A psychological construct for aiming in First-Person Shooters

A psychological construct for aiming in First-Person Shooters | 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 Short Report A psychological construct for aiming in First-Person Shooters Nami Koïdé, Hamza Altakroury, Alizée Poli, Loann Mahdar-Recorbet, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4624991/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 The complexities of the structure of aiming skills in competitive first-person shooters (FPS) have prompted much discussion within online communities but little-to-no empirical scrutiny within academia. This study aims to address this gap by employing psychometric methods to validate the construct validity and factorial structure of the aiming skills in FPS games, thought to rely on two "Tracking" and "Flicking" sub-skills. In this work, 61 FPS gamers were recruited to complete a playlist of six training exercises widely recognized within the gaming community designed to measure basic "Tracking" and "Flicking" abilities. Internal consistency was assessed using Cronbach’s alpha and Confirmatory factor analysis was conducted to test the two-factor model of aiming. Cronbach's alpha indicated high internal consistency across all exercises, but with values exceeding the recommended threshold. In other hands, confirmatory factor analysis revealed that the two-factor model for "Tracking" and "Flicking" fit satisfactorily, with strong factor loadings for each exercise and a significant correlation between the factors. These findings contribute to the evidence of the existence of specific psychomotor skillsets required in FPS games and suggest that such measures can effectively capture the dimensionality of aiming skills. Future research should further refine these measures to enhance training and performance in FPS gaming contexts. First-Person Shooters eSports Gaming Ethletes Psychometrics Factor Analysis Introduction Since the 1990s, there has been a growing interest in competitive first-person shooter (FPS) games (Wagner, 2006 ). Defined as video games centered on gunfighting and other weapon-based combat from a first-person perspective, FPS games immerse the player in the action through the eyes of the protagonist (Adams, 2014 ). A key attribute of this genre is player-guided navigation through a three-dimensional space, setting it apart from other shooting games that employ a first-person view, such as light gun shooters and rail shooters (Rollings & Adams, 2003 ). In the gaming community, theories have evolved around how specific skills are organized from both psychological and psychomotor perspectives (Aimer7, 2019b ; Sharma, 2020 ). Among others, these skills include "Tracking," where the player aims to follow the target smoothly and dynamically over time, and "Flicking," characterized by quick, precise movements of the crosshair onto a target (Aimlabs, 2023 ). Despite widespread discussion and the intuitive understanding of these skills among players, empirical evidence and structured measurement of these psychomotor constructs have been notably lacking. Thus, this study aims to fill this gap by systematically evaluating the psychometric properties of a training playlist designed to measure basic tracking and flicking skills using already widely used exercises. By employing canonical psychometric statistical techniques, including confirmatory factor analysis and internal consistency assessments, this research aims to contribute to the existing evidence that supports the theoretical constructs of tracking and flicking within the context of aiming in FPS games. Method Study design The exercises were selected according to Aimer7 and Voltaic aim-training guides (Aimer7, 2019a ; Voltaic, 2021 ). The playlist was then computerized using the KovaaK’s training software ( KovaaK’s Aim Trainer , s. d.). The sampling process was conducted through a random distribution of a contact form across various social networks, based on the principle of anonymous and voluntary participation. The study aimed for a minimum of 60 participants to ensure at least 10 individuals per exercise, conforming to the standard requirements for psychometric tool validation as described by (Boateng et al., 2018 ; Nunnally, 1967 ). Data collected included only date of completion, and socio-cultural level, thus guaranteeing participant anonymity. Subjects Through an online random voluntary sampling, 61 members (mean age = 28.46 years, standard deviation = 5.62) of the international gaming community participated in this experiment. Every participant finished each exercise, allowing all data to be available for every individual. The complete dataset analyzed is provided in Additional File 1. Detailed descriptions of the study's objectives