BUSI 830
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DESCRIPTIVE CASE STUDY ASSIGNMENT INSTRUCTIONS
OVERVIEW
We begin this Module: Week with analyzing case study evidence. Our overview said, analysis
then, begins with the data and how we see it - the interpretation or operative words are array and
display. That is, in different ways, so you may observe the data in a unique range or grouping to
help see new patterns, insights, and concepts. There are a number of strategies and techniques for
how to do this. We’ll wrap this module up with a deep-dive on the Descriptive Case.
INSTRUCTIONS
After reviewing the Learn material for the Module: Week, address the following paper in current
APA format:
1. Describe how to order and display case study data in different ways
2. Identify methods for setting-up (‘play’) with data to help detect promising patterns, insights,
and concepts in case study data
3. Explain the four generic analytical strategies and the five analytical techniques for case study
data
4. Draft a Descriptive Case Study that is unique and not normally available to researchers (see
Box 28, Box 3A (Yin, 2018))
Required Format
This 1600 minimum, 2400 maximum word paper needs to be written with these main sections:
Cover page
Abstract
Introduction
Array and Display Case Study Data
How to Detect Patterns, Insights, and Concepts
Generic Analytical Strategies and Analytical Techniques
Descriptive Case Study
Conclusion
References
Other Requirements
Materials submitted to fulfill requirements in one course may not be submitted in another course.
Concerns about the propriety of obtaining outside assistance and acknowledging sources should
be addressed to the instructor of the course before the work commences and as necessary as the
work proceeds.
The cover page must include this statement as an author’s note: “By submitting this
assignment, I attest this submission represents my own work, and not that of another
student, scholar, or internet source. I understand I am responsible for knowing and
BUSI 830
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correctly utilizing referencing and bibliographical guidelines. I have not submitted this
work for any other class.”
In addition to the course textbook(s) and the Bible, this paper must include at least 5
references from scholarly articles that have publication dates no older than 5 years. Do
not use any books other than the Bible and the textbook. Do not conduct interviews.
There should be at least one instance of biblical integration (at least one scripture
reference).
In-text citations are required to support your statements, points, assertions, issues,
arguments, concerns, paragraph topic sentences, and statements of fact and opinion.
The required cover page, abstract and the reference pages are not included in the required
assignment word count but are required as part of your paper.
The APA required abstract and conclusion section headings and subject headings (see
above) are expected. For papers this length, there should be at least two (2) ‘levels of
headings’.
The introduction and conclusion sections should not be longer than ½ page each since the
assignment is short in word count.
The required abstract should be written as a stand-alone document and not written as an
introduction since an introduction section is required. Therefore, refrain from using
phrases such as, “in this paper,” and do not use citations. See example in APA manual.
Sources of information from Wikipedia, dictionaries, and encyclopedia will not be
accepted.
Paragraph lengths: Each paragraph should have a topic sentence unless it continues from
or provides support to the prior paragraph. A paragraph is defined in this course as being
at least 4 sentences in length.
All parts of the assignment must be based on scholarly and biblical literature.
Avoid clichés, slang, jargon, exaggerations, abbreviations, figurative language, and
language that is too informal and too subjective.
Submit your final document for grading with file name syntax: Last NameFirst Initial
Project#. For example: PhilebaumJ Project4.doc (no .pdfs)
Grading Metrics
Consult the accompanying rubric for how your instructor will grade this assignment. Also, any
form of plagiarism, including cutting and pasting, will result in zero points for the entire
assignment. All quoted materials must be properly cited in current APA format.
Note: Your assignment will be checked for originality via the Turnitin plagiarism tool. The tool
is a starting point for instructors to check overall Academic Integrity and higher scores generally
indicate a higher probability of Academic Misconduct. The higher a score, the higher the
probability that there are too many quotations included in the narrative, and/or there are passages
that have not been properly cited.
