The
Data Analysis and Computers II
|
Course
Number: SW 388R7 |
Faculty:
Jim Schwab |
|
Unique
Number: 63175 |
Office
Number: SSW 2.228 |
|
Semester:
Spring 2006 |
Office
Phone: 471-9816 |
|
Time: Wednesday:
8:30am to 11:30am |
Email:
jimSchwab@mail.utexas.edu |
|
Place:
SSW 1.214 |
Office Hours: Wednesdays, 7:30
to 8:30am, |
Course Description:
This course is designed to build upon the concepts and procedures introduced in Data Analysis and Computers I, to enable students to do a more thorough job of data analysis by introducing multivariate statistical procedures into their repertoire of statistical techniques. The primary focus is on using the SPSS statistical package for calculating multivariate statistics and the utilization of the statistical output in research findings.
Course Objectives:
1. To understand how the analysis of data derives from the
statement of a research problem or hypothesis and the availability of empirical
data.
2. To understand how to conduct a variety of statistical analyses, including
testing of statistical assumptions, data transformations, and validation of
statistical findings.
3. To understand how to present and interpret the results of statistical
analyses.
4. To be able to design a data analysis strategy that answers a research
question or hypothesis, including specifications for data elements,
requirements of the statistic, and limitations to the interpretation.
Teaching Methods:
Course content will be covered using class lecture, focused discussions, computer demonstrations, and regular homework assignments involving data analysis exercises and computer applications. Students are expected to ask questions, share experiences, and actively participate in class discussions. While most statistical calculation will be done on the computer, some hand calculation is inherent in statistical analysis. Pocket calculators or Microsoft Excel can be used for to compute these calculations.
Course Website:
Course materials, announcements, assignments, and grading of homework problems will be done in BlackBoard. Through BlackBoard, the syllabus and any updates are available for downloading; datasets for problems are available for downloading; homework assignments and exams will be made available and completed online; your grades on exams and homework will be available online to you; a public bulletin board and access to email is supported for reporting problems on assignments, requesting assistance, and checking for announcements.
While the University has invested additional resources in support of BlackBoard, there are still periodic outages and slow-downs. If you wait until the last minute to complete assignments, you may encounter difficulties.
Required Texts and Materials:
The required text for the course is:
Hair, Joseph F., Jr; Black, William
C.; Babin, Barry J.; Anderson, Rolph
E.; and Tatham, Ronald L. Multivariate Data Analysis, Sixth Edition.
In addition, you will need access to version 13.0 of SPSS on a
PC computer and access to the Internet using either Internet
Explorer, Firefox, or the Netscape browser. If you do
not have a personal computer, the necessary hardware and software are available
in the LRC computer lab in the
All data sets used in this course will be available as SPSS system data files (".SAV") for downloading via the course web page.
Class Policies:
The
If a student has any questions concerning the application of
the rules prohibiting scholastic dishonesty in regard to a particular
assignment, it is the responsibility of that student to seek clarification from
the instructor of the course. Violations of the University's policy on
scholastic dishonesty will result in a grade of F for the course and may result
in reporting to the Dean of the
As part of professional social work education, students may have assignments that involve working in agency settings and/or the community. As such, these assignments may present some risks. Sound choices and caution may lower risks inherent to the profession. It is the student's responsibility to be aware of and adhere to policies and practices related to agency and/or community safety. Students should also notify instructors regarding any safety concerns.
All students should
become familiar with the University's official e-mail student notification policy.
It is the student's responsibility to keep the University informed as to
changes in his or her e-mail address. Students are expected to check e-mail on
a frequent and regular basis in order to stay current with University-related
communications, recognizing that certain communications may be time-critical.
It is recommended that e-mail be checked daily, but at a minimum, twice per
week. The complete text of this policy and instructions for updating your
e-mail address are available at http://www.utexas.edu/its/policies/emailnotify.html.
