{"_buckets": {"deposit": "45be76ba-bfcd-4c80-9196-0371e60be588"}, "_deposit": {"id": "17855", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "17855"}, "status": "published"}, "_oai": {"id": "oai:soar-ir.repo.nii.ac.jp:00017855", "sets": ["1310"]}, "author_link": ["49922", "49923", "49924"], "item_1628147817048": {"attribute_name": "出版タイプ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_ab4af688f83e57aa", "subitem_version_type": "AM"}]}, "item_6_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2015-01-02", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "1", "bibliographicPageEnd": "189", "bibliographicPageStart": "186", "bibliographicVolumeNumber": "48", "bibliographic_titles": [{"bibliographic_title": "JOURNAL OF BIOMECHANICS"}]}]}, "item_6_description_20": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Kinematic and force trajectories are often normalized in time, with mean and variance summary statistic trajectories reported. It has been shown elsewhere, for simple one-factor experiments, that statistical testing can be conducted directly on those summary statistic trajectories using Random Field Theory (RFT). This technical note describes how RFT extends to two-factor designs, and how bizarre \"non-phasic interactions\" can occur in multi-factor experiments. We reanalyzed a public dataset detailing stance phase knee flexion during walking in (a) patellofemoral pain vs. controls, and (b) females vs. males using both a full model (with interaction effect) and a main-effects-only model. In both models the main effect of PAIN failed to reach significance at alpha=0.05. The main effect of GENDER reached significance over 5-40% stance (p=0.0005), but only for the full model. The interaction effect (in the full model) reached significance over 0-15% of stance (p=0.030), and resulted from greater flexion in females but decreased flexion in males in PFP vs. controls. Thus there was a non-phasic interaction in which a non-significant interaction (over 20-40% stance) suppressed the main effect of GENDER. Similarly, if we had only analyzed 20-40% stance, we would have committed Type II error by failing to reject the null PAIN-GENDER interaction hypothesis. The possible presence of non-phasic interactions implies that trajectory analyses must be conducted at the whole-trajectory level, because a failure to do so will generally miss non-phasic interactions if present. (C) 2014 Elsevier Ltd. 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It has been shown elsewhere, for simple one-factor experiments, that statistical testing can be conducted directly on those summary statistic trajectories using Random Field Theory (RFT). This technical note describes how RFT extends to two-factor designs, and how bizarre \"non-phasic interactions\" can occur in multi-factor experiments. We reanalyzed a public dataset detailing stance phase knee flexion during walking in (a) patellofemoral pain vs. controls, and (b) females vs. males using both a full model (with interaction effect) and a main-effects-only model. In both models the main effect of PAIN failed to reach significance at alpha=0.05. The main effect of GENDER reached significance over 5-40% stance (p=0.0005), but only for the full model. The interaction effect (in the full model) reached significance over 0-15% of stance (p=0.030), and resulted from greater flexion in females but decreased flexion in males in PFP vs. controls. Thus there was a non-phasic interaction in which a non-significant interaction (over 20-40% stance) suppressed the main effect of GENDER. Similarly, if we had only analyzed 20-40% stance, we would have committed Type II error by failing to reject the null PAIN-GENDER interaction hypothesis. The possible presence of non-phasic interactions implies that trajectory analyses must be conducted at the whole-trajectory level, because a failure to do so will generally miss non-phasic interactions if present. (C) 2014 Elsevier Ltd. All rights reserved."}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Pataky,  Todd C."}], "nameIdentifiers": [{"nameIdentifier": "49922", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Vanrenterghem,  Jos"}], "nameIdentifiers": [{"nameIdentifier": "49923", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Robinson,  Mark A."}], "nameIdentifiers": [{"nameIdentifier": "49924", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2016-02-04"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "Two-way_ANOVA_scalar_trajectories_experimental_evidence.pdf", "filesize": [{"value": "1.4 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensefree": "© 2015. 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