Statistical Characteristics of K6 as an Explanatory Variable in the United States and China | 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 Statistical Characteristics of K6 as an Explanatory Variable in the United States and China steven stern, xin lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5829871/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 We consider many ways to use K6 as an explanatory variable in equations for marriage and employment. We find that a) it is inappropriate transform K6 into a binary variable; b) it is inappropriate to add the K6 variables into a single sum of Lykert scales; c) there are serious issues associated with using each of the K6 variables separately; and d) each possible way to use K6 has critical issues making its use problematic. Health Economics & Outcomes Research Full Text Additional Declarations The authors declare no competing interests. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5829871","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402196980,"identity":"2b78622a-042d-44f0-807d-67a160ea6a51","order_by":0,"name":"steven stern","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie2PvWrDMBRGrwjIi4I7yhjsV1DRkBYCehUbgya7dPRYCCiLvTtTXyFjx0BAWfQAGVsCnkpx6dKCCyVpKKEopdkK1YH7M3wHXQE4HH+ebtfJtiX7soD3c5tDDbATlQH5jRLfTjR9vgMhvLrdjPs+GtFCd10pwfdyZlOYxjKYGUgrshrxQjF+2Vxls8bkEFSPdgUTzocKEkIlDosbls7XOR8MVQlsbX8lVv4Lf1cgSNzi8KI/UMQRBTRBG6QAVRTjEPCXkgOjx/4izx9qRdPKSBzUinNmnjLUGEmoaa+th02W94s3NRbeVGP62kcRWxVL6Mos8qfZ3HrYJ/RgP0t2g/wQ/46/OCHscDgc/4EP1JNXxDfPOWsAAAAASUVORK5CYII=","orcid":"","institution":"stony brook university","correspondingAuthor":true,"prefix":"","firstName":"steven","middleName":"","lastName":"stern","suffix":""},{"id":402196981,"identity":"84114286-0a3a-4bf7-97ce-8bc024ca4777","order_by":1,"name":"xin lu","email":"","orcid":"","institution":"stony brook university","correspondingAuthor":false,"prefix":"","firstName":"xin","middleName":"","lastName":"lu","suffix":""}],"badges":[],"createdAt":"2025-01-14 21:49:29","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5829871/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5829871/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73816718,"identity":"3e59ab32-f201-4157-9d57-a6f113601065","added_by":"auto","created_at":"2025-01-15 03:11:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":346880,"visible":true,"origin":"","legend":"","description":"","filename":"k6explainhsorm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5829871/v1_covered_0f2765a9-7d8c-4faf-898c-fe334f0ad2ef.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eStatistical Characteristics of K6 as an Explanatory Variable in the United States and China\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-5829871/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5829871/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe consider many ways to use \u003cstrong\u003eK6\u003c/strong\u003e as an explanatory variable in equations for marriage and employment. We find that a) it is inappropriate transform \u003cstrong\u003eK6\u003c/strong\u003e into a binary variable; b) it is inappropriate to add the \u003cstrong\u003eK6\u003c/strong\u003evariables into a single sum of Lykert scales; c) there are serious issues associated with using each of the \u003cstrong\u003eK6\u003c/strong\u003e variables separately; and d) each possible way to use \u003cstrong\u003eK6\u003c/strong\u003e has critical issues making its use problematic.\u003c/p\u003e","manuscriptTitle":"Statistical Characteristics of K6 as an Explanatory Variable in the United States and China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-15 02:55:28","doi":"10.21203/rs.3.rs-5829871/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":"35b19c3b-a74a-4f92-84e7-e54a4a7bf429","owner":[],"postedDate":"January 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42868677,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2025-01-15T02:55:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-15 02:55:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5829871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5829871","identity":"rs-5829871","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
Text is read by the "Ask this paper" AI Q&A widget below.
Extraction quality varies by source — PMC NXML preserves structure
cleanly, OA-HTML may include some navigation residue, and OA-PDF can
have broken hyphenation. The publisher copy
(via DOI)
is the canonical version.