• Be sure to use your own words, Make sure to answer all parts of each question.
• Please write your reasoning for your answers and why you chose to pursue the answer the way that you did.
• Some of the questions are very open-ended and more than one good answer is possible.
• i want it in 1,200 words total.
Below is a data set. A researcher created a 3-question test that she hopes will determine whether or not someone is ready to take a calculus-based physics college course. She has piloted the test with 10 students. The table shows the points awarded for each question by a physics teacher, the total scores, and the gender of each subject.
Subject# Gender Q1 Q2 Q3 Total
1 M 18 76 13 107
2 F 23 71 22 116
3 F 4 27 1 32
4 M 43 21 4 68
5 M 31 30 20 81
6 M 40 14 5 59
7 F 12 62 19 93
8 F 4 74 2 80
9 M 12 82 14 108
10 F 4 61 16 81
1. Consider the variables “Gender,” “Q1,” “Q2,” and “Q3.” Which ones are nominal, ordinal, or interval, and why?
2. (a) Conceptually, how might the researcher empirically decide who scored “above average” or “below average” on this test? (b) Computationally, which subjects higher than average or lower than average? How did you find out? (c) Are there any outliers, that is, subjects that scored very highly above average or very below average? Which ones? What procedure did you use to decide?
3. It’s often important when creating tests like this to make sure that the items on the test do not show “bias” (sometimes called “disproportionate impact”) towards one group over another.
a. How might we determine whether the questions on this test are biased towards the gender of the subject?
b. Are there any questions on this test that show a gender bias? How would you decide?
c. Which one(s), if any, did you find?
4. The researcher wants her test to assess just one characteristic (“readiness for a course”) and that characteristic only. How might she use correlation coefficients to decide if the items on her test are accomplishing that objective?
5. The researcher decided to use the mean score on her test as the cut-off for whether or not the test would predict that someone would pass the class. Anyone below the mean score she predicts “No—would not pass the course,” and anyone above the mean score she predicts “Yes—would pass the course.” All 10 students then took the class, and she found out which students passed it.
Here is the updated data set below. “Predicted” is what her test predicted, but “Passed” is whether or not they actually passed the course.
Subject# Sex Q1 Q2 Q3 Total Predicted Passed
1 M 18 76 13 107 “Yes” No
2 F 23 71 22 116 “Yes” Yes
3 F 4 27 1 32 “No” No
4 M 43 21 4 68 “No” No
5 M 31 30 20 81 “No” Yes
6 M 40 14 5 59 “No” Yes
7 F 12 62 19 93 “No” Yes
8 F 4 74 2 80 “No” Yes
9 M 12 82 14 108 “Yes” Yes
10 F 4 61 16 81 “No” No
a) Conceptually, what can she do to find out empirically whether her test was any good at predicting whether or not students would pass the course?
b) Computationally, was her test any good at predicting whether or not students would pass the course?
c) From your evaluation, should this test be put to use as is? Why or why not?
I will attach the same thing in a file to be clearer because the numbers above are in a table
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