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Statistical Generalization in Research

Discuss about the Professional Research and Analysis for Statistical Generalizability.
 

In research, generalizability refers to increasing research results, outcomes, and conclusions which are based on a study of people, settings, and institutions. There are mainly two types of strategies are used in research such as statistical and theoretical generalization. Statistical is also called quantitative generalization in research. According to Yin, statistical generalization is defined as a strategy in research which is occurring when interference is made by population or individuals on the basis of collected data. It includes results, data, and statistics of any research (Barnham, 2015). A quantitative generalization in research is considered as a critical evaluation of data and provides quality of any research. It begins by measuring the population which can help to generalize the results of any research. The most common strategy to identify sample data of any research is the use of probability, which provides a data of information on every person (Hoy, and Adams, 2015). Through statistical generalization, a goal, objective and aim of any research can be analyzed and achieved. Random sampling is a process which is used in statistical generalization to identify the outcomes, and results of any research. Generalizability is a complex issue is researches which are considered with high-quality evidence. The quantitative research is defined as the systematic investigation of research, observation and also provides statistical information on any research. The main objective of the statistical research is to develop quantitative data or information, mathematical models, and statistical graphs regarding any research topic. It is a process which is used to provide a fundamental connection between observation, data, and mathematical models. Quantitative information is utilized to find any issue as far as numeric material which can be transformed into statistics information. It is used to find evaluation states of mind, results, and information from a sample populace (LoPilato, Carter, and Wang, 2015). It can be linked as a number; examples of statistical generalizations are a number of hours of study, and scores archive in tests. It includes different kinds of reviews, for example, portable, on the web, paper overviews, telephonic meeting, and precise perceptions.

Examples: to identify the age of graduate students in an MBA program, investigate data or results of any organization, annual income drawn by a blue shield, and grade distribution of students. 

Theoretical generalization in research projects uses two types of methods such as data collection, and data analysis. In qualitative analysis, the problem of theoretical generalization is discussed under the fundamental concept of experimental information’s. Theoretical generalization plays an important role in qualitative research and it is a form of argument generalization which is used in research to analysis data collation. Theoretical generalization is also called qualitative research which is consisting of theoretical information about any research (Isaacs, 2014). It is also used in case studies in which information of previously theory is used. Theoretical research is a kind of essential research which is used to realize feelings, hidden intention, and inspirations. It provides a platform to produce thoughts for quantitative research. Theoretical generalization can shift using unstructured procedures, and it can be utilized to clarify thought and opinions in an exploration (Leung, 2015). There are numerous cases of theoretical research, for example, gather dialogs, Sight, contact, interviews, and hearing. A theoretical generalization consists of theory, information’s, definitions, and concepts related to research. It is a framework which provides a platform to understand the theory, and the key concept of any research idea which is related to your research. Theoretical generalization plays an significant role in research because it provides a critical evolution of any research (Smith, 2018). There are a few steps that are used in theoretical generalization such as identify the title of thesis, literature review, list of variables, review of social theories, discuss the assumption of theory. Theoretical generalization includes knowledge, theory, information, and approaches for a specific research topic. This generalization includes three types of strategies such as empirical, analytical, and case studies. However, all these strategies are used in different types of research. A qualitative generalization is a type of research approach which is used to understand the theory, the key concept of research topics. It focuses on case studies, information’s, and theories related to research ideas, and goals. There are main three focus areas of theoretical generalization in research such as people, societies, and cultures (Morse, 2015). This research strategy provides common theoretical information and also provides information on your research topic. 

Theoretical Generalization in Research

Examples: interviews, ethnographic research, case studies, group discussions, focus group, and data analysis. 

Statistical research identifies a large number of individuals by providing data, mathematical expiration, and numeric answers. Theoretical research identifies a small number of individuals, and it provides much information, and theories related to research topics. Theoretical research is defined as a physical research which helps to understand information, case studies, and theories (Choy, 2014). The most common process is used in theoretical generalization is that face to face communication or interviews. But statistical research helps to identify data, mathematical expiration, and charts which are generally used in research. This generalization research includes behaviour, numeric data, and graphs that provide information about research ideas (Hussein, 2015). Theoretical generalization is represented by theories and case studies while statistical generalization is represented by graphs, charts, and data. Theoretical research is soft in nature while statistical research is hard in nature. Statistical research requires a lot of efforts as compare to theoretical research.

