EDUR 8131
Educational Statistics 1
(16 Week Format)

Spring 2011

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

 

Announcements ---

Updated ANCOVA notes highlighted in yellow.


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. Test 1 (Covers content found in Notes 1, 2, 3, and 4. Test 1 may be posted as early as 2/15)
  7. Test 2 (Covers content found in Notes 1 through 7. Test 2 may be as early as 3/8)
  8. Test 3 (due 5/10 in PDF form sent in GeorgiaView)
  9. Exercises
  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)
  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
            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
  8. Notes 8a Simple Regression (read also Regression with one quantitative IV )
  9. Notes 8b Multiple Regression (read also Regression with two quantitative IVs )
  10. Notes 8 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  
  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 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

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 (1/18): 5pm to 7:45pm 

  1. Syllabus 
  2. Begin Notes 1
  3. Variables and scales of measurement (variable/constant; scales; quan./qual.; IV/DV)  (practice exercise)
  4. Hypotheses (directional/nondirectional/null; qual. vs. quan. wording; categories compared; writing; problematic-no difference between IV and DV, qual. IV affects DV [e.g., type of instruction affects DV]) (practice exercise #1, practice exercise #2)
  5. Displaying Data (frequency distributions, stem-and-leaf, bar charts, histograms, pie charts, box plots, scatterplots; practice exercise for displaying data
  6. Overview of things to come; read on your own - Descriptive Statistics and Graphical Displays (correlation coefficients and scatterplots) and Inferential Statistics (practice exercise)

Session 2 (1/25)

  1. Resume discussion from Session 1 (spr 2011: resume at scatterplots; fall 09, covered through displaying data)
  2. Central tendency (mode, median, mean)
  3. Variability (range, variance, standard deviation)
  4. Homework activity for material covered above (variability, central tendency, graphical displays), do on your own:
    tests/edur_8131_homework_central_tendency_variability_graphics.pdf
  5. Begin Notes 2
  6. Normal curve and skew
  7. Percentiles and percentile rank
  8. Standard scores -- z score, T score
  9. Probability and finding area under standard normal curve (Excel table with area to left of Z: Z Table); below is supplemental reading on calculating area under normal curve: http://www-stat.stanford.edu/~naras/jsm/NormalDensity/NormalDensity.html
  10. Homework activity for z scores:

    edur_8131_homework__zscores_z_test.pdf

  11. Supplemental reading materials on topics covered above in case you find the course notes or textbook is unclear.

    Additional readings and useful links follow.

Session 3 (2/1)

  1. Review Notes 2 (fall 09, cover conversion z to raw, find two-tailed .05 and .01 z scores)
  2. Begin Notes 3
  3. 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 4 (2/8)

  1. Resume Notes 3 (note, fall 09 spring 10 session 3 ended at Notes 3.8 confidence intervals)
  1. Confidence Intervals
  2. 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 
  3. Properties of Estimators
  1. Begin Notes 4 (Hypothesis Testing and One-sample Z test):
    1. One Sample z-test (two tailed vs. one tail tests and probability) (Instructor's note: partially covered this, did not get to material below)
    2. Hypothesis testing -- power, type 1 and type 2 errors, p-values
    3. One sample t-test; maybe start two-independent samples t-test (seldom are t-tests covered during session 5).
    4. Some readings on t-test can be found here:
  1. Homework activity for z scores and z test:

    edur_8131_homework__zscores_z_test.pdf

Session 5 (2/15)

  1. Resume Notes 4 (fall 09 and spring 10, finished one sample z test notes 4.2 session 4; begin at 4.3):
  1.  Notes 4.3: Short Cut to P-values
  2. Hypothesis testing -- power, type 1 and type 2 errors, p-values
  3. Some additional readings on the logic of hypothesis testing:
     
  1. Answer Questions about Test 1 (assuming we complete Notes 4 this chat session)
  2. Begin Notes 5: t-tests - covered one-sample t-test

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

Session 6 (2/22)

