Skip to main content
SpringerLink
Log in

Datenanalyse

  • Chapter
  • First Online:
Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften

  • 57k Accesses

Zusammenfassung

Dieses Kapitel vermittelt folgende Lernziele: Wissen, was man unter qualitativer Datenanalyse versteht und verschiedene interpretative Auswertungsverfahren kennen. Wissen, was man unter quantitativer Datenanalyse versteht und unterschiedliche statistische Auswertungsansätze voneinander abgrenzen können. Die Logik des klassischen statistischen Signifikanztests zur Überprüfung von Hypothesen erläutern können. Bei quantitativen explorativen (gegenstandserkundenden und theoriebildenden) Studien Methoden der explorativen Datenanalyse beschreiben können. Bei quantitativen deskriptiven (populationsbeschreibenden) Studien die Parameterschätzung mittels Punkt- und Intervallschätzung hinsichtlich unterschiedlicher Arten von Parametern und Stichproben erklären können. Bei quantitativen explanativen (hypothesenprüfenden) Studien die Hypothesenprüfung mittels klassischem statistischem Signifikanztest hinsichtlich verschiedener Arten von Unterschieds-, Zusammenhangs- und Veränderungs-Hypothesen sowie Einzelfall-Hypothesen erläutern können.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 64.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Literatur

  • Ahrbeck, B., Lehmann, R., Fickler-Stang, U., Kretschmer, A. & Maué, E. (2009). ENEBS. Evaluationsstudie Berliner Schülerfirmen. Berlin: Humboldt-Universität zu Berlin.

    Google Scholar 

  • Allolio-Näcke, L. (2010). Diskursanalyse. In G. Mey & K. Mruck (Hrsg.), Handbuch Qualitative Forschung in der Psychologie (S. 662–675). Wiesbaden: VS.

    Google Scholar 

  • Amozurrutia, J., & Servós, C. (2011). Excel spreadsheet as a tool for social narrative analysis. Quality & Quantity, 45, 953–967.

    Google Scholar 

  • Ayres, L., Kavanaugh, K., & Knafl, K. A. (2003). Within-case and across-case approaches to qualitative data analysis. Qualitative Health Research, 13, 871–883.

    PubMed  Google Scholar 

  • Basit, T. N. (2003). Manual or electronic? The role of coding in qualitative data analysis. Educational Research, 45, 143–154.

    Google Scholar 

  • Bernard, H. R., & Ryan, G. W. (2009). Analyzing qualitative data: Systematic approaches. Thousand Oaks: Sage.

    Google Scholar 

  • Billmann-Mahecha, E. (2010). Auswertung von Zeichnungen. In G. Mey & K. Mruck (Hrsg.), Handbuch Qualitative Forschung in der Psychologie (S. 707–722). Wiesbaden: VS.

    Google Scholar 

  • Bohnsack, R. (2011). Qualitative Bild- und Videointerpretation. Die dokumentarische Methode (2. Aufl.). Stuttgart: UTB.

    Google Scholar 

  • Bohnsack, R. & Nentwig-Gesemann, I. (Hrsg.). (2010). Dokumentarische Evaluationsforschung: Theoretische Grundlagen und Beispiele aus der Praxis. Opladen: Budrich.

    Google Scholar 

  • Bohnsack, R., Nentwig-Gesemann, I. & Nohl, A.-M. (2007). Einleitung: Die dokumentarische Methode und ihre Forschungspraxis. In R. Bohnsack (Hrsg.), Die dokumentarische Methode und ihre Forschungspraxis: Grundlagen qualitativer Sozialforschung (S. 9–27). Wiesbaden: VS.

    Google Scholar 

  • Bohnsack, R., Pfaff, N., & Weller, W. (Eds.). (2010). Qualitative research and documentary method in educational science – Results from Brazilian-German cooperations. Opladen: Budrich.

    Google Scholar 

  • Bryman, A. (2006). Integrating quantitative and qualitative research: How is it done? Qualitative Research, 6, 97–113.

    Google Scholar 

  • Burman, E., & Parker, I. (Eds.). (1993). Discourse analytic research: Repertoires and readings of texts in action. London: Routledge.

    Google Scholar 

  • Coffey, A., Holbrook, B., & Atkinson, P. (1996). Qualitative data analysis: Technologies and representations. Sociological Research Online, 1, 80–91. Retrieved 2021, August 13, from https://www.socresonline.org.uk/1/1/4.html

  • Davis, N. W., & Meyer, B. B. (2009). Qualitative data analysis: A procedural comparison. Journal of Applied Sport Psychology, 21, 116–124.

    Google Scholar 

  • Deppermann, A. (1999). Gespräche analysieren: Eine Einführung in konversationsanalytische Methoden. Wiesbaden: Leske & Budrich.

    Google Scholar 

  • Deppermann, A. (2010). Konversationsanalyse und diskursive Psychologie. In G. Mey & K. Mruck (Hrsg.), Handbuch Qualitative Forschung in der Psychologie (S. 643–661). Wiesbaden: VS.

    Google Scholar 

  • Erjavec, K., & Volčič, Z. (2010). ‚Target‘, ‚cancer‘ and ‚warrior‘: Exploring painful metaphors of self-presentation used by girls born of war rape. Discourse & Society, 21, 524–543.

    Google Scholar 

  • Evers, J. C., Silver, C., Mruck, K., & Peeters, B. (2011). Introduction to the KWALON experiment: Discussions on qualitative data analysis software by developers and users. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12, Art. 40. Retrieved 2021, August 13, from https://www.qualitative-research.net/index.php/fqs/article/view/1637

  • Flick, U. (2011). Triangulation. Eine Einführung (3. Aufl.). Wiesbaden: VS.

    Google Scholar 

  • Foucault, M. (1982). Subjekt und Macht. In D. Defert & F. Ewald (Hrsg.), Michel Foucault Schriften in vier Bänden Dits et Ecrits (Band 2, S. 269–294). Frankfurt/Main: Suhrkamp.

    Google Scholar 

  • Frost, N. A., Holt, A., Shinebourne, P., Esin, C., Nolas, S.-M., Mehdizadeh, L. et al. (2011). Collective findings, individual interpretations: An illustration of a pluralistic approach to qualitative data analysis. Qualitative Research in Psychology, 8, 93–113.

    Google Scholar 

  • Gibbs, G. R. (2008). Analyzing qualitative data. Thousand Oaks: Sage.

    Google Scholar 

  • Grbich, C. (2007). Qualitative data analysis: An introduction. Thousand Oaks: Sage Publications.

    Google Scholar 

  • Gregorio, S. D., & Davidson, J. (2008). Qualitative research design for software users. New York: McGraw-Hill.

    Google Scholar 

  • Hardy, M., & Bryman, A. (Eds.). (2004). The handbook of data analysis. Thousand Oaks: Sage.

    Google Scholar 

  • Harrison, R. L., & Westwood, M. J. (2009). Preventing vicarious traumatization of mental health therapists: Identifying protective practices. Psychotherapy: Theory, Research, Practice, Training, 46, 203–219.

    Google Scholar 

  • Hutchby, I., & Wooffitt, R. (2008). Conversation analysis. Cambridge: Polity Press.

    Google Scholar 

  • Hwang, S. (2008). Utilizing qualitative data analysis software. A review of Atlas.ti. Social Science Computer Review, 26, 519–527.

    Google Scholar 

  • Kluge, S. (1999). Empirisch begründete Typenbildung. Zur Konstruktion von Typen und Typologien in der qualitativen Sozialforschung. Opladen: Leske & Budrich.

    Google Scholar 

  • Kluge, S. (2000). Empirisch begründete Typenbildung in der qualitativen Sozialforschung. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 1, Art. 14. Abgerufen am 13. August 2021, unter https://www.qualitative-research.net/index.php/fqs/article/view/1124/2498

  • Knoblauch, H., Schnettler, B., Raab, J., & Soeffner, H.-G. (2006). Video-analysis. Methodology and methods – Qualitative audiovisual analysis in sociology. Frankfurt/Main: Lang.

    Google Scholar 

  • König, H.-D. (1994a). Mutter und Sohn und ein Mann aus Stahl. Tiefenhermeneutische Rekonstruktion von ‚Terminator II‘. Teil 1. medien praktisch, 1, 12–18.

    Google Scholar 

  • König, H.-D. (1994b). Mutter und Sohn und ein Mann aus Stahl. Tiefenhermeneutische Rekonstruktion von ‚Terminator II‘. Teil 2. medien praktisch, 2, 45–49.

    Google Scholar 

  • König, H.-D. (1994c). Mutter und Sohn und ein Mann aus Stahl. Tiefenhermeneutische Rekonstruktion von ‚Terminator II‘. Teil 3. medien praktisch, 3, 52–60.

    Google Scholar 

  • König, H.-D. (1995). Sexualität zwischen Lust und Tod. Tiefenhermeneutische Rekonstruktion des in dem Film Basic Instinct inszenierten Geschlechterkampfes. In S. Müller-Doohm & K. Neumann-Braun (Hrsg.), Kulturinszenierungen (S. 141–165). Frankfurt/Main: Suhrkamp.

    Google Scholar 

  • Konopásek, Z. (2008). Making thinking visible with Atlas.ti: Computer assisted qualitative analysis as textual practices. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 9, Art. 12. Retrieved 2021, August 13, from https://www.qualitative-research.net/index.php/fqs/article/view/420

  • Kuckartz, U. & Rädiker, S. (2022). Qualitative Inhaltsanalyse. Methoden, Praxis, Computerunterstützung (5. Aufl.). Weinheim: Beltz.

    Google Scholar 

  • La Pelle, N. (2004). Simplifying qualitative data analysis using general purpose software tools. Field Methods, 16, 85–108.

    Google Scholar 

  • Leech, N. L., & Onwuegbuzie, A. J. (2007). An array of qualitative data analysis tools: A call for data analysis triangulation. School Psychology Quarterly, 22, 557–584.

    Google Scholar 

  • Leech, N. L., & Onwuegbuzie, A. J. (2008). Qualitative data analysis: A compendium of techniques and a framework for selection for school psychology research and beyond. School Psychology Quarterly, 23, 587–604.

    Google Scholar 

  • Leech, N. L., & Onwuegbuzie, A. J. (2011). Beyond constant comparison qualitative data analysis: Using NVivo. School Psychology Quarterly, 26, 70–84.

    Google Scholar 

  • Leithäuser, T. & Volmerg, B. (1988). Psychoanalyse in der Sozialforschung. Opladen: Westdeutscher Verlag.