Introduction
Data analysis is a crucial step after collecting case study data
Array and Display Case Study Data
According to Yin (2018), "playing" with obtained data is an important first step in any
data analysis process
How to Detect Patterns, Insights, and Concepts
A pattern is used in some contexts to refer to a regularity in the world
General Analytical Strategies and Techniques
To ensure effective case study data analysis, Yin (2018) suggests four general analytical
methodologies
Descriptive Case Study
The projected research is a single-case study that would look at Eagle Hill School in
Hardwick, Massachusetts' special education services
Conclusion
For case study researchers, data analysis has been confirmed to be a hard undertaking
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Descriptive Case Study
Name
Institution
Course
Instructor
Date
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Authors Note
I declare that this assignment is my original work. It does not contain materials of any
other scholar. I am fully aware that I am responsible for understanding and referencing
accordingly by strictly adhering to the bibliographical procedures. This work has never been
presented in any other class.
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Abstract
Case study data processing is a sophisticated and demanding undertaking since its components
are not developed. Case study investigators frequently lack a developed understanding of how to
analyze the data they collect. Data visualization and analysis are made easier by arranging and
displaying case study data. As a result, you'll be able to spot useful patterns, ideas, and concepts
with ease. Pattern corresponding, structuring of description, time-series analysis, employing
logic replicas, and cross-case investigation are the main analytical tools that are employed in a
case study interposition. In assessing data, a Christian case study researcher should follow the
biblical honesty principle. "Giving candid things, not just in the eyes of the Lord, but also in the
look of men," says Second Corinthians 8. If the Christian researcher does not follow the rules of
honesty, they will report fraudulent findings. As a result, God and men see you as a failure.
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Introduction
Data analysis is a crucial step after collecting case study data. The analysis is also a hard
undertaking. Because it is one of the least established components of the process, the analysis is
complex and difficult. Case study researchers frequently lack a well-developed understanding of
how to analyze the data they collect. A researcher's failure to develop all-important patterns,
ideas, and concepts in case study data might be caused by a lack of clarity about interpreting the
data. The goal of the debate is to generate ideas about how to examine data from case studies.
Array and Display Case Study Data
According to Yin (2018), "playing" with obtained data is an important first step in any
data analysis process. Playing with data entails modifying data in a variety of ways. The goal of
the analysis is to come up with helpful patterns, ideas, and concepts. In raw data, meaningful
patterns, insights, and concepts are not always visible. Data manipulation aids in the discovery of
previously unknown patterns, ideas, and concepts. Data manipulation and data analysis efforts
can be aided by arranging and showing case study data.
Arraying Case Study Data
Arraying case study data is one technique to present data for easier manipulation. An
array is a logical grouping of things, usually rows and columns (Stott, 2016). As a result,
arranging case study data frequently entails dividing the information into rows and columns.
Display Case Study Data
A case study investigator can utilize a computer-aided instrument like Atlas.ti or NVIvo
to fix case study data for scrutiny and examine the data, according to Yin (2018). The
information gathered can be veiled and entered into the computer-aided instrument. The
computer-aided instrument would show the inscribed data and make it easier to analyze it. The
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inscribed data can also be shown in print, which will aid in data visualization. As a result, data
analysis becomes easier. Electronic or print media to display case data would aid in manipulating
it to develop meaningful patterns, insights, and concepts. Visualization tools such as graphs,
tables, and charts can display information produced from case data analysis (Luo et al., 2018).
Such a display can also assist in learning a great deal about the facts.
How to Detect Patterns, Insights, and Concepts
A pattern is used in some contexts to refer to a regularity in the world. Elements of a
pattern repeat predictably. In research, patterns are vital to explaining a research phenomenon
(Jiang et al., 2016). Insight refers to a clear and deep understanding of something. A key goal of
any study is to gain insights regarding a research phenomenon (Sivarajah et al., 2017). A study
can be aimed at discovering concepts (Vishwanathan et al., 2020). Studies are also conducted to
test concepts (Vishwanathan et al., 2020). The detection of patterns, ideas, and concepts in data
is also difficult due to case study data analysis difficulties.
