit\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eSRMR\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.056\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.048\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.048\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e0.079\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003e\u0026ge; 0.08\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cem\u003e\u003cspan dir=\"LTR\"\u003eExcellent Fit\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eLegend:\u003c/em\u003e RMSEA: Root Mean Square Error of Approximation; CFI: Comparative Fit Index; NFI: Normed Fit Index; NNFI: Non-Normed Fit Index; PNFI: Parsimony Normed Fit Index; IFI: Incremental Fit Index; RFI: Relative Fit Index; AGFI: Adjusted Goodness of Fit Index; GFI: Goodness of Fit Index; RMR: Root Mean Square Residual; SRMR: Standardized Root Mean Square Residual: (BM); Model fit indexes of MDGA before modification, (AM); Model fit after modification\u003c/p\u003e\n\u003cp\u003eBased on the literature, three-factor model was exanimated through Confirmatory factor analysis (CFA), model provided marginal fit to data.\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThe Confirmatory Factor Analysis (CFA) and Fitness indexes results for a three-dimension structure are illustrated in Table 3. Confirmatory factor analysis displayed that three-factor structure provided a good fit to the data: \u003csub\u003esb\u003c/sub\u003eX\u003csup\u003e2\u003c/sup\u003e = 426.18 (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01); SRMR= 0.048; RMR= 0.061, GFI= 0.93, AGFI= 0.92, \u003cem\u003eRFI=0.96, IFI=0.98, PNFI=0.83, NNFI=0.97, NFI=0.97; CFI= 0.98;\u003c/em\u003e RMSEA =0.051. These results demonstrated all standardized factor loadings for all items were statistically significant (p\u0026lt;0.01), that supporting each item as adequately each component (Table 3). As shown in Tables 2 and Figure 1, all items of loads show a significant factor, and standardized factor loading for all items over 0.50 except item 18.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1 Internal consistency reliability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInternal consistency reliability was investigated using data from the main study and based on the Cronbach\u0026apos;s alpha, McDonald\u0026rsquo;s omega, and Guttman\u0026rsquo;s lambda coefficient test, in which Cronbach\u0026rsquo;s alpha, McDonald\u0026rsquo;s omega, and Guttman\u0026rsquo;s lambda coefficient for Multidimensional Dispositional Greed Assessment (MDGA) was measured 0.88, 0.88 and 0.78 that indicate excellent internal reliability.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eInternal consistency reliability of the Multidimensional Dispositional Greed Assessment (MDGA)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026omega;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lambda;6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eInsatiable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDesire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eThe Multidimensional Dispositional Greed Assessment (MDGA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: \u0026alpha;= Cronbach\u0026rsquo;s alpha; \u0026omega;= McDonald\u0026rsquo;s omega; \u0026lambda;6= Guttman\u0026rsquo;s lambda-6\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Convergent, divergent, and discriminant validity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used indexes, including Average variance Extracted (AVE), Maximum shared Squared Variance (MSV), and Average shared Squared Variance (ASV) for investigation validity of MDGA. \u0026nbsp;As shown in Table 5, for all components, CR was higher than 0.50, which indicates acceptable construct reliability. In all components, CR was higher than AVE and AVE \u0026gt; 0.50 for all factors except desire (AVE=0.444), that indicates acceptable convergent validity. Also, findings demonstrated AVE were higher than MSV and ASV in all components, which illustrates that the discriminant validity of the Multidimensional Dispositional Greed Assessment (MDGA) was acceptable. the person correlations acquired between the MDGA with GR\u0026euro;\u0026euro;D scale, INCOM and Short Dark Triad (SD3) indicate good convergent validity (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 5. Descriptive statistics, Validity and correlations between GR\u0026euro;\u0026euro;D scale, INCOM and Short Dark Triad (SD3) with the MDGA\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eMSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eASV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eGR\u0026euro;\u0026euro;D scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eINCOM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eMachiavellianism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eNarcissism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePsychopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eInsatiable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e.306**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.301**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.193**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e.112**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e.328**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eDesire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e.617**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.323**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.162**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e.199**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e.107**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eRetention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e.134**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.341**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.184**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e.166**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eMDGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e.504**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.439**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e.248**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e.183**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e.289**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e49.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e35.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e27.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e27.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e21.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: M=Man; SD=Standard deviation; CR=Construct Reliability; AVE= Average variance Extracted; MSV= Maximum shared Squared Variance; ASV= Average shared Squared Variance; N= 607; all \u003cem\u003eP\u003c/em\u003e values are significant at the 0.01 level\u003c/p\u003e\n\u003cp\u003eThe findings of Table 5 show the relationship among MDGA with other psychological variables in non-clinical population, which there was significant positive relationship between the total score of MDGA with GR\u0026euro;\u0026euro;D scale (r=0.50, P\u0026lt;0.01), INCOM (r=0.43, P\u0026lt;0.01), Machiavellianism (r=0.24, P\u0026lt;0.01), Narcissism (r=0.183, P\u0026lt;0.01) and Psychopathy (r=0.289, P\u0026lt;0.01). in other word, Findings demonstrated good convergent validity for\u0026nbsp;the Multidimensional Dispositional Greed Assessment (MDGA)\u0026nbsp;(see more details in Table 5).\u003c/p\u003e\n\u003cp\u003eWe investigate relationship sociodemographic characteristics with greed, MANOVA and independent t-test was conducted to compare Iranian and Azerbaijan participants on subscales and total mean score of MDGA. finding of MANOVA showed significant difference between Iranian and Azerbaijan [F (1,605) = 20.63, P = 0.001, partial \u0026eta;2=0.033], and mean score of insatiable in Iranian participants (M=17.76, SD=6.10) was significantly higher than Azerbaijan (M=15.28, SD=7.33), but in mean score of retention [F (1,605) = 3.79, P = 0.026, partial \u0026eta;2=0.006] Iranian participants (M=11.83, SD=4.26) lower than Azerbaijan(M=12.55, SD=4.83), in subscale of desire retention [F (1,605) = 0.004, P = 0.95, partial \u0026eta;2=0.001] \u0026nbsp;there was no significant difference between Iranian (M=26.40, SD=5.42) and Azerbaijan (M=26.43, SD=6.36) participants. Independent t-test for comparing Iranian (M=56.011, SD=11.43) and Azerbaijan (M=54.27, SD=13.67) participants on total score MDGA indicated no significant difference [t(605) =1.69,P=0.091] between two groups. A One-way ANOVA was conducted to compare the effect of age on the total score of MDGA. An analysis of variance showed that the effect of age on MDGA was significant, [F (5,601) = 6.63, P = 0.001, partial \u0026eta;2=0.052]. post hoc comparisons using Bonferroni test indicated that the mean score for under 24 years old (M=57.44, SD=11.09) was significantly higher than mean score for 40-49 years (M=50.72, SD=12.89) and over 50 years (M=47.20, SD=14.35) age groups. mean score for over 50 age group was significantly lower than mean score of 25-29 (M=55.91, SD=13.25), 30-34 (M=56.12, SD=10.88), and 35-39 years age groups (M=55.05, SD=13.18) see graph 2. For comparing marital state on the total score of MDGA, finding ANOVA showed significant effect of marital status on greed score [F (2,604) = 21.43, P = 0.001, partial \u0026eta;2=0.066]. post hoc comparisons indicated that the mean score for single participants (58.14, SD=11.68) was significantly higher than mean score for married people (M=51.77, SD=12.47). however, there was not significant difference between single and divorce participant (M=59.28,11,39) on the total score of greed. Finding of ANOVA demonstrated no significant effect of educational status on greed score [F (4,602) = 1.97, P = 0.098, partial \u0026eta;2=0.013]. however, the mean score of greed in higher educated groups include a (M=53.69, SD=14.40) and master\u0026rsquo;s degree (M=53.82, SD=12.91) years old (M=57.44, SD=11.09) was significantly higher than slightly higher than bachelor\u0026rsquo;s degree (M=56.62, SD=), Diploma (M=56.60, SD=11.70) and under diploma participants (M=57.11, SD=7.57). finding of ANOVA showed significant difference among socioeconomical classes on total greed score [F (2,604) = 5.45, P = 0.005, partial \u0026eta;2=0.018]. post hoc comparisons indicated that the mean score of greed for high socioeconomic participants (54.35, SD=14.01) was significantly lower than low socioeconomic participants (M=58.60, SD=12.59) than mean score for married people (M=51.77, SD=12.47), and there was not significant difference between socioeconomic middle class participants (M=54.46, 