    Google Scholar 

  • Lewins, A., & Silver, C. (2007). Using software in qualitative research: A step-by-step guide. Thousand Oaks: Sage.

    Google Scholar 

  • Lewis, R. B. (2004). NVivo 2.0 and ATLAS.ti 5.0: A comparative review of two popular qualitative data-analysis programs. Field Methods, 16, 439–464.

    Google Scholar 

  • Lorenzer, A. (1986). Tiefenhermeneutische Kulturanalyse. In A. Lorenzer (Hrsg.), Kultur-Analysen (S. 11–98). Frankfurt/Main: Fischer.

    Google Scholar 

  • Lucius-Hoene, G. (2010). Narrative Analysen. In G. Mey & K. Mruck (Hrsg.), Handbuch Qualitative Forschung in der Psychologie (S. 584–600). Wiesbaden: VS.

    Google Scholar 

  • Lucius-Hoene, G. & Deppermann, A. (2004). Rekonstruktion narrativer Identität: Ein Arbeitsbuch zur Analyse narrativer Interviews (2. Aufl.). Wiesbaden: VS.

    Google Scholar 

  • Lyons, E., & Coyle, A. (2007). Analysing qualitative data in psychology. Thousand Oaks: Sage.

    Google Scholar 

  • Mey, G. & Mruck, K. (Hrsg.). (2010). Grounded theory reader. Wiesbaden: VS.

    Google Scholar 

  • Meyer, D. Z., & Avery, L. M. (2009). Excel as a qualitative data analysis tool. Field Methods, 21, 91–112.

    Google Scholar 

  • Nideröst, B. (2002). Die technikunterstütze Analyse von qualitativen Daten mit Word. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 3, Art. 22. Abgerufen am 13. August 2021, unter https://www.qualitative-research.net/index.php/fqs/article/view/861

  • Oevermann, U. (2002). Klinische Soziologie auf der Basis der Methodologie der objektiven Hermeneutik – Manifest der objektiv hermeneutischen Sozialforschung. Abgerufen am 13. August 2021, unter https://www.ihsk.de/publikationen/Ulrich_Oevermann-Manifest_der_objektiv_hermeneutischen_Sozialforschung.pdf

  • Oevermann, U. (2010). Strukturprobleme supervisorischer Praxis. Eine objektiv hermeneutische Sequenzanalyse zur Überprüfung der Professionalisierungstheorie (3. Aufl.). Frankfurt am Main: Humanities Online.

    Google Scholar 

  • Rädiker, S. & Kuckartz, U. (2019). Analyse qualitativer Daten mit MAXQDA. Text, Audio, Video. Wiesbaden: Springer VS.

    Google Scholar 

  • Reichertz, J. (2003). Die Abduktion in der qualitativen Sozialforschung. Opladen: Leske & Budrich.

    Google Scholar 

  • Reichertz, J. (2004). Abduktion, Deduktion und Induktion in der qualitativen Forschung. In U. Flick, E. von Kardorff & I. Steinke (Hrsg.), Qualitative Forschung: Ein Handbuch (S. 276–286). Reinbek: Rowohlt.

    Google Scholar 

  • Riessman, C. (2008). Narrative methods for social sciences. Thousand Oaks: Sage.

    Google Scholar 

  • Roberts, K. A., & Wilson, R. W. (2002). ICT and the research process: Issues around the compatibility of technology with qualitative data analysis. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 3, Art. 23. Retrieved 2021, August 13, from https://www.qualitative-research.net/index.php/fqs/article/view/862

  • Schmitt, R. (2010). Metaphernanalyse. In G. Mey & K. Mruck (Hrsg.), Handbuch Qualitative Forschung in der Psychologie (S. 676–691). Wiesbaden: VS.

    Google Scholar 

  • Schulze, H. (2010). Biografische Fallrekonstruktion. In G. Mey & K. Mruck (Hrsg.), Handbuch Qualitative Forschung in der Psychologie (S. 569–583). Wiesbaden: VS.

    Google Scholar 

  • Steinke, I. (2000). Gütekriterien qualitativer Forschung. In U. Flick, E. von Kardorff & I. Steinke (Hrsg.), Qualitative Forschung: Ein Handbuch (S. 319–331). Hamburg: Rowohlt.

    Google Scholar 

  • Strauss, A. L. & Corbin, J. (1996). Grundlagen Qualitativer Sozialforschung. Weinheim: Beltz.

    Google Scholar 

  • Tashakkori, A., & Teddlie, C. (Eds.). (2003). Handbook of mixed methods in social & behavioral research. Thousand Oaks: Sage.

    Google Scholar 

  • Vandall-Walker, V., & Clark, A. M. (2011). It starts with access! A grounded theory of family members working to get through critical illness. Journal of Family Nursing, 17, 148–181.

    Google Scholar 

  • Wertz, F. J., Charmaz, K., McMullen, L. M., Josselson, R., Anderson, R., & McSpadden, E. (2011). Five ways of doing qualitative analysis: Phenomenological psychology, grounded theory, discourse analysis, narrative research, and intuitive inquiry. New York: Guilford.

    Google Scholar 

  • Williams, A. L., & Merten, M. J. (2009). Adolescents’ online social networking following the death of a peer. Journal of Adolescent Research, 24, 67–90.

    Google Scholar 

  • Bortz, J., Lienert, G. A. & Boehnke, K. (2008). Verteilungsfreie Methoden in der Biostatistik (3. Aufl.). Heidelberg: Springer.

    Google Scholar 

  • Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Boca Raton: CRC Press.

    Google Scholar 

  • Hatzinger, R., Hornik, K. & Nagel, H. (2011). R – Einführung durch angewandte Statistik. München: Pearson.

    Google Scholar 

  • Kline, R. B. (2004). Beyond significance testing. Reforming data analysis methods in behavioral research (2nd ed.). Washington: American Psychological Association.

    Google Scholar 

  • Koch, R. (2012). Einführung in die Bayes-Statistik. Heidelberg: Springer.

    Google Scholar 

  • Lee, P. M. (2012). Bayesian statistics. An introduction (4th ed.). New York: Wiley.

    Google Scholar 

  • Lee, M. D., & Wagenmakers, E.-J. (2005). Bayesian statistical inference in psychology: Comment on Trafimow (2003). Psychological Review, 112, 662–668.

    PubMed  Google Scholar 

  • Luhmann, M. (2020). R für Einsteiger: Einführung in die Statistiksoftware für die Sozialwissenschaften (5. Aufl.). Weinheim: Beltz.

    Google Scholar 

  • Manderscheid, K. (2011). Sozialwissenschaftliche Datenanalyse mit R: Eine Einführung. Wiesbaden: VS.

    Google Scholar 

  • Trafimow, D. (2003). Hypothesis testing and theory evaluation at the boundaries: Surprising insights from Bayes’s theorem. Psychological Review, 110, 526–535.

    PubMed  Google Scholar 

  • Trafimow, D. (2005). The ubiquitous Laplacian assumption: Reply to Lee and Wagenmakers (2005). Psychological Review, 112, 669–674.

    Google Scholar 

  • Wagenmakers, E.-J., Lodewyckx, T., Kuriyal, H., & Grasman, R. (2010). Bayesian hypothesis testing for psychologists: A tutorial on the Savage-Dickey method. Cognitive Psychology, 60, 158–189.

    PubMed  Google Scholar 

  • Behrens, J. T. (1997). Principles and procedures of exploratory data analysis. Psychological Methods, 2, 131–160.

    Google Scholar 

  • Behrens, J. T., & Yu, C.-H. (2003). Exploratory data analysis. In J. A. Schinka, & W. F. Velicer (Eds.), Handbook of psychology – Volume 2: Research methods in psychology (Vol. 2, pp. 33–64). New Jersey: Wiley.

    Google Scholar 

  • Berry, M. J. A., & Linoff, G. (2000). Mastering data mining, The art and science of customer relationship management. New York: Wiley.

    Google Scholar 

  • Borg, I., & Groenen, P. J. F. (2010). Modern multidimensional scaling: Theory and applications (2nd ed.). New York: Springer.

    Google Scholar 

  • Bortz, J. & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler (7. Aufl.). Berlin, Heidelberg: Springer

    Google Scholar 

  • Bühner, M. (2021). Einführung in die Test- und Fragebogenkonstruktion (4. Aufl.). München: Pearson.

    Google Scholar 

  • Charters, S., & Menival, D. (2011). Wine tourism in Champagne. Journal of Hospitality & Tourism, 35, 102–118.

    Google Scholar 

  • Chen, C., Härdle, W., & Unwin, A. (Eds.). (2008). Handbook of data visualization. Heidelberg: Springer.

    Google Scholar 

  • Cleveland, W. S. (1993). Visualizing data. Summit: Hobart.

    Google Scholar 

  • Degen, H. (2010). Graphische Datenexploration. In C. Wolf & H. Best (Hrsg.), Handbuch sozialwissenschaftliche Datenanalyse Teil III (S. 91–116). Wiesbaden: VS.

    Google Scholar 

  • Dolce, G., Riganello, F., Quintieri, M., Candelieri, A., & Conforti, D. (2008). Personal interaction in the vegetative state. A data-mining study. Journal of Psychophysiology, 22, 150–156.

    Google Scholar 

  • Emerson, J. D., & Hoaglin, D. (1983). Steam-and-leaf-displays. In D. Hoaglin, F. Mosteller, & J. W. Tukey (Eds.), Understanding robust and exploratory data analysis (pp. 1–32). New York: Wiley.

    Google Scholar 

  • Epstein, I. (2009). Promoting harmony where there is commonly conflict: Evidence-informed practice as an integrative strategy. Social Work in Health Care, 48, 216–231.

    PubMed  Google Scholar 

  • Hoaglin, D. C., Mosteller, F., & Tukey, J. W. (Eds.). (1985). Exploring data, tables, trends, and shapes. New York: Wiley.

    Google Scholar 

  • Hoaglin, D. C., Mosteller, F., & Tukey, J. W. (Eds.). (2000). Understanding robust and exploratory data analysis. New York: Wiley.

    Google Scholar 

  • Holz-Ebeling, F. (1995). Faktorenanalyse und was dann? Zur Frage der Validität von Dimensionsinterpretationen. Psychologische Rundschau, 46, 18–35.

    Google Scholar 

  • Kerr, N. L. (1998). HARKing: Hypothesizing After the Results are Known. Personality and Social Psychological Review, 2, 196–217.

