2-9-2017
Intl 500 Week 6: Intelligence Officer In Training
Intelligence and Global Studies, 2nd Master’s
Degree
American Public University System (APUS)
Due: Thursday, February 9, 2019
Original Work By: Miss. Bayo Elizabeth Cary, AA, BA,
MLIS
Forum Participation Week 6: Miss. Bayo Elizabeth Cary,
AA, BA, MLIS
Think about how you might design a quantitative
research project. What methods would you use to collect your data?
What would you need to do to demonstrate that your study had a high degree of
validity and reliability?
Instructions: Your initial post should be at least 300 words. Please respond to at least 2 other students. Responses should be a minimum of 250 words and include direct questions.
Initial Post Due: Thursday, by 11:55pm ET
Responses Due: Sunday, by 11:55pm ET
Instructions: Your initial post should be at least 300 words. Please respond to at least 2 other students. Responses should be a minimum of 250 words and include direct questions.
Initial Post Due: Thursday, by 11:55pm ET
Responses Due: Sunday, by 11:55pm ET
Introduction:
Quantitative
Analysis of Experimental Data:
·
What is a “quantitative experiment?”;
·
How are “quantitative methods,” applied to
an experiment? and;
·
How is “quantitative methods,” applied to
collection of data? and;
·
What “quality assurance” techniques, are
utilized, to check quantitative data? and;
·
What is “validity,” and how does it relate
to “quantitative data?” and;
·
What is “reliability,” and how does it
relate, to “quantitative data?” and;
·
Why are both: “validity” and “reliability,”
so important, in regards to: “true experiments, and: “empirical data” results?
Body
of Research Paper: What is quantitative data, and how is it applied to the
experimental processes?
Twitter:
“A Longitudinal Case Study Analysis Qualitative, and Quantitative, and Why
Both:”
My case study of Twitter, is at an intersection,
between: US social networking online networks, and US politics. It is a longitudinal
case study. I began the study on Twitter, and the sharing of intelligence
information-through, US social networking websites-in 2013. While, it would
seem, that the application, of only: qualitative, or quantitative methods-would
best satisfy, the requirements, for a successful design-I am now stating
otherwise.
How
I might Design a research experiment, with both: qualitative and quantitative
data sets:
There is, empirical evidence, that,
utilizing, both: qualitative, and quantitative data-can, in many ways, improve
the overall quality, of both: the results of an experiment, and, the final
analysis, of the data collected (Bidart et al. 2013, 2496):
Mixed methods designs (sic.)
allow consideration of an object from several points of view and in several dimensions.
They generally combine qualitative and quantitative methods in order to
articulate, sequentially or simultaneously, positivist and constructivist paradigms.
(Bidart, et al. 2013, 2495)
Because, qualitative, and
quantitative data, are collected, and are expressed-in different ways, what is
studied-for: methods reports, and, for analysis reasons, is more varied,
dependent, on whether: qualitative, or quantitative methods, of data
collection, and analysis-are utilized. In other words: qualitative and quantitative,
data collection, and analysis, are such different methods-that, necessarily,
what they examine-also differ, to an extreme extent-as well (Bidart et al. 2013,
2496).
What
types of methods analysis, are applied to quantitative data, and why?:
The choice, of both: qualitative,
and quantitative data, is a move towards, a higher quality of data, and
analysis. However, how the data is analyzed, is just as important, as, what
kind of data, is collected. Qualitative data, and quantitative data, are
analyzed, in different ways. There is no standardized way, to evaluate-either:
qualitative, or quantitative data. The computer programs, chosen, to analysis
data, are just as important-as, the data itself. Software, simplifies, the “coding,”
and analysis portion, of my data collection, methods section production, and
required presentation, of a final research paper, and report.
Analytical
Analyzation Process (AHP) (Al-Faifi et al. 2012, 40):
Computer systems, are based on: “bi-numeral
theory.” Technically, bi-numeral theory, is just a choice, between the numbers:
“1,” and the number “0.” With-this-in-mind, computer languages, are written, to
operate, on a fundamental choice system-the answer is either: “1,” or the
answer, is: “0.” The number: “1,” is never just the number: “1,” and the number:
“0”-is never just the number: “0,” it is symbolic. If I chose: “Yes,” if I
select: “1,” and, if I chose: “No,” I, then select the number: “0.” Information
systems, breaks this type of basic and relied upon computer language, down, to
a logic-which, is reflected in: “bi-numeral truth tables.”