and purpose were provided to all participants, who expressed their consent to participate by providing online written approval. The protocol was approved by the Institutional Review Board Commission Nationale de l’Informatique et des Libertés (registration n°2234564) in accordance with the Declaration of Helsinki. Playlist For the Tracking factor, "Smoothsphere" involves targeting a fast-moving spherical object that changes speed and direction across a 360° plane; "Smoothbot Invincible Goated" features a pill-shaped target maneuvering unpredictably in a similar 360° scope; and "Bounce 180 Tracking Invincible Always Bounce," where participants aim at a spherical target that consistently bounces horizontally within a 180° range. For the Flicking factor, three exercises were employed: "Grindshot – Aimlab," which challenges participants to hit three large, static, spheric targets that disappear upon being shot; "Reflex Micro Flick 250ms Fixed," requiring the precision shooting of a single small, static, spheric target that disappears after 250ms or upon impact; and "Micro Dynamic Flick 250ms," where participants aim at a small, moving pill-shaped target that vanishes after 250ms, with additional points for headshots. Each participant performed each exercise three times in a row, and the performance was estimated using the geometric mean of the three successive attempts. Full descriptions for all exercises are available in (Additional File 2) and video captures of an experimenter performing each exercise are available in (Additional File 3–8). Internal consistency and reliability Cronbach’s alpha was employed to evaluate the internal consistency and reliability of the residual exercises. An acceptability criterion deemed reasonable was set within the range of .70 to .90 for α. Falling below this range signifies lower reliability, and surpassing it suggests a redundancy of similar exercises, which decrease the real playlist’s reliability (Bland & Altman, 1997 ; DeVellis, 2003 ). Factor structure To evaluate the construct validity of our two-factor model for the training playlist, we executed a confirmatory factor analysis. The factor structure’s fit was assessed using the generalized least squares method. We measured model fit with multiple indices: the χ² test statistic for absolute fit; the Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) as relative fit indices against a null model (Bentler, 1990 ; Tucker & Lewis, 1973 ). Similarly, for overall fit assessment, we performed measure of the Standardized Root Mean Square Residual (SRMR (Bentler, 1989 )) along with the Root Mean Square Error of Approximation (RMSEA (Steiger, 1980 )). Following the criteria set by (Hu & Bentler, 1999 ), the two-factor model was considered adequate if TLI > .95, CFI > .95, SRMR < .08, and RMSEA < .06. Data were rescaled to a 1–5 range to standardizes measurement scales for all variables and to minimizes the influence of outliers. Statistical analysis was carried out in R, utilizing the Lavaan package, and results were interpreted in RStudio v2023.12.1. Results Descriptive statistics The study sampled sixty-one participants from the general worldwide population. The mean age of participants was 28.46 years with a standard deviation of 5.62. Of these participants, 49 were male and 12 were female, highlighting a gender disparity in our sample. Participants also provided data on their preferred method of controlling the mouse during gameplay. On a slider from 1 (primarily using the wrist) to 6 (primarily using the arm), the average score was 3.08, with a standard deviation of 1.33, indicating a moderate preference with a slight inclination towards wrist-based control. Regarding the grip style used to handle the mouse, 28 participants reported using a claw grip (where the hand is not rested flat on the mouse and the fingers are slightly curled), 18 a palm grip (where the base of the palm aligns with the bottom of the mouse and the fingers extend along the mouse to reach the buttons), and 15 a fingertip grip (which involves manipulating the mouse solely with the thumb, index, and middle fingers). Internal consistency and reliability Table 1 presents results for internal consistency and reliability. The data reveal that dropping an exercise does not affect the high internal consistency and reliability of the training playlist. Table 1 : Reliability and internal consistency of the training playlist when an exercise is dropped. Exercise Factor Reliability if dropped Bounce 180 Tracking Invincible Always Bounce Tracking .96 Smoothbot Invincible Goated Tracking .97 Smoothsphere Tracking .96 Grindshot – Aimlab Flicking .97 Micro Dynamic Flick 250ms Flicking .96 Reflex Micro Flick 250ms fixed Flicking .96 The measurement of Cronbach’s alpha for the complete