    Google Scholar 

  • Leijon, O., Härenstam, A., Waldenström, K., Alderling, M., & Vingård, E. (2006). Target groups for prevention of neck/shoulder and low back disorders: An exploratory cluster analysis of working and living conditions. Work: A Journal of Prevention, Assessment & Rehabilitation, 27, 189–204.

    Google Scholar 

  • MacKay, D. G. (1993). The theoretical epistemology: A new perspective on some long-standing methodological issues in psychology. In G. Keren, & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences. Methodological Issues (pp. 229–255). Hillsdale: Erlbaum.

    Google Scholar 

  • Mano, H., & Davis, S. M. (1990). The effects of familiarity on cognitive maps. Advances in Consumer Research, 17, 275–282.

    Google Scholar 

  • Mathar, R. (1997). Multidimensionale Skalierung: Mathematische Grundlagen und algorithmische Aspekte. Stuttgart: Teubner.

    Google Scholar 

  • Oldenbürger, H. A. (1996). Exploratorische, graphische und robuste Datenanalyse. In E. Erdfelder, R. Mausfeld, T. Meiser & G. Rudinger (Hrsg.), Handbuch Quantitative Methoden (S. 71–86). Weinheim: Beltz.

    Google Scholar 

  • Payne, D., & Trumbach, C. C. (2009). Data mining: proprietary rights, people and proposals. Business Ethics: A European Review, 18, 241–252.

    Google Scholar 

  • Polasek, W. (1994). EDA Explorative Datenanalyse: Einführung in die deskriptive Statistik. Heidelberg: Springer.

    Google Scholar 

  • Schnell, R. (1994). Graphisch gestützte Datenanalyse. München, Wien: Oldenbourg.

    Google Scholar 

  • Torgerson, W. S. (1958). Theory and methods of scaling. New York: Wiley.

    Google Scholar 

  • Tsymbal, A., Pechenizkiy, M., Cunningham, P., & Puuronen, S. (2008). Dynamic integration of classifiers for handling concept drift. Information Fusion, 9, 56–68.

    Google Scholar 

  • Tukey, J. W. (1977). Exploratory data analysis. Reading/MA: Addison-Wesley.

    Google Scholar 

  • Uncu, Y., Bayram, N., & Bilgel, N. (2007). Job related affective well-being among primary health care physicians. European Journal of Public Health, 17, 514–519.

    PubMed  Google Scholar 

  • Unwin, A., Theus, M., & Hofmann, H. (2006). Graphics of large datasets. Visualizing a Million. Heidelberg: Springer.

    Google Scholar 

  • Velleman, P. F., & Hoaglin, D. C. (1981). Applications, basics, and computing of exploratory data analysis. Boston, Massachusetts: Duxbury.

    Google Scholar 

  • Wilke, C. O. (2020). Datenvisualisierung – Grundlagen und Praxis. Wie Sie aussagekräftige Diagramme und Grafiken gestalten. Heidelberg: O’Reilly.

    Google Scholar 

  • Wolff, H.-G. & Bacher, J. (2010). Hauptkomponentenanalyse und explorative Faktorenanalyse. In C. Wolf & H. Best (Hrsg.), Handbuch sozialwissenschaftliche Datenanalyse, Teil III (S. 333–365). Wiesbaden: VS.

    Google Scholar 

  • Assenmacher, W. (2009). Induktive Statistik (2. Aufl.). Berlin: Springer.

    Google Scholar 

  • Bortz, J. (2005). Statistik (6. Aufl.). Heidelberg: Springer.

    Google Scholar 

  • Bortz, J. & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler (7. Aufl.). Heidelberg: Springer.

    Google Scholar 

  • Brandstätter, E. (1999). Konfidenzintervalle als Alternative zu Signifikanztests. Methods of Psychological Research Online, 4, 1–17.

    Google Scholar 

  • Daniel, C., & Wood, F. S. (1971). Fitting equations to data. New York: Wiley.

    Google Scholar 

  • Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Boca Raton: CRC Press.

    Google Scholar 

  • Fischer, G. H. (1974). Einführung in die Theorie psychologischer Tests. Bern: Huber.

    Google Scholar 

  • Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 222, 309–368.

    Google Scholar 

  • Fisher, R. A. (1925). Theory of statistical estimation. Mathematical Proceedings of the Cambridge Philosophical Society, 22, 700–725.

    Google Scholar 

  • Hays, W. L., & Winkler, R. L. (1970). Statistics. New York: Holt, Rinehart & Winston.

    Google Scholar 

  • Helten, E. (1974). Wahrscheinlichkeitsrechnung. In J. v. Koolwijk & M. Wieken-Mayser (Hrsg.), Techniken der empirischen Sozialforschung (Band 6, S. 9–38). München: Oldenbourg.

    Google Scholar 

  • Huber, P., & Ronchetti, E. (2009). Robust statistics (2nd ed.). Hoboken: Wiley.

    Google Scholar 

  • Kendall, M. G., & Stuart, A. (1973). The advanced theory of statistics (2nd ed.). London: Griffin.

    Google Scholar 

  • Kreyszig, E. (1973). Statistische Methoden und ihre Anwendungen. Göttingen: Vandenhoeck & Ruprecht.

    Google Scholar 

  • Lampert, T., Sygusch, R. & Schlack, R. (2007). Nutzung elektronischer Medien im Jugendalter. Ergebnisse des Kinder- und Jugendgesundheitssurveys (KiGGS). Bundesgesundheitsblatt – Gesundheitsforschung – Gesundheitsschutz, 50, 643–652.

    PubMed  Google Scholar 

  • Marronna, R., Martin, D., & Yohai, V. (2006). Robust statistics. Theory and methods. Hoboken: Wiley.

    Google Scholar 

  • Meeker, W. Q., Hahn, G. J., & Escobar, L. A. (2017). Statistical intervals. A guide for practitioners and researchers (2nd ed.). Hoboken: Wiley.

    Google Scholar 

  • Neyman, J. (1937). Outline of a theory of statistical estimation based on the classical theory of probability. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 236, 333–380.

    Google Scholar 

  • Sachs, L. (2002). Statistische Auswertungsmethoden (10. Aufl.). Berlin: Springer.

    Google Scholar 

  • Schwarz, H. (1975). Stichprobenverfahren. München: Oldenbourg.

    Google Scholar 

  • Stegmüller, W. (1985). Probleme und Resultate der Wissenschaftstheorie und analytischen Philosophie (2. Aufl.). Berlin: Springer.

    Google Scholar 

  • Student. (1908). The probable error of a mean. Biometrika, 6, 1–25.

    Google Scholar 

  • Wirtz, M. & Nachtigall, C. (2012). Deskriptive Statistik. Statistische Methoden für Psychologen Teil 1 (6. Aufl.). Weinheim: Beltz Juventa.

    Google Scholar 

  • Yamane, T. (1976). Statistik. Frankfurt/Main: Fischer.

    Google Scholar 

  • Baird, G. L., & Harlow L. L. (2016). Does one size fit all? A case for context-driven null hypothesis statistical testing. Journal of Modern Applied Statistical Methods, 15, 100–122. https://doi.org/10.22237/jmasm/1462075560

    Article  Google Scholar 

  • Bakan, D. (1966). The test of significance in psychological research. Psychological Bulletin, 66, 423–437.

    PubMed  Google Scholar 

  • Bortz, J. & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler (7. Aufl.). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Brandstätter, E. (1999). Konfidenzintervalle als Alternative zu Signifikanztests. Methods of Psychological Research Online, 4, 1–17.

    Google Scholar 

  • Bredenkamp, J. (1970). Über Maße der praktischen Signifikanz. Zeitschrift für Psychologie, 177, 310–318.

    Google Scholar 

  • Bredenkamp, J. (1972). Der Signifikanztest in der psychologischen Forschung. Frankfurt/Main: Akademische Verlagsanstalt.

    Google Scholar 

  • Bredenkamp, J. (1980). Theorie und Planung psychologischer Experimente. Darmstadt: Steinkopff.

    Google Scholar 

  • Carver, R. P. (1978). The case against statistical significance testing. Harvard Educational Review, 48, 378–399.

    Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New York: Erlbaum.

    Google Scholar 

  • Cohen, J. (1994). The earth is round (\(p<0.05\)). American Psychologist, 49, 997–1003.

    Google Scholar 

  • Cook, T. D., Grader, C. L., Hennigan, K. M., & Flay, B. R. (1979). The history of the sleeper effect: Some logical pitfalls in accepting the null hypothesis. Psychological Bulletin, 86, 662–679.

    Google Scholar 

  • Cowles, M., & Davis, C. (1982). On the origins of the .05 level of statistical significance. American Psychologist, 37, 553–558.

    Google Scholar 

  • Crane, J. A. (1980). Relative likelihood analysis versus significance tests. Evaluation Review, 4, 824–842.

    Google Scholar 

  • Eid, M., Gollwitzer, M. & Schmitt, M. (2017). Statistik und Forschungsmethoden (5. Aufl.). Weinheim: Beltz.

    Google Scholar 

  • Erdfelder, E. & Bredenkamp, J. (1994). Hypothesenprüfung. In T. Herrmann & W. T. Tack (Hrsg.), Enzyklopädie der Psychologie: Themenbereich B, Serie I, Methodologische Grundlagen der Psychologie (Bd. 1, S. 604–648). Göttingen: Hogrefe.

    Google Scholar 

  • Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191.

    PubMed  Google Scholar 

  • Fiedler, K., Kutzner, F., & Krueger, J. I. (2012). The long way from \(\upalpha\)-error control to validity proper: Problems with a short-sighted false-positive debate. Perspectives on Psychological Science, 7, 661–669.

    PubMed  Google Scholar 

  • Fisher, R. (1925). Statistical methods for research workers. Edinburgh, UK: Oliver & Boyd.

    Google Scholar 

  • Fisher, R. (1956). Statistical methods and scientific inference. Edinburgh, UK: Oliver & Boyd.

    Google Scholar 

  • Greenwald, A. G. (1975). Consequences of prejudice against the null hypothesis. Psychological Bulletin, 82, 1–20.

    Google Scholar 

  • Hager, W. (1987). Grundlagen einer Versuchsplanung zur Prüfung empirischer Hypothesen in der Psychologie. In G. Lüer (Hrsg.), Allgemeine experimentelle Psychologie (S. 43–264). Stuttgart: Fischer.

    Google Scholar 

  • Hager, W. (2004). Testplanung zur statistischen Prüfung psychologischer Hypothesen. Göttingen: Hogrefe.

    Google Scholar 

  • Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (Eds.). (1997). What if there were no significance tests? Hillsdale: Erlbaum.

