Quant vs Qual Research.

One major difference between quantitative and qualitative approaches is the nature of data and the methods used for data collection and analysis.

Quantitative Approaches:

1. Data Type: Quantitative research primarily deals with numerical data. It involves the collection of structured data that can be measured and analyzed statistically.
2. Research Questions: Quantitative approaches are well-suited for research questions that seek to establish relationships, patterns, and generalizability. They are often used to answer questions about the extent, frequency, or strength of associations between variables.
3. Methodology: Researchers in quantitative studies typically use standardized surveys, experiments, or large-scale data analysis to collect and analyze data.
4. Data Analysis: Statistical techniques such as regression analysis, t-tests, and ANOVA are commonly employed to analyze quantitative data.
5. Generalizability: Quantitative research aims for generalizability, seeking to draw conclusions that apply to a broader population.

Qualitative Approaches:

1. Data Type: Qualitative research deals with non-numerical, text-based, or observational data. It emphasizes the richness and depth of information rather than numerical precision.
2. Research Questions: Qualitative approaches are better suited for research questions that seek to understand complex phenomena, explore perspectives, or uncover underlying meanings. They focus on context, experiences, and social processes.
3. Methodology: Qualitative researchers use methods like interviews, participant observation, content analysis, and case studies to gather data.
4. Data Analysis: Qualitative data analysis involves techniques like thematic analysis, content analysis, and constant comparative analysis to identify themes, patterns, and narratives within the data.
5. Contextual Understanding: Qualitative research aims to provide a deep contextual understanding of a particular case or phenomenon. It often leads to rich, descriptive accounts.

Regarding the use of both correlational evidence and process-tracing evidence in research, this approach is often employed to strengthen the validity and persuasiveness of research findings. Here’s why researchers use both types of evidence:

1. Correlational Evidence (Quantitative): Correlational evidence helps establish associations or relationships between independent variables (IV) and dependent variables (DV) across a larger sample. It quantifies the strength and direction of these relationships. Researchers use this evidence to demonstrate statistical significance and the extent to which changes in IV are related to changes in DV. It provides a broader perspective by showing patterns across cases.

   Citation: Ragin, C. C. and L. M. Amoroso. 2011. Using Quantitative Methods to Study Covariation.

2. Process-Tracing Evidence (Qualitative): Process-tracing evidence helps establish the causal mechanisms and the logical sequence of events by which changes in IV cause changes in DV. It provides a detailed, context-specific understanding of how and why certain outcomes occur. This evidence is particularly useful for explaining the “why” and “how” behind correlations.

   Citation: Fearon, J. D. 1991. Counterfactuals and hypothesis testing in political science.

By combining both types of evidence, researchers can build a more comprehensive and convincing argument. Quantitative evidence establishes the presence of a relationship, while qualitative evidence delves into the mechanisms and context, offering a deeper understanding of the underlying processes. This strengthens the overall validity and persuasiveness of the research findings.

Note: Specific examples and further elaboration on this approach would require access to the provided readings, which I don’t have access to.