training playlist was .97 [CI 95% = .96–.98]. In robustness checks, where each exercise was sequentially removed from the analysis, the alpha values remained notably stable, ranging from .96 to .97 with mean ɑ = .97, SD = .01). All values exceeded the lower acceptable limit of .70, but were also greater than the maximum expected value of .9. Confirmatory Factor Analysis : The confirmatory factor analysis revealed a robust fit between the training playlist and the proposed model. Selected exercises were categorized into two factors corresponding to the psychomotor modules postulated within aiming communities. The exercises "Bounce 180 Tracking Invincible Always Bounce," "Smoothbot Invincible Goated," and "Smoothsphere" were amalgamated into the "Tracking" factor. The "Flicking" factor included "Grindshot – Aimlab," "Micro Dynamic Flick 250ms," and "Reflex Micro Flick 250ms fixed". The analysis suggested that the dual-factor model provided an adequate fit for the training playlist, with metrics slightly below the established benchmarks [χ2(8) = 37.104, p < .001, CFI = .952, TLI = .9, RMSEA = .24, SRMR = .029]. Standardized factor loadings for the training playlist are outlined in Table 2. For the Tracking factor, factor loadings ranged from .96 to .98, and for the Flicking factor, from .89 to .95. A significant correlation of r = .92 was observed between the Tracking and Flicking factors. Table 2 : Training playlist’s two-factor model standardized factor loadings Exercise Tracking Flicking Bounce 180 Tracking Invincible Always Bounce .98 - Smoothbot Invincible Goated .96 - Smoothsphere .98 - Grindshot – Aimlab - .95 Micro Dynamic Flick 250ms - .89 Reflex Micro Flick 250ms fixed - .95 Discussion Through the application of canonical psychometric statistical techniques, the current research validated the construct validity and factorial organization of aiming competencies in Competitive First-Person Shooters. Values of Cronbach's alpha suggested that the selected playlist had a marginally excessive internal reliability, as alpha levels surpassed the recommended .90 interval outlined by Bland & Altman ( 1997 ) and DeVellis ( 2003 ). The confirmatory factor analysis conducted subsequently supported the bifactorial structure of our training playlist, with “Bounce 180 Tracking Invincible Always Bounce”, “Smoothbot Invincible Goated”, and “Smoothsphere” included in the "Tracking" factor ; in contrast to “Grindshot – Aimlab”, “Micro Dynamic Flick 250ms”, “Reflex Micro Flick 250ms fixed” exercices aggregated in the "Flicking" factor. This structural model achieved a satisfactory fit as per the Hu & Bentler ( 1999 ) criteria. Furthermore, each exercise demonstrated strong factor loadings on its designated dimension and significant correlations among both factors were measured. Thus, through the application of established psychometric methods, our study is the first to have contributed to the community-based theories of “Tracking” and “Flicking” skills within the “Aiming” construct. Moreover, the results suggest that the psychometric approach could be relevant for constructing more optimized training routines. Indeed, the measurement performed at the level of internal consistency analysis, which exceeded the threshold value, suggests that the selected exercises were redundant in the skills they mobilized (Tavakol & Dennick, 2011 ). These data thus introduce the perspective of using this specific measure to ensure the diversity of skills developed in this type of training. Future research could expand upon this groundwork by exploring the application of factorial analysis in different gaming contexts or integrating them with training protocols or methods to optimize performance, or to offer a practical framework for players and trainers to evaluate and develop these skills systematically. Limitations Several limitations must be acknowledged in our study. First, there was an uneven gender distribution with fewer females than males participating, which could influence the generalizability of our findings. Additionally, participants' handedness was not measured. We were also unable to perform experiments to assess positive control or convergent validity due to a lack of available tools. Finally, as the study was conducted remotely and participants were not observed directly, we could not verify the absence of artifact or erroneous data, which might affect the reliability of the results. Declarations Competing interests The authors declare no competing interests. Funding Not applicable. Author Contribution A.T. and N.K. conceptualized the study. A.T. curated the data, performed formal analysis, led the methodology, managed the project administration, acquired resources, validated the findings, and prepared the original draft. N.K. conducted the investigation. N.K. prepared the original draft. H.A. developed the software and prepared the visualization. L.M.