Over all the chapter number, 14 was very interesting and relatively easy to understand than other chapters. It was because of some easy concepts such as mean and variation that we had learnt many times before this, as well. However, the most interesting concept among all the topics discussed in the chapter was about editing the collected data before analyzing it. This was a short topic but still attracted my attention the most. Perhaps, the reason behind this was I felt this was one of the most crucial phases of quantitative data analysis. In the process of editing, the researcher edits the data before coding and entering it into the database for analysis. He decides which data to use further in his research. For this purpose, the researcher eliminates any unfilled spaces in questionnaires and checks the level of consistency in each part of data. The entire results and conclusion depend upon the data collected. Therefore, I felt editing the data was most important because any data collected from an un-reliable resource, or any part of data, which is thought to be bias will affect the analysis and hence results. However, it was a little surprising to know there are not as such specific techniques to use for editing the data. Researchers often use their opinion and perception for this purpose. However, in my opinion I believe a special technique must be determined to edit the data. This will help the researchers to utilize time efficiently to analyze only the data, which is best related to the research topic, and will enable them to conclude the most reliable theory.
Interesting Question:
Although coding makes analyzing easier for the researcher and avoids confusions, there is no recommended method to be used by researchers in coding of non-responses. Thus, each researcher codes non-responses using his/her own method; this poses a great challenge for many researchers in the analyzing and interpretation process. It is very common to have an outlier response in the research. However, it is extremely difficult to know if this outlier value is a data error or not. Therefore, it is important to determine the source and reliability of the outlier value. In the editing process, researchers edit information to match other questionnaires, how do researchers avoid bias while editing data to ensure that the results of the study give accurate information. What is the procedure used in coding contrasting data.
CHAPTER 15
Interesting Point:
Chapter 15 was full of new concepts and formulae. I felt it a little difficult than the other two assigned readings. For instance, topics like testing hypothesis and regression required some practice before developing complete familiarity with it. Also, concepts such as Logistic regression, conjoint analysis, canonical correlation, and two-way ANOVA were difficult concepts. However, found each of them was equal interesting. The most interesting was their real life applications. I was very surprised to see how statistics could be linked easily with life. The most interesting concept of all the terms in this chapter was regression and multiple regression analyses. This statistical process is used to estimate the relationship existing between a dependent and more than one independent variable. It explains how one values of the independent variable affects those of dependent variable. I also found how regression could be used for future predicting and forecasting. This also gave me the idea of how wide this field’s scope was.
Interesting Question:
There are two types of statistical errors described in the very beginning of chapter 15: Type I error and Type II error. Type I error is described as “probability of rejecting null hypothesis, when it is actually true” and type II error as “probability of failing to reject the null hypothesis.” While reading about both the errors, I was very much interested to know about the consequences of such errors in real lives. Reading only the content in the book it was a little difficult to relate the topic with one’s daily lives. Therefore, I was very keen to know about this. Another most interesting question regarding the same topic was that; based on real life experiences, which one of the errors was more dangerous?
CHAPTER 16
Interesting Point
The most interesting point that I found in this chapter was about the importance of data display in qualitative analysis. The process of data displaying involves organizing and arranging data in a well-integrated and condensed manner. It was also nice to know that there are so many data displaying methods. Researcher can choose any one of them depending upon his preferences. For instance, some methods include; pie charts, line graphs, bar graphs, flow charts, Venn diagrams, taxonomy, boxed display, diagram, and matrices. It was very fascinating to know how displaying data in this form enables the researcher to determine the relationship between different variables and the trend followed by each variable used in the study. In addition, it was even more fascinating how a researcher can predict future performance of an organization using past trends after displaying data.
Throughout chapter 16, I was comparing qualitative analysis with quantitative analysis, their importance, use and techniques. I found qualitative analysis more easy and fun. IT involved making charts and graphs, while quantitative was all about numbers and formulae. In fact, quantitative analysis was more time consuming. However, I was unable to figure out which one of them played a more important role in conducting research Although, quantitative analysis gives a much accurate answer, I believed qualitative analysis have equal importance. This is because qualitative analysis gives an overview of the research questions, trend in preceding years and quick conclusions. Therefore, I believe any research should compromise of both the things.
Interesting Question
Although, both qualitative and quantitative research analysis is important, which one of them is more reliable? Is it important to include both of them in research? I believe the answer to this question is yes, because whenever you collect a data, if you analyses it using quantitative techniques you will definitely need qualitative tools to elaborate the result. However, I am very eager to know is it possible to include only one of them? Can a research survive and be completed with only one of these techniques? If yes, then which of them is considered a better approach and why? Which of them can be neglected and why? How can we decide this?
