Geänderte Inhalte

Strack, M. (2005). Organizing diverse sets of data with the Schwartz´ Value Circle. The First EASR Conference (European Association for Survey Research), July 18-22. Barcelona, Spain.

Strack, M. & Boer, D. (2006). Symbolische Selbstergänzung im Jugendalter: Musikstil indiziert Werthaltungen. (18 S). In: Hartmut Schröder (Hrsg). Stil als Zeichen. Funktionen -Brüche - Inszenierungen. Universitätsschriften - Schriftenreihe der Europa-Universität Viadrina, Band 24. CD-ROM (ISSN 0941-7540).

Strack, M., Gennerich, C. & Hopf, N. (2008). Warum Werte? S. 90-130 in: Witte, E. H. (Hrsg). Sozialpsychologie und Werte. 23. Hamburger Symposium zur Methodologie der Sozialpsychologie. Lengerich: Pabst.

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Kanos Begeisterungsfaktoren mit quadratischem Term in der Treiberanalyse auffinden. Strack, M.; Heise, N. & Karger, J. (2009)

Strack, M., Boultgen, D., Kenkmann, C. & Held, M. (2004): Sozialperspektivische Imagepositionierung als Feedbackinstrument zur Unterstützung kundenorientierten Managements. S. 362-369 in: Bungard, W., Koop, B. & Liebig, C. (Hrsg.). "Psychologie und Wirtschaft leben". Aktuelle Themen der Wirtschaftspsychologie in Forschung und Praxis. München: Hampp.

Sequential Bayesian analyses have been suggested as a means for efficient hypothesis-testing within psychology, often substantially reducing the sample sizes required. In the context of clinical psychology, trial designs using sequential Bayesian analyses have been proposed as a way to accelerate psychological treatment development. However, a focus on Bayesian analyses as a means to conduct sequential testing may hinder uptake of such methods due to a lack of familiarity amongst many researchers. Demonstrating how equivalent sequential analyses are possible using non-Bayesian (e.g., frequentist) analyses could increase the accessibility of these methods and thus the extent to which psychological treatment development research can benefit from more time and resource-efficient trial designs. This paper demonstrates a simple method for sequential testing based on confidence intervals around effect size estimates, and illustrates comparative efficiency to an approach based on Bayes factors.