Topic 1 DQ 1:2

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Grand Canyon University *

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Statistics

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Jan 9, 2024

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Topic 1 DQ 1 Statistics can be a foreign topic to many people and/or cause anxiety. This checklist may help you succeed. Schedule 20-25 hours a week for studying and completing assignments Review the specific terms for each topic on the Statistics Visual Learner website Read the assigned chapters from the textbook and complete the Progress Checks as you are reading for practice Use the Electronic Resources for extra assistance (Khan videos, SPSS tutorials, SAGE resources, etc.) Complete the assignment(s) for each topic Having reviewed this checklist, what challenges might you face? What steps can you take to succeed in the class? Review the levels of measurement terms in the Statistics Visual Learner media piece. Compare and contrast Stevens's four scales of measurement and explain when each type of scale should be used. Challenges you might face when studying statistics and steps to succeed in the class: 1. Time Management: Scheduling 20-25 hours a week for studying and completing assignments can be challenging if you have a busy schedule. To succeed, create a weekly study plan that incorporates these hours. Prioritize your tasks and avoid procrastination. 2. Complex Concepts: Statistics can be difficult to grasp, especially if you are new to the subject. To overcome this challenge, use additional resources like the Statistics Visual Learner website, textbooks, and electronic resources. Seek clarification from your instructor or a tutor if you find specific concepts confusing. 3. Practical Application: Understanding the theory is one thing, but applying statistical concepts in assignments or real-life scenarios can be daunting. Practice is key. Complete progress checks and assignments as you review the material to reinforce your learning. 4. Technical Tools: Depending on the course, you may need to use software like SPSS. Learning how to navigate and utilize these tools effectively can be a challenge. Take advantage of tutorials and resources available to master the required software. 5. Motivation: Maintaining motivation throughout the course can be challenging, especially if statistics are anxiety-inducing. Connect with classmates for study groups, set small achievable goals, and remind yourself of the practical importance of statistics in various fields. Regarding the comparison of Stevens's four scales of measurement:
1. Stevens's four measurement scales are nominal, ordinal, interval, and ratio. Each type of scale has distinct characteristics and is used in different situations: 2. Nominal Scale: This is the most superficial level of measurement and involves categorizing data into distinct groups or categories. Nominal data can't be ranked or ordered. Examples include gender, ethnicity, or eye color. Nominal scales are suitable when you only need to differentiate between categories without quantitative meaning. 3. Ordinal Scale: Ordinal data can be ranked or ordered, but the intervals between values are not meaningful. Examples include education levels (e.g., high school, bachelor's, master's), Likert scale responses, or movie ratings. Ordinal scales are used when you need to represent a relative ranking but can't precisely compare values. 4. Interval Scale: Interval data have meaningful intervals between values but lack an actual zero point. Temperature measured in Celsius or Fahrenheit is an example. In an interval scale, you can perform addition and subtraction, but you can't meaningfully perform multiplication or division. Interval scales are suitable for data where the absence of a value (zero) doesn't imply the absence of the measured attribute. 5. Ratio Scale: Ratio data have meaningful intervals between values and an actual zero point, which allows for significant addition, subtraction, multiplication, and division. Examples include age, height, weight, and income. Ratio scales are the most versatile and are used when precise quantitative measurements are necessary. The choice of scale depends on the nature of the data and the type of analysis you intend to perform. Nominal and ordinal scales are often used for qualitative data, while interval and ratio scales are used for quantitative data where you need to perform more advanced mathematical operations (Witte & Witte, 2017). References Witte, R. S., & Witte, J. S. (2017). Statistics . John Wiley & Sons. https://www.google.com/books/edition/Statistics/KcxjDwAAQBAJ?hl=en&gbpv=1&dq=Witte, +R.+%26+Witte,+S.+(2017).+Statistics+(11th+ed.).+Wiley&pg=PA1&printsec=frontcover DQ 2 The professor teaching a large introductory class gives a final exam that has alternate forms, A, B, and C. A student taking the exam using Form B is upset because she claims that Form B is much harder than Forms A and C. Discuss how percentile point data might be useful to determine if the student is correct.
Percentile point data can be a valuable tool to assess whether the student's claim that Form B of the final exam is much more complex than Forms A and C is valid or if it's a matter of perception or individual performance. Percentile point data can help provide a more objective and statistically sound comparison of the exam forms. Here's how percentile point data can be used in this context (Kaur et al., 2018): 1. Normalization of Scores: By collecting and analyzing the scores of all students who took the different forms (A, B, and C) of the final exam, you can normalize the scores. This means converting the raw scores into percentile ranks. Percentile ranks indicate the percentage of students who scored lower than a particular student. 2. Comparison of Percentile Ranks: Once you have percentile ranks for each exam form, you can compare them across the three forms. If the student who claimed that Form B is much harder performed similarly or worse on Form B compared to the other forms based on percentile ranks, this would indicate that it was more challenging for them than their peers. 3. Overall Class Performance: You can also look at the class's overall performance on each form. If a significant portion of the course scored lower on Form B than Forms A and C, it might suggest that Form B was more challenging for most students. 4. Statistical Analysis: You can conduct statistical analyses to determine whether the differences in scores between the forms are statistically significant. This can help you determine if the observed variations in scores are likely due to chance or if they reflect fundamental differences in exam difficulty. 5. Student Feedback: Alongside percentile data, it's vital to consider qualitative feedback from students who took the different forms. While percentiles provide an objective measure, student comments and feedback can provide insights into specific aspects of the exam that make it more challenging. 6. Adjustments and Fairness: If the percentile data and statistical analysis confirm that Form B was more challenging for most students, adjusting the grading scale or considering other fairness measures to account for the perceived difficulty may be appropriate. Percentile point data can be a valuable tool for assessing the relative difficulty of different exam forms and determining if objective data supports the student's claim. It allows for a fair and data- driven evaluation of exam difficulty, which can inform future decisions regarding exam design and grading. Reference Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. International Journal of Academic Medicine, 4 (1), 60-63. https://journals.lww.com/ijam/Fulltext/2018/04010/Descriptive_statistics.7.aspx
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