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    Quantitative Research Methodologies 2

    English | 10 Weeks

    Facilitated
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    From data cleaning to predictive modelling—master the next level of quantitative analysis in just 10 weeks online!

    Perfect for:

    This course is ideal for postgraduate students, early-career researchers, and professionals who are looking to deepen their understanding of statistical analysis and expand their quantitative research skillset.

    Quantitative Research Methodologies 2 builds on foundational concepts of quantitative inquiry and introduces participants to intermediate and advanced data analysis techniques. Delivered online over 10 weeks, the course equips learners with hands-on skills in inferential statistics, data cleaning, hypothesis testing, and predictive modelling using real-world datasets. It also provides a comparative understanding of parametric and non-parametric methods, enabling participants to make sound methodological decisions in their research practice.

    Hands-On Learning:

    This course emphasises active, practice-based learning. Participants will work with real-world data sets to:

    • Clean and prepare data for analysis
    • Conduct statistical tests using user-friendly software tools
    • Interpret results in context and draw meaningful conclusions
    • Apply what is learned through guided exercises, case studies, and peer discussions
    70 notional hours (5-7 hours per week).

    Live Zoom sessions:

    A one-hour Zoom session each week.

    Target group:

    This course is aimed at postgraduate students, early-career researchers, and professionals across disciplines who are looking to deepen their understanding of statistical analysis and expand their quantitative research skillset.

    Learning Outcomes (four main points):

    By the end of the course, participants will be able to:

    • Differentiate between qualitative vs. quantitative research and parametric vs. non-parametric data.
    • Apply key inferential statistical concepts such as hypothesis testing, p-values, and confidence intervals.
    • Conduct data cleaning, Exploratory Factor Analysis (EFA), and reliability testing using Cronbach’s alpha.
    • Select and perform appropriate statistical tests including t-tests, ANOVA, Chi² tests, and non-parametric equivalents.
    • Build predictive models using simple linear regression and identify contexts for using advanced techniques like multiple regression and repeated measures ANOVA.

    Requirements:

    To ensure a meaningful learning experience, participants should meet one of the following prerequisites:

    • Completion of the DIGI-FACE Quantitative Research Methodologies 1 module; or
    • Demonstrated basic knowledge of statistical concepts, including descriptive statistics, basic hypothesis testing, and familiarity with statistical software.
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    Open Access License: CC-BY-SA

    Rights of Use of the DIGI-FACE Generic Modules

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    Quantitative Research Methodologies 2





      DIGI-FACE – Digital Initative for African Centres of Excellence represented by:

      University of Applied Sciences Kehl
      Kehl Institute of Applied Research (KIAF)
      Projects International Cooperation and Development

      Kinzigallee 1, D- 77694 Kehl
      +49 7851 894143
      https://www.hs-kehl.de/

      Contact: digiface[at]hs-kehl.de

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        • Staff is everybody who works in the frame of a university which is participating at DIGI-FACE (African Centres of Excellence and its network) [(i.e. as coordinator, financial/administrative support, IT-support, learning designer, administrator etc.). If you are working for DAAD or DIGI-FACE you are also staff.
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      5. Researcher
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