2.1. What steps or actions should be taken after you collect data and before you run the analyses aimed at answering your research questions or testing your research hypotheses? A1. 2.2. Why should you label the values of nominal variables? A1. 2.3. Why would you print a codebook or dictionary? A1. 2.4. What do you do if you look at your data file and see words or letters instead of numbers? Why is this important to do? A1. 2.5. Why would you use the Mean function to create a variable, as we did for the pleasure scale? A1. 2.6. (a) Why is it important to check your raw (questionnaire) data before and after entering them into the data editor? (b) What are ways to check the data before entering them? After entering them? SPSS Problems Chapter 2 Using the CollegeStudentData.sav file (see Appendix A in the Morgan, Barrett, Leach and Glockner) do the following problems. Print your outputs and circle the key parts for discussion. Compute the N, minimum, maximum, and mean for all the variables in the College StudentData.sav file. How many students have complete data? Identify any statistics on the output that are not meaningful. Explain. What is the mean height of the students? What about the average height of the same-sex parent? What percentage of students are males? What percentage have children? Make Sure to: Attach your word document for review and grading. Other file formats are not accepted and will not be graded. Use the following filename format: LastName_BUSI820_AssignmentX.docx Include a table of contents and a reference section. Number your pages in the footer along with the date. Include a header starting on page 2 with the Course and assignment number. Write the problem number and the problem title as a level one heading (Example _ A.1.1: Chapter 2, Problem 2.1, and then provide your response. Use level two headings with short titles for multi part questions (Example _ A1.1.a, Short Title, A1.1.b, Short Title II, etc.) Use appropriate level headings for key elements of your discussion such as Research Questions, Hypotheses, Descriptive Statistics, Assumptions
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