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Doing intersectional quantitative research (RSS2.10) - Confirmed

Intersectionality studies draw attention to the interrelatedness of inequalities or power hierarchies along social dimensions, such as gender, race, class, sexuality, religion, able-bodiedness, and it is often associated with qualitative research. Still many who are interested in conducting it are struggling with the question: How to do intersectional quantitative research?  That is exactly what you will focus on in this one-week crash course. Building on an emerging consensus, this course makes clear why adding an interaction term to your regression model is neither sufficient, nor necessary for doing intersectionality research. Having that said, you do focus on which statistical tools are suitable for doing intersectional research and how they can be applied.

Duration: one-week.



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    Part 1: Doing what exactly? 
    In the first part of the course, the course discusses both the seminal and most recent methodological literature on (doing quantitative) intersectional research in order to enable you to position this course and your own research in the larger debate. It also provides an overview of the different techniques applied, such as descriptive statistics, regression-based analysis, latent class analysis, and (fuzzy-set) qualitative comparative analysis. This first part covers one to two days, in which you discuss the texts in a seminar style. There will be ample room to discuss your own research (plans) and how it fits in this debate and the different quantitative techniques. 

    Part 2: Really doing it? 
    Next, you focus on descriptive and regression-based analyses and how the various ways in which statistical practices can be applied in either testing hypotheses derived from taking an intersectional perspective or exploring how inequalities and hierarchies are intersectional. This includes both practicing with different tools on your own or provided data (the first being preferred) as well as critically reflecting on how to interpret and report results, doing justice to your intersectional perspective. The second part of the course covers two to three days, including introductory lectures and practical sessions in which instructors walk around and are available for advice-giving and discussion. You close the days with brief wrap-ups which might include participant presentations. 

    Part 3: How to do it with success? 
    Lastly,  we close off the course by zooming out again to discuss the organizational practice of doing intersectional statistics: how to publish intersectionality quantitative research, what to considered when you are applying for grants, and the opportunities and limitations in survey design.


    Watch what our participants say about their experience!

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    24 June 2024, 9 am
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    24 June 2024, 9 am - 28 June 2024, 5 pm
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    Niels Spierings, professor of Sociology: processes of inclusion and exclusion

    Niels Spierings

    Prof. Dr. Niels Spierings researches issues of inclusion and exclusion, particularly in politics, society and labor markets. He focuses mainly on the Netherlands and Western Europe and on the Middle East. In his work he combines statistics and survey research with case studies, interviews and 'big data' analysis.

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    • Regular: €1049 (application deadline 13th of May)
    • Student & PhD's: €699 (application deadline 13th of May)

    Includes: your course, short morning and late afternoon courses, coffee and tea during breaks, a warm lunch every day, Official Opening, MethodsNET Café (including some drinks and snacks) Official Closing (with some drinks and snacks) and a 1-year (2024 calendar year) free membership as MethodsNET regular member.

    Excludes: transport, accommodation, social events and other costs. 

    Discounts and Scholarships


    Level of participant: 

    • Master
    • PhD
    • PostDoc
    • Professional

    Admission requirements: 

    ​​A basic understanding of regression analysing and the skill to conduct regression analysis is needed. The course work can be done in any program you prefer (e.g. SPSS, R, Excel, STATA), particularly if you bring data from you own project (which is recommended). If this is not possible, contact the course coordinator.

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