EDUR 8132
Educational Statistics 2

Fall 2010

Instructor: Bryan W. Griffin (bwgriffin@GeorgiaSouthern.edu)
My personal web pages: http://coe.georgiasouthern.edu/foundations/bwgriffin/

 

Announcements ---

Dec 1 -- Several Updates:

Nov 30 -- Added notes on Standardized Coefficients, see Lectures notes 9.i.

Nov 20 -- A number of updates:

Session 15 Nov 30 (Last Chat) - will focus on Standardized Regression Coefficients. I will create a summary set of notes on these, but in the meantime read Agresti text on standardized regression coefficients pages 270, 351-354, 529-532. Also, see my discussion in Notes 8a Regression with One Quantitative Predictor (see page 10) and Notes 8b Regression with Two Quantitative Predictors (also starting on page 10).


PDF Files

Submissions in this course must be as a PDF attachment. Below are links to creating PDF files:

Here are a few free web page that convert files to PDF over the internet: 

Links to free software to create PDF files (it leaves no watermark):
 http://www.primopdf.com/ 
 http://www.nitropdf.com/free/index.htm

I also use OpenOffice to create some free PDF files. Open Office is a free Office Suite similar to Microsoft Office (Open Office leaves no watermark): http://www.openoffice.org/ 

If you want further tips and links for converting to free PDFs, read this site http://www.pruittfamily.com/paul/freepdf.htm or this one http://www.masternewmedia.org/2002/03/31/creating_pdfs_without_adobe_acrobat_part_ii.htm 


Course Index (e.g., tests, activities, reading and other material to review for each class session)

  1. Syllabus
  2. Course Reading Assignments
  3. Course Calendar
  4. Reporting statistical results in APA style: http://www.bwgriffin.com/gsu/courses/edur8131/content/reporting_statistics_sept_2009.doc
  1. Regression
  1. ANOVA
  1. Test 1 (posted about Sept 15 [5th class session], due Sept 21 at 5pm; responses due in GeorgiaView as PDF attachment)
  2. Test 2 (posted shortly after 10th class session; responses due in GeorgiaView as PDF attachment)
  3. Test 3 (posted about 1 week before final tests responses due; responses due in GeorgiaView as PDF attachment)
  4. Exercises
  1. See course notes below for exercises embedded within notes.
  2. Others may be added here if needed.
  1. Lecture Notes 
  1. Introductory notes (modeling behavior, central tendency, variability, correlation, t-test, hypothesis testing)
  2. Simple Regression with One Quantitative Predictor
  3. Multiple Regression with Multiple Quantitative Predictors
  4. Semi-partial Correlation (ΔR2)
    • Summary notes for chat: Notes 8c Regression Semi-partial Correlation 
    • Detailed notes: Read Notes 8b page 5 section entitled "ΔR2, Semi-partial Correlation, and the Partial F Test of ΔR2"
    • Video: ΔR2 Discussed and Illustrated in SPSS ΔR2 Notes used in video; Test Score Data (in SPSS format) used in video
    • (Note Fall 2010: SPSS syntax offers easy way to obtain several ΔR2 values and F ratios simultaneously: change Method=enter x1 x2 x3 x4 to Method=test (x1) (x2 x3) (x4). For Video data above, this command change produces summary output: "/METHOD=ENTER Study_Time IQ Griffin Moore" to this "/METHOD=TEST (Study_Time) (IQ) (Griffin Moore)" Add video segment to video above demonstrating this.
    • Table of Critical F-ratios: Critical F at .05 and .01
  5. Regression with One Qualitative Predictor
  6. Regression with Multiple Qualitative and Quantitative Predictors
  7. Effect sizes in regression
  8. Sample size for regression
  9. Standardized Regression Equation: Notes 8h Regression Standardized Coefficients (Note - update formatting, also check standardized equation subscripts)
  10. ANOVA Models
  11. Multi-way ANOVA
  12. Effect sizes in ANOVA
  13. Sample size for ANOVA
  14. Interactions in Regression
  15. Polynomial Regression
  16. Model fitting and assessment
  17. Common Research Designs and Related Statistical Analysis (e.g. Post-test only control, Pretest-posttest control, Non-equivalent control group, etc.)
  18. Logistic regression or Factor Analysis
  1. Types of Statistical Procedures and Their Characteristics  
  2. SPSS Tutorials (obtained from various on-line sources: Dr. McKnight at U. Oklahoma,  Dr. Elvers at U. of Dayton, Guang-Hwa Change at Youngstown State U.)
  1. Entering data into SPSS (variable labels, value labels, setting variable as numeric, etc.)
            Data Entry A, Data Entry B, http://www.nau.edu/~its/acad/stats/docs/spsswin.html, http://www.cam.ac.uk/cs/docs/leaflets/m583/
  2. Frequencies/Explore Commands (including descriptive statistics, box plots, histograms, stem-and-leaf, etc.)
            Frequencies A, Frequencies B, Stem and Leaf, Various Graphical Displays
  3. t-tests (one sample, independent sample, and correlated samples)
            t-tests
  4. Correlation 
            Correlation A, Correlation B, For scatter plots see Various Graphical Displays above
  5. Regression 
            Regression A, Regression B
  6. ANOVA
            ANOVA A (using ONEWAY command), ANOVA B (using ONEWAY command), ANOVA C (using UNIVARIATE command)
  7. ANCOVA
            ANCOVA A
  1. Don't remember introductory statistics? Below are some notes
  1. Notes 1 Descriptive Statistics  
            Excel file: 1. SD, Variance, and Z scores
  2. Notes 2 Normal Distribution and Standard Scores
  3. Notes 3 Statistical Inference
            Excel file: 2. Population, Sample, and Sampling Distributions
  4. Notes 4 Hypothesis Testing and One Sample Z Test
            Excel file: 3. Coin Flip
  5. Notes 5 t-tests
            Excel files:
                4. One Sample t-test,
                5. Two Sample t-test,
                6. Correlated Sample t-test ;
            Critical t-values table:
                t-tables.pdf
  6. Notes 6 Correlation
            Excel files:
                7. Correlation 
  7. Notes 7 Chi-square
            Excel files:
               8. Goodness of fit chi-square,
               9. Association chi-square
  1. Instructional statistical videos can be found here: http://www.learner.org/resources/series65.html
  2. Refresher Exercises for content covered in EDUR 8131 (Educational Statistics 1)
  1. One and Two Samples t-test (answers)
  2. Correlated Samples t-test (answers)
  3. Correlation (answers)
  4. Chi-square (with answers)
  5. ANOVA (answers note this is a Word Document)
  6. Regression (see regression notes)
  7. ANCOVA (to be added)