    Google Scholar 

  • Harnatt, J. (1975). Der statistische Signifikanztest in kritischer Betrachtung. Psychologische Beiträge, 17, 595–612.

    Google Scholar 

  • Heerden, J. H. van, & Hoogstraten, J. van. (1978). Significance as a determinant of interest in scientific research. European Journal of Social Psychology, 8, 141–143.

    Google Scholar 

  • Kerr, N. L. (1998). HARKing: Hypothesizing After the Results are Known. Personality and Social Psychological Review, 2, 196–217.

    Google Scholar 

  • Kline, R. B. (2004). Beyond significance testing (2nd ed.). Washington: American Psychological Association.

    Google Scholar 

  • Krause, B. & Metzler, P. (1978). Zur Anwendung der Inferenzstatistik in der psychologischen Forschung. Zeitschrift für Psychologie, 186, 244–267.

    Google Scholar 

  • Lakens, D. (2021). The practical alternative to the \(p\) value is the correctly used \(p\) value. Perspectives in Psychological Science, 16, 639–648.

    Google Scholar 

  • Lane, D. M., & Dunlap, W. P. (1978). Estimating effect size: Bias resulting from the significance criterion in editorial decisions. British Journal of Mathematical and Statistical Psychology 31, 107–112.

    Google Scholar 

  • Lykken, D. T. (1968). Statistical significance in psychological research. Psychological Bulletin, 70, 151–157.

    PubMed  Google Scholar 

  • Maxwell, S. E. (2004). The persistence of underpowered studies in psychological research. Causes, consequences, and remedies. Psychological Methods, 9, 147–163.

    PubMed  Google Scholar 

  • Morrison, D. E., & Henkel, R. E. (2007). The significance test controversy (new ed.). Chicago: Aldine.

    Google Scholar 

  • Neyman, J., & Pearson, E. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference. Part I. Biometrika, 20A, 175–240.

    Google Scholar 

  • Neyman, J., & Pearson, E. (1933). The testing of statistical hypotheses in relation to probability a priori. Mathematical Proceedings of the Cambridge Philosophical Society, 29, 492–510.

    Google Scholar 

  • Nickerson, R. S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5, 241–301.

    PubMed  Google Scholar 

  • Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594–604.

    Google Scholar 

  • Willmes, K. (1996). Neyman-Pearson-Theorie statistischen Testens. In E. Erdfelder, R. Mausfeld & T. Meiser (Hrsg.), Handbuch Quantitative Methoden (S. 109–122). Weinheim: Psychologie Verlags Union.

    Google Scholar 

  • Witte, E. H. (1977). Zur Logik und Anwendung der Inferenzstatistik. Psychologische Beiträge, 19, 290–303.

    Google Scholar 

  • Wottawa, H. (1990). Einige Überlegungen zu (Fehl-)Entwicklungen der psychologischen Methodenlehre. Psychologische Rundschau, 41, 84–107.

    Google Scholar 

  • Agresti, A. (2007). An introduction to categorical data analysis (2nd ed.). New York: Wiley.

    Google Scholar 

  • Alf, E. F., Jr., & Abrahams, N. M. (1975). The use of extreme groups in assessing relationships. Psychometrika, 40, 563–572.

    Google Scholar 

  • Andreß, H. J., Hagenaars, J. A. & Kühnnel, S. (1997). Analyse von Tabellen und kategorialen Daten. Heidelberg: Springer.

    Google Scholar 

  • Arminger, G. (1982). Klassische Anwendungen verallgemeinerter linearer Modelle in der empirischen Sozialforschung. ZUMA Arbeitsberichte, Nr. 1982/03. Mannheim.

    Google Scholar 

  • Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.

    PubMed  Google Scholar 

  • Beck-Bornholdt, H. P. & Dubben, H. H. (2001). Der Hund, der Eier legt. Erkennen von Fehlinformationen durch Querdenken. Reinbeck: Rowohlt.

    Google Scholar 

  • Bennett, R. E., Gottesman, R. L., Rock, D. A., & Cerullo, F. (1993). Influence of behavior perceptions and gender on teachers’ judgments of students’ academic skill. Journal of Educational Psychology, 85, 347–356.

    Google Scholar 

  • Benninghaus, H. (1989). Deskriptive Statistik. Statistik für Soziologen, Bd. 1 (6. Aufl.). Stuttgart: Teubner.

    Google Scholar 

  • Benninghaus, H. (1998). Einführung in die sozialwissenschaftliche Datenanalyse (5. Aufl.). München: Oldenbourg.

    Google Scholar 

  • Bentler, P. M. (1980). Multivariate analysis with variables: Causal modeling. Annual Review of Psychology, 31, 419–456.

    Google Scholar 

  • Bishop, Y. M. M., Fienberg, S. E., & Holland, P. W. (1975). Discrete multivariate analysis. Cambridge: MIT Press.

    Google Scholar 

  • Blalock, H. M. (Ed.). (1971). Causal models in the social sciences. London: MacMillan.

    Google Scholar 

  • Bortz, J. & Lienert, G. A. (2003). Kurzgefaßte Statistik für die klinische Forschung. Ein praktischer Leitfaden für die Analyse kleiner Stichproben (2. Aufl.). Heidelberg: Springer.

    Google Scholar 

  • Bortz, J., Lienert, G. A. & Boehnke, K. (2008). Verteilungsfreie Methoden in der Biostatistik (3. Aufl.). Heidelberg: Springer.

    Google Scholar 

  • Bortz, J. & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler (7. Aufl.). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Campbell, D. T. (1963). From description to experimentation: Interpreting trends as quasi-experiments. In C. W. Harris (Ed.), Problems in measuring change (pp. 212-254). Madison: University of Wisconsin Press.

    Google Scholar 

  • Clausen, S. E. (1998). Applied correspondence analysis. An introduction. Thousand Oaks: Sage.

    Google Scholar 

  • Cook, T. D., & Campbell, D. T. (1976). The design and conduct of quasi-experiments and true experiments in field settings. In M. Dunnette (Ed.), Handbook of industrial and organizational research (pp. 223–326). Chicago: Rand McNally.

    Google Scholar 

  • Cook, T. D., & Shadish, W. R. (1994). Social experiments: Some developments over the past fifteen years. Annual Review of Psychology, 45, 548–580.

    Google Scholar 

  • Dawes, R. M., Faust, D., & Meehl, P. E. (1993). Statistical prediction versus clinical prediction: Improving what works. In G. Keren, & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences. Methodological issues (pp. 351–367). Hillsdale: Erlbaum.

    Google Scholar 

  • Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual Review of Psychology, 41, 417–440.

    Google Scholar 

  • Draper, N., & Smith, H. (1998). Applied regression analysis (3rd ed.). New York: Wiley.

    Google Scholar 

  • Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5, 155–174.

    PubMed  Google Scholar 

  • Eid, M., Gollwitzer, M. & Schmitt, M. (2017). Statistik und Forschungsmethoden (5. Aufl.). Weinheim: Beltz.

    Google Scholar 

  • Eye, A. von (1990). Introduction to configural frequency analysis: The search for types and antitypes in cross-classification. Cambridge: Cambridge University Press.

    Google Scholar 

  • Eye, A. von, & Schuster, C. (1998). Regression analysis for social sciences. San Diego: Academic Press.

    Google Scholar 

  • Eysenck, H. J. (1969). Personality structure and measurement. London: Routledge & Paul.

    Google Scholar 

  • Faraone, S. V., & Dorfman, D. D. (1987). Lag sequential analysis: Robust statistical methods. Psychological Bulletin, 101, 312–323.

    Google Scholar 

  • Fasel, N., Green, E. G. T., & Sarrasin, O. (2013). Unveiling naturalization. A multilevel study on minority proportion, conservative ideologies, and attitudes toward the Muslim veil. Zeitschrift für Psychologie, 221, 242–251.

    Google Scholar 

  • Gaensslen, H. & Schubö, W. (1973). Einfache und komplexe statistische Analyse. München: Reinhardt.

    Google Scholar 

  • Gifi, A. (1990). Nonlinear multivariate analysis. New York: Wiley.

    Google Scholar 

  • Greenacre, M. J. (1993). Correspondence analysis in practice. London: Academic Press.

    Google Scholar 

  • Gunter, P. L., Jack, S. L., Shores, R. E., Carrell, D. E., & Flowers, J. (1993). Lag sequential analysis as a tool for functional analysis of student disruptive behavior in classrooms. Journal of Emotional and Behavioral Disorders, 1, 138–148.

    Google Scholar 

  • Hox, J. J. (2002). Multilevel analysis. Techniques and applications. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Hox, J. J., & Roberts, J. K. (Eds.). (2010). Handbook of advanced multilevel analysis. New York, NY: Routledge.

    Google Scholar 

  • Hoyt, W. T., Imel, Z. E., & Chan, F. (2008). Multiple regression and correlation techniques: Recent controversies and best practices. Rehabilitation Psychology, 53, 321–339.

    Google Scholar 

  • Kenny, D. A., & Harackiewicz, J. M. (1979). Cross-lagged panel correlation: Practice and promise. Journal of Applied Psychology, 64, 372–379.

    Google Scholar 

  • Koolwijk, J. van & Wieken-Mayser, M. (Hrsg.). (1986). Techniken der empirischen Sozialforschung. Bd. 8 Kausalanalyse. München: Oldenbourg.

    Google Scholar 

  • Köbben, A. (1970). Cause and intention. In R. Naroll, & R. Cohen (Eds.), A handbook of method in cultural anthropology (pp. 89–98). Garden City: Natural History Press.

    Google Scholar 

  • Krämer, W. (1995). So lügt man mit Statistik. Frankfurt: Campus.

    Google Scholar 

  • Krauth, J. (1993). Einführung in die Konfigurationsfrequenzanalyse (KFA). Weinheim: Beltz.

    Google Scholar 

  • Krauth, J. & Lienert, G. A. (1973). Die Konfigurationsfrequenzanalyse (KFA) und ihre Anwendungen in Psychologie und Medizin. Freiburg: Alber.

    Google Scholar 

  • Kubinger, K. D. (1990). Übersicht und Interpretation der verschiedenen Assoziationsmaße. Psychologische Beiträge, 22, 290–346.

    Google Scholar 

  • Langeheine, R. (1980). Multivariate Hypothesentestung bei qualitativen Daten. Zeitschrift für Sozialpsychologie, 11, 140–151.

    Google Scholar 

  • Latane, B., & Darley, J. M. (1970). The unresponsive bystander: Why doesn’t he help? New York: Appleton Crofts.

