You must follow the instructions below to get credits for this assignment.
Hint: Make sure to discuss study’s goal, subjects, and variables in the data.
The Early Childhood Longitudinal Study was conducted in the late 1990s and it was to measure the academic progress of kindergarten through fifth grade. This study included more than 20,000 children from across America. The variables in this experiment were sixteen factors and whether or not they affected a child’s academic progress. Eight of these factors showed a strong positive correlation and the other eight didn’t matter to the child’s academic progress, they had no correlation.
Hint: A correct answer must have a discussion on a main concept of regression, often called as, “all else being equal”, “controlling for other variables”, or “Ceteris paribus”. The author explained this concept using “the circuit board analogy”.
A regression analysis uses correlation to see if variables move together in a set of data. It uses statistical techniques to figure out correlations. In a regression analysis when you are looking at one variable you are holding all of the other variables constant and this is referred to “Ceteris paribus” as the Romans would have said.
Hint: A correct answer must have a discussion on causality versus correlation.
A regression analysis demonstrates a correlation well but it doesn’t show cause for the correlation, also called causality. For example on the reading mentions “A regression alone can’t tell you whether it snows because it’s cold, whether it’s cold because it snows, or if the two just happen to go together” (148). This sentence is basically saying that a regression analysis showing a correlation does not give you a reason for why that correlation is happening. The correlation could be happening for many reasons and without further research we cannot find out the causality.
Hint: See page 150.
The quality of schools plays a huge role in a students academic performance and in this reading it talks about how people mostly worry about the black-white test scores but the bad-school/good-school gap seems to be a more serious issue. In studies done it shows that students in better school systems outperformed kids that were in a bad school system regardless of their race. According to the data, a child’s school has a huge impact on a child’s education in their early years.
Hint: For this question, you may need additional information in addition to the assigned reading. You may Google search something like “how does regression control for variables”.
When doing a regression analysis when you’re looking at one variable you have to hold the other variables constant. For example in this case if you wanted to look at one variable in regard to the quality of the school, you could make one side of a school renovated and new and one side poor and bad quality. The bad and poor quality side of the school would be considered the controlled variables. Then you would measure the students academic progress according to which section of the school they work on. Whether it’s the renovated and good quality side of the school or whether it’s the nonrenovated side of the school.
The main takeaways from this reading are that regression analysis is more than just correlations. Regression is a good way to start research and eventually dive in deeper to an idea. Regression analysis and its correlations are more art than science because we can come up with many meaningful connections through a regression analysis. A child’s academic progress can be affected by many things but also not affected by other things. It’s important that we continue this research to continue improving a students education. Regression analysis is one of the many steps in research towards better academic progressions for students.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.