Psychometric Properties and Standardization of the Shortened Latvian Personality Inventory (LPI-v3s) in Athlete Sample: Implications for Evidence-Based Assessment

Psychometric Properties and Standardization of the Shortened Latvian Personality Inventory (LPI-v3s) in Athlete Sample: Implications for Evidence-Based Assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Psychometric Properties and Standardization of the Shortened Latvian Personality Inventory (LPI-v3s) in Athlete Sample: Implications for Evidence-Based Assessment Katrina Volgemute, Viktorija Perepjolkina, Gundega Ulme, Agita Abele, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7957250/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Personality traits play an important role in athletic performance, yet existing sport-specific measures often lack psychometric refinement and predictive validation. This research comprised two interlinked studies aimed to create and validate a short version of the Latvian Personality Inventory (LPI-v3s) and examine its utility for identifying personality predictors of athletic achievement. Methods A total of 925 athletes (aged 15–45 years; 84 sports) participated. Following data screening, two subsamples were formed: Study 1 (n = 436) for psychometric evaluation and Study 2 (n = 753) for predictive analysis. Study 1 employed exploratory and confirmatory factor analyses and multi-group CFA to test gender invariance. Continuous regression-based norming produced age and gender specific T-scores. Study 2 applied hierarchical logistic regression to examine the predictive validity of personality traits for elite/pre-elite versus non-elite status. Results The 53-item LPI-v3s demonstrated a stable five-factor, 14-trait structure, satisfactory internal consistency (α = 0.72–0.88), and configural, metric, and scalar invariance across gender. Predictive models indicated that lower Anxious Insecurity (N1) and Sociability (E1), and higher Orderliness (C1 ) , particularly in team sports which significantly predicted higher athletic achievement (AUC = 0.72). Conclusions The validated LPI-v3s offers a reliable and efficient measure of athlete personality with robust psychometric properties and practical value for sport psychology, talent identification, and applied performance research. personality assessment athletes psychometrics measurement invariance sport environment Figures Figure 1 1. Introduction An athlete's personality shapes patterns of thinking, feeling and behavior that influence not only performance but also their ability to overcome challenges and maintain motivation [ 1 , 2 ]. The concept of personality has attracted research attention across various scientific fields, including sport psychology. One of the most widely used theoretical frameworks is the Five-Factor Model [ 3 ]. Many measuring instruments used today to diagnose personality aspects are based on the concept of this model. The Five Factor Model divides personality traits into five broad domains: openness, conscientiousness, extraversion, agreeableness, and neuroticism. These traits have been associated with key psychological attributes in athletes, such as mental toughness and resilience [ 4 , 5 ]. Previous studies have explored how personality traits vary by gender, competitive level, and sport type, underscoring the complexity of individual differences within athletic populations [ 5 , 6 , 7 ]. Such findings continue to spark interest in the role of personality in sport as well as highlight the need for context-specific assessment approaches tailored to athletic environments. Although personality traits are widely acknowledged as influential in athletic performance, research findings in this area remain mixed. Studies often report inconsistent relationships between specific traits and performance outcomes, reflecting the complex interplay between personality, psychological skills, and situational factors [ 2 , 8 ]. These contrasting perspectives continue to drive interest and highlight the need for further investigation within applied sport contexts. 1.1. Personality Assessment in Sport Personality assessment holds practical value in sport psychology by offering insight into athletes’ mental strengths, challenges, and developmental needs [ 9 ]. Understanding personality profiles can support individualized psychological interventions and guide coaching strategies. Research also indicates that personality traits differ across competitive levels, suggesting that high-performance sport may attract certain personality profiles [ 10 ]. Some traits, such as low neuroticism, have been consistently linked with long-term athletic success, as they are associated with emotional regulation, confidence and physiological stability [ 11 , 12 ]. Personality traits also influence performance indirectly by shaping psychological skills. For example, Fabbricatore et al. [ 13 ] emphasized that traits help develop psychological skills that are essential for success. Lower neuroticism is linked to greater emotional stability and better decision-making [ 2 , 14 ], while extraversion and conscientiousness correlate positively with performance [ 6 ]. Additionally, sport participation may promote adaptive personality development [ 15 ]. Links between personality and competitive anxiety also support the value of tailored psychological interventions [ 16 ]. These findings are consistent with broader personality patterns observed in athletes. Piepiora [ 15 ] notes that, according to the Five-Factor model, athletes tend to exhibit low neuroticism, high extraversion and conscientiousness, and moderate levels of openness and agreeableness. However, it is important to recognize that sport-specific and demographic differences remain critical factors, especially when considering variation across disciplines and gender. For example, Kemarat et al. [ 5 ] reported that both the gender of athletes and the type of sport significantly affect personality characteristics and levels of competitive anxiety, with neuroticism emerging as the strongest predictor. These findings reinforce the need to consider sport-specific and demographic factors when evaluating personality in athletic populations. 1.2. Limitations of Existing Personality Measures in Athletic Populations Personality inventories grounded in the Five-Factor Model have been widely applied in sport psychology. While useful, their generalizability to sport-specific contexts has been questioned. Recent research emphasizes the need for psychometric tools designed specifically for athletes, to ensure accurate and contextually relevant assessments [ 17 , 18 ]. Studies often highlight that these tools fail to account for the unique psychological and situational challenges associated with different sports disciplines, and typically do not provide athlete-specific norms, which limit their interpretability in performance contexts [ 15 , 19 ]. It is important to mention that such personality assessments should consider cultural and sport contextual factors. These practical issues also limit their use in sport practice. The NEO Personality Inventory and its revised version (NEO-PI-R) are one of the most commonly used personality assessment tools within sport [ 20 ]. It assesses five broad traits but can take up to 60 minutes to complete, limiting their practicality in sport settings. Shorter tools like the Mini-IPIP [ 21 ] are more efficient but, as with many brief measures, may capture constructs with less detail. Others instrument like the Sports Mental Toughness Questionnaire (SMTQ) focus on narrow constructions [ 22 ]. These limitations highlight the need for validated, sport-specific tools that are both comprehensive and practical. Existing inventories rarely incorporate cultural or regional adaptations, which is especially problematic when assessments are used across diverse linguistic and national populations. This is a critical concern in countries such as Latvia, where adaptations of foreign tools like the NEO-PI-R or Big Five Inventory have demonstrated limited cultural sensitivity and reduced interpretive depth [ 23 , 24 ]. Taking together, these theoretical, contextual, and practical shortcomings highlight a clear need for validated personality instruments that are designed with athletes in mind, culturally relevant, psychometrically sound, and efficient to administer. This forms the basis for evaluating and refining tools like the Latvian Personality Inventory (LPI-v3) for use in sport-specific contexts. 1.3. The Latvian Personality Inventory (LPI-v3) LPI-v3 is a validated, multidimensional tool designed to assess a broad range of personality traits. It was developed in response to the limitations of existing instruments adapted for Latvia, such as the NEO-PI-R [ 23 ] and the Big Five Inventory (BFI) [ 24 , 25 ]. While widely used, these tools have shown limited cultural sensitivity and offer only a general overview of traits, restricting their interpretive depth in applied settings. The LPI-v3 [ 26 ] addresses these shortcomings by integrating elements from both the Five-Factor Model and the HEXACO framework. It consists of 100 items in Latvian and measures six broad personality domains: Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness, and Honesty-Humility, with four subscales in each. Initial validation studies support the LPI-v3 internal consistency and factor structure. However, its utility in applied contexts such as sport remains unexplored. Further psychometric evaluation, particularly among athletes, is needed to ensure its relevance and accuracy across demographic and performance-related variables, and to enable the development of athlete-specific population norms. 1.4. The Present Research: Study 1 And Study 2 The present research comprised two sequential studies designed to develop, validate, and apply the LPI-v3s in an athlete population. Study 1 focused on the psychometric refinement and standardization of the instrument, aiming to create a psychometrically sound and time-efficient measure suitable for athletes. Study 2 built on these findings by examining the predictive validity of the LPI-v3s in differentiating between athletes of varying achievement levels. Keeping that in mind, the overarching aim of the current study was to establish the LPI-v3s as reliable, valid and contextually appropriate personality assessment tool for sports settings by confirming its factorial structure and measurement invariance (Study 1), developing age and gender specific normative data and identifying the key personality traits that predict elite/pre-elite sport achievement status (Study 2). The main objectives of this study were as follows: To develop a shortened version of the LPI-v3s suitable for athletes, ensuring stable factor structure and measurement invariance across male and female athlete subsamples (Study 1). To establish normative indicators for the scales by determining which scales require the development of gender- and/or age-specific norms (Study 1). To identify which personality scales (at both the trait and factor level) significantly predict sport achievement status (elite/pre-elite vs. non-elite) (Study 2). Despite the availability of various conceptual personality surveys based on the five-factor model, most of the available tools lack validation in athlete populations and often show limited cultural sensitivity when adapted to smaller language contexts, such as Latvian. It should be noted that currently, in many countries, there are no validated, sport-specific norms and their factorial structure and measurement invariance have not been empirically tested among athletes. This indicates that there is a need for a psychometrically sound, time-efficient, and culturally valid personality measure adapted to the needs of sport psychology practice. 2. Methods 2.1. Participants The Initial Sample included 931 participants aged 15 to 45. Five participants who did not identify as male or female were excluded, as gender was a primary stratification element for subsequent analyses. The remaining set of participants formed the total sample ( N = 925). This sample was used as the source pool for: (1) selecting participants for the normative sample ( n = 436) used in the development and psychometric validation of LPI-v3s (Study 1), and (2) forming the final analytical sample (n = 753) for the evaluation of the relationship between the athletes' personality traits and sport achievement status (Study 2). Together, athletes represented 84 different sports, including basketball ( n = 107), hockey ( n = 96), football ( n = 94), volleyball ( n = 65), athletics ( n = 56), fitness ( n = 47), handball ( n = 42), floorball ( n = 41), luge sport ( n = 31), swimming ( n = 28), orienteering ( n = 27), judo ( n = 15), sports dances ( n = 15), table tennis ( n = 13), rugby ( n = 13), weightlifting ( n = 12), boxing ( n = 10), artistic gymnastics ( n = 10), cross-country skiing ( n = 9), tennis ( n = 9), karate ( n = 9), cycling ( n = 8), beach volleyball ( n = 8), equestrian sport ( n = 8), alpine skiing ( n = 7), gymnastics ( n = 6), kayaking ( n = 6), taekwondo ( n = 6), figure skating ( n = 6), climbing sport ( n = 5), running ( n = 5), shooting ( n = 5), biathlon ( n = 4), bodybuilding ( n = 4), curling ( n = 4), triathlon ( n = 4), road cycling ( n = 4), disc golf ( n = 4) and, other ( n = 82). 2.1.1. Total Sample (Study 1 and Study 2) The total sample comprised participants, with ages ranging from 15 to 44 years ( M = 21.2, SD = 5.6 years). The sample consisted of 535 males (57.8%), aged 15 to 44 years ( M = 20.9, SD = 5.8), and 390 females (42.2%), aged 15 to 43 years ( M = 21.1, SD = 4.8). Sports experience among the participants ranged from one year in specific sport to 35 years ( M = 7.2 years, SD = 4.8 years), and training intensity ranged from 1 to 25 hours per week ( M = 7.8 hours/week, SD = 4.8 hours/week); 490 (53.0%) participants represented Team sports (356 males, 134 females) and 435 (47.0%) participants represented Individual sports (179 males, 256 females). Sport Achievement Level initially was classified in three categories: elite ( n = 46, 5.0%), pre-elite ( n = 433, 46.8%), or non-elite ( n = 446, 48.2%). Athletes’ competitive status was categorized based on training intensity and performance criteria. Elite athletes were defined as those who trained at least 8 sessions per week (or > 12 hours weekly) and had achieved at least one high-level competitive result, such as a podium at a national championship, a top placement in a regional league or participation in a major international competition (European Championships at junior or senior level, World Cup, World Championships, or Olympic Games). All athletes in the elite category also had a minimum of five years of sport-specific experience. pre-elite athletes trained at least 5 times per week (≈ 7.5 hours weekly) and had competitive experience at the national championship level (in any age category), in regional leagues, or at university-level events such as Universiade. Non-elite athletes engaged in a minimum of 2 training sessions per week (≈ 3 hours weekly), and their competition experience was limited to lower- or mid-tier competitions. Missing data per participant did not exceed 5% and deletion was applied to maintain data integrity. Although the sample was broad and diverse, it represents a convenience sample of active athletes rather than a random selection from the national athlete population. 2.1.2. Normative Sample (Study 1) Participants from the total sample ( N = 925) were stratified based on three criteria: gender (male/female), age (15–17, 18–20, 21–29, 30–45), and sport type (individual/team). The respondent distribution across these strata in the Analysis Sample was highly uneven. Since no official population data on the distribution of athletes by these specific characteristics was available, it was decided that an equal number of respondents per stratum would be selected to ensure maximum structural uniformity and direct comparability across groups. Following the principle of the smallest available cell size, the final sample size for most strata was determined by the number of participants in the smallest available group, which was the female 15–17 age group in team sports ( n = 32). To ensure a uniform representation across all other strata, a total of 32 participants were randomly selected from each of the remaining strata that had more than 32 available respondents. The randomization was performed using an online random number generator (e.g., randomizer.org), ensuring that each participant within a stratum had an equal chance of being selected for the final sample. An exception was made for the 30–45 age group, which had a significantly smaller number of participants. To ensure sufficient power for the gender-specific norms, it was decided to use the highest number of available respondents for each gender within this age group. This meant including all 21 available male respondents in each of their respective sport type strata. For female respondents, due to the low number of participants in team sports ( n = 5), a proportional number of participants ( n = 5) were randomly selected from the female individual sports group to maintain proportional representation within the female 30–45 age group. This decision prioritized obtaining the largest possible sample size for each gender while preserving the sport type balance within the older female group. Therefore, to ensure minimally sufficient representation (or adequate sample sizes) within age strata for the development of both gender- and age-specific norms where required, a decision was made to consolidate the initial four age groups into two broader categories: adolescents/young adults (15–20 years) (combining 15–17 and 18–20 age groups) and Adults (21–45 years) (combining 21–29 and 30–45 age groups). This approach allowed for the standardization of LPI-v3s scores for males and females, with the flexibility to implement age-based norms for specific scales identified as being significantly influenced by age (as detailed in Section 2.2.3). The final composition of the normative sample is detailed in Appendix A Table S1 , which illustrates the initial and final counts for each stratum. The data from this normative sample ( n = 436) were utilized to develop and validate the abbreviated version of the LPI-v3s and establish the necessary normative scores for the athlete population (see Appendix A Table S2 ). 2.1.3. Final Analytical Sample and Data Quality Control (Study 2) The total sample ( N = 925) was subsequently subjected to a final data quality control procedure to form the Final Analytical Sample used for the main predictive analyses. Crucially, scores on the Lie Scale (M) from the shortened version of the LPI-v3s (as detailed in Section 2.2.2 .) were utilized as a criterion for data exclusion to ensure the validity of the personality profile analysis and minimize the impact of socially desirable responding within the final analytical sample. Participants who scored above the established cutoff on the Lie Scale (the T score > 60) were identified as providing potentially invalid data. Consequently, a total of 160 participants (≈ 17.3% of the sample) were excluded. The final Analytical sample used for all subsequent statistical analyses (prediction of sport achievement) consisted of 753 participants (437 males and 316 females). Detailed distributions by sport type, performance level, and gender are presented in Appendix A Table S3 . 2.2. Measures 2.2.1. The Original Version of the Latvian Personality Inventory (LPI-v3) The Latvian Personality Inventory (LPI-v3; Perepjolkina, Renge, 2012) is a 100-item self-report questionnaire used to assess six personality factors: Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness, and Honesty-Humility. The instrument is hierarchically structured, also measuring four narrower personality traits (facets) within each of the six factors. Each of the six factors is measured by 16 items, while each facet (subscale) is measured by 4 items. An additional 4 items form a Lie scale, which assesses the tendency toward socially desirable responding. Participants respond using a 5-point Likert scale ranging from 1 ("Does not correspond to me") to 5 ("Corresponds to me"). Scale scores for both the personality factors and the facets were derived by summing the scores of the respective items, dividing the sum by the number of items (to yield the mean score), and finally multiplying this mean score by 10. The resulting scaled scores range from 10 to 50. Higher scores indicate a greater expression of the trait as defined by the scale name. LPI-v3 demonstrates a stable factor structure and satisfactory psychometric properties in the general adult population sample. However, the stability of the factor structure and its invariance across gender groups have not yet been empirically tested. Internal consistency (Cronbach's alpha, n = 1294) for the factor scales range from 0.81 to 0.88, and for the facet scales, from 0.61 to 0.86. The inventory also shows high test-retest reliability (Perepjolkina, Renge, 2012). Retest coefficients ( n = 166; mean interval of 19 weeks) ranges from 0.85 to 0.90 at the factor level and from to at the subscale level. The complete LPI-v3s short-form questionnaire (Latvian) and scoring key are provided in Appendix B. 2.2.2. Shortened Version of the LPI-v3 (LPI-v3s) Development and Use in Current Study The data from the normative sample ( n = 436) was utilized to develop a psychometrically sound abbreviated version of the LPI-v3s specifically adapted for the extended age and athlete population represented in this study. The primary objective of developing this shortened version was to ensure it achieved measurement invariance across gender groups, which is a prerequisite for correctly comparing mean scores between male and female athletes. Furthermore, the short version was designed to be robust enough to allow for the development of gender-specific and, where necessary, age-specific norms tailored to this athletic population (Study 1). The subsequent analyses for predicting sports achievement status were based on the T-scores derived from this validated short version of LPI-v3s (Study 2). To ensure accurate interpretation of personality traits, the necessity for developing separate age-based norms (adolescents/young adults [ 15 – 20 ] vs. adults [ 21 – 45 ]) was examined. This decision was informed by a preliminary analysis of the normative sample ( n = 436), with the goal of detecting systematic variation of traits across the lifespan. The analysis was conducted separately for males and females. A scale was deemed to require age-specific norms if: (1) Age correlated statistically significantly with the scale score ( p < 0.05); and (2) The Independent Samples t-test or Mann-Whitney U test revealed a statistically significant mean difference ( p < 0.05) between the 15–20 age group and the 21–45 age group, with a corresponding effect size of at least a small to moderate magnitude (e.g., Cohen’s d or r ≥ 0.20). Scales meeting both criteria were subsequently normed separately by age group, resulting in four distinct normative tables: (1) male adolescents/young adults, (2) male adults, (3) female adolescents/young adults, and (4) female adults. Figure 1 provides a visual summary of the sequential steps and methodological structure for the two-study research design, including sample flow, psychometric analyses, and predictive modeling. Raw-to-T conversions and continuous norm tables are available in Appendix C (Excel). 