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Please respond to the following two post , each post response should be one paragraph with at least one citation included .

Post #1

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

According to Saunders et al. (2015), there are four threats to reliability: participants error, participants bias, researcher error, and researcher bias. The first threat is participant error. While holding the interview session, the participant answers the phone, allows other people to ask business-related questions; these interruptions can cause are participants’ errors. These interruptions take the participant’s focus off the purpose of our meeting (Saunders et al., 2015). The second threat that affects quality is participant bias. Participant bias is caused by any element that might encourage a false response. As a scholar being sensitive to the participant’s privacy is of utmost importance. Any sensitive question that is asked in an open environment might cause the participant to answer in a way that is not truthful (Saunders et al., 2015). The third threat that may have a negative impact is the error of the researcher. Researchers’ error is a result of misinterpreting a response that the participant has shared. If participants use terms that scholars are not familiar with, research errors may occur. Alternatively, if the researcher is poorly prepared for the interview. These types of errors may have an adverse impact (Saunders et al., 2015). The fourth threat that may have a negative impact on doctoral research is the bias of researchers. The needs of researchers must be open to new theories and behaviors. Each of the items listed above could cause bias. Any distraction can cause the interview session to go in another direction. A twenty-minute discussion could turn into an hour. Redirecting the participant may be challenging.

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

Validity is the outcome of what the scholar is seeking to obtain. It is also defined as the “appropriateness of the measure used, accuracy of the analysis of the results” Saunders et al., 2015). Reliability is the ability to consistently reproduce the same findings, using the research design repeatedly (Scott et al., 2019, p. 1121). For example, you set the alarm clock to 5 pm every day. This clock will continuously sound an alarm at 4 pm every day. This clock is reliable, but the clock lacks validity. It did not ring at 5 pm. A scholar must be able to provide research to scholars that are accurate and are able to be reproduced.

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

Reliability and validity can be achieved by implementing the following four design tests; structure, internal and external validity, and reliability. The first test is construct validity applies multiple sources of evidence and encourages the involvement of other scholars to participate. The second test Internal validity looks for correlations, logic models and addresses “rival explanations” (Yin,2018). In the third test, the scholar can perform a” single case study and use replication logic in multiple-case studies” (Yin,2018, p.43). The fourth test is external validity, which implements a theoretical single case study. The final test is the reliability test. Reliability testing uses a case study database and manages a series of evidence. It is suggested that several of these test designs are utilized by independent scholars in their case studies (Yin, 2018, p.43).

Post #2

Sources of Data Quality Issues

Scholars are responsible for ensuring the quality of their data to avoid negative impacts on their doctoral studies. A fundamental aspect of the research design is the quality of the research and its outcomes (Saunders et al., 2016). However, various data quality issues could negatively impact a doctoral research study. Risks to the reliability of a study include participant error, participant bias, researcher error, and researcher bias (Saunders et al., 2016). First, participant error pertains to elements that could negatively affect how a participant performs (Saunders et al., 2016). An example of participant error may include interviewing a participant before a critical meeting or appointment. Next, participant bias relates to factors that could provoke a dishonest response (Saunders et al., 2016). Third, researcher error involves any influences that could change the researcher’s understanding (Saunders et al., 2016). Finally, researcher bias is associated with factors that stimulate bias in how the researcher documents a response (Saunders et al., 2016), such as predispositions or prejudices. The previously mentioned issues are data quality issues that represent a threat to reliability.

Furthermore, additional data quality issues related to semi-structured and in-depth interviews include cultural disparities and generalizability/transferability (Saunders et al., 2016). When interviewing participants from different cultures, scholars should use cultural reflexivity to remain unbiased to ensure data quality (Saunders et al., 2016). Generalizability can occur when one case or a small number of cases are used to draw conclusions in a study, thereby limiting a study’s ability to sufficiently test theories (Saunders et al., 2016). Data quality issues involving transferability occur when scholars use an existing study to generalize about an entire population (Saunders et al., 2016). Mitigating the potential barriers to research quality will help scholars achieve a positive outcome for their research study.

Importance of Reliability and Validity

Reliability and validity are essential elements of research commonly associated with quantitative studies. Validity pertains to the ability to sufficiently measure variables (Heale & Twycross, 2015). Heale and Twycross (2015) correlated reliability to the consistency of a measure. Conversely, qualitative studies rely on credibility, transferability, dependability, and confirmability to establish the research quality (Houghton et al., 2013). First, credibility refers to a study’s internal validity and believability (Houghton et al., 2013; Morse, 2015). Next, transferability is the external validity of research and the ability to transfer findings to similar contexts or individuals while maintaining the original intent of the data (Houghton et al., 2013; Morse, 2015). Third, dependability coincides with reliability and examines the stability (Houghton et al., 2013; Morse, 2015). Finally, confirmability references the impartiality or accuracy of the data (Houghton et al., 2013; Morse, 2015). Like the elements of quality quantitative research, qualitative research incorporates validity and reliability to establish data quality.

How to Achieve Reliability and Validity

Reliability and validity can be achieved through several methods in the doctoral research process. Yin (2018) cited construct validity, internal validity, external validity, and reliability as four methods for testing the quality of research designs. Each of the four tests offers tactics researchers can use to judge data quality for case study research. Additionally, using multiple sources of evidence enables scholars to ensure the reliability and validity of research through triangulation or member validation. Walden University (n.d.) requires scholars to use a minimum of two sources of evidence. Data triangulation relies on more than one source of evidence to verify the results of a study (Yin, 2018). Additionally, member validation ensures data quality by allowing participants to validate the accuracy of interview transcripts (Saunders et al., 2016)