Mixed methods research methodology: qualitative and quantitative data collection

¶ … Sigma

This chapter presents research methodology and data collection method through qualitative and quantitative research. The study combines both qualitative and quantitative method to enhance the reliability and validity of the research findings.

Qualitative Method of Data Collection

Qualitative research is the process of collecting data through in-dept interview, analyzing document and observation method. This study uses the in-dept interview and document analysis to collect data through qualitative method. Generally, qualitative research relies on the in-dept interview to collect data, and using the in-dept interview, the research collects data within the natural environment as well as immersing in the phenomenon. The qualitative in-dept interview that a researcher uses to collect data is to explore the research topic to answer the research questions as well as achieving research objectives. Typically, the in-dept interview unfolds participants' views, and to unfold participant view, the study uses systematic method to question the interviewees to explore the phenomenon.

Interview is a technique of collecting data through asking questions verbally and expects participants to answer the questions posed. The structured interview is the data collection technique where a researcher phrases questions in such a way to make respondents having a limited range of response. The structured interview involves asking participants to answer one of these responses: "Strongly agree, Agree, Somewhat agree, Agree and strongly agree."

On the other hand, semi-structured interview involves using a list of open-ended questions that are based on the research topic. The benefit of open-ended interview is that it assists a researcher to discuss certain concepts, which are relevant to the research topics. The unstructured interview is the same as in-dept interview where a researcher tries to obtain information about a specific topic. Typically, face-to-face interview is a technique of collecting quality data from participants.

While qualitative research is the process of collecting data from social and cultural phenomenon, however, quantitative research is the process of collecting data using survey method and statistical method of presenting data. The motivation of qualitative research as opposed to quantitative data collection is that the qualitative research is designed to assist a researcher to understand people within their natural environment, and the goal of qualitative research is to understand the phenomenon based on the natural environment of participants. While there are both advantages and disadvantages associated to both qualitative and quantitative research, this study enjoys the advantages of the two research methods. The combination of the two research method assists in the triangulation of research which enhances the validity and reliability of the research findings.

The advantages that this study derives from using qualitative research is that it assists the study to sample population within their natural phenomena, and the technique yields results that could be generalized to similar phenomena. Moreover, qualitative research allows a researcher to uncover the candid perception about the study, which assists in generating ideas, as well as developing hypothesis that could be tested quantitatively.

Despite the benefits that this study derives from the qualitative study, a qualitative research is time consuming and costly. While the goal of qualitative and quantitative research is to arrive at valid research findings, there are still differences within the two research. Typically, the qualitative research uses the epistemology research; on the other hand, the quantitative research uses the positivistic approach for data collection.

Primary and Secondary Data

Primary research is the data collection technique, which originates from interview, survey, direct observation, and focus group. The quantitative source of primary data is from survey and questionnaire, and data collected through voice recording. Typically, the research collects primary data to achieve a specific goal. On the other hand, the secondary data are the data collected from the published academic or non- academic literatures. This research collects data from both primary and secondary sources. The primary data collection technique is through survey, and interview technique. Typically, primary data is undertaken after a researcher has gained insight on the secondary research. There are many advantages that this study derives from the primary data. First, the primary data assists the study to address the specific research topic. For example, the primary data assists the researcher to explore the method the Six sigma and DFSS methodology could assist in improving the existing products. The data collected through the primary data also assists the research to understand the importance of Cp and Cpk in the production line, which could assist an organization to achieve production efficiency. Despite the advantages that could be derived from the primary data, the data collected through primary data is relatively expensive. Typically, higher costs could be incurred to produce effective questionnaire. The costs of transportation and telephone costs could be high before completing the research. Moreover, the primary data is time consuming. Sometimes, it may take longer time before collecting data that could be used for the research findings. Thus, a researcher might not have ability to collect valuable data that could assist a researcher in generating the research findings because of the unwillingness of target population to release the valuable information.

Apart from primary data collection technique that this study implements, the study also collects secondary data to arrive at research findings and achieve research objectives. The study collects secondary data from electronic academic database such as JSTOR, Academic Journals Database, EBSCO Publishing, ProQuest, Wiley InterScience, Elsevier, Mendeley, and ScienceDirect. This electronic database consists of varieties of academic research papers, and quality high journals that this study uses in preparing the research study. Apart from the data collected from the electronic database, a researcher also collects data from the academic books which are sourced from the university library, private and public library.