2.3. Study Protocol and Ethics The data collection process for the LPI-v3 was conducted using a mixed-mode approach to ensure maximum reach and engagement among participants. Athletes were given the option to complete either a digitally accessible questionnaire through Microsoft Forms or a paper-based version, which was administered directly by members of the research team. The target population included athletes from various sports and competitive levels. Participant recruitment and data collection occurred over a 13-month period, beginning on 1 June 2024 and concluding on 1 July 2025. The inventory was distributed directly to athletes by the research team. In addition to completing the LPI-v3 and rating each item, participants were requested to share demographic details, such as their age, gender, city of residence, type of sport, hours spent training each week, highest achievements, and years of practicing in sport. Participation in the study was anonymous and voluntary. All participants were informed that their data would be used exclusively for research purposes. Written informed consent was obtained prior to participation, ensuring that all individuals were aware of the study’s aims and how their data would be handled. In the case of participants under the age of 16, written informed consent was additionally obtained from their parents or legal guardians. The study was approved by the Ethics Committee of the Latvian Academy of Sport Education (Protocol No. 8, Statement No. 1, April 19, 2024) and was conducted in accordance with the ethical standards set forth in the Declaration of Helsinki. Confidentiality was strictly maintained. All data were anonymized, encrypted, and stored on secure servers, accessible only to authorized members of the research team. The study adhered to applicable data protection regulations, with a registered data management plan submitted via the ARGOS (OpenAIRE) system. Participants were also informed of their right to withdraw from study at any time without penalty. In such cases, any identifiable data were immediately deleted to protect participant privacy. 2.4. Statistical Analysis Statistical analyses were performed separately for Study 1 (psychometric evaluation and norm development) and Study 2 (predictive validity). All analyses were conducted using JASP v0.95.4 and JAMOVI v2.6. Statistical significance was evaluated at p < 0.05, and effect sizes were reported where appropriate. Sample-size adequacy was verified using G*Power 3.1.9.6 (α = 0.05, power = 0.80). Internal consistency was assessed using Cronbach’s α and McDonald’s ω coefficients, with values ≥ 0.70 considered acceptable. 2.4.1. Study 1: Psychometric evaluation and norm development The factorial evaluation proceeded iteratively. Exploratory factor analysis (EFA) using principal axis factoring with Promax rotation was first conducted to identify a parsimonious structure. The resulting configuration was then evaluated using hierarchical confirmatory factor analysis (HCFA) based on a polychoric correlation matrix and the diagonally weighted least squares (DWLS) estimator, appropriate for ordinal data. Model fit was evaluated using the Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA, 90% CI), and Standardized Root Mean Square Residual (SRMR). Thresholds for acceptable fit were CFI and TLI ≥ 0.90, RMSEA ≤ 0.08, and SRMR ≤ 0.08. In line with Marsh et al. (2004, 2005) and McNeish, Wolf (2020), slightly lower CFI/TLI values were tolerated for complex hierarchical models. Measurement invariance across gender groups (male vs. female athletes) was examined using multigroup CFA. Configural, metric, and scalar invariance were tested sequentially. Invariance was supported when ΔCFI < 0.01 and ΔRMSEA < 0.015 (Cheung, Rensvold, 2002; Chen, 2007). To determine the need for age-specific norms, Pearson’s correlations were calculated between age and personality-scale scores separately for males and females. Scales showing significant correlations ( p < 0.05) were further compared between age groups (15–20 vs. 21–45 years) using independent-samples t-tests or Mann–Whitney U tests. Effect sizes were expressed as Cohen’s d (parametric) and rank-biserial r (non-parametric), with values ≥ 0.20 interpreted as practically meaningful. Continuous norming was performedusing the cNORMj package in Jamovi [ 27 ], supplemented by conventional T-score transformations ( M = 50, SD = 10) to facilitate comparability across methods. The quality of norm models was verified by coefficients of determination (R² ≥ 0.996), indicating empirical equivalence between continuous and conventional norms. 2.4.2. Study 2: Predictive Validity (Trait and Factorial Level Analysis) Predictive validity of the LPI-v3s was evaluated using hierarchical binary logistic regression. Analyses were performed at two hierarchical levels: (1) Trait-level model: Step 1 included all trait-scale T -scores and sport type (Team = reference). Step 2 added interaction terms (Sport Type × Personality Trait) to test for moderating effects; (2) Factor-level model: A parallel analysis was conducted using the six broad personality factors. Model performance was evaluated via Nagelkerke R², overall classification accuracy, and Receiver Operating Characteristic (ROC) curves. Discrimination was interpreted using the Area Under the Curve (AUC), with thresholds of 0.60 = fair, 0.70 = good, ≥ 0.80 = very good. Sensitivity and specificity were calculated using a 0.50 cut-off. 3. Results 3.1. Study 1: Psychometric Evaluation and Norm Development of the LPI-v3s 3.1.1 Initial Model Fit Analysis Considering that the LPI-v3 was developed and standardized on a general population sample aged 18 and older, and this study's data were collected from an athlete sample aged 15 and older, it was first necessary to confirm that the inventory’s theoretical model was appropriate for this dataset before calculating standardized scores. The model fit was initially tested on the full normative sample (n = 436) using hierarchical confirmatory factor analysis (HCFA). The initial hierarchical model showed a poor fit to the data. The chi-square statistic was significant, χ² (4424) = 7484, p .90), indicating insufficient model fit. The Standardized Root Mean Square Residual (SRMR = 0.087) also exceeded the recommended limit (< 0.08), whereas the Root Mean Square Error of Approximation (RMSEA = 0.040; 90% CI [0.038, 0.041]) met the acceptable standard, suggesting a reasonable approximation of the population covariance matrix. Taking together, these indices indicated that the initial measurement model did not adequately represent the relationships among the observed variables in the normative sample. Therefore, further modifications were required to improve overall fit. 3.1.2. Development and Evaluation of Modified Models (Models 1–4) To improve model fit, several modified models were sequentially tested, aiming to enhance the fit indices while retaining the original theoretical model as much as possible. This sequential process was guided primarily by the analysis of standard estimates of factor loadings and finally by modification indices. In the first modified model, the O2 (Tolerance) scale was excluded from O (Openness) factor, as its standard estimate on the O factor was 0.153, which is too low. Additionally, eight other items were removed (J41 from the E2 (Cheerfulness) scale, J3 from the H1 (Honesty) scale, J59 from the H3 (Modesty) scale, J78 and J91 from the H4 (Integrity) scale, J64 from the N3 (Depressivity) scale and J37 because their standard estimates representing factor loadings were below 0.35. The fit indices improved slightly but as seen in Appendix A Table S4, all indices except for RMSEA remained inadequate. In the next step, using the same procedure, one more item was identified and removed: J15 from the H1 (Honesty) scale. The fit indices for this second modified model also improved slightly but were still inadequate. Therefore, the model modification process continued. Analyzing the second model, all items showed appropriate factor loadings, but four scales (A1: Compliance, H3: Modesty, H4: Integrity and O2: Tolerance) showed unsatisfactory internal consistency metrics (Coefficient α and ω in the range of 0.381 to 0.591), indicating low reliability. Consequently, it was decided to exclude these scales from further analysis completely. This resulted in Modified Model 3. Although all fit indices for this model improved slightly (see Appendix A Table S4), they still indicated inadequate model fit, with the exception of the RMSEA. Analysis of the third modified model's results showed that the H2 (Humility) scale's factor loading in the H factor was inappropriate (λ = 0.287), leading to its removal from the H factor. However, since only one scale was left in this factor, it was also removed from the higher-order factors. But two separate scales (H1 and H2) were left in the model, which did not fit into any of the higher-order factors. Additionally, it was found that the J34 item had a reduced factor loading (λ = 0.366), and it was also excluded. Furthermore, at this stage, the proposed modification indices were carefully analyzed, and the model was supplemented with several relaxations, allowing four subscales to correlate with the N factor, one with the A factor, three with the C factor, and four with the O factor. Four correlations between the subscales were also permitted. All permitted correlations are theoretically justified and reflect complex relationships between personality factors and their constituent traits. As a result, the fourth modified model was obtained. Despite slight improvements, this fourth modified model still revealed an unsatisfactory fit for the proposed model (CFI = 0.830) on the overall normative sample. This inadequate model fit was more pronounced in the male athlete subsample, where the CFI was below the 0.80 threshold, but a little better in the female subsample (CFI = 0.832) (see Appendix A Table S4). A more detailed analysis revealed that in the male subsample, three items (J100 from O4: Creativity, J52 from N3: Depressivity, and J28 from H2: Humility) showed excessively low factor loadings, and some scales dropped from their factors (the E3 scale and O1: Aesthetic Interests scale). However, subsequent modifications to the model, including those based on modification indices, did not lead to significant improvements. The results remained illogical, at least with respect to some items and scales in the male subsample. 3.1.3. EFA-Guided Model and Final HCFA (Model 5a) To better understand the underlying factor structure of the LPI-v3s and address the poor model fit, an exploratory factor analysis (EFA) was performed on the overall normative sample. The EFA utilized a polychoric correlation matrix and the Principal Axis Factoring method for factor extraction. The number of factors were determined using Kaiser's criterion (eigenvalues > 1), which suggested a 14-factor solution (representing 14 lower-order scales). Promax rotation was applied to allow for correlations between the factors, a decision based on the expectation that the personality traits being measured are not independent. The EFA results revealed a more parsimonious and well-fitting structure compared to the priori model. The 14-factor solution explained a total of 53.6% of the variance. This new structure differed slightly from the final modified CFA model from the previous stage (Model 4). Specifically: 1. The H1: Honesty scale was lost entirely, while only the H2: Humility scale remained from the H factor. 2. The E4: Social Activity scale was removed as its items merged with the O4 scale (Creativity), and the C3 (Perfectionism) scale was also removed as its items merged with the O3 (Curiosity) scale, which in both cases was theoretically unacceptable. 3. Similar to the previous CFA Model 4, both the O2 (Tolerance) and A1(Compliance) scales dropped out as well. 4. The second-order EFA showed that the E3 (Sensation-seeking) scale did not load on the general E factor. Based on the EFA findings, a new, shortened model was developed, which included 53 items, 14 scales, and 5 higher-order factors. This revised model was then subjected to a new hierarchical confirmatory factor analysis (CFA) (Model 5). The fit indices for this initial model were gathered to establish a baseline. Based on these results and the modification indices, several theoretically justified correlations between specific scales and factors were allowed (e.g., based on covariances > 0.35). This refined version of the model, subsequently referred to as Model 5a, was found to provide a significantly better fit to the data. Although the CFI for Model 5a was 0.885 (see Table 5), it still did not meet the recommended threshold of > 0.90. However, it was nearing this threshold and given that while Model 5a is significantly simpler than the initial model, it remains complex. Authors such as Marsh et al. [ 28 , 29 ] and McNeish, Wolf [ 30 ] note that in the case of psychometric instruments with many scales and items, there is a lower probability of achieving the stringent benchmarks (CFI and TLI > 0.95) recommended by Hu and Bentler [ 31 ]. These authors acknowledge that for complex models, such as those in personality tests, the CFA model fit may be lower than ideal, and values around 0.80 or 0.85 can be considered acceptable. Therefore, the decision was made to use this model for further analysis. The goal of this CFA was to identify a measurement model that provided an acceptable fit and was suitable for subsequent measurement invariance testing, which is crucial for the planned gender-based norming and comparisons of personality traits related to athletic performance. Model 5a's fit indices for the overall normative sample and the separate male and female subsamples are presented in Table 5. As can be seen, the fit indices were higher in the female subsample than in the male subsample (CFI = 0.878 and 0.849, respectively), and in both subsamples, they were lower than in the overall sample. Nevertheless, these values were significantly better compared to the fit indices of the fourth modified model and reached the minimum required criterion of > 0.80, which could be considered acceptable for such a complex survey structure. Consequently, this model was subsequently used for measurement invariance testing. Fit indices for the modified Model 5a for the overall, male, and female subsamples are presented in Table 1 . Table 1 Goodness-of-Fit Indices of the HCFA Modified Model 5a Model Sample N χ 2 (df) p CFI TLI RMSEA [90% CI] SRMR Modified Model 5 Normative sample 436 2505 (1291) < 0.001 0.853 0.843 0.046 [0.044; 0.049] 0.074 Modified Model 5a Normative sample 436 2217 (1276) < 0.001 0.885 0.876 0.041 [0.038; 0.044] 0.068 Modified Model 5a Male athletes 234 1857 (1276) < 0.001 0.849 0.837 0.044 [0.040; 0.049] 0.085 Modified Model 5a Female athletes 202 1662 (1276) < 0.001 0.878 0.868 0.039 [0.033; 0.044] 0.083 Note . Estimator is DWLS. Model test is scaled and shifted. Information matrix is expected. Standard errors are robust. Fit indices are based on the scaled test statistics. χ²/df: Degrees of freedom; CFI: Confirmatory Fit Index; TLI: Tucker-Lewis Index; RMSEA: Root Mean Square; SRMR: Standardized Root Mean Square Residual Given its improved parsimony and satisfactory model fit, Model 5a was retained as the final structure of the LPI-v3s for subsequent analyses, including measurement invariance and normative development (Study 1), and predictive analyses (Study 2). 3.1.4. Measurement Invariance Factorial invariance was tested using multigroup confirmatory factor analysis (MG-CFA) to determine if the measurement model for the modified shortened version (Model 5a) of LPI-v3s was equivalent across male and female athlete samples. The analysis followed a hierarchical approach, starting with the least restrictive model and progressing to more restrictive models. The goodness-of-fit was evaluated using the chi-square statistic (χ 2 ), degrees of freedom (df), comparative fit index (CFI), Tucker-Lewis Index (TLI), and the root mean square error of approximation (RMSEA) with its 95% confidence interval. A significant drop in fit for the more restricted models was assessed using the change in CFI (ΔCFI), with a value less than 0.01 considered acceptable [ 32 , 33 ]. The first step was to test for configural invariance, which assesses whether the factor structure is the same across both groups without imposing equality constraints on the model parameters. The results of the configural model indicated an acceptable, but not excellent, fit to the data (χ 2 (2552) = 3516; p < 0.001). The fit indices were CFI = 0.863, TLI = 0.852, and RMSEA = 0.042 [0.038; 0.045]. These results support the notion that the same number of factors and the same pattern of factor loadings were present in both male and female samples. Following the establishment of configural invariance, metric invariance was tested by constraining the factor loadings to be equal across both groups. This model showed a slight deterioration in fit compared to the configural model (χ 2 (2603) = 3553; p < 0.001). The change in fit was minimal (Δχ 2 (51) = 37; ΔCFI = 0.002), which is below the recommended threshold of 0.01. This suggests that the factor loadings are equivalent across both male and female samples, supporting the comparability of the factor-item relationships between the two groups. The final step was to test for scalar invariance by adding the constraint that the item interceptions are equal across both groups. This model provides the strongest test of measurement equivalence. The results showed a significant drop in model fit compared to the metric invariance model based on the chi square test (χ 2 (2741) = 3746; p < 0.001), nevertheless, the change in CFI was 0.008 (ΔCFI = 0.008), which is below the recommended threshold of 0.01. This suggests that the item interceptions are equivalent, allowing for direct comparison of latent mean scores between the groups. Based on these results, full scalar invariance was supported (see Table 2 ). Table 2 Sequential Tests of Measurement Invariance Across Gender Groups For the LPI-v3s Model χ2 df CFI TLI RMSEA CI 95% [lower; upper] Δ χ2 (Δdf) ΔCFI Configural invariance 3516 2552 0.863 0.852 0.042 [0.038; 0.045] -- -- Metric invariance 3553 2603 0.865 0.857 0.041 [0.038; 0.044] 37 (51) 0.002 Scalar invariance 3746 2741 0.857 0.85 0.041 [0.038; 0.044] 193 (138) 0.008 Note . χ²: Chi-square; df: degrees of freedom; CFI: Comparative Fit Index; TLI: Tucker–Lewis Index; RMSEA: Root Mean Square Error of Approximation; Δχ² = Chi-square difference test; Δdf = difference in degrees of freedom; ΔCFI = change in Comparative Fit Index. 3.1.5. Gender Differences and Decision for Gender Norms To determine which scales require gender-specific norms, mean differences between male and female athletes were examined using the Mann-Whitney U test due to non-normality across most scales. The analysis identified statistically significant differences ( p 0.20): N1 (Anxious Insecurity), N2 (Irritability), N (Neuroticism), A4 (Self-Control), and O1 (Aesthetic Interests). The largest effect sizes were observed for N ( r = 0.32) and N1 ( r = 0.29). Based on the combined criteria of statistical significance and meaningful effect size, gender-specific norms were deemed necessary for the five identified scales. All remaining scales (including those that reached statistical significance but had r < 0.20) showed negligible differences between male and female athletes, suggesting that unified norms can be applied for these traits (see Table 3 ). Table 3 Mean Differences and Effect Sizes Between Gender Groups in the LPI-v3s Normative Sample LPI-v3 Scale (Short Form) Mean ( SD ) normative sample ( n = 436) Mean ( SD ) male ( n = 234) Mean ( SD ) female ( n = 202) Test statistic ( U ) p -value Effect size r Norms required N1: Anxious-Insecurity 26.3 (7.3) 24.5 (6.6) 28.3 (7.4) 16719 < 0.001 0.29 Yes N2: Irritability 27.9 (9.7) 26.0 (9.3) 30.0 (9.7) 18213 < 0.001 0.23 Yes E1: Sociability 30.0 (9.1) 29.6 (9,0) 30.4 (9.3) 22899 0.573 0.03 No E2: Cheerfulness 37.8 (7.6) 38.1 (7.5) 37.6 (7.8) 22743 0.493 -0.04 No E3: Sensation-Seeking 34.6 (8.5) 34.9 (8.1) 34.3 (8.9) 22930 0.590 -0.03 No C1: Orderliness 33.9 (8.3) 33.8 (8.4) 34.3 (8.3) 23402 0.859 0.01 No C2: Self-discipline 32.2 (8.5) 31.6 (4.9) 31.0 (9.2) 19941 0.005 -0.16 No C4: Prudence 32.1 (8.0) 33.2 (7.6) 31.8 (7.6) 22487 0.378 -0.05 No A2: Peacefulness 32.8 (8.4) 32.6 (8.4) 33.0 (8.4) 23120 0.694 0.02 No A3: Compliance 32.8 (5.7) 32.5 (6.1) 33.1 (5.1) 22668 0.453 0.04 No A4: Self-Control 32.1 (9.7) 34.0 (9.3) 30.0 (9.7) 18213 < 0.001 -0.23 Yes O1: Aesthetic Interests 24.9 (9.4) 23.2(8.7) 26.8 (9.8) 18533 < 0.001 0.22 Yes O3: Curiosity 32.2 (7.8) 32.6 (7.7) 31.7 (7.9) 22603 0.430 -0.04 No O4: Creativity 32.5 (8.0) 32.8 (7.1) 32.2 (8.9) 22882 0.564 -0.03 No H2: Humility 30.8 (8.7) 30.1 (9.0) 31.6 (8.3) 21168 0.058 0.10 No N: Neuroticism 26.8 (6.8) 25.0 (6.3) 28.8 (6.8) 15957 < 0.001 0.32 Yes E: Extraversion 33.9 (6.9) 33.8 (6.7) 34.0 (7.1) 23461 0.895 0.01 No C: Conscientiousness 32.9 (6.4) 33.2 (6.5) 32.5(6.2) 21972 0.205 -0.07 No A: Agreeableness 32.4 (6.5) 32.9 (6.4) 31.9(6.6) 21820 0.166 -0.08 No O: Openness to Experience 30.1 (6.1) 29.8 (5.4) 30.4(6.7) 22394 0.344 0.05 No M: Lie Scale 25.1 (7.2) 25.0 (6.9) 25.1(7.5) 23470 0.900 0.01 No 3.1.6. Age Effect and Decision for Age Norms Correlations with age (Criterion 1). To empirically determine the necessity of establishing age-based norms, Pearson correlational analyses were conducted between respondent age and all shortened version of the LPI-v3s scales’ scores within the normative sample, with analyses performed separately for the female and male subsamples. The results are detailed in (see Appendix A Table S6). Findings in the female subsample. Age was statistically associated with two factor-level scales and four facet scales in the female subsample. Consistent with general population trends, a negative, weak but statistically significant correlation was between age and Neuroticism ( r = -0.18, p = 0.012). This relationship was reflected in the facet Anxious-Insecurity ( r = -0.18, p = 0.009), suggesting that women tend to report lower levels of emotional instability or worry with increasing age. Similarly, Sensation-Seeking showed a small, significant decline with age ( r = -0.19, p = 0.006). Conversely, a positive, weak but statistically significant correlation was found between age and Conscientiousness ( r = 0.20, p = 0.005), which was primarily driven by the facet Orderliness ( r = 0.19, p = 0.008), and weakly by Prudence ( r = 0.14, p = 0.046). Findings in the male subsample. The male subsample displayed more numerous and often stronger correlations between age and personality traits, suggesting a greater influence of age on self-reported scores in this group. Positive, statistically significant correlations were found with the following factors: 1. Openness to Experience ( r = 0.23, p < 0.001), which was largely accounted for by the facet Curiosity ( r = 0.28, p < 0.001). 