12.15) and high socioeconomic on the total score of greed. Finding of ANOVA showed significant difference among the type of social media used on greed score [F (5,601) = 2.84, P = 0.015, partial \u0026eta;2=0.023]. post hoc comparisons indicated that the mean score of greed for Instagram users (56.85, SD=11.70) was significantly higher than WhatsApp users (M=52.36, SD=11.20). there was no significant difference between other types social media users on total score of greed. Finally, independent t test was conducted to compare male and female on the mean score of MDGA. Finding demonstrated that there was significant difference between male and female score [t (605) = 2.97, P = 0.003], and mean score of greed in men (n=168, M=57.69, SD=13.85) was significantly higher than mean score in women (n=439, M=54.36, SD=11.74).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe conducted a study to investigate the psychometric properties, factor structure, and evidence of validity of MDGA scores among samples of adults from two countries, Iran and Azerbaijan. The results of the confirmatory factor analysis (CFA) indicated that the three-factor structure provided a good fit to the data (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01). These findings demonstrated that all standardized factor loadings for each item were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01), supporting the adequacy of each item in representing its respective component. These results are consistent with the findings from the CFA study by Lambie et al. (2022), which was conducted on a validating sample of adults in the United States.\u0026nbsp;The Cronbach\u0026rsquo;s alpha values for the three factors Insatiable (0.873), Desire (0.875), and Retention (0.924) were obtained that indicated excellent internal validity of MDGA\u003c/p\u003e\n\u003cp\u003eOur results also provided evidence for the concurrent validity of MDGA scores. The findings indicated a strong positive relationship between the total MDGA scores and the GR\u0026euro;\u0026euro;D scale scores (13), which supports the concurrent validity of the MDGA. These results are consistent with previous research and provide further evidence for the concurrent validity of MDGA scores (11). Additionally, we examined the relationship between the individual MDGA subscale scores and the GR\u0026euro;\u0026euro;D scores. Our findings revealed strong positive correlations between the GR\u0026euro;\u0026euro;D scores and three MDGA subscales. This strong relationship aligns with the multidimensional definition of greed proposed by Lambie and Stickl Haugen (10). Furthermore, the aspect of retention motivation measured within the MDGA is unique. The MDGA is recognized as the only known greed scale that includes an individual retention motivation component, highlighting the need for further research to explore the retention aspect of dispositional greed and its relationship to the overall construct.\u003c/p\u003e\n\u003cp\u003eThere was also a significant positive correlation between MDGA and three subscales related to the dark triad (Machiavellianism, narcissism, and psychopathy) as well as social comparisons. There are conceptual similarities between greed and the current dark triad. Firstly, both phenomena have similar developmental origins linked to childhood uncertainty. For example, Chen (19), using an evolutionary life history approach, noted that childhood unpredictability may lead to the emergence of greed. Additionally, Jonason et al (20) have examined the connections between childhood unpredictability and dark traits in their research. Secondly, greed and the traits of the dark triad share many central characteristics. For instance, both are associated with conspicuous consumption (21), diminished self-control, and a tendency towards unethical behavior (22), such that greed shows a relationship with psychopathy across various games (13). Therefore, these traits should be examined as a dark trait.\u003c/p\u003e\n\u003cp\u003eSome distinctive features make the MDGA unique. This three-factor model presents greed as a multidimensional construct, whereas previous research has primarily identified a unidimensional structure. The MDGA contributes to the literature by offering a multidimensional assessment of greed. Lambie and Stickl Haugen (10) proposed a multidimensional definition of greed that includes six characteristics: (1) excessive desire for material things; (2) excessive desire for non-material things; (3) disregard for the costs associated with obtaining one\u0026rsquo;s desires; (4) insatiability; (5) acquisition motivation; and (6) retention motivation. Although the MDGA reveals only three factors, its items combine multiple theoretical dimensions and support the conceptual foundations of a multidimensional definition of greed. The MDGA is notable for incorporating the motivation for retention (the desire to hold onto what one possesses). While research findings in this area are varied, initial evidence suggests that retention may be a component of greedy behavior (15). This aspect has rarely been explored in the existing literature, highlighting the need for further empirical research on the retention dimension of greed (10,2). The MDGA serves as a suitable tool for conducting this additional research.