No answer, to an experimental question,
is ever as simple, as: “Yes,” or “No.” Values, have to be attached, to the: “Yes,”
or to the: “No.” The values must coincide, with the hierarchy, of the computer
program-and, in such a way, that-the answers provided, allow one’s input, to lead
to varying results, if the input too-is varied. In the world of experiments,
there are, a variety of results, and the results, are confounded, by-unexpected
“mitigating” factors. Some, of the 3rd party factors, that an
experimenter, traditionally calls, secondary factors, are as follows:
·
Experimenter bias
·
Software analysis glitch and application
mis-match
·
Participant attrition
·
Double-blind coding mistakes
·
Unintentional data collection and
reporting errors
·
Failed results
·
Scientific standards constraints: IRB,
etc.
Associations
and The Relationship To Analysis (Breseghello et al. 2006, 1323):
For many experiments,
with the applications, of both: qualitative, and quantitative data, the
analysis, has to be, considered, in such a specific way-so that, all the data, has
been collected as required-can also be factored, into the analysis. A specific
software, such as: SPSS, is chosen, because, of HOW it analyses, the
data (Al-Faifi et al. 2012, 40). The specific application, of the correct
software, or other data analysis, to both: qualitative, and quantitative
data-improves the, quality, of the results (Breseghello et al. 2006, 1323).
Specific data sets, require, specific types, and applications, of various data analyses-software,
and otherwise (Breseghello et al. 2006, 1323).
Certified
Materials References List (CMR) (Dybczynski 2002, 928):
Part,
of establishing, the validity, and the reliability, of an experiment, and the
empirical evidence collected, as data-pertains to the reference resources,
utilized, to support, the research study, and, consequently-the research paper,
as well (Dybczynski 2002, 928). The introductory portion, of an experiment, is
intended, to establish a framework, for collecting the data, for appropriate analysis,
of data collection methods.
When results, have been attained,
part of supporting, the: “validity,” and “reliability,” of the experiment, are-in
locating appropriate, and high quality reference materials, to apply. A research
paper, must be written, and submitted, in the form, of a final-results report.
The research paper, of final results form, of the experimental data sets, must
be: “narrowly construed”-as to be specifically suited, to: the experiment, and the
results analysis, and, the final data report-itself.
Summary
and Conclusion:
“Four
Ways To Improve The Quality of Results, That Are Provided, By, A: ‘True
Experiment (Dybczynski 2002, 929).’”
1. Diversity
data collected, include both: qualitative and quantitative collection and
analysis, and;
2. Apply
the correct analysis, to the data collection-so results, will not leave, any
required data out, and;
3. Have
concerns for both: the “validity” and the “reliability” of results-by
understanding the specific definitions of both: “validity and “reliability,”
and-that, they are different, and not the same, and;
4. Chose
to verify, both: “validity,” and “reliability,” by certifying, that reference
materials, that are utilized, to support the experiment, are, of a sufficient:
quality and quantity-know the deliminated requirements, for both: quality
versus quantity, and overall requirements, for a valid final research paper and
report.
References
Al-Faifi, Abdullah M., and Al-Naeem, Tariq.
2012.
“Quantitative
Evaluation of IS Applications.” IJCSNS
International Journal of Computer Science and Network Security, vol.12. 5.:
39-50. Accessed February 9, 2017.
Bidart, Clarie and Cacciuttolo, Patrice.
2013. “Combining qualitative,
quantitative and
structural dimensions in a longitudinal perspective. The case of network
influence.” Springer Science and Business
Media B.V., vol. 47.: 2495–2515. Accessed February 9, 2017.
Breseghello, Flavio and, Sorrells, Mark E.
2006. “Association Analysis
as a Strategy for
Improvement of Quantitative Traits in Plants.” Crop Science, vol. 46. 3.: 1323-1330. Accessed February 9, 2017.
Dybczynski, R. 2002. “Preparation and use
of reference materials
for quality
assurance in inorganic trace analysis.” Food
Additives and Contaminants, vol. 19. 10.: 928-938. Accessed February 9,
2017.
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