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Hint: Make sure to discuss study’s goal, subjects, and variables in the data.
The ECLS or the Early Longitudinal Study was a project that was created to measure the academic progress of 20,000 American school children from Kindergarten-5th grade. The children were picked all at random from all different regions of the country to get a large wide spread of data. The kids that they picked they took note of all different types of data looking at the students gender, race, family structure, families social status, and the parents level of education, and many more physiological factors. Researchers then took the time to dive even further into the research creating questionaires for the school administrators and the parents to determine how good the school was, and different types of parenting styles which could be affecting the childs progress. The objective of this project was to measure correlations about every aspect of a childs life to academic progress. Researches wanted to figure out what can families do in order to see there child succeed in school.
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”.
The point of regression anaylisis is to allow researchers to look at a widespread of data to determine the strength of the correlation between different variables. The goal is to be able to hold a constant in the variables so that they can look at the most miniscule piece of data to determine the strength of its correlation and how two variables can co-vary. This all works like a circuit board in a type of way because imagine looking at a circuit board as a a massive set of data and each switch is a variable, what researchers and economists can then do is flip the switches to look at only a few variables at once to determine the strength of the correlation and determine which variables are relevant and could change the outcome and which variables are not relevant. Being able to analyze variables this way allows for researchers and economists to look at massive sets of data that are equal and find slight variations in the data and the correlations between the massive data sets.
Hint: A correct answer must have a discussion on causality versus correlation.
The problem with regression analysis is the accuracy of the data. Unfortunaltley the analysis is not 100% all the time and can only show trends which can be skewed by outliers in the data sets. The example that the author used was, “Does having tons of books in your home lead to your child doing better in school?” While the answers is yes most of the time its not 100% becuase there are kids that do not preform well and will still struggle even if they grow up with tons of books. So a better question that should have been asked would be “Does having tons of books in your home TEND to lead to better academic succsess.” This way its not saying that its definently going to happen it just is saying that its more likely to happen than homes with less books. The difference between the two questions us casuality cersus correlation because the first question that was asked can not give you the exact answer which is casuality, and the second question that was asked can give you trends of the data set which represents correlation.
Hint: See page 150.
School quality is determined by class size, average teacher education, and student to computer ratio. This infromation is all pulled from when the researched sent out the questionaire to the school administration at the beginning of this study. That being said there is a definiate correlation and impact in the test scores with the level of the school, the higher quality of the school the higher the test grades, at least from ages kindergarten-5th grade.
Hint: For this question, you may need additional information in addition to the assigned reading. You may Google search someting like “how does regression control for variables”.
Being able to control information all works off of dependent and independent variables. Independent variables are just random sets of data that could effect the dependent variable. So when researchers and economists want to control data they have to control a few of independent variables to make sure that they are all consistent to see what variable combined with that controlled variable has the greatest effect. So as I talked about befrom indepent variabels can be your average class size and researchers could just look at every school with the same average class size, for example they are looking at 50 schools all with the same average class size of 20 students which would be their controlled variable, they can then look at another random variable which could be teachers education level which will vary throughout all 50 of those schools. If the goal was to then look at test scores between all 50 of these schools researchers can then look at their controlled variable and say all class sizes are the same and then schools with the higher education teachers are preforming better than schools with the lower level of teachers.
The biggest takeway that I took from the reading is that nothing is ever guareented in this world and that we have to look at the trends and be able to say that this is likely to happen this way but its not a 100% guareentee. I also learned a lot about independent and dependent variables and learned about how we can control our data sets to try to find correlations between a widespread of data. I will be able to use this as a future coach becuase I can look at my athletes and be able to determine what variables can affect their play the most if one week I work them out 3 times and we win and the other week I work them out 4 times and we lose I can determine that that extra workout can be to much on my athletes.
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