The Envisioned Context
Introduction
This study gives a comprehensive activity enumeration from the previous research done on Desktop Virtualization. It will look at the problem statements as well as the research questions used to give clarity of the research design and methods preferred for the research. The paper will also reflect on different data collection and analysis methods to identify the most suited for desktop virtualization as well as elaborating the specific reasons for their preference. Finally, the proposed plans for the validity and reliability of the research results will be discussed.
Research Questions
The research employed four research questions in the approach of researching by giving the specific areas of its focus as listed below.
- What are the issues faced by organizations that implement desktop virtualization?
- What are the challenges of desktop virtualization in computing resource management?
- How does desktop virtualization reduce operating costs?
- What are the advantages of effective implementations of desktop virtualization?
Hypotheses
It was important to design hypotheses to direct the study on the intended outcome of the research.
- H1: Desktop Virtualization hurts computing resources management.
- H2: Desktop Virtualization reduces the cost of operations.
Variables in the Proposed Research
There are two main types of variables in research namely; dependent variables and independent variables (Trochim & Donnelly, 2008). As far as the research topic is concerned, the impact of energy consumption depends on desktop virtualization. Desktop virtualization is a concept whereby hardware and software systems deploy the client-server model of computing to separate the personal desktop computer environment from the physical machine. Therefore, the study’s dependent variable will be ‘the impact of energy consumption and the independent variable will be ‘desktop virtualization. The conceptual framework for the study will derive its components from these two variables.
Discussion of Envisioned Data Collection Method
Methodology
Most researches have been using the two research approaches, namely, qualitative, and quantitative. The quantitative approach becomes very fundamental when the study is dealing with measurable statistical information. On the other hand, the qualitative approach is used when the study requires immeasurable facts, such as respondents’ beliefs, reactions, and opinions on the research topic. However, the importance and relationship between these two approaches are inseparable (Hamel, 2000). Therefore, the researcher in this study considered the differences analyzed in the table below before making a choice.
Table 1: variations linking Quantitative, and Qualitative Research
Based on the above differences, the study considered the quantitative approach as the primary priority. This will be possible after addressing the different specific approaches within this approach. This approach became very successful among researchers in predicting and quantifying certain theories. According to Grinnell and Yvonne (2010), this approach qualified as primary research because there was the acquisition of firsthand data from experiments. Primary data offer better and up-to-date information, unlike secondary data that can be outdated or biased.
Mixed Methods
From the previous write-ups, the issue of the mixed method became clear on the application of both the qualitative and quantitative approaches in data collection and interpretation. Qualitative insights blend well with the quantitative data reflecting the topic of study. As a result, a theoretical overview of mixed methods approaches was necessary for ensuring the elimination of hitches in data analysis.
Various theorists had varying opinions on the mixed-method approach in studies as witnessed in various kinds of literature (Leedy, 1997; Yin, 2009; Patton, 2002; Sekaran & Bougie, 2010). However, it is clear that these methods are inseparable hence; the issue of complementarities among these methods could be the major issue to handle in this framework. One of the suggested approaches revealed triangulation of the dataset as a major proposal toward the achievement of complementing the mixed method (Leedy, 1997). For instance, qualitative interviews directed toward interviewees on the research topic contribute to the questionnaires set by the researcher. As a result, the findings from these interviews result in quantitative data based on the quantitative questionnaires from the responses to certain questions (Patton, 2002).
Nevertheless, Leedy’s perspective on the mixed method facilitated more room for discussion on the complementarity of the two research approaches. Among those theories, emanating from the work of Leedy was Patton’s theory. On his theory, Patton (2002) said that quantitative and qualitative methods “applied to the same research topic invited the research to compare and contrast the two methods’ analytical outputs” (p. 11).
Research design remained a key factor in the comparison, and contrast of the mixed research approach. For instance, there is a difference between sequential triangulation and explanatory research designs. The former examines both disagreements and similarities, whereas the latter does not recognize the convergence between the outputs generated by the study (Leedy, 1997). Therefore, academic literature remains a fundamental tool in relating the study design to triangulation approaches.
Triangulation and embedding pose major differences rather than what people may think. Precisely, the purpose of triangulation is to merge different methods of data collection into one comprehensive interpretation, emphasizing both types of data. In the embedded design approach, one dataset supports the other (Schram, 2006). The focus should be the concept, data-merged interpretation, and the support provided by different data conceptually.
Moreover, the “Embedded data is not primarily significant in the study but just important” (Sekaran & Bougie, 2010, p. 22). To expand on the embedded study, the qualitative-dominant study can serve as an example in which demographic characteristics offer the descriptive statistics required by the study subjects. The major focus of the study integrates both gender and age demographic characteristics (Schram, 2006).
Single and integrated research activity forms the data collection process in sequential triangulated approaches. As a result, this study employed a different sequential triangulated approach. Therefore, it deviated from Schram’s theory approach of sequential exploration. The study may forego hypothesis formulation toward qualitative data interaction with quantitative data. To sum up, the study design section revisited and reviewed more information on the mixed research approach in data analysis procedures (Trochim & Donnelly, 2008).
Study Design
Research instruments
This study used questionnaires in the collection of data. The questionnaires had multiple-structured and open-ended questions to make them more user-friendly. Classmates, friends, and casual workers within the project management industry were issued with the questionnaires. The questionnaires were distributed and picked randomly among the respondents because it was large-scale research. The focus of the questionnaires was an analysis of the impact of desktop virtualizations on energy consumption.
