Justify Your Qualitative Research Design Rationale

Description In this assignment, you will review the template for the research prospectus and then DRAFT a QUALITATIVE RESEARCH METHOD AND DESIGN RATIONALE typical to what would be presented in a Method and Design section of the research prospectus in your future CMP-9601E or CMP-9701E course. Be sure to revisit the prior assignments submitted for each of these study design components and the sources cited previously as you draft this rationale. The rationale must describe and cite scholarly sources to support justification for each of the following components: Justification for the choice of the qualitative method to examine an educational research problem. Justification for the choice of research design as appropriately aligned with the qualitative method. Justification for the choice of sampling method and sample size as aligned with the conventions of qualitative research and the specific research design proposed. Justification for the choice of data collection method as aligned with the conventions of qualitative research and the specific research design proposed. Justification for the choice of data analysis method as aligned with the conventions of qualitative research and the specific research design proposed. Describe the qualitative analysis software application to be used for the proposed for the data analysis method proposed. Length: 2-3 pages (does not include title page and References pages) EDUCATIONAL RESEARCH PROBLEM – The physical and social effects of sports on adolescents Qualitative method to examine research problem – Grounded Theory ATTACHED IS ALL OF THE WORK I’VE DONE THAT WILL GIVE YOU AN IDEA OF WHAT ALL I HAVE TO PUT TOGETHER FOR THIS SIGNATURE ASSIGNMENT. UNFORMATTED ATTACHMENT PREVIEW QUALITATIVE RESEARCH DESIGNS GROUNDED THEORY: STRENGTHS AND CHALLENGES  Strengths  Fosters creativity-the researcher is not confined to testing preconceived hypothesis, but rather explores empirical data to identify associations  Has a systematic data analysis protocol  It is instinctive-this design allows intensive engagement with the data  Allows researchers to conceptualize-researcher can generate valid concepts from data (El Hussein et al. 2014)  Challenges  Time consuming-open data coding is laborious and exhaustive  Vulnerable to methodological errors-complexity of this approach exposes novice researchers to an array of technical errors (Pulla, 2016)  Has numerous approaches CASE STUDIES: STRENGTHS AND CHALLENGES   Strengths  Accuracy in relating theory to practice-this approach allows researchers to evaluate associations between theory and practice  Efficient in hypothesis formulation- case studies lead to identification of unanticipated information  Discloses pertinent correlations- intensive analysis of a case allows the researcher to unearth crucial details Challenges  Lack of rigor-reliability and internal validity are questionable (Lock and Seele, 2018)  Potential of collecting useless data-researcher may collect vast quantities of data of minimal scientific value (Crowe et al. 2011)  Conceptualizing an inappropriate case BENEFITS OF THE RESEARCH DESIGNS  Both research methodologies are guided by a scientific framework and lead to the collection of comprehensive data on the subject  These designs have a prescribed approach for data analysis which ensures that the gathered data is critically and accurately analyzed to give valid and reliable information  The designs confer some degree of freedom to the researcher, which creates room for creativity and exploration of instinctive ideologies, which may not be particularly compatible with other designs  Researcher bias is minimal in these designs which fosters reliability of research findings and the generalization of the findings  Importantly, these study designs allow the researcher to capture the actual state of events regardless of their research prowess SELECTED QUALITATIVE RESEARCH DESIGN  Grounded theory is a design marked with a logical procedure of analyzing data  The design engages inductive reasoning  Research  It framed by this designs may start in the form of the question may also begin with some sets of qualitative data  Researchers utilize codes to tag data they have collected GROUNDED THEORY GENERAL PRINCIPLES, TENETS, TRADITIONS, AND PARADIGMATIC PERSPECTIVES  It encompasses various traditions in positivism and sociology  Its principle stresses on the significance of social engagement  Based on the design, human beings can interpret the world by using symbols  One will realize the behaviors of human beings based on the symbols  The researcher will learn about the world by interpreting the human engagements REFERENCES  Crowe, S., Cresswell, K., Robertson, A., Huby, G., Avery, A., & Sheikh, A. (2011). The case study approach. BMC Medical Research Methodology, 11(100).  