    Google Scholar 

  • Lehmann, G. (1980). Nichtlineare „Kausal-“ bzw. Dominanz-Analysen in psychologischen Variablensystemen. Zeitschrift für experimentelle und angewandte Psychologie, 27, 257–276.

    Google Scholar 

  • MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York: Erlbaum.

    Google Scholar 

  • McKim, V. R., & Turner, S. P. (Eds.). (1997). Counseling in crisis? Statistical methods in the search for causal knowledge in the social sciences. Notre Dame: University of Notre Dame Press.

    Google Scholar 

  • Meehl, P. E. (1950). Configural scoring. Journal of Consulting Psychology, 14, 165–171.

    PubMed  Google Scholar 

  • Meehl, P. E. (1954). Clinical versus statistical prediction. Minneapolis: University of Minnesota Press.

    Google Scholar 

  • Meehl, P. E., & Waller, N. G. (2002). The path analysis controversy: A new statistical approach to strong appraisal of verisimilitude. Psychological Methods, 7, 283–300.

    PubMed  Google Scholar 

  • Meulman, J. J. (1992). The integration of multidimensional scaling and multivariate analysis with optimal transformations of the variables. Psychometrika, 57, 539–565.

    Google Scholar 

  • Moosbrugger, H. (1978). Multivariate statistische Analyseverfahren. Stuttgart: Kohlhammer.

    Google Scholar 

  • Moosbrugger, H. (2002). Lineare Modelle. Regressions- und Varianzanalysen (3. Aufl.). Bern: Huber.

    Google Scholar 

  • Mulaik, S. A. (1975). Confirmatory factor analysis. In D. J. Amick, & H. J. Walberg (Eds.), Introductory multivariate analysis (pp. 170–207). Berkeley: McCutchan.

    Google Scholar 

  • Ott, J. C. (2011). Government and happiness in 130 nations: Good governance fosters higher level and more equality of happiness. Social Indicators Research, 102, 3–22.

    PubMed  Google Scholar 

  • Overall, J. E., & Klett, C. J. (1972). Applied multivariate analysis. New York: McGraw Hill.

    Google Scholar 

  • Paulhus, D. L., & Williams, K. M. (2002). The Dark Triad of personality: Narcissism, Machiavellianism, and psychopathy. Journal of Research in Personality, 36, 556–563.

    Google Scholar 

  • Pelz, D. C., & Andrews, F. M. (1964). Detecting causal priorities in panel study data. American Sociological Review, 29, 836–848.

    Google Scholar 

  • Preacher, K. J., Rucker, D. D., MacCallum, R. C., & Nicewander, W. A. (2005). Use of the extreme groups approach: A critical reexamination and new recommendations. Psychological Methods, 10, 178–192.

    PubMed  Google Scholar 

  • Rochel, H. (1983). Planung und Auswertung von Untersuchungen im Rahmen des allgemeinen linearen Modells. Heidelberg: Springer.

    Google Scholar 

  • Rogosa, D. R. (1980). A critique of cross-lagged correlation. Psychological Bulletin, 88, 245–258.

    Google Scholar 

  • Rogosa, D. R. (1995). Myths and methods: „Myths about longitudinal research“ plus supplemental questions. In J. M. Gottman (Ed.), The analysis of change (pp. 3–66). Mahwah: Erlbaum.

    Google Scholar 

  • Schäfer, B. (1983). Semantische Differentialtechnik. In H. Feger & J. Bredenkamp (Hrsg.), Enzyklopädie der Psychologie: Themenbereich B, Serie I, Bd. 2, Datenerhebung (S. 154–221). Göttingen: Hogrefe.

    Google Scholar 

  • Schlosser, O. (1976). Einführung in die sozialwissenschaftliche Zusammenhangsanalyse. Reinbek: Rowohlt.

    Google Scholar 

  • Schmitz, B. (1989). Einführung in die Zeitreihenanalyse. Modelle, Softwarebeschreibung, Anwendung. Bern: Huber.

    Google Scholar 

  • Schmitz, B., Kruse, F. O. & Tasche, K. G. (1985). Anwendung zeitreihenanalytischer Verfahren bei prozeßorientierter Paardiagnostik. In H. Appelt & B. Strauß (Hrsg.), Ergebnisse einzelfallstatistischer Untersuchungen (S. 84–113). Berlin: Springer.

    Google Scholar 

  • Selg, H. (1971). Einführung in die experimentelle Psychologie. Stuttgart: Kohlhammer.

    Google Scholar 

  • Shadish, W. R. (1996). Meta-analysis and the exploration of causal mediating processes: A primer of examples, methods, and issues. Psychological Methods, 1, 47–65.

    Google Scholar 

  • Sievers, W. (1977). Über Dummy-Variablen-Kodierung in der Varianzanalyse. Psychologische Beiträge, 19, 454–462.

    Google Scholar 

  • Silbereisen, R. K. (1977). Prädiktoren der Rollenübernahme bei Kindern. Psychologie in Erziehung und Unterricht, 24, 86–92.

    Google Scholar 

  • Snijders, T. A. B., & Bosker, R. (1993). Standard errors and sample sizes for two-level research. Journal of Educational Statistics, 18, 237–259. https://doi.org/10.2307/1165134

    Article  Google Scholar 

  • Snijders, T. A. B., & Bosker, R. (2011). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). London: Sage.

    Google Scholar 

  • Spaeth, J. L. (1975). Path analysis. In D. J. Amick, & H. J. Walberg (Eds.), Introductory multivariate analysis (pp. 53–89). Berkeley: McCutchan.

    Google Scholar 

  • Spector, P. E. (1981). Research designs. Thousand Oaks: Sage.

    Google Scholar 

  • Stelzl, I. (1982). Fehler und Fallen der Statistik. Bern: Huber.

    Google Scholar 

  • Steyer, R. (1992). Theorie kausaler Regressionsmodelle. Stuttgart: Fischer.

    Google Scholar 

  • Steyer, R. (2003). Wahrscheinlichkeit und Regression. Heidelberg: Springer.

    Google Scholar 

  • Ulrich, R., & Wirtz, M. (2004). On the correlation of a naturally and an artificially dichotomized variable. British Journal of Mathematical and Statistical Psychology, 57, 235–251.

    PubMed  Google Scholar 

  • Waller, N. G., & Meehl, P. E. (2002). Risky tests, versimilitude, and path analysis. Psychological Methods, 7, 323–337.

    Google Scholar 

  • Weber, R. (2000). Prognosemodelle zur Vorhersage der Fernsehnutzung. Neuronale Netze, Tree-Modelle und klassische Statistik im Vergleich. München: Fischer.

    Google Scholar 

  • Werner, J. (1997). Lineare Statistik. Allgemeines lineares Modell. Weinheim: Psychologie Verlags Union.

    Google Scholar 

  • Williams, K. M., Nathanson, C., & Paulhus, D. L. (2010). Identifying and profiling scholastic cheaters: Their personality, cognitive ability, and motivation. Journal of Experimental Psychology: Applied, 16, 293–307.

    PubMed  Google Scholar 

  • Wolf, G., & Cartwright, B. (1974). Rules for coding dummy variables in multiple regression. Psychological Bulletin, 81, 173–179.

    Google Scholar 

  • Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20, 557–585.

    Google Scholar 

  • Yarnold, P. R. (1996). Characterizing and cirumventing Simpson’s paradox for ordered bivariate data. Educational and Psychological Measurement, 56, 430–442.

    Google Scholar 

  • Young, F. W. (1981). Quantitative analysis of qualitative data. Psychometrika, 46, 357–387.

    Google Scholar 

  • Agresti, A. (2007). An introduction to categorical data analysis (2nd ed.). Hoboken: Wiley.

    Google Scholar 

  • Bittrich, K. & Blankenberger, S. (2011). Experimentelle Psychologie: Experimente planen, realisieren, präsentieren. Landsberg: Beltz.

    Google Scholar 

  • Bortz, J. (2005). Statistik (6. Aufl.). Berlin: Springer.

    Google Scholar 

  • Bortz, J., Lienert, G. A. & Boehnke, K. (2008). Verteilungsfreie Methoden in der Biostatistik (3. Aufl.). Heidelberg: Springer.

    Google Scholar 

  • Bortz, J. & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler (7. Aufl.). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Bredenkamp, J. (1982). Verfahren zur Ermittlung des Typs der statistischen Wechselwirkung. Psychologische Beiträge, 24, 56–75.

    Google Scholar 

  • Cochran, W. G., & Cox, G. M. (1966). Experimental designs. New York: Wiley.

    Google Scholar 

  • Eid, M., Gollwitzer, M. & Schmitt, M. (2017). Statistik und Forschungsmethoden (5. Aufl.). Weinheim: Beltz.

    Google Scholar 

  • Feldt, L. S. (1958). A comparison of the precision of three experimental designs employing a concomitant variable. Psychometrika, 23, 335–354.

    Google Scholar 

  • Guo, S., & Fraser, M. W. (2010). Propensity score analysis. Statistical methods and applications. Advanced quantitative techniques in the social sciences. Thousand Oaks: Sage.

    Google Scholar 

  • Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010). Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. Psychological Methods, 15, 234–249.

    PubMed  PubMed Central  Google Scholar 

  • Leigh, J. H., & Kinnear, T. C. (1980). On interaction classification. Educational and Psychological Measurement, 40, 841–843.

    Google Scholar 

  • Lieberson, S. (1985). Making it count. The improvement of social research and theory. Berkeley: University of California Press.

    Google Scholar 

  • Little, R. J., An, H., Johanns, J., & Giordani, B. (2000). A comparison of subset selection and analysis of covariance for the adjustment of confounders. Psychological Methods, 5, 459–476.

    PubMed  Google Scholar 

  • Luellen, J. K., Shadish, W. R., & Clark, M. H. (2005). Propensity scores. An introduction and experimental test. Evaluation Review, 29, 530–558.

    Google Scholar 

  • Maxwell, S. E. (2004). The persistence of underpowered studies in psychological research: Causes, consequences, and remedies. Psychological Methods, 9, 147–163.

    PubMed  Google Scholar 

  • Perry, R. B., Abrami, P. C., Leventhal, L., & Check, J. (1979). Instructor reputation: An expectancy relationship involving student ratings and achievement. Journal of Educational Psychology, 71, 776–787.

    Google Scholar 

  • Preacher, K. J., Rucker, D. D., MacCallum, R. C., & Nicewander, W. A. (2005). Use of the extreme groups approach: A critical reexamination and new recommendations. Psychological Methods, 10, 178–192.

