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Principles and Methods of Qualitative Data Collection
The four principle associated with data collection as discussed by Yin (2014) will play an instrumental role in enhancing my capability to address the research question. The principle related to the use of multiple evidence sources will ensure that I collect data from various sources with an aim of answering the research question. According to Yin (2014), having multiple sources of data helps in enhancing the construct validity of a study. I will also ensure that during the data collection process a chain of evidence will be maintained. The objective of having a chain of evidence will be to make the claims made in relation to answering the research questions strong. This will make the study findings reliable to the target audience. The principle will also help in increasing the capability of supporting the arguments made in an attempt to answer the research question by making use of different schools of thoughts. In addition, the use of the principles will help in creating a data base for the case study and this will make the process of data collection easier in answering the research questions (Cohen, Manion and Marrison, 2013).
Using multiple sources of evidence and triangulation within the data collection process is of great importance. It aids in enhancing the quality of data collected. Additionally, it makes it possible to investigate a concept using multiple points of view, thereby enhancing construct validity which is key in ensuring that the study findings are acceptable in the scholarly context (Saunders, Lewis and Thornhill, A. (2015). Finally, it ensures that data collected using different methodology is used in an attempt to understand the research concept.
References
Cohen, L., Manion, L., & Morrison, K. (2013). Research methods in education. Routledge.
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2015). Research methods for business students (7th ed.). Essex, England: Pearson Education Unlimited.
Yin, R. K. (2014). Case study research: Design and methods (5th ed.). Thousand Oaks, CA: Sage.
Part 3A
Consider the different data quality issues that can arise within the research process. Be sure to take into consideration such concerns as credibility, transferability, dependability, and confirmability.
Reliability and Validity
There are different data quality issues in a quantitative research. One of the issues of concern is data manipulation. Data manipulation is when a researcher changes the collected information to fit a certain narrative related to the topic of the study (Taylor, Bogdan and DeVault, 2015). Bias during the data collection process is also a quality issue that impact negatively on the findings of quantitative MASTERS research. When there is bias in selection of study respondents, the data collected is not reliable at all and this impact negatively on the quality of the inferences generated from the study. Additionally, lack of credibility, particularly sources of data, makes the findings generated in a MASTERS masters study invalid and unreliable.
According to Bernard (2010), reliability can be defined as the extent to which a similar study about a topic produces similar results and predictions. In the case of quantitative design, the concept of reliability focuses on dependability and trustworthiness of the research data generated. Therefore, it ensures that research findings are dependable in the course of completing a research. On the other hand, validity ensures that the research findings and inferences are credible. It makes sure that the findings are not manipulated by any way but are presented in a manner that they are collected and analyzed from the field or secondary sources (Saunders, Lewis and Thornhill, 2015).
In a MASTERS masters research, reliability and validity can be attained through data triangulation. Data triangulation involves various perspectives and methods being brought together with an aim of producing a comprehensive set of study findings (Bernard 2010). It can also be achieved through reducing personal bias during the data collection process.
References
Bernard, H. R. (2010). Analyzing quantitative data: Systematic approaches. Los Angeles, CA: Sage Publications.
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2015). Research methods for business students (7th ed.). Essex, England: Pearson Education Unlimited.
Taylor, S. J., Bogdan, R., & DeVault, M. (2015). Introduction to quantitative research methods: A guidebook and resource. John Wiley & Sons.
PART 4A
You are a researcher conducting a qualitative research study and have scheduled a series of interviews. As you begin to conduct your interviews, you encounter a number of problems: Particiant A does not expand on his or her responses, providing mostly one-word answers, and refuses outright to answer some questions. Participant B continually goes off topic, sharing personal anecdotes, and takes up interview time with unrelated information. Finally, Participant C has been difficult to schedule and attempts to withdraw his or her contribution to the process after showing initial interest. Consider strategies you might employ to address these challenges and any potential insights you may gain toward your growth as an independent scholar.
Difficult participants
Participant A – Semi-structured and in-depth interviews can achieve a high level of validity/credibility. Where conducted carefully using clarifying probe questions, you want your interviewees to explain, or build on, their responses. Probing will add significance and depth to the data obtained by exploring responses from a variety of angles. Probing questions can also be used to seek an explanation where you do not understand the interviewee’s meaning or where the response does not reveal the reasoning involved. Probes may be used to follow up initial responses and obtain greater detail from your participants.
Participant B – Although some digression should be tolerated, as it can lead to aspects in which you are interested, you will need to impose more direction. This must be done subtly so as not to cause offense, such as by referring to an earlier relevant point and asking them to tell you more, or requesting that they pause so you can note down what they have just said.
