[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128005-en":3,"doc-seo-128005-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128005,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Unveiling the Impact of Socioeconomic and Demographic Factors on Graduate Salaries - A Machine Learning Explanatory Analytical Approach","Graduate salaries represent a major concern for graduates, employers, and policymakers due to the wide range of factors that shape earnings. This study examines determinants of UK graduate salaries using Higher Education Statistical Agency survey data and advanced machine-learning explanatory methods integrated with statistical analyses. Multi-stage modeling with decision trees and random forests, supported by SHAP explainability, assesses the effects of 21 socioeconomic and demographic variables. Institutional reputation, graduation age, socioeconomic classification, job qualification requirements, and domicile are identified as key drivers. ANOVA confirms statistical significance and reveals interaction effects validated through domain expert perspectives.","This is a peer-reviewed, final published version of the following document, © 2025 by the authors. Licensee MDPI, Basel, Switzerland. and is licensed under Creative Commons:  \nAttribution 4.0 license:  \nHenshaw, Bassey, Mishra, Bhupesh Kumar, Sayers, William ORCID logoORCID: [https://orcid.org/0000-0003-1677-4409](https://orcid.org/0000-0003-1677-4409) and Pervez, Zeeshan (2025) Unveiling the Impact of  \nSocioeconomic and Demographic Factors on Graduate Salaries: A Machine Learning Explanatory Analytical Approach Using Higher Education Statistical Agency Data . Analytics, 4  \n(1) . p . 10. doi:10.3390/analytics4010010  \nOfficial URL: [https://doi.org/10.3390/analytics4010010](https://doi.org/10.3390/analytics4010010)  \nDOI: [http://dx.doi.org/10.3390/analytics4010010](http://dx.doi.org/10.3390/analytics4010010)  \nEPrint URI: [https://eprints.glos.ac.uk/id/eprint/14895](https://eprints.glos.ac.uk/id/eprint/14895)  \nDisclaimer  \nThe University of Gloucestershire has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material.  \nThe University of Gloucestershire makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited.  \nThe University of Gloucestershire makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights.  \nThe University of Gloucestershire accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement.  \nPLEASE SCROLL DOWN FOR TEXT.  \nArticle  \nUnveiling the Impact of Socioeconomic and Demographic Factors on Graduate Salaries: A Machine Learning Explanatory Analytical Approach Using Higher Education Statistical Agency Data  \nBassey Henshaw 1, Bhupesh Kumar Mishra 2, *, William Sayers 1 and Zeeshan Pervez 3  \nAcademic Editor: Carson K. Leung  \nReceived: 2 January 2025  \nRevised: 18 February 2025  \nAccepted: 4 March 2025  \nPublished: 11 March 2025  \nCitation: Henshaw, B.; Mishra, B.K.; Sayers, W.; Pervez, Z. Unveiling the Impact of Socioeconomic and Demographic Factors on Graduate Salaries: A Machine Learning Explanatory Analytical Approach Using Higher Education Statistical Agency Data. Analytics 2025, 4, 10 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)analytics4010010  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 School of Computing and Technology, University of Gloucestershire, Cheltenham GL50 2RH, UK; [henshaw49@gmail.com](henshaw49@gmail.com) (B.H.)  \n2 Centre of Excellence for Data Science, Artificial Intelligence and Modelling (DAIM), University of Hull, Cottingham Road, Hull HU6 7RX, UK  \n3 School of Engineering, Computing, and Mathematical Sciences, University of Wolverhampton, Wolverhamton WV1 1LY, UK  \n* [Correspondence: bhupesh.mishra@hull.ac.uk](Correspondence: bhupesh.mishra@hull.ac.uk)  \nAbstract: Graduate salaries are a significant concern for graduates, employers, and policymakers, as various factors influence them. This study investigates determinants of graduate salaries in the UK, utilising survey data from HESA (Higher Education Statistical Agency) and integrating advanced machine learning (ML) explanatory techniques with statistical analytical methodologies. By employing multi-stage analyses alongside machine learning models such as decision trees, random forests and the explainability with SHAP stands for (Shapley Additive exPanations),","cbCaijhuPo0jSJo9","https://ap.wps.com/l/cbCaijhuPo0jSJo9","pdf",3176667,5,1,33,"English","en",105,"# Introduction\n## Methods and Data\n## Machine Learning Models and Explainability\n## Statistical Validation (ANOVA)\n## Findings and Key Determinants\n## Limitations and Future Work","[{\"question\":\"What data and methods are used to study determinants of UK graduate salaries?\",\"answer\":\"The study uses Higher Education Statistical Agency (HESA) survey data and combines multi-stage machine learning with statistical analytical techniques.\"},{\"question\":\"Which machine learning and explainability approaches are applied?\",\"answer\":\"It applies decision trees and random forests, and uses SHAP for model explainability to identify influential variables.\"},{\"question\":\"How are the relationships validated beyond machine learning predictions?\",\"answer\":\"ANOVA (Analysis of Variance) is used to validate statistical significance and uncover interaction effects among the socioeconomic and demographic variables.\"}]","Unveiling the Impact of Socioeconomic and Demographic Factors on Graduate Salaries - A Machine Learning Explanatory Analytical Approach | PDF",1785943771,83,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"unveiling-the-impact-of-socioeconomic-and-demographic-factors-on-graduate-salaries-a-machine-learning-explanatory-analytical-approach","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/unveiling-the-impact-of-socioeconomic-and-demographic-factors-on-graduate-salaries-a-machine-learning-explanatory-analytical-approach/128005/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What data and methods are used to study determinants of UK graduate salaries?","Question",{"text":77,"@type":78},"The study uses Higher Education Statistical Agency (HESA) survey data and combines multi-stage machine learning with statistical analytical techniques.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which machine learning and explainability approaches are applied?",{"text":82,"@type":78},"It applies decision trees and random forests, and uses SHAP for model explainability to identify influential variables.",{"name":84,"@type":75,"acceptedAnswer":85},"How are the relationships validated beyond machine learning predictions?",{"text":86,"@type":78},"ANOVA (Analysis of Variance) is used to validate statistical significance and uncover interaction effects among the socioeconomic and demographic variables.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]