{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:XLTZSQXKRU7I6BJGLES7FFTXHB","short_pith_number":"pith:XLTZSQXK","schema_version":"1.0","canonical_sha256":"bae79942ea8d3e8f05265925f29677387e23878594249dfb96c93bf4b5a49d46","source":{"kind":"arxiv","id":"2110.02805","version":3},"attestation_state":"computed","paper":{"title":"Penalized Optimal Scaling for Ordinal Variables with an Application to International Classification of Functioning Core Sets","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.AP","authors_text":"Aisouda Hoshiyar, Henk A.L. Kiers, Jan Gertheiss","submitted_at":"2021-10-06T14:25:50Z","abstract_excerpt":"Ordinal data occur frequently in the social sciences. When applying principal component analysis (PCA), however, those data are often treated as numeric implying linear relationships between the variables at hand, or non-linear PCA is applied where the obtained quantifications are sometimes hard to interpret. Non-linear PCA for categorical data, also called optimal scoring/scaling, constructs new variables by assigning numerical values to categories such that the proportion of variance in those new variables that is explained by a predefined number of principal components is maximized. We prop"},"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":"2110.02805","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.AP","submitted_at":"2021-10-06T14:25:50Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"599a1d9754106f92f6562591cd2b337058f9dec61f055cc2b494d8c7b29d03a5","abstract_canon_sha256":"6f379040aa759f3b9be55dde026330f119c0efe17ada72ba28e2d35005153f7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:33:06.459889Z","signature_b64":"GZtLQKmwRBqVixLWDycOZc+qnMH4d/2yfoO4WI9R7rcKANodHNq/TCVbgw6VIbvTYbiFmjO3hc5sw+JdKk6PCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bae79942ea8d3e8f05265925f29677387e23878594249dfb96c93bf4b5a49d46","last_reissued_at":"2026-07-05T05:33:06.459481Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:33:06.459481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Penalized Optimal Scaling for Ordinal Variables with an Application to International Classification of Functioning Core Sets","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.AP","authors_text":"Aisouda Hoshiyar, Henk A.L. Kiers, Jan Gertheiss","submitted_at":"2021-10-06T14:25:50Z","abstract_excerpt":"Ordinal data occur frequently in the social sciences. When applying principal component analysis (PCA), however, those data are often treated as numeric implying linear relationships between the variables at hand, or non-linear PCA is applied where the obtained quantifications are sometimes hard to interpret. Non-linear PCA for categorical data, also called optimal scoring/scaling, constructs new variables by assigning numerical values to categories such that the proportion of variance in those new variables that is explained by a predefined number of principal components is maximized. We prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.02805","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2110.02805/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2110.02805","created_at":"2026-07-05T05:33:06.459532+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.02805v3","created_at":"2026-07-05T05:33:06.459532+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.02805","created_at":"2026-07-05T05:33:06.459532+00:00"},{"alias_kind":"pith_short_12","alias_value":"XLTZSQXKRU7I","created_at":"2026-07-05T05:33:06.459532+00:00"},{"alias_kind":"pith_short_16","alias_value":"XLTZSQXKRU7I6BJG","created_at":"2026-07-05T05:33:06.459532+00:00"},{"alias_kind":"pith_short_8","alias_value":"XLTZSQXK","created_at":"2026-07-05T05:33:06.459532+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/XLTZSQXKRU7I6BJGLES7FFTXHB","json":"https://pith.science/pith/XLTZSQXKRU7I6BJGLES7FFTXHB.json","graph_json":"https://pith.science/api/pith-number/XLTZSQXKRU7I6BJGLES7FFTXHB/graph.json","events_json":"https://pith.science/api/pith-number/XLTZSQXKRU7I6BJGLES7FFTXHB/events.json","paper":"https://pith.science/paper/XLTZSQXK"},"agent_actions":{"view_html":"https://pith.science/pith/XLTZSQXKRU7I6BJGLES7FFTXHB","download_json":"https://pith.science/pith/XLTZSQXKRU7I6BJGLES7FFTXHB.json","view_paper":"https://pith.science/paper/XLTZSQXK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.02805&json=true","fetch_graph":"https://pith.science/api/pith-number/XLTZSQXKRU7I6BJGLES7FFTXHB/graph.json","fetch_events":"https://pith.science/api/pith-number/XLTZSQXKRU7I6BJGLES7FFTXHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XLTZSQXKRU7I6BJGLES7FFTXHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XLTZSQXKRU7I6BJGLES7FFTXHB/action/storage_attestation","attest_author":"https://pith.science/pith/XLTZSQXKRU7I6BJGLES7FFTXHB/action/author_attestation","sign_citation":"https://pith.science/pith/XLTZSQXKRU7I6BJGLES7FFTXHB/action/citation_signature","submit_replication":"https://pith.science/pith/XLTZSQXKRU7I6BJGLES7FFTXHB/action/replication_record"}},"created_at":"2026-07-05T05:33:06.459532+00:00","updated_at":"2026-07-05T05:33:06.459532+00:00"}