RES832 Foundations of Research Design 2
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Topic 8: Next Steps – Dissertation Process
Objectives:
Apply methodology to the dissertation.
Choose qualitative or quantitative methodology
.
Read
Chapter 9 in GCU Doctoral Research
: Introduction to Sampling, Data Collection, and Data Analysis
DQ1
What are your three most important personal and professional priorities regarding the completion of your dissertation research and life beyond graduation? Why are these important to you and the scholarly community? How does your current plan for your dissertation research meet each one of your priorities? If a certain priority is not sufficiently met, what changes to your plan could you make to adequately meet it?
Explain.
Setting priorities for personal happiness and professional success is a must; therefore, learning to
define professional and personal goals and how to influence the future, be it work, academic and personal relationships, will significantly help to work on self-motivation and the fulfillment of goals. Since these can lead to the individual's emotional and cognitive development, the personal needs must first be raised, then the personal objectives depending on it to establish professional objectives (Shmeleva et al., 2017). In other words, putting the professional field above the personal objectives, the person tends to have an imbalance in some moments affecting both aspects. Therefore, it is always advisable to know yourself and know what your weaknesses and strengths are to get to determine professional goals or in any other area to know the steps to achieve them successfully.
Now, the three most important personal and professional priorities regarding the completion of the dissertation research and life beyond graduation, I can express on a personal level that knows
through the development of the dissertation research, my strengths and weaknesses, strategies to improve, and reinforcement in the first, to be in the workplace to achieve a balance that benefits both the professional, those who work in my environment. Second, know how to correctly handle professional ethics and good social values, leaving aside the belief system since one must be partial and objective, contributing to the profile as a professional. Lastly, learn to establish that each day has its priorities and learn to manage both time and activities through the organization so that it does not affect the performance and thus achieve the established objectives. Knowing how to handle this personal aspect will contribute to knowing how to handle other aspects concerning priorities and time management. On the other hand, in the professional aspect, it to develop the dissertation research topic in a profound way to achieve the expected objectives, including findings that serve as new scientific
research, contributing discoveries to the proposed topic, and upon reaching the female underrepresentation, and presented various cases that can be developed more easily to understand the topic developed from the problem space (Isaac-Smith & McClendon, 2021). Since the purpose is to develop a professional career considering the practice and theory of investigative how organizational methods and principles of business strategy impact success, prevent turnover, and, most important, embrace female diversity and inclusion in a senior executive position. Moreover, upon completion, I will become a specialist in one or more to achieve higher status at a professional level through the development of a specific discipline when consulting the private or public sector to implement methods and principles proposing an improvement plan to establish regulatory reforms that eventually benefit the organization and improve gender diversity. Overall, when an academic work has clear objectives on the part of the researcher to comply with them, they do not reflect incorrect or false information, confusing those who follow their field of study because they have the responsibility of setting an example for the students who follow him (Sverdlik et al., 2018). As a result, having both personal and professional goals is very important to the scholarly community. Therefore, taking the necessary daily action steps is necessary to achieve these purposes. In other words, if the student knows how to structure specific steps to obtain results in a research topic, the problem space, and the problem statement (Isaac-Smith & McClendon, 2021). Then without a doubt, they can establish priorities when facing a professional case.
Furthermore, having a structured plan for developing the research questions following the objectives set meets each of the priorities because schedules are fulfilled. Effective working times were established, which are respected and are neither too long nor too short—identifying the things that must be done, the personal tasks, academic and leisure times—considering all that, assigning a realistic schedule because the search for material is a fundamental process stage that should not be overlooked. Find material and organize it and take the time to cover as much as possible, but always consider that for the material to be valid, it must be carefully classified (Isaac-Smith & McClendon, 2021).
Nevertheless, the most important task is writing. Doctoral Learners need to write constantly by maintaining a stable routine, being able to perform free writing exercises, and developing preliminary ideas, including reflections on the readings on how they feel about specific topics so that they can sustain the practice of writing permanently and overcome a little of the anguish of the blank page. Lastly, one piece of feedback I received during the first doctoral classes was that try to avoid broad topics. The takeaway of this feedback was that it is about the work that gives status to our career; therefore, if we set unrealistic goals that include an exhaustive investigation that can take years to complete and identify these points, it might not add a problem space beforehand should avoid it.
As a result, a plan or strategy fulfillment of priorities could be to expand the investigative line if the information is verifiable, as well as to rethink the work times by extending them to be able to solve the problem now and that does not delay the date established for the completion of the dissertation or graduation. Another strategy is to let the dissertation committee know the
priorities' findings and keep in permanent contact with the dissertation committee, student counselor, doctoral learner peer, professor, and DC Network by making inquiries to ask and establishing a professional trust link to reach a strategy. Finally, do not despair or lose the direction of the research and study when observing some failure in the priorities or one does not meet the established standards. Analyzing all the scenarios consistently and professionally maintains control over the investigation, ensuring its success (Sverdlik et al., 2018; Shmeleva et al., 2017; Skakni, 2018; Isaac-Smith & McClendon, 2021).
References
Isaac-Smith & McClendon (2021). Argumentation in Support of Methodology. In Grand Canyon
University (Ed) GCU Doctoral Research:
Introduction to Sampling, Data Collection, and Data Analysis
. Shmeleva, E. A., Kislyakov, P. A., Kartashev, V. P., Romanova, A. V., & Abramov, A. V. (2017). Innovative activities and socio-psychological security in professional and personal development. In
The European Proceedings of Social & Behavioural Sciences
(pp. 921-926).
Skakni, I. (2018). Reasons, motives and motivations for completing a PhD: a typology of doctoral studies as a quest.
Studies in Graduate and Postdoctoral Education,
9
(2), 197-212. https://doi.org/10.1108/SGPE-D-18-00004
Sverdlik, A., Hall, N. C., McAlpine, L., & Hubbard, K. (2018). The Phd Experience: A Review of the Factors Influencing Doctoral Students’ Completion, Achievement, and Well-Being. International Journal of Doctoral Studies, 13, 361–388. https://doi-org.lopes.idm.oclc.org/10.28945/4113
Peer Review
The priorities in which I have in my life are a major reflection of the principles and values in which my foundation was established from. It is my life purpose to serve in a way where I am supporting children and providing support as an advocate to individuals seeking assistance within the healthcare system. Through this journey of working on my dissertation, I have hit a lot
of brick walls in my professional and personal life. There have been some rather intense and severe obstacles that have come my way that have forced me to think about my current plan in my doctoral program. I would associate the emotions dealing with the obstacles as a bit of anxiety
. It was the anxiety associated with my circumstances that led me to unnecessary stressing. There is some anxiety that is experienced by doctoral learners with regards to wondering about the future courses that are present in the designed program (Isaac-Smith & McCLendon, 2021).
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One of the priorities that I have identified as being the most important with regards to completing
my dissertation and life beyond graduation would be to complete my dissertation in accordance to the timeline I have in place for completion. I want to pass all of my courses, and have no delays in my program plan so that I can transition into my after graduation plan. In the beginning
of the establishing of a problem space, it is important for the learner to prepare an argument that will assists in justifying how the methodology that is selected would address the aspects of the study (Isaac-Smith & McClendon, 2021). I went through a transition of changing the methodology for my topic as I started narrowing down the ideas that I was focus on with regards to the specifics in which I was researching. It was a bit of a headache as I was working through my thoughts, but I found ways to work through it.
Another priority I have is to make time for myself daily. The time I dedicate for me has to be a minimum of 45 minutes a day. I spend so much time giving so much of myself, and I started to realize that I was forgetting about my own self-care. A professional goal of mines is to open up a
behavior and mental health clinic within the next two years. The clinic will be for me to live out my passion of giving back to others in the best way I can within communities that are in need of the type of care the most. My area of focus will be to serve veterans, individuals in economically disadvantaged communities, foster families, children existing in the child protective system, as well as families dealing with tragedies.
The goals I identified are important because accomplishing the goals will ensure that I am at my best as I move forward into this doctoral program. The dynamic of choosing the methodology is not only for the dissertation, but for the usage of post-doctoral research (Greenberger & Miron, 2021). I know the road to completion will not be easy, but my goals provide me with a platform to build my success on top of as I am seeing the fruits of my labor transition into reality.
Hi Marilyn and Dr. Nicholson, Anxiety when entering a higher academic level like a Doctoral program is a familiar feeling amongst doctoral learners. The American Psychiatric Association defined anxiety as a response to a real, imagined, or exaggerated threat (Black, n.d.). As a result, academic anxieties have been
cited as one of the leading causes among students (Spielberger, 2013). However, Al Majali (2020) noted that stress is something that everyone experiences at some point in their lives. Therefore, identifying the factors contributing to the anxiety is essential to creating a plan to address and reduce the anxiety effectively and adaptively.
Furthermore, stress can motivate someone to improve themselves and seek better opportunities, which benefits a person in the long run. It can serve to make people evaluate their situation and make better choices for their future (Al Majali, 2020). It became evident that many doctoral learners felt some anxiety levels; as a result, engaging in activities to reduce anxiety is essential. There are various techniques to reduce or prevent academic anxiety, such as planning tasks using
a paper planner. Another technique is setting deadlines for themselves or self-care. Moses et al. (2016) opined that including activities that maintain and promote physical and emotional health is essential during the dissertation. Moses et al. (2016) recommends balancing the road of the
dissertation that doctoral learners should have enough sleep, eat well, take time away, exercise, spend time with friends and family, and most importantly, ask for help. Therefore, doctoral learners and everyone should find time for self-care. For instance, the doctoral learner can build a morning routine for meditation, yoga, or exercises as self-care. One technique I use often is to schedule self-care on my calendar; for example, schedule a meeting with a friend for coffee or time to take a walk in nature.
References
Al Majali, S. (2020). Positive anxiety and its role in motivation and achievements among university students.
Black, D. (n.d.). Anxiety disorders. Psychiatry.org - Anxiety Disorders. https://www.psychiatry.org/patients-families/anxiety-disorders
Hyseni Duraku, Z., & Hoxha, L. (2018). Self-esteem, study skills, self-concept, social support, psychological distress, and coping mechanism effects on test anxiety and academic performance.
Health Psychology Open
. https://doi.org/10.1177/2055102918799963
Spielberger, C. D. (Ed.). (2013).
Anxiety: Current trends in theory and research
. Elsevier.
Moses, J., Bradley, G. L., & O'Callaghan, F.,V. (2016). When College Students Look after Themselves: Self-Care Practices and Well-Being.
Journal of Student Affairs Research and Practice,
53
(3), 346-359. https://doi.org/10.1080/19496591.2016.1157488
I am glad you mentioned feedback
. I have struggled with accepting feedback since my program
began.
During the first two or three courses, I saw feedback as a failure on my part. Alternatively, not meeting the expectations of the professor. Once I entered my fourth course, I
began to see the feedback as supportive and started looking forward to the feedback from the professors.
I was nervous about the feedback in week three of the current course because I had switched my topic weeks before. Plus, I knew I would have a tough time defending the topic as
a quantitative study versus a qualitative study
. However, I remember Dr. Nicholson reminding us in an announcement that the students are still in the first rough drafts of the topic or dissertation. A few days before residency, I was terrified of going to the campus because my topic was in the rough stages. However, each professor on campus for residency, including the residency director, highly supported every student. One day I was sitting in a shared area reading a possible article I wanted to reference. One of the professors walked by and said, “I can tell you are stuck because you are pondering and not reading.” The professor was right, and I did not realize it until they said it aloud. The professor grabbed a chair and sat down with
me without me asking for help to refine my topic.
After the announcement Dr. Nicholson made in week three and my experience at residency two
weeks ago, I crave feedback from the professors and students
. I hope you have a positive experience at residency like I did (if you have not gone yet). At the beginning of the residency week, only the two class professors gave feedback when each student announced their topics. However, by the end of the week, fellow students and the professors voluntarily provided students with supportive feedback. I hope residency will allow you to become more open to feedback as I have.
Hi Dennis, I enjoyed ready your response to Vijay's post! You brought a point about feedback and thank you for sharing your residency experience. Positive or negative feedback can be tricky because
it is not always easy to give or receive. However, feedback is vital to organizations' success in business, school, sports, and life. Good feedback can help an individual improve weaknesses, maximize strengths, and get a sense of where they stand (Hall, 2018).
Nevertheless, what happens when feedbacks are unspecific and leaves the doctoral learner more confused than clarify? The doctoral learner should receive detailed feedback about where
they fell short and helpful guidance on how to improve next time. However, there is a possibility that the person marking feedback probably does not have time for that, so instead, it
leaves a very brief remark that the doctoral learner needs to decode the feedback to understand how they can do better. Even though doctoral learners are encouraged to ask for more feedback
if what the instructor said is not clear (Yu & Jiang, 2022), it might cause more confusion and even discouragement when the feedback is unspecific (Carter & Kumar, 2017).
References
Carter, S., & Kumar, V. (2017). ‘Ignoring me is part of learning’: Supervisory feedback on doctoral writing.
Innovations in Education & Teaching International, 54
(1), 68–75. https://doi-
org.lopes.idm.oclc.org/10.1080/14703297.2015.1123104
Hall, D. M. (2018). The Power of Feedback: An Indicator of Mentor Effectiveness during Student Teaching (Order No. 10274286). https://lopes.idm.oclc.org/login?url=https://www.proquest.com/dissertations-theses/power-
feedback-indicator-mentor-effectiveness/docview/1904974572/se-2?accountid=7374
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Yu, S., & Jiang, L. (2022). Doctoral students’ engagement with journal reviewers’ feedback on academic writing.
Studies in Continuing Education
,
44
(1), 87-104. https://doi-
org.lopes.idm.oclc.org/10.1080/0158037X.2020.1781610
Thank you for responding, and I hope your residency will benefit you as it was to me
. Today in
my residency course, I need to submit chapter 1,2,3 of my dissertation. I cannot believe I will complete two courses today, bringing my course total to six, and I am already writing chapters of my dissertation. You mentioned students receiving confusing feedback
. I have experienced confusing feedback, and most professors have taken the time to break down the feedback into simpler terms. However, I had a professor who was unwilling to break down the feedback, and I never understood her feedback.
Thankfully, the next professor supplied similar feedback and provided the breakdown I needed.
As a paramedic, instructors, mentors, and certified paramedics can make almost anything sound or look complex. However, for the last seven years of my career, I taught paramedics and was a mentor and field training officer for the last fifteen years
. When I supplied feedback or taught students and new paramedics, I kept terms and feedback as simplistic as possible
. While people may have certifications and college degrees, everyone’s comprehension d
evelops
at various levels. So, why not keep feedback and information as simple as possible for everyone to understand?
Hi Dennis, Thank you for your response. Yes, I have experienced the same thing, and you always will have those professors that goes the extra miles and break down the feedback. Most definitely I agree that everyone’s comprehension is different and those go beyond certifications and college degrees. In my workplace, managers are required to give feedback using the acronym SIMPLE. ‘S’ stands for sensitive, ‘I’ issue-related, ‘M” meaningful, ‘P’ prompt, ‘L’ listens, and ‘E’ easy to understand. The last
DQ2
Are you planning to pursue a quantitative or a qualitative methodology for your proposed research study? Why? What things did you learn in this class that strengthened your decision to use one methodology and not the other? Which of the GCU core research designs (introduced in a previous course) might best serve the needs of both your proposed research study and your personal and professional priorities? Explain. What things did
you learn in this class that you used to advance, refine, and/or improve your dissertation research plan?
Explain.
In research, both methodologies have their areas of finding and recommendations. Which research methodology the doctoral learner wants to use in their research would lay on the problem space and what they want to find. A larger target population is needed to investigate, understand, and address why women are underrepresented in the Senior Executive role in the governmental sector. As a result, a quantitative method and design of descriptive phenomenon by synthesizing participants’ experiences and information suited this study better. As noted by Steefes & Jacobs (2021), descriptive phenomenology is a phenomenon of synthesizing the experience of participants and information from prior researchers.
Furthermore, the term description includes the data one collects from those who have experienced the phenomenon and what the research crafts to communicate the invariant meaning-based analysis (Greenberger, 2021). Consequently, during this class, I learned that in descriptive phenomenology, the researcher uses human consciousness as the approach to study human consciousness. At the same time, the researcher must bracket his/her knowledge of the phenomenon. As a result, the sample size in quantitative studies emerges from the current practices used for the design and guidelines from the organization supporting the research. I feel like I learned a lot from this class. Overall, this class helps me better understand the problem statement involving gender and how it affects society. Exploring the GCU Core research, I learned how much gender roles influence everyday life and how society promotes and
encourages the belief that a person’s gender creates different innate, natural attitudes and attributes. Searching to understand how vital society views gender and how it affects women to be promoted into Senior Executives roles has helped me refine and improve the dissertation plan especially reviewing what other researchers have found and their limitations. For instance, one improvement or refinement is the dissertation topic.
References
Greenberger, S. (2021). Foundations of Quantitative and Qualitative Research. In Grand Canyon University (Ed) GCU Doctoral Research: Foundational Principles of Research Design
. Steffes, D. & Jacbos, J. (2021) Qualitative Data Sources and Data Collection Methods. In Grand Canyon University (Ed) GCU Doctoral Research:
Introduction to Sampling, Data Collection, and Data Analysis
. Peer Review
My proposed research study involves the experiences
and challenges of the sample population with regard to accessing healthcare services
. As this study leans toward subjective data as experienced by the study sample (Cuthbertson et al., 2020), the proposed work will be following a qualitative methodology.
Through this course, I've learned more about the inductive nature and exploratory approaches of qualitative research (Pressman, 2021). This further supported and strengthened my decision of utilizing a qualitative approach to examine the experiences of the sample population. Using a semi-structured interview format will allow for an in-depth inquiry into the experiences and insights of the sample population, with open-ended questions that allow flexibility for dialogue
and follow-up questioning
(Steffes & Jacobs, 2021). The qualitative core design of phenomenology is an approach that is being considered, as it allows for the reflective review of
how participants experience the situations (Pressman, 2021). Through the upcoming attendance
of the first week of residency, I am planning to be able to more firmly decide on the methodology to use. With a goal of gaining increased knowledge and sensitivity to healthcare access issues and challenges, it appears that the qualitative approach will be the more appropriate selection.
Hi Patrick
A semi-structured interview is often open-ended; as a result, it allows more flexibility to ask to set a question to allow a dialogue. However, like any other data collection methodology, there are some limitations. Therefore, less structure can help researchers see patterns while allowing the dialogue of respondents' experiences regarding accessing healthcare services (DeJonckheere & Vaughn, 2019). One disadvantage of the semi-structured interview might be that the open-ended questions can lead to the temptation to ask leading questions, causing partial response (DeJonckheere & Vaughn, 2019). Conversely, the participant may seek to give
the answers they think the researcher wants to hear, leading to social desirability bias. Bergen & Labonté (2020) define social desirability bias as the methodical mistake caused by participants' intention in a study to respond to interview questions in a manner to believe their social interests. Another concern about open-ended questions is the ethical issues. DeJonckheere & Vaughn (2019) emphasized that the researcher should consider ethical issues when the participant must reveal sensitive and personal information. As a result, the researcher
should ensure that the interviewee and the answers did not influence the relationship between the researcher, healthcare provider, and participant (DeJonckheer & Vaighn, 2019).
References
Bergen, N., & Labonté, R. (2020). “Everything Is Perfect, and We Have No Problems”: Detecting and Limiting Social Desirability Bias in Qualitative Research. Qualitative Health Research, 30(5), 783–792. https://doi.org/10.1177/1049732319889354
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DeJonckheere, M., & Vaughn, L. M. (2019). Semistructured interviewing in primary care research: a balance of relationship and rigour.
Family medicine and community health
,
7
(2).
Topic 7: Comparing Qualitative and Quantitative Methodologies
Objectives
Compare qualitative and quantitative methodologies.
Read
Chapter 8 in GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis.
Notes:
What are the most significant philosophical underpinnings and assumptions of qualitative and quantitative research methodologies?
It relates to the philosophical and epistemological foundations of world knowledge and studies challenge. The methodology is different from techniques since it refers to the intellectual and logical premises of specific ways of research.
