Understanding panel data QCA in R with data from postconflict employment of women / Preya Bhattacharya.

Format:

Book

Author/Creator:

Bhattacharya, Preya, author.

Language:

English

Subjects (All):

Women--Employment--Developing countries--Statistical methods.

Women.

R (Computer program language)--Handbooks, manuals, etc.

R (Computer program language).

Panel analysis--Statistical methods.

Panel analysis.

Physical Description:

1 online resource : illustrations

Place of Publication:

London : SAGE Publications Ltd, 2026.

Summary:

In this dataset, I will demonstrate how students should build and analyze a panel-data Qualitative Comparative Analysis (QCA) model in the software RStudio and the QCA packages of SetMethods (Oana & Schneider, 2018) and QCA (Duşa, 2019).As a research approach, QCA was first developed by Charles C. Ragin in his book "The Comparative Method" in 1987 to help social scientists compare and analyze an intermediate number of cases (around 10-50), which is usually too small for quantitative analysis and too big for qualitative analysis (Ragin & Fiss, 2019; Rihoux & Lobe, 2015, p. 1044). Along with this, QCA is important because it can help researchers understand the causal pathway/s through which a condition or a combination of conditions causes the outcome by testing for necessary and sufficient conditions. And finally, QCA can also help researchers test and build new theories, and analyze how the context of the cases/countries studied determines the impact of conditions on an outcome (Bhattacharya, 2019, p. 4; Ragin & Fiss, 2019).To date, there are many different types of QCA models, depending on whether a researcher is analyzing cross-sectional or panel data (Oana, Schneider, & Thomann, 2021, pp. 61-170). In this dataset, I will focus on panel data QCA models and demonstrate one of the approaches towards building a panel data QCA model, that is, Cluster QCA (Bhattacharya, 2024, pp. 44-76; Garcia-Castro & Ariño, 2016).To do this, I will first discuss the most important assumptions of Cluster QCA (Bhattacharya, 2024, pp. 44-76; Garcia-Castro & Ariño, 2016). I will then demonstrate how to build Cluster QCA models, with the RStudio QCA packages of SetMethods (Oana & Schneider, 2018), and QCA (Duşa, 2019), and my own research data on 'whether the application of microfinance institutions has increased the economic participation of women, in the postconflict countries of Bosnia-Herzegovina (BiH), Croatia (HRV), Serbia (SRB), Montenegro (MNE), North Macedonia (MKD), and Kosovo (XKX), between the years of 1999-2020. (Bhattacharya, 2020, 2023b). I will then discuss how to interpret Cluster QCA analysis results and finally conclude this dataset by discussing a few weaknesses of Cluster QCA models and alternate QCA approaches that can help address these weaknesses.To summarize, by the end of this dataset, students should be able to build their own Cluster QCA models, and also learn how to analyze and interpret the Cluster QCA results in the context of the cases studied. To do this, I have attached a Student Guide and a How-to Guide for creating Cluster QCA models in RStudio as accompaniments to this dataset file.

Notes:

Description based on XML content.

ISBN:

9781036244798

OCLC:

1594889612

Publisher Number:

T301265