2. Conscientiousness ( r = 0.15, p = 0.022), which was largely accounted for by the facet Prudence ( r = 0.22, p < 0.001), indicating increased cautiousness in decision-making with age. 3. The facet Humility ( r = 0.31, p < 0.001) under the Honesty-Humility factor, which was the strongest correlation observed across all scales, indicating that male athletes report becoming substantially less status-conscious and more modest as they age. Only two negative correlations in the male group were observed for the Agreeableness factor ( r = -0.17, p = 0.08), which was largely accounted for by the facet Compliance ( r = -0.28, p < 0.001), suggesting that older male athletes may be less inclined to defer to others or compromise compared to their younger counterparts. Mean differences between age groups. Following the correlational analysis which served as the first filtering criterion for age-specific norms, the second criterion for requiring age-specific norms was tested. Independent Samples t-tests (or the Mann-Whitney U test, based on normality assumptions) were performed only on those scales that had shown a statistically significant correlation with age in the respective gender subsamples. This step was necessary to confirm that the observed relationship translated into a meaningful mean difference (criterion) between the newly defined age groups: adolescents/young adults (15–20) and adults (21–45). Prior to conducting mean difference analyses, assumptions of normality (Shapiro-Wilk test) and homogeneity of variances (Levene's test) were examined for all scales demonstrating a significant correlation with age in their respective gender subsamples. In the female subsample, Levene's test indicated that the assumption of homogeneity of variances was met for all scales ( p > 0.05). However, the normality assumption was violated for the Sensation-Seeking (E3) and Orderliness (C1) scales ( p Shapiro < 0.05). Consequently, mean differences for these two non-normally distributed scales were analyzed using the non-parametric Mann-Whitney U test, while the remaining scales were analyzed using the Independent Samples t-test (assuming equal variances). A similar pattern was observed in the male subsample: Levene's test confirmed the homogeneity of variances assumption for all correlating scales ( p > 0.05). However, the Shapiro-Wilk test revealed a violation of the normality assumption ( p Shapiro < 0.05) for four facets: Prudence (C4), Compliance (A3), Curiosity (O3), and Humility (H2). Therefore, the Mann-Whitney U test was applied to these four non-normally distributed scales, and the remaining scales were tested using the Independent Samples t-test (assuming equal variances). Female subsamples mean differences. For the female subsample, five scales met the initial correlation criterion ( p < 0.05). Subsequent mean comparison tests (Independent Samples -test or Mann-Whitney test, based on normality) confirmed that four out of five scales met the dual criteria for requiring age-specific norms (i.e., < 0.05 AND d or r ≥ 0.20) (see Appendix A Table S7): 1. Neuroticism ( t (200) = 3.35, p < 0.001, d = 0.49) and its facet Anxious-Insecurity ( t (200) = 2.79, p < 0.006, d = 0.41) showed higher scores among younger women, indicating greater emotional stability with age. 2. Conscientiousness ( t (200) = -2.97, p < 0.001, d = -0.43) and its facet Orderliness ( U = 3775, p = 0.013, r = -0.20) showed higher scores among adult women, suggesting increased organization and diligence with age. The Sensation-Seeking facet (E3) failed to meet the effect size criterion ( r = − 0.08), despite the statistically significant value ( p = .033), and thus did not warrant age-specific norms. Male Subsample Mean Differences. In the male subsample, seven scales met the initial correlation criterion. Mean comparison tests confirmed that six out of the seven scales met the criteria for requiring age-specific norms (see Appendix A Table S8): 1. Conscientiousness ( t (232) = -2.60, p = 0.01, d = -0.34) and its facet Prudence ( U = 5167, p < 0.001, r = 0.24) showed higher scores among adult men, indicating increased responsibility and long-term planning with age. 2. Compliance ( U = 4920, p < 0.001, r = -0.27) showed lower scores among adult men, confirming the finding from the correlation analysis that older male athletes report being less yielding. 3. Openness to Experience ( t (232) = -2.14, p = 0.034, d = -0.28) and the facet Humility ( U = 4348, p 0.20, despite the statistically significant p-value ( p = 0.014). Similarly, the Agreeableness factor (A) failed to meet the statistical significance criterion ( p = 0.103), despite meeting the effect size criterion ( d = 0.21). Therefore, these two scales did not warrant age-specific norms. Based on the sequential analyses, age-specific norms were established for four scales in the female subsample and six scales in the male subsample. For all other scales, a single, gender-specific norm was retained, as these traits did not show a simultaneous statistically significant and practically meaningful change across the two age groups. 3.1.7. Development of Norm Construction Normative scores for the LPI-v3 Short Form scales were calculated using a dual approach to ensure both high-quality norm tables and simplified scoring for subsequent analysis. For the creation of the final norm tables, a regression-based continuous norming approach was employed, utilizing the cNORMj for single group module within the JAMOVI statistical software. This method offers significant advantages over conventional approaches, as it models the relationship between raw scores and normative scores using polynomial regression, thereby smoothing the percentile curve and eliminating gaps or abrupt changes that often occur with traditional percentile calculations [ 34 ]. Although the continuous function for age-normed scores was constrained to predefined groups (due to the single-group approach), this method effectively optimized the endpoints and filled any sparsity in the data. For the purposes of the subsequent analytical steps (e.g., regression analysis) involving the Total Analytic Sample, T-scores were calculated using the conventional approach (using a linear transformation where the mean is set to M = 50 and the standard deviation to SD = 10). Based on the preliminary statistical analysis of the athlete sample, it was determined that gender- and/or age-specific norms were necessary only for a subset of the scales. Specifically, gender-specific norms were developed for the following scales: N1, N2, N, C1, C4, C, A3, A4, O1, O and H2. Furthermore, a detailed review of the data indicated the need for age-specific norms within genders for several key traits: 1. For female athletes, separate norms for two age groups were created for N1, N, and C1. 2. For male athletes, separate norms for two age groups were created for A3, O, and H2. For the remaining scales (E, E1, E2, E3, C2, A2, O3, O4, A, and M), a unified set of norms was developed, without subdivision by age or gender, as no statistically significant differences were observed across these demographic variables. The cNORMj package allows for a direct comparison between conventional norms and the regression-based continuous norms. The quality of the continuous norming model was excellent, with the final model's being no lower than 0.996 for all individual norm tables. This high degree of fit indicates that the two methods are empirically equivalent, and that no systematic error exists between the linear transformation and the regression model. Therefore, the T-scores calculated using the conventional approach are considered equivalent to those that would be assigned based on the newly created continuous norm tables, justifying their use in the primary data analysis. 3.2. Study 2: Predictive Validity of the LPI-v3s for Sport Achievement A hierarchical binary logistic regression was performed to examine the unique contributions of personality traits, sport type, and their interaction on the prediction of elite/pre-elite status (see Appendix A Table S5 for coding and full results). The elite and pre-elite groups were merged into a single elite/pre-elite achievement category due to the limited number of participants in the elite category ( n = 34). Insufficient sample size in the highly specific elite group would have compromised the statistical power and stability of the regression model's parameter estimates, particularly when analyzing complex interactions. Merging these two categories, which represent the highest levels of competitive performance, ensured adequate cell sizes for robust analysis while still maintaining a meaningful distinction from the non-elite group. Model 1: Personality Traits and Sport Type (Main Effects). Step 1 included the main effects of all personality traits and the Sport Type variable. The initial model was found to be statistically significant, χ 2 (15) = 26.3, p = 0.035, the model accounted for 4.58% of the variance in sport achievement (Nagelkerke R 2 = 0.046). In this first step, the significant predictors were: 1. Anxious Insecurity (N1): Significantly reduced the odds of being in the higher achievement group (OR = 0.96, p = 0.004). 2. Sociability (E1): Significantly reduced the odds of being in the higher achievement group (OR = 0.98, p = 0.013). 3. The main effect of sport type was not significant (OR = 0.43, p = 0.577). Model 2: Interaction Effects. Step 2 introduced the interaction terms (sport type x personality traits) to the model from Step 1. The addition of the interaction terms resulted in a non-significant improvement in the model's overall fit, Δχ 2 (14) = 20.8, p = 0.107. The full model was statistically significant (Model 2), χ 2 (29) = 47.1, p = 0.018, and accounted for a slightly larger proportion of the variance (Nagelkerke R 2 = 0.081). After the inclusion of the interaction terms, the following results were observed: 1. Main Effects (Model 2). The significance and direction of the main effects of Anxious Insecurity (OR = 0.96, p < 0.001), Sociability (OR = 0.97, p = 0.004), remained consistent with Model 1, but this time Orderliness became a significant predictor (OR = 1.03, p = 0.018) 2. Interaction Effects: The only statistically significant interaction term was Sport Type x Orderliness (C1) (OR = 0.965, p = 0.023). The negative coefficient (B = -0.04) indicates that the positive relationship between Orderliness and elite/pre-elite status (as evidenced by the main effect B = 0.03) is significantly attenuated (reduced) for athletes in Individual Sports compared to those in Team Sports (the reference category). The effect of Orderliness becomes non-significant (or slightly negative, B = -0.01) within the Individual Sport group. The remaining interaction terms were not statistically significant ( p > 0.05) Predictive performance of the Second Model. The full model correctly classified 61.1% of cases. The overall predictive power of the final model was assessed using the ROC curve, yielding an Area Under the Curve (AUC) of 0.642 which indicates acceptable (or fair) discrimination. The model achieved a Sensitivity (correctly identifying elite/pre-elite athletes) of 72.7% and a Specificity (correctly identifying non-elite athletes) of 47.6 (using a cut-off value of 0.5) (see Table 4 ). Table 4 Summary Of Hierarchical Binary Logistic Regression Analysis Predicting Sport Achievement Level Variable Estimate (B) SE p Odds Ratio (OR) 95%CI for OR Intercept 2.75 1.57 0.079 15.67 [0.73, 337.35] N1: Anxious-Insecurity -0.04 0.01 < 0.001 0.96 [0.94, 0.98] N2: Irritability 0.01 0.01 0.251 1.01 [0.99, 1.03] E1: Sociability -0.03 0.01 0.004 0.97 [0.95, 0.99] E2: Cheerfulness 0.01 0.01 0.277 1.01 [0.99, 1.03] E3: Sensation-Seeking 0.00 0.01 0.632 1.00 [0.98, 1.01] C1: Orderliness 0.03 0.01 0.018 1.03 [1.01, 1.06] C2: Self-discipline 0.01 0.01 0.397 1.01 [0.99, 1.03] C4: Prudence -0.01 0.01 0.102 0.99 [0.97, 1.00] A2: Peacefulness 0.00 0.01 0.887 1.00 [0.98, 1.02] A3: Compliance 0.00 0.01 0.528 1.00 [0.99, 1.02] O1: Aesthetic Interests -0.01 0.01 0.301 0.99 [0.98, 1.01] O3: Curiosity -0.01 0.01 0.284 0.99 [0.97, 1.01] O4: Creativity 0.00 0.01 0.791 1.00 [0.99, 1.02] H2: Humility 0.00 0.01 0.547 1.00 [0.98, 1.01] Sport type: individual – team -0.85 1.52 0.577 0.43 [0.02, 8.40] N1 ✻ Sport Type 0.031 0.02 0.107 1.03 [1.00, 1.06] N2 ✻ Sport Type 0.020 0.02 0.306 1.02 [0.98, 1.06] E1 ✻ Sport Type 0.020 0.02 0.224 1.02 [1.00, 1.06] E2 ✻ Sport Type 0.003 0.02 0.880 1.00 [0.97, 1.04] E3 ✻ Sport Type 0.022 0.02 0.189 1.02 [0.99, 1.06] C1 ✻ Sport Type -0.038 0.02 0.034 0.96 [0.94, 0.10] C2 ✻ Sport Type 0.033 0.02 0.085 1.03 [1.00, 1.07] C4 ✻ Sport Type -0.017 0.02 0.354 0.98 [0.95, 1.02] A2 ✻ Sport Type -0.014 0.02 0.445 0.99 [0.95, 1.02] A3 ✻ Sport Type 0.001 0.01 0.945 1.00 [0.98, 1.03] O1 ✻ Sport Type -0.002 0.02 0.927 1.00 [0.97, 1.03] O3 ✻ Sport Type -0.007 0.02 0.701 0.99 [0.96, 1.03] O4 ✻ Sport Type 0.013 0.02 0.473 1.01 [0.98, 1.05] H2 ✻ Sport Type -0.002 0.02 0.877 1.00 [0.97, 1.03] Note. The table displays the results of the second model. Personality scales were measured using T-scores. Estimates represent the log odds of "performance level = 1 = elite/pre-elite" vs. "performance level = 0 = non-elite (reference group). Sport type was coded 0 = team sport (reference group) and 1 = individual sport." A parallel hierarchical binary logistic regression was conducted at the broader personality factor level. The full factor-level model demonstrated significance, χ 2 (11) = 26.00, p = 0.006. However, only the Neuroticism factor emerged as a statistically significant predictor of elite/pre-elite status (OR = 0.97, p = 0.001), indicating that higher Neuroticism slightly reduced the odds of being in the higher achievement group. Crucially, none of the interaction terms between personality factors and sport type were found to be statistically significant ( p > 0.05). The predictive performance of this factor-level model was slightly weaker compared to the trait-level model. The model correctly classified 58.6% of cases. The overall predictive power was assessed by the AUC of 0.605, indicating acceptable discrimination. The model achieved a Sensitivity of 77.1% and a Specificity of 36.9% (using a cut-off value of 0.5). 4. Discussion 4.1. Psychometric Evaluation and Structural Validity The aim of this study was to refine the psychometric properties of the LPI-v3 in an athlete population by developing and standardizing a shortened version (LPI-v3s), and secondly, to explore group-level differences and generate athlete-specific norms to support evidence-based personality assessment in sport contexts. Based on both practical experience and previous research, it is often emphasized that a large number of items in psychometric instruments may compromise data quality, as respondents can become fatigued [ 35 , 36 ]. There is a growing trend toward developing shortened personality inventories, such as the MINI-IPIP, which are convenient to use across various contexts but often lack the depth required for more detailed interpretation. The results obtained confirm the factorial validity of the LPI-v3s, with satisfactory model fit indices and adequate internal consistency across scales. Measurement invariance analyses also showed that the inventory structure and item functioning were equivalent across gender groups, confirming that personality scores can be meaningfully compared between male and female athletes. Measurement invariance indicates that the construct is interpreted and measured in the same way across compared groups [ 37 , 38 ], ensuring that any observed score differences reflect true trait differences rather than measurement artifacts. Recent cross-cultural findings further support the robustness and gender invariance of the Five-Factor Model across populations [ 39 , 40 ]. These results suggest that the LPI-v3s can serve as a psychometrically sound and effective tool for assessing a broad range of personality traits in a sports context. Beyond confirming structural validity, the study also revealed systematic group-level variations, highlighting the importance of demographic differentiation in athlete personality assessment. 4.2. Age and Gender-Related Variations and Norm Development Studies indicate that there are systematic age-related differences in several personality dimensions among athletes, confirming the need for age-specific norms when measuring and interpreting personality traits, including the LPI-v3s scales developed in this study. Athlete personality differences were noticeable already in early adolescence, suggesting that personality maturation processes relevant to sport contexts begin relatively early [ 41 , 42 ]. Younger athletes in this research, aged 15 to 20 years, showed higher levels of Neuroticism and lower levels of Conscientiousness compared to older athletes aged 21 to 45 years. Other research has also indicated that this pattern reflects greater emotional instability and less developed self-regulation abilities during adolescence [ 43 , 44 ]. With increasing age, both male and female athletes demonstrated higher levels of Conscientiousness reflecting growing responsibility, discipline, and goal-directed behavior associated with accumulated sport experience and general psychological maturity [ 9 ]. Gender-specific patterns were also observed in age-related changes. Among female athletes, Neuroticism and its Anxiety–Insecurity facet decreased with age, while Conscientiousness increased, indicating greater emotional regulation and self-discipline across the developmental period. In male athletes, increases in Openness to Experience and Honesty–Humility was found, while Agreeableness showed a significant decline with age. This pattern may reflect shifting social roles and competitive attitudes, as older male athletes tend to emphasize self-confidence and autonomy over conformity. Similar tendencies have been documented in prior research [ 45 , 46 ]. The identification of these systematic age-related variations highlights the importance of developing differentiated norms. The introduction of age- and gender-specific normative data in this study ensures that trait scores are interpreted relative to appropriate reference groups, reducing the risk of biased conclusions when assessing athletes at different developmental stages. The inclusion of athletes as young as 15 years also underscores the value of early psychological profiling in sport, allowing practitioners to monitor personality development trajectories throughout adolescence and into adulthood, keeping in mind that personality is still forming during this period and that the social environment plays a significant role in shaping personality. 4.3. Personality as a Predictor of Sport Achievement Although the predictive power of the models was modest, the results underscore the practical importance of trait-level analysis. Lower Anxious-Insecurity appears to reflect better emotional regulation and performance stability under pressure which is a quality frequently emphasized in elite athlete profiles. The negative association of Sociability with elite status may indicate that highly social individuals expend more cognitive and emotional resources on interpersonal engagement at the expense of task focus, particularly in high-pressure competitive environments. The sport-type interaction involving Orderliness reveals that structured behavioral tendencies may facilitate success in team contexts, where rule adherence and role clarity are vital, whereas individual sports may reward autonomy and flexibility. These findings align with person–environment fit perspectives and suggest that personality–context interactions, rather than global personality factors, provide the most meaningful predictors of performance outcomes. The present findings highlight that specific personality traits offer a superior predictive framework for understanding sport achievement compared to broader personality factors. While the factor-level analysis confirmed the general relevance of emotional stability (Neuroticism factor), the overall model's predictive performance was marginal and lacked specificity. The significant findings at the trait level demonstrate that nuance is critical. Not only were traits like Anxious Insecurity and Sociability inversely related to elite status, but the positive role of Orderliness was found to be context dependent. The trait-level findings highlight the importance of personality contextualization in sport. For example, lower levels of sociability contradict previous findings linking extraversion to elite achievement [ 47 ], suggesting that in certain sports or cultures, reduced social engagement may promote goal-directed individual achievement. Similarly, while orderliness predicted elite status, this effect was particularly pronounced in team sports, highlighting the context-specific value of structure and discipline [ 15 ]. Specifically, the positive effect of Orderliness was significantly attenuated for Individual Sport athletes compared to Team Sport athletes. This implies that while Orderliness is generally beneficial, its specific importance as a driver for high achievement is likely stronger within the highly structured, cooperative, and often regimented environment of team sports, where adherence to rules and roles is critical. For individual athletes, other personality aspects or training autonomy might play a more dominant role. The trait-level analysis provides a more nuanced and superior predictive framework, capable of identifying specific drivers and context dependent effects, which were obscured at the higher factor level. This supports the use of detailed trait measures in high-performance sport psychology research. 