 


Course Reading Assignments

Topic Readings
1. Hypothesis Testing Logic Chapters 5, 6
2. Regression Chapter 9
3. Multiple Regression Chapters 10, 11
4. ANOVA Chapter 12
5. ANCOVA/Regression Chapter 13
6. Modeling building in Regression/ANOVA Chapter 14
7. Logistic Regression Chapter 16
   

Course Calendar (will be revised throughout term)

Session 1 (8/17): 5pm to 7:45pm  (Note: Fall 2010 covered through correlation review; begin hypothesis testing Session 2)

  1. Syllabus 
  2. Review of modeling behavior/outcomes with statistics, descriptive statistics, t-test, and correlation (SPSS use illustrated throughout)
  3. Logic and practice of hypothesis testing (fairness of coin), Errors in hypothesis testing
  1. Statistical Inference, Hypothesis Testing (read this: logic of hypothesis testing)
  2. Central Limit Theorem Illustrated
  3. Excel file showing 95% CI and Standard Error of the Mean: http://www.bwgriffin.com/gsu/courses/edur8131/content/Example_Calculating_SE_of_Mean_adv.xls 
  4. Some additional readings on the logic of hypothesis testing:

Session 2 (8/24) (Note: Fall 2010 covered through student ratings example; resume at Additional Examples for Interpretation p. 7)

  1. Logic and practice of hypothesis testing (fairness of coin), Errors in hypothesis testing
  1. Statistical Inference, Hypothesis Testing (read this: logic of hypothesis testing)
  2. Central Limit Theorem Illustrated
  3. Excel file showing 95% CI and Standard Error of the Mean: http://www.bwgriffin.com/gsu/courses/edur8131/content/Example_Calculating_SE_of_Mean_adv.xls 
  4. Some additional readings on the logic of hypothesis testing:
  1. Linear Regression (one quantitative predictor)

Session 3 (8/31) (Note: Fall 2010 covered through notes 8a.)

