Nichtparametrische Statistik: Eine Einführung in Die Grundlagen
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H�ufig sind Sozialwissenschaftler mit der Tatsache konfron- tiert, da die von Ihnen zu analysierenden Daten nicht den Annahmen gen�gen, die in den zur Anwendung vorgesehenen statistischen Verfahren explizit als Modellvoraussetzungen formuliert sind. Dies gilt insbesondere f�r die klassischen statistischen Modelle. Als Ausweg bieten sich hier eine Viel- zahl von Verfahren an, die in ihrer Gesamtheit recht global und oft auch ungenau mit der Bezeichnung nichtparametrische oder auch verteilungsfreie Statistik belegt werden. Zwar gelten auch hier jeweils spezifische Modellvoraussetzungen, diese sind in der Regel weniger einschr�nkend und damit der besonderen Datenlage der Sozialwissenschaften angemessener. Der vorliegende Band handelt von diesen statistischen Ver- fahren. Das besondere didaktische Konzept der Darstellung ergibt sich aus der vorgefundenen allgemeinen Lehrbuchliteratur zu dem in Frage stehenden Sachbereich. Diese zeichnet sich ent- weder aus durch rigorose mathematische Abstraktion auf ho- hem Niveau oder begn�gt sich mit rezeptartiger Pr�sentation einzelner Verfahren. Ubersteigt die erstgenannte Darstellungs- form bei weitem die Notwendigkeiten sozialwissenschaftlicher Datenanalyse, so kann die zweite Form erst recht nicht be- friedigen. Zwar haben sich die vorhandenen "Kochb�cher" mit der in ihnen enthaltenen Vielzahl von Tests und anderen Ver- fahren als Kompendien f�r den praktisch Forschenden durchaus bew�hrt, beim Novizen des Bereichs der nichtparametrischen Statistik hinterlassen sie jedoch eher Verwirrung als Ein- sicht in die Zusammenh�nge. Letztlich ist eine solche Ein- sicht aber das einzige Mittel gegen eine falsche Anwendung statistischer Verfahren. 6 Demgegen�ber soll hier ein Kompromi zwischen diesen beidenDarstellungsformen versucht werden. Nicht alle m�glichen nichtparametrischen Verfahren werden behandelt.
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