In this course e-mail
will be used as a means of communication with students. You will be responsible
for checking your e-mail regularly for class work and announcements. Note: if
you are an employee of the University, your e-mail address in Blackboard is
your employee address.
Students with disabilities who require special accommodations need to get a letter that documents the disability from the Services for Students with Disabilities area of the Office of the Dean of Students (471-6259 voice or 471-4641 TTY for users who are deaf or hard of hearing). This letter should be presented to the instructor in each course at the beginning of the semester and accommodations needed should be discussed at that time. Five business days before an exam the student should remind the instructor of any testing accommodations that will be needed. See following web site for more information: http://deanofstudents.utexas.edu/ssd/providing.php or contact the Office of the Dean of Students at 471-6259, 471-4641 TTY.
Religious holy days sometimes conflict with class and
examination schedules. If you miss an examination, work assignment, or other
project due to the observance of a religious holy day you will be given an
opportunity to complete the work missed within a reasonable time after the
absence. It is the policy of The University of Texas at
Course Assignments:
Midterm and final exams will be given to assess your mastery of key concepts and procedures involved in the course The midterm and final examination will be in the format of questions anticipated to be on future departmental qualifying exams. In addition to outlining the analysis to solve a research problem, the qualifying exam includes interactive data analysis. Note, however, that there is no warranty, expressed or implied, between completion of this course and success on the qualifying exams. This course will help you prepare for the qualifying exam, but it is expected that your preparation for that exam will go beyond the limits of this course.
If any student requires an accommodation for taking tests, they must notify the instructor prior to the first exam.
Homework assignments requiring students to use SPSS to analyze data will be made available on the course web site after every class. Homework problems will be objective style questions on the datasets provided for the course. Each homework assignment draws from a large test bank from which a subset of problems are randomly selected. The homework assignment may be redone as many times as you wish. You will be given a different selection of questions each time you redo the assignment. You will find two identical versions of each homework assignment. Your grade for the assignment will be the higher grade on either version of the assignment. Since BlackBoard will record your grade for the last attempt, you can use the other version to retake the assignment to improve your grade.
Practice problems that go through each analysis step by step are also available, but the results do not count toward your grade for homework assignments. It is suggested that you begin your work with the practice problems first until you have mastered all of the steps required to complete a statistical analysis. The homework problems assume mastery of the practice problems.
Grading Policies:
Your grade will be based upon your performance on mid-term, final exam and regular homework assignments involving computer applications. The weighting for the final course grade will be as follows:
|
Homework
|
50% |
|
Midterm
Exam |
25% |
|
Final
Exam |
25% |
Final grades will be assigned using the scale below:
100 - 94 = A
93 - 90 = A-
89 - 87 = B+
86 - 84 = B
83 - 80 = B-
79 - 77 = C+
76 - 74 = C
73 - 70 = C-
69 - 67 = D+
66 - 64 = D
63 - 60 = D-
59 and below = F
Assistance Outside of Class:
Blackboard supports an email system and I can post announcements for all class members to see. If you identify errors or ambiguities in my materials, please inform me and I will post the clarification announcement. You may want to consult announcements before raising an issue to see if it has already been asked and answered.
In addition to posting requests on the bulletin board, you may request help via personal email, which I check several times during a typical workday and generally once a day on weekends. Usually you may anticipate a response within 24 hour. My email address is listed at the top of this syllabus. If I think your question is of general interest to the class, I may post it as an announcement unless you explicitly request that I do not post it. If you need to meet with me individually, the best method for setting an appointment is via email.