Qualitative research approach. Subjective experience attaches considerable significance. The ontology is that there is no objective truth but that the phenomenon contains several realities. In addition to this, every person perceives, interprets, and experiences a scenario or phenomena of interest from a different perspective, as individuals have various realities.
The theory is that knowledge is derived from subjective perception and is richly described; according to qualitative studies, truth is diverse and complex. It can only be found when humans engage within and with each other. Therefore, phenomenological approach, phenomena, rather than quantifying data requiring a predefined tool or set of questions, can best comprehend and sort by embedding researchers within the scenario.
In addition, it is time and context. Qualitative investigation is usually carried out in naturalistic environments rather than in artificial laboratories. To gather knowledge about the exciting phenomenon, researchers interact with volunteers and examine thoughts, emotions, beliefs, and conduct to participate in the study. Grove, This strategy includes planned actions before the investigator enters the environment for observations and inquiries.
In qualitative research, the focus is usually significant and not reductive, as the aim is to mean the whole thing. In this strategy, data is gathered through thorough talks, diary maintenance, broad interviews, protracted observation, and focus group interviews to gain insights into this subjective reality. Qualitative data are written in the form of detailed information, register the discussions, and identify categories that assist in sorting and organizing the data. The aim is to have a personalized interpretation to characterize the examined phenomenon in the structuring of
the data. In addition, the study.
From the optimistic worldview, a quantitative method emerges. Rationality, objectivity, prediction, and control are essential considerations in positivist paradigms. The ontological
premise is that one reality exists the senses can authenticate that. Epistemologically speaking, knowledge can be defined and explored by carefully measuring the phenomenon of interest. Researchers think all human conduct is objective, targeted, and measurable.
This includes studying research hypotheses that identify the movie's frequency and feature, test the relationship, evaluate the relation between causes and effects, and intervention efficiency testing. To avoid personal values and biases impacting study outcomes, researchers must locate or construct the instrument or tool for measuring the phenomenon while researchers remain unattached. Research is based on the collection of numerical data.
Moreover, Quantitative research involves control to identify and reduce the problem and restrict the effects of non-focused external or external variables. To verify that the research findings reflect actual reality, control, tool, and statistical analysis are utilized to extend the finding Explicitly states, association models, conceptual frameworks, and developed semi designs are the
four types of research design most typically employed in nursing research.
The approach used is dependent on what you want to do; the purpose and question researchers should study and not engage with a particular paradigm. The methods must therefore meet a specific phenomenon of interest.
In the preparation and design of the study, the qualitative research leads the researcher to achieve
the stated purpose The ability to select and implement a research design can improve the quality of the research and consequently the utility of the results; Descriptive cross-sectional design would therefore be adopted to attain this objective. The situation of occurrences or relationships between phenomena is appropriately described at a fixed period.
The general objective is to discover new significance, describe how this exists, ascertain recurrence, characterize, count, or remain informed
. Information data from so many other participants is acquired under environmental circumstances in quantitative descriptive research without attempting to modify the situation.
Research methods at several levels can be stated, examined, and classed, the philosophical level of which is the fundamental All the study is carried out in a philosophical context, as it helps the researchers to comprehend the philosophical assumptions that are behind their methodological decisions directly Protects feels it is vital for every research project to be supported and justified by the coherence between the aims of a research study, the research issues, the methods, and the scientist's personal belief.
Assumptions of philosophical quantitative and qualitative research
Quantitative research Presumes a postpositivist claim for knowledge that postulates the determination of outcomes or effects of a deterministic philosophy. It has an objective that reduces thoughts into little discreet collections of ideas to evaluate a reductionist approach.
Four Assumptions of Philosophy They believe in ontology Epistemology of Reality Nature What
is considered information and the justification of knowledge claims to develop and created the role of research and methodology values the investigation procedure.
The theoretical framework utilized by researchers for collecting, analyzing, and interpreting data acquired in a particular field of study is a philosophical presupposition. It sets the background for
findings or judgments
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DQ1
What are the most significant philosophical underpinnings and assumptions of qualitative and quantitative research methodologies? How do the kinds of useful knowledge produced vary from quantitative to qualitative research? How does the methodology employed require you to adapt your perspective of a problem? Explain.
Hi Dr. Nicholson and Doctoral Learner, Both qualitative and quantitative research methodologies have different philosophical underpinnings and assumptions. For instance, qualitative researchers often assume that only the subjective experience of individuals can yield genuinely accurate and complete understanding. On the other hand, quantitative researchers often assume that complex phenomena can be best understood and predicted using statistical analysis (Pressman, 2021). However, these researchers use qualitative and quantitative methods, sometimes called "design-based research" or "comparative research." Design-based research is a strategy that allows researchers to explore the
impact of different design decisions in the same context, providing insight into how different policies or interventions would affect the same population. Comparative research is a strategy that allows researchers to compare and evaluate the effects of two or more different interventions
or policies, often in the same population, providing insight into which intervention or policy is most effective (Brannen, 2017). Therefore, the way researcher goes about answering their research questions has a profound impact on the answers they find. Even though there are many different methodologies for generating research data, some are better suited to answering some questions than others. For instance, quantitative methods generate data through numerical data collection and manipulation, while qualitative methods involve collecting and interpreting words and texts (Pressman, 2021; Rutberg & Bouikidis, 2018).
From examining the literature and textbook, doctoral learners can assume that qualitative research might often be viewed as superior because it focuses on an in-depth understanding of a topic rather than on the accuracy of a conclusion (Steffes & Jacobs, 2021). This assumption is due to the reliance on primary sources, such as observation, interviews, and documents, which provide a more holistic view of a topic than secondary sources, such as statistical analysis, which
tend only to provide a narrow perspective. However, in quantitative research, data is collected and analyzed according to a set of established criteria and rules. In contrast, qualitative research collects and analyzes data based on the researcher's interests, beliefs, and values which often allows for a deeper and more in-depth understanding of a given topic than is possible with quantitative research (Rutberg & Bouikidis, 2018). Furthermore, when conducting qualitative research, the researcher typically works with a small sample that has been carefully chosen. As a result, it can make getting to know people easier and build trust.
In contrast, the quantitative researcher typically works with large numbers of people, making it more challenging to build trust (Pressman, 2021). Therefore, to understand each research method's valuable knowledge, the doctoral learner should think about the different ways to use prior researchers and understand the phenomena better. For instance, understanding which information yields quantitative data is essential because data that is measurable and can be expressed in numbers is often reliable and valid. The same thing for the qualitative data; however, this approach is often overlooked in the day-to-day conversations about research, but it is just as important, if not more so, than quantitative research (Rutberg & Bouikidis, 2018). Lastly, quantitative researchers tend to use more formal methods, like questionnaires and surveys, which can make it easier to stay focused and reduce confusion. Qualitative researchers, on the other hand, tend to use more natural methods, like in-depth interviews and observations, which can be more time-consuming and difficult to standardize (Pressman, 2021).
That said, the methodology for solving a problem can determine how doctoral learners or researchers perceive and approach the problem space. Quantitative approach, such as a scientific study or an experiment, is often documented in a set of rules and procedures to be followed to reach a particular outcome. A qualitative approach, such as an exploration or research, often requires adapting the perspective or viewpoint to understand the problem better (Pressman, 2021). Sometimes, the best approach combines both methods to get the best results (Brannen, 2017). Furthermore, qualitative and quantitative research are often used interchangeably to describe research that involves collecting data through observation, interviews, and other research methods—the difference between the two lies in the quantity and quality of the data being collected (Brannen, 2017). Overall, in qualitative research, the emphasis is placed on data that provide rich and detailed information about the phenomenon of interest. On the other hand, in quantitative research, the emphasis is placed on the amount of data being collected rather than the quality of that data (Brannen, 2017; Rutberg & Bouikidis, 2018).
References
Brannen, J. (2017). Combining qualitative and quantitative approaches: an overview.
Mixing methods: Qualitative and quantitative research
, 3-37.
Pressman, M. S., (2021) Comparison of Qualitative and Quantitative Designs. In Grand Canyon University (Ed) GCU Doctoral Research:
Introduction to Sampling, Data Collection, and Data Analysis
. Rutberg, S., & Bouikidis, C. D. (2018). Focusing on the fundamentals: A simplistic differentiation between qualitative and quantitative research. Nephrology Nursing Journal, 45
(2),
209-213.
Steffes, D. & Jacobs, J. (2021) Qualitative Data Sources and Data Collections Methods. In Grand
Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Peer Review
Greetings All,
As you navigate this post this week, make sure you are looking at the last component of the post.
Go beyond discussing the underpinnings and defining them. How do those underpinning change as your perspective of the problem changes?
In other words, how did the assumptions change based on the methodology?
Hi Dr. Nicholson
Greetings All,
Thank you for your insightful posts, I really saw the development of your ideas related to the assumptions of qualitative and quantitative methodologies. Remember, the philosophical foundations are the root of the why for the methodology. The assumptions are the things that would occur (or not) as a result of that foundation.
Leave nothing behind. These perspectives will be helpful as you finalize your study.
Qualitative research is the process of naturalistic inquiry that seeks an in-depth understanding of social phenomena within the natural settings (Gardner et al., 2021). Qualitative research can provide important information about social phenomena and human experiences (Gardner et al.,
2021). The understanding of the philosophical assumptions begins with assessing where it fits within the overall process of research
. Quantitative research is the process of collecting and analyzing numerical data and answering numerical related research questions (Gardner et al., 2021). Qualitative research can provide useful knowledge about patterns in the data, averages, and answer how much or how many questions (Gardner et al., 2021). Qualitative research focuses on answering the why questions (Gardner et al., 2021). Understanding the philosophical underpinnings is important for researchers. The methodology employed require the researcher to adapt their perspective of a problem. The type of methodology employed changes the research questions, underpinnings, and assumptions being researched.
The researcher will need to determine the research questions they are looking to answer.
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Hi Anamaria,
Great post! It relates to the philosophical and epistemological foundations of world knowledge and studies challenge each methodology is different from techniques since it refers to the intellectual and logical premises of specific ways of research. For instance, the qualitative research approach is a subjective experience (Pressman, 2021), and the ontology is that there is
no objective truth but that the phenomenon contains several realities. In addition, every person perceives, interprets, and experiences a scenario or phenomenon of interest from a different perspective, as individuals have various realities (Tracy, 2019). Therefore, the theory is that knowledge is derived from subjective perception and is richly described; according to qualitative studies, truth is diverse and complex. It can only be found when humans engage within and with each other (Tracy, 2019; Pressman, 2021). Therefore, the phenomenological approach, phenomena, rather than quantifying data requiring a predefined tool or set of questions, can best comprehend and sort by embedding researchers within the scenario. In addition, it is time and context where the qualitative investigation is usually carried out in naturalistic environments rather than in artificial laboratories (Pressman, 2021). Furthermore, quantitative research involves control to identify and reduce the problem and restrict the effects of non-focused external or external variables. To verify that the research findings reflect actual reality, control, tool, and statistical analysis are utilized to extend the finding. This approach depends on what the researcher wants, the purpose, and the question researchers should study and not engage with a particular paradigm (Tracy, 2019). Overall, research methods at several levels can be stated, examined, and classed, the philosophical level
of which is the fundamental All the study is carried out in a philosophical context, as it helps the researchers comprehend the philosophical assumptions behind their methodological decisions directly. Therefore, protects feels it is vital for every research project to be supported
and justified by the coherence between the aims of a research study, the research issues, the methods, and the scientist's personal belief (Pressman, 2021).
References
Pressman, M. S., (2021) Comparison of Qualitative and Quantitative Designs. In Grand Canyon University (Ed) GCU Doctoral Research:
Introduction to Sampling, Data Collection, and Data Analysis
. Tracy, S. J. (2019). How qualitative research is distinct from quantitative research.
Qualitative research methods: Collecting evidence, crafting analysis, communicating impact
. John Wiley & Sons
Tracy, S. J. (2019). Three Core Qualitative Concepts: self-reflexivity, context, and thick description.
Qualitative research methods: Collecting evidence, crafting analysis, communicating impact
. John Wiley & Sons.
Hi Amber,
Great definitions of the underpinnings of research. What are some of the epistemological assumptions we might make for qualitative research?
Hi Dr. Nicholson and Amber, Brenda,
How or why questions are more aligned with qualitative research. Is this aligned with the epistemology of qualitative research?
Hi Dr. Nicholson and Amber, DQ2
What are the relative strengths and shortcomings of quantitative and qualitative methodologies? Consider the comparative advantages and disadvantages in terms of design
components including sampling strategies, methods of data collection, and data analysis approaches.
Each study has its strengths, weaknesses, limitations, and delimitations, which can determine other studies' success or failure. As a result, what each contains may add new knowledge. Kitme (2021) found that credible data will provide a scope and progression for one's research and will equip the researcher with enlightenment toward the validity of
their research. Furthermore, extant research can show strengths and weaknesses and add to a researcher's assumptions, beliefs, strengths, and weaknesses, which can influence the research and cause issues with credibility if personal biases are not controlled. Therefore, when reviewing
literature reviews, the researcher determines what should or should not be included to remain with the focus of the research, which can result in limitations and affect the validity and reliability of the research; however, delimitations allow for the simplification of goals (Tracy, 2019). Quantitative methodology leaves no objectivity for concise feedback regarding positives and needs of room for improvement. A practical evaluation system should first be in place when preparing for inquiry and must be concise with all participants (Tracy, 2019). Observations and questionnaires would have to be performed in a categorical, quantitative study to derive data for the quantitative performance review. Criteria and measures must focus on specific, measurable, achievable, relevant, and timely goals through self-evaluation with an opportunity to self-assess
(Pressman, 2021). Data can be obtained from observations, questionnaires, and performance reviews to determine contributions or offer feedback that leads to self-reflection and change (Pressman, 2021; Tracy, 2019). In contrast, qualitative data allows observations that cannot use quantitative data to obtain the information needed for a study through processes and products that explain through words instead of numbers. It allows for understanding through empathy, focuses on the positive, and empowers the participants (Tracy, 2019). The human experience is imperative to a qualitative study (Pressman, 2021; Steffes & Jacobs, 2021). Furthermore, qualitative data is derived from a subject's attitude, behavior, and emotions, and it offers information regarding why something happens and provides additional insight into underlying issues (Steffes & Jacobs, 2021). In other words, "lived" experience. Ryan & Russell Bernard (2003) opined that information obtained from studies could be organized according to the themes. As a result, others can view this information for guidance or future studies. A focus group can be used to obtain data, and then a subsequent survey can provide more data to find themes that exhibit the most identified prevailing issue/theme. Grounded Theory Design uses the
data from focus groups, observations, and documents to examine a phenomenon (Steffes & Jacobs, 2021). For instance, Izvercian et al. (2016) utilized Grounded Theory Design to obtain a hypothesis from data collected to understand a phenomenon and prevent it from recurring by using constant comparisons and theoretical sampling. Using constant comparisons and theoretical sampling, the researcher can find this data source would tell why something is happening and what factors influence it. Other data sources will provide ongoing data to develop a theory to address those concerns and allow for preventative measures to be put into place.
References
Izvercian, M., Potra, S., & Ivascu, L. (2016). Job satisfaction variables: A grounded theory approach.
Procedia-Social and Behavioral Sciences
,
221
, 86-94. https://doi.org/10.1016/j.sbspro.2016.05.093
Kitme, E. (2021). Understanding wisdom of literature review for efficient research writing skills.
Pressman, M. S., (2021) Comparison of Qualitative and Quantitative Designs. In Grand Canyon University (Ed) GCU Doctoral Research:
Introduction to Sampling, Data Collection, and Data Analysis
. Ryan, G. W., Russell Bernard, H. (2003). Techniques to identify themes. Field Methods, 15
(1), 85–109. https://doi.org/10.1177%2F1525822X02239569
Steffes, D. & Jacobs, J. (2021) Qualitative Data Sources and Data Collections Methods. In Grand
Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
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Tracy, S. J. (2019). How qualitative research is distinct from quantitative research.
Qualitative research methods: Collecting evidence, crafting analysis, communicating impact
. John Wiley & Sons
Peer Review
I am seeing some good foundation to our discussion! What are some analysis approaches for
each of the designs in more specific terms?
Hi Dr. Nicholson, Really dive into the strengths AND weaknesses of the methodologies. Go beyond a general discussion of the approach. Talk about the sampling methods, how you collect data, and other aspects of the methodology chosen
.
Hi Dr. Nicholson, Quantitative and qualitative research explains a specific phenomenon; as a result, different designs, methodology, and measures are used. Quantitative research is data represented by numbers that describe and explain reflective observations ranging from social sciences to biology, sociology, and geology, to name a few, that do not reflect any underlying assumptions
and researcher interpretations that are not realistic to that data. Qualitative research does not use numerical data or statistics, and since phenomenological research can be unlimited, so can the data, making quantitative research more flexible (Astroth & Chung, 2018). I am focusing more on quantitative research because that is what I have been leaning toward in
my study; however, I would like to look more into qualitative research to obtain more information from the "lived" of women who aspire to a senior leadership position in a particular setting.
As a result, surveying a data collection method using close-ended questions where the respondents can answer yes or no questions or the respondent can answer firmly agrees, agrees,
disagrees, or strongly disagrees about gender stereotypes and women underpromoted into a senior leadership position. Another data collection method would be interviewing using face-
to-face meetings or focus groups. This approach is like filling out a close-ended survey, except that the exchange is verbal; as a result, the sample target is represented in the data analysis, but
not the "lived" experiences. However, using an integrated approach on quantitative and qualitative data collection methods can provide a better insight into the problem space. While qualitative data gives content into the whys of participants' behavior and help understand
people's thoughts, beliefs, and experiences, quantitative data is necessary to confirm or test a hypothesis or theory by gathering objective and conclusive answers (Taylor, 2019). References
Astroth, K. S., & Chung, S. Y. (2018). Focusing on the Fundamentals: Reading Quantitative Research with a Critical Eye.
Nephrology Nursing Journal,
45
(3), 283-287. https://lopes.idm.oclc.org/login?url=https://www.proquest.com/scholarly-journals/focusing-on-
fundamentals-reading-quantitative/docview/2063390700/se-2?accountid=7374
Tracy, S. J. (2019). How qualitative research is distinct from quantitative research.
Qualitative research methods: Collecting evidence, crafting analysis, communicating impact
. John Wiley & Sons
The two main research methods are used differently to enable data analysis based on the type of data being collected and purpose of the research. Quantitative method has a few advantages over qualitative method. Some of these advantages include but not limited to: Researchers tend
to use less time in analyzing quantitative data because of the use of statistical software’s
. Once the correct data is imported into these software’s, it automatically analyzes the data.
Also, due to the large and most often sufficient size of the sample, the results are often generalized. Additionally numeric data which it uses is viewed as more credible than qualitative methods, as it relies on instruments to provide value
(Waldschmidt & Casteel, 2021) Researchers can run the risk of not seeing the bigger picture
when it comes to quantitative research and run the risk of invalidated a result(s) if a numeric error is made. (Rahman, 2020) also suggested that quantitative research only gets a snapshot of the phenomenon and not the in-depth. On the other hand, the main advantage of qualitative studies is the fact that its people’s experiences and issues can be described
. The downside though of qualitative is the variability of perception
of the participants which can be influenced by other factors.
Hi Divine, I enjoy reading your post. Quantitative studies clarify the statistics researcher employed (logistic regression, a double-ended test, or factor analysis). In contrast, qualitative articles discuss the type of interview they conducted (recorded semi-structured interview) or the type of ethnography they conducted (i.e., participant observation with fieldnotes) (Pressman, 2021).
The philosophical assumptions for qualitative and quantitative research are based on the distinction. There are assumptions: ontological (nature of reality), epistemological (knowledge), axiological (role of values), and methodological (research strategies). For instance, a researcher is conducting a study about the psychological factors of poverty in America, if he or she takes for granted that poverty is something real. Then, this assumption is called ontological.
