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Conceptualization, Operationalization, and Measurement
Research begins with a “problem” or topic. Thinking about the problem results in identifying concepts that capture the phenomenon being studied. Concepts, or CONSTRUCTS, are ideas that represent the phenomenon. Conceptualization is the process whereby these concepts are given theoretical meaning. The process typically involves defining the concepts abstractly in theoretical terms.
Describing social phenomena and testing hypotheses require that concept(s) be operationalized. Operationalization moves the researcher from the abstract level to the empirical level, where variables rather than concepts are the focus. It refers to the operations or procedures needed to measure the concept(s). Measurement is the process by which numerals (or some other labels) are attached to levels or characteristics of the variables. The actual research then involves empirically studying the variables to make statements (descriptive, relational, or causal) about the concepts.
Conceptualization
Although research may begin with only a few and sometimes only loosely defined concepts, the early stages usually involve defining concepts and specifying how these concepts are related. In exploratory research, a goal is often to better define a concept or identify additional important concepts and possible relationships.
Because many concepts in social science are represented by words used in everyday conversation, it is essential that the concepts be defined. For example, the concepts norms, inequality, poverty, justice, and legitimacy are a part of our everyday experiences and thus carry various meanings for different people. The definitions provided by the researchers are usually referred to as nominal definitions (i.e., concepts defined using other words). No claim is made that these definitions represent what these concepts “really” are. They are definitions whose purpose it is to communicate to others what the concept means when the word is used. A goal in social science is to standardize definitions of the key concepts. Arguably, the most fruitful research programs in social science—those that produce the most knowledge—are those in which the key concepts are agreed on and defined the same way by all.
Concepts vary in their degree of abstractness. As an example, human capital is a very abstract concept, whereas education (often used as an indicator of human capital) is less abstract. Education can vary in quality and quantity, however, and thus is more abstract than years of formal schooling. Social science theories that are more abstract are usually viewed as being the most useful for advancing knowledge. However, as concepts become more abstract, reaching agreement on appropriate measurement strategies becomes more difficult.
Operationalization
Operationalization of concepts involves moving from the abstract to the empirical level. Social science researchers do not use this term as much as in the past, primarily because of the negative connotation associated with its use in certain contexts. One such use has been in the study of human intelligence. Because consensus on the meaning of this concept has been hard to come by, some researchers simply argued that intelligence is what intelligence tests measure. Thus, the concept is defined by the operations used to measure it. This so-called “raw empiricism” has drawn considerable criticism, and as a consequence, few researchers define their concepts by how they are operationalized. Instead, nominal definitions are used as described above, and measurement of the concepts is viewed as a distinct and different activity. Researchers realize that measures do not perfectly capture concepts, although, as described below, the goal is to obtain measures that validly and reliably capture the concepts.
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- Analysis of Variance
- Association and Correlation
- Association
- Association Model
- Asymmetric Measures
- Biserial Correlation
- Canonical Correlation Analysis
- Correlation
- Correspondence Analysis
- Intraclass Correlation
- Multiple Correlation
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- Time-Series Data (Analysis/Design)
- Trend Analysis
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