\u003c/p\u003e\n\u003cp\u003ePsychologists and mental health professionals can utilize the MDGA to explore the concept of greed and enhance their understanding of clients. By considering greed as an inherent motivation, the MDGA provides a tool for assessing motivations and behaviors related to greed. For instance, MDGA Factor 1, titled \u0026ldquo;Insatiable Pursuit for More at All Costs,\u0026rdquo; may indicate individuals\u0026rsquo; disregard for the needs of others and highlight social and relational issues. Greed can lead to serious consequences, such as financial ruin and damage to interpersonal relationships (3), which may contribute to clients\u0026rsquo; concerns. Therefore, evaluating levels of greed with high MDGA scores (i.e., \u0026lt;80) can be effective in setting appropriate treatment goals. Additionally, the MDGA can be used to examine the relationship between childhood experiences and the emergence of greed. For example, Liu et al. (23) found that higher socioeconomic status in childhood was associated with greed in adolescence, while Chen (19) explored the connection between childhood unpredictability and levels of greed in adulthood, indicating that attachment may mediate this relationship. Thus, psychologists can adopt a developmental approach to assess and explore greed.\u003c/p\u003e\n\u003cp\u003eIn the present study, the results of the demographic variables indicated that there are differences between the two countries in two subcomponents of greed. Specifically, the Iranian population exhibited higher levels of Insatiable greed, while the Azerbaijani population demonstrated higher levels of Retention. These findings necessitate an examination of cultural differences and their impact on the extent and type of greed exhibited by individuals. It appears that in certain cultures, one subtype of greed may be more predominant, which could contribute to a better understanding of human behaviors related to greed. In terms of gender differences, the results also showed that men scored higher than women in the Insatiable component and the total score, and this difference is significant. In studies that have examined greed as a personality trait, it has been observed that men generally score higher than women (23). Additionally, research indicates that men are more likely than women to engage in unethical behavior, which may be related to greed (24). In terms of differences in greed across various age groups, the results indicated that individuals under 24 years of age and those aged 25 to 35 exhibited higher levels of greed compared to participants over 50 years old. In other words, younger participants displayed greater levels of greed relative to their older counterparts. This finding is consistent with the research conducted by Hoyer et al. (25), which demonstrated that greed was negatively correlated with age. Additionally, in terms of marital status, the results indicated that single individuals exhibited higher levels of greed compared to married individuals. The claim that single individuals exhibit higher levels of greed compared to married individuals is a complex issue. Some studies suggest that marriage may lead to a focus on family and a decrease in social connections. Reasons for the higher greed of single individuals include: (1) Focus on individual wealth: Greater attention to career advancement and financial security (26). (2) Fewer social obligations: Lack of family responsibilities that allows for a focus on personal gain (27). (3) Potential for social isolation: Feelings of isolation that can lead to greater reliance on financial resources (28). No differences in greed were observed in terms of educational level. Individuals who spent more time online showed higher levels of greed compared to those who spent less time online. \u0026nbsp;Research indicates that increased time spent online is associated with higher levels of materialism and potentially greed. One study has shown that individuals who spend more time on social media exhibit greater materialism compared to those who read newspapers. These findings suggest that online environments may enhance attitudes or behaviors related to materialism, which could be linked to greed (29). In terms of economic status, individuals with a poor economic situation exhibited higher levels of greed compared to those with a moderate financial status. Research on the relationship between economic status and greed shows mixed results. Some studies suggest that individuals with lower socioeconomic status may exhibit more greed, while other research indicates that those with higher economic status may be more prone to unethical behaviors. Overall, the impact of economic status on greed may vary depending on the circumstances (30). Ultimately, among social media users, Instagram users exhibited higher levels of greed compared to WhatsApp users. While some studies indicate that Instagram users may exhibit certain personality traits more than WhatsApp users or show a greater tendency toward specific behaviors, there is no direct connection between the use of this platform and a general trait like \u0026ldquo;greed.\u0026rdquo; \u0026nbsp;However, high levels of \u0026ldquo;neuroticism\u0026rdquo; and low levels of \u0026ldquo;agreeableness\u0026rdquo; have been identified as predictors of excessive Instagram use (31), which may indirectly relate to greed.