Free «Operational Definition of Variables, Validity, Reliability and Their Measurement» Essay
In the nursing field, variables are significant being the ‘basic units’ of the research process. The Office of Research Integrity (ORI) (n.d.) has issued a tutorial, which ascertains that studying and interpreting such leads to a better conclusion in the research project. To better understand how variables work, they are divided into two broad categories, dependent and independent ones. The first type changes in relation to the second. They consistently vary over time (Flannelly, Flannelly, & Jankowski, 2014). Most often, x denotes independent variables on the horizontal axis on a graph, while y denotes dependent ones on the vertical axis (“What are Independent and Dependent Variables,” 2016). While variables just focus on a range of values, their operational definition represents a specific way of measuring the latter. Reliability and validity characterize these variables, which are later illustrated by comprehensive measurements.
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Operationalizing variables is the method of rigorously describing them as measurable factors (Naik, Gantasala, & Prabhakar, 2010). A better explanation of the operationalization of definitions stems from understanding conceptual variables. The latter are ideas of what to be measured (Jabareen, 2009). For instance, in the research process that intends to use weight as a measurement, the one is a conceptual variable. Then, there is a need to explain what weight is and how it is measured. It is then defined in specific measurable terms like kilograms, grams and so forth. Explaining weight in such a way is what is referred to as operationalizing a variable. Furthermore, various studies measure conceptual values differently. For instance, when measuring the maximum weight a human being can have, it may be 50 kilograms in children and 150 kilograms in adults. The operational definition of this dependent variable is vital in designing a study (Australia Bureau of Statistics, 2013). This process requires a reliable measurement tool.
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The operational definition of reliability is that it is the quality of being able to deliver compatible results in different tests (Noble & Smith, 2015). It is used quite often in quantitative research. Tests with reliable measurements are also characterized by test-retest reliability because they are repeatedly conducted to verify it. Furthermore, researchers use results of such investigations to determine the ‘accuracy’ of the research process and the appropriateness of conclusions. Since qualitative studies do not involve the use of reliable tests, they are often criticized because of poor justification. Thus, any research process that requires proper rationalization and a valid conclusion needs to use reliable methods of measurement. Other techniques of testing whether results are reliable apart from test-retest reliability include parallel-form, inter-rater, and internal consistency types (Phelan & Wren, 2006). Phelan and Wren (2006) define parallel-form reliability as a situation where the initial test may affect the second one by administering a different version of the same assessment tool. The results of studies are compared to analyze their consistency. They further help to define inter-rater reliability as the extent to which various assessors agree in their decisions. Here, reliability is tested by analyzing their judgments. Finally, internal consistency reliability refers to the way in which different tests produce the same results (McLeod, 2016). All the tools used to measure reliability need to be valid.
Validity is the quality of a tool being able to measure what is supposed (Noble & Smith, 2015). By doing so, it ensures the accuracy of operational definitions. However, the validity of a measurement tool is not necessarily universal. For instance, a stethoscope can measure the heart rate, but cannot determine the severity of the heart failure. Similarly, a pulse oximeter can only measure the level of oxygen saturation in the body, but cannot find out the severity of anemia or the hemoglobin level in the blood. Considerably, measurement tools are described by means of how suitable they are in measuring a particular variable, disregarding whether they are valid or not. Therefore, some techniques may have high validity in measuring one variable, but low in determining another. Reliability and validity are closely connected, since their combination is very useful in measuring variables.
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The measurement of variables requires proper planning and the establishment of the reliability and validity of research tools. It is conducted on two main levels that specify the accuracy of measurement (Wright & Lake, n.d.). They are categorical (nominal and ordinal) and metric (interval and ratio). Nominal measurement, also known as categorical one, involves two measurements that do not have any natural order. Data generated from the use of this tool are called discrete because there exists no connection between the categories. A good example is the variable of gender (male and female). Similarly, in ordinal measurements, there is no relation between the two variables. However, the latter can be ordered in such a manner that they become meaningful. For example, the distance between 10 and 30 is equal to the distance between 40 and 60. As opposed to the categorical system, the metric one involves the connection between values. This relationship is like in the case of centimeters, meters, and kilometers. The ordering of these variables happens in a logical sequence (Wright & Lake, n.d.).
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In conclusion, in case of the presence of all these factors, namely, the operational definition of variables, reliability, validity and measurement, a research project has better results. When variables are well analyzed, and methods of measurement are proven to be reliable and valid, the appropriateness of the study is inevitable. The proper judgment of research results from rational and clear methods of measurement.