On the other hand, if the researcher assumes that poverty can be studied productively and we can learn something meaningful and valuable about it, then those assumptions are
epistemological since they are about what can be known. However, if it takes for granted that learning about poverty would be a good thing and that there are right and wrong ways to do it, this assumption is axiological. Lastly, if the researcher takes specific methods for learning about poverty for granted, and he or she assumes that some methods of inquiry will be workable and others will not, then this assumption is called methodological (Ataro, 2020). As a
result, these assumptions influence how researchers approach challenges and interpret data. In other words, the paradigm a researcher chooses to work within; then those paradigms can be defined under positivistic research (quantitative) or interpretivism, or social constructivism (qualitative) (Ataro, 2020). Therefore, it is critical that the researcher and the doctoral learner first comprehend the differences between the two research approaches before begin writing our
academic paper (Tracy, 2019). References
Ataro, G. (2020). Methods, methodological challenges and lesson learned from phenomenological study about OSCE experience: Overview of paradigm-driven qualitative approach in medical education.
Annals of Medicine and Surgery
,
49
, 19-23.
Pressman, M. S., (2021) Comparison of Qualitative and Quantitative Designs. In Grand Canyon University (Ed) GCU Doctoral Research:
Introduction to Sampling, Data Collection, and Data Analysis. Tracy, S. J. (2019). How qualitative research is distinct from quantitative research.
Qualitative research methods: Collecting evidence, crafting analysis, communicating impact
. John Wiley & Sons
Specialization
assumptions derived from the school of thought whose point of view supports the research topic. These include assumptions about the kinds of research that can be done within the specialization, and topical assumptions derived from the literature about the specific
topic of the dissertation. Philosophical
assumptions derived from a paradigm that guides the design. These include
Ontological assumptions about the nature of reality.
Epistemological assumptions about what can be known.
Axiological assumptions about what is important and valuable in research.
Methodological assumptions about what methods and procedures are allowable within the paradigm.
Positivism (Logical Positivism)
Positivism is a philosophy that holds that empirical evidence obtained through the senses as the only firm foundation for knowledge. Further, it insists that valid knowledge can only be
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assumed if all observers come up with essentially the same description of a thing. Last, it requires that these descriptions be uniform across all researchers or observers, which leads to the requirement that measurement is the royal road to knowledge. Thus, positivism leads to the following four sets of assumptions:
Ontological assumptions
(nature of reality): There is one defined reality, fixed, measurable, and observable.
Epistemological assumptions
(knowledge): Genuine knowledge is objective and quantifiable. The goal of science is to test and expand theory.
Axiological assumptions
(role of values): Objectivity is good, and subjectivity is inherently misleading.
Methodological assumptions
(research strategies): Using quantitative research methods such as experiments, quasi-experiments, surveys, correlation studies, and so on—which require objective measurement and analysis—is the only acceptable method to generate valid knowledge.
Interpretivism (Social Constructivism)
A second main paradigm or philosophical camp is known as
interpretivism
, or
social constructivism
. This philosophy has been more recent in development, but its roots are in the philosophy of Plato and his teacher Socrates, who held that the truth, even if it is only dimly shadowed by human approximations of it, can only be approached through careful reflection and dialog with others. Simply put, we can only interpret the truth, not measure it. We can only know what we can learn in thoughtful discussion with other seekers. Human beings, that is, construct their realities and truths by talking together about them.
Here is how the four groups of assumptions look to an interpretivist (a social constructivist):
Ontological assumptions
(nature of reality): There must be multiple realities, socially constructed by individuals together.
Epistemological assumptions
(knowledge): Knowledge is gained through an empathic understanding of participants’ lived social realities; the goal of science is to describe people’s subjective lived realities, experiences, and understandings.
Axiological assumptions
(role of values): The researcher’s subjective values, intuition, and biases are important—they play a role in the dialog of social construction and inform his or her interpretation of the data.
Methodological assumptions
(research strategies): Using qualitative research methods such as phenomenology, ethnography, case study, grounded theory, and ethnography provides access to participants inner, subjective experiences.
Assignment: Justification for Quantitative or Qualitative Methodology
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This week, the focus is on a comparison of the designs. What are some of the strengths? What
are the weaknesses?
Be sure to provide a clear rationale for the comparison. Support your assertions! There should be
noticeable citations and evidence provided throughout the paper. Also, be sure you are "swapping lenses". Make sure you are looking at the pros and cons of each design through its own lens. What does each approach bring to the table?
I look forward to your responses!
Assessment Description
Upon completion of this course, you will have a window of time in which you must declare whether you intend to pursue your dissertation research using a quantitative or qualitative methodology. Consider the work you have done in this course and previous courses in which you
explored your potential dissertation topic through both quantitative and qualitative lenses.
In this assignment, you will choose to conduct your potential dissertation research using either a quantitative or qualitative methodology. You will justify your choice of methodology based on the problem statement and supported by the extant literature and the nature of the research questions.
General Requirements
Use the following information to ensure successful completion of the assignment:
Refer to the discussions in which you have engaged to date in the forums in this course as well as
your assignments submitted in Topics 2 and 5 and any feedback from faculty and peers.
Refer to the dissertation topic you have been developing in your previous research courses as well as any feedback from faculty and peers.
If you have attended your first Residency, refer to the Prospectus PowerPoint from Residency and any feedback given by faculty or peers. The most current template can be found in the DC Network.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
Refer to the Publication Manual of the American Psychological Association for specific guidelines related to doctoral level writing. The Manual contains essential information on manuscript structure and content, clear and concise writing, and academic grammar and usage.
This assignment requires that at least two additional scholarly research sources related to this topic, and at least one in-text citation from each source be included.
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You are required to submit this assignment to LopesWrite. A link to the LopesWrite technical support articles is located in Class Resources if you need assistance.
Directions
Write a paper (1,250-1,500 words) in which you create an argument for your chosen methodology. Include the following in your paper:
1.
A refined statement of your potential dissertation topic.
2.
A single-sentence problem statement that aligns with your potential dissertation topic.
3.
A cohesive, literature-based argument for your choice of employing a quantitative or qualitative methodology for your study. How does this methodology align with your problem statement?
4.
A cohesive argument for not choosing the alternate method to explore your study. Why does this methodology not align with your problem statement?
When the issue to be solved is simple and the person in charge of solving the issue has encountered similar issues in the past, qualitative techniques can be used for resolving. This method makes use of one’s shrewdness and familiarity with similar issues for solving the difficulty. However, when the issues are complex or if the person in charge has not encountered similar issues in the past, then the quantitative technique can be used for resolving the complication. The quantitative method makes use of facts and data related to the issue. Scientific
tools are used for analyzing the related data and then making a choice based on the results of this
analysis. While the qualitative method uses intuition and knowledge, the quantitative method makes use of
skills that needs to be mastered. However, if both these techniques are simultaneously applied, then the final choice made will be optimal with both factual as well an intuitive basis which is why the person in charge needs to have knowledge of both these methods.
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Topic 6: Qualitative Data Analysis Objectives:
Analyze qualitative data analysis approaches.
Align qualitative data analysis with other study components.
Read Chapter 7 in
GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3
(2)
Ryan, G. W., Russell Bernard, H. (2003). Techniques to identify themes. Field Methods, 15
(1), 85–109.
Saldaña, J. (2013). The coding manual for qualitative researchers. Sage. (Chapter 3-6)
Discussion: Analysis Help
"Qualitative Data Analysis From Start to Finish" by Jamie Harding
Simplicity and clarity
"Doing Qualitative Research in Education" by J Amos Hatch
“Case Study Research and Applications” by Robert K. Yin
“The Coding Manual for Qualitative Researchers” by Johnny Saldana
Johnny Saldaña’s unique and invaluable manual demystifies the qualitative coding process with a comprehensive assessment of different coding types, examples and exercises. The ideal reference for students, teachers, and practitioners of qualitative inquiry, it is essential reading across the social sciences and neatly guides you through the multiple approaches available for coding qualitative data. Its wide array of strategies, from the more straightforward to the more complex, is skillfully explained and carefully exemplified providing a complete toolkit of codes and skills that can be applied to any research project. For each code Saldaña provides information about the method's origin, gives a detailed description of the method, demonstrates its practical applications, and sets out a clearly illustrated example with analytic follow-up.
Concrete steps and methods.
DQ1:
In your response, be sure to detail what the data sets would be and how thematic analysis
might specifically be used
. Provide detail of the order and process of the data analysis.
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Imagine you are a state-level health policy maker interested in health care access. You want to explore health care access barriers in underserved rural communities. You have contacts in these communities and decide to do a qualitative case study to explore how residents in one small rural
community experience difficulties accessing health care services. You plan to use semi-
structured interviews, focus groups, and a sociogram as your sources of data. How would you use a thematic analysis approach to analyze these data sets? Should the analysis be inductive or deductive? Why? What is the preferred sequence for analyzing each data set? Explain. In this study, how would you move accordingly from the analysis of raw data to development of codes and thematic findings? Explain.
As a state-level health policymaker fascinated by healthcare access and exploring barriers to access healthcare in the rural communities, a researcher might use the thematic or inductive analysis to conduct a qualitative study. Using the data sources from a semi-structured interview, focus group, and sociogram to explore how residents in one small rural community have trouble getting healthcare services. Chess and Adams-Thies (2021) emphasize that thematic analysis is a practical, flexible, and commonly used qualitative methodology which identifies, analyzes, and interprets meaningful patterns from the data source. In other words, this qualitative data methodology allows the researcher to read data to obtain meaning from it. As a result, the best method to analyze each data set would be to become familiar with the data, assign codes, look for data review, and define and name themes and patterns that produce the data (Braun, 2019). From examining the literature, the researcher would look at the data derived from the semi-
structured interviews, focus groups, and sociograms to code into applicable data sets to find out what it means regarding the lack of healthcare services in rural communities. For instance, in this
scenario, the data set might be community contacts available to the healthcare policymaker and the community's personal experiences. As a result, the researcher would use a thematic analysis approach to have the flexibility to interpret the data with a massive data set that can be sorted into themes. Therefore, using an inductive analysis will be beneficial because the actual data is driving it from the bottom up, and the inference is made from the observation and not on-premises (Maguire & Delahunt, 2017). In other words, the thematic inductive analysis is best suited for this scenario because it is built on observation of practical reality, and the researcher does not expect what the data might contain (Chess & Adams-Thies, 2021). Furthermore, the inductive analysis came from narrative inquiry, identifying stories, stories-within-stories, and versions of stories (Chess & Adams-Thies, 2021). As a result, the data collected will come directly from the target participants, which in the study will be community contact and people's experience from the rural communities facing a lack of healthcare services. Lastly, data can be moved accordingly from the analysis of raw data to develop codes and a thematic finding by following the process of reading the data first, assigning a code, then grouping the data or categorizing the data to find themes; and lastly, get the result (Chess & Adams-Thies, 2021). In other words, Chess and Adams-Thies (2021) reaffirm that the process
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should include two cycles of coding to find the themes and obtain the result. The first coding cycle includes the code, and the second coding cycle includes the category. After which further coding is required to re-examine what the researcher has learned and ensure that each code is grouped properly, thematically, and a complete final narrative is obtained. Overall, the qualitative coding will assist in obtaining intuitive and inductive data that represents the information and where the narratives come from.
References
Braun, V. (08/2019). "Reflecting on reflexive thematic analysis". Qualitative research in sport, exercise, and health, 11
(4), 589. https://doi-org.lopes.idm.oclc.org/10.1080/2159676X.2019.1628806
Chess, P.C. & Adams-Thies, B. (2021) Qualitative Data Analysis.
In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Maguire, M., & Delahunt, B. (2017). Doing a thematic analysis: A practical, step-by-step guide for learning and teaching scholars.
All Ireland Journal of Higher Education
,
9
(3).
Peer Review:
Are there other reasons why several rounds of coding might be appropriate for a qualitative study?
Hi Dr. Nicholson, Thanks for the question! Bachman et al. (2015) opinioned coding is an essential step in an essential step in qualitative analysis to categorize the various variables and make it easier to document the research when the concept is coded. Furthermore, coding makes identifying and locating the data more accessible to facilitate data analysis. Snelson (2016) emphasizes that multiple approaches can be used to review the content from a different point of view and time and to cross-check results for consistency. Furthermore, Snelson (20216) conducted a study and suggested that conducting the second round of coding can bring different perspectives to the data, interpret the data differently, and expand the range of concepts developed to understand the
correlative relationships between data. Lastly, it helps interpret the data (Bachman et al., 2015). As a result, employing several rounds of coding in the qualitative analysis process might be an essential strategy to navigate and contribute to the research if necessary.
References
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Bachman, R. D., Schutt, R. K., & Plass, P. S. (2015).
Qualitative Methods and Data Analysis. Fundamentals of research in criminology and criminal justice: With selected readings
. Sage Publications.
Snelson, C. L. (2016). Qualitative and mixed methods social media research: A review of the literature. International Journal of Qualitative Methods, 15
(1), 1609406915624574.
This study could really be used to help different communities in today's world. Many urban communities experience difficulty accessing health care services. The best way to use the thematic analysis approach to analyze these data sets would be by understanding the common them. The preferred sequence for analyzing each set can be determined by having a great understanding of inductive and deductive coding. Inductive or bottom-up coding is also described as inductive thematic analysis. One can think of this as an emergent or unstructured approach to coding in which the structure emerges from patterns identified in the data. ( Chess & Adams-Thies, 2021) Top-down coding is known
as deductive thematic analysis, typological, theoretical, or preset coding (Chess & Adams-Thies, 2021). This analysis should be inductive coding. This is because inductive coding helps one find what is common amongst the communities. This gives a better understanding of how to get better health care services in rural communities. . In this study it would be best to analyze the raw data to develop the best codes and thematic to have a great change. Having the raw data give the researcher the tools to work with no bias or other interpretation. This research will be extremely beneficial for many communities. I hope this sparks the entrance to get a better understanding of the lack of resources in small rural communities.
Hi Charvette, You suggest that having the raw data will help the researcher work without bias and other interpretations. When a researcher uses existing statistics, they are not analyzed the raw data and instead compare the statistics that another researcher or organization computed based on their data. Determining the data collection methods for any study should weigh the advantages and disadvantages of each method (i.e., raw data or secondary data). The lack of healthcare services in many urban or rural communities is a significant problem in today's society; therefore, a researcher might use secondary data analysis because the data collection has already been done for the researcher (Chess & Adams-Thies, 2021). As a result, it saves time and money. On the other hand, the disadvantage is the validity because the data has already been collected; as a result, the researcher cannot change the variables and measures of the original researcher. These can cause the researcher to miss important data or information about the lack of healthcare services for a specific population in the rural community. Now, raw data can provide some insights on its own from interviews or surveys, transcripts, or videos. For this study, the raw data analysis can find a pattern, and when the raw data is found, the researcher can run a query to summarize the result of the database. These can be beneficial
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because the raw data has not been worked, analyzed, or processed. However, one disadvantage is
the time and cost (Sinaci et al., 2020). The first step in planning data collection involves identifying which data is needed for the study; the researcher can determine the best approach between raw and secondary data.
References
Chess, P.C. & Adams-Thies, B. (2021) Qualitative Data Analysis. In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Sinaci, A. A., Núñez-Benjumea, F. J., Gencturk, M., Jauer, M. L., Deserno, T., Chronaki, C., ... & Parra-Calderón, C. L. (2020). From raw data to FAIR data: the FAIRification workflow for health research. Methods of information in medicine, 59
(S 01), e21-e32. DOI: 10.1055/s-0040-
1713684
Why is order important in data analysis? And what led you, specifically, to select the order of data analysis you use in the post?
Hi Dr. Nicholson, To properly execute a critical data analysis, it is important that researcher keep in mind data structures and organization
If
I was conducting a qualitative study to understand the barriers in rural communities when it came to accessing health care then I would utilize several qualitative research tools such as surveys, interviews, and observations
(Greenberger, et al., 2020). I would also use semi-structure interviews, focus groups, and a sociogram
. The semi-structured interviews would allow for the researcher to ask the individuals questions regarding their barriers in the community to answer research questions (Chess & Adams-Thies, 2021). The focus groups would allow for the researcher to gather data that can be compared and contrasted from the answers of the individuals about their experiences regarding accessibility to health care in their rural community
(Chess & Adam-Theis, 2021). The sociogram can be used to understand the social network between the health care in the community and the individuals in the community
(Chess & Adam-Theis, 2021). Thematic analysis can be used to identify the common things reported from the individuals and to complete an indicative analysis to code the patterns identified (Steffes & Jacobs, 2021).
Hi Mirage, Thanks for your response and the insight! Which data sources it more suitable for this study?
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DQ2:
In your response, please discuss the rationale of the data sources chosen for a phenomenological design. Remember the focus and goal of a phenomenological study. There will be several data sources that may not yield appropriate data. Be sure to fully explore the "why" and "so what" for each point made.
Now, imagine you are the CEO of a large hospital. You are interested in reducing the turnover among nurses. You wish to understand the reasons for leaving their jobs on a more personal, open, and in-depth manner. You plan a phenomenological study to obtain the lived experiences of a small sample of participants who recently left the hospital. What qualitative sources of data and data analysis approaches are aligned and ideal for use in such a study? Explain. What sources of data and data analysis approaches are not aligned and ideal for such a study? Why?
Any CEO of a hospital interested in reducing employee turnover among nurses should understand the reason why there are leaving or attempting to leave. As a result, using a phenomenological study, the CEO can obtain the lived experiences of a small sample of participants who recently left or attempted to leave the hospital. GCU Core Design emphasized that phenomenological study is a "lived" experience study; as a result, the interview questions must be open-ended and should be 60-90 minutes (
GCU Core Designs
). In conducting a qualitative phenomenological study, the CEO would gather data using interview and survey to discover, explore, and understand nurses' turnover. It an essential for the CEO to keep in mind that the source of data and the analysis approaches aligned with the research questions; as a result, a "lived" experience the CEO can gather is interviewing patients to determine the disposition of the nurses and interview the nurses that already left the hospital. By conducting an unstructured interview, the CEO can use an open-ended question to show qualitative data as the information comes directly from the participant's experience (O. Nyumba et al., 2018). From examining the literature, Kiger and Varpio (2020), along with Chess and Adams-Thies (2021), opined that thematic analysis is a way to identify the data and then analyze the data in set themes. This data set would show the most noticeable sequences essential to a phenomenological
study. Furthermore, it is flexible and can be used with many other research designs (Kiger & Varpio, 2020).
Valizadeh et al. (2018) conducted a qualitative study where 21 nurses were interviewed using a semi-structured interview. They found three main themes and nine categories. They found a lack of professional pride, an oppressive work environment, and a lack of appreciation for the coding phases. As a result, from the semi-structured interview, they determine content to determine that nurses went through experiences of humiliation, feeling ignored, and abuse which ultimately affected their job performance and satisfaction in the nursing profession. As a result, they
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concluded that dignity is a critical facet of work environments; therefore, the CEO needs to dignify the environment to thrive. Overall, obtaining insight into nursing job satisfaction can help employee recruitment and retention, but most importantly, it focuses on interventions to address any issues and provide satisfaction for retention (Karen et al., 2019).
The sources of data and data analysis approach that are not aligned and ideal for this study would
come from content analysis instead of thematic analysis. Schreier et al. (2019) define a content analysis as used for textual words but not "lived" experiences. Although coding is prevalent in both designs, with thematic analysis, coding is based on themes; as a result, data is supported and
relative to the research questions (Chess & Adams-Thies, 2021). In contrast, content analysis is based on categories and subcategories that provide conceptual maps and models for the presented research (Chess & Adams-Thies, 2021).
References
Chess, P.C. & Adams-Thies, B. (2021) Qualitative Data Analysis.
In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
GCU Core Designs
. DC3V3. (n.d.). Retrieved June 4, 2022, from https://dc.gcu.edu/dissertation/findingcontentexpert/content_expert/content_expert_resources/
gcucoredesignsdocx
Karem, M. A., Mahmood, Y. N., Jameel, A. S., & Ahmad, A. R. (2019). The effect of job satisfaction and organizational commitment on nurses’ performance. Journal of Humanities and Social Sciences Reviews. eISSN
, 2395-6518. https://doi.org/10.18510/hssr.2019.7658
Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131.
Medical teacher
,
42
(8), 846-854.