4.4. Implications for Evidence-Based Personality Profiling in Sport The psychometrically validated LPI-v3s offers a reliable and contextually adapted tool for assessing key personality domains in athletes. Its structural validity makes it suitable for both applied sports and research environments, providing potentially effective personality assessment without compromising measurement quality. Furthermore, it provides an opportunity for a deeper understanding of personality domains across a diverse range of athletes. Research indicates that establishing age- and gender-specific norms improves the accuracy of survey interpretation and allows practitioners to assess athletes’ psychological profiles against comparable reference groups [ 48 , 49 ]. This helps to understand developmental trends and identify individual differences that may affect both athlete performance and team dynamics, as well as athletes’ psychological preparedness. From an applied perspective, the validated short version enables efficient large-scale screening, supports selection and talent identification procedures, and enhances the precision of psychological support planning. The invariance-supported comparability across gender groups further strengthens its utility in mixed-gender teams and cross-sectional athlete research. Standardized use of the LPI-v3s can promote more systematic, data-driven approaches to athlete assessment, talent development, and planning psychological support interventions. Integrating personality profiling into sport psychology practice can help coaches and practitioners tailor interventions that are relevant to athletes’ personality traits, promoting adaptive motivation, resilience, and interpersonal effectiveness in the sport environment [ 50 , 51 , 52 ]. 4.5. Cultural and Contextual Relevance of the LPI-v3/LPI-v3s Cross-cultural research highlights that psychometric instruments developed in one language or cultural context often do not retain their full conceptual and structural equivalence when adapted elsewhere [ 17 , 18 ]. This issue is particularly relevant in language-minority regions such as Latvia, where imported measures, including the NEO-PI-R and the Big Five Inventory, have shown reduced sensitivity to local linguistic nuances and social norms [ 23 , 24 ]. The LPI-v3 was initially designed to address these shortcomings by integrating both the Five Factor and HEXACO perspectives in a linguistically and culturally valid format. The methodological rigor applied in this study sets a precedent for culturally responsive psychometric development in other small or underrepresented populations. Further cross-national collaboration could further explore the equivalence of LPI-v3s measurements in the Baltic and European sports context, contributing to the development of a more unified, yet culturally sensitive, personality assessment framework in the sports environment. In addition to its national significance, the LPI-v3s exemplifies a scalable model for developing psychometrically valid, linguistically grounded personality measures in smaller cultural contexts. Such regionally adapted instruments can enhance inclusion in international sport psychology research, which has historically been dominated by English-language tools. 4.6. Limitations The development of the shortened version of the LPI-v3s and the development of norms for athletes were carried out in accordance with the guidelines for the validation of both surveys. It is important to note that the study sample of Latvian athletes is significantly large, but at the same time there are several limiting factors. One of these limitations is the reliance on self-report data, which may not be completely accurate and objective in all aspects. Personality measurements are largely based on self-report, which is inherently subject to various response biases, including social desirability, impression management, and self-perception. Although the inclusion of the lying scale in the LPI-v3s helped to identify and control potentially invalid responses, self-report instruments cannot completely eliminate subjective bias. Future studies could benefit from incorporating multi-method assessment designs, including peer or coach ratings, behavioral indicators, and objective psychological indicators, to strengthen validity. Limitations are also related to the cross-sectional nature of the study. Since the data were collected at a single point in time, it is not possible to examine the developmental changes in personality traits or to infer causal relationships between personality traits and sports performance outcomes. Longitudinal research designs would allow for a more comprehensive understanding. It is also important to note that the generalizability of the results is limited by the cultural and linguistic specificity of the sample. The norms and psychometric properties determined in this study apply to Latvian-speaking athletes. Therefore, additional cross-cultural validation in populations of other countries is needed to ensure broader applicability and assess the equivalence of measurements in different sports contexts. 5. Conclusions The present study validated a shortened version of the LPI-v3s in an athletic population, confirming its structural validity, reliability, and gender invariance. The development of age and gender-specific norms enhances the precision and contextual relevance of personality assessment in Latvian sport settings. The LPI-v3s offers a practical, culturally grounded, and psychometrically robust tool for evidence-based profiling of athletes, supporting individualized approaches to performance and psychological development. Future research should further evaluate its predictive value for sport achievement and cross-cultural applicability across broader European contexts. Declarations Ethics Approval and Consent to Participate This study was approved by the Ethics Committee of the Latvian Academy of Sport Education (Protocol No. 8, Statement No. 1, April 19, 2024) and adhered to the ethical guidelines outlined in the Declaration of Helsinki. Informed consent was obtained from all participants included in the study and participants were fully informed that their data would be used solely within the framework of this research. Confidentiality was strictly maintained, with all data securely stored to protect participant privacy. Participants were also informed of their right to withdraw from study at any time without penalty. Consent for Publication Not applicable. Data Availability The used datasets can be accessed at Riga Stradiņš University Dataverse repository: Volgemute K, Ulme G, Perepjolkina V, Līcis R, Abele A, Laviņš R. Latvian Personality Inventory (LPI-v3) in an athlete population. Riga Stradiņš University Dataverse. 2025 Aug 1. https://doi.org/10.48510/FK2/AMV9WA. For long-term access or institutional inquiries, data requests may also be directed to the RSU Research Department at [email protected] . Funding Sources The author(s) declared financial support was received for the research, authorship, and/or publication of this article. This research is funded under the Grant No. RSU/LSPA-PA-2024/1-0010 of the project No. 5.2.1.1.i.0/2/24/I/CFLA/005 “RSU Internal and RSU with LASE External Consolidation” (funded by the European Union Recovery and Resilience Facility and the budget of the Republic of Latvia). Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Authors Contributions Conceptualization: Katrina Volgemute, Viktorija Perepjolkina, Gundega Ulme. Data curation: Katrina Volgemute, Gundega Ulme, Krister Ansons, Renars Licis, Agita Abele, Rodrigo Lavins. Formal analysis: Viktorija Perepjolkina, Katrina Volgemute, Gundega Ulme. Funding acquisition: Katrina Volgemute. Investigation: Katrina Volgemute. Methodology: Katrina Volgemute, Viktorija Perepjolkina, Gundega Ulme. Software: Viktorija Perepjolkina, Katrina Volgemute. Supervision: Katrina Volgemute. Validation: Katrina Volgemute, Viktorija Perepjolkina, Gundega Ulme. Visualization: Katrina Volgemute. Writing – original draft: Katrina Volgemute, Viktorija Perepjolkina, Gundega Ulme. Writing – review & editing: Katrina Volgemute, Gundega Ulme, Viktorija Perepjolkina, Agita Abele, Alina Klonova. References Padli P, Prasetyo T, Kurniawan R, Putra RA, Candra O. The influence of environment and social interaction on the formation of athlete character: a descriptive study. Rev Iberoam Psicol Ejerc Deporte. 2024;19(4):430-4. Li Q, Xiao D, Zeng Q. Exploring performance of athletic individuals: Tying athletic behaviors and big-five personality traits with sports performance. PLoS One. 2024;19(12):e0312850. doi:10.1371/journal.pone.0312850 McCrae RR, Costa PT Jr. A five-factor theory of personality. In: Handbook of Personality: Theory and Research. 2nd ed. 1999. p.139-53. Guntoro TS, Putra MFP, Németh Z, Setiawan E. 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14:11:27","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":55607,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixC.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7957250/v1/8fe37817a7906359da126e29.xlsx"},{"id":96816383,"identity":"d92c0d12-b3b7-42da-9dd1-110719b27eb5","added_by":"auto","created_at":"2025-11-26 11:10:31","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":212134,"visible":true,"origin":"","legend":"","description":"","filename":"d8e2c4d83d67432eb12a7f3824385c561enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7957250/v1/c31c8924f1906b1b01a88c69.xml"},{"id":96917846,"identity":"a93f62e5-df2e-413f-96e5-4d143e00eab6","added_by":"auto","created_at":"2025-11-27 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11:10:31","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":208295,"visible":true,"origin":"","legend":"","description":"","filename":"d8e2c4d83d67432eb12a7f3824385c561structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7957250/v1/c7723286bc772885a79d1e86.xml"},{"id":96917316,"identity":"e2cef4d6-a688-4ee9-adaf-140f45c2e274","added_by":"auto","created_at":"2025-11-27 14:09:32","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":226614,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7957250/v1/5ad76fdde1cdb99708da12f7.html"},{"id":96816368,"identity":"9a2e4e17-b897-43ae-9476-b6ec6859b6ed","added_by":"auto","created_at":"2025-11-26 11:10:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151650,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the Research Process (independent sample flow, psychometric evaluation, and predictive validity analysis)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7957250/v1/fd6c08f7a3047821c436be42.png"},{"id":97977299,"identity":"fbd8bd62-5c2e-4db1-adcc-6ae49a9ff060","added_by":"auto","created_at":"2025-12-11 12:08:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1974289,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7957250/v1/04e822e6-fd3d-44a1-b062-3ec4b7d7ca07.pdf"},{"id":96816372,"identity":"51f204e9-27a9-46b9-9b46-e340098ff716","added_by":"auto","created_at":"2025-11-26 11:10:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38673,"visible":true,"origin":"","legend":"\u003cp\u003eAppendix A. Supplementary Tables.\u003c/p\u003e","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7957250/v1/9da8eff7f21535952c90e45e.docx"},{"id":96816375,"identity":"241aba2b-a808-4be7-9820-d9681ee1267a","added_by":"auto","created_at":"2025-11-26 11:10:31","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":50772,"visible":true,"origin":"","legend":"\u003cp\u003eAppendix B. LPI-v3s Manual.\u003c/p\u003e","description":"","filename":"AppendixB.docx","url":"https://assets-eu.researchsquare.com/files/rs-7957250/v1/9b0092ea8a0b7e8383eac994.docx"},{"id":96917189,"identity":"305ecb71-b9b3-42e2-bbbc-d5e527749a25","added_by":"auto","created_at":"2025-11-27 14:09:20","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":55607,"visible":true,"origin":"","legend":"\u003cp\u003eAppendix C. Continuous Norm Tables (Excel supplement).\u003c/p\u003e","description":"","filename":"AppendixC.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7957250/v1/7238bdd526c4d116999c6820.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychometric Properties and Standardization of the Shortened Latvian Personality Inventory (LPI-v3s) in Athlete Sample: Implications for Evidence-Based Assessment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAn athlete's personality shapes patterns of thinking, feeling and behavior that influence not only performance but also their ability to overcome challenges and maintain motivation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The concept of personality has attracted research attention across various scientific fields, including sport psychology. One of the most widely used theoretical frameworks is the Five-Factor Model [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Many measuring instruments used today to diagnose personality aspects are based on the concept of this model. The Five Factor Model divides personality traits into five broad domains: openness, conscientiousness, extraversion, agreeableness, and neuroticism. These traits have been associated with key psychological attributes in athletes, such as mental toughness and resilience [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies have explored how personality traits vary by gender, competitive level, and sport type, underscoring the complexity of individual differences within athletic populations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Such findings continue to spark interest in the role of personality in sport as well as highlight the need for context-specific assessment approaches tailored to athletic environments. Although personality traits are widely acknowledged as influential in athletic performance, research findings in this area remain mixed. Studies often report inconsistent relationships between specific traits and performance outcomes, reflecting the complex interplay between personality, psychological skills, and situational factors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These contrasting perspectives continue to drive interest and highlight the need for further investigation within applied sport contexts.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1. Personality Assessment in Sport\u003c/h2\u003e\u003cp\u003ePersonality assessment holds practical value in sport psychology by offering insight into athletes\u0026rsquo; mental strengths, challenges, and developmental needs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Understanding personality profiles can support individualized psychological interventions and guide coaching strategies. Research also indicates that personality traits differ across competitive levels, suggesting that high-performance sport may attract certain personality profiles [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Some traits, such as low neuroticism, have been consistently linked with long-term athletic success, as they are associated with emotional regulation, confidence and physiological stability [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePersonality traits also influence performance indirectly by shaping psychological skills. For example, Fabbricatore et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] emphasized that traits help develop psychological skills that are essential for success. Lower neuroticism is linked to greater emotional stability and better decision-making [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], while extraversion and conscientiousness correlate positively with performance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, sport participation may promote adaptive personality development [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Links between personality and competitive anxiety also support the value of tailored psychological interventions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These findings are consistent with broader personality patterns observed in athletes. Piepiora [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] notes that, according to the Five-Factor model, athletes tend to exhibit low neuroticism, high extraversion and conscientiousness, and moderate levels of openness and agreeableness. However, it is important to recognize that sport-specific and demographic differences remain critical factors, especially when considering variation across disciplines and gender. For example, Kemarat et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] reported that both the gender of athletes and the type of sport significantly affect personality characteristics and levels of competitive anxiety, with neuroticism emerging as the strongest predictor. These findings reinforce the need to consider sport-specific and demographic factors when evaluating personality in athletic populations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2. Limitations of Existing Personality Measures in Athletic Populations\u003c/h2\u003e\u003cp\u003ePersonality inventories grounded in the Five-Factor Model have been widely applied in sport psychology. While useful, their generalizability to sport-specific contexts has been questioned. Recent research emphasizes the need for psychometric tools designed specifically for athletes, to ensure accurate and contextually relevant assessments [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Studies often highlight that these tools fail to account for the unique psychological and situational challenges associated with different sports disciplines, and typically do not provide athlete-specific norms, which limit their interpretability in performance contexts [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It is important to mention that such personality assessments should consider cultural and sport contextual factors. These practical issues also limit their use in sport practice.\u003c/p\u003e\u003cp\u003eThe NEO Personality Inventory and its revised version (NEO-PI-R) are one of the most commonly used personality assessment tools within sport [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It assesses five broad traits but can take up to 60 minutes to complete, limiting their practicality in sport settings. Shorter tools like the Mini-IPIP [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] are more efficient but, as with many brief measures, may capture constructs with less detail. Others instrument like the Sports Mental Toughness Questionnaire (SMTQ) focus on narrow constructions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These limitations highlight the need for validated, sport-specific tools that are both comprehensive and practical. Existing inventories rarely incorporate cultural or regional adaptations, which is especially problematic when assessments are used across diverse linguistic and national populations. This is a critical concern in countries such as Latvia, where adaptations of foreign tools like the NEO-PI-R or Big Five Inventory have demonstrated limited cultural sensitivity and reduced interpretive depth [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Taking together, these theoretical, contextual, and practical shortcomings highlight a clear need for validated personality instruments that are designed with athletes in mind, culturally relevant, psychometrically sound, and efficient to administer. This forms the basis for evaluating and refining tools like the Latvian Personality Inventory (LPI-v3) for use in sport-specific contexts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3. The Latvian Personality Inventory (LPI-v3)\u003c/h2\u003e\u003cp\u003eLPI-v3 is a validated, multidimensional tool designed to assess a broad range of personality traits. It was developed in response to the limitations of existing instruments adapted for Latvia, such as the NEO-PI-R [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and the Big Five Inventory (BFI) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. While widely used, these tools have shown limited cultural sensitivity and offer only a general overview of traits, restricting their interpretive depth in applied settings. The LPI-v3 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] addresses these shortcomings by integrating elements from both the Five-Factor Model and the HEXACO framework. It consists of 100 items in Latvian and measures six broad personality domains: Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness, and Honesty-Humility, with four subscales in each. Initial validation studies support the LPI-v3 internal consistency and factor structure. However, its utility in applied contexts such as sport remains unexplored. Further psychometric evaluation, particularly among athletes, is needed to ensure its relevance and accuracy across demographic and performance-related variables, and to enable the development of athlete-specific population norms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.4. The Present Research: Study 1 And Study 2\u003c/h2\u003e\u003cp\u003eThe present research comprised two sequential studies designed to develop, validate, and apply the LPI-v3s in an athlete population.\u003c/p\u003e\u003cp\u003eStudy 1 focused on the psychometric refinement and standardization of the instrument, aiming to create a psychometrically sound and time-efficient measure suitable for athletes. Study 2 built on these findings by examining the predictive validity of the LPI-v3s in differentiating between athletes of varying achievement levels. Keeping that in mind, the overarching aim of the current study was to establish the LPI-v3s as reliable, valid and contextually appropriate personality assessment tool for sports settings by confirming its factorial structure and measurement invariance (Study 1), developing age and gender specific normative data and identifying the key personality traits that predict elite/pre-elite sport achievement status (Study 2).\u003c/p\u003e\u003cp\u003eThe main objectives of this study were as follows:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo develop a shortened version of the LPI-v3s suitable for athletes, ensuring stable factor structure and measurement invariance across male and female athlete subsamples (Study 1).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo establish normative indicators for the scales by determining which scales require the development of gender- and/or age-specific norms (Study 1).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo identify which personality scales (at both the trait and factor level) significantly predict sport achievement status (elite/pre-elite vs. non-elite) (Study 2).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eDespite the availability of various conceptual personality surveys based on the five-factor model, most of the available tools lack validation in athlete populations and often show limited cultural sensitivity when adapted to smaller language contexts, such as Latvian. It should be noted that currently, in many countries, there are no validated, sport-specific norms and their factorial structure and measurement invariance have not been empirically tested among athletes. This indicates that there is a need for a psychometrically sound, time-efficient, and culturally valid personality measure adapted to the needs of sport psychology practice.