El Hussein, M., Hirst, S., Salyers, V., & Osuji, J. (2014). Using Grounded Theory as a Method of Inquiry: Advantages and Disadvantages. The Qualitative Report, 19(27), 1-15.  Holt, N. L., Neely, K. C., Slater, L. G., Camiré, M., Côté, J., Fraser-Thomas, J., … & Tamminen, K. A. (2017). A grounded theory of positive youth development through sport based on results from a qualitative meta-study. International review of sport and exercise psychology, 10(1), 1-49. https://doi.org/10.1080/1750984X.2016.1180704  Johnson, J. S. (2015). Qualitative sales research: An exposition of grounded theory. Journal of Personal Selling & Sales Management, 35(3), 262-273. https://doi.org/10.1080/08853134.2014.954581  Leung, L. (2015). Validity, reliability, and generalizability in qualitative research. Journal of family medicine and primary care, 4(3), 324. https://www.ncbi.nlm.nih.gov/pubmed/26288766  Lock, I., & Seele, P. (2018). Gauging the Rigor of Qualitative Case Studies in Comparative Lobbying Research. A Framework and Guideline for Research and Analysis. Journal Of Public Affairs, 18(4), 1-5.  Pulla, V. (2016). An Introduction to the Grounded Theory Approach in Social Research. International Journal Of Social Work And Human Services Practice, 4(4), 75-81. Running head: QUALITY CHECKLIST 1 Qualitative Data Quality Checklist Lenard Hudnal QUALITY CHECKLIST 2 Qualitative Data Quality Checklist Criteria for Data Trustworthiness Based on Cohen & Crabtree (2008) and Amankwaa (2016) No Yes Cannot Answer Credibility 1. Did the researchers spend enough time observing, talking to people, building relationships, checking for misinformation coming from the informants? 2. Did the researchers use multiple theories, investigators, and data sources to enhance comprehension as well as ensure a robust and rich account of their study inquiry? 3. Did the researchers take their collected data, analyses, conclusions, interpretations back to participants to validate the trustworthiness of their account? Transferability 1. Did the authors achieve to provide a sense of authenticity? 2. Did the authors describe in a detailed manner every part of their research (fully describing participants, procedures and settings, length and location of interviews, QUALITY CHECKLIST interviewee’s and interviewer’s reaction, recording procedures, results, for instance, long quotes or interview dialogue? 3. Was the sample enough for the study? Dependability 1. Did the researchers use an external audit to examine their research process and product? 2. Did the research process follow all the established ethical and professional guidelines? 3. Were data taken from a range of suitable settings, respondents, and times? 4. Were quality and coding checks made? Did they demonstrate adequate agreement? Confirmability 1. Is the data used to support conclusions? 2. Were any methods utilized to control for any bias? Were they adequate? 3. Did the authors consider compelling hypotheses and rival conclusions? 3 QUALITY CHECKLIST 4. Were the researchers self-aware and explicit as possible about their personal values, assumptions, and biases? 4 QUALITY CHECKLIST 5 References Cohen, D. J., & Crabtree, B. F. (2008). Evaluative criteria for qualitative research in health care: controversies and recommendations. The Annals of Family Medicine, 6(4), 331-339. Amankwaa, L. (2016). Creating Protocols for Trustworthiness in Qualitative Research. Journal Of Cultural Diversity, 23(3), 121–127. Retrieved from https://search-ebscohostcom.proxy1.ncu.edu/login.aspx?direct=true&db=mdc&AN=29694754&site=eds-live SURVEY AND SECONDARY DATA Data Collection Method The Appropriate Data Collection Method  The research will use data collected from people and the study of secondary sources.  Using the two methods will lead to a vast amount of data  Through the two methods there will be a covering of a wide scope of the research  In the process, the results of the study will capture the issue on the ground  Using both methods encourages an analysis method that has the potential of protecting the validity of the results Advantages and Disadvantages of the Data Collection Methods  Survey – One advantage of the survey data collection method is that its accurate as its based on the current issues on the ground. -Its disadvantage is that its time consuming and requires a human power.  