Intl 500: Rough Draft: "Application of Quantitative Data Sets To 'True Experiment'-Answers Related To Quality Improvements."

2-9-2017

Intl 500 Week 6: Intelligence Officer In Training

Intelligence and Global Studies, 2^nd 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

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, and an intersection, between: US social networking online networks, and US politics-is a longitudinal study. I began the study, in 2013. While, it would seem, that the application, of only: qualitative, or quantitative methods-would best satisfy, that requirements, for a successful design, I am no stating otherwise.

         There is, empirical evidence, that utilizing, both: qualitative, and quantitative data-can, in many ways, improve the overall quality, of both: results of an experiment, and the final analysis, of 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 report, and analysis reasons, is more varied, dependent, on whether: qualitative, or quantitative methods 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-differ, to an extreme differential-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, is just as important-as the data itself-should one chose, to rely on something faster, and something, that makes the “coding,” simpler.

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 1, is never really the number 1, and the number 0-is never the number 0, it is symbolic. If I chose yes, I select 1, and, if I chose no, I 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, has to be attached, to the: yes, or to the: no. The values must coincide, with the hierarchy, of the computer program-in such a way, that, the answers provided, allow one’s input, to lead to varying results, if the input, 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 3^rd 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, that can be submitted, can also be factored into the analysis-that, 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 (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 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-in the research paper and written report form, of the experiment-that, are “narrowly construed”-as to be specifically suitable, to: the experiment, the results, and, the final report.

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 are specific in definition, and, 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.

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.