Discussion Post Response

Discussion Post,

Post an analysis of reliability and validity within the context of qualitative research data analysis. In your analysis, do the following:

· Identify sources for data quality issues that could negatively impact a doctoral research study.

· Explain the importance of reliability and validity within a DBA doctoral research study.

· Explain how reliability and validity can be achieved within the doctoral research process.

Be sure to support your work with a minimum of two specific citations from this week’s Learning Resources and one or more additional scholarly sources.

Guillermo,

Sources for data quality issues that negatively impact a doctoral research study

In qualitative studies, the researcher is the data collection tool and cannot be objective or separate from the research (Walden University, n.d.). According to Dr. Knight-Lynn (2016), a researcher’s worldview and experiences affect data collection, interpretation and can jeopardize the study’s validity and reliability. Saunders, Lewis, & Thornhill (2015) explain the researcher’s, as well as the participant’s bias and errors, can influence the quality and reliability of the qualitative case-study research (QSR). Yin (2018) explains that errors of perception can hinder the credibility of the design, execution, and analysis of information and the ability to repeat the study to obtain the same results (Reliability).

Importance of reliability and validity within a DBA doctoral research study

The goal of a qualitative study is to establish the relationship between the research question, the empirical data, and its conclusions (Yin, 2018). A researcher’s responsibility is to mitigate bias as much as possible to ensure that the study will overcome scrutiny of design and that the method is repeatable and will produce the same results using a different sample (Walden University, n.d.). Reliability is the ability to consistently measure the phenomena regardless of the sample (Kilicer, Coklar, & Ozeke, 2017). The researcher’s contribution increases in value as the reliability increases. Researchers presenting studies that fail to meet reliability and validity requirements reduce the value of their contribution (Houghton, Casey, Shaw, & Murphy, 2013) and may not meet the requirements to obtain the doctoral degree (Walden University, n.d.). 

Reliability and validity within the doctoral research process. 

Achieving reliability and validity includes (a) design (b) rigor, and (c) data saturation. Design includes the measurement of the phenomena (construct), the link between cause and effect (internal), the ability to apply the findings to other cases (external) and the repeatability of the study (Yin, 2018). Rigor relates to data is collection analysis, and coding; Involving credibility of the process, dependability of the method, the ability to validate the data, as well as the ability to transfer the results to other populations (Houghton et al. 2013). Reaching data saturation involves gathering and analyzing data to the point where additional coding is not possible, additional data is not available, and there is enough information to replicate the study (Fusch, &  Ness,2015). Validity and reliability are paramount to ensure the quality of the study. In some cases, statistical analysis of qualitative data can help ensure, and support the reliability of the findings (Kilicer et al., 2017).

References 

Fusch, P., &  Ness, L. (2015). Are we there yet? Data saturation in qualitative research. The Qualitative Report, 20(9), 1408–1416.  Retrieved from http://tqr.nova.edu/

Houghton, C., Casey,  D., Shaw, D., & Murphy, K. (2013). Rigor in qualitative case-study research. Nurse Researcher, 20(4), 12–17.  doi:10.7748/nr2013.03.20.4.12.e326

Kilicer, K., Coklar, A. N., & Ozeke, V. (2017). Cyber human values scale (i-value): The study of development, validity, and reliability. Internet Research, 27(5), 1255-1274. doi:http10.1108/IntR-10-2016-0290

Laureate Education (Producer). (2012a). Ensuring quality in qualitative research [Video file]. Baltimore, MD: Author.

Saunders, M. N. K., Lewis, P., & Thornhill, A. (2015). Research methods for business students (7th ed.).  Essex, England: Pearson Education Unlimited.

Yin,  R. K. (2018). Case study research and applications: Design and methods (6th  ed.). Thousand Oaks, CA: Sage

Brent,

Data quality issues that negatively impact a doctoral research study

Researchers define the parameters of their research study to account for data quality issues (Saunders, Lewis, & Thornhill, 2015; Yin, 2018). For example, a researcher creates an interview protocol to use with all interviewees in the study (Saunders et al., 2015; Walden University, 2019). Researchers may use other methods too such as multiple sources of evidence, case study database, and chain of evidence techniques (Yin, 2018). However, the researcher’s personal bias, experiences, and assumptions about their research topic can influence the findings (Saunders et al., 2015; Walden University, 2019). Also, the researcher should use open-ended questions that steer away from personal bias or assumptions (Saunders et al., 2015; Laureate Education, 2012).

Other data quality issues can come from a researcher not achieving data saturation (Fusch & Ness, 2015; Yin, 2018). Data saturation impacts both the external validity and reliability of a study (Houghton, Casey, Shaw, & Murphy, 2013; Fusch & Ness, 2015; Yin, 2018). A researcher should be able to apply or replicate the findings to another target group in order to validate the findings (Fusch & Nexx, 2015; Yin, 2018).

Importance of reliability and validity within a DBA doctoral research study

A researcher will contribute to the extant body of literature when their research study adheres to the rigor and richness of the qualitative research process (Houghton et al., 2013; Yin, 2018). In order for the user or readers of the doctoral research study to use the findings, the validity of the data must be established (Houghton et al., 2013; Yin, 2018). Depending on the research design, a researcher will use either internal or external validity (Yin, 2018). A researcher will use internal validity for explanatory or causal studies and external validity for descriptive or exploratory studies (Yin, 2018). 

Achieving reliability and validity within a doctoral research process

Researchers achieve reliability and validity within a doctoral research process through the research design, data collection, and data analysis (Yin, 2018). External validity means that the finding in the case study can apply to another target population (Yin, 2018). The data collection and evidence handling are two processes that a researcher will use to achieve reliability and validity (Houghton et al., 2013; Saunders et al., 2015; Yin, 2018). Other methods a researcher can employ are peer review or member checking (Houghton et al., 2013; Laureate Education, 2012; Saunders et al., 2015; Yin, 2018). Also, a researcher achieves data saturation by finding common themes or repeated occurrences within the evidence collected (Fusch & Ness, 2015; Yin, 2018). Researchers must still be aware of personal biases which can limit data collection short of saturation (Fusch & Ness, 2015; Houghton et al., 2013; Yin, 2018). 

References

Fusch, P., & Ness, L. (2015). Are we there yet? Data saturation in qualitative research. The Qualitative Report, 20, 1408-1416. Retrieved from http://tqr.nova.edu/

Houghton, C., Casey, D., Shaw, D., & Murphy, K. (2013). Rigour in qualitative case-study research. Nurse Researcher, 20(4), 12-17. doi:10.7748/nr2013.03.20.4.12.e326

Laureate Education (Producer). (2012). Ensuring quality in qualitative research [Video file]. Baltimore, MD: Author.

Saunders, M. N. K., Lewis, P., & Thornhill, A. (2015). Research methods for business students (7th ed.). Essex, England: Pearson Education Unlimited.

Walden University. (2019). DBA doctoral study rubric and research handbook. Available from http://academicguides.waldenu.edu/researchcenter/osra/dba 

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Thousand Oaks, CA: Sage.