O. Nyumba, T., Wilson, K., Derrick, C. J., & Mukherjee, N. (2018). The use of focus group discussion methodology: Insights from two decades of application in conservation. Methods in Ecology and evolution, 9
(1), 20-32.
Schreier, M., Stamann, C., Janssen, M., Dahl, T., & Whittal, A. (2019). Qualitative Content Analysis: Conceptualizations and Challenges in Research Practice—Introduction to the FQS Special Issue "Qualitative Content Analysis I". Forum : Qualitative Social Research, 20
(3) https://doi-org.lopes.idm.oclc.org/10.17169/fqs-20.3.3393
Valizadeh, L., Zamanzadeh, V., Habibzadeh, H., Alilu, L., Gillespie, M., & Shakibi, A. (2018). Threats to nurses’ dignity and intent to leave the profession.
Nursing ethics
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25
(4), 520-531. https://doi-org.lopes.idm.oclc.org/10.1177%2F0969733016654318
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Peer Review
Qualitative data analysis is a systematic approach to understanding commonalities across a dataset. Qualitative datasets are complex, including many, many pages of transcripts, and depending upon the research purpose, might also include photographs, drawings, audio, and video recordings, social media, archival records, documents-in-use, observational records, demographic information, and questionnaire response (Chess & Adams-Thies, 2021). The qualitative source of data and data analysis approaches that aligned and are ideal to use for this study are observational records. This is beneficial for this study because an interview would cause one to feel pressure to state things they may not completely believe or agree with. Interviews could also ask leading questions to get certain answers. A survey could also be very beneficial as one feels they can be honest if their identity is not being exposed. Many approaches
could be aligned and be ideal depending on who is doing this study. The key is to gather as much
information to determine why employees are leaving the hospital. This will also give one the information to guide the company to give the idea workspace.
Hi Charvette, Great post! You stated that target participants might feel pressure to answer the interview question even though they do not entirely believe or agree with it. However, this study’s goal is to obtain insight into personal experiences, feelings, and perceptions to determine the reason for nurses’ turnover. Chess & Admas-Thies (2021) explained that interviews come in three forms: structured, semi-structured, and unstructured. Structured interviews are surveys. As the name implies, the questions are more structured; however, a little rapport is built to get an in-depth analysis of why employee nurses are leaving the hospital.
In comparison, the semi-structured interviews are less stringent and more flexible with open or closed-ended questions that follow a guide, and the questions are not asked in order, unlike structured interviews. The unstructured interviews have no predetermined perceptions, questions,
or hypotheses. As a result, the researcher determines whether the questions from the participant’s
responses following their conversations and themes are derived from that experience, nor is the participant’s viewpoint (Chess & Adams-Theis, 2021). On the other hand, Howe (2020) opined that there are some challenges in using unstructured interviews due to process, control over the study pace, directions, and analyzing the gathered data systematically from the context of the phenomena. However, it was also determined that unstructured interviews provide more insight and understanding from the participant’s view, which is the hospital’s nurses in this study. This insight cannot be found using a survey or structured interview process. As a result, the researcher
should know something about the problem space or phenomenon before beginning the research.
Furthermore, several things can be inferred from a deductive analysis of why nurses are leaving or want to leave their profession. However, in this study, the best way is an inductive analysis of a response to the phenomena. This response can be obtained throughout the unstructured
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interviews provided directly from the interviewees as to why there could be a high turnover in nurses.
References
Chess, P.C. & Adams-Thies, B. (2021) Qualitative Data Analysis.
In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Howe, D. C. (2020). CandiDating Game: An Exercise in Interviewing and Hiring.
Management Teaching Review
, 2379298120956268. https://doi-org.lopes.idm.oclc.org/10.1177%2F2379298120956268
Hi Dr. Nicholson, phenomenology is uniquely positioned to help health professions education (HPE) scholars learn from the experiences of others. A phenomenology is a form of qualitative research that focuses on the study of an individual’s lived experiences within the world (Chess &
Thies,2021). For this study, an unstructured interview provides the best source of qualitative data
as it allows open-end questions to be asked. Qualitative data, also space highlights research approaches that push readers and scholars deeper into qualitative methods and methodologies (Neubauer et al., 2019). Contributors to a Qualitative Space may: advance new ideas about qualitative methodologies, methods, and/or techniques; however, most qualitative researchers will develop their own specific means of preparing the data and engaging in the analysis (Chess & Thies,2021). These specific means develop with experience and over time. Documents archived data in the past cannot convey the full or provide insight into the participant’s lived experiences. A focus group can also be a good way for participants to express their views, but the data cannot be used effectively.
Hi Saundra, I enjoy reading your post. This phenomenological study of qualitative research is to obtain the lived experience of a small sample of participants who recently left the hospital. The selection of a particular methodology informs the practical steps of conducting a phenomenological research study. While all approaches seek to capture participants' lived experiences, they utilize different concepts and methods of data collection (Gill, 2020). However, the researcher needs to discover a group of people who have been through the event and can share their experiences. It is worth noting that each methodology emphasizes the importance of its analytical stages. Rather than using a methodical approach to decision-making, the participants moved through these processes
when they experienced positive emotions. As a result, the interview is the most common data collecting method in phenomenological design. The researcher structures the interview, allowing
interaction with the participants and beginning to enter the interviewee's view of the experience (Chess & Adams-Theis, 2021). However, the phenomenological interview should be structured flexibly and structured.
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A phenomenological study will typically incorporate a thematic analysis regarding the data analysis because themes are to be expected regardless of the form of phenomenological investigation (which is the occurrence of nurses departing from the hospital). Furthermore, themes will emerge from a process of reading and rereading interview transcripts, removing parts
that do not pertain to the core subject of inquiry and synthesizing the remaining parts, which constitute the object of the inquiry. Overall, the phenomenological study will not be practical if the study has a large sample size and the participants' unfamiliarity with the phenomena. Also, any type of non-thematic data analysis will not be appropriate. For phenomenological investigations, teamwork and excellent interaction with the participants are essential.
References
Chess, P.C. & Adams-Thies, B. (2021) Qualitative Data Analysis.
In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Gill, M. J. (2020). How can I study who you are? Comparing grounded theory and phenomenology as methodological approaches to identity work research. The Oxford Handbook of Identities in Organisations, 295-310.
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Topic 5: Qualitative Data Sources and Data Collection
Objectives:
1.
Analyze qualitative sources of data.
2.
Analyze qualitative data collection approaches.
Read:
Chapter 6
in
GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis.
DQ1
Think again of the study on the influence of high school principals’ leadership styles and academic achievement in their schools in your state. The sources of data must be aligned with the research questions and study design, and they must be feasible for administration of the study. Identify five different qualitative data sources that could be used to examine participants' experiences and perceptions about the phenomenon. Which ones are most appropriate for use with each of the GCU core qualitative research designs? What are some concerns you may have about the feasibility of using each one of the five data sources identified in the qualitative study described above?
Hi Dr. Nicholson and Doctoral Learners, Researchers can use different qualitative data sources to examine academic achievement and the influence of high school principals’ leadership styles in North Carolina’s schools: case study, narrative study, phenomenological study, grounded theory study, ethnography, and descriptive study (Steffes & Jacobs, 2021). However, GCU recommends the following qualitative designs: qualitative descriptive, design phenomenology, narrative inquiry, case study, and grounded theory. As a result, the most appropriate design for this scenario is qualitative descriptive. Steffes & Jacobs (2021) inform us that the qualitative descriptive design uses primary data collection (semi-structured interviews) and secondary data (i.e., focus groups, questionnaires, observations). A narrative study uses semi-structured interviews with questions for primary data collection and timelines to look at chronological events by allowing 60-90 minutes to share those
experiences. Then, the case study uses three data sources that have research questions and only provide profile information but do not answer the questions. In contrast, phenomenology design uses semi-structured interviews for primary data and secondary data collection of interviews. Those interviews include observations and reflection to obtain a more in-depth look at phenomenal experiences, including probing questions. However, the interviews, focus groups, journals, and archival documents could come from a mixed methodology. However, a researcher should keep in mind that quantitative instruments must focus on the research questions. Lastly,
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the Grounded theory uses varied processes and numerous approaches to collect data until all information is found (Steffes & Jacobs, 2021). The data sources mentioned above are interviews that speak to information acquired from another source. The interview can be face-to-face or via telephone; observations are viewing another person’s actions or behaviors in a phenomenon; focus groups discuss a particular phenomenon, and information derived from internet sources and surveys that can be emailed or mailed to obtain information from responses to questions.
Overall, Bourke et al. (2004) utilized Prescribing Indicators National Group (PING) quality indicators so that the researchers could make a more knowledgeable conclusion regarding their study focus and added the component of bringing awareness and creating better ways to record data through feedback reporting continuously. Consequently, there are some concerns about the feasibility of using each of the five data sources identified in the qualitative study described above would be obtaining valid information from the sources. Do they cover all that is needed? Primary source information validity and whether it addresses as much of the phenomenon as the focus study wishes to address.
References
Bourke, A., Dattani, H., & Robinson, M. (2004). Feasibility study and methodology to create a quality-evaluated database of primary care data.
Journal of Innovation in Health Informatics
,
12
(3), 171-177. http://dx.doi.org/10.14236/jhi.v12i3.124
Steffes, D. & Jacobs, J. (2021) Qualitative Data Sources and Data Collections Methods. In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Peer Review
The practice of data collection will include gathering evidence for systematic exploration in an attempt to discover a new understanding of a phenomenon (Steffes & Jacobs, 2021). With qualitative research, the data include perspectives and fragments of a personal story derived from
conversations with individuals and may include interpretations (Steffes & Jacobs, 2021).
With qualitative studies, the researcher may obtain data from various sources including interviews, questionnaires, observations, field notes, and focus groups (Steffes & Jacobs, 2021). These primary sources of data are those that the researcher directly collects. Interviews are a common data collection method used in qualitative studies but have a high risk of bias. Focus groups are considered an expansion of the interview methods, in that it allows multiple individuals to participate in the discussion (Steffes & Jacobs, 2021). The use of focus groups is believed to result in enhanced data quality as it encouraged dynamic dialogue between participants to gain additional insights (Shekhar et al., 2019).
With feasibility concerns, each source of data collection will require direct access to research participants and will need respective institutional review board approval for ethical
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considerations. Observations, for example, would be an acceptable method for data collection in an area discussion with participants yield limited information (Shekhar et al., 2019). The researchers will need to gain access to the classroom and its participants for direct classroom observation, again requiring IRB approval.
Hi Patrick,
Interviews and observations might have difficulty regarding interview expectations or opinions, interviewees' disposition, and time. Furthermore, their response might come from their personality, experiences, culture, background, and behaviors; as a result, it can cause an adverse effect. Therefore, researchers should keep in mind that each of these characteristics might harm the feasibility of the study. McKinney and Spialek (2017) opinioned that the cooperation and motivation of the interviewee are vital factors when it comes to measuring quality and the actual value. In other words, the interviewer's interaction, stance, and disposition can drive the interviewee to respond to their expectations and not their reality. Therefore, depending on the study and research, the environment and lack of time could be detrimental to obtaining adequate information from interviews or observation sources. However, examining the findings study reveals the amount of time needed to have an adequate qualitative interview from various disciplines and academics, and the number of interviews enough to identify common themes can help researchers to answer the research question, the study's purpose after obtaining the information, the actual environment, and the time available for the research (Hagaman & Wutich, 2017). References
Hagaman, A. K., & Wutich, A. (2017). How many interviews are enough to identify metathemes
in multisited and cross-cultural research? Another perspective on Guest, Bunce, and Johnson’s (2006) landmark study.
Field methods
,
29
(1), 23-41.
McKinney, M. S., & Spialek, M. L. (2017). The SAGE Encyclopedia of Communication Research Methods.
Do you foresee any concerns with questionnaires? Is there another data source you might
consider or is there a need to be more intentional with interviews for your study?
Hi Dr. Nicholson and Marilyn, Typically, interviews serve as a foundation for qualitative inquiry, whether or not other data sources are used
. For example, observation is a tool that can deepen the learning in an ethnography but may lack depth if we do not talk to people to understand the nuances of the
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culture we are observing
. Are there other considerations for the document analysis for the case study? How might you determine context from documents alone?
Hi Dr. Nicholson and Serah, Thank you for your response! You mention some concerns with questionnaires. Are these open-ended questionnaires? How might they be useful in your study? Why are they often not helpful?
Hi Dr. Nicholson and Jacqueline, Open-ended questionnaires are easier to administer. For this scenario, the open-ended questionnaires might be helpful because the kinds of questions a researcher asks will determine
the kind of response, they get from their target population. Steffes and Jacobs (2021) opinioned
that opened-ended questions are questions that cannot simply be answered with Yes/No, but instead, it requires the participant to provide a free response by elaborating in detail. However, the researcher should keep in mind the type of response they need; otherwise, they may end up with an unnecessary response that does not help achieve the study goal. In this scenario, the data need to go deeper to delve into the opinions and thoughts of the target population to find out the influence of high school principals’ leadership styles and academic achievement in the school system. Overall, open-ended questions allow for a complete response, provide more detail, deliver new insights, and offer deeper qualitative data (Steffes & Jacobs, 2021). However, like any other data collection methodology, open-ended questions have disadvantages. For instance, it is time-consuming to answer, irrelevant information, hard to analyze, and difficult to compare (Dalati & Marx Gómez, 2018). Zhou et al. (2017) conducted a study, and they concluded that more than 75% of the respondents did not answer any of the open-ended questions. In other words, researchers most likely will get lower response rates than with close-ended questions. However, Krosnick (2018) opinioned that the wording and organization of the survey questionnaires can benefit the response rates. As a result, researchers should find a helpful questionnaire from early surveys before writing their own. References
Dalati, S., & Marx Gómez, J. (2018). Surveys and questionnaires. In Modernizing the Academic Teaching and Research Environment
(pp. 175-186). Springer, Cham.
Krosnick, J. A. (2018). Questionnaire design. In The Palgrave handbook of survey research
(pp. 439-455). Palgrave Macmillan, Cham.
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Steffes, D. & Jacobs, J. (2021) Qualitative Data Sources and Data Collections Methods. In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Zhou, R., Wang, X., Zhang, L., & Guo, H. (2017). Who tends to answer open-ended questions in
an e-service survey? The contribution of closed-ended answers.
Behaviour & Information Technology
,
36
(12), 1274-1284. https://doi.org/10.1080/0144929X.2017.1381165
Paragraph Development
As you move forward with your next paper, I wanted to share a tip that can help keep paragraphs
fully developed:
M
Every paragraph should have one main idea. If you find that your paragraphs have more than one
main idea, separate your paragraphs so that each has only one main point. The idea behind a paragraph is to introduce an idea and expand upon it. If you veer off into a new topic, begin a new paragraph.
E
Your main idea needs support, either in the form of evidence that buttresses your argument or examples that explain your idea. If you don’t have any evidence or examples to support your main idea, your idea may not be strong enough to warrant a complete paragraph. In this case, re-
evaluate your idea and see whether you need even to keep it in the paper. A
Analysis is the heart of academic writing. While your readers want to see evidence or examples of your idea, the real “meat” of your idea is your interpretation of your evidence or examples: how you break them apart, compare them to other ideas, use them to build a persuasive case, demonstrate their strengths or weaknesses, and so on. Analysis is especially important if your evidence (E) is a quote from another author. Always follow a quote with your analysis of the quote, demonstrating how that quote helps you to make your case. If you let a quote stand on its own, then the author of that quote will have a stronger voice in your paragraph (and maybe even your paper) than you will.
L
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Links help your reader to see how your paragraphs fit together. When you end a paragraph, try to
link it to something else in your paper, such as your thesis or argument, the previous paragraph or main idea, or the following paragraph. Creating links will help your reader understand the logic and organization of your paper, as well as the logic and organization of your argument or main points. Focus Groups
Greetings All,
Several of you have mentioned focus groups in your discussions. You are starting to explore data collection methods - great work!
A couple of things to consider with focus groups:
1. Remember that one focus group produces one transcript, no matter how many people participate. So, it might be a good idea to have 5-6 people x two groups
2. A key value of a focus group is for there to be a lot of discussion, where participants bounce ideas off one another and where the discussion has people thinking more and coming up with new perspectives/insights/stuff to talk about. This will produce a more robust dataset and will better amplify and extend what gets learned in 1:1 interview
DQ2
Imagine once again that you are an automobile manufacturing executive tasked with increasing
sales in your state. You wish to do a qualitative study to obtain the perspective of sales personnel regarding an incentive program you implemented at few dealerships that quantitatively proved to be successful. The three sources of data for your case study are individual semi-structured interviews, archival documents, and field observations
. What are the most significant strengths and weaknesses of the methods for collecting data from these data sources? Why are these significant? What skills are needed to collect the data effectively? Explain. What concerns do you have about the feasibility of implementing these methods of data collection for this study? Explain.
Hi Dr. Nicholson and Doctoral Learners,
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Like every data source, individual semi-structured interviews, archival documents, and field observations have their strengths and weaknesses in collecting data. For instance, an individual
semi-structured interview allows the researcher to expand on the questioning. This expansion gives more insight into the problem space; however, the interview process might be intimidating and not allow building a rapport (Kakilla, 2021). In contrast, archival documents cannot be ignored because much information already exists documented. However, it might encounter difficulty from a feasibility perspective because it is plagiarized, newer information is ignored or overlooked, and nonexistent guidance for archival works (L’Eplattenier, 2009).
In comparison, the field observation data source observes the participant in their natural environment, and observations and other methods can be added to accompany the data (Steffes & Jacobs, 2021). These will allow us to see employees and obtain a substantiated analysis of each incentive work better according to their behavior. In other words, researchers can see the research subjects at work; therefore, field observation would be the best data source for this scenario. As a result, showing the strengths and weaknesses of using one data source disallows various views into a phenomenon and assists with a better understanding of the problem space;
furthermore, it shows how valuable the information is to further research (Hageman, 2008). Now, why are these significant? It is significant because researchers might encounter difficulty
obtaining, locating, or facilitating data for new studies. Therefore, researchers must have the skills needed to collect data effectively. For instance, the inquiry approach is literate, reasoning
thinking, good communication, problem-solving, judgment, decision making, and foundational
occupancy (Wolf et al., 2016). However, like any other study, concerns about the feasibility of the data collection implementation should be considered. For this scenario, the feasibility of implementing these data collection methods is time-consuming, and information can be overlooked or limited. Another concern is bias due to personal experiences or influences. From examining the literature review, Choy (2014) found that secondary data collection plays a significant role in research. The role determines the strengths and weaknesses in the study and supporting documents. However, the primary data method is what prepares the analysis of the phenomenon. Overall, each data source has its strengths and weaknesses, and it comes from the reliability, validity, and credibility perspective, even if they are feasible. References
Choy, L. T. (2014). The strengths and weaknesses of research methodology: Comparison and complimentary between qualitative and quantitative approaches. IOSR Journal of Humanities and Social Science, 19
(4), 99-104
Hageman, A. M. (2008). A review of the strengths and weaknesses of archival, behavioral, and
qualitative research methods: recognizing the potential benefits of triangulation. Advances in Accounting Behavioral Research
. http://doi.org/10.1016/S1475-1488(08)11001-8
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Kakilla, C. "Strengths and Weaknesses of Semi-Structured Interviews in Qualitative Research: A Critical Essay." (2021).
L'Eplattenier, B. E. (2009). An argument for archival research methods: thinking beyond methodology. College English, 72
(1), 67-79.
Steffes, D. & Jacobs, J. (2021) Qualitative Data Sources and Data Collections Methods. In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Wolff, A., Gooch, D., Montaner, J. J. C., Rashid, U., & Kortuem, G. (2016). Creating an understanding of data literacy for a data-driven society. The Journal of Community Informatics, 12
(3). http://doi.org/10.15353/joci.v12i3.3275
Peer Review
A knowledgeable automobile executive who understands the inherent potential of using the following data sources of individual semi-structured interviews, archival documents and field observation can implement incentive program to increase sales in some dealerships in the states. Any interview aims to capture data from participants by asking questions and encouraging them to respond verbally, describing their experiences. However, many researchers find it helpful to strike a compromise by employing a semi-structured interview format. Semi-structured interviews are designed for maximum flexibility, they can allow the researcher to create questions, open dialogue with the participant and then generate follow-up questions based on the direction and context of the participant’s replies (Steffes and Jacobs, 2021).