Theoretical generalization

Statistical generalization

It is a process which is used to develop theoretical information about any research idea

It is a process which is used to provide quantitative data, numeric values, and the mathematical expiration of the research topic.

Soft in nature

Hard in nature

It uses a subjective approach

It uses an objective approach

It is an exploratory type of research

It is a conclusive type if research

It requires purposive sampling

It requires random sampling

It provides verbal data

It provides measurable data

Words, objects are elements of analysis

Numerical data are elements of analysis

There are many characteristics of scientific research such as rigor, testability, confidence, purposiveness, objectivity, parsimony, and generalizability. Parsimony is a type scientific research which is used to identify simple theories of any research idea. It is a fundamental concept of science, and economics which is used in business research. According to law of parsimony, a theory or information should provide explanation or concept about any research topic (Imus, and Ryan, 2017). It provides a platform to identify a simplest explanation about research in business. It is a universal process which is used in research to identify a simple theories and explanation of research ideas. Parsimony is not a statement about evaluation or theories and it is a fundamental critical analysis which can explain in different way. Parsimony is defined as a simplicity which is used to explain the phenomenon of any study and producing solution of any problem in research (Rekker, 2016). This research strategy always preferred to critical research outlines which consider as many unmanageable factors. Research should be directed in a parsimony that is straightforward and efficient way. Directness in clarifying the issues and summing up answers for the issues it wanted to a mind-boggling research system. Economy in look into models can be accomplished by process for considering less number of factors encouraging more prominent difference as opposed to considering more number of factors prompting less change. Clear understanding with respect to the issue and the components affecting a similar will prompt stinginess in inquiring about exercises (Stango, Young, and Zinman, 2017). According to research parsimony is a process which is used to understand any problem in research and identify important factors that affect it. Parsimony consists of theoretical model and literature review of any research topic. It requires number of critical evolution events for example: amino acid replacements, and nucleotide substitutions. An advanced guideline of stinginess might be expressed as takes after: Where we have no motivation to do generally and where two hypotheses represent similar certainties, we ought to lean toward the one which is briefer, which influences presumptions with which we to can undoubtedly administer, which alludes to observables, and which has the best conceivable generality Psychologists frequently damage this rule, especially in ascribing complex conduct to intellectual procedures. There are many benefits of this research such as simple, logical, can be used for both molecular and non-molecular data, and it can be sued for rate analysis (Braun, 2015). The economics of any research model can be improved by reducing number of variables which reduce efficiency of any organization (Zhang, Cole, and Chancellor, 2015). An excellent theoretical model can be realized by face to face communication, interviews and literature review. Parsimony in research consists of two main factors that are simplicity and economic. Simplicity is preferred to a critical research outline in terms of problems, situations, and generalizing clarifications for the difficulties.

Comparison of Statistical and Theoretical Generalization

Examples: if at least 2 to 3 variables in any situation are recognized, that would increase the company commitment of workers by almost 45%, that would be more valuable to employees and manager if it were recommended that manager should change almost 10 various variables to improve company commitment by 46%. 

Rigor is defined as a way to establish confidence to identify results, and outcomes or any research study.it is very important to establish the study methods and process which is used in the representation of population information and studies. In other words, it is defined as a process which is used to investigate different research theories and studies for research models. According to the Oxford dictionary, rigor is defined as a quality of data which is used in research and case studies. To understand the meaning of rigor first identifies qualitative and quantitative research. The main difference between qualitative and quantitative data is that qualitative data require subjective approach while quantitative data require an objective approach. Rigor is a quality of data which is used to define the research process. The Rigor Metric speaks to the modified meaning of meticulousness that rose up out of the investigation, which outlines the idea of thoroughness as the composite of different process qualities. This multi-property metric describes these pointers as free segments of the examination procedure which, when totaled, uncover a composite appraisal of logical meticulousness. Rigor refers to the degree of exactitude in any research and main advantage of this process is that it cannot carefulness during the investigation (Storbjörk, Garfield, and Larner, 2017). Rigor is defined as a quantitative research that consists of two factors such as validity and reliability. There are many advantages of rigor in research such as provide deep knowledge related to research ideas, problem-solving, and complex thinking, provide support and provide quantitative analysis. The main benefit of this research process is that it provides a degree of exactitude in any research.  In which research methods should be free from emotional biases. There are many reasons that reduce rigor in an organization such as incorrect conclusions, the manner of framing, and lack of good theoretical outline.  Scientific rigor is defined as a rigor which is an application of scientific method to certify healthy, and unbiased experimental proposal.