  1. Continue Notes 5 (spring 10 covered one and two group t-test; fall 09 - covered one sample t-test, resume at two group t-test):
  1. One Sample t-test
  2. Confidence Intervals About Sample Means
  3. Two Independent Samples t-test
  4. Confidence Intervals About Mean Difference (cont. two independent samples t-test)
  5. Two Correlated Samples t-test
  6. CI with correlated samples t-test (same formula as above)
  1. Some readings on t-test can be found here:
  1. One sample, two independent sample, and correlated samples t-tests exercises with answers:

    http://coe.georgiasouthern.edu/foundations/bwgriffin/edur8131/edur_8131_homework_t_tests.pdf 

  2. See also Exercises in Course Index above for more on t-tests

Session 7 (3/1)

1. Finished correlated samples t-test (spring 10; began at correlated t-test)

2. Began Notes 6, Correlation (useful information on reviewing scatterplots: correlation coefficients and scatterplots ).

3. Supplemental correlation notes below:

4. Begin Notes 7 (chi-square)

5. Some readings on chi-square can be found here:

Session 8 (3/8)

Spring 2011 -- unable to enter chat session; chat canceled.

 

1. Continue Notes 6 (spring 10 resume at 6.8, correlation matrices, illustrate correlations in spss/excel; fall 09: covered correlation, need now to illustrate finding r in Minitab/Excel and reporting results in APA style)

2. Begin Notes 7 Chi-square (fall 09 covered goodness of it)

3. Some readings on chi-square can be found here:

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

 

Spring Break (3/15)

 

Session 9 (3/22)

  1. Begin (and Finish) Notes 7, Chi-square, if needed (fall09 - covered test of association)
  2. Begin Notes 8a Simple Regression (fall 09 - ended at finding residuals for ratings data)

Session 10 (3/29)

Resume Notes 8a Simple Regression (fall 09 - resume with finding residuals for rating data, covered through model inference -- reviewed with several examples of literal interpretation of coefficients; end at coefficient inference)

Session 11 (4/5)

  1. Continue with simple regression (f 09 -- resume this chat  with coefficient interpretation in simple regression, ended at ice cream data)
  2. Begin multiple regression (end at ice cream example)

Session 12 (4/12)

  1. Continue with multiple regression (fall 09 -- begin ice cream data - show predicted values, residuals and R, R^2)
  2. Begin One-way ANOVA

Session 13 (4/19)

  1. Resume ANOVA
  2. Begin Multiple Comparison Procedures under ANOVA (called Post Hoc) (fall 09, illustrated logic of Bonferroni correction, demonstrated comparisons in Zoho)

Session 14 (4/26)

  1. Address any remaining issues on ANOVA or multiple comparisons (s10, began ANCOVA; f 09 resume Zoho, and presenting results in APA style)
  2. ANCOVA

Session 15 (5/3)

  1. Finish ANCOVA
  2. Q&A about Test 3

 

Test 3 responses due (5/10)

 

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 an introduction to statistics, both descriptive and inferential, and the application of statistics to educational research. Topics covered range from simple descriptive statistics, such as mean and variance, to inferential methods, such as t-tests, chi-square tests of association, analysis of variance, and analysis of covariance. 

Texts  

Required - Moore, D. & McCabe, G. (2006, 2009, 2012). Introduction to the practice of statistics (5th, 6th, or 6h ed. acceptable). New York: Freeman.

Note that 5th and 6th editions may be found on-line for less than $20. Try searching for ISBN 0716764008 on google.com shopping, www.amazon.com, or www.alibris.com, for example.

Web Pages

http://www.bwgriffin.com/gsu/courses/edur8131/

Software (Recommended)

SPSS (PASW) Version 10.0 or higher. Version 18 of the Graduate Pack Base can be rented for 6 months for $35 here:

http://www.onthehub.com/spss

Other student options and pricing for SPSS/PASW can be found here:

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. Measurement (scales, variables)
  2. Hypotheses
  3. Descriptive Statistics
  4. Central Tendency 
  5. Variability
  6. Displaying Data
  7. Normal Curve and Skew 
  8. Percentile Ranks 
  9. Standard Scores 
  10. Hypothesis Testing
  11. t-test 
  12. Correlation 
  13. chi-square 
  14. Regression
  15. ANOVA
  16. multiple comparisons
  17. ANCOVA (if time is available)

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