Integrated Inferences: Causal Models for Qualitative and Mixed-Method Research

Integrated Inferences combines quantitative and qualitative tools of inference to develop a framework for causal models and Bayesian updating. It provides an introduction to fundamental principles and demonstrates how these tools can be used to implement and justify inferences using within-case evidence,correlational patterns,or a mix of the two.

Format: Hardback
Length: 300 pages
Publication date: 31 October 2023
Publisher: Cambridge University Press

The social sciences are increasingly embracing research strategies that combine quantitative and qualitative tools of inference, recognizing their potential for enhanced understanding and analysis. Integrated Inferences presents a comprehensive framework for utilizing causal models and Bayesian updating in qualitative and mixed-methods research. By making, updating, and querying causal models, researchers can effectively integrate information from diverse data sources, bridging the gap between theory and empirics in a more systematic and transparent manner than traditional qualitative and quantitative approaches allow.

This book serves as an introductory guide to fundamental principles of causal inference and Bayesian updating, demonstrating how these tools can be applied to implement and justify inferences using within-case (process tracing) evidence, correlational patterns across multiple cases, or a combination of the two. The authors further showcase how causal models can inform research design, guiding the selection of cases, observations, and methods that will be most valuable in addressing specific research questions.

The integration of quantitative and qualitative methods in research offers numerous advantages. It enables researchers to capture a broader range of perspectives and experiences, enhancing the richness and depth of the analysis. By combining quantitative data with qualitative insights, researchers can uncover nuanced patterns, relationships, and meanings that may be overlooked in single-method approaches. Moreover, the use of causal models and Bayesian updating allows for a more rigorous and scientific approach to inference, providing a framework for testing and validating hypotheses based on empirical evidence.

However, the integration of quantitative and qualitative methods also presents challenges. One of the key challenges is the need for careful data management and analysis, as different data types and methodologies require specific techniques and approaches. Additionally, researchers must navigate the complex interplay between theory and empirical evidence, ensuring that their inferences are grounded in robust and reliable evidence.

Despite these challenges, the growing consensus in the social sciences on the virtues of research strategies that combine quantitative and qualitative tools of inference suggests that this approach will continue to gain prominence in the years to come. By leveraging the strengths of both methods, researchers can achieve a more comprehensive understanding of complex social phenomena and develop more effective interventions and policies.

In conclusion, Integrated Inferences provides a valuable resource for researchers seeking to integrate quantitative and qualitative tools of inference in their research. By offering a comprehensive framework for utilizing causal models and Bayesian updating, the book enables researchers to make more informed decisions about research design, data collection, and analysis. The integration of quantitative and qualitative methods in research has the potential to revolutionize our understanding of social phenomena and drive positive social change.

ISBN-13: 9781107169623