EDUR 8131 Summer 2011 Instructor: Bryan W. Griffin (bwgriffin@GeorgiaSouthern.edu) 
Announcements 
Added file showing which statistical test to use given type of variables involved. See Course Index #13, second link.
PDF Files
Test answer submissions will be required as a PDF document. Below are some links that show how to create 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: 1. SD, Variance, and Z scores Notes 2 Normal Distribution and Standard Scores
 Notes 3 Statistical Inference
Excel file: 2. Population, Sample, and Sampling Distributions 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:
4. One Sample ttest,
5. Two Sample ttest,
6. Correlated Sample ttest ;
Critical tvalues table:
ttables.pdf Notes 6 Correlation (note, use car data to illustrate correlation matrix in SPSS)
Excel files:
7. Correlation Notes 7 Chisquare
Excel files:
8. Goodness of fit chisquare,
9. Association chisquare Notes 8a Simple Regression (sample data files located here: http://www.bwgriffin.com/gsu/courses/edur8131/data
 Notes 8b Multiple Regression
 Notes 9 ANOVA
Multiple Comparisons in Excel: Bonferroni and Scheffe
Multiple Comparisons in Zoho (online spreadsheet): Bonferroni and Scheffe (this spreadsheet does not work correctly) 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 (5/24): 5pm to 9:30pm
 Syllabus
 Notes 1
 Notes 1 Supplemental Reading and Exercises:
 Variables and scales of measurement (variable/constant; scales; quan./qual.; IV/DV) (practice exercise)
 Hypotheses (directional/nondirectional/null; qual. vs. quan. wording; categories compared; writing; problematicno difference between IV and DV, qual. IV affects DV [e.g., type of instruction affects DV]) (practice exercise #1, practice exercise #2)
 Displaying Data (frequency distributions, stemandleaf, bar charts, histograms, pie charts, box plots, scatterplots)
 Overview of things to come; read on your own  Descriptive Statistics and Graphical Displays (correlation coefficients and scatterplots) and Inferential Statistics (practice exercise)
 Notes 2
 Notes 2 Supplemental Reading:
 Probability and finding area under standard normal curve (Excel table with area to left of Z: Z Table); below is information on calculating area under normal curve: http://wwwstat.stanford.edu/~naras/jsm/NormalDensity/NormalDensity.html
 Additional Coverage of Notes 2 Material:
Session 2 (5/26): 5pm to 9:30pm (note: session 1 covered through start of finding area under curve)
 Resume Notes 2 if needed
 Notes 3
 Notes 3 Supplemental Reading and Notes:
 Statistical Inference, Hypothesis Testing (read this: logic of hypothesis testing)
 Central Limit Theorem Illustrated
 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
 Additional Coverage of Hypothesis Testing
 Notes 4
Session 3 (5/31): 5pm to 9:30pm (note: session 2 covered through much of notes 4)
 Resume Notes 3 if needed
 Notes 4
 Notes 4 Supplemental Reading:
 http://www2.chass.ncsu.edu/garson/pa765/ttest.htm
 http://www2.sjsu.edu/faculty/gerstman/StatPrimer/Hyptest.PDF
 Notes 5
Test 1 posted sometime after Session 3 (assuming Notes 4 is completed, otherwise Test 1 posted after Session 4)
Session 4 (6/2): 5pm to 9:30pm (note: session 3 covered through two group ttest, review one example session 4, then move to correlated ttest)
 Resume Notes 4 if needed
 Notes 5
 Notes 5 Supplemental Readings:
 Notes 6  see below in Session 5 for supplemental readings
Session 5 (6/7): 5pm to 9:30pm (note: session 4 covered through correlation, start session 5 with APA example of correlation, then move to notes 6)
 Resume Notes 5 if needed
 Notes 6
 Notes 6 Supplemental Readings:
 Read this: Correlation Coefficients and Scatterplots
 http://www.wadsworth.com/psychology_d/special_features/ext/workshops/correlation.html
 http://www2.chass.ncsu.edu/garson/pa765/correl.htm
 http://davidmlane.com/hyperstat/A34739.html
 http://www.wellesley.edu/Psychology/Psych205/pearson.html
 http://www.une.edu.au/WebStat/unit_materials/c6_common_statistical_tests/test_signif_pearson.html
 Notes 7
Session 6 (6/9): 5pm to 9:30pm (note: Session 5 covered through notes 7)
 Resume Notes 6 if needed
 Notes 7
 Notes 7 Supplemental Readings:
 Notes 7 SPSS Tutorials
 Notes 8a and 8b
Test 2 posted sometime after Session 6 (assuming coverage of chisquare is completed, otherwise Test 2 posted after Session 7)
Session 7 (6/14): 5pm to 9:30pm (note: Session 6 covered through end of simple regression, did not cover APA style, but will once MR is covered)
 Resume Notes 8a and 8b
 Notes 9
 Notes 9 Supplemental Reading:
Session 8 (6/16): 5pm to 9:30pm (note: Session 7 stopped at ANOVA SS calculation, start there with session 8)
 Resume Notes 9
 Begin Notes 9d
Test 3 will be posted sometime after 6/16
Session 9 (6/21): 5pm to 9:30pm
 Resume Notes 9d
 Q&A about Test 3
Test 3 responses due (6/23)
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. 
(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