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"creatorType": "author" }, { "lastName": "Stroppe", "firstName": "Anne-Kathrin", "creatorType": "author" }, { "lastName": "Suchow", "firstName": "Jordan", "creatorType": "author" }, { "lastName": "Teltemann", "firstName": "Janna", "creatorType": "author" }, { "lastName": "Tibajev", "firstName": "Andrey", "creatorType": "author" }, { "lastName": "Tung", "firstName": "Brian B", "creatorType": "author" }, { "lastName": "Vagni", "firstName": "Giacomo", "creatorType": "author" }, { "lastName": "Van Assche", "firstName": "Jasper", "creatorType": "author" }, { "lastName": "van der Linden", "firstName": "Meta", "creatorType": "author" }, { "lastName": "van der Noll", "firstName": "Jolanda", "creatorType": "author" }, { "lastName": "Van Hootegem", "firstName": "Arno", "creatorType": "author" }, { "lastName": "Vogtenhuber", "firstName": "Stefan", "creatorType": "author" }, { "lastName": "Voicu", "firstName": "Bogdan", "creatorType": "author" }, { "lastName": "Wagemans", "firstName": "Fieke", "creatorType": "author" }, { "lastName": "Wehl", "firstName": "Nadja", "creatorType": "author" }, { "lastName": "Werner", "firstName": "Hannah", "creatorType": "author" }, { "lastName": "Wiernik", "firstName": "Brenton M", "creatorType": "author" }, { "lastName": "Winter", "firstName": "Fabian", "creatorType": "author" }, { "lastName": "Wolf", "firstName": "Christof", "creatorType": "author" }, { "lastName": "Wu", "firstName": "Cary", "creatorType": "author" }, { "lastName": "Yamada", "firstName": "Yuki", "creatorType": "author" }, { "lastName": "Zakula", "firstName": "Björn", "creatorType": "author" }, { "lastName": "Zhang", "firstName": "Nan", "creatorType": "author" }, { "lastName": "Ziller", "firstName": "Conrad", "creatorType": "author" }, { "lastName": "Zins", "firstName": "Stefan", "creatorType": "author" }, { "lastName": "Żółtak", "firstName": "Tomasz", "creatorType": "author" } ], "date": "2021-05-18", "DOI": "10.31235/osf.io/j7qta_v1", "abstractNote": "This paper reports findings from a crowdsourced replication. Eighty-five independent teams attempted a computational replication of results reported in an original study of policy preferences and immigration by fitting the same statistical models to the same data. The replication involved an experimental condition. Random assignment put participating teams into either the transparent group that received the original study and code, or the opaque group receiving only a methods section, rough results description and no code. The transparent group mostly verified the numerical results of the original study with the same sign and p-value threshold (95.7%), while the opaque group had less success (89.3%). Exact numerical reproductions to the second decimal place were far less common (76.9% and 48.1%), and the number of teams who verified at least 95% of all effects in all models they ran was 79.5% and 65.2% respectively. Therefore, the reliability we quantify depends on how reliability is defined, but most definitions suggest it would take a minimum of three independent replications to achieve reliability. Qualitative investigation of the teams’ workflows reveals many causes of error including mistakes and procedural variations. Although minor error across researchers is not surprising, we show this occurs where it is least expected in the case of computational reproduction. Even when we curate the results to boost ecological validity, the error remains large enough to undermine reliability between researchers to some extent. The presence of inter-researcher variability may explain some of the current “reliability crisis” in the social sciences because it may be undetected in all forms of research involving data analysis. The obvious implication of our study is more transparency. Broader implications are that researcher variability adds an additional meta-source of error that may not derive from conscious measurement or modeling decisions, and that replications cannot alone resolve this type of uncertainty.", "archiveID": "j7qta_v1", "libraryCatalog": "OSF Preprints", "repository": "SocArXiv", "shortTitle": "The Reliability of Replications", "url": "https://osf.io/preprints/socarxiv/j7qta_v1/", "attachments": [ { "title": "Preprint PDF", "mimeType": "application/pdf" } ], "tags": [ { "tag": "Immigration" }, { "tag": "Meta-Reliability" }, { "tag": "Noise" }, { "tag": "Policy Preferences" }, { "tag": "Replication" }, { "tag": "Researcher Variability" }, { "tag": "Secondary Observer Effect" } ], "notes": [], "seeAlso": [] } ] }, { "type": "web", "url": "https://osf.io/preprints/psyarxiv/7eb4g", "items": [ { "itemType": "preprint", "title": "Revisiting the Digital Jukebox in Daily Life: Applying Mood Management Theory to Algorithmically Curated Music Streaming Environments", "creators": [ { "lastName": "Ernst", "firstName": "Alicia", "creatorType": "author" }, { "lastName": "Dietrich", "firstName": "Felix", "creatorType": "author" }, { "lastName": "Rohr", "firstName": "Benedikt", "creatorType": "author" }, { "lastName": "Reinecke", "firstName": "Leonard", "creatorType": "author" }, { "lastName": "Scharkow", "firstName": "Michael", "creatorType": "author" } ], "date": "2024-09-30", "DOI": "10.31234/osf.io/7eb4g_v1", "abstractNote": "Experimental evidence has profoundly contributed to our understanding of Mood Man-agement Theory (MMT) in the context of music. Extant research, however, lacks insights into everyday mood regulation through music listening, especially on music streaming services where selections can be guided by algorithmic recommendations. Hence, we tested MMT in a naturalistic setting by combining experience sampling with logged music streaming data, while accounting for algorithmic curation as a boundary condition to users’ music choices. In a pre-registered study utilizing T = 6,864 surveys from N = 144 listeners, results showed that mood, music selection, and algorithmic curation varied substantially from situation to situation. How-ever, we found no effects between mood and music choices that would confirm MMT’s selection hypotheses, yet in part, small congruent effects between mood and music. Algorithmic curation did not establish novel MMT-related patterns. Our findings suggest re-specifying MMT and related media use theories for daily life.", "archiveID": "7eb4g_v1", "libraryCatalog": "OSF Preprints", "repository": "PsyArXiv", "shortTitle": "Revisiting the Digital Jukebox in Daily Life", "url": "https://osf.io/preprints/psyarxiv/7eb4g_v1/", "attachments": [ { "title": "Preprint PDF", "mimeType": "application/pdf" } ], "tags": [ { "tag": "Mood Management Theory" }, { "tag": "algorithmic curation" }, { "tag": "digital behavioral data" }, { "tag": "experience sampling method" }, { "tag": "music use" } ], "notes": [], "seeAlso": [] } ] }, { "type": "web", "url": "https://osf.io/search?search=curiosity&tab=5", "defer": true, "items": "multiple" } ] /** END TEST CASES **/