    PubMed  Google Scholar 

  • Pruzek, R. M. (2011). Introduction to the special issue on propensity score methods in behavioral research. Multivariate Behavioral Research, 46, 389–398.

    PubMed  Google Scholar 

  • Raudenbusch, S. W., & Liu, X. (2000). Statistical power and optimal design for multisite randomized trials. Psychological Methods, 5, 199–213.

    Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.

    Google Scholar 

  • Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127, 757–763.

    PubMed  Google Scholar 

  • Spector, P. E. (1981). Research designs. Thousand Oaks: Sage.

    Google Scholar 

  • Wampold, B. E., & Serlin, R. C. (2000). The consequence of ignoring a nested factor on measures of effect size in analysis of variance. Psychological Methods, 5, 425–433.

    PubMed  Google Scholar 

  • Adam, J. (1978). Sequential strategies and the separation of age, cohort, and time of measurement contributions to developmental data. Psychological Bulletin, 85, 1309–1316.

    PubMed  Google Scholar 

  • Agresti, A. (2007). An introduction to categorical data analysis (2nd ed.). Hoboken: Wiley.

    Google Scholar 

  • Baer, J., & Schmitz, M. F. (2000). Latent growth curve modeling with a cohort sequential design. Social Work Research, 24, 243–248.

    Google Scholar 

  • Baltes, P. B. (1967). Längsschnitt- und Querschnittsequenzen zur Erfassung von Alters- und Generationseffekten. Saarbrücken: Universität des Saarlandes.

    Google Scholar 

  • Bereiter, C. (1963). Some persisting dilemmas in the measurement of change. In C. W. Harris (Ed.), Problems in measurement of change (pp. 3–20). Madison: University of Wisconsin Press.

    Google Scholar 

  • Bierhoff, H. W. & Rudinger, G. (1996). Quasi-experimentelle Untersuchungsmethoden. In E. Erdfelder, R. Mausfeld & T. Meiser (Hrsg.), Handbuch Quantitative Methoden (S. 47–58). Weinheim: Psychologie Verlags Union.

    Google Scholar 

  • Blossfeld, H. P., Hamerle, A. & Mayer, K. U. (1986). Ereignisanalyse. Statistische Theorie und Anwendung in den Wirtschafts- und Sozialwissenschaften. Frankfurt/Main: Campus.

    Google Scholar 

  • Bohrnstedt, G. W. (1969). Observations on the measurement of change. In E. F. Borgetta (Ed.), Sociological methodology (pp. 113–133). San Francisco: Bass.

    Google Scholar 

  • Boker, S. M., Xu, M., Rotondo, J. L., & King, K. (2002). Windowed crosscorrelation and peak picking for the analysis of variability in the association between behavioral time series. Psychological Methods, 7, 338–355.

    PubMed  Google Scholar 

  • Bortz, J. & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler (7. Aufl.). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. San Francisco: Holden-Day.

    Google Scholar 

  • Braden, J. P., & Bryant, T. J. (1990). Regression discontinuity designs: Applications for school psychologists. School Psychology Review, 19, 232–239.

    Google Scholar 

  • Braver, M. C., & Braver, S. L. (1988). Statistical treatment of the Solomon four-groups design: A meta-analytic approach. Psychological Bulletin, 104, 150–154.

    Google Scholar 

  • Campbell, D. T., & Kenny, D. A. (1999). A primer on regression artifacts. New York: Guildford.

    Google Scholar 

  • Collins, L. M. (1996). Is reliability obsolete? A commentary on „Are simple gain scores obsolete?“. Applied Psychological Measurement, 20, 289–292.

    Google Scholar 

  • Cook, T. D., & Shadish, W. R. (1994). Social experiments: Some developments over the past fifteen years. Annual Review of Psychology, 45, 548–580.

    Google Scholar 

  • Corder-Bolz, C. R. (1978). The evaluation of change: New evidence. Educational and Psychological Measurement, 38, 959–976.

    Google Scholar 

  • Cronbach, L. J., & Furby, L. (1970). How should we measure „change“: Or should we? Psychological Bulletin, 74, 68–80.

    Google Scholar 

  • Cudeck, R., & Klebe, K. J. (2002). Multiphase mixed-effects models for repeated measures data. Psychological Methods, 7, 41–63.

    PubMed  Google Scholar 

  • Davis, C. S. (2002). Statistical methods for the analysis of repeated measurements. New York: Springer.

    Google Scholar 

  • Delucchi, K., & Bostrom, A. (1999). Small sample longitudinal clinical trials with missing data: A comparison of analytic methods. Psychological Methods, 4, 158–172.

    Google Scholar 

  • Deutsch, S. J., & Alt, F. B. (1977). The effect of Massachusetts’ gun control law on gun-related crimes in the city of Boston. Evaluation Quarterly, 1, 543–568.

    Google Scholar 

  • Du Bois, P. H. (1957). Multivariate correlational analysis. New York: Harper.

    Google Scholar 

  • Eckensberger, L. H. (1973). Methodological issues of cross-cultural research in development psychology. In J. Nesselroade, & H. W. Reese (Eds.), Life-span developmental psychology – Methodological issues. (pp. 43–64). New York: Academic Press.

    Google Scholar 

  • Eid, M., Gollwitzer, M. & Schmitt, M. (2017). Statistik und Forschungsmethoden (5. Aufl.). Weinheim: Beltz.

    Google Scholar 

  • Erdfelder, E., Rietz, C. & Rudinger, G. (1996). Methoden der Entwicklungspsychologie. In E. Erdfelder, R. Mausfeld & T. Meiser (Hrsg.), Handbuch Quantitative Methoden (S. 539–550). Weinheim: Beltz.

    Google Scholar 

  • Fischer, G. H. (1995). Linear logistic models for change. In G. H. Fischer, & J. W. Molenaar (Eds.), Rasch models. Foundations, recent developments and applications (pp. 157–180). New York: Springer.

    Google Scholar 

  • Formann, A. K., & Ponocny, I. (2002). Latent change classes in dichotomous data. Psychometrika, 67, 437–457.

    Google Scholar 

  • Furr, R. M., & Rosenthal, R. (2003). Repeated-measures contrasts for „multiple-pattern“ hypothesis. Psychological Methods, 8, 275–293.

    PubMed  Google Scholar 

  • Galton, F. (1886). Family likeness in stature. Procedings of the Royal Society, 40, 42–73.

    Google Scholar 

  • Glass, G. V., Tiao, G. O., & Maguire, T. O. (1971). The 1900 revision of German divorce laws. Analysis of data as a time-series quasi-experiment. Law & Society Review, 5, 539–562.

    Google Scholar 

  • Glass, G. V., Willson, V. L., & Gottman, J. M. (1975). Design and analysis of time-series experiments. Boulder: University Press.

    Google Scholar 

  • Gottman, J. M. (Ed.). (1995). The analysis of change. Mahwah: Erlbaum.

    Google Scholar 

  • Gudat, U. & Revenstorff, D. (1976). Interventionseffekte in klinischen Zeitreihen. Archiv für Psychologie, 128, 16–44.

    PubMed  Google Scholar 

  • Guilford, J. P. (1954). Psychometric methods. New York: McGraw Hill.

    Google Scholar 

  • Gulliksen, H. (1968). Methods of determining equivalence of measures. Psychological Bulletin, 70, 534–544.

    Google Scholar 

  • Hamilton, J. (1994). Time series analysis. Princeton: Princeton University Press.

    Google Scholar 

  • Hedeker, D., & Gibbons, R. D. (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2, 64–78.

    Google Scholar 

  • Helmreich, R. (1977). Strategien zur Auswertung von Längsschnittdaten. Stuttgart: Klett.

    Google Scholar 

  • Hennigan, K. M., Del Rosario, M. L., Heath, L., Cock, T. D., Calder, B. J., & Wharton, J. D. (1979). How the introduction of television affected the level of violent and instrumental crime in the United States. Washington: National Science Foundation.

    Google Scholar 

  • Hibbs, D. (1977). On analyzing the effects of policy interventions: Box-Jenkins and Box-Tiao vs. structural equation models. In D. R. Heise (Ed.), Sociological methodology (pp. 137–179). San Francisco: Bass.

    Google Scholar 

  • Holland, P. W. (1986). Statistical and causal inference. Journal of the American Statistical Association, 81, 945–960.

    Google Scholar 

  • Hoppe, S., Schmid-Schönbein, C. & Seiler, T. B. (1977). Entwicklungssequenzen. Bern: Huber.

    Google Scholar 

  • Hultsch, D. F., & Bortner, R. W. (1974). Personal time perspective in adulthood: A time-sequential study. Developmental Psychology, 10, 835–837.

    Google Scholar 

  • Jenkins, G. M. (1979). Practical experiences with modelling and forecasting time series. Jersey: Channel Islands.

    Google Scholar 

  • Johnson, C. W. (1986). A more rigorous quasi-experimental alternative to the one-group pretest-posttest design. Educational and Psychological Measurement, 46, 585–591.

    Google Scholar 

  • Kenny, D. A. (1975). A quasi-experimental approach to assessing treatment effects in the nonequivalent control group design. Psychological Bulletin, 82, 345–362.

    Google Scholar 

  • Keren, G. (1993). Between- or within-subjects design: A methodological dilemma. In G. Keren, & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences. Methodological issues (pp. 257–272). Hillsdale: Lawrence Erlbaum Associates.

    Google Scholar 

  • Kette, G. (1990). Determinanten der Geschworenenentscheidung: Ein Anwendungsbeispiel für die Zeitreihenanalyse der Rechtspsychologie. Archiv für Psychologie, 142, 59–81.

    PubMed  Google Scholar 

  • Klemmert, H. (2004). Äquivalenz- und Effekttests in der Psychologischen Forschung. Frankfurt/Main: Lang.

    Google Scholar 

  • Lacey, J. I., & Lacey, B. C. (1962). The law of initial value in the longitudinal study of autonomic constitution: Reproducibility of autonomic responses and response patterns over a four-year interval. Annals of the New York Academy of Sciences, 98, 1257–1290.

    PubMed  Google Scholar 

  • Langeheine, R. & Pol, F. van de. (1990). Veränderungsmessung bei kategorialen Daten. Zeitschrift für Sozialpsychologie, 21, 88–100.

    Google Scholar 

  • Linn, R. L., & Slinde, J. A. (1977). The determination of the significance of change between pre- and posttesting periods. Review of Educational Research, 47, 121–150.

    Google Scholar 

  • Lord, F. M. (1953). On the statistical treatment of football numbers. American Psychologist, 8, 750–751.