Survey Design
The study employed a descriptive survey. Creswell (2003) further analyzed that a descriptive survey approach describes, explores, and analyzes relationships among geographically gathered subjects. A descriptive survey captured and compared the key parameters of this research on analyzing the energy consumption of Desktop virtualization. A descriptive survey is also the best for this survey because it determines the perceptions of respondents on a particular subject and, in this case, Desktop Virtualization in an encoded structured way. The study adopted a quantitative approach as socio-economic, political, and cultural parameters assessable using empirical data (Creswell, 2003). Therefore, a quantitative approach was the best-suited approach in meeting the research objectives because it aims at gathering, analyzing, and measuring data from a large sample to test the relationship between different variables (Shank, 2006).
Ethical Implications in qualitative studies
Several ethical issues are associated with qualitative research. For example, Informed consent is required in research where qualitative research is included. This entails updating the respondents, orally or in writing, of the need for the research and the importance of taking part (Leedy, 1997). Another ethical issue involved in qualitative research is the responsibility of the researcher to research respondents. It is the responsibility of the researcher to ensure the respondents are confident and ready to give feedback.
In this case, the participants are asked not to give confidential information if they are not comfortable in doing so. There should be no risk of harm to the participants whatsoever. Participants expect at least to get something out of the research project after giving out their time and experiences to the success of the research. They should at least get results on the conducted study as a form of appreciation.
Sampling and Ethical Protections
Data collection and activities in research have a relationship to the anthropogenic aspect. Therefore, researchers must have practices that should not expose the confidentiality and privacy of the respondents. As a result, various ethical practices become very crucial when performing research. They are significant in protecting the participants who may be employees, students, or a company. Therefore, IRB norms became very important to the study because there were three critical components.
According to various researchers, Informed consent, participant protection, and data privacy remained substantially crucial in research (Shank, 2006; Yin, 2009; Crepau, 2000). The study revealed that participants in the study were voluntarily in which a consent form was signed. Other measures taken during the research to safeguard the privacy of the respondents included the generation of random numbers to the participants instead of their names. The study maintained a high level of confidentiality of the participants to prevent consequences such as defamation, job termination, and victimization (Stake, 1995).
Data Collection Procedure
Questionnaires and observation were the two main methods used for data collection in this research. The respondents gave the information as enumerated in the questionnaires. This data formed the basis of all the research findings. Coding was through SPSS v16 statistical software, which also produced processed data for analysis. This was not a major component of the qualitative study although it generated complementation to the main quantitative course of the study (Stake, 1995; Yin, 2009). However, data from the above data collection technique offered the best source of information for the validity and reliability of the study.
Analyses Along With the Reasoning behind the Selection
After collecting the data, it was necessary to compile and analyze the results so that decision could be made regarding the questions being tested. This was done by the researcher after coding the data in meaningful descriptive parts. Before this, the collected data was used to compare the research findings with related literature to develop accurate results that could be relied on in making recommendations.
Since data coding and analysis was through SPSS v16 statistical software, a mixed research approach was used in data analysis. Therefore, descriptive statistics, which fall into two categories, were used. These are the measure of central tendency and measure of dispersion. These measures included mean, median, mode, standard deviation, and variance. This research is concerned with analyzing the impact of desktop virtualization on energy consumption.
Therefore, the measure of central tendency was relied upon because it enabled a researcher to group large data into single values for analysis. Secondly, the measure of central tendency and measure of dispersion allowed condensation of data, comparison of data, and gave space for the data to be used for further statistical analysis. Given the research questions to be tested, this method of data analysis ensured all questions are answered by the results generated in the analysis.
Research Qualitative Design
Positivist research about phenomenological research design became pivotal and a determinant factor in the completion of this study. According to Creswell (2003), these two philosophies greatly differ in their basic belief. In the positivist paradigm, the world is objective and external, whereas, with the phenomenological paradigm, the world is subjective and socially constructed.
In addition, the observer is independent in the positivist paradigm, whereas in the phenomenological paradigm the observer forms part of the observation (Creswell, 2003; Rogers, 2000). This study employed the phenomenological research design because the information used in the study occurred in a natural setting but not in a laboratory setting (Rogers, 2000). Induced research presents qualitative data used in the study only when data collection involves the researcher.
Proposed Plans to Ensure Validity and Reliability of Outcomes
Validity
It is important in ascertaining if the findings of the study are true and certain. In research, validity refers largely and broadly to the “Goodness” or “soundness” of a study (Thomas, Nelson & Silverman, 2010, p. 25). There has been an emergence of a large number of approaches, and conceptualizations of validity. In quantitative data, the researcher will consider facts and content validities in the research process. The researcher will also compare the data collection methods with those of previous studies. Finally, the researcher will do a preliminary investigation to check the methods of data collection likely to be most effective within the context of the study. This will ensure the outcome of the study is valid.
Reliability
This research tool measures how far results obtained are consistent over time to consider an accurate representation of the whole population of the study. It is the consistency, dependability, and repeatability of a project’s information, and data collection, interpretation, and analysis. In this study, the researcher intends to carry out a test-retest method to examine an indicator’s degree of stability and reliability (Yin, 2009). This will ensure the reliability of the data and therefore of the results produced after data analysis.
The researcher will visit the place or institution in which data is to be collected before the actual data collection takes place for reliability. Triangulation and a combination of various methods such as focus groups, observation, and interview methods to collect data will be considered by the researcher to ensure the research outcome given are correct. A combination of various data collection strategies, which enhances reliability, will also be used in the study as stated in the data collection procedure section.
References
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