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants\u003c/h2\u003e\u003cp\u003eThe Initial Sample included 931 participants aged 15 to 45. Five participants who did not identify as male or female were excluded, as gender was a primary stratification element for subsequent analyses. The remaining set of participants formed the total sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;925). This sample was used as the source pool for: (1) selecting participants for the normative sample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;436) used in the development and psychometric validation of LPI-v3s (Study 1), and (2) forming the final analytical sample (n\u0026thinsp;=\u0026thinsp;753) for the evaluation of the relationship between the athletes' personality traits and sport achievement status (Study 2).\u003c/p\u003e\u003cp\u003eTogether, athletes represented 84 different sports, including basketball (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;107), hockey (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96), football (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;94), volleyball (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;65), athletics (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;56), fitness (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;47), handball (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;42), floorball (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;41), luge sport (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31), swimming (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28), orienteering (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27), judo (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15), sports dances (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15), table tennis (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13), rugby (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13), weightlifting (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12), boxing (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10), artistic gymnastics (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10), cross-country skiing (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9), tennis (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9), karate (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9), cycling (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8), beach volleyball (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8), equestrian sport (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8), alpine skiing (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7), gymnastics (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6), kayaking (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6), taekwondo (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6), figure skating (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6), climbing sport (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), running (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), shooting (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), biathlon (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4), bodybuilding (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4), curling (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4), triathlon (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4), road cycling (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4), disc golf (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4) and, other (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;82).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e\u003cem\u003e2.1.1. Total Sample (Study 1 and Study 2)\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eThe total sample comprised participants, with ages ranging from 15 to 44 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21.2, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.6 years). The sample consisted of 535 males (57.8%), aged 15 to 44 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20.9, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.8), and 390 females (42.2%), aged 15 to 43 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21.1, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.8). Sports experience among the participants ranged from one year in specific sport to 35 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.2 years, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.8 years), and training intensity ranged from 1 to 25 hours per week (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.8 hours/week, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.8 hours/week); 490 (53.0%) participants represented Team sports (356 males, 134 females) and 435 (47.0%) participants represented Individual sports (179 males, 256 females). Sport Achievement Level initially was classified in three categories: elite (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;46, 5.0%), pre-elite (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;433, 46.8%), or non-elite (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;446, 48.2%).\u003c/p\u003e\u003cp\u003eAthletes\u0026rsquo; competitive status was categorized based on training intensity and performance criteria. Elite athletes were defined as those who trained at least 8 sessions per week (or \u0026gt;\u0026thinsp;12 hours weekly) and had achieved at least one high-level competitive result, such as a podium at a national championship, a top placement in a regional league or participation in a major international competition (European Championships at junior or senior level, World Cup, World Championships, or Olympic Games). All athletes in the elite category also had a minimum of five years of sport-specific experience. pre-elite athletes trained at least 5 times per week (\u0026asymp;\u0026thinsp;7.5 hours weekly) and had competitive experience at the national championship level (in any age category), in regional leagues, or at university-level events such as Universiade. Non-elite athletes engaged in a minimum of 2 training sessions per week (\u0026asymp;\u0026thinsp;3 hours weekly), and their competition experience was limited to lower- or mid-tier competitions.\u003c/p\u003e\u003cp\u003eMissing data per participant did not exceed 5% and deletion was applied to maintain data integrity. Although the sample was broad and diverse, it represents a convenience sample of active athletes rather than a random selection from the national athlete population.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2. Normative Sample (Study 1)\u003c/h2\u003e\u003cp\u003eParticipants from the total sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;925) were stratified based on three criteria: gender (male/female), age (15\u0026ndash;17, 18\u0026ndash;20, 21\u0026ndash;29, 30\u0026ndash;45), and sport type (individual/team). The respondent distribution across these strata in the Analysis Sample was highly uneven. Since no official population data on the distribution of athletes by these specific characteristics was available, it was decided that an equal number of respondents per stratum would be selected to ensure maximum structural uniformity and direct comparability across groups.\u003c/p\u003e\u003cp\u003eFollowing the principle of the smallest available cell size, the final sample size for most strata was determined by the number of participants in the smallest available group, which was the female 15\u0026ndash;17 age group in team sports (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32). To ensure a uniform representation across all other strata, a total of 32 participants were randomly selected from each of the remaining strata that had more than 32 available respondents. The randomization was performed using an online random number generator (e.g., randomizer.org), ensuring that each participant within a stratum had an equal chance of being selected for the final sample.\u003c/p\u003e\u003cp\u003eAn exception was made for the 30\u0026ndash;45 age group, which had a significantly smaller number of participants. To ensure sufficient power for the gender-specific norms, it was decided to use the highest number of available respondents for each gender within this age group. This meant including all 21 available male respondents in each of their respective sport type strata. For female respondents, due to the low number of participants in team sports (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), a proportional number of participants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5) were randomly selected from the female individual sports group to maintain proportional representation within the female 30\u0026ndash;45 age group. This decision prioritized obtaining the largest possible sample size for each gender while preserving the sport type balance within the older female group.\u003c/p\u003e\u003cp\u003eTherefore, to ensure minimally sufficient representation (or adequate sample sizes) within age strata for the development of both gender- and age-specific norms where required, a decision was made to consolidate the initial four age groups into two broader categories: adolescents/young adults (15\u0026ndash;20 years) (combining 15\u0026ndash;17 and 18\u0026ndash;20 age groups) and Adults (21\u0026ndash;45 years) (combining 21\u0026ndash;29 and 30\u0026ndash;45 age groups). This approach allowed for the standardization of LPI-v3s scores for males and females, with the flexibility to implement age-based norms for specific scales identified as being significantly influenced by age (as detailed in Section 2.2.3). The final composition of the normative sample is detailed in Appendix A Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, which illustrates the initial and final counts for each stratum. The data from this normative sample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;436) were utilized to develop and validate the abbreviated version of the LPI-v3s and establish the necessary normative scores for the athlete population (see Appendix A Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.1.3. Final Analytical Sample and Data Quality Control (Study 2)\u003c/h2\u003e\u003cp\u003eThe total sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;925) was subsequently subjected to a final data quality control procedure to form the Final Analytical Sample used for the main predictive analyses. Crucially, scores on the Lie Scale (M) from the shortened version of the LPI-v3s (as detailed in Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e2.2.2\u003c/span\u003e.) were utilized as a criterion for data exclusion to ensure the validity of the personality profile analysis and minimize the impact of socially desirable responding within the final analytical sample. Participants who scored above the established cutoff on the Lie Scale (the T score\u0026thinsp;\u0026gt;\u0026thinsp;60) were identified as providing potentially invalid data. Consequently, a total of 160 participants (\u0026asymp;\u0026thinsp;17.3% of the sample) were excluded.\u003c/p\u003e\u003cp\u003eThe final Analytical sample used for all subsequent statistical analyses (prediction of sport achievement) consisted of 753 participants (437 males and 316 females). Detailed distributions by sport type, performance level, and gender are presented in Appendix A Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Measures\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. The Original Version of the Latvian Personality Inventory (LPI-v3)\u003c/h2\u003e\u003cp\u003eThe Latvian Personality Inventory (LPI-v3; Perepjolkina, Renge, 2012) is a 100-item self-report questionnaire used to assess six personality factors: Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness, and Honesty-Humility. The instrument is hierarchically structured, also measuring four narrower personality traits (facets) within each of the six factors. Each of the six factors is measured by 16 items, while each facet (subscale) is measured by 4 items. An additional 4 items form a Lie scale, which assesses the tendency toward socially desirable responding. Participants respond using a 5-point Likert scale ranging from 1 (\"Does not correspond to me\") to 5 (\"Corresponds to me\"). Scale scores for both the personality factors and the facets were derived by summing the scores of the respective items, dividing the sum by the number of items (to yield the mean score), and finally multiplying this mean score by 10. The resulting scaled scores range from 10 to 50. Higher scores indicate a greater expression of the trait as defined by the scale name.\u003c/p\u003e\u003cp\u003eLPI-v3 demonstrates a stable factor structure and satisfactory psychometric properties in the general adult population sample. However, the stability of the factor structure and its invariance across gender groups have not yet been empirically tested. Internal consistency (Cronbach's alpha, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1294) for the factor scales range from 0.81 to 0.88, and for the facet scales, from 0.61 to 0.86. The inventory also shows high test-retest reliability (Perepjolkina, Renge, 2012). Retest coefficients (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;166; mean interval of 19 weeks) ranges from 0.85 to 0.90 at the factor level and from to at the subscale level. The complete LPI-v3s short-form questionnaire (Latvian) and scoring key are provided in Appendix B.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Shortened Version of the LPI-v3 (LPI-v3s) Development and Use in Current Study\u003c/h2\u003e\u003cp\u003eThe data from the normative sample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;436) was utilized to develop a psychometrically sound abbreviated version of the LPI-v3s specifically adapted for the extended age and athlete population represented in this study. The primary objective of developing this shortened version was to ensure it achieved measurement invariance across gender groups, which is a prerequisite for correctly comparing mean scores between male and female athletes. Furthermore, the short version was designed to be robust enough to allow for the development of gender-specific and, where necessary, age-specific norms tailored to this athletic population (Study 1). The subsequent analyses for predicting sports achievement status were based on the T-scores derived from this validated short version of LPI-v3s (Study 2).\u003c/p\u003e\u003cp\u003eTo ensure accurate interpretation of personality traits, the necessity for developing separate age-based norms (adolescents/young adults [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] vs. adults [\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]) was examined. This decision was informed by a preliminary analysis of the normative sample (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;436), with the goal of detecting systematic variation of traits across the lifespan. The analysis was conducted separately for males and females.\u003c/p\u003e\u003cp\u003eA scale was deemed to require age-specific norms if: (1) Age correlated statistically significantly with the scale score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); and (2) The Independent Samples t-test or Mann-Whitney U test revealed a statistically significant mean difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the 15\u0026ndash;20 age group and the 21\u0026ndash;45 age group, with a corresponding effect size of at least a small to moderate magnitude (e.g., Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e or \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.20). Scales meeting both criteria were subsequently normed separately by age group, resulting in four distinct normative tables: (1) male adolescents/young adults, (2) male adults, (3) female adolescents/young adults, and (4) female adults. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a visual summary of the sequential steps and methodological structure for the two-study research design, including sample flow, psychometric analyses, and predictive modeling. Raw-to-T conversions and continuous norm tables are available in Appendix C (Excel).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Study Protocol and Ethics\u003c/h2\u003e\u003cp\u003eThe data collection process for the LPI-v3 was conducted using a mixed-mode approach to ensure maximum reach and engagement among participants. Athletes were given the option to complete either a digitally accessible questionnaire through Microsoft Forms or a paper-based version, which was administered directly by members of the research team. The target population included athletes from various sports and competitive levels. Participant recruitment and data collection occurred over a 13-month period, beginning on 1 June 2024 and concluding on 1 July 2025. The inventory was distributed directly to athletes by the research team. In addition to completing the LPI-v3 and rating each item, participants were requested to share demographic details, such as their age, gender, city of residence, type of sport, hours spent training each week, highest achievements, and years of practicing in sport.\u003c/p\u003e\u003cp\u003eParticipation in the study was anonymous and voluntary. All participants were informed that their data would be used exclusively for research purposes. Written informed consent was obtained prior to participation, ensuring that all individuals were aware of the study\u0026rsquo;s aims and how their data would be handled. In the case of participants under the age of 16, written informed consent was additionally obtained from their parents or legal guardians. The study was approved by the Ethics Committee of the Latvian Academy of Sport Education (Protocol No. 8, Statement No. 1, April 19, 2024) and was conducted in accordance with the ethical standards set forth in the Declaration of Helsinki.\u003c/p\u003e\u003cp\u003eConfidentiality was strictly maintained. All data were anonymized, encrypted, and stored on secure servers, accessible only to authorized members of the research team. The study adhered to applicable data protection regulations, with a registered data management plan submitted via the ARGOS (OpenAIRE) system. Participants were also informed of their right to withdraw from study at any time without penalty. In such cases, any identifiable data were immediately deleted to protect participant privacy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed separately for Study 1 (psychometric evaluation and norm development) and Study 2 (predictive validity). All analyses were conducted using JASP v0.95.4 and JAMOVI v2.6. Statistical significance was evaluated at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and effect sizes were reported where appropriate. Sample-size adequacy was verified using G*Power 3.1.9.6 (α\u0026thinsp;=\u0026thinsp;0.05, power\u0026thinsp;=\u0026thinsp;0.80). Internal consistency was assessed using Cronbach\u0026rsquo;s α and McDonald\u0026rsquo;s ω coefficients, with values\u0026thinsp;\u0026ge;\u0026thinsp;0.70 considered acceptable.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1. Study 1: Psychometric evaluation and norm development\u003c/h2\u003e\u003cp\u003eThe factorial evaluation proceeded iteratively. Exploratory factor analysis (EFA) using principal axis factoring with Promax rotation was first conducted to identify a parsimonious structure. The resulting configuration was then evaluated using hierarchical confirmatory factor analysis (HCFA) based on a polychoric correlation matrix and the diagonally weighted least squares (DWLS) estimator, appropriate for ordinal data.\u003c/p\u003e\u003cp\u003eModel fit was evaluated using the Comparative Fit Index (CFI), Tucker\u0026ndash;Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA, 90% CI), and Standardized Root Mean Square Residual (SRMR). Thresholds for acceptable fit were CFI and TLI\u0026thinsp;\u0026ge;\u0026thinsp;0.90, RMSEA\u0026thinsp;\u0026le;\u0026thinsp;0.08, and SRMR\u0026thinsp;\u0026le;\u0026thinsp;0.08. In line with Marsh et al. (2004, 2005) and McNeish, Wolf (2020), slightly lower CFI/TLI values were tolerated for complex hierarchical models.\u003c/p\u003e\u003cp\u003eMeasurement invariance across gender groups (male vs. female athletes) was examined using multigroup CFA. Configural, metric, and scalar invariance were tested sequentially. Invariance was supported when ΔCFI\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and ΔRMSEA\u0026thinsp;\u0026lt;\u0026thinsp;0.015 (Cheung, Rensvold, 2002; Chen, 2007).\u003c/p\u003e\u003cp\u003eTo determine the need for age-specific norms, Pearson\u0026rsquo;s correlations were calculated between age and personality-scale scores separately for males and females. Scales showing significant correlations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were further compared between age groups (15\u0026ndash;20 vs. 21\u0026ndash;45 years) using independent-samples t-tests or Mann\u0026ndash;Whitney U tests. Effect sizes were expressed as Cohen\u0026rsquo;s d (parametric) and rank-biserial \u003cem\u003er\u003c/em\u003e (non-parametric), with values\u0026thinsp;\u0026ge;\u0026thinsp;0.20 interpreted as practically meaningful.\u003c/p\u003e\u003cp\u003eContinuous norming was performedusing the cNORMj package in Jamovi [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], supplemented by conventional T-score transformations (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10) to facilitate comparability across methods. The quality of norm models was verified by coefficients of determination (R\u0026sup2; \u0026ge; 0.996), indicating empirical equivalence between continuous and conventional norms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2. Study 2: Predictive Validity (Trait and Factorial Level Analysis)\u003c/h2\u003e\u003cp\u003ePredictive validity of the LPI-v3s was evaluated using hierarchical binary logistic regression. Analyses were performed at two hierarchical levels: (1) Trait-level model: Step 1 included all trait-scale \u003cem\u003eT\u003c/em\u003e-scores and sport type (Team\u0026thinsp;=\u0026thinsp;reference). Step 2 added interaction terms (Sport Type \u0026times; Personality Trait) to test for moderating effects; (2) Factor-level model: A parallel analysis was conducted using the six broad personality factors.\u003c/p\u003e\u003cp\u003eModel performance was evaluated via Nagelkerke R\u0026sup2;, overall classification accuracy, and Receiver Operating Characteristic (ROC) curves. Discrimination was interpreted using the Area Under the Curve (AUC), with thresholds of 0.60\u0026thinsp;=\u0026thinsp;fair, 0.70\u0026thinsp;=\u0026thinsp;good, \u0026ge; 0.80\u0026thinsp;=\u0026thinsp;very good. Sensitivity and specificity were calculated using a 0.50 cut-off.