The risk of bias is high in interviewing and varies inversely with the structure of the method increases as the number of structures decreases (Steffes and Jacobs, 2021). Therefore, the researcher must strive to avoid interviewer's effect, an influence on an interviewee’s behavior that results from the conduct of the interviewer rather than the actual thoughts or beliefs. Additionally, it is essential to realize that while interviewing can be an effective way to obtain valuable data, it can be time-consuming and expensive if done by a professional interviewer. Furthermore, observation occurs in a natural environment where the researchers are unlikely to be noticed and confident that they are not influencing the participants’ behavior. The principal value of observation lies in its ability to give the researcher a direct view of behavior. There is, of
course, the risk of the researcher affecting the behavior of the participants. Nevertheless, observation is inexpensive and very effective in researching topics. Generally, archival (secondary) data sources relate to the past or existing information (Steffes and Jacobs, 2021). Archival data sources are used for qualitative research to support and offer further information to
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supplement primary data sources to help answer the research questions. Together, primary and secondary data provide the invaluable description desired in qualitative studies.
Hi Martin, Steffes and Jacobs (2021) opinion that researcher bias must be removed. I enjoy reading your post. You stated there is a high bias risk in an interview data source. Furthermore, if bias is present during the research, the research is invalid and unethical; as a result, it can be harmful to the individual being interviewed (Steffes & Jacobs, 2021). Therefore, researchers should be mentally prepared to set aside their prior knowledge and experience regarding the phenomenon to exclude any interference in gathering information that would support the research. Another point you explained was the primary and secondary data. Doctoral learners must understand that the primary data source gives them direct access to the phenomena of their research, and the secondary data source provides second-hand information and commentary from
other researchers (Steffes & Jacobs, 2021). As a result, the primary data source is more credible as evidence; however, researchers might use primary and secondary data sources. Moreover, both data have positive and negative features to determine the possibilities of the problem space, But the advantage of primary data is that scientists can be sure of their reliability and directly answer the research question (Johhson & Sylvia, 2018). In other words, the researchers formulate
the survey or the experimental method in such a way as to obtain the most accurate answer to the
question. However, collecting such data is usually costly and requires significant funding. For example, Trochim (2006) notes that qualitative research takes an enormous amount of time and effort, and the most accurate and convenient research methods are more expensive than the sources of funding would like. At the same time, secondary data have the advantage that they are
cheap or free because they are collected from literature, reports, and Internet sources (Johhson & Sylvia, 2018).
References
Johhson, E., & Sylvia, M. L. (2018). Secondary data collection. Critical Analytics and Data Management for the DNP, 61.
Steffes, D. & Jacobs, J. (2021) Qualitative Data Sources and Data Collections Methods. In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Trochim, W. M. K. (n.d.). Qualitative measures
. Research Methods Knowledge Base. https://conjointly.com/kb/qualitative-measures/
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You mentioned that these data collection methods can be time consuming which can result in concerns about the data being feasible. Increased amount of time can increase the cost to carry out the data. Certain data collection formats may require more time and resources to ethically secure permission, access, and participation from human subjects
. In interview methods the validity is reliant on if the participants are truthful and are open to share (Given, 2008).
You pointed out that bias due to personal experiences or influences can be a concern
. Bias information from individuals can cause knowledge to be the results of subjective information from individuals making it challenging to evaluate.
Credibility can be a problem and allow some information that is developed to be perspective based on their own individual contributions in his/her own individual lived experiences within the environment (Steffes & Jacobs, 2021).
You also mentioned that primary data is what prepares the individual to understand the phenomenon.
Primary data is information collected directly from first-hand experience of something that someone experienced themselves. This is the information that you gather for the purpose of a particular research project
. Primary data include observations, interviews, and anything else that is learned during the data collection process (Hox, 2005).
Hi Vijay, Thanks for your response and feedback! You mentioned that biased information cause knowledge, but it might be challenging to evaluate. So, two questions come into my mind: Can
we obtain knowledge despite bias and selection in science? Or if there is any bias when a researcher has selected data that proves his or her idea? Joober et al. (2012) opinioned biased information can affect the integrity of the entire experimental framework or study. However, some studies with bias might be published due to the study's positive results than those with negative or null findings. As a result, the studies do not provide new knowledge. Therefore, Mlinaric et al. (2017) emphasized publishing negative results because it is an ethical obligation
to be considered when reporting results of studies on human subjects as people have exposed themselves during the performance of the study. Overall, the researcher should try their best to avoid bias during their research (
Mlinarić et al., 2017). Reference
Joober, R., Schmitz, N., Annable, L., & Boksa, P. (2012). Publication bias: what are the challenges, and can they be overcome?
Journal of Psychiatry and Neuroscience
,
37
(3), 149-152.
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Mlinarić, A., Horvat, M., & Šupak Smolčić, V. (2017). Dealing with the positive publication bias: Why you should really publish your negative results.
Biochemia medica
,
27
(3), 447-452. https://doi.org/10.11613%2FBM.2017.030201
Assignment: Building a Potential Qualitative Study
Assessment Description
Upon completion of this course, you will have a window of time in which you must declare whether you intend to pursue your dissertation research using a quantitative or qualitative methodology. In this assignment, you will apply what you learned thus far in this course and in your previous research courses to develop your potential dissertation topic as a qualitative study.
General Requirements
Use the following information to ensure successful completion of the assignment:
Refer to the discussions in which you have engaged to date in the forums in this course.
Refer to the dissertation topic you have been developing in your previous research courses as well as any feedback from faculty and peers.
If you have attended your first Residency, refer to the Prospectus PowerPoint from Residency and any feedback given by faculty or peers.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
Refer to the Publication Manual of the American Psychological Association for specific guidelines related to doctoral-level writing. The Manual contains essential information on
manuscript structure and content, clear and concise writing, and academic grammar and usage.
This assignment requires that at least two additional scholarly research sources related to this topic, and at least one in-text citation from each source be included.
You are required to submit this assignment to LopesWrite. A link to the LopesWrite technical support articles is located in Class Resources if you need assistance.
Directions
Write a paper (750-1,000 words) in which you discuss the attributes of qualitative methodology that could be used to study your potential dissertation topic. Include the following in your paper:
A statement of your potential dissertation topic as refined over the last several courses.
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A discussion of the attributes of qualitative methodology that could be used to study your
potential dissertation topic.
A rationale for approaching your potential dissertation topic as a qualitative study. You must support your rationale with examples including at least two qualitative studies from the extant literature that justify pursuing the topic using a qualitative methodology
.
Devotional
"I consider that our present sufferings are not worth comparing with the glory that will be revealed in us." (Romans 8:18)
Reflection: "Life is pain. Anyone who says differently is selling you something." This line from the movie "Princess Bride" is poignantly accurate. However, it is not eternally accurate! Better days are ahead for those who follow Jesus, and these better days are far better than anything we can begin to imagine. As the old song says, "O that will be, glory for me ... when by Thy grace I shall look on His face, that will be glory ..."
Notes:
A Qualitative methodology ensures the question about why a phenomenon occurs is given an answer through thorough research. The methods utilized in a Qualitative study will help researchers understand the factors that influence a phenomenon to occur and could also potentially help researchers determine if there is a correlation between these factors. Qualitative methods are often used when a researcher desires to uncover data that
cannot be collected using numerical or statistical methods. This is why so many prefer to use a qualitative method; the researcher is working with individuals through interviews and conversation to understand why something occurs.
Case Study A case study is a type of qualitative methodology that can help a researcher follow the progress of research participants throughout the course of a study to measure a
specific phenomenon. This type of method is excellent for allowing a researcher to be descriptive, compare and contrast results, as well as assess and comprehend all aspects of the research problem.
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Topic 4: Qualitative Sampling Plan
Qualitative Strategy
This week we begin out exploration of qualitative inquiry. We will look at the same general topic
in our sample discussion with a new lens. Remember that qualitative study is holistic and subjective; you are looking at the experiences, understandings, perceptions, etc. related to the phenomenon
. How does this align with your general study topic. Soon you will also receive feedback on your quantitative study papers. Remember to leave nothing behind! Cite appropriately - if you need assistance,
www.apastyle.org
is a great resource. Check for grammatical and punctuation errors -
www.grammarly.com
is a great resource. Full develop your
argument and make sure your concepts are aligned - ALL ITEMS IN A ROW. I have included an example of alignment between problem space/need, problem, and purpose below:
Need:
In the Baptist organizational culture, there has been the endorsement from patriarchal leadership (limited study regarding reasons why women are not allowed to move forward or promotes males moving forward, despite so many women interested in pursuing leadership).
Study Baptist organization leadership’s perspective regarding why women are not ‘allowed’ to move into pastoral positions
Study Baptist organization leadership’s perspective regarding why men make good pastors
Problem statement:
It is not known
how
Baptist organization leaders perceive the influence of gender on pastoral roles
Purpose statement:
The purpose of this
(method)(design)(problem statement)(target population)
(location) qualitative case study is to explore
how
Baptist organization leaders perceive the influence of gender on pastoral roles for Baptist leaders in the South.
Now, your work may not be this developed. But I want to give you an idea of how the concepts flow - there should be a clear connection between each piece.
Objectives:
Differentiate between qualitative sampling approaches.
Describe implementation of a sampling plan.
Read:
Chapter 5 Greenberger, S. & Steffes, D. (2021) Qualitative Sampling Plan. In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis.
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Emmel, N. (2013). Sampling and choosing cases in qualitative research: A realist approach. SAGE.
Guest, G., Namey, E. E., Mitchell, M. L. (2017). Sampling in qualitative research. In G. Guest, E. E. Namey, & M. L. Mitchell (Eds.), Collecting qualitative data: A field manual for applied research. SAGE.
DQ1: Qualitative research tends to use small samples to examine a problem instead of using large samples as in quantitative research. With fewer people in the sample, it is important for the participants to be information-rich informants. With that in mind, consider the strengths and weaknesses of purposeful, convenience, and random sampling approaches in qualitative research. Then, assume you are an automobile manufacturing executive tasked with increasing sales in your state. You wish to do a qualitative study to obtain the perspective of sales personnel
regarding an incentive program you implemented at a few dealerships that quantitatively proved to be successful.
What sampling approach would you use to identify and select the 12-15 information-rich personnel from the target population? What eligibility criteria would you use in addition to being sales personnel in a dealership of this manufacturer in the given state? What logistic difficulties would you anticipate in drawing your sample? Explain your answers.
Hi Dr. Nicholson and Doctoral Learner, An automobile manufacturing executive can use Purposive Sampling to select the 12-15 information-rich personnel from the target population. Purposive sampling is a technique wherein the researcher depends on their judgment when choosing population members to participate in the study (Greenberger & Steffes, 2021; Etikan et al., 2016). This sampling method
may prove effective when only a limited number of people can serve as primary data sources due
to the nature of the research design, its aims, and objectives. The research aims to gather the perspective of sales personnel regarding an incentive program you implemented at a few dealerships. However, there are advantages and disadvantages of this sampling method. For instance, three advantages are: cost-effective and time effective. The second is the appropriate sampling method if there are only limited primary data sources. Lastly, it effectively explores anthropological situations (Palinkas et al., 2015). Three disadvantages are vulnerable to errors in the researcher's judgment, low level of reliability, and high levels of bias; lastly, the inability to generalize research findings (Etikan et al., 2016). Now, eligibility criteria describe the characteristics that all participants must share. As a result, there are some eligibility criteria that automobile manufacturing executives should consider in selecting their participants. For instance, sales personnel who have been experiencing the incentive program for at least six months and sales personnel who have a piece of extensive knowledge about the incentive program could be the two eligibility criteria for this study. In other words, the criterion for random sampling selections must depend on the participant's role
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within the organization and include variant sampling for inclusivity. On the other hand, the logistical difficulties the automobile manufacturing executive must anticipate are the participants'
availability and willingness to participate in the study. Another challenge is variation due to difficulty understanding the same range of deviation from the sampling at the beginning o each study; therefore, sampling and resampling are necessary, according to Palinkas et al. (2016). In the same line, DeFeo (2013) opined that the participants' selections when they are no longer in the study may be psychologically harmful. One harmful one might be to feel included and then be excluded. Lastly, the examination of the selection process can be logistically adverse, as well. References
DeFeo, D. J. (2013). Toward a model of purposeful participant inclusion: Examining deselection as a participant risk.
Qualitative Research Journal
.
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling.
American journal of theoretical and applied statistics
,
5
(1), 1-4. http://doi.org/10.11648/j.ajtas.20160501.11
Greenberger, S. & Steffes, D. (2021) Qualitative Sampling Plan. In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015).
Purposeful sampling for qualitative data collection and analysis in mixed method implementation
research.
Administration and policy in mental health and mental health services research
,
42
(5), 533-544. http://doi.org/10.1007/s10488-013-0528-y
Peer Review
A small sample is narrowly focused, but mighty! What are some considerations of the detail you are making regarding your sample for the project we are developing in the discussion thread?
Hi Dr. Nicholson, Greenberger & Steels (2021) state several practical considerations about sampling: thinking about accessibility, exploring site authorization, planning for attrition, planning for IRB, and finalizing the sampling plan. Before doctoral learners start data collection with people, they must
submit a research proposal to an Intuitional Review Board (IRB). The purpose of submitting a research proposal to an IRB is to check whether the research aims and design are ethically acceptable and follow the institution’s code of conduct (Greenberger & Steels, 2021). It an essential to know that there are several ethical issues that doctoral learners must pay attention to during their research design. For instance, voluntary participation, informed consent, anonymity,
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confidentiality, potential harm, and communication results should follow the GCU research code of conduct and guidelines (Grand Canyon University, Academic Catalog & Policies, 2022). After successful approval, the doctoral learners can begin to collect data according to the approved procedures. Another consideration is site permission or site authorization. Greenberger & Steffes (2021) noted that an authorization letter to conduct research is most likely required before starting a study. From examining the finding, the considerations doctoral learners or researchers should make regarding their sample for their project are consulting other researchers doing a pilot study to test
it or have done a study in the study field before spending the time and energy to do the significant study. Nevertheless, the most crucial consideration is gaining permission to proceed because ethical issues are essential when the doctoral learners or researchers plan a research study using phenomenological techniques (Walker, 2013).
References
Academic Catalog & Policies. GCU. (n.d.) https://www.gcu.edu/academics/academic-
policies.php
Greenberger, S. & Steffes, D. (2021) Qualitative Sampling Plan. In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Walker, W. (2007). Ethical considerations in phenomenological research.
Nurse Researcher (through 2013),
14
(3), 36-45. https://lopes.idm.oclc.org/login?url=https://www.proquest.com/scholarly-journals/ethical-
considerations-phenomenological-research/docview/200781616/se-2?accountid=7374
You made a lot of really great points in your post. It looks like you have a really strong understanding of purposeful sampling. I like that you mentioned how purposeful sampling can be effective with a limited number of participants.
There are pros and cons of utilizing a smaller sample size. Using a smaller sample size can be quick to conduct regarding gathering participants, performing studies, and reviewing the collected data
(Trafirmow et al., 2021). I like conducting research with a smaller sample size because for me the pros outweigh the cons.
As an emerging researcher, smaller sample sizes are more appealing as far as conducting my research. I like that the cost is lower, and it is easier to round up participants when there are fewer to find
. I like that you mentioned that purposeful sampling is cost-effective and time effective.
This is a huge pro for me. You made a lot of great points and I enjoyed reading through your post.
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Hi Anamaria, Thanks for your response and feedback! Like each sampling method, there are advantages and disadvantages (Sharma, 2017). Nevertheless, one of the essential advantages is that it allows researchers to communicate the significance of their discoveries to the public. Furthermore, sampling ensures convenience, intensive and exhaustive data collection, suitability in limited resources, and better rapport (Greenberger & Steffes, 2021; Sharma, 2017). Suri (2011) explained Patton’s 16 purposeful sampling strategies to the process of qualitative research. Patton illustrates how different purposeful sampling strategies might be particularly suited to constructing multi-perspectival, emancipatory, participatory, and deconstructive interpretations
of published research (Suri, 2011). From examining the literature review, purposeful sampling allows the researcher to gather qualitative responses that might lead to better insights and more
precise research results. Definitively, purposeful sampling is a cost-effective sample selection method; as a result, it can save not only time but also research costs. Most importantly, it lowers the margin of error in the data because the data source fits with the research context (Sharma, 2017; Suri, 2011). References
Greenberger, S. & Steffes, D. (2021) Qualitative Sampling Plan. In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Sharma, G. (2017). Pros and cons of different sampling techniques.
International journal of applied research
,
3
(7), 749-752.
Suri, H. (2011). Purposeful Sampling in Qualitative Research Synthesis.
Qualitative Research Journal,
11
(2), 63-75
. https://doi.org/10.3316/QRJ1102063
DQ2: Assume you wish to study the influence of high school principals’ leadership styles and academic achievement in their schools in your state.
How would you sample 12-15 potential participants for a single case study using convenience sampling and purposeful sampling? What factors would make you choose one approach over the other? Why? What logistic difficulties could you expect in drawing the sample?
Hi Dr. Nicholson and Doctoral Learners, Studying the influence of high school principals' leadership styles and academic achievement in North Carolina school systems, a researcher might use convenience sampling or purposeful sampling at the same time. By using convenience sampling, the researcher would find those who are more accessible as a participant in the study (Greenberger & Steffes, 2021). In other words, researchers might begin with a convenience sample since it is used because the participants want
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to participate; as a result, they come forward and identify themselves. However, qualitative studies aim to extract the most meaningful information from a few people; convenience sampling
may not provide enough information. Therefore, purposive sampling would be a better approach since it is more selective. Greenberger & Steffes (2021) noted researchers deliberately choose the
types of cases that will best contribute to purposeful sampling. In other words, sample participants are selected purposefully based on the information that needs to emerge from the early findings. Furthermore, the qualitative methodology uses purposeful sampling to obtain a more detailed, comprehensive, and rigorous look into the phenomenon. As a result, simply selecting 12-15 participants for the study is not enough but choosing only those who meet specific criteria that the study is focused on and who stand to benefit from the research the most is more feasible and are factors that would make me select purposive sampling over convenience
sampling approach. But like any other study, there are logistic difficulties; in this scenario, researchers could expect to draw the sample in errors. Another logistic difficulty might be bias and generalization of findings. Sharma (2017) opined purposeful sampling is highly prone to researcher bias no matter what method is used to collect the data. Furthermore, Sharma (2017) emphasized that when the judgments are either poorly considered or ill-conceived, the problem becomes a significant disadvantage that can provide a roadblock. In comparison, Palinkas et al. (2015) opined that there is no set of guidelines or objectives for the appropriateness of samples with any purposeful approach or unclear comprehension of range. Therefore, due to participants' selective process, people could view the study as judgmental. Furthermore, they could call the selection of the participants into question. However, purposeful sampling is an ideal approach because the flexibility of purposeful sampling allows researchers to save time and money while they are collecting data. But the final decision usually occurs during the data collection (Greenberger & Steffes, 2021).
References
Greenberger, S. & Steffes, D. (2021) Qualitative Sampling Plan. In Grand Canyon University (Ed) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015).
Purposeful sampling for qualitative data collection and analysis in mixed method implementation
research.
Administration and policy in mental health and mental health services research
,
42
(5), 533-544. http://doi.org/10.1007/s10488-013-0528-y
Sharma, G. (2017). Pros and cons of different sampling techniques.
International journal of applied research
,
3
(7), 749-752.
Peer Review:
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The process of choosing participants for data collection using the convenience sampling technique always brings the issue of data reliability to the forefront which tends to deter researchers from using this method except as a last resort. Using this method to sample participants for a study on the leadership style of principals and academic achievement would be easy in the sense that teachers and academic staff are more likely to show interest in this research.
Letting school districts and individual schools share information on the research topic
to garner interest will help more participants sign up for the research based on their availability
and accessibility.
