EDUR 8131 Fall 2011 Instructor: Bryan W. Griffin (bwgriffin@GeorgiaSouthern.edu) 
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)
 Central Tendency, Variability, Graphical Displays (with answers)
 Displaying Data (with answers)
 Z scores, Percentile Ranks, and Z test (with answers)
 One and Two Samples ttest (answers)
 Correlated Samples ttest (answers)
 One Sample, Two Samples, and Correlated ttests (with answers)
 Correlation (answers)
 Chisquare (with answers)
 ANOVA (answers note this is a Word Document)
 Regression (see regression notes)
 ANCOVA (to be added)
 Notes 1 Descriptive Statistics
 Excel file: SD, Variance, and Z scores
 Video  Averages: On Average, Swedes have 1.99 Legs (4 minutes)
 Video  Pie Chart (Polar Area Display): Showing the Dead: Which is more Deadly, Battlefield or Hospital? (4 minutes)
 Video  Scatterplot: Life Expectancy and Wealth over 200 Years (4 minutes)
 Notes 2 Normal Distribution and Standard Scores
 Notes 3 Statistical Inference
 Notes 4 Hypothesis Testing and One Sample Z Test
 Excel file: 3. Coin Flip
 Notes 5 ttests (note, update CI for one sample ttest)
 Excel files:
 Critical tvalues table: ttables.pdf
 Notes 6 Correlation/a> (note, use car data to illustrate correlation matrix in SPSS)
 Excel files: Correlation
 Video  Correlation and Causation: English is the Cause of Death (4 minutes)
 Notes 7 Chisquare
 Excel files:
 Online Chisquare Calculators:
 http://faculty.vassar.edu/lowry/newcs.html (test of association; Important  specify number of rows and columns to get correct df and p)
 http://faculty.vassar.edu/lowry/csfit.html (goodnessoffit)
 Notes 8a Simple Regression (
 sample data files located here: http://www.bwgriffin.com/gsu/courses/edur8131/data )
 Critical F ratio: http://www.bwgriffin.com/gsu/courses/edur8132/notes/critical_f_values.pdf
 Notes 8b Multiple Regression
 Notes 9 ANOVA
 Multiple Comparisons in Excel: Bonferroni and Scheffe
 ANOVA Group Separation Example: http://www.buseco.monash.edu.au/mkt/resources/applets/onewayanova.html
 Multiple Comparisons in Zoho (online spreadsheet): Bonferroni and Scheffe (this spreadsheet does not work correctly)
 Critical F ratio: http://www.bwgriffin.com/gsu/courses/edur8132/notes/critical_f_values.pdf
 Notes 9d ANCOVA
Sample Data
 Supplemental Reading: Stevens Chapter 7 ANCOVA
 Excel Spreadsheet for Interactions
 Video Tutorial: Oneway ANCOVA with three groups
 Video Tutorial: Twoway ANCOVA (not covered in EDUR 8131 this semester)
 Video Tutorial: Oneway ANCOVA with Factor*Covariate Interaction (Unfortunately sound spikes and breaks occasionally; not covered in EDUR 8131 this semester)
 Sample Data 1: Fictional salary discrimination
 Sample Data 2: Gun control and homicides by state
 Sample Data 3: Fictional achievement data  homework with parental support
 Sample Data 4: Fictional data showing ANCOVA power vs ANOVA
 Sample Data 5: Car MPG by country of origin controlling for horsepower and weight
 Add Waldron data to illustrate difference between gain score and ANCOVA
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 402403 
Pages 374376 
Pages

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

Chapter

Chapter

6.
Central Tendency 
Chapter 1  Chapter 1  Chapter 1 
7.
Variability 
Chapter

Chapter

Chapter

8.
Standard Scores 
Chapter

Chapter

Chapter

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 ztest 
Chapter 6  Chapter 6  Chapter 6 
12.
ttest (onesample, independent samples, correlated) 
Chapter 7  Chapter 7  Chapter 7 
13.
Correlation (Pearson r) 
Chapter 2, 10  Chapter 2, 10  Chapter 2, 10 
14.
Chisquare (c^{2}) (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
Session 2 (8/30) (Instructor note: F11 stopped at scatterplots; SP11 stopped at scatterplots; F09, covered through displaying data)
Session 3 (9/6) (Instructor note: F11 found p for positive Z/IQ score; F099 cover conversion z to raw, find twotailed .05 and .01 z scores)
Session 4 (9/13) (Instructor note: fall 09 spring 10 session 3 ended at Notes 3.8 confidence intervals)
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):
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 ttest; fall 09  covered one sample ttest, resume at two group ttest)
Session 7 (10/4) (Instructor note: spring 10; began at correlated ttest)
Session 8 (10/11) (Instructor note: fall09  covered test of association)
Test 2 posted sometime after Session 8 (assuming coverage of chisquare 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)
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)
Session 11 (11/1) (Instructor note: f 09  resume this chat with coefficient interpretation in simple regression, ended at ice cream data)
Session 12 (11/8) (Instructor note: fall 09  begin ice cream data  show predicted values, residuals and R, R^2)
Session 13 (11/15) (Instructor note: fall 09, illustrated logic of Bonferroni correction, demonstrated comparisons in Zoho)
Thanksgiving Holiday Nov 22
Session 14 (11/29) (Instructor note: s10, began ANCOVA; f 09 resume Zoho, and presenting results in APA style)
Session 15 (12/6)
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) ttest for independent groups 
Qualitative 
Quantitative 
IV has only 2 categories 
There will be a difference between boys and girls on mathematics
achievement scores. 
(3) ChiSquare c2 
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) Oneway 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) Multiway 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 chisquare. 
(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
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