Conceptualization refers to the process where the researcher specify what they imply in their use of particular terms. Conceptualization brings about an agreed upon meaning of a concept for research purposes. Bhattacherjee (2012) describes it as a mental process in which constructs that are unclear and imprecise are defined in precise and concrete terms. Many a time, researchers conceptualize a single concept in slightly different ways. Operationalization, on the other hand, refers to the coming up with specific research procedures that will bring about empirical observations that are representative of the concepts. According to Bhattacherjee (2012), it is "the process of developing indicators or items for measuring these constructs." Measurement is an important issue under operationalization. It involves deciding the values that an indicator can take. There are four main levels of measurement namely nominal, ordinal, interval and ratio.
The two concepts I am interested in are health and employment. Health stands out an important issue in the modern day society given the continued emergence of life-threatening viruses and bacteria and the ever increasing cost of healthcare services. Employment is also a major issue since more people are graduating from colleges and universities, and are looking for jobs that will guarantee them a favorable income and work-life balance.
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Starting with employment, I will conceptualize it as having a paid job. This implies that anyone who has a job that pays at the end of the day or the month will be deemed employed. To operationalize the unobservable theoretical construct that is employment, I will use an indicator that asks participants the question: Are you currently working for pay in any job? To measure this indicator, I will utilize the nominal scale. According to Bhattacherjee (2012), nominal scales are used to measure categorical data. They are utilized for indicators that have mutually exclusive attributes. Nominal scales just offer labels or names for the different attribute values. In the current cases, the nominal scale will include two answers namely Yes and No. Respondents who tick Yes will be deemed employed and those who tick no will be deemed unemployed. It is impossible to define the mean and median when using a nominal scale, but the mode can be defined. Frequency distribution and chi-square are some of the statistical techniques that can be used with nominal scales.
For health, I will conceptualize it as the absence of illness or disease. To operationalize the unobservable theoretical construct that is healthy, I will use an indicator that asks participants the question: Taking all things together how would you describe your health status? To measure this indicator, I will utilize the ordinal scale. Bhattacherjee (2012) points out that ordinal scales measure ranked-ordered data. However, this measure does not facilitate the assessment of the actual or relative values of the attributes. For the current case, the ordinal scale will include four answers namely Very healthy, healthy, unhealthy and very unhealthy. It is quite evident that with this scale, it will be impossible to quantify the health status of respondents. With an ordinal scale, it is impossible to interpret the means, but one can get other central tendency measures namely mode and median. A researcher may incorporate non-parametric analysis and percentiles as statistical techniques.
The following is the hypothesis that I will seek to test for the first concept of "employment."
H1: A majority of people residing in the district are employed
To test this hypothesis, I will run a frequency distribution once I have collected and entered all the data in statistical software. The frequency distribution will highlight the percentage of respondents who indicated that they are working in a paying job and those who indicated that they are in no paying job. A higher percentage of "Yes" responses will mean that the hypothesis is correct.
For the second concept of "health," I will test the following hypothesis
H1: Most residents in the district are healthy
I will also run a frequency distribution to test this hypothesis. A higher percentage of "healthy" responses will mean that the hypothesis is correct.
References
Bhattacherjee, A (2012). Social Science Research: Principles, Methods, and Practices . Retrieved from http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1002&context=oa_textbooks
