{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:GP5OW7ZPMJGYVIF43ZC7Z424FF","short_pith_number":"pith:GP5OW7ZP","schema_version":"1.0","canonical_sha256":"33faeb7f2f624d8aa0bcde45fcf35c29553c272a8c40744efea70e6a6a7a5411","source":{"kind":"arxiv","id":"1508.05502","version":1},"attestation_state":"computed","paper":{"title":"Evaluating the quality of survey and administrative data with generalized multitrait-multimethod models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Antje Kirchner, Daniel Leonard Oberski, Frauke Kreuter, Stephanie Eckman","submitted_at":"2015-08-22T12:31:33Z","abstract_excerpt":"Administrative register data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audits or comparison with a survey that is assumed perfect.\n  We introduce the \"generalized multitrait-multimethod\" (GMTMM) model, which can be seen as a general framework for evaluating the quality"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1508.05502","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2015-08-22T12:31:33Z","cross_cats_sorted":[],"title_canon_sha256":"67d8b901512779b132945541b6e47a5155d145e233ec893b0a47d2769253344e","abstract_canon_sha256":"9af945494c69403b18e1e9765200a897861bf5378cfa42ef8cfdbb27104ad1d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:34:53.769387Z","signature_b64":"7X+87um5MuTqoJupp1pd66wUbFkH97OtvoF4n01aU35j6jvAqsjbo4TvUUG3cVqAvLXiAjWkds2Iao3erZLtAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33faeb7f2f624d8aa0bcde45fcf35c29553c272a8c40744efea70e6a6a7a5411","last_reissued_at":"2026-05-18T01:34:53.768624Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:34:53.768624Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating the quality of survey and administrative data with generalized multitrait-multimethod models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Antje Kirchner, Daniel Leonard Oberski, Frauke Kreuter, Stephanie Eckman","submitted_at":"2015-08-22T12:31:33Z","abstract_excerpt":"Administrative register data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audits or comparison with a survey that is assumed perfect.\n  We introduce the \"generalized multitrait-multimethod\" (GMTMM) model, which can be seen as a general framework for evaluating the quality"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.05502","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1508.05502","created_at":"2026-05-18T01:34:53.768733+00:00"},{"alias_kind":"arxiv_version","alias_value":"1508.05502v1","created_at":"2026-05-18T01:34:53.768733+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1508.05502","created_at":"2026-05-18T01:34:53.768733+00:00"},{"alias_kind":"pith_short_12","alias_value":"GP5OW7ZPMJGY","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"GP5OW7ZPMJGYVIF4","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"GP5OW7ZP","created_at":"2026-05-18T12:29:22.688609+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GP5OW7ZPMJGYVIF43ZC7Z424FF","json":"https://pith.science/pith/GP5OW7ZPMJGYVIF43ZC7Z424FF.json","graph_json":"https://pith.science/api/pith-number/GP5OW7ZPMJGYVIF43ZC7Z424FF/graph.json","events_json":"https://pith.science/api/pith-number/GP5OW7ZPMJGYVIF43ZC7Z424FF/events.json","paper":"https://pith.science/paper/GP5OW7ZP"},"agent_actions":{"view_html":"https://pith.science/pith/GP5OW7ZPMJGYVIF43ZC7Z424FF","download_json":"https://pith.science/pith/GP5OW7ZPMJGYVIF43ZC7Z424FF.json","view_paper":"https://pith.science/paper/GP5OW7ZP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1508.05502&json=true","fetch_graph":"https://pith.science/api/pith-number/GP5OW7ZPMJGYVIF43ZC7Z424FF/graph.json","fetch_events":"https://pith.science/api/pith-number/GP5OW7ZPMJGYVIF43ZC7Z424FF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GP5OW7ZPMJGYVIF43ZC7Z424FF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GP5OW7ZPMJGYVIF43ZC7Z424FF/action/storage_attestation","attest_author":"https://pith.science/pith/GP5OW7ZPMJGYVIF43ZC7Z424FF/action/author_attestation","sign_citation":"https://pith.science/pith/GP5OW7ZPMJGYVIF43ZC7Z424FF/action/citation_signature","submit_replication":"https://pith.science/pith/GP5OW7ZPMJGYVIF43ZC7Z424FF/action/replication_record"}},"created_at":"2026-05-18T01:34:53.768733+00:00","updated_at":"2026-05-18T01:34:53.768733+00:00"}