    Google Scholar 

  • Lord, F. M. (1956). The measurement of growth. Educational and Psychological Measurement, 16, 421–437.

    Google Scholar 

  • Lord, F. M. (1963). Elementary models for measuring change. In C. W. Harris (Ed.), Problems in measuring change (pp. 21–38). Madison: University of Wisconsin Press.

    Google Scholar 

  • Ludwig, D. A. (1979). Statistical considerations for the univariate analysis of repeated-measures experiments. Perceptual and Motor Skills, 49, 899–905.

    Google Scholar 

  • Malgady, R. G., & Colon-Malgady, G. (1991). Comparing the reliability of difference scores and residuals in analysis of covariance. Educational and Psychological Measurement, 51, 803–807.

    Google Scholar 

  • Maxwell, S. E. (1994). Optimal allocation of assessment time in randomized pretest-posttest designs. Psychological Bulletin, 115, 142–152.

    Google Scholar 

  • Maxwell, S. E. (1998). Longitudinal designs in randomized group comparison: When will intermediate observation increase statistical power? Psychological Methods, 3, 275–290.

    Google Scholar 

  • Mayer, K.-U. & Huinink, J. (1990). Alters-, Perioden- und Kohorteneffekte in der Analyse von Lebensverläufen oder: Lexis ade? In K.-U. Mayer (Hrsg.), Lebensverläufe und sozialer Wandler, Sonderheft 31 der Kölner Zeitschrift für Soziologie und Sozialpsychologie (S. 442–459). Opladen: Westdeutscher Verlag.

    Google Scholar 

  • McCain, L. J., & McCleary, R. (1979). The statistical analysis of the simple interrupted times series quasi-experiment. In T. D. Cook, & D. T. Campbell (Eds.), Quasi-experiment: Design and analysis issues for field settings (pp. 207–233). Chicago: Rand-McNally.

    Google Scholar 

  • McCleary, R., & Hay, J. R. A. (1980). Applied time series analysis for the social sciences. Thousand Oaks: Sage.

    Google Scholar 

  • McDowall, D., McCleary, R., Meidinger, E. E., & Hay Jr., R. A. (1980). Interrupted time series. Thousand Oaks: Sage.

    Google Scholar 

  • McNemar, Q. (1958). On growth measurement. Educational and Psychological Measurement, 18, 47–55.

    Google Scholar 

  • Meier, F. (Hrsg.). (1988). Prozeßforschung in den Sozialwissenschaften. Anwendungen zeitreihenanalytischer Methoden. Stuttgart: Fischer.

    Google Scholar 

  • Mellenberg, G. J. (1999). A note on simple gain score precisions. Applied Psychological Measurement, 23, 87–89.

    Google Scholar 

  • Metzler, P. & Nickel, B. (1986). Zeitreihen- und Verlaufsanalyse. Leipzig: Hirzel.

    Google Scholar 

  • Minsel, W. R. & Langer, I. (1973). Methodisches Vorgehen zum Erfassen von psychotherapeutisch bedingten Veränderungen. In G. Reinert (Hrsg.), Bericht über den 27. Kongreß der Deutschen Gesellschaft für Psychologie in Kiel 1970 (S. 646–648). Göttingen: Hogrefe.

    Google Scholar 

  • Mittring, G. & Hussy, W. (2004). Die Ermittlung der kleinsten hinreichend großen Stichprobe bei wissenschaftlichen Experimenten mit Randomisierung. Kölner Psychologische Studien. Beiträge zur natur-, kultur-, sozialwissenschaftlichen Psychologie, IX(1), 1–57.

    Google Scholar 

  • Miyazaki, Y., & Raudenbusch, S. W. (2000). Tests for linkage of multiple cohorts in an accelerated longitudinal design. Psychological Methods, 5, 44–63.

    PubMed  Google Scholar 

  • Mosteller, F. (1990). Improving research methodology: An overview. In L. Sechrest, E. Perrin, & J. Bunker (Eds.), Research methodology. Strengthening causal interpretations of nonexperimental data (pp. 221–230). Rockville: AHCPR, PHS.

    Google Scholar 

  • Nachtigall, C. & Suhl, U. (2002). Der Regressionseffekt. Mythos und Wirklichkeit. Jena: Friedrich-Schiller-Universität Jena.

    Google Scholar 

  • Nelson, C. R. (1973). Applied time series analysis for managerial forecasting. San Francisco: Holden-Day.

    Google Scholar 

  • O’Connor, E. F. (1972). Extending classical test theory to the measurement of change. Review of Educational Research, 42, 73–98.

    Google Scholar 

  • Orth, U., Trzesniewski, K. H., & Robins, R. W. (2010). Self-esteem development from young adulthood to old age: A cohort-sequential longitudinal study. Journal of Personality and Social Psychology, 98, 645–658.

    PubMed  Google Scholar 

  • Pawlik, K. & Buse, L. (1994). „Psychometeorologie“: Zeitreihenanalytische Ergebnisse zum Einfluß des Wetters auf die Psyche aus methodenkritischer Sicht. Psychologische Rundschau, 45, 63–78.

    Google Scholar 

  • Petermann, F. (1978). Veränderungsmessung. Stuttgart: Kohlhammer.

    Google Scholar 

  • Plewis, I. (1981). A comparison of approaches to the analysis of longitudinal categoric data. British Journal of Mathematical and Statistical Psychology, 34, 118–123.

    Google Scholar 

  • Preacher, K. J., Rucker, D. D., MacCallum, R. C., & Nicewander, W. A. (2005). Use of the extreme groups approach: A critical reexamination and new recommendations. Psychological Methods, 10, 178–192.

    PubMed  Google Scholar 

  • Razvodosky, Y. E. (2009). Beverage-specific alcohol sale and suicide in Russia. Crisis: The Journal of Crisis Intervention and Suicide Prevention, 30, 186–191.

    Google Scholar 

  • Rennert, M. (1977). Einige Anmerkungen zur Verwendung von Differenzwerten bei der Veränderungsmessung. Psychologische Beiträge, 19, 100–109.

    Google Scholar 

  • Rogers, J. L., Howard, K. I., & Vessey, J. T. (1993). Using significance test to evaluate equivalence between two experimental groups. Psychological Bulletin, 113, 553–565.

    PubMed  Google Scholar 

  • Rogosa, D. R. (1995). Myths and methods: „Myths about longitudinal research“ plus supplemental questions. In J. M. Gottman (Ed.), The analysis of change (pp. 3–66). Mahwah: Erlbaum.

    Google Scholar 

  • Rogosa, D. R., Brandt, D., & Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 92, 726–748.

    Google Scholar 

  • Rogosa, D. R., & Willett, J. B. (1983). Demonstrating the reliability of the difference score in the measurement of change. Journal of Educational Measurement, 20, 335–343.

    Google Scholar 

  • Rogosa, D. R., & Willett, J. B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometrika, 50, 203–228.

    Google Scholar 

  • Rossi, P. H., & Freeman, H. E. (1985). Evaluation: A systematic approach (3rd ed.). Thousand Oaks: Sage.

    Google Scholar 

  • Rost, J. (2004). Lehrbuch Testtheorie Testkonstruktion (2. Aufl.). Bern: Huber.

    Google Scholar 

  • Rothermund, K., & Brandstädter, J. (2003). Depression in later life: Cross-sequential patterns and possible determinants. Psychology and Aging, 18, 80–90.

    PubMed  Google Scholar 

  • Rottleuthner-Lutter, M. (1985). Evaluation mit Hilfe der Box-Jenkins-Methode. Berlin: Technische Universität Berlin.

    Google Scholar 

  • Rubin, D. B. (1977). Assignment to treatment groups on the basis of a covariate. Journal of Educational Statistics, 2, 1–26.

    Google Scholar 

  • Rudinger, G. (1981). Tendenzen und Entwicklungen entwicklungspsychologischer Versuchsplanung – Sequenzanalysen. Psychologische Rundschau, 32, 118–136.

    Google Scholar 

  • Schaie, K. W. (1965). A general model for the study of developmental problems. Psychological Bulletin, 64, 92–107.

    PubMed  Google Scholar 

  • Schaie, K. W. (1977). Quasi-experimental designs in the psychology of aging. In J. E. Birren, & K. W. Schaie (Eds.), Handbook of the psychology of aging (pp. 39–58). New York: Van Nostrand Reinhold.

    Google Scholar 

  • Schaie, K. W. (1994). Developmental designs revisited. In S. H. Cohen, & H. W. Reese (Eds.), Life-span developmental psychology. Methodological contributions (pp. 45–64). Hillsdale: Erlbaum.

    Google Scholar 

  • Schlittgen, R. & Sattarhoff, C. (2020). Angewandte Zeitreihenanalyse mit R (4. Aufl.). München: De Gruyter Oldenbourg.

    Google Scholar 

  • Schmitz, B. (1989). Einführung in die Zeitreihenanalyse. Modelle, Softwarebeschreibung, Anwendung. Bern: Huber.

    Google Scholar 

  • Shadish, W. R., Galindo, R., Wong, V. C., Steiner, P. M., & Cook, T. D. (2011). A randomized experiment comparing random and cutoff-based assignment. Psychological Methods, 16, 179–191.

    PubMed  Google Scholar 

  • Shumway, R. H., & Stoffer, D. S. (2000). Time series analysis and its applications. New York: Springer.

    Google Scholar 

  • Singer, J. D., & Willett, J. B. (1991). Modeling the days of our lifes: Using survival analysis when designing and analyzing longitudinal studies of duration and the time of events. Psychological Bulletin, 110, 268–290.

    Google Scholar 

  • Stelzl, I. (1982). Fehler und Fallen der Statistik. Bern: Huber.

    Google Scholar 

  • Swaminathan, H., & Algina, J. (1977). Analysis of quasi-experimental time-series designs. Multivariate Behavioral Research, 12, 111–131.

    PubMed  Google Scholar 

  • Thistlethwaite, D. L., & Campbell, D. T. (1960). Regression-discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology, 51, 309–317.

    Google Scholar 

  • Thome, H. (2005). Zeitreihenanalyse. Eine Einführung für Sozialwissenschaftler und Historiker. München: Oldenbourg.

    Google Scholar 

  • Trochim, W. M. K. (1984). Research design for program evaluation: The regression discontinuity approach. Thousand Oaks: Sage.

    Google Scholar 

  • Trochim, W. M. K., & Cappelleri, J. C. (1992). Cutoff assignment strategies for enhancing randomized clinical trials. Controlled Clinical Trials, 13, 190–212.