-R. assisted in software development. A.P. helped in acquiring resources. A.T., N.K., H.A., A.P., and L.M.-R. reviewed and edited the manuscript. Data Availability All data used in this study are available in (Additional File 1). References Adams, E. (2014). Fundamentals of game design . Pearson Education. Aimer7. (2019a). AIM WORKOUT ROUTINES WITH KOVAAK’S FPS AIM TRAINER . https://www.dropbox.com/s/vaba3potfhf9jy1/KovaaK%20aim%20workout%20routines.pdf?e=1&dl=0 Aimer7. (2019b). Heuristic About Geometric Positioning and Applications . https://www.dropbox.com/s/aif0jy1prxe0rjm/Heuristic%20about%20geometric%20positioning.pdf?e=1&dl=0 Aimlabs, M. (2023, septembre 18). Aim Training & FPS Mechanics Glossary : Key Terms Explained. Aimlabs.Com Articles . https://aimlabs.com/articles/aimlabs/aim-training-fps-mechanics-glossary-key-terms-explained/ Bentler, P. M. (1989). EQS : Structural equations program manual . Undefined. https://www.semanticscholar.org/paper/EQS-%3A-structural-equations-program-manual-Bentler/3b39d1d27934a461f04e0e076ddb6da5b87193b0 Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin , 107 (2), 238‑246. https://doi.org/10.1037/0033-2909.107.2.238 Bland, J. M., & Altman, D. G. (1997). Cronbach’s alpha. BMJ (Clinical Research Ed.) , 314 (7080), 572. https://doi.org/10.1136/bmj.314.7080.572 Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research : A Primer. Frontiers in Public Health , 6 , 149. https://doi.org/10.3389/fpubh.2018.00149 DeVellis, R. F. (2003). Scale Development : Theory and Applications . SAGE. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal , 6 (1), 1‑55. https://doi.org/10.1080/10705519909540118 KovaaK’s Aim Trainer . (s. d.). Consulté 6 mai 2024, à l’adresse https://kovaaks.com/kovaaks/main Nunnally, J. C. (1967). Psychometric theory. Rollings, A., & Adams, E. (2003). Andrew Rollings and Ernest Adams on game design . New Riders. Sharma, S. (2020, juin 4). Theory of Aiming in First Person Shooter (FPS) Games. Medium . https://medium.com/@flummoxedshubh/theory-of-aiming-in-first-person-shooter-fps-games-bbdebef24fd3 Steiger, J. H. (1980). Statistically based tests for the number of common factors . https://doi.org/null Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International journal of medical education , 2 , 53. Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika , 38 (1), 1‑10. https://doi.org/10.1007/BF02291170 Voltaic. (2021). Voltaic x KovaaKs—Fundamental Aim Training Routines 2.5 . Google Docs. https://docs.google.com/document/d/1iunv6vXKWZpjpFvclGLGBeFg6WudwsavozZ-TlGDq_c/edit?usp=sharing&usp=embed_facebook Wagner, M. G. (2006). On the Scientific Relevance of eSports. 437‑442. Additional Declarations No competing interests reported. Supplementary Files nt24padditionalFile1data.xlsx nt24padditionalFile2exercisesdescriptions.docx nt24padditionalFile3Grindshot.mp4 nt24padditionalFile4ReflexFlick.mp4 nt24padditionalFile5DynamicFlick.mp4 nt24padditionalFile6Bounce.mp4 nt24padditionalFile7SmoothBot.mp4 nt24padditionalFile8Smoothsphere.mp4 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4624991","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":328580136,"identity":"4457ffbd-0825-4fa6-8dd5-69d1f6ee7c5c","order_by":0,"name":"Nami Koïdé","email":"","orcid":"","institution":"CLINICOG","correspondingAuthor":false,"prefix":"","firstName":"Nami","middleName":"","lastName":"Koïdé","suffix":""},{"id":328580137,"identity":"532a1c44-cfd5-4088-aba2-9123ac1e1c43","order_by":1,"name":"Hamza 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competitive first-person shooter (FPS) games (Wagner, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Defined as video games centered on gunfighting and other weapon-based combat from a first-person perspective, FPS games immerse the player in the action through the eyes of the protagonist (Adams, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A key attribute of this genre is player-guided navigation through a three-dimensional space, setting it apart from other shooting games that employ a first-person view, such as light gun shooters and rail shooters (Rollings \u0026amp; Adams, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the gaming community, theories have evolved around how specific skills are organized from both psychological and psychomotor perspectives (Aimer7, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Sharma, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among others, these skills include \"Tracking,\" where the player aims to follow the target smoothly and dynamically over time, and \"Flicking,\" characterized by quick, precise movements of the crosshair onto a target (Aimlabs, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite widespread discussion and the intuitive understanding of these skills among players, empirical evidence and structured measurement of these psychomotor constructs have been notably lacking.