University of South Florida

Department al Course Syllabus

 

 

1.             Course Prefix and Number:           EDF 7488

                             

2.             Course Title:                                        Problems in Educational Data Analysis

              

   

3.             Course Prerequisite:         EDF 7408 (or equivalent)

 

 

4.             Course Description:                          Strategies and techniques for data processing and quantitative

analysis using statistical software, including data screening, transfromation, diagnostic indices, and interpretation.

 

5.             Course Goals and Objectives:        This lab is intended to provide students with practical experience in

the computer analysis of educational data. Using microcomputer packaged statistical software, the students will learn to (a) structure data for computer analysis, (b) examine statistical assumptions,

(c) conduct hypothesis tests, (d) read and interpret computer-generated results, and (e) produce statistical tables and graphs. A second purpose is to reinforce and extend student knowledge of

current issues (and some long-standing but still unresolved issues) in the analysis of data. For the most part (p<.001), the problems and data sets we will be working with are based on actual

research data and issues that have arisen therein.

 

 

Course Objectives:                             Students who successfully complete all course requirements should be able  to:

 

a.             Translate social science data and research questions into computer readable code. 

b.             Read and comprehend software manuals and user's guides.

c.                    Check data sets for accuracy of data, statistical

assumptions, and outliers. Correct errors or violations of assumptions prior to statistical calculations.

d.            Generate and interpret descriptive statistics (both graphical and tabular) and inferential statistics.


6.             Course Outline:                 

 

Topics

 

Readings

 

Course overview                            

Basics of SAS                              

Descriptive statistics

(MEANS, FREQ, UNIVARIATE)

 

 

 

Ch 1 & 2

Appndx A - C

How Data Arrive to the analyst             

Variety of INPUT requirements        

Remember the t-test?                

 

 

Ch 6 & 10

Screening Data #1                          

ARRAY Operations                     

INFILE statement

                    

 

Ch 11 & 13

Contingency tables (PROC FREQ)            

Statistics for contingency tables  

Labels for variables and values 

  

 

Ch 3

 

 

Handling multiple data files #1            

Subsamples of cases (IF and BY)    

Screening Data #2: Transformations

 

 

Ch 14 & 16

Handling multiple data files #2            

Working with output files                                   

Combining data files (SET and MERGE)

 

 

Ch 14

Ch  4

Producing reports (FILE PRINT)             

Writing files (FILE)               

 

 

Ch 11    

Issues in ANOVA                       

    

 

Ch 7 & 8

Issues in Regression #1                  

 

 

Ch 5 & 9

Issues in Regression #2             

      

 

Ch 5 & 9

 

     

7.             Evaluation of Student Outcomes:

 

Criteria for Evaluation of Student Performance: Computer assignments will be provided at

each meeting and must be completed for discussion at the following class meeting. Although these assignments will not be collected and graded, the amount of knowledge obtained in this course is proportional to the amount of effect applied to it.

 

8.             Grading Criteria:

 

Grades in the course will be based on a semester project that involves the analysis of a set of actual field data to answer specific research questions. Students may provide their own data and research questions for the project (probably the best circumstance) or a set of data will be provided by the instructor.

 

9.             Require Text and Readings:

 

   Cody, R. P. & Smith, J. K. (1991). Applied Statistics and the SAS Programming Language. New

      York: North-Holland.

 

   The following journal readings are strongly suggested (other readings may be added as the course

   progresses):

 

   Chamberlin, T. C. (1965). The method of multiple working hypotheses. Science, 148, 754-759.

 

   Chatfield, C. (1985). The initial examination of data. Journal of the Royal Statistical Society

      (Series A), 148, 214-253.

 

   Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45, 1304-1312.

 

   Cohen, J. (1994). The earth is round (p<.05). American Psychologist, 49, 997-1003.

 

   Ehrenberg, A. S. C. (1977). Rudiments of numeracy. Journal of the Royal Statistical Society

      (Series A), 140, 277-297.

 

   Finney, D. J. (1988). Was this in your statistics textbook? II. Data handling. Experimental

      Agriculture, 24, 343-353.

 

   Platt, J. R. (1964). Strong inference. Science, 146, 347-352.

 

   Thompson, B. (1989). Statistical significance, result importance, and result generalizability: Three

      noteworthy but somewhat different issues. Measurement and Evaluation in Counseling and

      Development, 22, 2-5.

 

   Tukey, J. W. (1969). Analyzing data: Sanctification or detective work? American Psycologist, 24,

      83-91.

 

 

                                                     COLLEGE OF EDUCATION

 

                                           DEPARTMENTAL COURSE SYLLABUS

 

                                                               ATTACHMENT I

 

 

Please respond to each of the following questions and complete the attached Matrix:

 

1.         Rationale for Setting Goals and Objectives:  What sources of information (e.g., research, best practices) support the formulation and selection of course goals and objectives.

 

This course is to develop skills in accessing and using computer programs (ASAS,  SPSS, BMDP, etc.) avalaible for statistical analysis of research data in education. 

 

2.         List the specific competencies addressed from the relevant national guidelines.

 

Successful completion of this course should enable students to use the computer in making calcuations necessary in applying various statistical analyses appropriate for educatonal research.  It should also develop student skills in readding and interpreting computer printouts.

 

3.         Are there field-based experiences in this course?  If so, please briefly indicate nature and duration.

 

No

 

4.         Is technology used in this course?  If so, please briefly indicate type of technology and how it is used to manage, evaluate and improve instruction.  Are students provided opportunities to access and/or demonstrate use of technology in instruction in this course?  If so, please briefly describe.  (See Accomplished Practice #12)

 

Yes, statistical packages such as SAS, SPSS, BMDP.

 

5.         List the specific competencies addressed from the Florida Adopted Subject Area Competencies, if applicable.

 

N/A

 

6.         Are there any components of the course designed to prepare teacher candidates to help K-12 students achieve the Sunshine State Standards?  Is so, please identify.

 

N/A

 

                                                                                                                                        (Continued)

                                                                                                   Attachment I (cont'd)

 

                                                                                                              MATRIX

 

                                                                                        (For College of Education files only)

           

 

             Course Objectives

 

(Note:  Objectives should be numbered 1.0, 2.0, 3.0, etc.)

 

                      Topics

 

  What topics are used to fulfill each objective?

 

                 Evidence of

                Achievement

 

            Predominant Accomplished

               Practices*

(For Undergraduate and Plan II Masters Courses Only)

 

1.0       Translate social science data into computer readable code

 

 

1.1  Research questions/hypotheses

1.2  Null hypotheses

1.3  Statistical estimation

 

Homework assignments

Class project

 

 

 

2.0       Read and comprehend software manuals and users guides

 

 

2.1 Basic computational operations

2.2 Sources of documentation

 

Homework assignments

Class project

 

 

 

3.0       Check datasets for accuracy of data, statistical assumptions, and outliers. Correct errors or violations of assumptions prior to statistical calculations

 

 

3.1 Validation of data integrity

3.2   Evaluation of assumptions

3.3   Transformations

 

Homework assignments

Class project

 

 

 

4.0       Generate and interpret descriptive statistics (both graphical and tabular) and inferential statistics

 

 

4.1  Descriptive statistics

4.2   Inferential statistics

4.3   Limits and caveats on interpretations

 

Homework assignments

Class project