This study derives several advantages from secondary data. First, the secondary data is easier to collect and cost effective. Unlike primary data that are time-consuming, the secondary data is less time consuming, and a research could collect secondary data to answer the research questions as well as achieving research objectives. Boslaug (2007) argues that the economy is the major advantage of secondary data. Since data have already been collected by another researcher, a researcher does not need to devote his/her resources to collect the data. More importantly, the saving of time is another major advantage of secondary data. Since the data has already been collected and analyzed, the research only needs to collect the data for his or her research study. Boslaug (2007) further points out that secondary data are characterized with the informed expertise, which may not be available with primary data.

Despite the advantages that this research derives from secondary data, there are still disadvantages associated with secondary data. The secondary data may not be suitable to answer the research questions. Typically, the secondary data collected may not be applicable to a specific research topic.

The study collects both primary and secondary data investigate the strategy to improve the existing product using the DMAIC (Define, Measure, Analysis, Improve and Control) as an effective improvement process. Moreover, the data are collected is to investigate the effectiveness of IDOV (Identify, Design, Optimize and Validate) or DFFS methodology as an effective process. It is essential to realize DFFS is an analytical tool that could be used to prevent predictive product defect when deciding to design a product. Typically, a significant decline in costs could enhance higher level of performances. Thus, the data are collected to establish that DMAIV sequence could enhance high level of product design performances.

ANOVA (Analysis of Variance) and Pareto Analysis

ANOVA is a statistical test to identify whether the means of several groups are equal. ANOVA has benefits of comparing two, three or more means, and ANOVA is very useful for the confirmatory data analysis. This study uses the ANOVA as a statistical tool to compare the means of the following:

Red color, auto feeding

Red color, manual feeding

Blue color, auto feeding

Blue color, manual feeding

The study uses the one-way ANOVA to calculate the value of StDev using power and sample feature. The ANOVA test includes blue input and red input. The test higher than 0.05 is assumed to have high significant level. The ANOVA analysis is also used to test the mean of manual feed and auto feed, and the results are compared with the mean of blue input group and red input group. In this study, one-way ANOVA is used to confirm the equal variance.

Pareto Analysis

Pareto analysis is a statistical technique to enhance decision-making, and is used in selecting limited number of task to produce overall significant effect. Pareto analysis is a creative technique in identifying the cause of problems, and this technique assists in identifying 20% cause of the problem, which could assist in solving 80% of the problems. Typically, Pareto analysis assists in focusing 20% of the problem that could have impact on the project. With reference to this study, the Pareto analysis is used to identify the scraps that have the major impact on costs and quantity. The analysis and separation was carried out from the information collected from scrap. Thus, the material in the top ten ranks was placed in the Pareto diagram. The Pareto analyses aims to classify data into group, which range from smallest to largest. Typically, Pareto analysis is a process improvement tool that facilitates data presentation and organization as well as revealing the root cause of the problem. Pareto analysis is also the tool to understand the problem and identify the opportunities that have highest values. Typically, Pareto analysis is useful in assisting the Six Sigma team. This study uses the Pareto analysis to assist the study:

To understand the relative importance of the problems and method to provide the simple and quick and visual solution to the problem.

To measure progress using an easy to understand and visible format.

Reduce the probability of the problem that may lead to vital the consequence.

Summary

This chapter provides the research methodology that discusses both quantitative and qualitative research. The study combines both research method and combining the advantages associated with the two-research technique to generate valid and reliable research findings. Moreover, the study collects primary and secondary data to prepare this study. The study uses the ANOVA to compare the means of the following:

Red color, auto feeding

Red color, manual feeding

Blue color, auto feeding

Blue color, manual feeding

Pareto analysis also assists in focusing 20% of the problem, which could have impact on the project. For example, Pareto analysis identifies the scraps that could have the major impact on the costs and quantity. Combination of these research techniques assists the study to generate valid and reliable research findings.

List of References

Borlaug, S. (2007). Secondary Data Sources for Public Health: A Practical Guide.

Cambridge University Press.UK.

Hox, J.J. & Boeije, H.R. (2010). Data Collection Primary vs. Secondary. Utrecht University. The Nethertland.

Morrell, K. (2010). Quantitative Data Basic Introduction. USA.

Texas State Auditor's Office, (1995). Methodology Manual. Data Analysis: Analyzing Data - Hypothesis Testing. ..USA.