Sigma This Chapter Presents Research 'Methodology' chapter

Excerpt from 'Methodology' chapter :

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.


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…

Cite This 'Methodology' chapter:

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