Secondary Data – A major advantage of secondary data is that it is peer reviewed and consists of arguments developed by scholars (Goodwin, 2014). – The method is disadvantageous in the sense that it could be outdated. Benefits of the two Data Collection Methods  Using the two methods will increase the accuracy of the data collected which will reflect on the validity of the results.  Studies with valid results become references for future studies  Allows for the use of both qualitative and quantitative data analysis methods (Guest et al. 2017).  Reduces the need of researchers input  The issue reduces researcher’s bias a matter that increases the validity of the developed results. References  Goodwin, J. (2014). SAGE Secondary Data Analysis. SAGE.  Guest, G., Namey, E. E., Mitchel, M. L. (2017). Collecting Qualitative A Field Manual for Applied Research. SAGE.  Wolf, C., Joye, D., Smith, W. T. & Fu, Y. (2017). The SAGE Handbook of Survey Methodology. SAGE. Data: Qualitative Data Analysis 1 Qualitative Data Analysis Methods Lenard Hudnal QUALITATIVE DATA ANALYSIS METHODS 2 Qualitative Data Analysis Methods Globally, research processes are accompanied by several infinite factors of consideration which works towards the maximization of the formulated principles and procedures (Williams, 2003). Data collection marks one of the core factors depicted in any research design process; however, the type of the specified method of data collection directly affects the core foundational maximization towards the objected vision. Under this scope, the strategized kind of data collection lies under the parameters of the use of administered questionnaire towards the qualitative research design (Kirakowski, 1994). There exist different types of data depending on the core objectives of the research, namely, qualitative and quantitative types of data. Therefore the formulated questions in the questionnaire directly exhibit the collection of the qualitative data, that is, through the use of open-ended and rigid questions. The analysis of the qualitative data lies under several channels of evaluations, for example, content analysis, narrative analysis, discourse analysis, and grounded theory (Mayring, 2004). However, concerning this analysis, the selected types of qualitative data are the content analysis and narrative analysis. Qualitative type of data refers to the type of data that lies under the dictation of the categorical variables under the authorization of constrictions of ‘what type.’ The first method of analysis is the content analysis; it illustrates the breakdown of documented information. The sources of the recorded information include; texts, whereby the researcher should gather the sources related to the type of analysis depending on the topic of study of the entire research. This means that the researcher should first use diverse platforms to locate the relevant source of the qualitative data and then evaluate the overall texts and extract the essential details to help in the maximization of the strategized vision. On the other hand, the narrative analysis involves evaluations of the contents from other sources, for example, recorded interview sessions and related stories from the target population (Sgier, 2012). It requires the researcher to search for these sources compressively and utilize the information to extract the qualitative data of interest. For example, if the researcher was dealing with the identification of factors affecting the field of industrialization, then he can search for the recorded interviews coping with this type of topic and extract the required information. Therefore, based on my qualitative research design, I would entirely apply these two types of analysis accordingly due to the exhibited merits. Under a detailed evaluation of the two proposed methods, narrative analysis marks the selected plan. The selection of the method was due to the more merits than demerits related to the topic of the research. The main advantages of this method are; there is already existing information to evaluate, time minimization, the technique utilizes more straightforward techniques that do not require high levels of skills, and it’s easily flexible (Tetnowski, 2001). On the other hand, the disadvantage resulting from this is the availability of language barriers. Since my research deals with the reaction of the audience in the arts festivals, then the narrative analysis will enable interaction with the viewers in the form of interviews. Since the language barrier may emerge some conflicts, I would find other people