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Theoretical models
Critical theory
Critical theory is an essential perspective used to analyze science, politics, social action, and human activities. According to critical theory, it is necessary to understand the target group’s real experiences (McKie & Doan, 2018). The theory analyses society’s symbols and acts for a clear understanding of how the communities are oppressed. Also, critical theory takes into consideration the social conditions to help discover various structures. It focuses on the adage that knowledge is power in that the only way to solve people’s problems is to understand what is affecting them. The theory, in short, incorporates action and approach and serves to bring change.
Grounded theory
Grounded theory is a theory that is connected to qualitative research and quantitative research methodologies. It gives step by step guidelines for collecting, synthesizing, evaluating, and hypothesizing qualitative data needed for theory construction. The theory was developed by Anselm Strauss, Glaser, and Barney. The First step or analyzing data according to Grounded Theory is coding (open, axial, and selective coding) (Bryant, 2017). The theory also involves both inductive and abductive theory generation processes. The theory is mainly used for theory development to explain the significant objective of the target population and offers how the objective is processed. Since the Grounded theory is a general research method, it applies to all disciplines.
Fundamentals of research methodologies
Quantitative research methodologies
Quantitative data mainly deals with quantities such as numbers and statistical figures. The type of data can be used to draw tables and graphs that produce averages and patterns. By testing causal links between variables, quantitative research data is applied to reject and confirm the hypothesis (Goertzen, 2017). The major aim of using quantitative research is to develop general behavior laws and occurrences in varied contexts. Questionnaires and controlled observations can give quantitative information as well as experimental methods. To turn quantitative data into practical information that can help in making decisions, statistics is used. The problem with quantitative research is that it does not occur in normal settings and, at the same time, gives no room for participants to given meaning to their choices. The technique also requires high expertise for the research to be a success and accurate. It is also prone to bias as it is likely to miss accurate observation as it mainly focuses on the hypothesis and not the generation of a hypothesis. The good thing about quantitative data is that it is rational and objective. One also enjoys various features such as rapid analysis, can be replicated, and hypotheses are testable (Queirós, Faria & Almeida, 2017).
Qualitative research, on the other hand, collects and processes data that is not in numerical form. Qualitative research data is obtained from interviews, diary accounts, and surveys. The analysis of qualitative research is done through thematic analysis and grounded theory (Mohajan, 2018). Theories and hypotheses are usually generated from qualitative research. Qualitative research has disadvantages such as not being adequately reliable and valid as it is subjective and drawn from a small pool. The technique also requires too much time to collect and analyze data before coming up with conclusions. However, the method is practical as the researcher obtains information from the field directly. It also helps to understand the causes, relationships, dynamic processes, and impacts (Korstjens & Moser, 2018). The technique is also beneficial as it can be converted to qualitative reports to gain more insights.
Sources of Information and Ethical issues related to data collection and handling
Data sources can be secondary or primary. Primary data sources collect information on a first-hand basis, such as interviews, surveys, questionnaires, case studies, and artwork and statistical data (Morton, Bunker, Shaw & Terlecki, 2017). On the other hand, secondary sources provide research commentaries and second-hand information. Secondary data sources include book reviews, encyclopedias, articles, journals, and academic books. Secondary sources’ main aim is to synthesize, interpret, and describe primary sources (Johnston, 2017).
It is always a good gesture to let the people involved in your data collection if it is interactive to know the researcher in depth. It is also ethically correct to state the main reason why the data is collected. Also, before collecting data, one should seek permission from the participants. The participants should also be given the freedom to withdraw from the research at any point in their study. The study should also never harm the participants, and if any harm is likely to occur, it should be stated before they get involved in the research (Markham, Tiidenberg & Herman, 2018). Bias should also be avoided at all costs by not avoiding misconceptions or pre-convinced ideas from getting on the way during research. It is also essential to keep the participants’ confidentiality and always have a third party sign before gaining access to the collected data. If the data collection requires confidentiality to be broken, it should be stated to the participants. Also, collecting data from those we can easily get information from is unethical. It is also vital to respect participants, especially in terms of time and if necessary, compensate them. They are sharing data with the participants after the analysis is also a good gesture during data collection. In secondary data collection, it is mandatory to acknowledge data sources to avoid plagiarism or get in trouble with copyright crimes (Dooly, Moore & Vallejo, 2017).
Reference
Bryant, A. (2017). Grounded theory and grounded theorizing: Pragmatism in research practice. Oxford University Press.
Dooly, M., Moore, E., & Vallejo, C. (2017). Research Ethics. Research-publishing. Net.
Goertzen, M. J. (2017). Introduction to quantitative research and data. Library Technology Reports, 53(4), 12-18.
Johnston, M. P. (2017). Secondary data analysis: A method of which the time has come. Qualitative and quantitative methods in libraries, 3(3), 619-626.
Markham, A. N., Tiidenberg, K., & Herman, A. (2018). Ethics as methods: doing ethics in the era of big data research—introduction. Social Media+ Society, 4(3), 2056305118784502.
McKie, D., & Doan, M. A. (2018). Critical theory. The International Encyclopedia of Strategic Communication, 1-14.
Mohajan, H. K. (2018). Qualitative research methodology in social sciences and related subjects. Journal of Economic Development, Environment, and People, 7(1), 23-48.
Morton, R., Bunker, R., Shaw, M., & Terlecki, P. (2017). U.S. Patent No. 9,633,076. Washington, DC: U.S. Patent and Trademark Office.
Queirós, A., Faria, D., & Almeida, F. (2017). Strengths and limitations of qualitative and quantitative research methods. European Journal of Education Studies.