  1. Linear Regression -- resume at Additional Examples for Interpretation; add reading simple regression tables in literature

Session 4 (9/7) (Note: Fall 2010 covered summary notes 8b but did not illustrate APA presentation of final example)

  1. Multiple Regression (two or more quantitative predictors)

Session 5 (9/14)

  1. Review Multiple Regression (two or more quantitative predictors) and work through example to illustrate APA styled presentation
  2. Confidence intervals -- calculating 99% by hand and SPSS
  3. Review Model Fit Summary
  4. Begin Semi-partial Correlation (ΔR2)

Session 6 (9/21)

  1. Finish Semi-partial Correlation (ΔR2) if needed
  2. Begin Notes 8d Regression with One Qualitative Predictor

Session 7 (9/28) (Note: Fall 2010 covered 8d One Qualitative Predictor)

  1.  Notes 8d Regression with One Qualitative Predictor if needed
  2. Begin Notes 8e Regression Multiple Comparisons
  3. Possibly begin Notes 8f Regression with Two Qualitative Predictors

Session 8 (10/5) (Note: Fall 2010 completed one qual. predictor, briefly discussed interpretation with two qual IVs, began multiple comparisons)

  1. Resume Notes 8d Regression with One Qualitative Predictor at "The Regression Equation for One Categorical IV" (p. 13)
  2. Begin Notes 8e Regression Multiple Comparisons
  3. Possibly begin Notes 8f Regression with Two Qualitative Predictors

Session 9 (10/12) (Note: Fall 2010 covered notes on multiple comparisons)

  1. Resume notes Notes 8e Regression Multiple Comparisons
  2. Begin Notes 8f Regression with Two Qualitative Predictors
  3. Begin Notes 8g Regression with Both Qualitative and Quantitative Predictors 

Session 10 (10/19) (Note: Fall 2010 covered notes on multiple comparisons and two qual IVs.)

  1. Resumed multiple comparisons
  2. Covered Notes 8f Regression with Qual IVs Qualitative IVs (Added Examples with two IVs with 3+ categories; reference use with ANOVA easier at this point)
  3. Possibly begin Notes 8g Regression with both Qual and Quan Predictors.

Test 2 likely sometime after 10/19 or 10/26

Session 11 (10/26)

  1. Begin Notes 8g Regression with both Qual and Quan Predictors.
  2. Possibly begin notes on ANOVA.

Test 2 after 10/26

Session 12 (11/2)

  1. Resume, if needed, Notes 8g Regression with both Qual and Quan Predictors
  2. Begin notes on ANOVA.

Session 13 (11/9)

  1. ANOVA
  2. Multi-way ANOVA (plus interactions)

Session 14 (11/16)

  1. Multi-way ANOVA (plus interactions)
  2. ANCOVA

Thanksgiving 11/23

Session 15 (11/30)

  1. ANCOVA
  2. Standardized regression coefficients

Session 16 (12/7) Test 3 due

NOTE -- Material below this point is not yet organized into sessions

Course Assessment Questionnaire

General Readings on ANOVA:

ANCOVA

 


Note to self -- add links to on-line statistical programs, e.g.

http://home.ubalt.edu/ntsbarsh/Business-stat/otherapplets/SampleSize.htm#rmenu 

http://noppa5.pc.helsinki.fi/koe/flash/flash.html


Syllabus

Office Information

Hours

By chance or appointment. Best to contact me via e-mail to arrange an appointment. 

Telephone Numbers

Office (Room 2128 College of Education Building): 912-478-0488
Department of Curriculum, Foundations, and Research: 912-478-5091

E-Mail

Use GeorgiaView to contact me. If GeorgiaView is not working, my regular e-mail address is bwgriffin@GeorgiaSouthern.edu, but please use GeorgiaView for course-related communications.

Mail

Department of Curriculum, Foundations, and Reading
P.O. Box 8144
College of Education
Georgia Southern University
Statesboro, GA 30460

Course Description

This course is will provide cover modeling relations among variables with a focus on regression and various analysis of variance models. If time permits, other modeling methods will be covered such as logistic regression or factor analysis. 

Texts  

Required - Agresti, A. & Finlay, B. (2009). Statistical methods for the social sciences (4th ed.). Pearson/Prentic Hall. (Note that 3rd edition will also suffice for this course and may be cheaper if found used on-line)

Web Pages

http://www.bwgriffin.com/gsu/courses/edur8132/

Software (Required)

SPSS (PASW) Version 10.0 or higher. Version 18 of the Graduate Pack Base can be rented for 6 months for $35 here  IBM/SPSS Statistics Standard GradPack 18 for Windows can be rented for 6 months here for $85:

http://www.onthehub.com/spss

Other student options and pricing for SPSS/PASW can be found here (the Grad Pack can be bought for $200 and the license is for 4 years, e.g., www.journeyed.com ):

http://www.spss.com/vertical_markets/education/online.htm

Course Activities

To facilitate learning of statistical analysis of data and reporting of statistical results, numerous computer replications of examples from readings and out-of-class exercises will be provided.  