Participant C – Credibility may also be promoted through the supply of relevant information to participants before the interview. Providing participants with a list of the interview themes before the event, where this is appropriate, should help this difficulty. The interview location will be arranged by those whom you interview. For example, when you interview organizational participants such as managers in their offices, this has the advantage that they can find documents which support points they are making. The researcher needs to be knowledgeable about the research topic and organizational or situational context in which the interview is to take place. The ability to draw on this type of information in the interview will help to demonstrate credibility, and allow the accuracy of responses and encourage the interviewee to offer a more detailed account of the topic under discussion.
Participant D – If a participant starts interviewing you. This can suggest that you have created rapport. However, you need to stress that you are interested in their opinions and that, if they wish, they can ask you questions at the end.
Effectiveness of strategies
The conduct of semi-structured and in-depth interviews will be affected by the appropriateness of the researcher’s appearance, opening comments when the interview commences, approach to questioning, appropriate use of different types of question, nature of the interviewer’s behavior during the interview, demonstration of attentive listening skills, scope to summarize and test understanding, ability to deal with difficult participants and ability to record data accurately and fully (Saunders, Lewis & Thornhill 2015). These aspects are intended to avoid forms of bias that would affect the reliability and validity of the data produced.
Successfully interviewing participants from different cultures requires some knowledge about those cultures. Brickman and Kvale (2015) highlighted some of the verbal and non-verbal cues that may have contrary or different meanings between cultures. For example, the importance of being aware of social conventions in a culture to understand the way answers are constructed and not to cause offense (Brickman & Kvale 2015). Cultural differences exist not only between countries but between groups, social classes and organizations and some prior knowledge about those you wish to interview will invariably be helpful (Saunders, Lewis & Thornhill2015). Developing a trusting relationship with an interviewee is an effective strategy that facilitates more comfort in helping a participant open about their experiences (Elmir, Schmied, Jackson, & Wilkes, 2011). The ability to deal with difficult participants and ability to record data accurately and fully (Laureate Education Producer, 2012c).
References
Brinkmann, S. and Kvale, S. (2015) InterViews (3rd ed). Thousand Oaks, CA: Sage
Elmir, R., Schmied, V., Jackson, D., & Wilkes, L. (2011). Interviewing people about potentially sensitive topics. Nurse Researcher, 19(1), 12–16. Retrieved fromhttp://nurseresearcher.rcnpublishing.co.uk
Laureate Education (Producer). (2012c). Interview techniques part II [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 Limited.
PART 5A
Write an explanation for your choice of data analysis technique for qualitative research. In your explanation, do the following:
- Briefly describe the business problem identified for your Prospectus.
- Explain which data analysis process is most appropriate to your research, providing a rationale for your choice using supportive scholarly examples.
Discussion: Data Analysis Processes
The general business problem is that most project managers view project success as delivering a service or product and are unable to identify real reasons of failure and interruption which is the increased inconsistency, miscommunication, mismanagement, and confusion about the acceptance criteria factor (Kendrick, 2015). The specific business problem is that project managers do not understand how to identify failures on projects and lack to understand effective practices, leadership, culture, and corporate processes which will aid in increasing the rate of project success.
According to Taylor, Bogdan and DeVault (2015) qualitative data analysis can be defined as the range of procedures and process where a researcher moves from qualitative data collected to drawing some inference and interpretations on the subject of investigation. The data analysis technique will be employed in the case of the study will be inductive approach. Yin (2014) notes that inductive technique is applied in the data analysis process when qualitative methods are the major approach of inquiry. The use of inductive technique will involve grouping the data into certain categories and using them to look for the relationships that exist. Hence, the data analysis process will involve grouping the data collected into certain related items and them using them to come up with inferences related to the business problem (Saunders, Lewis and Thornhill, 2015).
The main reason for choosing inductive data analysis technique is that it allow for one to group data into certain related categories and then using it to draw conclusions on the subject of the research. Hence, in this case the technique will help accurately determine the relationship that exists between the business problem and the qualitative data collected. Thus, the conclusions that will be drawn for this study will be reliable and valid in nature.
References
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2015). Research methods for business students (7th ed.). Essex, England: Pearson Education Unlimited.
Taylor, S. J., Bogdan, R., & DeVault, M. (2015). Introduction to qualitative research methods: A guidebook and resource. John Wiley & Sons.
Yin, R. K. (2014). Case study research: Design and methods (5th ed.). Thousand Oaks, CA: Sage.