\u003c/p\u003e \u003cp\u003eThus, this study aims to fill this gap by systematically evaluating the psychometric properties of a training playlist designed to measure basic tracking and flicking skills using already widely used exercises. By employing canonical psychometric statistical techniques, including confirmatory factor analysis and internal consistency assessments, this research aims to contribute to the existing evidence that supports the theoretical constructs of tracking and flicking within the context of aiming in FPS games.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThe exercises were selected according to Aimer7 and Voltaic aim-training guides (Aimer7, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Voltaic, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The playlist was then computerized using the KovaaK\u0026rsquo;s training software (\u003cem\u003eKovaaK\u0026rsquo;s Aim Trainer\u003c/em\u003e, s. d.). The sampling process was conducted through a random distribution of a contact form across various social networks, based on the principle of anonymous and voluntary participation. The study aimed for a minimum of 60 participants to ensure at least 10 individuals per exercise, conforming to the standard requirements for psychometric tool validation as described by (Boateng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nunnally, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1967\u003c/span\u003e). Data collected included only date of completion, and socio-cultural level, thus guaranteeing participant anonymity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eThrough an online random voluntary sampling, 61 members (mean age\u0026thinsp;=\u0026thinsp;28.46 years, standard deviation\u0026thinsp;=\u0026thinsp;5.62) of the international gaming community participated in this experiment. Every participant finished each exercise, allowing all data to be available for every individual. The complete dataset analyzed is provided in Additional File 1.\u003c/p\u003e \u003cp\u003e Detailed descriptions of the study's objectives and purpose were provided to all participants, who expressed their consent to participate by providing online written approval. The protocol was approved by the Institutional Review Board Commission Nationale de l\u0026rsquo;Informatique et des Libert\u0026eacute;s (registration n\u0026deg;2234564) in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePlaylist\u003c/h2\u003e \u003cp\u003eFor the Tracking factor, \"Smoothsphere\" involves targeting a fast-moving spherical object that changes speed and direction across a 360\u0026deg; plane; \"Smoothbot Invincible Goated\" features a pill-shaped target maneuvering unpredictably in a similar 360\u0026deg; scope; and \"Bounce 180 Tracking Invincible Always Bounce,\" where participants aim at a spherical target that consistently bounces horizontally within a 180\u0026deg; range.\u003c/p\u003e \u003cp\u003eFor the Flicking factor, three exercises were employed: \"Grindshot \u0026ndash; Aimlab,\" which challenges participants to hit three large, static, spheric targets that disappear upon being shot; \"Reflex Micro Flick 250ms Fixed,\" requiring the precision shooting of a single small, static, spheric target that disappears after 250ms or upon impact; and \"Micro Dynamic Flick 250ms,\" where participants aim at a small, moving pill-shaped target that vanishes after 250ms, with additional points for headshots.\u003c/p\u003e \u003cp\u003eEach participant performed each exercise three times in a row, and the performance was estimated using the geometric mean of the three successive attempts.\u003c/p\u003e \u003cp\u003eFull descriptions for all exercises are available in (Additional File 2) and video captures of an experimenter performing each exercise are available in (Additional File 3\u0026ndash;8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eInternal consistency and reliability\u003c/h2\u003e \u003cp\u003eCronbach\u0026rsquo;s alpha was employed to evaluate the internal consistency and reliability of the residual exercises. An acceptability criterion deemed reasonable was set within the range of .70 to .90 for α. Falling below this range signifies lower reliability, and surpassing it suggests a redundancy of similar exercises, which decrease the real playlist\u0026rsquo;s reliability (Bland \u0026amp; Altman, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; DeVellis, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFactor structure\u003c/h2\u003e \u003cp\u003eTo evaluate