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Mixed Method Research - Qualitative Comparative Analysis

©2006 Hausarbeit (Hauptseminar) 15 Seiten


Traditionally most social researchers either employ purely qualitative or quantitative methods, even though a mixed method strategy may promise better results. The present paper introduces Qualitative Comparative Analysis (QCA) as a mixed method alternative for data analysis. It may be of particular value when dealing with small-n case studies, which typically do not permit profound statistical testing. QCA enables researchers to filter those variables or combinations of variables that empirically result in (and possibly explain) a certain outcome. As such, the method can also be used to analyze the impact of social networks on companies’ innovation performance and promises valuable new insights in the field.



1. Introduction

2. Methods of social research
2.1 Quantitative method
2.2 Qualitative method
2.3 Mixed method

3. Qualitative Comparative Analysis (QCA)
3.1 Procedure and logic of QCA
3.2 Strengths and weaknesses of QCA

4. Applying QCA to innovation networks studies
4.1 Research setting
4.2 QCA and innovation networks

5. Conclusion

6. Executive summary

7. Literature

1 Introduction

Following common guidelines most researchers either apply a qualitative or a quantitative methodology. Many pragmatic scholars have criticized this dichotomy as artificial and counterproductive for advancing scientific knowledge. Johnson & Onwuegbuzie (2004) found a vast number of major errors and myths in educational research textbooks concerning the proper use of qualitative and quantitative techniques and suggest to overcome the strict separation. Consequently, they promote the use of mixed methods to combine the strengths and overcome the limitations of traditional approaches.

The purpose of this paper is to introduce Qualitative Comparative Research as an alternative mixed method for data analysis. Following the author’s interest in the complex relationship between social factors and company’s innovation performance, chapter four takes a look at the study of innovation networks and explores how Qualitative C omparative Analysis (QCA) may lead to new insights in this specific field.

2 Methods of social research

Research design can be broadly categorized into three distinct groups: a) quantitative designs, which focus on testing theory and hypothesis, b) qualitative designs which focus on developing theory and generating knowledge, and c) mixed designs which tend to combine or mix the two designs (Creswell 2003). The quantitative approach relies on a large number of cases, which are analyzed with statistical tools such as SPSS or Excel. Qualitative designs on the other hand frequently use in-depth analysis of a small number of cases. In between these two extremes one may apply a mixed research design.

In epistemological terms quantitative research is often associated with formalism (e.g. positivism, logical positivism and logical empiricism), while qualitative research tends to be rooted in constructivism or dialecticism.

The choice of research method depends on the type of study, the research goal and its corresponding setting. The design determines the strategy, the data collection process as well as the appropriate tools for data analysis.

2.1 Quantitative method

When the researcher’s intention is to generalise the findings across different cases and situations, they often make use of quantitative methods. The main goal of these scholars is to test hypothesis and propositions derived from theory. According to Creswell (1994: 2) the quantitative study may be defined as:

“…an enquiry into a social or human problem based on testing a theory composed of variables, measured with numbers, in order to determine whether the predictive generalizations of the theory hold true.”

In order to achieve generalizability, quantitative studies need to target many cases. Hence, they usually do not get closer to single cases, which would require an in-depth investigation. The focus rather lies on the overall picture that is the discernible relationship across many cases. The quantitative method attempts to isolate the causes of a phenomenon by examining correlations, in order to find out how each factor adds to a certain outcome. However, as Ragin (1987) notes this correlation is based on the assumption that the impact of the various factors are additive and deterministic in nature; which according to him is not the case. Hence, quantitative statistical analysis is limited by a simplified conception of cause, relying on mean values based on a large data set.

2.2 Qualitative method

Qualitative research is widely used when a researcher is interested in gaining in-depth knowledge of only a few specific cases in order to understand how different factors piece together and explain a specific phenomenon or outcome. It involves an interpretative, naturalistic approach. (Mertens, 1998)

Qualitative research is characterized by the inductive logic, which allows comprehending a situation without imposing pre-existing expectations on the subject. Creswell (1994: 1) defines qualitative research as:

“…an inquiry process of understanding a social or human problem, based on building a complex, holistic picture, formed with words reporting detailed views of informants, and conducted in a natural setting.”

The objective of qualitative research is not to establish relationships between mean valued variables but to elaborate certain patterns of unaltered empirical observations that lead to a certain outcome. These studies are usually strongly embedded in a specific and complex social setting. Due to the difficulty to sustain this complexity when the number of cases increases, which raises the question of comparability. The findings based on a single case or very few cases strongly depend on the specific situation. The lack of control for external factors (e.g. no random sampling) prevents valid claims of generalizable truths.

2.3 Mixed method

Mixed methods studies are those that combine the qualitative and quantitative approaches into the research methodology of a single study or multiphased study” (Tashakkori & Teddlie, 1998: 17-18). These studies use an interactive (systemic) approach to take advantage of each single method to get more valid answers to the underlying research questions (Maxwell & Loomis, 2003). Advocates of this strategy claim that all methods have limitations and that a combination of quantitative and qualitative methods may help neutralise biases inherent in any single method. Following Lacey (2001), most research is either based on case studies (N < 6) or on generalizable quantitative research supported by a variety of statistical tools, because researchers lack appropriate methods for handling multiple case studies (N=10-50). Mixed methodology may correct this problem. It is particularly suitable when researchers are interested in both, developing a detailed view of meaning of a phenomenon (in-depth qualitative analysis of a limited number of cases), and generalising the findings (quantitative methods).

Researchers that incorporate mixed methods tend to dismiss traditional epistemological philosophical paradigms such as the currently predominant positivistic view. Instead they argue for a pragmatic approach, allowing those methods to be applied that supposedly promise the highest epistemic returns.



ISBN (eBook)
ISBN (Buch)
502 KB
Institution / Hochschule
Wirtschaftsuniversität Wien – Europainstitut
2010 (Mai)
Sehr Gut
qualitative comparative method mixed method QCA logic ragin

Titel: Mixed Method Research - Qualitative Comparative Analysis