This article summarizes the paper “Bridging the Qualitative-Quantitative Divide: Guidelines for Conducting Mixed Methods Research in Information Systems” by Venkatesh (2013) (link to paper). This paper provides guidelines for conducting mixed methods research in information systems. For articles related to the basics of writing a thesis, see the following link.
This paper describes how to conduct a mixed-methods study and the guidelines needed to execute it effectively. Along with the benefits of mixed methodology, this book will help you understand the purpose of mixed methodology, how researchers can select and design an appropriate mixed methodology, and how to collect, analyze, and report data.
■ What is a Mixed method?
Let’s take a closer look at mixed methods. Mixed methodology is a methodology that combines quantitative and qualitative research methods to compensate for their limitations. The advantage is that by collecting and analyzing a wider variety of information, research results are more accurate and valid.
Quantitative methodology involves collecting and analyzing large amounts of numerical data and uses statistical methods to determine the relationships between variables. However, this methodology does not allow for in-depth exploration of the research topic because it uses a structured framework (i.e., a questionnaire with pre-determined questions), as qualitative methodology does..
Since participants are often asked to select from a predetermined set of answer categories, it tends to be difficult to identify causal relationships or implications, such as personal experiences or attitudes related to the research topic. In addition, quantitative data collection methods use objective, structured questionnaires or surveys, making it difficult to reflect personal experiences and thoughts.
On the other hand, qualitative methodologies collect and analyze small amounts of qualitative data and use methods such as interviews and observations to identify causal relationships and meanings in a natural context. It is useful for understanding personal experiences, attitudes, and contexts and situations.
However, the disadvantage of this methodology is that it is difficult to secure representativeness, and it is difficult to generalize because it is difficult to analyze the collected data statistically. There is also the potential for results to be subjective to the researcher’s interpretation.
Mixed methodology integrates quantitative and qualitative methodologies, combining their strengths to increase the validity and consistency of research findings. This methodology can supplement qualitative data that is lacking in a quantitative study, or it can supplement quantitative data that is lacking in a qualitative study to collect and analyze more diverse information. They can also compensate for limitations or biases that may occur in each methodology, resulting in more reliable research results.
■ Advantages of mixed methods
The paper emphasizes that mixed methods play an important role in conducting research. Here’s why.
First, by combining different methodologies, you can address both confirmatory and exploratory questions about your research topic. This ensures the validity and depth of your findings.
A confirmatory question is one that aims to confirm a hypothesis or expected outcome based on previous research or theory. They are often used in quantitative research, for example, “Does educational program A affect the performance of students B?”.
Exploratory questions, on the other hand, are designed to discover new topics or phenomena without relying on previous research or theoretical foundations. They are often used in qualitative research, for example, “What factors influence students’ academic performance?”.
Second, they provide stronger inferences than studies conducted with a single methodology. Because they combine multiple methodologies, they can produce more consistent and accurate results than studies using only one methodology.
Third, it can compensate for the shortcomings of a particular methodology. For example, it is difficult to collect the subjective experiences of individuals in a quantitative methodology, but by combining qualitative and quantitative methods, you can capture the experiences and attitudes of individuals. It also provides an opportunity to accommodate diverse opinions.
■ Seven objectives of using mixed methods
- Complementarity: Complementing quantitative and qualitative data to provide richer information and increase the validity of research findings.
- Development: Discover and develop new theories, concepts, models, etc.
- Initiation: Conducting initial research in a new topic or field to increase our understanding of the field.
- Expansion: Complementing or extending existing research, to provide richer data and gain new insights.
- Triangulation: Using different data sources or methods, to increase the validity of research findings.
- Explanation: Using a combination of quantitative and qualitative data to provide and explain a deeper understanding of the research topic.
- Understanding: Using a combination of quantitative and qualitative data to make sense of complex phenomena, seeking to develop an understanding of the field.
■ Considerations when using mixed methods
- Appropriateness: The research topic and the purpose of the mixed methodology must be aligned.
- Development of a meta-infrastructure: A meta-inference is a synthesis of the results of multiple studies to draw higher-level generalizations; that is, it brings together the results of individual studies to identify their similarities and differences and draws broader conclusions from them.
- Assessing the quality of the meta-inference: Evaluating whether the developed meta-inference is valid and reliable. This requires the researcher to evaluate the quantitative and qualitative data collected and assess the validity of the results using various validation methods. This will ensure that the developed meta-inference is valid and reliable.
- Bridging the Qualitative–Quantitative Divide: Guidelines for Conducting Mixed Methods Research in Information Systems (Venkatesh 2013)