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Study 1: Psychometric Evaluation and Norm Development of the LPI-v3s\u003c/h2\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Initial Model Fit Analysis\u003c/h2\u003e\n \u003cp\u003eConsidering that the LPI-v3 was developed and standardized on a general population sample aged 18 and older, and this study\u0026apos;s data were collected from an athlete sample aged 15 and older, it was first necessary to confirm that the inventory\u0026rsquo;s theoretical model was appropriate for this dataset before calculating standardized scores. The model fit was initially tested on the full normative sample (n\u0026thinsp;=\u0026thinsp;436) using hierarchical confirmatory factor analysis (HCFA).\u003c/p\u003e\n \u003cp\u003eThe initial hierarchical model showed a poor fit to the data. The chi-square statistic was significant, \u0026chi;\u0026sup2; (4424)\u0026thinsp;=\u0026thinsp;7484, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, which is common in large samples. The Comparative Fit Index (CFI\u0026thinsp;=\u0026thinsp;0.728) and Tucker\u0026ndash;Lewis Index (TLI\u0026thinsp;=\u0026thinsp;0.719) were below the acceptable threshold (\u0026gt;\u0026thinsp;.90), indicating insufficient model fit. The Standardized Root Mean Square Residual (SRMR\u0026thinsp;=\u0026thinsp;0.087) also exceeded the recommended limit (\u0026lt;\u0026thinsp;0.08), whereas the Root Mean Square Error of Approximation (RMSEA\u0026thinsp;=\u0026thinsp;0.040; 90% CI [0.038, 0.041]) met the acceptable standard, suggesting a reasonable approximation of the population covariance matrix. Taking together, these indices indicated that the initial measurement model did not adequately represent the relationships among the observed variables in the normative sample. Therefore, further modifications were required to improve overall fit.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003e\u003cem\u003e3.1.2. Development and Evaluation of Modified Models (Models 1\u0026ndash;4)\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eTo improve model fit, several modified models were sequentially tested, aiming to enhance the fit indices while retaining the original theoretical model as much as possible. This sequential process was guided primarily by the analysis of standard estimates of factor loadings and finally by modification indices.\u003c/p\u003e\n \u003cp\u003eIn the first modified model, the O2 (Tolerance) scale was excluded from O (Openness) factor, as its standard estimate on the O factor was 0.153, which is too low. Additionally, eight other items were removed (J41 from the E2 (Cheerfulness) scale, J3 from the H1 (Honesty) scale, J59 from the H3 (Modesty) scale, J78 and J91 from the H4 (Integrity) scale, J64 from the N3 (Depressivity) scale and J37 because their standard estimates representing factor loadings were below 0.35. The fit indices improved slightly but as seen in Appendix A Table S4, all indices except for RMSEA remained inadequate.\u003c/p\u003e\n \u003cp\u003eIn the next step, using the same procedure, one more item was identified and removed: J15 from the H1 (Honesty) scale. The fit indices for this second modified model also improved slightly but were still inadequate. Therefore, the model modification process continued.\u003c/p\u003e\n \u003cp\u003eAnalyzing the second model, all items showed appropriate factor loadings, but four scales (A1: Compliance, H3: Modesty, H4: Integrity and O2: Tolerance) showed unsatisfactory internal consistency metrics (Coefficient \u0026alpha; and \u0026omega; in the range of 0.381 to 0.591), indicating low reliability. Consequently, it was decided to exclude these scales from further analysis completely. This resulted in Modified Model 3. Although all fit indices for this model improved slightly (see Appendix A Table S4), they still indicated inadequate model fit, with the exception of the RMSEA.\u003c/p\u003e\n \u003cp\u003eAnalysis of the third modified model\u0026apos;s results showed that the H2 (Humility) scale\u0026apos;s factor loading in the H factor was inappropriate (\u0026lambda;\u0026thinsp;=\u0026thinsp;0.287), leading to its removal from the H factor. However, since only one scale was left in this factor, it was also removed from the higher-order factors. But two separate scales (H1 and H2) were left in the model, which did not fit into any of the higher-order factors. Additionally, it was found that the J34 item had a reduced factor loading (\u0026lambda;\u0026thinsp;=\u0026thinsp;0.366), and it was also excluded. Furthermore, at this stage, the proposed modification indices were carefully analyzed, and the model was supplemented with several relaxations, allowing four subscales to correlate with the N factor, one with the A factor, three with the C factor, and four with the O factor. Four correlations between the subscales were also permitted. All permitted correlations are theoretically justified and reflect complex relationships between personality factors and their constituent traits. As a result, the fourth modified model was obtained.\u003c/p\u003e\n \u003cp\u003eDespite slight improvements, this fourth modified model still revealed an unsatisfactory fit for the proposed model (CFI\u0026thinsp;=\u0026thinsp;0.830) on the overall normative sample. This inadequate model fit was more pronounced in the male athlete subsample, where the CFI was below the 0.80 threshold, but a little better in the female subsample (CFI\u0026thinsp;=\u0026thinsp;0.832) (see Appendix A Table S4). A more detailed analysis revealed that in the male subsample, three items (J100 from O4: Creativity, J52 from N3: Depressivity, and J28 from H2: Humility) showed excessively low factor loadings, and some scales dropped from their factors (the E3 scale and O1: Aesthetic Interests scale). However, subsequent modifications to the model, including those based on modification indices, did not lead to significant improvements. The results remained illogical, at least with respect to some items and scales in the male subsample.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3. EFA-Guided Model and Final HCFA (Model 5a)\u003c/h2\u003e\n \u003cp\u003eTo better understand the underlying factor structure of the LPI-v3s and address the poor model fit, an exploratory factor analysis (EFA) was performed on the overall normative sample. The EFA utilized a polychoric correlation matrix and the Principal Axis Factoring method for factor extraction. The number of factors were determined using Kaiser\u0026apos;s criterion (eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1), which suggested a 14-factor solution (representing 14 lower-order scales). Promax rotation was applied to allow for correlations between the factors, a decision based on the expectation that the personality traits being measured are not independent.\u003c/p\u003e\n \u003cp\u003eThe EFA results revealed a more parsimonious and well-fitting structure compared to the priori model. The 14-factor solution explained a total of 53.6% of the variance. This new structure differed slightly from the final modified CFA model from the previous stage (Model 4). Specifically:\u003c/p\u003e\u003cspan\u003e1. The H1: Honesty scale was lost entirely, while only the H2: Humility scale remained from the H factor.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e2. The E4: Social Activity scale was removed as its items merged with the O4 scale (Creativity), and the C3 (Perfectionism) scale was also removed as its items merged with the O3 (Curiosity) scale, which in both cases was theoretically unacceptable.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e3. Similar to the previous CFA Model 4, both the O2 (Tolerance) and A1(Compliance) scales dropped out as well.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e4. The second-order EFA showed that the E3 (Sensation-seeking) scale did not load on the general E factor.\u003cbr\u003e\u003c/span\u003e\n \u003cp\u003eBased on the EFA findings, a new, shortened model was developed, which included 53 items, 14 scales, and 5 higher-order factors. This revised model was then subjected to a new hierarchical confirmatory factor analysis (CFA) (Model 5). The fit indices for this initial model were gathered to establish a baseline. Based on these results and the modification indices, several theoretically justified correlations between specific scales and factors were allowed (e.g., based on covariances\u0026thinsp;\u0026gt;\u0026thinsp;0.35). This refined version of the model, subsequently referred to as Model 5a, was found to provide a significantly better fit to the data.\u003c/p\u003e\n \u003cp\u003eAlthough the CFI for Model 5a was 0.885 (see Table\u0026nbsp;5), it still did not meet the recommended threshold of \u0026gt;\u0026thinsp;0.90. However, it was nearing this threshold and given that while Model 5a is significantly simpler than the initial model, it remains complex. Authors such as Marsh et al. [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] and McNeish, Wolf [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] note that in the case of psychometric instruments with many scales and items, there is a lower probability of achieving the stringent benchmarks (CFI and TLI\u0026thinsp;\u0026gt;\u0026thinsp;0.95) recommended by Hu and Bentler [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. These authors acknowledge that for complex models, such as those in personality tests, the CFA model fit may be lower than ideal, and values around 0.80 or 0.85 can be considered acceptable. Therefore, the decision was made to use this model for further analysis.\u003c/p\u003e\n \u003cp\u003eThe goal of this CFA was to identify a measurement model that provided an acceptable fit and was suitable for subsequent measurement invariance testing, which is crucial for the planned gender-based norming and comparisons of personality traits related to athletic performance. Model 5a\u0026apos;s fit indices for the overall normative sample and the separate male and female subsamples are presented in Table 5. As can be seen, the fit indices were higher in the female subsample than in the male subsample (CFI\u0026thinsp;=\u0026thinsp;0.878 and 0.849, respectively), and in both subsamples, they were lower than in the overall sample. Nevertheless, these values were significantly better compared to the fit indices of the fourth modified model and reached the minimum required criterion of \u0026gt;\u0026thinsp;0.80, which could be considered acceptable for such a complex survey structure. Consequently, this model was subsequently used for measurement invariance testing. Fit indices for the modified Model 5a for the overall, male, and female subsamples are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGoodness-of-Fit Indices of the HCFA Modified Model 5a\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e (df)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA [90% CI]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eModified Model 5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormative sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2505 (1291)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003cp\u003e[0.044; 0.049]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eModified Model 5a\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormative sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2217 (1276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003cp\u003e[0.038; 0.044]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eModified Model 5a\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eathletes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1857\u003c/p\u003e\n \u003cp\u003e(1276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003cp\u003e[0.040; 0.049]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eModified Model 5a\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eathletes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1662\u003c/p\u003e\n \u003cp\u003e(1276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003cp\u003e[0.033; 0.044]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cem\u003eNote\u003c/em\u003e. Estimator is DWLS. Model test is scaled and shifted. Information matrix is expected. Standard errors are robust. Fit indices are based on the scaled test statistics. \u0026chi;\u0026sup2;/df: Degrees of freedom; CFI: Confirmatory Fit Index; TLI: Tucker-Lewis Index; RMSEA: Root Mean Square; SRMR: Standardized Root Mean Square Residual\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eGiven its improved parsimony and satisfactory model fit, Model 5a was retained as the final structure of the LPI-v3s for subsequent analyses, including measurement invariance and normative development (Study 1), and predictive analyses (Study 2).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.4. Measurement Invariance\u003c/h2\u003e\n \u003cp\u003eFactorial invariance was tested using multigroup confirmatory factor analysis (MG-CFA) to determine if the measurement model for the modified shortened version (Model 5a) of LPI-v3s was equivalent across male and female athlete samples. The analysis followed a hierarchical approach, starting with the least restrictive model and progressing to more restrictive models. The goodness-of-fit was evaluated using the chi-square statistic (\u0026chi;\u003csup\u003e2\u003c/sup\u003e), degrees of freedom (df), comparative fit index (CFI), Tucker-Lewis Index (TLI), and the root mean square error of approximation (RMSEA) with its 95% confidence interval. A significant drop in fit for the more restricted models was assessed using the change in CFI (\u0026Delta;CFI), with a value less than 0.01 considered acceptable [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe first step was to test for configural invariance, which assesses whether the factor structure is the same across both groups without imposing equality constraints on the model parameters. The results of the configural model indicated an acceptable, but not excellent, fit to the data (\u0026chi;\u003csup\u003e2\u003c/sup\u003e(2552)\u0026thinsp;=\u0026thinsp;3516; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The fit indices were CFI\u0026thinsp;=\u0026thinsp;0.863, TLI\u0026thinsp;=\u0026thinsp;0.852, and RMSEA\u0026thinsp;=\u0026thinsp;0.042 [0.038; 0.045]. These results support the notion that the same number of factors and the same pattern of factor loadings were present in both male and female samples.\u003c/p\u003e\n \u003cp\u003eFollowing the establishment of configural invariance, metric invariance was tested by constraining the factor loadings to be equal across both groups. This model showed a slight deterioration in fit compared to the configural model (\u0026chi;\u003csup\u003e2\u003c/sup\u003e(2603)\u0026thinsp;=\u0026thinsp;3553; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The change in fit was minimal (\u0026Delta;\u0026chi;\u003csup\u003e2\u003c/sup\u003e(51)\u0026thinsp;=\u0026thinsp;37; \u0026Delta;CFI\u0026thinsp;=\u0026thinsp;0.002), which is below the recommended threshold of 0.01. This suggests that the factor loadings are equivalent across both male and female samples, supporting the comparability of the factor-item relationships between the two groups.\u003c/p\u003e\n \u003cp\u003eThe final step was to test for scalar invariance by adding the constraint that the item interceptions are equal across both groups. This model provides the strongest test of measurement equivalence. The results showed a significant drop in model fit compared to the metric invariance model based on the chi square test (\u0026chi;\u003csup\u003e2\u003c/sup\u003e(2741)\u0026thinsp;=\u0026thinsp;3746; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), nevertheless, the change in CFI was 0.008 (\u0026Delta;CFI\u0026thinsp;=\u0026thinsp;0.008), which is below the recommended threshold of 0.01. This suggests that the item interceptions are equivalent, allowing for direct comparison of latent mean scores between the groups. Based on these results, full scalar invariance was supported (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSequential Tests of Measurement Invariance Across Gender Groups For the LPI-v3s\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSEA CI 95% [lower; upper]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta; \u0026chi;2 (\u0026Delta;df)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;CFI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eConfigural invariance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003cp\u003e[0.038; 0.045]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMetric invariance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003cp\u003e[0.038; 0.044]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e(51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eScalar invariance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003cp\u003e[0.038; 0.044]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003cp\u003e(138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003eNote\u003c/em\u003e. \u0026chi;\u0026sup2;: Chi-square; df: degrees of freedom; CFI: Comparative Fit Index; TLI: Tucker\u0026ndash;Lewis Index; RMSEA: Root Mean Square Error of Approximation; \u0026Delta;\u0026chi;\u0026sup2; = Chi-square difference test; \u0026Delta;df\u0026thinsp;=\u0026thinsp;difference in degrees of freedom; \u0026Delta;CFI\u0026thinsp;=\u0026thinsp;change in Comparative Fit Index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.5. Gender Differences and Decision for Gender Norms\u003c/h2\u003e\n \u003cp\u003eTo determine which scales require gender-specific norms, mean differences between male and female athletes were examined using the Mann-Whitney U test due to non-normality across most scales.\u003c/p\u003e\n \u003cp\u003eThe analysis identified statistically significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the central tendency (median) for six scales. However, only five scales demonstrated a meaningful effect size (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.20): N1 (Anxious Insecurity), N2 (Irritability), N (Neuroticism), A4 (Self-Control), and O1 (Aesthetic Interests). The largest effect sizes were observed for N (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32) and N1 (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29).\u003c/p\u003e\n \u003cp\u003eBased on the combined criteria of statistical significance and meaningful effect size, gender-specific norms were deemed necessary for the five identified scales. All remaining scales (including those that reached statistical significance but had \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.20) showed negligible differences between male and female athletes, suggesting that unified norms can be applied for these traits (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMean Differences and Effect Sizes Between Gender Groups in the LPI-v3s Normative Sample\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLPI-v3 Scale (Short Form)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (\u003cem\u003eSD\u003c/em\u003e) normative sample\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;436)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (\u003cem\u003eSD\u003c/em\u003e) male\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;234)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (\u003cem\u003eSD\u003c/em\u003e) female\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;202)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest statistic (\u003cem\u003eU\u003c/em\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003cp\u003esize\u003c/p\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNorms required\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN1: Anxious-Insecurity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.3 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.5 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.3 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN2: Irritability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.9 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.0 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.0 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE1: Sociability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.0 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.6 (9,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.4 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE2: Cheerfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.8 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.1 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.6 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE3: Sensation-Seeking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.6 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.9 (8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.3 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1: Orderliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.9 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.8 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.3 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2: Self-discipline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.2 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.6 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.0 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC4: Prudence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.1 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.2 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.8 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA2: Peacefulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.8 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.6 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.0 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA3: Compliance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.8 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.5 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.1 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA4: Self-Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.1 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.0 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.0 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO1: Aesthetic Interests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.9 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.2(8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.8 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO3: Curiosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.2 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.6 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.7 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO4: Creativity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.5 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.8 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.2 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH2: Humility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.8 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.6 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN: Neuroticism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.8 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.0 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.8 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE: Extraversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.9 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.8 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.0 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC: Conscientiousness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.9 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.2 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.5(6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA: Agreeableness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.4 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.9 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.9(6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO: Openness to Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.8 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.4(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM: Lie Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.1 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.0 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.1(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.6. Age Effect and Decision for Age Norms\u003c/h2\u003e\n \u003cp\u003eCorrelations with age (Criterion 1). To empirically determine the necessity of establishing age-based norms, Pearson correlational analyses were conducted between respondent age and all shortened version of the LPI-v3s scales\u0026rsquo; scores within the normative sample, with analyses performed separately for the female and male subsamples. The results are detailed in (see Appendix A Table S6).