One limitation of a convenient sample is that it can only be generalized if the
sample was randomly drawn from the population (Andrade, 2020)
On the other hand, if participants are purposefully selected for the research, it entails the researcher has reached out to the participant who has been identified as belonging to the target population and has shown interest in the studies. Through contacts, researchers can identify individuals that are knowledge-rich and can provide the necessary needed information for the research. Purposeful sampling is preferable because the researcher strategically selects participants that are insightful and information-rich
(Greenberger & Steffes, 2021)
Hi Divine, I enjoy reading your post! You stated that allowing school districts and other school individuals would be beneficial because they can share information. It is evident from Gordon & Louis' (2009) study that stakeholders and, other than high school principal input, are needed to obtain a better understanding of leadership styles and academic achievement. Those stakeholders might be parents, educators, and individuals involved in a student's educational journey. Furthermore, Gordon & Louis opined that more than addressing the school environment, all stakeholders and students must participate in the process through continual and reflective actions that share what each can offer for student success (2009). For the circumstance, community relationship, professional capacity, and the school environment are essential factors to include when attempting to understand better leadership styles and how they affect students academically and the discrepancies in learning regarding educators on that campus. On the same line, Sebastian and Allensworth's (2012) study reflects a relationship between instruction, leadership, school structure equation model. Furthermore, it was determined that instructional deviated due to leadership from administrators and the caliber of professional development offered within a school environment. References
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Gordon, M. F., & Louis, K. S. (2009). Linking parent and community involvement with student achievement: Comparing principal and teacher perceptions of stakeholder influence. American journal of education, 116
(1), 1-31.
Sebastian, J., & Allensworth, E. (2012). The influence of principal leadership on classroom instruction and student learning: A study of mediated pathways to learning.
Educational administration quarterly
,
48
(4), 626-663. http://doi.org/10.1177/0013161X11436273
In trying to get the best data from participants in this research, selecting individuals with knowledge of leadership styles and how they correlate to student achievement will help the researcher get detailed knowledgeable, reliable, and trustworthy information. Some of the participants I would select would-be teachers, vice-principals, and School District administrators
. Purposeful sampling is judgmental in selection because participant selection depends on the judgment of the researcher
. The fact that purposeful sampling can be generalized gives this type of research technique credibility However, the element of bias created by the researcher being judgmental and subjective in participant selection when such a judgment is ill-conceived or poorly considered (Sharma, 2017). With this technique, it can be difficult to convince readers that the judgment used was appropriate
. In this research, the most likely logistical issues that might arise will be getting the green light to meet with participants on school grounds as this will depend on a variety of factors.
Hi Divine, Hi, certainly a researcher may use both convenience sampling and purposeful sampling at the same time. Any school system can benefit from studying the effect of high school leadership styles on academic achievement. The defining characteristic of using a purposeful sample is that you used a specific goal (purpose) as the basis for your sample. A convenience sample would not
have any purpose other than ease of data collection. Convenience sampling is common in quantitative research, although less common, used in qualitative research (Greenberger & Steffes,2021). However
, if you decided that you want to compare three backgrounds with two interviews per background, that would indeed be a purposeful sample. Therefore, in this study I also think that purposeful sampling is the best choice for this study, qualitative studies aim to extra the most meaningful information from a few people as you mention earlier; convenience sampling might not provide enough information. It is for this reason that purposeful is the right choice for this study. The amount of time spent with participants is a concern as well. The number of interviews or participants has little meaning without knowing the amount and quality of data collected (Greenberger & Steffes,2021).
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Hi Saundra,
Thanks for your response and insight! For this scenario, purposeful sampling is adequate approach because it allows researcher to target the niche demographics to obtain specific data for
their research. As a reminder:
Review the definition of convenience sampling
A sampling strategy in which targeted individuals ‘self-select’ for research participation based on
their availability and personal motives (e.g., interest in the topic, or incentives for participation). Here, the word ‘self-select’ means that the individual has 1) received mass-distributed* information about the study; 2) has self-identified as belonging to the target population of the study; and 3) has responded to the researcher with an expressed interest to participate [which is affirmed through a signed Informed Consent document]. *Examples of ‘mass-distribution’ are given below
Compare with purposive sampling definition:
A sampling strategy in which the researcher ‘hand-picks’ individuals for participation based off a
pre-developed list of candidates (usually obtained from institutional contacts, or public/private databases). Here, the word ‘
hand-pick’ means that the individual has 1) been pre-identified as belonging to the target population; 2) has been contacted by the researcher and given information
about the study; 3) has responded to the researcher with an expressed interest to participate [which is affirmed through a signed Informed Consent document].
A key distinction in these sampling strategies is the agency in recruitment. For convenience sampling, the recruitment is driven by candidates who ‘self-select.’ For purposive sampling, the recruitment is driven by researchers who wield a list of candidates, and therefore ‘hand-pick’.
Notes:
12 interviews provide a starting point; however, the researcher needs to consider other factors when considering whether a sample size is sufficient to reach saturations.
Three factors to considering during saturation: nature of the study topic, the intended time
spent with study participants, and perceived similarity of beliefs, values, and knowledge of intended sample (Boddy, 2016).
due to the complex and emergent nature of qualitative data collection and analysis, there is not a simple answer to determining sample size in qualitative research. Each college, committee, and chair will have expectations as to what is considered a sufficient sample size.
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Topic 3: Quantitative Data Analysis
Objectives:
Analyze quantitative data analysis approaches.
Align quantitative data analysis with other study components.
Read Chapter 4 in GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis.
Pressman, M. S. (2021). Quantitative Data Analysis. In Grand Canyon University. (Ed.) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis.
Use "Which Stats Test," located on the SAGE Research Methods website to answer the discussions questions in this topic. http://methods.sagepu
DQ1
Imagine you are a state-level health policy maker interested in health care access. You want to identify geographic areas and ethnic communities that are underserved to be able to correct problems and ensure fair access. You can only use archival data for your study. What data do you need for your study? What variables will you use? Do you expect any difficulties in obtaining those data? Why or why not? What kind of design would you use? Why? What parametric and nonparametric statistical tests can you use to obtain the information you need?
Explain.
Hi Dr. Nicholson and Doctoral Learners, When it comes to the policymaker in health care topic, it is evident that access to health care refers to the individual’s ability to obtain and use needed services. However, there are geographic areas and ethnic communities that are underserved. For instance, uninsured, underinsured, elderly, lower socioeconomic class, minorities, and people that live in remote areas
are at the highest risk for lack of access to health care. Therefore, researchers would need to use archival data from economic, social, demographic, and community statistics to identify the geographic regions for a specific geographic variable. Pressman (2021) opined that variable structure is done by completing the conceptual, operational, and measurement levels from the purpose reason to identifying the variable type. As a result, in this case, doctoral learners can use geographic, ranking, and subject tables to derive data from and find the population by age, gender, access to health care, and lack of access to healthcare compared to those who have sought healthcare.
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From examining other studies, the Multiple Attribute Primary Care Targeting Strategy (MAPCATS) is the most acceptable way of utilizing descriptive data from a community with resultant healthcare information that targets areas that would benefit significantly from primary and preventive care access (Dulin et al., 2010). Future research will attend to locating communities in need, which can also compare to other communities receiving adequate care to see what variables affect primary care decisions or lack thereof. The process will allow for future
benefits to growing communities and prepare for future studies.
Overall, doctoral learners and researchers can use the descriptive design and statistics to investigate numerous variables to describe the phenomenon in underserved communities and present an accurate analysis of the existent phenomenon. It uses a survey questionnaire to determine who has sought care and who requires care; furthermore, to inquire about information the community may not have or be privileged to. However, in each study, it might be difficult in s in obtain data from confidentiality with patient information course. Still, even more, difficult would-be get information from unpublished or missing data studies, which some do contain information from individuals that were more willing to participate; however, limitations and weaknesses within these studies are hindrances. Young and Hopewell (2011) found this type of data can be viewed as being biased in that it may lack significant findings. However, due to US legislation passed in 2007 enacted, the government research was included in its first year on the clinical trials government website and allowed for a contribution of additional unpolished and missing information.
References
Dulin, M. F., Ludden, T. M., Tapp, H., Smith, H. A., de Hernandez, B. U., Blackwell, J., & Furuseth, O. J. (2010). Geographic information systems (GIS) demonstrating primary care needs for a transitioning Hispanic community.
The Journal of the American Board of Family Medicine
,
23
(1), 109-120.
Pressman, M. S. (2021). Quantitative Data Analysis. In Grand Canyon University. (Ed.) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Sedgwick, P. (2012). Parametric v non-parametric statistical tests. Bmj, 344
.
Young, T., & Hopewell, S. (2011). Methods for obtaining unpublished data. Cochrane Database of Systematic Reviews
, (11).
Peer Review
If I was a state level health policy maker interested in health care access and I wanted to identify geographic areas and ethnic communities that are underserved to be able to correct the problem and ensure fair access, there would be a lot of information that would be necessary to obtain. If I could only use archival data for the study
, I would access sources such as textbooks, newspapers,
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and peer-reviewed journals that have information previously found on the topic (Waldschmidt & Casteel, 2021). It is important for the researcher to utilize caution when using archived data and to ensure the information is supported with evidence (Waldschmidt & Casteel, 2021).The data that would be necessary for the study would include the type of health care facility accessible, the types of transportation available in the community, and the number of health care available in
the community. It is important to consider the transportation as well because though there may be health care accessible the lack of transportation may make it hard to access.
The variables that will be used would be the races, religion, occupation, businesses, educational background, and income in the community. The difficulties that are expected when obtaining the
data includes it being time consuming due to having to conduct research on each health care facility and transportation type in the community. The kind of design that would be best for this study would be a correlational research design because it identifies a relationship without control
on the research subjects (Lappe, 2000) . The parametric statistical tests that and nonparametric statistical tests that can be used to obtain the information necessary includes utilizing the Pressman correlation to understand if the data is skewed or not.
Hi Mirage, Great post! According to the U.S. Census, in 2020, 8.6 percent of people did not have health insurance, and children under the age of 19 in poverty were uninsured compared to 2018 (
Health
insurance coverage in the United States: 2020
). You stated, “the researcher should use caution when using archived data and ensure the information is supported with evidence.” One disadvantage is that the previous research may be unreliable or not collected to the researcher’s standards. As a result, the study does not control how researchers collected the data when they used archived information. But the most critical disadvantage is that it may not respond to the researcher’s specific research questions; as a result, it may be time-consuming to find and sort through archives that do not use standardized descriptive methods or the study is in original language (Fisher & Chaffee, 2018). Furthermore, Fisher & Chafee (2018) opined the data might be old. As a result, it is no longer relevant or generalizable to the current phenomenon under investigation.
Transportation is a significant issue regarding healthcare accessibilities, particularly in rural areas where travel distances are required and access to substitute means such as public transit or taxi services is limited or lacking. On the other hand, transportation is one of the critical social determinants of health, and the availability of convenient transportation impacts individuals’ ability to access quality healthcare. For instance, living in a rural area, people face a lack or limitation of public transportation, which delays access to healthcare services (Syed et al., 2014).
Using transportation as one of the variables can help determine health policymakers interested in health care access to find the underserved to correct the problem and ensure fair access.
References
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Bureau, U. S. C. (2021, October 18). Health insurance coverage in the United States: 2020
. Census.gov. https://www.census.gov/library/publications/2021/demo/p60-274.html
Fisher, G. G., & Chaffee, D. S. (2018). Research using archival data. In Advanced Research Methods for Applied Psychology
(pp. 76-84). Routledge.
Syed, S. T., Gerber, B. S., & Sharp, L. K. (2014). Traveling towards disease: Transportation Barriers to Health Care Access. Journal of Community Health
, 38
(5), 976–993. https://doi.org/10.1007/s10900-013-9681-1
If I was a CEO researching the turnover rate with the nurses at a large hospital I would utilize quantitative research. Quantitative research would be beneficial because it would allow the numerical data to be compared to find the answers regarding how to reduce the turnover rate (Waldschmidt & Casteel, 2021). It would be important for the researcher to use the variables including the satisfaction of the nurses, the number of hours that the nurses are working a week, and how stressed the nurses feel at work. As a researcher it would be best to use a correlational study to compare and to give the participants an anonymous survey. A parametric or a nonparametric statistical test would need to be utilized to analyze and compare the data. A parametric test would be used if the data is not heavily skewed and the nonparametric test would be used if the data is skewed (Pressman, 2021). Also, a Wilcoxon or Mann-Whitney t test may be
used to analyze the data (Pressman, 2021).
Hi Mirage, Thanks for your response! DQ2
Now, imagine you are the CEO of a large hospital. You are interested in reducing turnover among nurses. You wish to find out to what extent nurses’ turnover intention is related to their overall job satisfaction, their average number of hours worked per week, and their level of professional stress. You plan a correlational study, administer an anonymous survey, and collect interval data for all four variables. What parametric and nonparametric statistical tests can you use to analyze the data?
Hi Dr. Nicholson and Doctoral Learners,
Based on the result from the Which Stats Test, using interval data for all the four variables: overall job satisfaction, the average number of hours worked per week, turnover intention, and level of professional stress, it was determining the best test to use was the Ordinary least-squares
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(OLS) regression (Which stats test). The purpose that OLS regression is a type of linear least-
squares method for estimating the unknown parameters in a linear regression model (Meloun & Militky, 2001). The scenario does not know if or to what extent a relationship exists between job satisfaction, work hours, and stress levels resulting from nurse turnover. Therefore, the CEO should start with association variables, then predictive values of outcome variables from the independent values, and finally, interval values because the intent is to determine the correlation between turnover intention resulting from job satisfaction or lack, the hours per week worked. The professional level of stress determines the relationship between the variables, studies the subject, and ultimately ascertains the connection through an anonymous survey (Pressman, 2021). Along the same lines, Audette et al. (2020) emphasize that anonymous data collection allows more participation since the answers are not tied with the participants. As a result, it will enable a more truthful response. Finally, the Likert scale point system could be used for data derived from a quantitative survey, then analyzed with statistical software, and the researcher can
use a literature review to support all findings (Ali et al., 2015).
References
Ali, K., Noor, N., Wilson, P., & Ismeth, Y. (2015). Symptoms Versus Problems (SVP) Analysis On Job Dissatisfaction and Managing Employee Turnover: A Case Study in Malaysia. International Journal of Economics, Commerce and Management United Kingdom
, 3(4), 1-29.
Audette, L. M., Hammond, M. S., & Rochester, N. K. (2020). Methodological issues with coding
participants in anonymous psychological longitudinal studies. Educational and Psychological Measurement, 80
(1), 163-185.
Meloun, M., & Militký, J. (2001). Detection of single influential points in OLS regression model
building.
Analytica Chimica Acta
,
439
(2), 169-191
. https://doi-org.lopes.idm.oclc.org/10.1016/S0003-2670(01)01040-6
Pressman, M. S. (2021). Quantitative Data Analysis. In Grand Canyon University. (Ed.) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Which stats test. Sage Research Methods. (n.d.). http://methods.sagepub.com/which-stats-test
Peer Review
Burnout is very common amongst healthcare professionals, which can result in a turnover and a number of other problems. Trying to understand the turnover and find ways to mitigate or reduce
the number is beneficial for several reasons; this will be best completed using a quantitative correlational study. A correlational study allows the researcher to examine the relationship
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between sets of variables using data collected from a group of participants with the goal of assessing the course and strength of the relationship (Waldschmidt & Casteel, 2021).
With consideration to job satisfaction, an average number of hours per week, and professional stress as my variables, it is difficult to know what the data will look like and whether it is parametric or nonparametric. My problem statement would be something like “it is not known if and to what extent a difference exists in nurse turnover rate between overall job satisfaction, their
average number of hours worked, or their professional stress. The use of Pearson correlation is the inferential statistic to be used for the study if I obtain normally distributed data; if the assumption is not met, then the appropriate tool to utilize is the Spearman correlation (Waldschmidt & Casteel, 2021). Through analysis of the data collected, the organizational policy
could be implemented that would support the infrastructure for evidence-based interventions (Sancheznieto & Byars-Winston, 2021). Hi Candice, When creating a research study to reduce the turnover rate among nurses, the researcher should use parametric tests and nonparametric test, because both are techniques that make inferences about a population from a sample. In this scenario, the parametric test that can be used is the Pearson’s Correlation statistical test, specifically the Spearman’s Rho. Spearman’s rho provides an appropriate in-depth comparison when variables are in the form of ranks or ordinal scale (Obilor & Amadi, 2018, p.15). The purpose of the research is to understand the relationship between the turnover rate and the overall
job satisfaction, average numbers worked and professional stress. Data can be collected in the form of Likert Scale. The Likert scale would consist of options from strongly agree to strongly disagree based on their personal reason for leaving. In this scenario, the nonparametric test that can be utilized is the T-Test. The T-test examines the
methods used to test a hypothesis to compare groups (Mishra, Prabhaker, et al., 2019, p.407). After the parametric statistical test, Likert scale, the researcher would take an educated guess based on the results from the Likert survey. The Likert survey will produce results that will allow
the researcher to use the T-test to make assumption that can compare two independent groups.
Hi Amber, Nahum (2016) opinioned that nonparametric analysis testing had a reduction for inaccurate findings due to lack of assumptions in population. As a result, the nonparametric methods have been found to be effective, but not reliable in validity. In contrast, parametric methods allow
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normality in distribution; as a result, it lends to a normality in assumption. In other words, if there are assumption is unsatisfactory, then nonparametric methods can be utilized as an alternative. References
Nahm, F. S. (2016). Nonparametric statistical tests for the continuous data: the basic concept and
the practical use.
Korean journal of anesthesiology
,
69
(1), 8. DOI: https://doi.org/10.4097/kjae.2016.69.1.8
Saundra,
Will it be important to clearly define work enthusiasm, career identity, and work enjoyment? Why?
Hi Dr. Nicholson and Saundra, Our identity plays an essential role in our thoughts, behaviors, and every decision we make at work and at home. Therefore, true identity can help any employee to become more successful in the workplace, and in the scenario, nurse should have true identity about her career. Data analysis of the study will be conducted using the following two statistical tests: Parametric and nonparametric.
The parametric statistical tests that can be used to analyze the data are the Pearson correlation coefficient and the linear regression. The parametric test is conducted to determine the correlation between the nurse's turnover intention and overall job satisfaction, average number of hours worked per week (Aiken et al., 2017). There is a positive
correlation between a nurse's job satisfaction and their average number of hours worked per week and their level of professional stress. In this test, most of the variables are quantitative/interval data type and can be analyzed using the t-test for independent samples that
ascertains whether there is significant difference between means from two independent samples before conducting ANOVA.
ANOVA is an acronym for anova, or analysis of variance. This is a statistical method that allows you to see if different levels of each variable have an effect on one another.
There is a positive correlation between a nurse's job satisfaction and their level of professional stress (Aiken et al., 2017). The nonparametric statistical test that
can be used to analyze the data is the Spearman rank correlation coefficient
. Nonparametric
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tests which compare two groups by their medians instead of their averages, include Mann Whitney U Test simple analysis of variance.
Hi Serah, Great insight! Kim (2017) informs us ANOVA is the statistical method most frequently used in medical research. However, ANOVA has limitations with the assumption that like groups have standard deviations. Moreover, a vast difference allows for a bigger chance of inaccurate testing. But one way ANOVA statistical technique is used to test the means of the samples where only one independent variable is involved (Kim, 2017). As a result, one factor is applied, and, in the scenario, the level of interaction with nurse turnover is the only factor involved.
Consequently, if this value is greater than the critical value, the researcher has evidence against the null hypothesis. Hence the null hypothesis cannot withstand a 5% level of significance. In other words, the F test is significant (
ANOVA
). Overall, the ANOVA test analyzes the underlying
population means. In comparison, inferential statistics provide a practical and more straightforward way to analyze real-world situations such as nurse turnover. However, ANOVA is an essential part of inferential statistics. The main limitation of ANOVA is that the individual significance between the mean is not determined. Only the overall value is determined. A pro hoc test may overcome this difficulty.
References
ANOVA
. from Wolfram MathWorld. (n.d.). https://mathworld.wolfram.com/ANOVA.html
Guetterman, T. C. (2019). Basics of statistics for primary care research.
