Introduction to Empirical Research Methods

Semesters: WS 19/20, SS 20
Lecturer: Univ.-Lekt. Lena Sophie Kinner, BA, MA

In this semester (WS 19/20), students were taught how empirical studies can be understood, discussed and criticized. In addition, students were introduced to the basics of descriptive and inferential statistics by planning, implementing and evaluating their own empirical study.

As the course extended over two semesters, the winter semester 19/20 focused on the teaching of basic knowledge about empirical research. In doing so, the course combined theoretical input with practice. Thereby, the students were supported in developing the framework for their own empirical study, which was completed by the end of the semester. During the semester break, the students had to collect the data for their study by using their own questionnaires. Furthermore, the students had to do the data entry by using a data processing program.

The topics of the winter semester were: (differences between) quantitative and qualitative research methods; planning an empirical study; hypothesis formulation; operationalization; selecting a sample; creating a survey; scale levels; data collection; data entry.

In this course, students learned to be:

  • familiar with fundamental terms of empirical-quantitative research.
  • able to undertake a survey independently and to create a data mask for the acquired data in an electronic data processing program.
  • capable and know how to enter the collected data in the data processing program.

Literature

  • Bryman, Alan. Social Research Methods. Oxford: Oxford University Press, 2004.
  • Brink, Alfred. Anfertigung wissenschaftlicher Arbeiten. Berlin: Springer, 2013.

In the fourth semester (SS 20) course, the students continued to work with the collected data, as the semester specialized on data evaluation. They were taught how empirical studies can be understood, discussed and criticized. In addition, students were introduced to the basics of descriptive and inferential statistics by implementing and evaluating their own empirical study. For this purpose, students did the data collection (by using self-completion-questionnaires), the preparation of a data-mask, and the data entry (by using the statistic software SPSS). Afterwards, the data was analyzed and interpreted.

The topics of the summer semester were: Data collection, data mask, data entry; Electronically supported data analysis (important statistical characteristics and representation forms, filter setting, classification, recoding of variables, correlation and reliability analysis, indexing, diversity tests, etc.); Interpretation of data.

Literature

  • Bühl, A. SPSS 23. Einführung in die moderne Datenanalyse. Hallbergmoos: Pearson Deutschland GmbH, 2016.
  • Black, T. R. Understanding social science research. London: Thousand Oaks, 2002.
  • Bortz, J., & Döring, N. Forschungsmethoden und Evaluation für Human- und Sozialwissenschaftle Heidelberg: Springer Medizin Verlag, 2006.
  • Bortz, J.; Schuster, C. Statistik für Human- und Sozialwissenschaftler. Heidelberg: Springer Verlag, 2010.
  • Brink, A. Anfertigung wissenschaftlicher Arbeiten. Ein prozessorientierter Leitfaden zur Erstellung von Bachelor-, Master- und Diplomarbeiten. Wiesbaden: Springer Fachmedien, 2013.
  • Cleff, T. Deskriptive Statistik und Explorative Datenanalyse. Eine computergestützte Einführung mit Excel, SPSS und STATA. Pforzheim: Gabler Verlag, 2015.
  • Cramer, D. Advanced Quantitative Data Analysis. Maidenhead: Open University Press, 2003.
  • Diekmann, A. Empirische Sozialforschung: Grundlagen, Methoden, Anwendungen. Reinbek bei Hamburg: Rowohlt Taschenbuch Verlag, 2016.
  • Faherty, V. Compassionate statistics: applied quantitative analysis for social services with Exercises and Instructions in SPSS. Thousand Oaks: SAGE Publications, 2008.
  • Greasley, P. Quantitative data analysis using SPSS: an introduction for health & social science. Maidenhead: Open University Press, 2008.
  • Häder, M. Empirische Sozialforschung. Eine Einführung. Wiesbaden: Springer Fachmedien, 2015.
  • Hand, D. J. Statistics: A Very Short Introduction. Oxford: Oxford University Press, 2008.
  • Hirsig, R. Statistische Methoden in den Sozialwissenschaften. Eine Einführung im Hinblick auf computergestützte Datenanalysen mit SPSS. Zürich: Seismo Verlag, 2006.
  • Korb, C.; Völkl, K. Deskriptive Statistik. Eine Einführung für Politikwissenschaftlerinnen und Politikwissenschaftler. Halle: Springer VS, 2018.
  • Mood, D.; Morrow, J. Introduction to statistics in human performance using SPSS and R. New York, London: Routledge, 2019.
  • Mooi, E.; Sarstedt, M. A Concise Guide to Market Research. The Process, Data, and Methods Using IBM SPSS Statistics. Heidelberg: Springer-Verlag, 2011.
  • Stehlik-Barry, K.; Babinec, A. Data analysis with IBM SPSS Statistics. Birmingham: Packt Publishing, 2017.
  • Tavakol, M.; Dennick, R. “Making sense of Cronbach’s alpha.” International Journal of Medical Education (2011): 53-55.

Group Project 1
Group: Johanna Lindner, Maximilia Hogrebe, Adaeze Ike, Lisa-M. Weidl
Format: Research & Questionnaire Creation
Topic: Quantitative study/ Self completion questionnaire

Group Project 2
Group: Adaeze Ike, Lisa-M. Weidl
Format: Execution & Analyzation of Data
Topic: Quantitative study/ Self completion questionnaire

A quantitative design in the form of a self-completion questionnaire was chosen to obtain the participants’ attitudes concerning the topic under investigation. Self-completion questionnaires have the advantage of providing anonymity to the participants, can be quickly answered online, and are cost-effective. Since the topic of research represents currently unavailable market products, participants were encouraged by open-ended questions to express their views in detail. The self-completion questionnaire contained 45 questions – 21 single choices, 9 multiple choices, 15 open-ended questions – and was available in German and English.

Since many participants (27/50 contraceptive injection and 23/50 contraceptive pill) wanted more information on MHC, we used the extracted data as a building block for the different personas of the podcast “Contraception Is a Man’s Business” – the project of the class Cross Disciplinary Capabilities. The podcast addresses the social implications and problems of the contraceptive focus on women since 1960 and creates awareness for future possibilities on male (hormonal) contraception.