Course Schedule:
The following schedule is the weekly sequence of activities for
the semester. The instructor reserves the right to make adjustments to the
course schedule if deemed necessary. Any changes will be made prior to the date
of the class.
|
Class/Date |
|
Solving Problems in SPSS |
Practice Problems |
Homework
Problems |
|
Class 1 |
Chapter 1,
“Introduction” in Multivariate Data Analysis, pages 1-34. Chapter 2, “Examining Your Data” in Multivariate Data Analysis., pages 35 – 73. |
Analyzing
Missing Data |
Level of Measurement Evaluating Missing Data |
Level of Measurement Missing Data Analysis |
|
Class 2
|
Chapter
2, “Examining Your Data” in Multivariate
Data Analysis., pages 73 – 100. |
Detecting outliers Evaluation the assumption of
normality Evaluating the assumption of homoscedasticity Evaluating the assumption
of linearity |
Evaluating outliers Assumption of normality Assumption of homoscedasticity Assumption of linearity |
Outliers Multivariate Assumptions |
|
Class 3
|
Chapter
3, “Factor Analysis” in
Multivariate Data Analysis, pages 101-166. |
Principal
Components Analysis |
Principal
Components Analysis – Basic Relationships |
Principal
Components Analysis – Basic Relationships |
|
Class 4
|
Principal Component
Analysis – Validity, Outliers, and Reliability |
Principal
Component Analysis – Validity and Reliability |
Principal
Component Analysis – Validity and Reliability |
|
Class 5
|
Chapter
4, “Multiple Regression” in Multivariate
Data Analysis., pages 169-268. |
Multiple
Regression – Basic Relationships |
Multiple
Regression – Basic Relationships |
Multiple
Regression – Basic Relationships |
|
Class 6
|
Multiple
Regression - Validation |
Multiple
Regression – Complete Analyses |
Multiple
Regression – Complete Analyses |
|
|
Class 7
|
Multiple
Regression – Hierarchical Models |
Multiple
Regression – Sequential Models |
Multiple
Regression – Sequential Models |
|
|
Class 8
|
Multiple
Regression – Stepwise Analysis |
Multiple
Regression – Statistical Models |
Multiple
Regression – Statistical Models |
|
|
Class 9
|
Regression Exam |
|||
|
Class
10 |
Chapter 5, Multiple Discriminant Analysis
and Logistic Regression in Multivariate Data Analysis, pages 269 – 355. |
Discriminant Analysis – Basic Relationships |
Discriminant Analysis – Basic Relationships |
Discriminant Analysis – Basic Relationships |
|
Class
11 |
Discriminant Analysis – Complete Analysis |
Discriminant Analysis – Complete Analysis |
Discriminant Analysis – Complete Analysis |
|
|
Class
12 |
Chapter 5, Multiple Discriminant
Analysis and Logistic Regression in Multivariate
Data Analysis, pages 355 - 382. *Nancy Morrow-Howell and Enola K. Proctor, "The Use of
Logistic Regression in Social Work Research." In David F. Gillespie and
Charles Glisson, Eds., Quantitative Methods
in Social Work: State of the Art. *Barbara
G. Tabachnick and Linda Fidell,
Using
Multivariate Statistics, Fourth Edition. Chapter 12, Logistic Regression. *SPSS Regression
Models 10.0, Chapter 8, Logistic Regression Analysis
Examples. |
Logistic Regression and Odds Ratios Binary Logistic Regression
– Basic Relationships
|
Binary
Logistic Regression – Basic Relationships |
Binary
Logistic Regression – Basic Relationships |
|
Class
13 |
|
Binary
Logistic Regression – Complete Analysis |
Binary Logistic
Regression – Complete Analysis |
Binary
Logistic Regression – Complete Analysis |
|
Class
14 |
|
Binary
Logistic Regression – Sequential and Statistical Models |
Binary
Logistic Regression – Sequential and Statistical Models |
Binary Logistic
Regression – Sequential and Statistical Models |
|
Class
15 |
*SPSS
Regression Models 10.0, Chapter 9, Multinomial Logistic Regression Examples |
Multinomial
Logistic Regression |
Multinomial
Logistic Regression – Basic Relationships |
Multinomial
Logistic Regression – Basic Relationships |
|
May 16,
2 to 5 pm |
Final Exam |
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