Yin (2018) offers several data grounding and examination strategies that aid case study
investigators in detecting hidden patterns, insights, and concepts in raw data. Coding is one
method for discovering patterns, insights, and concepts. Coding is the process of organizing
acquired data into logical and consistent categories. Data arraying is another method. Data can be
manipulated and visualized more easily when organized into rows and columns, which leads to
the discovery of important designs, insights, and ideas.
Data visualization, which is critical for uncovering patterns, insights, and concepts, can
be aided by displaying data using graphs, tables, and charts (Luo et al., 2018). The fourth
strategy is to build a matrix of opposing categories and then place evidence in it. Using the
matrix to compare and contrast distinct categories might aid in the discovery of important
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patterns, ideas, and concepts concealed in acquired data or information. Another method is to
keep track of the frequency of various events.
This type of tabulation can aid in the discovery of important patterns or ideas for a
researcher. Case study researchers might identify important patterns, ideas, and concepts by
organizing data or information sequentially or chronologically. Data analysis can aid in the
discovery of patterns, insights, and concepts utilizing statistical approaches such as mean
calculation, correlation analysis, and regression analysis, among many others (Mishra et al.,
2019). To get the greatest findings, a researcher must choose the best technique for their data or
topic.
General Analytical Strategies and Techniques
To ensure effective case study data analysis, Yin (2018) suggests four general analytical
methodologies. Relying on theoretical propositions is one of the four techniques. The data
collected in the case of studies based on specific theoretical notions are frequently shaped by the
concepts themselves (Ridder, 2017). The propositions assist the structure of the analysis;
therefore, basing data analysis on them would make it easier (Ridder, 2017). The propositions
aid data analysis by making it easier. Working with data from the ground up is the second
general data analysis method that case study researchers might use (Yin, 2018). Playing with
data to identify patterns, ideas, and concepts is recommended by the strategy (Yin, 2018). When
utilizing the strategy to summarize data into relevant categories, data coding is advised. As a
result, data manipulation is made easier. The second technique does not organize data using
theoretical propositions.
Analytical strategies appear in different parts within the qualitative research literature.
Therefore, it plays a significant role in embracing everything within the research literature,
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including the purpose of the research study to the nitty-gritty, which is most of the tasks executed
by data analysis. Therefore, analytical strategies help determine what needs to be done within the
research literature and the plan that needs to be developed to achieve all those activities. On the
other hand, the term analytical technique is rarely mentioned within the research literature.
Therefore, it is important to understand the difference between the two, especially while using
qualitative data analysis. That is mainly because analytical strategies differ from the analytical
tactics, but they tend to be contradictory too. Most of the qualitative analyses are either
reassessed or re-characterized based on the prior information provided. However, it is impossible
to predict the outcome of different tasks even after being provided with a full understanding of
the workings of a specific task. It is not possible to take any action after being challenged with
the contradiction between the two not until such contradiction has been solved. However, a key
strategy of resolving such contradiction is ensuring ATLAS.ti has been applied powerfully.
The framework can aid in the organization and analysis of data. When the theoretical
hypotheses and ground-up analysis procedures are difficult to use, Yin (2018) suggests utilizing
the third strategy. Examining feasible alternative explanations is the fourth broad strategy.
Examining the feasible alternative hypotheses can aid in the data analysis process. As a result, it
assists in the identification of useful patterns, insights, and concepts. According to Yin (2018),
the method is frequently used with the other three strategies. Besides the general strategies
utilized in case study data examination, Yin (2018) adds five analytical methodologies.
However, the author warns that the five tactics are tough to master and need a lot of effort to
master.
Pattern matching is one of the analytical approaches. The method involves evaluating
acquired data using a pattern-matching algorithm. The technique aids in the evaluation of
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potential explanations for a studied phenomenon (Sinkovics, 2018). Building explanations is the
second analytical strategy (Yin, 2018). The method entails using case study data to explain the
study phenomenon (Yin, 2018). It's a harder approach than pattern matching.
