Business Question

User Generated

Xvxv12345

Business Finance

busi830

Liberty University

Description

We begin this module 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/week up with a deep-dive on the Descriptive Case. After reviewing the Reading & Study material for the module, address the following paper in current APA 7 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))

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Explanation & Answer

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Descriptive Case Study

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Author Note

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another student, scholar, or internet source. I understand I am responsible for knowing and
correctly utilizing referencing and bibliographical guidelines. I have not submitted this work for
any other class.

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Abstract
Case study data analysis is a complicated and difficult task because case study evidence analysis
is one of the least developed parts of the case study approach. Case study researchers often lack a
well-developed notion of analyzing collected data. Arraying and displaying case study data helps
ease data visualization and data analysis. Thus, easily detect useful patterns, insights, and
concepts. Techniques that a case study researcher can use to detect patterns, insights, and
concepts in data are coding, data arraying, displaying data, creating a matrix of contrasting
categories and placing evidence in the matrix, tabulating different events' frequencies, organizing
data or information in sequential or chronological order, and statistical data analysis. Four
general strategies that can be used to analyze case study data are using a study's theoretical
propositions, conducting a ground-up analysis, using a case study description, and examining
plausible explanations. Five analytical techniques that a case study researcher can use are pattern
matching, explanation building, time-series analysis, using logic models, and cross-case
synthesis. The biblical honesty principle should guide a Christian case study researcher in
analyzing data. Second Corinthians 8 states, “Providing for honest things, not only in the sight of
the Lord, but also in the sight of men” (King James Bible, 2017). The failure to adhere to
honesty would lead to the Christian researcher reporting false findings. Thus failing in the eyes
of God and men.
Keywords: array, display, patterns, insights, concepts, generic analytical strategies,
analytical techniques, descriptive case study

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Introduction
Case study researchers have to analyze case study data after collecting the data.
According to Yin (2018), case study data analysis is a complicated and difficult task. The data
analysis is complicated and difficult because case study evidence analysis is one of the least
developed parts of the case study approach (Yin, 2018). Case study researchers often lack a welldeveloped notion of analyzing collected data (Yin, 2018). The lack of a clear idea regarding how
to analyze case study data can result in a researcher failing to generate all vital patterns, insights,
and concepts in case study data. This discussion will focus on arraying and displaying case study
data, detecting patterns, insights, and concepts in such data, generic analytical strategies and
analytical techniques, and the descriptive case study design. The discussion is aimed at the
development of notions on how to analyze case study data.
Array and Display Case Study Data
Yin (2018) observes that a key starting point in any data analysis task is to “play” with
collected data. Playing with data regards manipulating the data in different ways. The analysis
process is aimed at generating useful patterns, insights, and concepts. The useful patterns,
insights, and concepts are not often obvious in raw data. The manipulation of data helps uncover
hidden patterns, insights, and concepts. Arraying and displaying case study data can help
manipulate the data and make vital data analysis progress (Yin, 2018).
Arraying Case Study Data
One way to present data for easier manipulation is through arraying case study data (Yin,
2018). An array refers to a systematic arrangement of objects, often in the form of rows and
columns. Therefore, the arraying of case study data would often regard organizing the data into
rows and columns. Yin (2018) recommends putting data or information into arrays based on

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themes and subthemes. Data can be arrayed based on criteria other than themes. Case study data
arraying is vital to data analysis as it helps compared data which can lead to the identification of
hidden patterns, insights, and concepts. The approach can help case study researchers address the
complicated and difficult task of analyzing case study data.
Display Case Study Data
Another way to present data for manipulation is through data displaying (Yin, 2018).
Data display regards outputting data on mediums like print or electronic. According to Yin
(2018), a case study researcher can use a computer-assisted tool like Atlas.ti and NVIvo to
prepare case study data for analysis and analyze the data. Collected data can be coded and fed
into the computer-assisted tool. The computer-assisted tool would display the coded data and
help ease the analysis of the same. The coded data can also be displayed in print, and that would
also help visualize the data. Thus, ease data analysis. The display of case data using electronic or
print mediums would help manipulate the same to generate useful patterns, insights, and
concepts. Information derived from case data analysis can also be displayed using visualization
tools like graphs, tables, and charts. Such display would also help discover patterns, insights, and
concepts hidden within the information.
How to Detect Patterns, Insights, and Concepts
A pattern is used in some contexts to refer to a regularity in the world (Jiang et al., 2016).
Elements of a pattern repeat predictably (Jiang et al., 2016). In research, patterns are vital to
explaining a research phenomenon (Jiang et al., 2016). Insight refers to a clear and deep
understanding of something (Sivarajah et al., 2017). A key goal of any study is to gain insights
regarding a research phenomenon (Sivarajah et al., 2017). A concept is an abstract idea. Another
goal of the research is to discover concepts. Studies are also conducted to test concepts. Patterns,

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insights, and concepts are often discovered following data analysis. Difficulties in case study
data analysis make the detection of patterns, insights, and concepts in data also difficult.
Techniques to Help Detect Patterns, Insights, And Concepts
Several data preparation and analysis techniques can help case study researchers detect
patterns, insights, and concepts hidden in raw data. One technique that can help discover
patterns, insights, and concepts is coding (Yin, 2018). In research, coding regards summarizing
collected data into meaningful and cohesive categories (Yin, 2018). It is much easier to discover
patterns, insights, and concepts in meaningful and cohe...

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