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAlthough the present study offers evidence for the validity of MDGA scores, it also has significant limitations. Examining further evidence of test\u0026ndash;retest reliability and concurrent validity with broader national samples could enhance the robustness of the findings. Moreover, there are limitations associated with self-report measures, especially when assessing negatively perceived traits like greed. Future researchers may explore levels of dispositional greed among participants through observational methods, such as examining the stability of greed-related behaviors across different conditions and utilizing a behavioral coding system for more accurate analysis. Overall, our objective was to examine the factor structure of MDGA scores in adult samples from Iran and Azerbaijan. The results indicated that the three-factor structure fits well with the data and aligns with the CFA study conducted by Lambie et al. (11) in a validated sample of American adults. The findings also provided additional evidence for the concurrent validity of MDGA scores through positive correlations with the GR\u0026euro;\u0026euro;D scale scores (13). Consequently, clinicians and researchers should consider these results and the utility of the MDGA in assessing individuals\u0026rsquo; levels of multidimensional dispositional greed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJA, Conceptualization, supervision, editing and review, writingMK, Data gathering, data analysis, visualizationNG, Methodology, data analysis, visualization\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHelzer, E. G. \u0026amp; Rosenzweig, E. 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Mental Health Addict.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (3), 628\u0026ndash;639 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dispositional Greed., Psychometric properties., Confirmatory Factor Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7603666/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7603666/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis article analyzes the psychometric properties of the Multidimensional Dispositional Greed Assessment (MDGA), which is designed to measure dispositional greed in adults. Dispositional greed is recognized as a key personality trait in social psychology and can have profound effects on individuals\u0026rsquo; behaviors and decision-making processes. In this study, we examined the factor structure of the 20-item MDGA scores using Confirmatory Factor Analysis (CFA). This analysis was conducted on a sample of adults from two countries, Iran and Azerbaijan, comprising 607 individuals. The aim of this research was to assess the validity and reliability of this tool in measuring dispositional greed across different cultures. The results of the confirmatory factor analysis indicated that a three-factor structure fits the data well and accounts for a significant portion of the variance in the scores (sbX2\u0026thinsp;=\u0026thinsp;426.18 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01); SRMR\u0026thinsp;=\u0026thinsp;0.048; RMR\u0026thinsp;=\u0026thinsp;0.061, GFI\u0026thinsp;=\u0026thinsp;0.93, AGFI\u0026thinsp;=\u0026thinsp;0.92, RFI\u0026thinsp;=\u0026thinsp;0.96, IFI\u0026thinsp;=\u0026thinsp;0.98, PNFI\u0026thinsp;=\u0026thinsp;0.83, NNFI\u0026thinsp;=\u0026thinsp;0.97, NFI\u0026thinsp;=\u0026thinsp;0.97; CFI\u0026thinsp;=\u0026thinsp;0.98; RMSEA\u0026thinsp;=\u0026thinsp;0.051). The Cronbach\u0026rsquo;s alpha values for the three factors Insatiable (0.873), Desire (0.875), and Retention (0.924) were obtained. Significant differences in the levels of greed were also observed among the cultural and sociodemographic variables. These findings help us gain a better understanding of the various dimensions of dispositional greed and suggest that MDGA can serve as an effective self-report tool for researchers in this field. Additionally, we emphasize the importance of this tool for future research and provide recommendations for various research domains. In particular, examining the cultural and social influences on dispositional greed, as well as its relationship with economic and social behaviors, can be vital topics that warrant further investigation. Ultimately, this study can contribute to the development of theories and models related to dispositional greed and enhance our understanding of this personality trait across different societies.\u003c/p\u003e","manuscriptTitle":"Psychometric Properties of Multidimensional Dispositional Greed Assessment (MDGA); and investigate the role of culture and sociodemographic characteristics on greed","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-13 13:08:54","doi":"10.21203/rs.3.rs-7603666/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":"fb44e427-b292-496b-b7d7-af1fbf2fbdbd","owner":[],"postedDate":"October 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56159064,"name":"Health sciences/Health care"},{"id":56159065,"name":"Biological sciences/Psychology"},{"id":56159066,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-10-13T13:08:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-13 13:08:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7603666","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7603666","identity":"rs-7603666","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}