Family medicine and community health
,
7
(2). http://doi.org/10.1136/fmch-2018-000067
Kim, T. K. (2017). Understanding one-way ANOVA using conceptual figures. Korean Journal of Anesthesiology, 70
(1), 22. https://doi.org/10.4097/kjae.2017.70.1.22
Waldschmidt and Casteel (2021) emphasize caution when using archival data such that appropriate permissions have been obtained for data that has been previously collected by another researcher and has been made available for use by another researcher
. If the researcher is diligent, he or she after ensuring data protection, will verify if the dataset is applicable for all
research studies, and provide measurements for the intended variable values for the study. The researcher is required to assess if the data needs to be paired, and the archival dataset must also
provide the appropriate variables that are already paired. An additional step involves verification that the dataset contains values for the needed variables. Waldschmidt and Casteel (2021) stress how some statistical analyses require the use of raw data and ensuring the archival dataset does contain raw data is a requirement.
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In this instance, my intent will be to utilize a survey design instrument with applicable screening questions and the use of inclusion criteria as a means of determining adequacy for participation. I will deploy a comparative study that makes use of dependent and independent variables, in order to determine how the factor of manipulation affects the outcomes for underserved communities. And since the measured outcome is the dependent variable, a change of the independent variable will have an effect on the values realized. Ultimately the researcher, in this case, is examining a causal effect to possibly explain impediments to adequate healthcare in under-resourced communities.
The use of informed consent templates follows strict guidelines that detail inclusion or exclusion criteria, potential risks, researcher contact information, and what is required of each participant. The study further directed the use of Survey Monkey, M-Turk, or Qualtrics for execution and to ensure accuracy.
Assumptions testing applies a set of rules to be met for the inferential statistic to be valid, and each contains a set of assumptions. The Laerd.com
source is noted in Pressman (2021) as a strong tool for understanding assumptions, and these must be identified by learners to describe how each assumption will be tested. When parametric violations occur, then options for alternative analyses including the acceptance or ignoring the violation, data transformation, or using nonparametric data can be used.
Are there any other cautions or considerations when using an existing quantitative data set?
Hi Dr. Nicholson, Bronaugh, and Anamaria, Researchers should take a few precautions while utilizing quantitative data since quantitative data is generalized. As a result, it is much narrower and can often be a superficial data set, and the researcher can be limited to the study results as it provides numerical data rather than personal narrative accounts that offer more information than solely numerical data (Pressman, 2021). Quantitative data experiments are typically carried out in unnatural and artificial environments to tweak the data a bit; however, this applies to this research as it seems the study was conducted in a natural area. Lastly, researchers' development of standard questions can lead to structural bias and false representation, also known as manipulating questions, so that researchers might formulate the answers that reflect the researcher's view and not the participant (Tripathy, 2013). Fagarasanu & Kumar (2002) opined and emphasized using a reliable and valid instrument suitable for the study. As a result, it is essential to find information that proves the
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reliability and validity of the device. Overall, it is believed that the use of archival data contains a
high level of ethical practice as it maximizes the values of the data collected and reduced the burden on resonance by ensuring replicability (Pressman, 2021; Tripathy, 2013).
Reference
Fagarasanu, M., & Kumar, S. (2002). Measurement instruments and data collection: a consideration of constructs and biases in ergonomics research.
International journal of industrial
ergonomics
,
30
(6), 355-369.
Pressman, M. S. (2021). Quantitative Data Analysis. In Grand Canyon University. (Ed.) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis
Tripathy J. P. (2013). Secondary Data Analysis: Ethical Issues and Challenges.
Iranian journal of public health
,
42
(12), 1478–1479.
Assignment: Building a Potential Quantitative Study
Assessment Description
Upon completion of this course, you will have a window of time in which you must declare whether you intend to pursue your dissertation research using a quantitative or qualitative methodology. In this assignment, you will apply what you learned thus far in this course and in
your previous research courses to develop your potential dissertation topic as a quantitative study.
General Requirements
Use the following information to ensure successful completion of the assignment:
Refer to the discussions in which you have engaged to date in the forums in this course.
Refer to the dissertation topic you have been developing in your previous research courses as well as any feedback from faculty and peers.
If you have attended your first Residency, refer to the Prospectus PowerPoint from Residency and any feedback given by faculty or peers.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
Doctoral learners are required to use APA style for their writing assignments. The APA
Style Guide is located in the Student Success Center.
Refer to the
Publication Manual of the American Psychological Association
for specific
guidelines related to doctoral-level writing. The Manual contains essential information on manuscript structure and content, clear and concise writing, and academic grammar and usage.
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This assignment requires that at least two additional scholarly research sources related to this topic, and at least one in-text citation from each source be included.
You are required to submit this assignment to LopesWrite. A link to the LopesWrite technical support articles is located in Class Resources if you need assistance.
Directions
Write a paper (750-1,000 words) in which you discuss the attributes of quantitative methodology that could be used to study your potential dissertation topic
. Include the following in your paper:
1.
A statement of your potential dissertation topic as refined over the last several courses.
2.
A discussion of the attributes of quantitative methodology that could be used to study your potential dissertation topic.
3.
A rationale for approaching your potential dissertation topic as a quantitative study. You must support your rationale with examples including at least two quantitative studies from the extant literature that justify pursuing the topic using a quantitative methodology
Hints:
Research has shown Using a _________ design, _______ did a study applying the conceptual framework ______________ examining perspectives of _____________________________ and found that experienced ___________. While _____________ were much more supportive than __________ of using ____________ to evaluate ____________. They suggested that ________________. Rubric
Dissertation Topic
:
A statement of potential dissertation topic is present and is clearly refined from the last several courses.
Attributes of the Quantitative Methodology
: A description of the attributes of the quantitative methodology is present and the argument is clear and convincing, presenting a persuasive claim in a distinctive and compelling manner.
Quantitative Study Rationale
: A rationale of the quantitative study methodology is thorough. Scholarly research is used for support and is current or seminal. Argument is clear and convincing, presenting a persuasive claim in a distinctive and compelling manner.
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Topic 2: Quantitative Instrumentation and Data Collection
Objectives:
1.
Analyze quantitative instrumentation.
2.
Analyze quantitative data collection approaches.
Read Chapter 3 in
GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis.
DQ1
Think again of that study on the predictive relationships of high school principals’ leadership styles and academic achievement in their schools in your state. The instrumentation must be aligned with the research questions and study design and must be feasible for administration of
the study. How do you identify instruments appropriate for use with GCU core quantitative research designs? How might you address concerns about the influence of instrumentation on study feasibility? Do you have any ethical concerns about recruitment
and data collection? Explain.
Hi Dr. Nicholson and Doctoral Learners
Identifying instruments appropriate for use with GCU core quantitative research designs depends on each study and the nature of the respondents involved. As a result, GCU has approved five core quantitative research designs: experimental, quasi-experimental, descriptive, correlational, and casual comparative (Quantitative research design methods for writing dissertations). From examining the textbook, the most common core design is the Pearson's correlations, even though other instruments such as online surveys and document analysis are essential instruments in quantitative research that help understand what people think, how they act in a particular way, and why, questionaries in most cases applicable when dealing with large sample sizes. A school setting has a large sample size (Goertzen M., 2017). Even though there are different concerns about the influence of instrumentation on study feasibility, they will be addressed depending on its complexity and impact on the entire process. As a result, the most important thing when choosing an instrument is understanding how the process will be carried out; this is key since different tools call for various procedures (Walschmidt & Casteel, 2021). Some strategies call for questionnaires; others call for probability sampling, observation, or interviews. Document reviews are the most common and widely used methods, either offline or online data collection. On the other hand, this study has some ethical concerns regarding the recruitment of respondents and data collection. The first concern is that this study deals with minors whose rights are closely restricted. The law does not require engaging a minor in high school to get information without their parent's or guardian's permission (Creswell, 2020). In other words, parents should be involved in the study as well. Again, there is also a concern about the anonymity of respondents since they are minors. These are simple and common ethical concerns that do not present many technical challenges in our research.
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References
Creswell, J. W., & Creswell, J. D. (2017).
Research design: Qualitative, quantitative, and mixed methods approaches
. Sage publications.
Goertzen, M. J. (2017). Introduction to quantitative research and data.
Library Technology Reports
,
53
(4), 12-18.
Quantitative research design methods for writing dissertations
. GCU. (n.d.). https://www.gcu.edu/blog/doctoral-journey/quantitative-research-design-methods-writing-
dissertations?msclkid=513e23e9cef911ec84339d085cf09842
Waldschmidt, J. & Casteel, A. (2021) Quantitative Instrumentation and Data Collection. In Grand Canyon University. (Ed.) GCU
Introduction to Sampling, Data Collection, and Data Analysis
Peer Review
Hello Dr. Nicholson and class,
The GCU Core Quantitative research designs include experimental, quasi-experimental, descriptive (survey), correlational, and causal-comparative (McClendon, Greenberger, & Bridges, 2020). When a researcher is conducting a quantitative study, an appropriate instrument to use must reliable and valid (Waldschmidt & Casteel, 2021). Waldschmidt and Casteel (2021) continue by stating that one way to determine if an instrument fits your study is to look at what previous researchers have used.
You can determine reliability using Cronbach’s alpha score. If an instrument has a score of 0.70 or higher, then it is considered valid
(Waldschmidt & Casteel, 2021).
Some issues with feasibility include how a researcher can obtain access to their population of interest, and then the subsequent population size of responses
(Waldschmidt & Casteel, 2021). Due to the fact that a quantitative research study is meant to take a target population and generalize the findings to a greater population, this could be an issue with feasibility if the target population responding to survey questions, for example, is too small, or doesn’t meet the demographic population (Waldschmidt & Casteel, 2021). As long as the researcher has respect for the persons participating in the study
, does not demand anything of the participants, does no harm to participants, and upholds the justice of the research,
there should be no ethical concerns related to the recruitment of participants or the data collection process
(Waldschmidt & Casteel, 2021).
Hi Andrea,
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Your post provided excellent insight into quantitative research for this week's discussion. Waldschmidt & Casteel (2021) also emphasize that the instrument must measure what it says it counts and consistently show validity and reliability. Furthermore, GCU recommends obtaining an instrument. Doctoral Learners should be able to examine how other researchers have measured the variable (Waldschmidt & Casteel, 2021), then conduct one of the research instruments used; the researcher can establish how reliable and valid the instrument was to collect data. However, when the instrument is selected, the doctoral learners need to obtain approval to use it. On the other hand, you stated "does not demand anything of the participants;" however, Waldschmidt & Casteel (2021) opined any information relevant (i.e., demographic information) to the study should be collected. However, doctoral learners should be aware of the problem of self-reported participants since the participant may not provide a truthful response. The ethical concern about sample participants and data collection is that the sample is genuine to obtain data accurately. Additionally, if the researcher is paying for the recruited participants, the researcher needs to ensure the data is not altered to remain valid and reliable.
Reference
Waldschmidt, J. & Casteel, A. (2021) Quantitative Instrumentation and Data Collection. In Grand Canyon University. (Ed.) GCU
Introduction to Sampling, Data Collection, and Data Analysis
Candice,
You mention what a reliable instrument should do as it relates to the data? What does validity tell us?
Hi Dr. Nicholson and Candice, Waldschmidt & Casteel (2021) opined validity is the amount to which a concept is correctly measured in a quantitative investigation or research. Therefore, validity is crucial since it defines which survey questions to employ and helps researchers guarantee that they ask questions that genuinely measure the problem space—for instance, a study designed to investigate depression but assesses anxiety. As a result, it would not be valid (Patino & Ferreira, 2018). On the other hand, Mahmood (2017) emphasizes six types of validation. Those
six types are predictive validity, concurrent validity, content validity, convergent validity, discriminant validity, and construct validity. The first two of these validities are considered criterion-oriented validation procedures. The convergent and discriminant validity are considered theoretically related or unrelated approaches. In comparison, the content validity measure people’s experience and available acknowledge. However, the construct validity measures the theoretical intention (Mahmood, 2017).
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Overall, validity tells researchers and doctoral learners that validity relates to the truthfulness of data and requires independent knowledge of the true nature or magnitude of the entity. As a result, it is understood that internal validity and external validity play an essential role in scientific research. The internal validity establishes the truth about inferences regarding cause-
effect or causal relationships. The external fact confirms the reality of a conclusion that involves generalization. As internal and external validity are fundamental to any experimental research, the researcher should be aware of threatening factors. In quantitative analysis, randomization and control groups reduce the threats to internal validity (Hoareau, 2017).
Reference
Hoareau, C., Querrec, R., Buche, C., & Ganier, F. (2017). Evaluation of internal and external validity of a virtual environment for learning a long procedure.
International Journal of Human–
Computer Interaction
,
33
(10), 786-798.
Mahmood, Khalid (10/2017). "Reliability and validity of self-efficacy scales assessing students’ information literacy skills A systematic review". Electronic library
(0264-0473), 35 (5), 1035.
Patino, C. M., & Ferreira, J. C. (2018). Internal and external validity: can you apply research study results to your patients?.
Jornal Brasileiro de Pneumologia : Publicacao Oficial da Sociedade Brasileira de Pneumologia e Tisilogia
,
44
(3), 183. https://doi.org/10.1590/S1806-
37562018000000164
Waldschmidt, J. & Casteel, A. (2021) Quantitative Instrumentation and Data Collection. In Grand Canyon University. (Ed.) GCU
Introduction to Sampling, Data Collection, and Data Analysis
Hello,
You bring up some interesting points that I did not think about. You said when participants self-report they may not give truthful responses. I am not completely sure why someone would lie about their demographic information if it were for a survey or questionnaire, but it creates a fascinating and unfortunate limitation of the study (Waldschmidt & Casteel, 2021). One concern I see with falsified demographic data is that when the researcher tries to extend the data to the general population, there may be a mismatch
. Waldshmidt and Casteel (2021) discuss how the type of instrument chosen for a quantitative study can help reduce the influx of
false answers, however, this was not regarding demographic information
. When doing a little more research on this, Punj (2017) adds that this type of falsifying of demographic data is mostly seen in consumer information input into online sites due to a mistrust of companies requesting such information
.
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Hi Andrea, Thanks for your response! PLOS One published a study where researchers have revealed what study participants are most likely to give biased answers regarding self-reported health to determine when it would be preferable to use on performance tests. The researchers argue that comparing self-reporting health in Southern Europe with other countries has led to a biased result. For instance, Southern Europeans might be healthier than they are. Furthermore, Spitzer
and Weber (2019) found that older individuals are most likely to misreport their health, and a person’s educational level might play a role in reporting their perceived cognitive ability. From
examining the finding, falsifying demographic data such as age, race, gender, politics, religion,
and education can negatively affect study results. However, the impact of falsified data on surveys is still unclear. Therefore, Simmons et al. (2016) emphasized since falsification can take occur in different ways, researchers can prevent it by having a variety of measures in place to mitigate the threat. Moreover, they inform us that prevention includes developing a relationship with the interviewers and designing a practical survey.. Reference
Simmons, K., Mercer, A., Schwarzer, S., & Kennedy, C. (2016). Evaluating a new proposal for detecting data falsification in surveys. Statistical Journal of the IAOS, 32
(3), 327–
338. https://doi.org/10.3233/sji-161019
Spitzer, S. & Weber, D. (2019). Reporting biases in self-assessed physical and cognitive health
status of older Europeans. PLOS ONE. 14
. e0223526. Doi:10.1371/journal.pone.0223526
DQ2
Imagine again that you are an automotive manufacturing executive tasked with increasing sales in your state. You wish to assess the effectiveness of an incentive program for sales personnel implemented at 10 dealerships in medium-size cities and 10 dealerships in small cities.
What three data collection approaches are most feasible for such a study? What are the most significant strengths and weaknesses of these data collection approaches? Why are these significant? What concerns do you have about the feasibility of implementing these
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approaches to data collection for this study? Explain. (
Make sure you consider the strengths and weaknesses of your approach. The approach is not always without pitfalls!)
Hi Dr. Nicholson and Doctoral Learners, For this type of scenario, the three more feasible data collection are surveys/questionnaires, documents/records, and observation to assess whether the incentive program given to sales personnel is effective. Considering that the company has passed the incentive program to sales personnel, the most significant strength of using questionnaires/surveys is the automotive manufacturing executive would know the direct impact on the recipient. Using this type of data collection approach, trying to understand their satisfaction rating or whether there is further improvement is needed with the program. As a result, it is very significant because it is a straightforward attempt to assess the efficiency of the incentive program (Nayak & Narayan, 2019). One weakness is that if the participant does not give consent, they are not allowed to continue the surveys. Another disadvantage is that it will not be cost-effective (Waldschmidt & Casteel, 2021).
In comparison, document/records, or archival data aside from the salesperson's account, researchers can observe a correlation between the incentive program and the company's sales by utilizing the business documents like attendance records, meeting minutes, and financial records to confirm the business success and data. The most significant strength of using archival data is that the research has already been available and can readily be available; a weakness is that it may not apply to the dissertation and may not contain raw data. These are significant because each utilizes primary or secondary data to replicate the data for the study at hand and can be used
without further complication. Lastly, along with the other two approaches, observation would serve as an additional verification point of the program's efficiency. The employee survey scored
high, and they also affirmed that the incentive program helped boost their productivity; these accounts should have been validated or aligned with their course of action. Observation would be
the best way to determine that. However, the weakness of this approach is the researchers need to
be well prepared and trained before they start using the observation data collection approach (Abdullah & Roman, 2001). Overall, the feasibility concerns using these types of approaches to data collection for this study are the target size, participant access, survey distribution, and the protections surrounding the data; but most importantly, understanding how to retrieve it (Waldschmidt & Casteel, 2021). Furthermore, based on previous experiences as an investigator from all three approaches, the survey/questionnaires and observation can be manipulated by researchers or companies. For instance, let us say the employee was notified or got a hint that their employer would be making a program satisfaction survey, they can manipulate the scores depending on their purpose, and along with this, the presence of an observer also impacts actual work behavior especially if it's your employer seeing. Therefore, depending on the study's state, a researcher may obtain vastly different information from the three sources. Merchant et al. (2011) found that automobile dealership incentives compared to those between Dutch and US companies prior research was
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not an acceptable form of data collection to form a theory. The study determined that questions remained regarding the number of incentives to offer, performance reward allocations, and measures to use, and more research is needed to address the limitations and obtain more extensive research. This study is an example of the vast differences and restrictions that can arise
from a study, whether the city, state, or countrywide.
As a result, as a researcher, the doctoral learner needs to ensure that all these potential sources of error are ruled out to yield accurate and relevant results.
References
Abdullah, S. H., & Raman, S. (2001). Quantitative and qualitative research methods: Some strengths and weaknesses.
Jurnal Pendidik dan Pendidikan, Jilid
,
17
, 1-15.
Merchant, K. A., der Stede, W. A. V., Lin, T. W., & Yu, Z. (2011). Performance Measurement and Incentive Compensation: An Empirical Analysis and Comparison of Chinese and Western Firms' Practices. European Accounting Review, 20
(4), 639–667. https://doi.org/10.1080/09638180.2011.593293
Nayak, M. S. D. P., & Narayan, K. A. (2019). Strengths and weaknesses of online surveys.
technology
,
6
, 7.
Waldschmidt, J. & Casteel, A. (2021) Quantitative Instrumentation and Data Collection. In Grand Canyon University. (Ed.) GCU
Introduction to Sampling, Data Collection, and Data Analysis
Peer Review
Saundra,
Thank you for your insight. What might an unhelpful statistic be? What steps might we take to ensure that we are gathering appropriate data for what we need to know and not extraneous data that will not be useful?
Blessings,
Dr. Nicholson
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Finding Tests and Survey Instruments (Quantitative Data Collection)
A student may use a published interview protocol and survey if they write and receive permission to use and modify the questions from the author.
How do I find tests and survey instruments?
Look at dissertations or theses and see what methods other scholars have used.
Find reviews of tests and surveys; try the Mental Measurements Yearbook - search for the broad topic related to your research, ex. self-concept, stress, etc.
Search test publisher websites or catalogs; try Educational Testing Service (ETS) TestLink.