\u003c/p\u003e\n \u003cp\u003eFindings in the female subsample. Age was statistically associated with two factor-level scales and four facet scales in the female subsample. Consistent with general population trends, a negative, weak but statistically significant correlation was between age and Neuroticism (\u003cem\u003er\u003c/em\u003e = -0.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012). This relationship was reflected in the facet Anxious-Insecurity (\u003cem\u003er\u003c/em\u003e = -0.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), suggesting that women tend to report lower levels of emotional instability or worry with increasing age. Similarly, Sensation-Seeking showed a small, significant decline with age (\u003cem\u003er\u003c/em\u003e = -0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Conversely, a positive, weak but statistically significant correlation was found between age and Conscientiousness (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), which was primarily driven by the facet Orderliness (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), and weakly by Prudence (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046).\u003c/p\u003e\n \u003cp\u003eFindings in the male subsample. The male subsample displayed more numerous and often stronger correlations between age and personality traits, suggesting a greater influence of age on self-reported scores in this group. Positive, statistically significant correlations were found with the following factors:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. Openness to Experience (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which was largely accounted for by the facet Curiosity (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e2. Conscientiousness (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), which was largely accounted for by the facet Prudence (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating increased cautiousness in decision-making with age.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. The facet Humility (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) under the Honesty-Humility factor, which was the strongest correlation observed across all scales, indicating that male athletes report becoming substantially less status-conscious and more modest as they age.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eOnly two negative correlations in the male group were observed for the Agreeableness factor (\u003cem\u003er\u003c/em\u003e = -0.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08), which was largely accounted for by the facet Compliance (\u003cem\u003er\u003c/em\u003e = -0.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that older male athletes may be less inclined to defer to others or compromise compared to their younger counterparts.\u003c/p\u003e\n \u003cp\u003eMean differences between age groups. Following the correlational analysis which served as the first filtering criterion for age-specific norms, the second criterion for requiring age-specific norms was tested. Independent Samples t-tests (or the Mann-Whitney U test, based on normality assumptions) were performed only on those scales that had shown a statistically significant correlation with age in the respective gender subsamples. This step was necessary to confirm that the observed relationship translated into a meaningful mean difference (criterion) between the newly defined age groups: adolescents/young adults (15\u0026ndash;20) and adults (21\u0026ndash;45).\u003c/p\u003e\n \u003cp\u003ePrior to conducting mean difference analyses, assumptions of normality (Shapiro-Wilk test) and homogeneity of variances (Levene\u0026apos;s test) were examined for all scales demonstrating a significant correlation with age in their respective gender subsamples.\u003c/p\u003e\n \u003cp\u003eIn the female subsample, Levene\u0026apos;s test indicated that the assumption of homogeneity of variances was met for all scales (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the normality assumption was violated for the Sensation-Seeking (E3) and Orderliness (C1) scales (\u003cem\u003ep\u003c/em\u003e\u003csub\u003eShapiro\u003c/sub\u003e \u0026lt; 0.05). Consequently, mean differences for these two non-normally distributed scales were analyzed using the non-parametric Mann-Whitney U test, while the remaining scales were analyzed using the Independent Samples t-test (assuming equal variances).\u003c/p\u003e\n \u003cp\u003eA similar pattern was observed in the male subsample: Levene\u0026apos;s test confirmed the homogeneity of variances assumption for all correlating scales (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the Shapiro-Wilk test revealed a violation of the normality assumption (\u003cem\u003ep\u003c/em\u003e\u003csub\u003eShapiro\u003c/sub\u003e \u0026lt; 0.05) for four facets: Prudence (C4), Compliance (A3), Curiosity (O3), and Humility (H2). Therefore, the Mann-Whitney U test was applied to these four non-normally distributed scales, and the remaining scales were tested using the Independent Samples t-test (assuming equal variances).\u003c/p\u003e\n \u003cp\u003eFemale subsamples mean differences. For the female subsample, five scales met the initial correlation criterion (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequent mean comparison tests (Independent Samples -test or Mann-Whitney test, based on normality) confirmed that four out of five scales met the dual criteria for requiring age-specific norms (i.e., \u0026lt; 0.05 AND \u003cem\u003ed\u003c/em\u003e or \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.20) (see Appendix A Table S7):\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. Neuroticism (\u003cem\u003et\u003c/em\u003e (200)\u0026thinsp;=\u0026thinsp;3.35, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49) and its facet Anxious-Insecurity (\u003cem\u003et\u003c/em\u003e (200)\u0026thinsp;=\u0026thinsp;2.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.006, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41) showed higher scores among younger women, indicating greater emotional stability with age.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e2. Conscientiousness (\u003cem\u003et\u003c/em\u003e (200) = -2.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e = -0.43) and its facet Orderliness (\u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3775, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013, \u003cem\u003er\u003c/em\u003e = -0.20) showed higher scores among adult women, suggesting increased organization and diligence with age.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eThe Sensation-Seeking facet (E3) failed to meet the effect size criterion (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.08), despite the statistically significant value (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.033), and thus did not warrant age-specific norms.\u003c/p\u003e\n \u003cp\u003eMale Subsample Mean Differences. In the male subsample, seven scales met the initial correlation criterion. Mean comparison tests confirmed that six out of the seven scales met the criteria for requiring age-specific norms (see Appendix A Table S8):\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. Conscientiousness (\u003cem\u003et\u003c/em\u003e (232) = -2.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, \u003cem\u003ed\u003c/em\u003e = -0.34) and its facet Prudence (\u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5167, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24) showed higher scores among adult men, indicating increased responsibility and long-term planning with age.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e2. Compliance (\u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4920, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003er\u003c/em\u003e = -0.27) showed lower scores among adult men, confirming the finding from the correlation analysis that older male athletes report being less yielding.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. Openness to Experience (\u003cem\u003et\u003c/em\u003e (232) = -2.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034, \u003cem\u003ed\u003c/em\u003e = -0.28) and the facet Humility (\u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4348, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32) also met the criteria.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eThe Curiosity facet (O3) failed to meet the effect size criterion (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19) with the established cut-off of r\u0026thinsp;\u0026gt;\u0026thinsp;0.20, despite the statistically significant p-value (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014). Similarly, the Agreeableness factor (A) failed to meet the statistical significance criterion (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.103), despite meeting the effect size criterion (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.21). Therefore, these two scales did not warrant age-specific norms.\u003c/p\u003e\n \u003cp\u003eBased on the sequential analyses, age-specific norms were established for four scales in the female subsample and six scales in the male subsample. For all other scales, a single, gender-specific norm was retained, as these traits did not show a simultaneous statistically significant and practically meaningful change across the two age groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.7. Development of Norm Construction\u003c/h2\u003e\n \u003cp\u003eNormative scores for the LPI-v3 Short Form scales were calculated using a dual approach to ensure both high-quality norm tables and simplified scoring for subsequent analysis.\u003c/p\u003e\n \u003cp\u003eFor the creation of the final norm tables, a regression-based continuous norming approach was employed, utilizing the cNORMj for single group module within the JAMOVI statistical software. This method offers significant advantages over conventional approaches, as it models the relationship between raw scores and normative scores using polynomial regression, thereby smoothing the percentile curve and eliminating gaps or abrupt changes that often occur with traditional percentile calculations [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although the continuous function for age-normed scores was constrained to predefined groups (due to the single-group approach), this method effectively optimized the endpoints and filled any sparsity in the data.\u003c/p\u003e\n \u003cp\u003eFor the purposes of the subsequent analytical steps (e.g., regression analysis) involving the Total Analytic Sample, T-scores were calculated using the conventional approach (using a linear transformation where the mean is set to \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50 and the standard deviation to \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10).\u003c/p\u003e\n \u003cp\u003eBased on the preliminary statistical analysis of the athlete sample, it was determined that gender- and/or age-specific norms were necessary only for a subset of the scales. Specifically, gender-specific norms were developed for the following scales: N1, N2, N, C1, C4, C, A3, A4, O1, O and H2. Furthermore, a detailed review of the data indicated the need for age-specific norms within genders for several key traits:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. For female athletes, separate norms for two age groups were created for N1, N, and C1.\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e2. For male athletes, separate norms for two age groups were created for A3, O, and H2.\u003c/p\u003e\n \u003c/span\u003e\n \u003cp\u003eFor the remaining scales (E, E1, E2, E3, C2, A2, O3, O4, A, and M), a unified set of norms was developed, without subdivision by age or gender, as no statistically significant differences were observed across these demographic variables.\u003c/p\u003e\n \u003cp\u003eThe cNORMj package allows for a direct comparison between conventional norms and the regression-based continuous norms. The quality of the continuous norming model was excellent, with the final model\u0026apos;s being no lower than 0.996 for all individual norm tables. This high degree of fit indicates that the two methods are empirically equivalent, and that no systematic error exists between the linear transformation and the regression model. Therefore, the T-scores calculated using the conventional approach are considered equivalent to those that would be assigned based on the newly created continuous norm tables, justifying their use in the primary data analysis.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Study 2: Predictive Validity of the LPI-v3s for Sport Achievement\u003c/h2\u003e\n \u003cp\u003eA hierarchical binary logistic regression was performed to examine the unique contributions of personality traits, sport type, and their interaction on the prediction of elite/pre-elite status (see Appendix A Table S5 for coding and full results). The elite and pre-elite groups were merged into a single elite/pre-elite achievement category due to the limited number of participants in the elite category (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34). Insufficient sample size in the highly specific elite group would have compromised the statistical power and stability of the regression model\u0026apos;s parameter estimates, particularly when analyzing complex interactions. Merging these two categories, which represent the highest levels of competitive performance, ensured adequate cell sizes for robust analysis while still maintaining a meaningful distinction from the non-elite group.\u003c/p\u003e\n \u003cp\u003eModel 1: Personality Traits and Sport Type (Main Effects). Step 1 included the main effects of all personality traits and the Sport Type variable. The initial model was found to be statistically significant, \u0026chi;\u003csup\u003e2\u003c/sup\u003e (15)\u0026thinsp;=\u0026thinsp;26.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035, the model accounted for 4.58% of the variance in sport achievement (Nagelkerke \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.046). In this first step, the significant predictors were:\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1. Anxious Insecurity (N1): Significantly reduced the odds of being in the higher achievement group (OR\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\n \u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e2. Sociability (E1): Significantly reduced the odds of being in the higher achievement group (OR\u0026thinsp;=\u0026thinsp;0.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e\n \u003c/span\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Heading\"\u003e3.\u0026nbsp; The main effect of sport type was not significant (OR\u0026thinsp;=\u0026thinsp;0.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.577).\u003c/div\u003e\n\u003cp\u003eModel 2: Interaction Effects. Step 2 introduced the interaction terms (sport type x personality traits) to the model from Step 1. The addition of the interaction terms resulted in a non-significant improvement in the model\u0026apos;s overall fit, \u0026Delta;\u0026chi;\u003csup\u003e2\u003c/sup\u003e(14)\u0026thinsp;=\u0026thinsp;20.8, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.107. The full model was statistically significant (Model 2), \u0026chi;\u003csup\u003e2\u003c/sup\u003e(29)\u0026thinsp;=\u0026thinsp;47.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, and accounted for a slightly larger proportion of the variance (Nagelkerke \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.081). After the inclusion of the interaction terms, the following results were observed:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. Main Effects (Model 2). The significance and direction of the main effects of Anxious Insecurity (OR\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Sociability (OR\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), remained consistent with Model 1, but this time Orderliness became a significant predictor (OR\u0026thinsp;=\u0026thinsp;1.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018)\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e2. Interaction Effects: The only statistically significant interaction term was Sport Type x Orderliness (C1) (OR\u0026thinsp;=\u0026thinsp;0.965, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023). The negative coefficient (B = -0.04) indicates that the positive relationship between Orderliness and elite/pre-elite status (as evidenced by the main effect B\u0026thinsp;=\u0026thinsp;0.03) is significantly attenuated (reduced) for athletes in Individual Sports compared to those in Team Sports (the reference category). The effect of Orderliness becomes non-significant (or slightly negative, B = -0.01) within the Individual Sport group. The remaining interaction terms were not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003ePredictive performance of the Second Model. The full model correctly classified 61.1% of cases. The overall predictive power of the final model was assessed using the ROC curve, yielding an Area Under the Curve (AUC) of 0.642 which indicates acceptable (or fair) discrimination. The model achieved a Sensitivity (correctly identifying elite/pre-elite athletes) of 72.7% and a Specificity (correctly identifying non-elite athletes) of 47.6 (using a cut-off value of 0.5) (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary Of Hierarchical Binary Logistic Regression Analysis Predicting Sport Achievement Level\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate (B)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003cp\u003e(OR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI for OR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.73, 337.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN1: Anxious-Insecurity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.94, 0.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN2: Irritability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99, 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE1: Sociability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.95, 0.99]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE2: Cheerfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99, 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE3: Sensation-Seeking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98, 1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1: Orderliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.01, 1.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2: Self-discipline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99, 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC4: Prudence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.97, 1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA2: Peacefulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98, 1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA3: Compliance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99, 1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO1: Aesthetic Interests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98, 1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO3: Curiosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.97, 1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO4: Creativity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99, 1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH2: Humility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98, 1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSport type: individual \u0026ndash; team\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.02, 8.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN1 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.00, 1.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN2 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98, 1.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE1 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.00, 1.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE2 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.97, 1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE3 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.99, 1.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.94, 0.10]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.00, 1.