EDUR 8131
Educational Statistics 1
(4 Week Format)

Summer 2011

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

 

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)

  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 5/31 or 6/2.
  8. Test 2: Covers content found in Notes 1 through 7. Test 2 may be as early as 6/9 or 6/14.
  9. Test 3: Covers all content found in Notes 1 through 9d. Test 3 may be posted by 6/17 and responses due 6/23.
  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  
            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 (note, update CI for one sample t-test)
            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 (note, use car data to illustrate correlation matrix in SPSS)
            Excel files:
                7. Correlation 
  7. Notes 7 Chi-square
            Excel files:
               8. Goodness of fit chi-square,
               9. Association chi-square
  8. Notes 8a Simple Regression (sample data files located here: http://www.bwgriffin.com/gsu/courses/edur8131/data
  9. Notes 8b Multiple Regression
  10. Notes 9 ANOVA 
            Multiple Comparisons in Excel: Bonferroni and Scheffe
            Multiple Comparisons in Zoho (on-line spreadsheet): Bonferroni and Scheffe (this spreadsheet does not work correctly)
  11. Notes 9d ANCOVA Sample Data
  1. Types of Statistical Procedures and Their Characteristics  (PDF Table Created June 21 during 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 data collection)
  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 (5/24): 5pm to 9:30pm 

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

Session 2 (5/26): 5pm to 9:30pm  (note: session 1 covered through start of finding area under curve)

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

Session 3 (5/31): 5pm to 9:30pm  (note: session 2 covered through much of notes 4)

  1. Resume Notes 3 if needed
  2. Notes 4
  3. Notes 4 Supplemental Reading:
  4. http://www2.chass.ncsu.edu/garson/pa765/ttest.htm
  5. http://www2.sjsu.edu/faculty/gerstman/StatPrimer/Hyp-test.PDF 
  6. 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 t-test, review one example session 4, then move to correlated t-test)

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

  1. Resume Notes 5 if needed
  2. Notes 6
  3. Notes 6 Supplemental Readings:
  4. Notes 7

Session 6 (6/9): 5pm to 9:30pm  (note: Session 5 covered through notes 7)

  1. Resume Notes 6 if needed
  2. Notes 7
  3. Notes 7 Supplemental Readings:
  4. Notes 7 SPSS Tutorials
  5. Notes 8a and 8b

Test 2 posted sometime after Session 6 (assuming coverage of chi-square 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)

  1. Resume Notes 8a and 8b
  2. Notes 9
  3. 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)

  1. Resume Notes 9
  2. Begin Notes 9d

Test 3 will be posted sometime after 6/16

Session 9 (6/21): 5pm to 9:30pm 

  1. Resume Notes 9d
  2. 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) 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

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