Introduction
Researching a topic can seem like a complex and strenuous study that takes much effort with minimal returns. However, there are scientific algorithms that streamline this process and make it less complicated. These include but are not limited to separating research into stages, understanding the unit analysis, comparing and contracting unit variables, and construing their connections. Learning scientifically proven methods is essential in getting acquainted with new concepts and ideas.
Stages of Research
Any research is conducted by following the same general pattern consisting of five stages. Stage and Manning (2015) break down the entire process into five steps: identification, collection, systematization, calculations, and recommendations. The first stage is identifying the problem that is to be researched. It is followed by data collection, which manifests itself in gathering all relevant information on the topic. This step is crucial for analyzing statistics since the quantity of information directly influences the accuracy of conclusions. The third stage is analytical, during which the gathered data is systematized and structured. After data is reorganized appropriately, researchers can consider future decisions or make the necessary calculations. The final stage implies drawing conclusions, providing recommendations or advice.
Unit Analysis
Unit analysis refers to the studying of various entities apart from each other. Strang (2015) explains the unit of analysis itself as “the factor, variable, tacit phenomena or plural combination thereof, which is at the focus of the study” (p. 369). It implies that anything can be a unit since the object of study determines its entities. For instance, a numerical unit of analysis would be the number of divorces in a particular year within a divorce rate study. A qualitative example would include a work of art that is evaluated based on the painter’s criteria, or the concepts of an art movement.
Variables
Variables are characteristics of units of analysis that allow researchers to distinguish between them. As Trochim (2012) suggested, if variables are mutually exclusive, their units of analysis possess only one characteristic that can exist at the same time, like the age of a person. When variables are exhaustive, a unit falls into one of the strict, predetermined categories, for example, a person is either a child, a teenager, or an adult. For a more diverse examination, researchers differentiate between discrete and continuous variables. Discrete variables do not have an intermediate state – the light in a room is either on or off. Continuous variables are not limited to a concrete value – the temperature in a room can take any degree.
Levels of Measurement
To interpret data, researchers need to analyze the relationship between the variable values. Out of four levels of measurement, nominal is the simplest (Qurotul et al., 2018). It only allows naming the category (male or female). The ordinal scale can arrange variables in a particular order (student grades). The interval measurement can classify the ordered variables and calculate their difference (IQ scale). The ratio level adds an absolute zero that implies that variables are equivalently distanced (the number of sales about the previous one).
Conclusion
Ascertaining unknown theories and ideas is incomprehensible without the scientific approach. Structuring the research into stages helps focus the necessary efforts. Understanding variables allows researchers to analyze units and as a whole as well as their values. Measurement scales are employed in categorizing, building hierarchies, and observing dynamics. Ultimately, every research tool enables a better way of relaying the complex nature of science clearly and concisely.
References
Stage, F. K. & Manning, K. (2015). Research in the college context: Approaches and methods. (2th ed.). Routledge.
Strang, D.S. (2015). Risk management research design ideologies, strategies, methods, and techniques. In Austin DeMarco (Ed.), Research methods: Concepts, methodologies, tools, and applications (pp. 362-381). IGI Global.
Trochim, W.M.K. (2020). Variables. Research Methods Knowledge Base. Web.
Qurotul, A., Zuliana, S. R. & Santoso, N. P. L. (2018). Management measurement scale as a reference to determine interval in a variable. Aptisi Transactions on Management, 2(1), 45-54. Web.