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC4 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.95, 1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA2 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.95, 1.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA3 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98, 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO1 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.97, 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO3 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.96, 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO4 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.98, 1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH2 ✻ Sport Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.97, 1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e The table displays the results of the second model. Personality scales were measured using T-scores. Estimates represent the log odds of \u0026quot;performance level = 1 = elite/pre-elite\u0026quot; vs. \u0026quot;performance level = 0 = non-elite (reference group). Sport type was coded 0 = team sport (reference group) and 1 = individual sport.\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA parallel hierarchical binary logistic regression was conducted at the broader personality factor level. The full factor-level model demonstrated significance, \u0026chi;\u003csup\u003e2\u003c/sup\u003e(11)\u0026thinsp;=\u0026thinsp;26.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006. However, only the Neuroticism factor emerged as a statistically significant predictor of elite/pre-elite status (OR\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), indicating that higher Neuroticism slightly reduced the odds of being in the higher achievement group. Crucially, none of the interaction terms between personality factors and sport type were found to be statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eThe predictive performance of this factor-level model was slightly weaker compared to the trait-level model. The model correctly classified 58.6% of cases. The overall predictive power was assessed by the AUC of 0.605, indicating acceptable discrimination. The model achieved a Sensitivity of 77.1% and a Specificity of 36.9% (using a cut-off value of 0.5).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Psychometric Evaluation and Structural Validity\u003c/h2\u003e\u003cp\u003eThe aim of this study was to refine the psychometric properties of the LPI-v3 in an athlete population by developing and standardizing a shortened version (LPI-v3s), and secondly, to explore group-level differences and generate athlete-specific norms to support evidence-based personality assessment in sport contexts. Based on both practical experience and previous research, it is often emphasized that a large number of items in psychometric instruments may compromise data quality, as respondents can become fatigued [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. There is a growing trend toward developing shortened personality inventories, such as the MINI-IPIP, which are convenient to use across various contexts but often lack the depth required for more detailed interpretation.\u003c/p\u003e\u003cp\u003eThe results obtained confirm the factorial validity of the LPI-v3s, with satisfactory model fit indices and adequate internal consistency across scales. Measurement invariance analyses also showed that the inventory structure and item functioning were equivalent across gender groups, confirming that personality scores can be meaningfully compared between male and female athletes. Measurement invariance indicates that the construct is interpreted and measured in the same way across compared groups [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], ensuring that any observed score differences reflect true trait differences rather than measurement artifacts. Recent cross-cultural findings further support the robustness and gender invariance of the Five-Factor Model across populations [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These results suggest that the LPI-v3s can serve as a psychometrically sound and effective tool for assessing a broad range of personality traits in a sports context. Beyond confirming structural validity, the study also revealed systematic group-level variations, highlighting the importance of demographic differentiation in athlete personality assessment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Age and Gender-Related Variations and Norm Development\u003c/h2\u003e\u003cp\u003eStudies indicate that there are systematic age-related differences in several personality dimensions among athletes, confirming the need for age-specific norms when measuring and interpreting personality traits, including the LPI-v3s scales developed in this study. Athlete personality differences were noticeable already in early adolescence, suggesting that personality maturation processes relevant to sport contexts begin relatively early [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eYounger athletes in this research, aged 15 to 20 years, showed higher levels of Neuroticism and lower levels of Conscientiousness compared to older athletes aged 21 to 45 years. Other research has also indicated that this pattern reflects greater emotional instability and less developed self-regulation abilities during adolescence [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. With increasing age, both male and female athletes demonstrated higher levels of Conscientiousness reflecting growing responsibility, discipline, and goal-directed behavior associated with accumulated sport experience and general psychological maturity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGender-specific patterns were also observed in age-related changes. Among female athletes, Neuroticism and its Anxiety\u0026ndash;Insecurity facet decreased with age, while Conscientiousness increased, indicating greater emotional regulation and self-discipline across the developmental period. In male athletes, increases in Openness to Experience and Honesty\u0026ndash;Humility was found, while Agreeableness showed a significant decline with age. This pattern may reflect shifting social roles and competitive attitudes, as older male athletes tend to emphasize self-confidence and autonomy over conformity. Similar tendencies have been documented in prior research [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe identification of these systematic age-related variations highlights the importance of developing differentiated norms. The introduction of age- and gender-specific normative data in this study ensures that trait scores are interpreted relative to appropriate reference groups, reducing the risk of biased conclusions when assessing athletes at different developmental stages. The inclusion of athletes as young as 15 years also underscores the value of early psychological profiling in sport, allowing practitioners to monitor personality development trajectories throughout adolescence and into adulthood, keeping in mind that personality is still forming during this period and that the social environment plays a significant role in shaping personality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Personality as a Predictor of Sport Achievement\u003c/h2\u003e\u003cp\u003eAlthough the predictive power of the models was modest, the results underscore the practical importance of trait-level analysis. Lower Anxious-Insecurity appears to reflect better emotional regulation and performance stability under pressure which is a quality frequently emphasized in elite athlete profiles. The negative association of Sociability with elite status may indicate that highly social individuals expend more cognitive and emotional resources on interpersonal engagement at the expense of task focus, particularly in high-pressure competitive environments. The sport-type interaction involving Orderliness reveals that structured behavioral tendencies may facilitate success in team contexts, where rule adherence and role clarity are vital, whereas individual sports may reward autonomy and flexibility. These findings align with person\u0026ndash;environment fit perspectives and suggest that personality\u0026ndash;context interactions, rather than global personality factors, provide the most meaningful predictors of performance outcomes.\u003c/p\u003e\u003cp\u003eThe present findings highlight that specific personality traits offer a superior predictive framework for understanding sport achievement compared to broader personality factors. While the factor-level analysis confirmed the general relevance of emotional stability (Neuroticism factor), the overall model's predictive performance was marginal and lacked specificity.\u003c/p\u003e\u003cp\u003eThe significant findings at the trait level demonstrate that nuance is critical. Not only were traits like Anxious Insecurity and Sociability inversely related to elite status, but the positive role of Orderliness was found to be context dependent. The trait-level findings highlight the importance of personality contextualization in sport. For example, lower levels of sociability contradict previous findings linking extraversion to elite achievement [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], suggesting that in certain sports or cultures, reduced social engagement may promote goal-directed individual achievement. Similarly, while orderliness predicted elite status, this effect was particularly pronounced in team sports, highlighting the context-specific value of structure and discipline [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Specifically, the positive effect of Orderliness was significantly attenuated for Individual Sport athletes compared to Team Sport athletes. This implies that while Orderliness is generally beneficial, its specific importance as a driver for high achievement is likely stronger within the highly structured, cooperative, and often regimented environment of team sports, where adherence to rules and roles is critical. For individual athletes, other personality aspects or training autonomy might play a more dominant role.\u003c/p\u003e\u003cp\u003eThe trait-level analysis provides a more nuanced and superior predictive framework, capable of identifying specific drivers and context dependent effects, which were obscured at the higher factor level. This supports the use of detailed trait measures in high-performance sport psychology research.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Implications for Evidence-Based Personality Profiling in Sport\u003c/h2\u003e\u003cp\u003eThe psychometrically validated LPI-v3s offers a reliable and contextually adapted tool for assessing key personality domains in athletes. Its structural validity makes it suitable for both applied sports and research environments, providing potentially effective personality assessment without compromising measurement quality. Furthermore, it provides an opportunity for a deeper understanding of personality domains across a diverse range of athletes.\u003c/p\u003e\u003cp\u003eResearch indicates that establishing age- and gender-specific norms improves the accuracy of survey interpretation and allows practitioners to assess athletes\u0026rsquo; psychological profiles against comparable reference groups [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This helps to understand developmental trends and identify individual differences that may affect both athlete performance and team dynamics, as well as athletes\u0026rsquo; psychological preparedness.\u003c/p\u003e\u003cp\u003eFrom an applied perspective, the validated short version enables efficient large-scale screening, supports selection and talent identification procedures, and enhances the precision of psychological support planning. The invariance-supported comparability across gender groups further strengthens its utility in mixed-gender teams and cross-sectional athlete research.\u003c/p\u003e\u003cp\u003eStandardized use of the LPI-v3s can promote more systematic, data-driven approaches to athlete assessment, talent development, and planning psychological support interventions. Integrating personality profiling into sport psychology practice can help coaches and practitioners tailor interventions that are relevant to athletes\u0026rsquo; personality traits, promoting adaptive motivation, resilience, and interpersonal effectiveness in the sport environment [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Cultural and Contextual Relevance of the LPI-v3/LPI-v3s\u003c/h2\u003e\u003cp\u003eCross-cultural research highlights that psychometric instruments developed in one language or cultural context often do not retain their full conceptual and structural equivalence when adapted elsewhere [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This issue is particularly relevant in language-minority regions such as Latvia, where imported measures, including the NEO-PI-R and the Big Five Inventory, have shown reduced sensitivity to local linguistic nuances and social norms [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The LPI-v3 was initially designed to address these shortcomings by integrating both the Five Factor and HEXACO perspectives in a linguistically and culturally valid format.\u003c/p\u003e\u003cp\u003eThe methodological rigor applied in this study sets a precedent for culturally responsive psychometric development in other small or underrepresented populations. Further cross-national collaboration could further explore the equivalence of LPI-v3s measurements in the Baltic and European sports context, contributing to the development of a more unified, yet culturally sensitive, personality assessment framework in the sports environment. In addition to its national significance, the LPI-v3s exemplifies a scalable model for developing psychometrically valid, linguistically grounded personality measures in smaller cultural contexts. Such regionally adapted instruments can enhance inclusion in international sport psychology research, which has historically been dominated by English-language tools.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec35\" class=\"Section2\"\u003e\u003ch2\u003e\u003cem\u003e4.6. Limitations\u003c/em\u003e\u003c/h2\u003e\u003cp\u003e The development of the shortened version of the LPI-v3s and the development of norms for athletes were carried out in accordance with the guidelines for the validation of both surveys. It is important to note that the study sample of Latvian athletes is significantly large, but at the same time there are several limiting factors. One of these limitations is the reliance on self-report data, which may not be completely accurate and objective in all aspects. Personality measurements are largely based on self-report, which is inherently subject to various response biases, including social desirability, impression management, and self-perception. Although the inclusion of the lying scale in the LPI-v3s helped to identify and control potentially invalid responses, self-report instruments cannot completely eliminate subjective bias. Future studies could benefit from incorporating multi-method assessment designs, including peer or coach ratings, behavioral indicators, and objective psychological indicators, to strengthen validity.\u003c/p\u003e\u003cp\u003eLimitations are also related to the cross-sectional nature of the study. Since the data were collected at a single point in time, it is not possible to examine the developmental changes in personality traits or to infer causal relationships between personality traits and sports performance outcomes. Longitudinal research designs would allow for a more comprehensive understanding. It is also important to note that the generalizability of the results is limited by the cultural and linguistic specificity of the sample. The norms and psychometric properties determined in this study apply to Latvian-speaking athletes. Therefore, additional cross-cultural validation in populations of other countries is needed to ensure broader applicability and assess the equivalence of measurements in different sports contexts.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe present study validated a shortened version of the LPI-v3s in an athletic population, confirming its structural validity, reliability, and gender invariance. The development of age and gender-specific norms enhances the precision and contextual relevance of personality assessment in Latvian sport settings. The LPI-v3s offers a practical, culturally grounded, and psychometrically robust tool for evidence-based profiling of athletes, supporting individualized approaches to performance and psychological development. Future research should further evaluate its predictive value for sport achievement and cross-cultural applicability across broader European contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Latvian Academy of Sport Education (Protocol No. 8, Statement No. 1, April 19, 2024) and adhered to the ethical guidelines outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants included in the study and participants were fully informed that their data would be used solely within the framework of this research. Confidentiality was strictly maintained, with all data securely stored to protect participant privacy. Participants were also informed of their right to withdraw from study at any time without penalty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe used datasets can be accessed at Riga Stradiņš University Dataverse repository: Volgemute K, Ulme G, Perepjolkina V, Līcis R, Abele A, Laviņš R. Latvian Personality Inventory (LPI-v3) in an athlete population. Riga Stradiņš University Dataverse. 2025 Aug 1. https://doi.org/10.48510/FK2/AMV9WA. For long-term access or institutional inquiries, data requests may also be directed to the RSU Research Department at [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declared financial support was received for the research, authorship, and/or publication of this article. This research is funded under the Grant No. RSU/LSPA-PA-2024/1-0010 of the project No. 5.2.1.1.i.0/2/24/I/CFLA/005 “RSU Internal and RSU with LASE External Consolidation” (funded by the European Union Recovery and Resilience Facility and the budget of the Republic of Latvia).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Katrina Volgemute, Viktorija Perepjolkina, Gundega Ulme. \u003c/p\u003e\n\u003cp\u003eData curation: Katrina Volgemute, Gundega Ulme, Krister Ansons, Renars Licis, Agita Abele, Rodrigo Lavins.\u003c/p\u003e\n\u003cp\u003eFormal analysis: Viktorija Perepjolkina, Katrina Volgemute, Gundega Ulme.\u003c/p\u003e\n\u003cp\u003eFunding acquisition: Katrina Volgemute.\u003c/p\u003e\n\u003cp\u003eInvestigation: Katrina Volgemute.\u003c/p\u003e\n\u003cp\u003eMethodology: Katrina Volgemute, Viktorija Perepjolkina, Gundega Ulme.\u003c/p\u003e\n\u003cp\u003eSoftware: Viktorija Perepjolkina, Katrina Volgemute.\u003c/p\u003e\n\u003cp\u003eSupervision: Katrina Volgemute.\u003c/p\u003e\n\u003cp\u003eValidation: Katrina Volgemute, Viktorija Perepjolkina, Gundega Ulme.\u003c/p\u003e\n\u003cp\u003eVisualization: Katrina Volgemute.\u003c/p\u003e\n\u003cp\u003eWriting – original draft: Katrina Volgemute, Viktorija Perepjolkina, Gundega Ulme.\u003c/p\u003e\n\u003cp\u003eWriting – review \u0026amp; editing: Katrina Volgemute, Gundega Ulme, Viktorija Perepjolkina, Agita Abele, Alina Klonova.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePadli P, Prasetyo T, Kurniawan R, Putra RA, Candra O. 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In: Strength and Conditioning for Football: From Science to Practice. 2025. p.219.\u003c/li\u003e\n \u003cli\u003eBurnell L, Ong CW, Pitt T, Butt J, Eubank MR. Exploring the perceived benefits of engaging with Spotlight personality profiling in performance domains. Sport Exerc Psychol Rev. 2023;18(1):76-95. doi:10.53841/bpssepr.2023.18.1.76\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"personality assessment, athletes, psychometrics, measurement invariance, sport environment","lastPublishedDoi":"10.21203/rs.3.rs-7957250/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7957250/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePersonality traits play an important role in athletic performance, yet existing sport-specific measures often lack psychometric refinement and predictive validation. This research comprised two interlinked studies aimed to create and validate a short version of the Latvian Personality Inventory (LPI-v3s) and examine its utility for identifying personality predictors of athletic achievement.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 925 athletes (aged 15\u0026ndash;45 years; 84 sports) participated. Following data screening, two subsamples were formed: Study 1 (n\u0026thinsp;=\u0026thinsp;436) for psychometric evaluation and Study 2 (n\u0026thinsp;=\u0026thinsp;753) for predictive analysis. Study 1 employed exploratory and confirmatory factor analyses and multi-group CFA to test gender invariance. Continuous regression-based norming produced age and gender specific T-scores. Study 2 applied hierarchical logistic regression to examine the predictive validity of personality traits for elite/pre-elite versus non-elite status.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe 53-item LPI-v3s demonstrated a stable five-factor, 14-trait structure, satisfactory internal consistency (α\u0026thinsp;=\u0026thinsp;0.72\u0026ndash;0.88), and configural, metric, and scalar invariance across gender. Predictive models indicated that lower Anxious Insecurity (N1) and Sociability (E1), and higher Orderliness (C1\u003cb\u003e)\u003c/b\u003e, particularly in team sports which significantly predicted higher athletic achievement (AUC\u0026thinsp;=\u0026thinsp;0.72).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe validated LPI-v3s offers a reliable and efficient measure of athlete personality with robust psychometric properties and practical value for sport psychology, talent identification, and applied performance research.\u003c/p\u003e","manuscriptTitle":"Psychometric Properties and Standardization of the Shortened Latvian Personality Inventory (LPI-v3s) in Athlete Sample: Implications for Evidence-Based Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 11:10:25","doi":"10.21203/rs.3.rs-7957250/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":"dfb887e0-b79f-4241-8066-12548f3922bd","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-11T12:08:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 11:10:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7957250","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7957250","identity":"rs-7957250","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}