    PubMed  Google Scholar 

  • Vagt, G. (1976). Korrektur von Regressioneffekten in Behandlungsexperimenten. Zeitschrift für experimentelle und angewandte Psychologie, 23, 284–296.

    Google Scholar 

  • Vagt, G. (1977). Meßinstrumente verändern sich im Laufe der Zeit. Psychologie und Praxis, 21, 117–122.

    Google Scholar 

  • Venter, A., Maxwell, S. E., & Bolig, E. (2002). Power in randomized group comparison: The value of adding a single intermediate time point to a traditional pretest-posttest design. Psychological Methods, 7, 194–209.

    PubMed  Google Scholar 

  • Wellek, S. (1994). Methoden zum Nachweis von Äquivalenz. Stuttgart: Fischer.

    Google Scholar 

  • Willett, J. B. (1989). Some results on reliability for the longitudinal measurement of change: Implications for the design of studies of individual growth. Educational and Psychological Measurement, 49, 587–602.

    Google Scholar 

  • Williams, R. H., & Zimmermann, D. W. (1977). The reliability of difference scores when errors are correlated. Educational and Psychological Measurement, 37, 679–689.

    Google Scholar 

  • Zielke, M. (1980). Darstellung und Vergleich von Verfahren zur individuellen Veränderungsmessung. Psychologische Beiträge, 22, 592–609.

    Google Scholar 

  • Zimmermann, D. W., & Williams, R. H. (1982). Gain scores in research can be highly reliable. Journal of Educational Measurement, 19, 149–154.

    Google Scholar 

  • Amthauer, R. (1971). Intelligenz-Struktur-Test (I-S-T 70). Göttingen: Hogrefe.

    Google Scholar 

  • Aster, M. von, Neubauer, A. & Horn, R. (2006). WIE – Wechsler Intelligenztest für Erwachsene. Frankfurt: Pearson.

    Google Scholar 

  • Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1, 91–97.

    PubMed  PubMed Central  Google Scholar 

  • Barker, M., Richards, C., Jones, R., & Monro, S. (2011). BiReCon: An international academic conference on bisexuality including the program for BiReCon. Journal of Bisexuality, 11, 157–170.

    Google Scholar 

  • Barlow, D. H., & Hersen, M. (1973). Single-case experimental designs. Uses in applied clinical research. Archives of General Psychiatry, 29, 319–325.

    Google Scholar 

  • Barlow, D. H., & Hersen, M. (Eds.). (1984). Single case experimental designs: Strategies for studying behaviour change. New York: Pergamon.

    Google Scholar 

  • Bird, K. D. (1991). Exploratory \(n=1\) profile analysis. Educational and Psychological Measurement, 51, 523–530.

    Google Scholar 

  • Bortz, J., Lienert, G. A. & Boehnke, K. (2008). Verteilungsfreie Methoden in der Biostatistik (3. Aufl.). Heidelberg: Springer.

    Google Scholar 

  • Bortz, J. & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler (7. Aufl.). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Bühner, M. (2021). Einführung in die Test- und Fragebogenkonstruktion (4. Aufl.). München: Pearson Studium.

    Google Scholar 

  • Cahan, S. (1989). A critical examination of the „relibability“ and „abnormality“ approaches to the evaluation of subtest score differences. Educational and Psychological Measurement, 49, 807–814.

    Google Scholar 

  • Callahan, C. D., & Barisa, M. T. (2005). Statistical process control and rehabilitation outcome: The single-subject design reconsidered. Rehabilitation Psychology, 50, 24–33.

    Google Scholar 

  • Edgington, E. S. (1975). Randomization tests for one-subject operant experiments. The Journal of Psychology, 90, 57–68.

    Google Scholar 

  • Edgington, E. S. (1980). Overcoming obstacles to single-subject experimentation. Journal of Educational Statistics, 5, 261–267.

    Google Scholar 

  • Edgington, E. S. (1995). Randomization tests. New York: Dekker.

    Google Scholar 

  • Fichter, M. M. (1979). Versuchsplanung experimenteller Einzelfalluntersuchungen in der Psychotherapieforschung. In F. Petermann & F. J. Hehl (Hrsg.), Einzelfallanalyse (S. 140–158). München: Urban & Schwarzenberg.

    Google Scholar 

  • Fisseni, H.-J. (2004). Lehrbuch der psychologischen Diagnostik (3. Aufl.). Göttingen: Verlag für Psychologie.

    Google Scholar 

  • Franklin, R. D., Allison, D. B., & Gorman, B. S. (Eds.). (1996). Design and analysis of single-case research. Mahwah: Erlbaum.

    Google Scholar 

  • Glass, G. V., Willson, V. L., & Gottman, J. M. (1975). Design and analysis of time-series experiments. Boulder: University Press.

    Google Scholar 

  • Good, P. (2000). Permutation tests (2nd ed.). New York: Springer.

    Google Scholar 

  • Gottman, J. M. (1973). \(N\)-of-one and \(N\)-of-two research in psychotherapy. Psychological Bulletin, 80, 93–105.

    PubMed  Google Scholar 

  • Huber, H. P. (1973). Psychometrische Einzelfalldiagnostik. Weinheim: Beltz.

    Google Scholar 

  • Kazdin, A. E. (1978). Methodological and interpretative problems of single-case experimental designs. Journal of Consulting and Clinical Psychology, 46, 629–642.

    PubMed  Google Scholar 

  • Kazdin, A. E. (2020). Single-case research designs: Methods for clinical and applied settings (3rd ed.). Oxford: Oxford University Press.

    Google Scholar 

  • Koehler, M. J., & Levin, J. R. (1998). Regulated randomization: A potentially sharper analytical tool for the multiple-baseline design. Psychological Methods, 3, 206–217.

    Google Scholar 

  • Kratochwill, T. R. (1978). Single subject research. Strategies for evaluating change. Waltham: Academic Press.

    Google Scholar 

  • Kratochwill, T. R., Alden, K., Demuth, D., Dawson, D., Panicucci, C., Arnston, P. et al. (1974). A further consideration in the application of an analysis-of-variance model for the intrasubject replication design. Journal of Applied Behavior Analysis, 7, 629–633.

    PubMed  PubMed Central  Google Scholar 

  • Kratochwill, T. R., & Levin, J. R. (Eds.). (1992). Single case research design and analysis. Hillsdale: Erlbaum.

    Google Scholar 

  • Krauth, J. (1995). Testkonstruktion und Testtheorie. Weinheim: Beltz.

    Google Scholar 

  • Levin, J. R., Marascuilo, L. A., & Hubert, L. J. (1978). \(N=1\). Nonparametric randomization tests. In T. R. Kratochwill (Ed.), Single subject research (pp. 167–196). New York: Academic Press.

    Google Scholar 

  • Levin, J. R., & Wampold, B. E. (1999). Generalized single-case randomization tests: Flexible analyses for a variety of situations. School Psychology Quarterly, 14, 59–93.

    Google Scholar 

  • Levy, P. S., & Lemeshow, S. (1999). Sampling of populations: Methods and applications. New York: Wiley.

    Google Scholar 

  • Lienert, G. A. (1978). Verteilungsfreie Methoden in der Biostatistik (Bd. II). Meisenheim: Hain.

    Google Scholar 

  • Maassen, G. H. (2000). Kelley’s formula as a basis for the assessment of reliable change. Psychometrika, 65, 187–197.

    Google Scholar 

  • Meyer-Bahlburg, H. F. L. (1969). Spearmans rho als punktbiserialer Rangkorrelationskoeffizient. Biometrische Zeitschrift, 11, 60–66.

    Google Scholar 

  • Morgan, D. L., & Morgan, R. K. (2008). Single-case research methods for the behavioral and health sciences. Thousand Oaks: Sage.

    Google Scholar 

  • Nicolich, M. J., & Weinstein, C. S. (1977). Time series analysis of behavioral changes in an open class-room. Paper presented at the annual meeting of the American Educational Research Association, New York.

    Google Scholar 

  • Noach, H. & Petermann, F. (1982). Die Prüfung von Verlaufsannahmen in der therapeutischen Praxis. Zeitschrift für personenzentrierte Psychologie und Psychotherapie, 1, 9–27.

    Google Scholar 

  • Parsonson, B. S., & Baer, D. M. (1978). The analysis and presentation of graphic data. In T. R. Kratochwill (Ed.), Single subject design (pp. 101–165). New York: Academic Press.

    Google Scholar 

  • Petermann, F. (1981). Möglichkeiten der Einzelfallanalyse in der Psychologie. Psychologische Rundschau, 32, 31–48.

    Google Scholar 

  • Petermann, F. (1982). Einzelfalldiagnose und klinische Praxis. Stuttgart: Kohlhammer.

    Google Scholar 

  • Petermann, F. (1996). Einzelfalldiagnostik in der klinischen Praxis. Weinheim: Psychologie Verlags Union.

    Google Scholar 

  • Rae, G. (1991). Another look at the reliability of a profile. Educational and Psychological Measurement, 51, 89–93.

    Google Scholar 

  • Revenstorf, D. & Keeser, W. (1979). Zeitreihenanalyse von Therapieverläufen. Ein Überblick. In F. Petermann & F. J. Hehl (Hrsg.), Einzelfallanalyse (S. 183–228). München: Urban & Schwarzenberg.

    Google Scholar 

  • Stevens, W. L. (1939). Distribution of groups in a sequence of alternatives. Annals of Eugenics, 9, 10–17.

    Google Scholar 

  • Westmeyer, H. (1979). Wissenschaftstheoretische Grundlagen der Einzelfallanalyse. In F. Petermann & F. J. Hehl (Hrsg.), Einzelfallanalyse (S. 17–34). München: Urban & Schwarzenberg.

    Google Scholar 

  • Yarnold, P. R. (1984). The reliability of a profile. Educational and Psychological Measurement, 44, 49–59.

    Google Scholar 

  • Yarnold, P. R. (1988). Classical test theory methods for repeated measures \(n=1\) research designs. Educational and Psychological Measurement, 48, 913–919.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Technische Universität Ilmenau, Ilmenau, Deutschland

    Nicola Döring

Authors
  1. Nicola Döring
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicola Döring .

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Der/die Autor(en), exklusiv lizenziert an Springer-Verlag GmbH, DE, ein Teil von Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Döring, N. (2023). Datenanalyse. In: Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-64762-2_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-64762-2_12

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-64761-5

  • Online ISBN: 978-3-662-64762-2

  • eBook Packages: Psychology (German Language)

Publish with us

Policies and ethics

Search

Navigation