the construct validity of our two-factor model for the training playlist, we executed a confirmatory factor analysis. The factor structure\u0026rsquo;s fit was assessed using the generalized least squares method. We measured model fit with multiple indices: the χ\u0026sup2; test statistic for absolute fit; the Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) as relative fit indices against a null model (Bentler, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Tucker \u0026amp; Lewis, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). Similarly, for overall fit assessment, we performed measure of the Standardized Root Mean Square Residual (SRMR (Bentler, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e)) along with the Root Mean Square Error of Approximation (RMSEA (Steiger, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1980\u003c/span\u003e)). Following the criteria set by (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), the two-factor model was considered adequate if TLI\u0026thinsp;\u0026gt;\u0026thinsp;.95, CFI\u0026thinsp;\u0026gt;\u0026thinsp;.95, SRMR\u0026thinsp;\u0026lt;\u0026thinsp;.08, and RMSEA\u0026thinsp;\u0026lt;\u0026thinsp;.06. Data were rescaled to a 1\u0026ndash;5 range to standardizes measurement scales for all variables and to minimizes the influence of outliers. Statistical analysis was carried out in R, utilizing the Lavaan package, and results were interpreted in RStudio v2023.12.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eDescriptive statistics\u003c/h2\u003e\n\u003cp\u003eThe study sampled sixty-one participants from the general worldwide population. The mean age of participants was 28.46 years with a standard deviation of 5.62. Of these participants, 49 were male and 12 were female, highlighting a gender disparity in our sample.\u003c/p\u003e\n\u003cp\u003eParticipants also provided data on their preferred method of controlling the mouse during gameplay. On a slider from 1 (primarily using the wrist) to 6 (primarily using the arm), the average score was 3.08, with a standard deviation of 1.33, indicating a moderate preference with a slight inclination towards wrist-based control.\u003c/p\u003e\n\u003cp\u003eRegarding the grip style used to handle the mouse, 28 participants reported using a claw grip (where the hand is not rested flat on the mouse and the fingers are slightly curled), 18 a palm grip (where the base of the palm aligns with the bottom of the mouse and the fingers extend along the mouse to reach the buttons), and 15 a fingertip grip (which involves manipulating the mouse solely with the thumb, index, and middle fingers).\u003c/p\u003e\n\u003ch2\u003eInternal consistency and reliability\u003c/h2\u003e\n\u003cp\u003eTable 1 presents results for internal consistency and reliability. The data reveal that dropping an exercise does not affect the high internal consistency and reliability of the training playlist.\u003c/p\u003e\n\u003cp\u003eTable 1 : Reliability and internal consistency of the training playlist when an exercise is dropped.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eExercise\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eReliability if dropped\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eBounce 180 Tracking Invincible Always Bounce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eTracking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eSmoothbot Invincible Goated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eTracking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eSmoothsphere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eTracking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eGrindshot \u0026ndash; Aimlab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eFlicking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eMicro Dynamic Flick 250ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eFlicking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eReflex Micro Flick 250ms fixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eFlicking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe measurement of Cronbach\u0026rsquo;s alpha for the complete training playlist was .97 [CI\u003csub\u003e95%\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.96\u0026ndash;.98]. In robustness checks, where each exercise was sequentially removed from the analysis, the alpha values remained notably stable, ranging from .96 to .97 with mean\u003csub\u003eɑ\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.97, SD\u0026thinsp;=\u0026thinsp;.01). All values exceeded the lower acceptable limit of .70, but were also greater than the maximum expected value of .9.