EDUR 8131
Educational Statistics 1
(16 Week Format)

Fall 2011

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

 

Announcements ---

Announcements such as calendar and course changes, updated material, and other revisions will be identified here and highlighted below.


PDF Files

Creating PDF files:

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

I use the following free software to create my PDF files (it leaves no watermark): http://www.primopdf.com/ 

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
  5. Sample Test 1 (material through one-sample t-test)
  6. Sample Test 2 (shows examples of data analysis section of Test 2 with t-tests, correlation, and chi-square; Sample Test 2 Answers)
  7. Test 1: Covers content found in Notes 1, 2, 3, and 4. Test 1 may be posted as early as 9/20 or 9/27.
  8. Test 2: Covers content found in Notes 1 through 7. Test 2 may be as early as 10/11 or 10/18.
  9. Test 3: Covers all content found in Notes 1 through 9d. Test 3 may be posted by 11/30 and responses due 12/13.
  10. Exercises
  1. Central Tendency, Variability, Graphical Displays (with answers)
  2. Displaying Data (with answers)
  3. Z scores, Percentile Ranks, and Z test (with answers)
  4. One and Two Samples t-test (answers)
  5. Correlated Samples t-test (answers)
  6. One Sample, Two Samples, and Correlated t-tests (with answers)
  7. Correlation (answers)
  8. Chi-square (with answers)
  9. ANOVA (answers note this is a Word Document)
  10. Regression (see regression notes)
  11. ANCOVA (to be added)
  1. Lecture Notes 
  1. Notes 1 Descriptive Statistics  
  2. Notes 2 Normal Distribution and Standard Scores
  3. Notes 3 Statistical Inference
  4. Notes 4 Hypothesis Testing and One Sample Z Test
  5. Notes 5 t-tests (note, update CI for one sample t-test)
  6. Notes 6 Correlation/a> (note, use car data to illustrate correlation matrix in SPSS)
  7. Notes 7 Chi-square
  8. Notes 8a Simple Regression (
  9. Notes 8b Multiple Regression
  10. Notes 9 ANOVA 
  11. Notes 9d ANCOVA Sample Data
  1. Types of Statistical Procedures and Their Characteristics/a>  (PDF Table Created During a Previous Semester's Chat)
  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
  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, independent samples t-tests
  4. Correlation 
            Correlation A, Correlation B, For scatter plots see Various Graphical Displays above
  5. Chi Square
            Goodness of Fit  and Test of Association; other examples:  Goodness of Fit A, Goodness of Fit B (note this is a Word document),  Test of Association
  6. Regression 
            Regression A, Regression B
  7. ANOVA
            ANOVA A (using ONEWAY command), ANOVA B (using ONEWAY command), ANOVA C (using UNIVARIATE command)
  8. ANCOVA
            ANCOVA A
  1. On-line Statistical Resources
  2. Course Assessment Questionnaire (may be used to collect data to illustrate statistical analysis)
  3. Links to on-line statistical calculators: http://home.ubalt.edu/ntsbarsh/Business-stat/otherapplets/SampleSize.htm#rmenu 

Course Reading Assignments

Topic Moore & McCabe
5th ed.
Moore, McCabe, & Craig
6th ed.
Moore, McCabe, & Craig
7th ed.
1. Measurement (scales, variables) Chapter 1 Chapter 1 Chapter 1
2. Hypotheses Pages 402-403 Pages 374-376 Pages 362-364
3. Overview of Descriptive and Inferential Statistics Chapter 3 (sampling) Chapter 3 (sampling) Chapter 3 (sampling)
4. Displaying Data with Graphs Chapters 1, 2 Chapters 1, 2 Chapters 1, 2
5. Percentile Ranks Chapter 1 Chapter 1 Chapter 1
6. Central Tendency Chapter 1 Chapter 1 Chapter 1
7. Variability Chapter 1 Chapter 1 Chapter 1
8. Standard Scores Chapter 1 Chapter 1 Chapter 1
9. Normal Curve and Probability Chapter 1 Chapter 1 Chapter 1
10. Hypothesis Testing Chapter 5, 6 Chapter 5, 6 Chapter 5, 6
11. One sample z-test Chapter 6 Chapter 6 Chapter 6
12. t-test (one-sample, independent samples, correlated) Chapter 7 Chapter 7 Chapter 7
13. Correlation (Pearson r) Chapter 2, 10 Chapter 2, 10 Chapter 2, 10
14. Chi-square (c2) (test for association) Chapter 9 Chapter 9 Chapter 9
15. Regression Chapter 2, 10, 11 Chapter 2, 10, 11 Chapter 2, 10, 11
16. ANOVA Chapter 12 Chapter 12 Chapter 12
17. ANCOVA (time permitting) --- --- ---

