\n Der MIND.set-IAT-Datensatz mit dem Titel „Implizite Assoziationen gegenüber arabischen Individuen im Vergleich zu weißen Individuen” umfasst zwei longitudinale Implicit Association Test (IAT)-Umfragen, die mit DeZIM-Panelteilnehmer*innen durchgeführt wurden. Die Daten enthalten Aufzeichnungen impliziter Bewertungen arabischer Individuen im Vergleich zu weißen Individuen auf der Grundlage spontaner Assoziationen mit positiven und negativen Attributen. Beide IATs verwendeten das gleiche Design und wiesen den Zielkategorien „ARABISCH“ und „WEISS“ sowie den Attributkategorien „GUT“ und „SCHLECHT“ zu. Für die Zielkategorie sahen die Teilnehmenden Fotos von arabischen und weißen Individuen, die auf der Grundlage ihrer wahrgenommenen ethnischen Typikalität aus der DeZIM Picture Database ausgewählt wurden. Die Attributkategorien bestanden aus Wörtern, beispielsweise wurde „glücklich“ für die Kategorie gut und „schrecklich“ für die Kategorie schlecht verwendet. Die Daten wurden im Rahmen einer zusätzlichen Umfrage in Welle 13 (EW1, 2024), N = 2813, und der Short Mindset Survey (ES2, 2025), N = 2158, des DeZIM-Panels unter Verwendung der MIND.set-Plattform erhoben. Die Testdaten umfassen sowohl die MIND.set-Rohdaten als auch den IAT-Bias-Score (D-Score), der auf der Grundlage des Best-Practice-Algorithmus von Greenwald et al. (2022) berechnet wurde. Die Testdaten können über eine persönliche Identifikationsnummer mit umfangreichen soziodemografischen und einstellungsbezogenen Paneldaten verknüpft werden. Dadurch kann der Datensatz für Querschnitts- und Längsschnittanalysen impliziter Einstellungen im Zusammenhang mit Migration, Diskriminierung und sozialem Zusammenhalt verwendet werden.\n
", "en": "\n The MIND.set IAT dataset, titled \"Implicit Associations toward Arab vs. White Individuals,\" comprises two longitudinal Implicit Association Test (IAT) surveys conducted with DeZIM panel participants. The data contains records of implicit evaluations of Arab individuals compared to white individuals based on spontaneous associations with positive and negative attributes. Both IATs used the same design, assigning target categories of \"ARAB\" vs. \"WHITE\" and attribute categories \"GOOD\" vs. \"BAD.\" For the target category, participants saw pictures of Arab and white individuals, which were selected based on their perceived ethnic typicality ratings from the DeZIM Picture Database. The attribute stimuli consisted of words; for example, \"happy\" was used for the good category, and \"horrible\" was used for the bad category. Data were collected as part of an additional survey in Wave 13 (EW1, 2024), N = 2813, and the Short Mindset Survey (ES2, 2025), N = 2158, of the DeZIM panel using the MIND.set platform. The test data includes raw MIND.set data as well as the IAT bias score (D score), computed based on Greenwald et al.'s (2022) best-practice algorithm. The test data can be linked to extensive sociodemographic and attitude-related panel data via a personal identification number. This allows the dataset to be used for cross-sectional and longitudinal analyses of implicit attitudes in the context of migration, discrimination, and social cohesion.\n
", "en-GB": "\n The MIND.set IAT dataset, titled \"Implicit Associations toward Arab vs. White Individuals,\" comprises two longitudinal Implicit Association Test (IAT) surveys conducted with DeZIM panel participants. The data contains records of implicit evaluations of Arab individuals compared to white individuals based on spontaneous associations with positive and negative attributes. Both IATs used the same design, assigning target categories of \"ARAB\" vs. \"WHITE\" and attribute categories \"GOOD\" vs. \"BAD.\" For the target category, participants saw pictures of Arab and white individuals, which were selected based on their perceived ethnic typicality ratings from the DeZIM Picture Database. The attribute stimuli consisted of words; for example, \"happy\" was used for the good category, and \"horrible\" was used for the bad category. Data were collected as part of an additional survey in Wave 13 (EW1, 2024), N = 2813, and the Short Mindset Survey (ES2, 2025), N = 2158, of the DeZIM panel using the MIND.set platform. The test data includes raw MIND.set data as well as the IAT bias score (D score), computed based on Greenwald et al.'s (2022) best-practice algorithm. The test data can be linked to extensive sociodemographic and attitude-related panel data via a personal identification number. This allows the dataset to be used for cross-sectional and longitudinal analyses of implicit attitudes in the context of migration, discrimination, and social cohesion.\n
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