Professional Research and Analysis: Parsimony versus Rigor Study

This essay provides a comprehensive analysis of parsimony and rigor in research, contrasting statistical and theoretical generalizability. Statistical generalizability focuses on quantitative data and mathematical models to generalize research findings, while theoretical generalizability uses qualitative methods to understand underlying theories and concepts. The discussion extends to parsimony, emphasizing simplicity and efficiency in research design, and rigor, highlighting the importance of validity and reliability in research methods. The essay concludes with a comparison of parsimony and rigor, offering insights into their distinct roles and applications in professional research. Desklib provides access to a wealth of study resources, including solved assignments and past papers, to support students in their academic endeavors.

Running Head: PROFESSIONAL RESEARCH AND ANALYSIS
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Professional Research And Analysis

PROFESSIONAL RESEARCH AND ANALYSIS
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Contents
Statistical generalizability in research...................................................................................................2
Theoretical generalizability in research.................................................................................................2
Statistical Generalizability versus Theoretical Generalizability.............................................................3
Parsimony in research............................................................................................................................4
Rigor in research...................................................................................................................................5
Parsimony versus rigor in research........................................................................................................6
References.............................................................................................................................................8

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Statistical generalizability in research
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.

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

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Statistical Generalizability versus Theoretical Generalizability
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
Parsimony in research
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

PROFESSIONAL RESEARCH AND ANALYSIS
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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.
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

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and manager if it were recommended that manager should change almost 10 various variables
to improve company commitment by 46%.
Rigor in research
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.

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Parsimony versus rigor in 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

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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.

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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.