\u003c/p\u003e\n\u003cp\u003eConfirmatory Factor Analysis :\u003c/p\u003e\n\u003cp\u003eThe confirmatory factor analysis revealed a robust fit between the training playlist and the proposed model. Selected exercises were categorized into two factors corresponding to the psychomotor modules postulated within aiming communities. The exercises \u0026quot;Bounce 180 Tracking Invincible Always Bounce,\u0026quot; \u0026quot;Smoothbot Invincible Goated,\u0026quot; and \u0026quot;Smoothsphere\u0026quot; were amalgamated into the \u0026quot;Tracking\u0026quot; factor. The \u0026quot;Flicking\u0026quot; factor included \u0026quot;Grindshot \u0026ndash; Aimlab,\u0026quot; \u0026quot;Micro Dynamic Flick 250ms,\u0026quot; and \u0026quot;Reflex Micro Flick 250ms fixed\u0026quot;. The analysis suggested that the dual-factor model provided an adequate fit for the training playlist, with metrics slightly below the established benchmarks [\u0026chi;2(8) = 37.104, p \u0026lt; .001, CFI = .952, TLI = .9, RMSEA = .24, SRMR = .029].\u003c/p\u003e\n\u003cp\u003eStandardized factor loadings for the training playlist are outlined in Table 2. For the Tracking factor, factor loadings ranged from .96 to .98, and for the Flicking factor, from .89 to .95. A significant correlation of \u003cem\u003er\u003c/em\u003e = .92 was observed between the Tracking and Flicking factors.\u003c/p\u003e\n\u003cp\u003eTable 2 : Training playlist\u0026rsquo;s two-factor model standardized factor loadings\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eExercise\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eTracking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eFlicking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eBounce 180 Tracking Invincible Always Bounce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eSmoothbot Invincible Goated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eSmoothsphere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eGrindshot \u0026ndash; Aimlab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eMicro Dynamic Flick 250ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eReflex Micro Flick 250ms fixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough the application of canonical psychometric statistical techniques, the current research validated the construct validity and factorial organization of aiming competencies in Competitive First-Person Shooters.\u003c/p\u003e \u003cp\u003eValues of Cronbach's alpha suggested that the selected playlist had a marginally excessive internal reliability, as alpha levels surpassed the recommended .90 interval outlined by Bland \u0026amp; Altman (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) and DeVellis (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe confirmatory factor analysis conducted subsequently supported the bifactorial structure of our training playlist, with \u0026ldquo;Bounce 180 Tracking Invincible Always Bounce\u0026rdquo;, \u0026ldquo;Smoothbot Invincible Goated\u0026rdquo;, and \u0026ldquo;Smoothsphere\u0026rdquo; included in the \"Tracking\" factor ; in contrast to \u0026ldquo;Grindshot \u0026ndash; Aimlab\u0026rdquo;, \u0026ldquo;Micro Dynamic Flick 250ms\u0026rdquo;, \u0026ldquo;Reflex Micro Flick 250ms fixed\u0026rdquo; exercices aggregated in the \"Flicking\" factor. This structural model achieved a satisfactory fit as per the Hu \u0026amp; Bentler (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) criteria. Furthermore, each exercise demonstrated strong factor loadings on its designated dimension and significant correlations among both factors were measured.\u003c/p\u003e \u003cp\u003eThus, through the application of established psychometric methods, our study is the first to have contributed to the community-based theories of \u0026ldquo;Tracking\u0026rdquo; and \u0026ldquo;Flicking\u0026rdquo; skills within the \u0026ldquo;Aiming\u0026rdquo; construct. Moreover, the results suggest that the psychometric approach could be relevant for constructing more optimized training routines. Indeed, the measurement performed at the level of internal consistency analysis, which exceeded the threshold value, suggests that the selected exercises were redundant in the skills they mobilized (Tavakol \u0026amp; Dennick, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These data thus introduce the perspective of using this specific measure to ensure the diversity of skills developed in this type of training.\u003c/p\u003e \u003cp\u003eFuture research could expand upon this groundwork by exploring the application of factorial analysis in different gaming contexts or integrating them with training protocols or methods to optimize performance, or to offer a practical framework for players and trainers to evaluate and develop these skills systematically.