Time-series analysis is the third analytical technique used to evaluate case study evidence
(Yin, 2018). The method entails arranging data into a sequence of specific intervals or periods
(Shrestha & Bhatta, 2018). If the technique is acceptable for a certain case study, it can be
applied. Logic models are the fourth analytical technique (Yin, 2018). The models can be used to
investigate a variety of activities. They're also valuable in case studies that are trying to figure
out cause-and-effect linkages. Cross-case synthesis is the fifth analytical strategy. In a case
study, the process entails examining and synthesizing the outcomes of several instances.
According to Yin (2018), the method can be used in multiple-case studies. To properly examine
acquired data, a case study researcher must choose analytical approaches that are appropriate for
their study.
Descriptive Case Study
The projected research is a single-case study that would look at Eagle Hill School in
Hardwick, Massachusetts' special education services. The School is a private institution that
caters to distinct education scholars in grades 8 through 12. Scholars having dyslexia, attention
deficit hyperactivity disorder (ADHD), and other difficulties are targeted by the School. The
purpose of the case study is to learn more about the School's special education programs.
The research will be grouped around five main themes. These main subjects are the
School's special education programs, teachers' experiences, the special education teaching
equipment utilized by the institution, Eagle Hill School's special education curriculum, and
student academic performance. The study is meant to help you learn more about the institution.
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Conclusion
For case study researchers, data analysis has been confirmed to be a hard undertaking.
Arrays and displays can aid data analysis and the detection of relevant patterns, ideas, and
concepts for case study researchers. The four main ways of analyzing case study data are
employing a research's theoretical assertions, directing a ground-up investigation, using a case
study explanation, and investigating possible clarifications. Pattern corresponding, elucidation
establishment, time-series examination, logic models, and cross-case blends are among the
analytical tools that a case study researcher can employ. The biblical honesty principle should
guide a Christian case study researcher's statistics analysis. The Christian researcher would
report fraudulent findings if they did not follow the rules of honesty. As a result, both God and
men judge us as failures.
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References
Eagle Hill School. (n.d.). About. https://www.eaglehill.school/about
Jiang, H., Yang, A., Yan, F., & Miao, H. (2016). Research on pattern analysis and data
classification methodology for data mining and knowledge discovery. International
Journal of Hybrid Information Technology, 9(3), 179-188.
https://doi.org/10.14257/ijhit.2016.9.3.17
King James Bible. (2017). King James Bible Online. https://www.kingjamesbibleonline.org/2-
Corinthians-8-21/ (Original work published 1769).
Luo, Y., Qin, X., Tang, N., & Li, G. (2018, April). Deepeye: Towards automatic data
visualization. In 2018 IEEE 34th international conference on data engineering
(ICDE) (pp. 101-112). IEEE. https://doi.org/10.1109/ICDE.2018.00019
Mishra, P., Pandey, C. M., Singh, U., Keshri, A., & Sabaretnam, M. (2019). Selection of
appropriate statistical methods for data analysis. Annals of cardiac anesthesia, 22(3),
297. https://doi.org/10.4103/aca.ACA_248_18
Ridder, H. G. (2017). The theoretical contribution of case study research designs. Business
Research, 10(2), 281-305. https://doi.org/10.1007/s40685-017-0045-z
Rusch, F., Wosniack, M. E., Raposo, E. P., Viswanathan, G. M., & da Luz, M. G. E. (2020).
Transient dynamics in a nonequilibrium superdiffusive reaction-diffusion process:
Nonequilibrium random search as a case study: physical Review E, 102(1), 012126.
Shrestha, M. B., & Bhatta, G. R. (2018). Selecting appropriate methodological framework for
time series data analysis. The Journal of Finance and Data Science, 4(2), 71-89.
https://doi.org/10.1016/j.jfds.2017.11.001
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Sinkovics, N. (2018). Pattern matching in qualitative analysis. The sage handbook of qualitative
business and management research methods, 468-485.
https://dx.doi.org/10.4135/9781526430236.n28
Weng, L., Zeng, X. Y., Yin, P., Wang, L. J., Wang, C. Y., Jiang, W., ... & Du, B. (2018). Sepsis-
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