Part VIII
Chapter 25: Covers the most common kinds of quantitative data statistics and the reasons behind them.
1. Chance in studies can act as its down fall as it will more often then not result in inaccurate information. "Because we are rarely able to study an entire population, we are almost always dealing with samples drawn from that population" (Grinnell 519).
2. The Dependent and Independent tests are the two kinds that can determine difference. The main way to tell them apart is how the groups being tested relate to one another.
3. Statistical measurement is critical in analysis and selecting the right measuring tools is impotent for that. The goal in an measuring is to eliminate as much error as possible before and during the test.
Chapter 26: is on analyzing data through a theoretical approach of the data.
1. Credibility in a study is very important for it to be considered a good one. If a study is not credible then it can never be used for further resurrect or in creating a solution to a problem.
2. When collecting data the most important kind of math to use is statistics. It lets you analyze your results with mathematical accuracy. this becomes more difficult when looking at qualitative data as it has no numbers directly related to it.
3. Qualitative data has time constraints to collecting, it has to be done sooner rather then later. If it is not then the situation your are studying might change and your questions will no longer be as applicable.
Chapter 27: Explains the systematic approach needed for a qualitative approach.
1. The first thing to do when creating a study is to create a frame work to build it off rather then just winging it. This will also let different people to tackle different aspects of a study.
2. The function of analysis data is to sort thought all the data that your study collects. It is quite possible that you will get a lot of information you do not need and this data will have to be effectively sorted out.
3. Coding your data helps with categorization making it much simpler to manage. Also once categorized it becomes possible to prioritize the research you need.
Question: If you account for error in your final report is it ok to leave it in during the test if it is no possible to remove?
Chapter 25: Covers the most common kinds of quantitative data statistics and the reasons behind them.
1. Chance in studies can act as its down fall as it will more often then not result in inaccurate information. "Because we are rarely able to study an entire population, we are almost always dealing with samples drawn from that population" (Grinnell 519).
2. The Dependent and Independent tests are the two kinds that can determine difference. The main way to tell them apart is how the groups being tested relate to one another.
3. Statistical measurement is critical in analysis and selecting the right measuring tools is impotent for that. The goal in an measuring is to eliminate as much error as possible before and during the test.
Chapter 26: is on analyzing data through a theoretical approach of the data.
1. Credibility in a study is very important for it to be considered a good one. If a study is not credible then it can never be used for further resurrect or in creating a solution to a problem.
2. When collecting data the most important kind of math to use is statistics. It lets you analyze your results with mathematical accuracy. this becomes more difficult when looking at qualitative data as it has no numbers directly related to it.
3. Qualitative data has time constraints to collecting, it has to be done sooner rather then later. If it is not then the situation your are studying might change and your questions will no longer be as applicable.
Chapter 27: Explains the systematic approach needed for a qualitative approach.
1. The first thing to do when creating a study is to create a frame work to build it off rather then just winging it. This will also let different people to tackle different aspects of a study.
2. The function of analysis data is to sort thought all the data that your study collects. It is quite possible that you will get a lot of information you do not need and this data will have to be effectively sorted out.
3. Coding your data helps with categorization making it much simpler to manage. Also once categorized it becomes possible to prioritize the research you need.
Question: If you account for error in your final report is it ok to leave it in during the test if it is no possible to remove?
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