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations must be acknowledged in our study. First, there was an uneven gender distribution with fewer females than males participating, which could influence the generalizability of our findings. Additionally, participants' handedness was not measured. We were also unable to perform experiments to assess positive control or convergent validity due to a lack of available tools. Finally, as the study was conducted remotely and participants were not observed directly, we could not verify the absence of artifact or erroneous data, which might affect the reliability of the results.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.T. and N.K. conceptualized the study. A.T. curated the data, performed formal analysis, led the methodology, managed the project administration, acquired resources, validated the findings, and prepared the original draft. N.K. conducted the investigation. N.K. prepared the original draft. H.A. developed the software and prepared the visualization. L.M.-R. assisted in software development. A.P. helped in acquiring resources. A.T., N.K., H.A., A.P., and L.M.-R. reviewed and edited the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data used in this study are available in (Additional File 1).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdams, E. (2014). \u003cem\u003eFundamentals of game design\u003c/em\u003e. Pearson Education.\u003c/li\u003e\n \u003cli\u003eAimer7. 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Undefined. https://www.semanticscholar.org/paper/EQS-%3A-structural-equations-program-manual-Bentler/3b39d1d27934a461f04e0e076ddb6da5b87193b0\u003c/li\u003e\n \u003cli\u003eBentler, P. M. (1990). Comparative fit indexes in structural models. \u003cem\u003ePsychological Bulletin\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e(2), 238‑246. https://doi.org/10.1037/0033-2909.107.2.238\u003c/li\u003e\n \u003cli\u003eBland, J. M., \u0026amp; Altman, D. G. (1997). Cronbach\u0026rsquo;s alpha. \u003cem\u003eBMJ (Clinical Research Ed.)\u003c/em\u003e, \u003cem\u003e314\u003c/em\u003e(7080), 572. https://doi.org/10.1136/bmj.314.7080.572\u003c/li\u003e\n \u003cli\u003eBoateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Qui\u0026ntilde;onez, H. R., \u0026amp; Young, S. L. (2018). 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(2006). \u003cem\u003eOn the Scientific Relevance of eSports.\u003c/em\u003e 437‑442.\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":"First-Person Shooters, eSports, Gaming, Ethletes, Psychometrics, Factor Analysis","lastPublishedDoi":"10.21203/rs.3.rs-4624991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4624991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe complexities of the structure of aiming skills in competitive first-person shooters (FPS) have prompted much discussion within online communities but little-to-no empirical scrutiny within academia. This study aims to address this gap by employing psychometric methods to validate the construct validity and factorial structure of the aiming skills in FPS games, thought to rely on two \"Tracking\" and \"Flicking\" sub-skills.\u003c/p\u003e \u003cp\u003eIn this work, 61 FPS gamers were recruited to complete a playlist of six training exercises widely recognized within the gaming community designed to measure basic \"Tracking\" and \"Flicking\" abilities. Internal consistency was assessed using Cronbach\u0026rsquo;s alpha and Confirmatory factor analysis was conducted to test the two-factor model of aiming.\u003c/p\u003e \u003cp\u003eCronbach's alpha indicated high internal consistency across all exercises, but with values exceeding the recommended threshold. In other hands, confirmatory factor analysis revealed that the two-factor model for \"Tracking\" and \"Flicking\" fit satisfactorily, with strong factor loadings for each exercise and a significant correlation between the factors.\u003c/p\u003e \u003cp\u003eThese findings contribute to the evidence of the existence of specific psychomotor skillsets required in FPS games and suggest that such measures can effectively capture the dimensionality of aiming skills. Future research should further refine these measures to enhance training and performance in FPS gaming contexts.\u003c/p\u003e","manuscriptTitle":"A psychological construct for aiming in First-Person Shooters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 10:09:11","doi":"10.21203/rs.3.rs-4624991/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":"416c6d2a-45a9-432d-ab51-0c4db5c848da","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-11T05:45:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-23 10:09:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4624991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4624991","identity":"rs-4624991","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}