Example: if the manager of an organization asks around 10 employees to how we increase the level of commitment if manager reach to a conclusion on the basis of slow response than the complete approach to the identification would be unscientific. 

Characteristics of Scientific Research

Parsimony is defined as a second research which is used to investigate simple theory or information about the research idea. Parsimony consists of qualitative data analysis while rigor consists of the quantitative data analysis. Rigor is a more complex research process as compare to parsimony (McNally, et al., 2016). Rigor provides an in-depth exploration of any research study while parsimony provides a broad explanation of the research idea. In rigor research data or information is collected by any author himself while in parsimony information is collected by secondary research. Rigor provides unique information about research theory while parsimony provides copied information’s. Rigor is a type of qualitative data while parsimony is a type of quantitative data. 

Rigor research

Parsimony research

Data stored by the researcher himself

Data stored by the third person

Unique information

Copied information

Qualitative data

Quantitative data

More reliable

Less reliable

More time consuming

Less time consuming

References

Barnham, C., (2015) Quantitative and qualitative research: Perceptual foundations. International Journal of Market Research, 57(6), pp.837-854.

Braun, D., (2015) 4. Between parsimony and complexity–system-wide typologies as a challenge in comparative politics. Comparative Politics: Theoretical and Methodological Challenges, 13, p.90.

Choy, L.T., (2014) the strengths and weaknesses of research methodology: Comparison and complementary between qualitative and quantitative approaches. IOSR Journal of Humanities and Social Science, 19(4), pp.99-104.

Hoy, W.K., and Adams, C.M., (2015) Quantitative research in education: A primer. Sage Publications.

Hussein, A., (2015) The use of triangulation in social sciences research: Can qualitative and quantitative methods be combined?. Journal of comparative social work, 4(1), p. 5

Imus, A.L., and Ryan, A.M., (2017) Relevance and rigor in research on the applicant's perspective: In pursuit of pragmatic science. The Blackwell handbook of personnel selection, 6, pp.291-305.

Isaacs, A.N., (2014) an overview of qualitative research methodology for public health researchers. International Journal of Medicine and Public Health, 4(4), pp. 10-12.

Leung, L., (2015) Validity, reliability, and generalizability in qualitative research. Journal of family medicine and primary care, 4(3), p.324.

LoPilato, A.C., Carter, N.T., and Wang, M.,(2015) Updating generalizability theory in management research: Bayesian estimation of variance components. Journal of Management, 41(2), pp.692-717.

McNally, J.J., Martin, B.C., Honig, B., Bergmann, H. and Piperopoulos, P., (2016) Toward rigor and parsimony: a primary validation of Kolvereid’s (1996) entrepreneurial attitudes scales. Entrepreneurship & Regional Development, 28(5-6), pp.358-379.

Morse, J.M., (2015) Critical analysis of strategies for determining rigor in qualitative inquiry. Qualitative health research, 25(9), pp.1212-1222.

Rekker, S., (2016) Converting planetary boundaries into action, a new approach to meeting global greenhouse gas targets: A pitch. Accounting and Management Information Systems, 15(1), pp.160-167.

Smith, B., (2018) Generalizability in qualitative research: Misunderstandings, opportunities, and recommendations for the sport and exercise sciences. Qualitative Research in Sport, Exercise, and Health, 10(1), pp.137-149.

Stango, V., Young, J. and Zinman, J., (2017) the quest for parsimony in behavioural economics: New methods and evidence on three fronts (No. w23057). National Bureau of Economic Research.

Storbjörk, J., Garfield, J.B. and Larner, A., (2017) Implications of eligibility criteria on the generalizability of alcohol and drug treatment outcome research: A study of real-world treatment seekers in Sweden and in Australia. Substance use & misuse, 52(4), pp. 439-450.

Zhang, Y., Cole, S.T. and Chancellor, C.H., (2015) Facilitation of the SUS-TAS application with parsimony, predictive validity, and global interpretation examination. Journal of Travel Research, 54(6), pp.744-757.

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