Content Covered (see Course Index and Course Calendar for assigned readings, supplemental readings, and date topics are covered)

  1. Regression models
  2. ANOVA models
  3. Multiple comparisons
  4. Supplemental material as time allows

Examinations

There will be three tests administered during the term. Each test will focus on conceptual components of statistical analysis, computer applications, choice of statistical procedures, and written results and interpretations. 

Grading

Each test will be weighted equally at 1/3 of the final grade. Final grades will be assigned based on the following table:

  90 and above =  A  
  80 to less than 89.999 =  B  
  70 to less than 79.999 =  C  
  60 to less than 69.999 =  D  
  59.999 and below   =  F  

You will be allowed to take any missed test for any absence (no excuse is necessary). Should you not provide responses to a missed test before the end of the term, an I (incomplete) will be assigned as your final course grade and will remain until all missed tests are completed, or for one year and then become an F.

If you fail to take the final test on the scheduled date, you may take it during a time that is convenient for the instructor. Tests cannot be taken early.  

Attendance

You may come and go as you please. 

Withdrawing from Class

The university sets a specific date in which you may withdraw from a course without an academic penalty. In this course, however, you may withdraw without an academic penalty (i.e., you will received a WP) until the last day of regular class (this excludes exam week), no questions asked, no matter what your current performance. My policy of assigning WPs is contingent upon the approval of the CFR department chair and COE Dean (i.e., a WP is not guaranteed). 

To withdraw after the drop date, contact the registrar's office to learn what form is needed (it may be called "petition to withdraw" or something similar). Complete that form and submit to me so I may sign and forward to my departmental chair. It may also be possible to withdraw via e-mail--again, contact the registrar's office to learn if possible and how.

Academic Integrity Expectations

Students are expected to abide by the GSU student conduct code and regulations regarding academic integrity. Academic misconduct such as cheating and plagiarism will be reported to the Office of Judicial Affairs and appropriate penalties imposed, such as a grade of zero on the targeted activity.  See information on student conduct in the relevant student handbook for details. 

Disability Accommodations

If a student has a documented and declared disability, reasonable accommodations will be provided if requested by the student according to the recommendations of the GSU Disabled Student Services office. 

How This Course Supports the Colleges Conceptual Framework

The College of Educations conceptual framework advances the theme of reflective educators for diverse learners. In this course information will be learned that should make each student a more knowledgeable consumer of educational research. With this knowledge you will be better able to evaluate critically current and recommended practices when analyzed empirically.

 


Types of Statistical Procedures and Their Characteristics

Statistical Test

Independent Variable

Dependent Variable

Special Feature

Example Hypothesis

(1) Pearson's r (correlation coefficient) 

Quantitative

Quantitative

There is a positive relationship between intelligence and mathematics achievement scores.

(2) t-test for independent groups 

Qualitative

Quantitative

IV has only 2 categories

There will be a difference between boys and girls on mathematics achievement scores.

(3) Chi-Square c

Qualitative

Qualitative

 Both IV and DV may have more than two categories

Males will be more likely to drop out of school than females.

(4) One-way ANOVA (Analysis of Variance) 

Qualitative

Quantitative

IV may have 2 or more categories.

There will be a difference among Black, Hispanic, and White students in mathematics achievement scores.

(5) Multi-way ANOVA (Analysis of Variance) 

Several Qualitative

Quantitative

IVs may have 2 or more categories.

There will be a difference among Black, Hispanic, and White, and between male and female, students in mathematics achievement scores.

(6) ANCOVA (Analysis of Covariance)

 

 

 

1. Qualitative

2. Covariate may be either qual. or quan., but usually quantitative

3. May also have several additional qualitative and quantitative variables

Quantitative

Qualitative IVs may have 2 or more categories.

Covariates used to make adjustments to DV means.

There will be a difference among Black, Hispanic, and White students in mathematics achievement scores after taking into account levels of motivation.

7) Regression 

Both Qualitative and Quantitative

Quantitative

 

Any of the above except for chi-square. Special coding procedures are required to in regression to handle qualitative IVs.

(9) Logistic Regression

Both Qualitative and Quantitative

Qualitative

 DV is qualitative with only 2 catgories

Any of the above, except the DV will be binary (e.g., dropout/persist; pass/fail; favor/oppose)


Copyright 2005, Bryan W. Griffin

Last revised on