{"created":"2023-05-15T12:28:03.662160+00:00","id":21437,"links":{},"metadata":{"_buckets":{"deposit":"7f278e58-dd3f-4059-8f16-27c4f74c8f3c"},"_deposit":{"created_by":10,"id":"21437","owners":[10],"pid":{"revision_id":0,"type":"depid","value":"21437"},"status":"published"},"_oai":{"id":"oai:kansai-u.repo.nii.ac.jp:00021437","sets":["528:1385:1386:2928"]},"author_link":["18563","49019","49021","49022","49023","49020"],"item_10_alternative_title_20":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"Trait-state distinction model with structured means : Methodologies and applications for longitudinal data sets of multiple occasions"}]},"item_10_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2021-09-30","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicPageEnd":"140","bibliographicPageStart":"69","bibliographicVolumeNumber":"53","bibliographic_titles":[{"bibliographic_title":"関西大学社会学部紀要"}]}]},"item_10_description_4":{"attribute_name":"概要","attribute_value_mlt":[{"subitem_description":"探索的因子分析の文脈で、Cattell (1965)は、「特定化方程式において、特性因子得点以外に状態因子得点を常に付け加えなければならない」と述べている。この方程式について、Cattell (1973)は、観測された変数が特性因子と状態因子に負荷すると定義している。彼の考えを複数機会の縦断データに構造方程式モデリングに適用しながら、われわれは、特性因子がすべての測定機会に対して因子パターン不変であり、一つの測定機会からなる状態因子が異なる測定機会の他の状態因子と不変であるとする特性・状態区分モデルを提案した。われわれのモデルとGeiser (2021)の単一特性・多状態モデルの違いは、特性と状態の因子分散が因子パターンの不変性の下で独立して推定され、特性と状態の因子得点の平均も推定されることである。特性・状態区別モデルは、2回測定の状態特性不安尺度、3回測定の大学での学習観尺度、5回測定のGrit尺度、そして、3回測定のBig Five形容詞30項目の尺度で使用された。これらの心理的変数の特性を明らかにするために、推定された因子の分散を、特性度と状態度の2次元空間にプロットした。因子の分散と因子の平均を組み合わせることの意義などが、特性・状態区分と関連づけて議論された。","subitem_description_type":"Other"},{"subitem_description":"In the context of exploratory factor analysis, Cattell (1965) noted that \"in the specification equation we must always add state factor scores along with trait factor scores.\" This equation was defined by Cattell (1973) as the observed variables loading on the trait factors and the state factors. Applying his idea to structural equation modeling for longitudinal data of multiple occasions, we proposed the traitstate distinction model, wherein the trait factor was invariant for all measurement occasions and the state factor of one measurement occasion was invariant with the other state factor of different measurement occasion. The differences between our model and the singletrait-multistate model of Geiser (2021) are that the trait factor variances and the state factor variances were estimated independently under the factor pattern invariance, and, the means of the trait and state factor scores were also estimated. The trait-state distinction model in this paper was utilized for the State Trait Anxiety Scale of two occasions, the College Learning Perspective Scales of three occasions, the Grit Scale of five occasions, and the Big Five Adjective Scale of 30 items of three occasions. To characterize these psychological variables, the estimated factors' variances were plotted in a two-dimensional space of trait and state proportions. The implications of combining factor variances and factor means were discussed in relation to trait-state distinction.","subitem_description_type":"Other"}]},"item_10_full_name_3":{"attribute_name":"著者別名","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"49021","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Shimizu, Kazuaki"}]},{"nameIdentifiers":[{"nameIdentifier":"49022","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Miho, Norihiro"}]},{"nameIdentifiers":[{"nameIdentifier":"49023","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Nishikawa, Kazuji"}]}]},"item_10_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.32286/00025457","subitem_identifier_reg_type":"JaLC"}]},"item_10_publisher_34":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"関西大学社会学部"}]},"item_10_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00046982","subitem_source_identifier_type":"NCID"}]},"item_10_source_id_8":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"02876817","subitem_source_identifier_type":"ISSN"}]},"item_10_version_type_17":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorAffiliations":[{"affiliationNameIdentifiers":[{"affiliationNameIdentifier":""}],"affiliationNames":[{"affiliationName":""}]}],"creatorNames":[{"creatorName":"清水, 和秋","creatorNameLang":"ja"},{"creatorName":"Shimizu, Kazuaki","creatorNameLang":"en"}],"familyNames":[{"familyName":"清水","familyNameLang":"ja"},{"familyName":"Shimizu","familyNameLang":"en"}],"givenNames":[{"givenName":"和秋","givenNameLang":"ja"},{"givenName":"Kazuaki","givenNameLang":"en"}],"nameIdentifiers":[{},{}]},{"creatorNames":[{"creatorName":"三保, 紀裕"}],"nameIdentifiers":[{},{}]},{"creatorNames":[{"creatorName":"西川, 一二"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2021-10-14"}],"displaytype":"detail","filename":"KU-1100-20210930-03.pdf","filesize":[{"value":"2.1 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"KU-1100-20210930-03.pdf","url":"https://kansai-u.repo.nii.ac.jp/record/21437/files/KU-1100-20210930-03.pdf"},"version_id":"6ed33bae-4725-4a0f-8f4c-ea08b253577c"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"特性・状態区分モデル","subitem_subject_scheme":"Other"},{"subitem_subject":"構造方程式モデリング","subitem_subject_scheme":"Other"},{"subitem_subject":"縦断的データ","subitem_subject_scheme":"Other"},{"subitem_subject":"因子パターン不変性","subitem_subject_scheme":"Other"},{"subitem_subject":"因子分散","subitem_subject_scheme":"Other"},{"subitem_subject":"因子平均","subitem_subject_scheme":"Other"},{"subitem_subject":"trait-state distinction model","subitem_subject_scheme":"Other"},{"subitem_subject":"structural equation modeling","subitem_subject_scheme":"Other"},{"subitem_subject":"longitudinal data","subitem_subject_scheme":"Other"},{"subitem_subject":"factor pattern invariance","subitem_subject_scheme":"Other"},{"subitem_subject":"factor variance","subitem_subject_scheme":"Other"},{"subitem_subject":"factor mean","subitem_subject_scheme":"Other"},{"subitem_subject":"関西大学","subitem_subject_scheme":"Other"},{"subitem_subject":"Kansai University","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"departmental bulletin paper","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"特性・状態の因子の平均を推定する区分モデル : 複数観測の縦断データの方法論と応用から","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"特性・状態の因子の平均を推定する区分モデル : 複数観測の縦断データの方法論と応用から"}]},"item_type_id":"10","owner":"10","path":["2928"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-10-14"},"publish_date":"2021-10-14","publish_status":"0","recid":"21437","relation_version_is_last":true,"title":["特性・状態の因子の平均を推定する区分モデル : 複数観測の縦断データの方法論と応用から"],"weko_creator_id":"10","weko_shared_id":-1},"updated":"2024-04-12T03:20:14.264823+00:00"}