Application of Relevant Theory and Research to Real Life Situations


A theory or hypothesis comprises meanings, interconnected ideas, and suggestions that explicate or foresee proceedings by identifying any associations that can be realized among variables. It provides a methodical strategy of understanding issues such as the proceedings, conduct, or any circumstance. Different hypotheses differ in scope based on the purpose for which they have been theoretically put in place to serve.

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Therefore, social science postulations are well comprehended as plans that hold in some contexts and not in others. This claim is contrary to the views of science, which have been confirmed to be universally true. Scholars have put in place numerous research theories. These theories are normally categorized as expressive, relational, or illustrative postulations. This paper seeks to explore the application of the descriptive research theory to real life situations including ethical, social, and cultural scenarios.

Application of the Descriptive Research Theory to Real Life Situations

Descriptive research gives an in-depth description of a crowd of citizens, society, a public setting, and state of affairs among other occurrences (Jansen et al., 2013). Descriptive research focuses on the environment, union, occupation, variations, or the affairs at hand that people encounter in their day-to-day life. It addresses realistic information to paint a precise image of a precedent or a recent happening.

For instance, descriptive research may scrutinize the condition of tutors in an elementary institution of learning. A popular example of descriptive research is a population survey, which presents all population particulars of a given population section. Chen, Chen, and Xiao (2013) reveal some descriptive research methods such as correlational studies, casual-comparative investigations, case studies, ethnography, document psychoanalysis, and investigative techniques. Correlational research depicts what is currently in place such as people’s occupations, day-to-day circumstances, social arrangement, and work procedures, hence qualifying as an evocative technique. It involves the process of getting data to find out if any association is evident together with the degree, to which an association occurs between two or more experimental parameters (Chen, Chen, and Xiao, 2013).

To determine this association, numerical information is used, with the coefficient of correlation revealing the degree of connection between the two parameters. This information gives an idea into the nature of the parameters, which helps to determine if the results of one variable are linked, or differ from those of the other variable. The researcher can hence use the correlations to make predictions about the variables. For example, to argue that some condition X is a sufficient cause of some outcome Y, one must be able to distinguish whole cases or partial members of situation X relative to those that do not have the specified condition X.

How Descriptive Research Theories guide or inform Practice

When analyzing a complex social phenomenon such as the population trend of a given country, it is crucial to note that the effect of any variable will not only depend on the circumstances within the variable, but more on the context within which the variable is situated. For instance, if a population is decreasing, interested stakeholders may use variables such as health, birth control, literacy, and child abuse amongst others to determine the cause of the population rise.

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Scholars therefore have to handle their matters as arrangements of features that are investigated as a unit or as general sets. Statistical scholars have a room to investigate areas in this method by using concentrated contact replicas to all of the aforementioned variables. However, these replicas may attract a challenge in terms of their analysis based on the evident relationship among the contact elements and the trend of several replicas to be accommodated by a similar data set.

Correlational research is based on the null hypothesis. For instance, in terms of learners’ performance, one might use the statement ‘there is an evident link between learners’ results on intellectual success in geography and their results on educational talent in arts’ as the null hypothesis. A good correlational research must address the problem under study. Therefore, a statement of the research problem is necessary in any research. In fact, the scholar has to carefully obtain the aspects to be analyzed based on a working supposition, prior study, inspection, and/or understanding. A logical deduction between the variables has to exist to enable the researcher to make sound interpretations of the findings of the study in an effort to derive a more consequential, applicable, and logical judgment.

The researcher also has to select a representative sample and tools that he or she needs to carry out his or her research. A statistically accepted sample size should be 30. This magnitude should be chosen using a suitable sampling technique. For instance, presume that the investigator wishes to find out the connection between a classroom atmosphere and educational triumph of learners. The tool for measuring class environment focuses only on the physical features of the classroom. Design and procedure of carrying out the research are other crucial factors that the researcher should have in mind.

The primary design of correlational study needs results that have been realized in more than one element from each component of the test. Once this requirement is fulfilled, the examiner will go ahead to analyze the correlation coefficient between the harmonized figures in the effort to illustrate the level and course of association between the elements under study. Descriptive research theory offers a room for the examiner to infer the findings of his or her study. In order to investigate the theorized relationships, a correlation coefficient is deduced in terms of its statistical importance. Some examples of questions that could be examined through descriptive or correlational research include:

  1. Is there any direct link between socio-economic condition of parents and their participation with the tuition?
  2. How does work contentment of a tutor connect with the degree of independence that is offered in the work?

Another type of descriptive research is the casual-comparative research. Casual-comparative research is a type of study where the researcher lacks express power over the autonomous variable since its appearance has already occurred or because it cannot be easily manipulated. In other words, it describes the circumstances that are already at hand. It endeavors to explore the reasons or causes of pre-existing distinctions within distinct variables. The examiner begins with an origin and proceeds to inspect its consequences on some other elements. For example, in a school scenery, the study dilemma could be examining the enduring outcome on the self-concept of learners who are assembled based on their intellectual aptitude in institutions of learning.

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Here, the researcher could hypothesize that such labeling of students could have a long-term effect of poor self-concept on students. Existing research elucidates that independent variables are non manipulable in the fields of sociology of both education and educational psychology due to ethical concerns especially when dealing with variables such as nervousness, aptitude, home surroundings, teachers’ traits, negative corroboration, and parity of opportunities. It is challenging to manage such elements in an investigational study. This makes casual-comparative research the best and suitable study design for investigating such educational themes and their weight on learners.