   


Course Calendar (will be revised throughout term)

Session 1 (8/23): 5pm to 7:45pm 

  1. Syllabus 
  2. Notes 1
  3. Notes 1 Supplemental Reading and Exercises:

Session 2 (8/30) (Instructor note: F11 stopped at scatterplots; SP11 stopped at scatterplots; F09, covered through displaying data)

  1. Notes 2
  2. Notes 2 Supplemental Reading:
  3. Notes 3

Session 3 (9/6) (Instructor note: F11 found p for positive Z/IQ score; F099 cover conversion z to raw, find two-tailed .05 and .01 z scores)

  1. Resume Notes 2 if needed
  2. Notes 3
  3. Notes 3 Supplemental Reading and Notes:
  4. Notes 4

Session 4 (9/13) (Instructor note: fall 09 spring 10 session 3 ended at Notes 3.8 confidence intervals)

  1. Resume Notes 3 if needed
  2. Notes 4
  3. Notes 4 Supplemental Reading:
  4. Notes 5

Session 5 (9/20) (Instructor note: fall 09 and spring 10, finished one sample z test notes 4.2 session 4; begin at 4.3):

  1. Resume Notes 4  if needed
  2. Notes 5
  3. Notes 5 Supplemental Readings:

Test 1 posted sometime after Session 5 (assuming Notes 4 is completed, otherwise Test 1 posted after Session 6)

Session 6 (9/27) (Instructor note: spring 10 covered one and two group t-test; fall 09 - covered one sample t-test, resume at two group t-test)

  1. Resume Notes 5 if needed
  2. Notes 6
  3. Notes 6 Supplemental Readings:

Session 7 (10/4) (Instructor note: spring 10; began at correlated t-test)

  1. Resume Notes 6 if needed
  2. Notes 7
  3. Notes 7 Supplemental Readings:
  4. Notes 7 SPSS Tutorials

Session 8 (10/11) (Instructor note: fall09 - covered test of association)

  1. Resume Notes 7 if needed
  2. Notes 8a

Test 2 posted sometime after Session 8 (assuming coverage of chi-square is completed [Notes 7], otherwise Test 2 posted after Session 9)

Session 9 (10/18) (Instructor note: fall 09 - ended at finding residuals for ratings data)

  1. Resume Notes 8a
  2. Notes 8b

Session 10 (10/25) (Instructor note: fall 09 - resume with finding residuals for rating data, covered through model inference, literal interpretation of coefficients; end at coefficient inference)

  1. Resume Notes 8a or 8b
  2. Notes 8b
  3. Notes 9
  4. Notes 9 Supplemental Reading:

Session 11 (11/1) (Instructor note: f 09 -- resume this chat  with coefficient interpretation in simple regression, ended at ice cream data)

  1. Resume Notes 8b or 9
  2. Notes 9
  3. Notes 9 Supplemental Reading:

Session 12 (11/8) (Instructor note: fall 09 -- begin ice cream data - show predicted values, residuals and R, R^2)

  1. Resume Notes 8b or 9

Session 13 (11/15) (Instructor note: fall 09, illustrated logic of Bonferroni correction, demonstrated comparisons in Zoho)

  1. Resume 9
  2. Notes 9d Begin Multiple Comparison Procedures under ANOVA (called Post Hoc)

Thanksgiving Holiday Nov 22

Session 14 (11/29) (Instructor note: s10, began ANCOVA; f 09 resume Zoho, and presenting results in APA style)

  1. Resume 9d

Session 15 (12/6)

  1. Complete 9d

Test 3 responses due (12/13)

 


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.

(8) Logistic Regression

Both Qualitative and Quantitative

Qualitative

 DV is qualitative with only 2 categories

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