Identify the difference between a concept and a variable in a research work.
A variable can be categorised into 3 different ways (refer Figure 6.2):
Figure 6.2: The three types of variables
Now, let us describe the details of each type of variables mentioned above.
6.3.1 The Causal Relationship
In research or studies that are attempting to study a causal based relationship, four sets of variables may operate ( Figure 6.3):
(a) Change variables that are responsible for bringing about change in a phenomenon;
(b) Variables which affect the link between cause and effect variables;
(c) Outcome variables which results from the effects of a change variable; and (d) Connecting or linking variables, which in certain situations important to
complete the relationship between cause and effect.
It is important to note here that in the field of ICT, cause and effect based relationships are much focused on information technology research which dominates the user level or application layer.
Figure 6.3: Types of variables in causal relationship
(Adapted from Research Method: Ranjit Kumar, SAGE Publications, page 60) In research methodology, change variables are referred to as independent variables while outcome variables are known as dependent variables. On the other hand, the unmeasured variables affecting the cause-effect relationship are called extraneous variables and the variables that link a cause and effect linking are called intervening variables. The following table (Table 6.2) summarises the details about each variable mentioned earlier.
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Table 6.2 Types of Variable
Independent variable Is the cause that is responsible for bringing changes in a situation
Dependent variable The changes occur due to introduction of an independent variable
Extraneous variable Some factors involved in real-life situation may influence changes in dependent variable. These factors which are not measured in the research study could increase or decrease the magnitude of relationship between the independent and dependent variables.
Intervening variable It links the independent and dependent variables. In some cases, relationship between both variables cannot be established unless with the intervention of another variable.
To explain in detail about the functionalities of those variables, let us delve further into an example. LetÊs say you want to study about digital divide and information technology awareness among the rural community. You want to study the relationship between education and IT awareness. You assume that education is the key cause for IT awareness. Studies have shown that there are many factors affecting the relationship, such as the number of schools, the availability of infrastructure, the age categories of community, early IT education and literacy and broadband infrastructure. All these factors may affect the extent to which education is important for IT awareness. These variables may either increase or decrease the magnitude of the relationship. In this example, education opportunity is an independent variable, IT awareness is a dependent variable and all other variables that might affect this relationship, either positively or negatively, are extraneous variables. Figure 6.4 shows the variables and their relationships.
Figure 6.4: Independent, dependent and extraneous variables
6.3.2 The Design of the Study
In research methodology, a study that investigates association or causation may be a controlled/ contrived experiment, a quasi-experiment or an ex post facto or non experimental study. Typically, in a controlled based environment, an independent variable is introduced or manipulated by the researcher or some other service provider. There are two sets of variables in this type of situations.
(a) Active variables ă variables that can be changed and controlled; and
(b) Attribute variables ă variables that cannot be changed or controlled and refer to characteristics of the research study population. For example;
demographic features like age, gender, education, qualification and income.
LetÊs say a researcher wants to measure the relative effectiveness of three enterprise architectures i.e. Zachman Framework, Service-Oriented Architecture (SOA) and The Open Group Architecture Framework (TOGAF) on open source software development.
The structure and the contents of these enterprise architectures could vary.
Therefore, any architecture can be tested on any population group. The contents, structure and testability of an architecture on a population group may also differ from one researcher to another.
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The researcher does not have any control over characteristics of the open source software development methodologies such as tools, language and hardware.
These characteristics of the study population are called attribute variables.
However, a researcher does have the ability to change the enterprise architectures. The researcher can decide what constitutes the enterprise architecture and on which group of the software development group it should be tested (if randomisation is not used).
1. Describe the different types of variables.
2. Identify the differences between active and attribute variable in the design of study category.
SELF-CHECK 6.1
6.3.3 Unit of Measurement
There are two ways to categorise variables in viewpoint of unit of measurement.
The unit of measurement can be categorical (nominal, ordinal) or continous in nature (interval, ratio). The other way is whether it is qualitative (nominal, ordinal) or quantitative (interval, ratio). Therefore, the variables thus classified are referred to as categorical and continuous, and qualitative and quantitative (Figure 6.5).
Nominal, Ordinal Interval, Ratio Categorical Continuous Qualitative Quantitative
Figure 6.5: Categorical and continuous versus qualitative and quantitative
We will explore more about qualitative and quantitative methods in coming topics. For now, let us see what categorical and continuous variables means.
Categorical variables are measured on nominal or ordinal scales whereas continuous variables are measured on interval or ratio scales. For a beginner or new researcher, it is important to understand the ways a variable is measured as it will determine the type of analysis that can be performed.