Issues involved in translating descriptive theories into practice

Scheck et al. (2013) identify three major setbacks involved in translating casual-comparative research theory into practice. These include “lack of appreciation for the value of participation and poor understanding about operations, costs, and the required workflow changes” (p. 29). Correlational study forms the foundation for the evident comprehensive enlightenment in the modern field of sociology. Several sociologists conclude causation using quantitative methods that rely much on the availability of bivariate associations.

Scheck et al. (2013, p. 30) confirm Caroll-Scott (2012) words, “Correlational analysis involves issues of organization, cost, and planning that may be of concern to hospitals, schools, the society, and any provider participants.” The problem associated with correlational study is that it stands out as an insufficient style of causal evaluation. Correlational analysis does not clearly identify the mechanisms that produce the observed associations. Hence, sociologists cannot make sense of empirical correlations in a correlational study. Correlational analysis is rested on well-built suppositions concerning elemental homogeneity or linear causation, which does not apply in a comparative study.

Extant research postulates that correlational methods assume causal heterogeneity (Caroll-Scott, 2012). Thus, they allow a room for analysis of the necessary and sufficient conditions. The major issue that is realized in this research methodology is that it presents a pathetic basis for put together various quantitative study results into one rational structure that can protract information gathering. This situation results in the lack of theoretical integration, which may lead to disintegration of information in social science.

Correlational analysis assumes that causation exists only to the extent that empirical irregularities exist. According to correlational analysts, this claim implies that causal forces do not exist where empirical irregularities are witnessed. Based on the significant study that uses several results of concern, this deduction can be disappointing. Lack of proper connection between independent and dependent variables in a causal relationship has been identified as another setback of correlational analysis. The ‘Black Box’ that establishes systematic co-variation between variables or events has not been explored.

How Casual-comparative Research Theory is applied

In spite of these weaknesses, cause-oriented research theory has a crucial role to play in social settings (Caroll-Scott, 2012). Upon moving from the rear side of the results to contributory systems, it is obvious that a closer attention to the real examples of the issue under study is indispensable for one to realize any underlying systems. A researcher who does not possess the appropriate information of the incidence of concern has low chances of coming up with sound judgment concerning how to handle the systems that trigger the incidence. Correlational analysis creates a platform for exploring all causal relationships with regard to the phenomena of interest.

Cause-oriented research theory also plays a substantive role in providing the researcher with significant theoretical knowledge of the particular cases. Most of the weaknesses of the conformist study methods arise because of the examiners’ insufficient information of the cases involved in an investigation (Jansen et al., 2013).

The use of empirical data is vital in a cause-oriented research analysis. The dimensions and characteristics of phenomena are measured in their natural state. Interviews and surveys are frequently used. Data is usually gathered by nonparticipant observation or self-report measures. Instruments usually include the fixed-choice observation checklists, standardized questionnaires, or rating scales.

This strategy is only possible because the dimensions or characteristics of the phenomena under study are believed to be known. These instruments yield a quantitative and qualitative data. Owing to the fact that numbers must be attached to raw data so that correlation coefficients can be calculated, qualitative data must be classified, with numbers being assigned to the categories (Cottler et al., 2013). Statistical analyses of the cause-oriented research data employ various nonparametric measures of association, which have helped many researchers to draw valid conclusions from their studies.


It is quite evident that correlational study does not spell out cause-and-effect relations between variables under study. It only indicates the connected variations in the scores on the variables. For instance, a null hypothesis, which depicts a link between learners’ results on educational success in geography and their results on intellectual success in the field of arts, does not imply that one of these variables is the source and the other is the outcome. In fact, another aspect, which is the learners’ cleverness, might be the reason for the learners’ educational attainment in both geography and arts.

However, as revealed in the paper, correlational studies fail to recognize the methods that produce the observed associations, hence clarifying the reasons why empirical relations exist. Despite the many setbacks of the cause-oriented research, it also comes in handy in practice. It provides a researcher with an opportunity to fully understand the phenomena under research. Extant research has shown that limitations of many research methods are because of the researcher lacking proper knowledge of the cases being analyzed. Cause-oriented research explores the causal relationships between variables under study, hence providing the researcher with a significant theoretical knowledge about the subject.

Reference List

Caroll-Scott, A. (2012). Results from the Data & Democracy Initiative to Enhance Community-Based Organization Data and Research Capacity. American Journal of Public Health, 102(7), 1384-91.

Chen, X., Chen, Y., & Xiao, P. (2013). The Impact of Sampling and Network Topology on the Estimation of Social Intercorrelations. Journal of Marketing Research (JMR), 50(1), 95-110.

Cottler, L., McCloskey, D., Aguilar-Gaxiola, S., Bennett, N., Strelnick, H., Dwyer-White, M., Collyar, D., Ajinkya, S., Seifer, S., O’Leary, C., Striley, C., Evanoff , B. (2013). Community Needs, Concerns, and Perceptions About Health Research: Findings From the Clinical and Translational Science Award Sentinel Network. American Journal of Public Health, 103(9), 1685-92.

Jansen, J., Hale, L., Mirfin-Veitch, B., & Harland, T. (2013). Building the Research Capacity of Clinical Physical Therapists using a Participatory Action Research Approach. Physical Therapy, 93(7), 923-34.

Scheck, A., Reiter, K., Weiner, B., Minasian, L., & Song, P. (2013). Challenges and Facilitators of Community Clinical Oncology Program Participation: A Qualitative Study. Journal of Healthcare Management, 58(1), 29-44.

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