in the knowledge of the language to help me. Generally, methods of data collection, types of data, and methods of data analysis determine the efficiency of the data to be collected at the end of any research process. Therefore, a detailed and practical report should be implemented in the development of the research design process to manage these principles. Eventually, when all these factors are applied effectively, then the degree of collection of high detailed information will maximize and automatically leading to an excellent decision-making process. QUALITATIVE DATA ANALYSIS METHODS References Kirakowski, J. (1994). The use of questionnaire methods for usability assessment. Unpublished manuscript. Recuperado el, 12. Williams, A. (2003). How to… Write and analyse a questionnaire. Journal of orthodontics, 30(3), 245-252. Mayring, P. (2004). Qualitative content analysis. A companion to qualitative research, 1, 159176. Sgier, L. (2012). Qualitative data analysis. An Initiat. Gebert Ruf Stift, 19-21. Tetnowski, J. A., & Damico, J. S. (2001). A demonstration of the advantages of qualitative methodologies in stuttering research. Journal of Fluency Disorders, 26(1), 17-42. http://sk.sagepub.com.proxy1.ncu.edu/video/qualitative-data-analysis-part-1/true?seq=1 3 Running head: CQDAS APPLICATIONS EVALUATION 1 Computer-Assisted Qualitative Data Analysis Software Evaluation Lenard Hudnal CQDAS Applications NVivo and Hyper Research Selection Criteria Minimum System Specifications Structure of the The user data or work interface (UI) Interoperability and data export CQDAS APPLICATIONS EVALUATION 2 According to research on the selection criteria above, Hyper Research is better than NVivo for use in qualitative data analysis. When choosing a Computer-Assisted Qualitative Data Analysis Software (CQDAS), there are many functionality features to analyze for each package (Lewins & Silver, 2009). Hyper Research software is cross-platform and requires minimum space for installation as recommended by the developer. Cross-platform means that it can operate on both Windows and Macintosh platforms. A file created on Windows PC can open on MAC OS. It requires a minimum space of 41 Megabytes in any computer (Silver & Silver, 2010). On the other hand, NVivo runs mostly on Windows and sometimes on Mac platform and requires eight gigabytes free space on the hard disk and four gigabytes of random access memory (RAM). It also requires an internet connection. The minimum system requirements for Hyper Research are attainable; hence it is the best option for me to use in qualitative data analysis. Additionally, hyper research has external databases in that source files are accessible by the study but not contained in it. On the contrary, in the NVivo package, all the data is imported into one file. In case of software failure, it is easy to recover data contained Hyper Research cases as compared to the one project file in NVivo, which loses all the data. This makes the Hyper Research method better for use in my project. Furthermore, considering the user interface (UI) for the two CQDAS packages, Hyper Research has more appealing multi-window interface that does not require a powerful computer. It contains hyperlinks and case cards to text and multimedia data. The NVivo user interface includes specific context ribbons and navigation views which make it complex to use. The simplicity of the Hyper Research package makes it more reliable and easy to use. Additionally, the hyper research package has a Word Cruncher and SPSS Export function that enables a researcher to bridge the qualitative-quantitative gap (Green, 2011). Finally, hyper research method has high import/export capabilities over all platforms as compared to NVivo. Interoperability makes it more useful to us students because we can carry our analysis on different machines (Schiller, 2019). References CQDAS APPLICATIONS EVALUATION 3 Green, R. A. (2011). Software for Linking Concepts. In R. A. Green, Case study research: a program evaluation guide for librarians (pp. 96-100). Santa Barbara: Santa Barbara, Calif. : Libraries Unlimited, ©2011. Lewins, A., & Silver, C. (2009, April). Choosing a CQDAS Package. Retrieved from Surrey: https://www.surrey.ac.uk/sites/default/files/2009ChoosingaCAQDASPackage.pdf Schiller, H. I. (2019, October 9). Research Tools Workshop. Retrieved from interoperability.ucsd.edu: http://interoperability.ucsd.edu/docs/researchtools.html Silver, A., & Silver, C. (2010, August 10). HyperRESEARCH: Distinguishing features and functions. 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