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Cumulative Final Assignments

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The Employee data is from a bank in the Midwest during the late 60's that was ... Data Bank; Input id gender $ bdate mmddyy12. educ jobcat sal Dollar10. ... – PowerPoint PPT presentation

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Title: Cumulative Final Assignments


1
Cumulative Final Assignments
2
MRGL34E1_5
  • Assignment Due April 22 (Tu).
  • MRGL34E1_5 - Please give this assignment name on
    your paper.
  • Also, please "echo" each question on your paper
    prior to answering it.
  • Exercises 1 5. Deals with aptitude scores
    predicting college stat course performance.
  • Include a residual plot with your Results
    sections.
  • Also include a Hierarchical MR Analysis Summary
    table modeled after Leech et al. class handout.

3
ParCorGL32E1_3
  • Assignment Due April 22 (Tu).
  • ParCorGL32E1_3 Name of assignment
  • GS Lesson 32, Exs. 1-3
  • When you answer specific questions, such as 1.a.,
    answer in a complete sentence rather than just
    giving the answer.
  • For example, 1.a. The bivariate correlation
    between num_pubs and cites was r whatever.
  • 1.b. Asks for a p value.
  • Given the number of correlations computed, it
    would be good to apply the Bonferroni correction
    to get your critical p. However, 1b just asks for
    the p for num_pubs instructor quality
    correlation, not whether it is significant.
  • Extra For 2, include a path diagrams for the
    instructor quality num_pubs relationship, with
    and without partialling out work ethic. Discuss
    whether the common cause hypothesis applies and
    to what extent.
  • At the end as an appendix, include partial
    correlation outputs from both SAS and SPSS plus
    minimal/critical procedure syntax for each.

4
AOV1(GL25)
  • Assignment Due April 17 (Th) by 5 PM.
  • AOV1(GL25) - Please give this assignment name on
    your paper. Also, please "echo" the questions on
    your paper prior to answering it.
  • 1. Create path diagrams for the two examples at
    the beginning of Green, Lesson 25.
  •   Based on your study of Green, Lesson 25 and the
    class notes, create 2 path diagrams one each for
    the examples at the beginning of the chapter
    (p.183) -- the Experimental study example dealing
    with colds and vitamin C dose, and the
    quasi-experimental study dealing with student
    behavior problems and teaching philosophy. 
  •   Note, in class I asked for 3 diagrams but am
    correcting that to 2 because the main example in
    the chapter is the same as the first example
    about vitamin C. 
  • 2. What new or different assumptions are
    indicated for Anova compared to multiple
    regression. Explain briefly.

5
ACOV1(GL27)
  • Assignment Due Monday, April 28 (by 5 PM) .
  • Be sure to echo the question before answering
    it.
  • 1. Create path diagram for the first example
    (same as main chapter example) on vitamin C and
    colds, Green L27. (2)
  • 2. Read what Green has to say about the areas of
    application and the adequacy of the Ancova for
    each type of application. Note the example,
    given on p. 210, of a study with potential
    confounding. What is said about using Ancova in
    studies with potential confounding? Discuss the
    reasoning associated with this issue from an
    experimental design perspective. (2)
  •  3. (a) What new or different main assumption is
    indicated for Ancova compared to Anova. Explain
    briefly. (2)
  • (b) Assume a 2-group situation where Y is the
    DV, X is the IV, and C is the covariate. Make up
    sketches of two scatterplots with regression line
    (one for each group) illustrating a violation of
    the assumption. Make two more sketches
    illustrating the assumption being met. (2)
  • 4. Use SPSS to create the graph shown in Fig.
    167, p. 219. Instructions are given in the first
    paragraph above on the same page. (2)
  • 5. Read the data from Lesson 27 Data File 1.sav
    into SAS, run just the Ancova, also getting the
    adjusted means (lsmeans). Show /report output
    only that corresponds to the ANCOVA source table
    and Estimates, p. 216 in Green. No written
    report requested here. (2)
  • ltassignment continues on next slidegt

6
ACOV1 Continued
  • This next part is optional bonus for 4990
    students nonoptional for 6990 students.
  • 6. (4 Points) Find the spss data file, Employee
    data.sav, found in the Program Files directory
    were spss is installed (file is one of many that
    comes with the spss program).
  • The Employee data is from a bank in the Midwest
    during the late 60s that was involved in a sex
    discrimination EEOC probe.
  • The assignment, basically, is to run an ANCOVA
    investigating gender differences in beginning
    salary (salbegin) controlling for education using
    SAS only. (Obviously, you can run spss to
    check/verify your results.) Gender will need to
    be recoded to numeric variable use SEX as the
    variable name.
  • Turn in a Results Section (and SAS output for
    the Ancova and adjusted means as an appendix).
    For the Results Section, following the model from
    Green, p. 220, and
  • Ignore and do not report (or test for) HOV of
    slopes. Just say, Homogeneity of slopes was
    assumed.
  • Do report/include the statement on the
    covariate modeled after the one I put in the
    Ancova lecture ppt, slide 10.
  • Your Results should include/address the question
    of whether there were gender differences in
    beginning salary, which gender, was
    disadvantaged, and whether and to what extent any
    differences were due to differences in education.
  • This is an individual assignment. If you have
    questions or need a little help, contact me.
  • Some SAS Help
  • Data Bank
  • Input id gender bdate mmddyy12. educ jobcat
    sal Dollar10. salbegin Dollar8. jobtime prevexp
    minority
  • if gender 'm' then sex 1
  • if gender 'f' then sex 0
  • datalines
  • ltinsert data here.gt
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