How do I find tests used in scholarly journal articles?
Go to the library; try PsycINFO or PsycARTICLES for psychological tests (you will want to use a keyword search for names of published tests).
If a test name is not known, try a variety of keywords such as, test, tests, assessments, questionnaires, instrument, intelligence tests, personality tests, aptitude tests, scales, inventories, etc.
Once you have identified the test you are interested in, you will need to purchase or obtain permission for administering. You
must
get permission to use the tests for your own research. If a test is not commercially available, you will need permission from the author. This process involves contacting and receiving permission to use the test in writing via letter or email.
Among G7 countries (Canada, France, Germany, Italy, Japan, United States, United Kingdom (What are the G7 Coutries?, 2017)), roughly 4 out of 10 businesses have absolutely no women in their senior management positions. There was a 1% increase in 2016, but this is an overall number.
Some countries have gone down in senior management roles held by women while others have increased (Medland, 2016).
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Topic 1: Quantitative Sampling Plan
Objectives:
Identify data sources.
Compute sample size.
Differentiate between quantitative sampling approaches
Read Chapters 1 and 2 in GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis.
Announcements
This week you will focus on molding the beginnings of a quantitative study. Think through each step of the process. What are considerations that need to be made along the way? Please be sure to support each step with a citation/rationale from the literature.
Fully develop your post!
Dr. Nicholson
DQ1
Quantitative research tends to require the use of relatively large samples. With that in mind, consider the strengths and weaknesses of purposeful, convenience, and random sampling approaches in quantitative research. Assume that you are an automobile manufacturing executive
tasked with increasing sales in your state. You wish to evaluate the effectiveness of an incentive program for sales personnel implemented at 10 dealerships in medium-size cities and 10 dealerships in small cities. All you have at hand are archived records of the incentives received by the sales staff and of their respective sales transactions. What information, data, and variables do you choose to analyze as relevant to your evaluation? Why? Which of the GCU core quantitative designs (introduced in a previous course) would best fit your evaluation plan? Why? How much data do you need to analyze in order to reach a meaningful conclusion? Explain. Do you anticipate any logistic difficulties or ethical concerns?
Explain.
What information, data, and variables do you choose to analyze as relevant to your evaluation? Why?
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An objective data based on statistics may be included in a sales appraisal. You may evaluate a salesperson's efforts by looking at how much time he spends on the job. You may, for example, keep track of how many sales calls he makes and how many customer meetings he attends.
What are the benefits of collecting and analyzing data for your evaluation? . The manner you gather data should correspond to how you want to evaluate and use it. Recording should be done concurrently with data collection, regardless of which method you use. The following are some ideas for what you could do with the data you've gathered.
Which of the GCU core quantitative designs (introduced in a previous course) would best fit your evaluation plan? Why? To support and develop current concepts and theories, both qualitative and quantitative research approaches are often used. In the humanities, such as sociology, psychology, public health, and politics, both methodologies are frequently used. Business, economics, healthcare, marketing, and education are some of the other disciplines that do considerable study.
>Qualitative research, on the other hand, is gathered and expressed through words and tales. This
method is used by researchers to delve into the specifics, context, and texture of people's ideas and experiences. Verbal interviews, open-ended questionnaires, focus groups, cultural or behavioral observations, and published literature reviews are all examples of qualitative approaches.
> Numbers and graphs are used to capture and convey quantitative research. This method is used
to evaluate or corroborate current theories by systematically examining trends in huge groups. Experiments closed-ended or scale questionnaires, and highly organized observations are examples of quantitative approaches.
How much data do you need to analyze in order to reach a meaningful conclusion? Explain. Data scarcity isn't an issue for most businesses and government bodies. In fact, there is frequently too much information available to make an informed judgment.
>You need something more from your data with so much to filter through:
You must be certain that the data is appropriate for answering your question; you must be able to
draw reliable conclusions from the data; and you must have data that guides your decision-
making process.
>In a nutshell, you require improved data analysis. What was once an overwhelming abundance of fragmented information becoming a simple, unambiguous decision point with the correct data analysis process and tools.
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Do you anticipate any logistic difficulties or ethical concerns? Explain
>It's one thing to have a personal issue with your boss, but it's quite another to report to someone
who is acting unethically. This can be overt, such as falsifying figures in a report or spending business funds on unrelated activities; but it can also be subtle, such as bullying, receiving inappropriate gifts from suppliers, or asking you to avoid a regular procedure.
>Organizations led by unethical leaders are much more likely to have a poisonous work environment. Leaders that don't mind taking bribes, distorting sales statistics, and data, or pressing employees or business associates for "favors" (personal or financial) will treat their personnel with contempt and bullying. As a result of the present focus in many organizations.
Hi Dr. Nicholson and Doctoral Learners
Evaluating Thompson et al. (2019) demonstrate that researchers can observe data using objective
data. Therefore, a sales appraisal may include accurate data based on statistics. With that in mind, the data required would be the number of sales made after an incentive. Then, a researcher can extract information from this data to determine whether the incentives worked to increase sales. Other data that could prove variable is the number of failed sales even after an incentive. These will help to evaluate whether the incentives had any effect on sales; if not, then the product sold is not up to the tastes and preferences of the customers. Greenberger and Miron (2021) emphasize that the variables are simplified portions of the phenomena a researcher intends to study. As a result, the variable required would be a relationship between the customers
and the salesperson who made the sale. These will help determine if the sales were made due to customer loyalty rather than the incentives. Another variable that could help is the quality and safety of the automobiles, which is data that can be sourced from the feedback of customers from
the sales personnel. The data must include sales made before and after the incentive (Casteel, 2021). As a result, the best design would be the correlational-predictive design. Seeram (2019) mentioned the correlational-predictive design would help compare the different variables and analyze how each variable affects the use and effectiveness of incentives (dependent variable) (p.
176). By using this design, the researcher can anticipate a change in the dependent variable (effectiveness of incentives); therefore, enabling the researcher to analyze whether the dependent
variable has any effect on the sales, i.e., the researcher will know whether the change in the incentives given had any impact on the sale made. The control variables (e.g., the relationship between sales personnel and customers) can be included in the design to show or connect the relationship between each variable. For example, variables on the quality and automobiles' safety
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can be used to know whether sales were made due to the quality of the product, or the incentives given. Hence a relationship will be developed.
With that in mind, the data needed to make a concrete conclusion will include all the sales made before the incentives were given and after the incentives were given to analyze the difference and determine whether the incentives are effective. Other data needed is the customer feedback from the ales made after the incentives were given to know what compelled the customers to buy
the automobiles, customer loyalty, or the incentives or brand strength in the market. Therefore, logistics difficulties will be rare since all the data will be collected from the automobile dealer or sales personnel. They would only arise if the automobile dealer had no direct access to the sales personnel or their sales record, which would mean the researcher will have to incur fuel and maybe driver costs to trace the sales personnel, i.e., tracking the sales made. Another difficulty that may arise would be tracking customers for whom the sales were made if the sales personnel did not record the customer feedback. Ethical concerns may occur during data analysis since misrepresentation of the facts would mislead the readers of the research paper. The researcher should refrain from falsifying data when they lack enough data to conclude. The researcher(s) should ensure no conflict of interest when doing the research. For example, a researcher may conduct a research project to get funding for other projects or employment from an automobile manufacturer.
References
Casteel A. (2021). Populations and Samples in Quantitative Research. In Grand Canyon University (Edu.). GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis.
Greenberger S. & Miron D. (2021) Introduction. In Grand Canyon University. (Ed.) GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis.
Seeram, E. (2019). An overview of correlational research.
Radiologic technology
,
91
(2), 176-
179.
Thomson, G., Johanna Nee‐Nee, Sutherland, K., Holland, R., Wilson, M., & Wilson, N. (2019). Observed vaping and smoking in outdoor public places: piloting objective data collection for policies on outdoor vaping.
Australian and New Zealand Journal of Public Health,
43
(4), 401-
402. https://lopes.idm.oclc.org/login?url=https://www.proquest.com/scholarly-journals/observed-
vaping-smoking-outdoor-public-places/docview/2268623331/se-2
Peer Review
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As an automobile manufacturing executive tasked with increasing my company's sales in my state, I would consider a few aspects. In considering the strengths and weaknesses of purposeful, convenient, and random sampling approaches in quantitative research, I would first
look into recent research articles
(Greenberger, 2021) that have already been conducted on the effectiveness of an incentive program for sales personnel. This way I can compare previous results with my task at hand as well as find any possible gaps
. The information, data, and variables that I would choose to analyze in order to conduct a meaningful conclusion would include the following. First, the information I would choose to analyze would revolve around the goal of quantitative methodologies, which is to generalize the results from the sample and compare them to a larger population
(Greenberger & Miron, 2021). This is because having a larger population in quantitative methodologies, as this question stated, often requires relatively large sample sizes. Then, for the data, I would use instrumentation or procedures, which will also help to measure variables (Greenberger & Miron, 2021). This is done after an extensive and in-depth investigation of available resources that will be used in the study (Greenberger & Miron, 2021). Of the GCU core quantitative designs, the experimental class would fit my evaluation plan best, because its goal is to examine its effectiveness (
Bainbridge, 2020).
Hi Brenda, Great post! An experimental design answers questions about behavior or invariable behavior between cause and effect (Bainbridge, 2020). In an experimental design, the researcher can manipulate the independent variables to observe their impact on the dependent variables. Therefore, when the researcher conducts experiment research, they will use random assignment group participation into a control group and the other group being experimented on. As a result, the data from an experimental class will compare with the impact of the control group and determine if the experiment was effective (Greenhill et al., 2020; Bainbridge, 2020). On the other
hand, Greenhill et al. (2020) emphasize researchers can transfer prior knowledge into new experiments. However, knowing the basics of experimental design is essential to planning during
an experiment because the reason for the circumstance is if doctoral learners or researchers threw
an experiment together that did not fit a known experimental design, then they would have no way of analyzing the data and conclude their research.
References
Bainbridge, C. (2020, August 10). Updated to quantitative GCU dissertation template. Dc network.
https://dc.gcu.edu/dissertation/dissertation-templates/6_core_designs/
quantitative_gcu_core_designs_08_10_20docx
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Greenhill S., Rana S. Gupta S, Vellanki P., and Venkatesh S. (2020). "Bayesian Optimization for
Adaptive Experimental Design: A Review," in IEEE Access (8)
13937-13948
. doi: 10.1109/ACCESS.2020.2966228.
Thank you, for your appreciation of my post. I like that you mentioned using prior knowledge and bringing it into a new study. I think that is an excellent approach, by not forgetting the past. As long as the research is mainly recent. I think that in a way we will be looking at this perspective when we write our dissertations, but from what I understand we will be using previous research to find the gap in it. Which is a little different than what we are discussing, but still useful. This way we can add to the previous knowledge without dwelling too much on it
during our studies. Would you agree?
Greenberger & Miron (2021) explain that sometimes researchers will use primary and secondary data sources in order to conduct their study. This relates to our discussion because primary data is when a researcher collects the original data and secondary is when a researcher used previously collected data on a similar topic to conduct
their own study (Greenberger & Miron, 2021).
Hi Brenda, Thanks for the response! Definitively, I agree. Therefore, it is essential to find enough resources to understand the phenomenon we are focusing on fully. As a result, Doctoral learners and researchers should analyze the data collected to understand the problem and find a
solution. Then, the data source can be categorized by data collection and type of data source (i.e., primary or secondary). Greenberger & Miron (2021) explained that using data from secondary sources can be a risk; therefore, researchers must be aware of the risk. In secondary data sources, bias is higher than the primary data sources (Cox et al., 2009). Another chance might be the use of the data is not relevant or the quality of the data due to the time-saving option. An automobile manufacturing executive might use an internal data source in the above scenario. This data can be collected from records, archives, and various other authorities within
the organization. The executive should check the relevance of the data before using it as secondary data and be sure that there is enough data on the evaluation of an incentive program for sales personnel. In other words, check for their feasibility, reliability, and suitability for the research project. References
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Cox, E., Martin, B. C., Van Staa, T., Garbe, E., Siebert, U., & Johnson, M. L. (2009). Good Research Practices for Comparative Effectiveness Research: Approaches to Mitigate Bias and Confounding in the Design of Nonrandomized Studies of Treatment Effects Using Secondary Data Sources: The International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report—Part II.
Value in Health, 12
(8), 1053-1061
.
https://doi.org/
https://doi-org.lopes.idm.oclc.org/10.1111/j.1524-
4733.2009.00601.x
Greenberger S. & Miron D. (2021) Introduction. In Grand Canyon University. (Ed.) GCU
Introduction to Sampling, Data Collection, and Data Analysis. One of the main strengths of purposeful sampling is that it allows researchers to target specific subgroups that may be of interest. This can be particularly useful when studying rare phenomena or when investigating a new research question. A key advantage of convenience sampling is that it is often easier and less expensive to implement than other sampling methods
(Scholtz, 2021). Random sampling provides a more representative sample of the population and is less susceptible to bias
(Amarteifio et al., 2021). However, one of the main weaknesses of random sampling is that it can be difficult to achieve, especially with large samples
. Additionally, random sampling can sometimes produce results that are not generalizable to the population as a whole (Amarteifio et al., 2021).
How might the use of different types of data (e.g., number of sales per week, customer satisfaction scores) or different types of analysis (e.g., chi-square, t-test, regression) impact the findings of your evaluation? The best design for this evaluation would be a randomized controlled trial. This would allow for the most accurate assessment of the effectiveness of the incentive program, as it would control for other variables that could impact sales (Amarteifio et
al., 2021). The use of different types of data or different types of analysis would not impact the
findings of the evaluation, as long as the data is appropriately analyzed. There are a few different ways to go about collecting the data for this project
. One option would be to use purposeful sampling, and select 10 dealerships in medium-size cities and 10 dealerships in small cities that have implemented the incentive program. Another option would be to use convenience sampling and collect data from the 20 dealerships that are easiest to access
.
Hi Serah,
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Great post! Casteel (2021) emphasizes the importance of random sample is it is simple and easy to use. Furthermore, the random sampling method is also considered by many researchers to be one of the fairest ways of identifying a sample that will give a clear representation of the entire population because it gives every member or item from the population an equal opportunity of being selected (Dobson et al., 2017; Sharma, 2017). As a result, this method has a meager chance
of being unbiased because no group will be favored in selecting the sample since it is chosen randomly (Sharma, 2017). Overall, the conclusion made from research in which random sampling has been employed is always a precise and reliable representation of the entire population because of its unbiased nature in selecting samples (Castell, 2021). Lastly, the key is to use statistically derived random sampling because it ensures the survey results can be defended as statistically representative of the dealership. Therefore, the survey must follow these
procedures because if not, it can produce an outcome that might lead to misguided market research, strategic, or policy decisions.
References
Casteel A. (2021). Populations and Samples in Quantitative Research. In Grand Canyon University (Edu.). GCU Introduction to Sampling, Data Collection, and Data Analysis
Dobson, C., Woller-Skar, M., & Green, J. (2017). An inquiry-based activity to show the importance of sample size and random sampling.
Science Scope,
40
(8), 76-81. https://lopes.idm.oclc.org/login?url=https://www.proquest.com/scholarly-journals/inquiry-based-
activity-show-importance-sample/docview/1884841275/se-2?accountid=7374
Sharma, G. (2017). Pros and cons of different sampling techniques.
International journal of applied research
,
3
(7), 749-752.
DQ2
Assume you wish to study the influence of high school principals’ leadership styles and academic achievement in their schools in your state. Do you need primary data, secondary data, or both? Explain. What logistic difficulties do you expect in gathering all necessary data? Explain. How are you going to combine all data into a single file for statistical analysis?
It is essential to use primary and secondary data to test bias and gather enough information to explore a topic thoroughly when it comes to data sources. Casteel (2021) explains that the researcher generated the preliminary data, surveys, interviews, or experiments to understand and solve the research question. In other words, it is the first-hand information collected by the researcher. In contrast, the secondary data is not collected initially and is instead obtained from
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published or unpublished sources. Therefore, it is second-hand information (Casteel, 2021). With
that in mind, to study the effect of high school principals' leadership styles and academic achievement in their schools, researchers can take any data, primary or secondary. However, if they have less time for study and secondary data recorded for each school about their principals' leadership, academic achievement, and passing rate of students is available, then it can use secondary data type.
While collecting preliminary data or taking a survey, we can expect several difficulties like- some students may be unwilling to respond to surveys. Some students may not respond genuinely. Some students may respond inappropriately. In contrast, collecting secondary data may not find the proper records for every selected student. As a result, difficulties will come out while gathering necessary data. If the researcher does not have secondary data available, they can
collect the primary data or take a survey from students. For instance, they collect the required data and variables. For example, a survey can be administered randomly by select students in the state.
Furthermore, the researcher will collect the information from each student about their passing rate, the principal's leadership style, and the academic achievement of their school. Then, the data will be in three columns, and in this way, data will be combined in one file for further analysis. For instance, the researcher gathered secondary data, selected some students' records from schools, collected the three variables, and then combined them into three columns of one file.
References
Casteel A. (2021). Populations and Samples in Quantitative Research. In Grand Canyon University (Edu.). GCU Introduction to Sampling, Data Collection, and Data Analysis
Peer Review
Anamaria’s Post
What are you considering accurate, relevant, and current data?
Hi Dr. Nicholson,
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Saundra’s post
What might be a scenario where secondary day might be more appropriate than primary data?
Dr. Nicholson
Hi Dr. Nicholson
Although secondary data have their advantages, such as new information from the same data becoming relevant because of new analytical tools, new theoretical perspectives, and new operationalization, one disadvantage doctoral learners and researchers should keep in mind is uncertain accuracy. When a researcher uses secondary data, it makes it possible to carry out a study without waiting for a long time to conclude, and it helps to generate new insights into existing primary data. A scenario where secondary data might be more appropriate than preliminary data could be when the problem is general. For instance, demographic structures of
a particular region; as a result, there is no meaning in collecting the primary data because the data are readily available in the Central Bureau of Statistics (Kamen, 2002). Another example, business-related data information and many others related to any company are easily accessible
in the Federation of Nepalese Chambers of Commerce and Industry (Kamen, 2009). The last example might be when the primary source is incomplete, then-doctoral learners or researchers
can explore further by finding a new piece of evidence after observing the primary source. References
Casteel A. (2021). Populations and Samples in Quantitative Research. In Grand Canyon University (Edu.). GCU Introduction to Sampling, Data Collection, and Data Analysis
Kamen, C. S. (2002). "Quality of life" research at the Israel Central Bureau of Statistics: Social indicators and social surveys.
Social Indicators Research,
58
(1-3), 141-162. https://lopes.idm.oclc.org/login?url=https://www.proquest.com/scholarly-journals/quality-life-
research-at-israel-central-bureau/docview/197671107/se-2?accountid=7374
Joshi, K. P. (2009).
Role of Federation of Nepalese Chambers of Commerce and Industry in Promotion of Trade and Industry in Nepal
(Doctoral dissertation, Faculty of Management).
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We had some good discussion in this post. The focus was to get you thinking about the implications of using a quantitative methodology. Sampling considerations are key and we almost must consider the core designs:
Experimental
Designed to demonstrate unambiguous cause-and-effect relationship between variables
Determines if there is an effect/outcome of some form of treatment(s) using random assignment of subjects to treatment and control groups. Includes a manipulation of an independent variable to determine its effects
Quasi-experimental
Designed to demonstrate cause-and-effect relationship between variables. Does not meet all requirements of an experimental design, thus cannot produce an unambiguous cause-and-effect
explanation.
Determines if there is an effect/outcome of some form of treatment(s) using pre-existing groups of subjects assigned to treatment and control groups
Non –Experimental
Attempts to demonstrate associative relationships between variables but does not attempt to produce an unambiguous cause and effect explanation.
Descriptive
(Survey)
Describes the opinions, attitudes, or trends of a population numerically
Correlational
Determines if there is a relationship between two or more variables on a single group of participants with the intent of predicting or defining a relationship
Comparative
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Determines the causes of differences that already exist
between or within two or more groups on two or more variables
Keep thinking on these as we continue the discussion into the next week!
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