Education | Intermediate Statistical Applied to Education
Y502 | 6237 | John Hansen

This is a course designed primarily for advanced graduate students
who anticipate future applications of quantitative analysis
techniques. Topics covered in this course include a brief review of
descriptive of statistics, and a more in depth discussion of
hypothesis testing, correlation techniques, regression, comparisons
of means (t-test, one-way and two-way analysis of variance).
Prerequisite of this course is successful completion of Y520 or P501
or equivalent.


1.To acquire basic skills necessary for applying statistical
principles of inference to well-defined behavioral and educational

2.To be able to objectively evaluate manuscripts in which analysis
techniques covered in this course were used.

3.To carry out numerical analyses of data by hand and to use, read
and interpret SPSS data applications and output in the Windows


Kirk, R. E. (1999). Statistics: An Introduction (4th ed.), Orlando,
FL: Harcourt Brace & Company.


Green, S. B., Salkind, N. J., Akey, T. M. (2000) Using SPSS for the
Windows: Analyzing and Understanding Data, (2nd ed.), Upper Saddle
River, NJ: Prentice Hall. ISBN: 0-13-020840-X
Green, S. B. & Salkind, N. J. (2003) Using SPSS for the Windows and
Macintosh: Analyzing and Understanding Data, (3rd ed.), Prentice
Hall. ISBN: 0-13-099004-3

Assignments, Exams, and Laboratory	

For each topic covered in this course, practice problems and readings
taken from Kirk will be assigned. Problems listed on the Chapter
Readings have corresponding solutions at the back of the text. For
students wishing to work additional problems at the end of each
chapter, solutions can be found in the Kirk Instructor Manual on
reserve in the School of Education library. Students are expected to
be up-to-date on the readings and problems since class time is
structured around in-depth understanding of statistical concepts.
Constant practice is essential for success in statistics. The utility
and language of statistics will come only with practice.
You will be required to take three in-class exams as well as complete
three take-home assignments. Ordinarily the take-home exam is
assigned on the same day as the in-class exam allowing one week for
students to complete the take-home questions.

You should have a valid student account on the university computing
system. This account will facilitate our communication via the e-mail
utility and enable you to retrieve reading lists and course lecture
notes from Oncourse. Each registered student should be able to access
Oncourse at: Login using your IU userID and

Laboratory attendance each Monday is a required part of the course.
SPSS operations and exercises will be an integral part of the
learning experience. In the laboratory we will address real research
questions with datasets too large to compute by hand. Students will
expected to read and interpret SPSS output and can expect exam and
take-home assignments to address laboratory components. In past
courses we have found that many “Ah ha!” moments occur during lab.

Additionally articles used for class can be found and printed from
the library electronic reserve: Password: edstats.

Grading System	

Student’s performance in this course will be evaluated based on three
required exams.  Each exam contributes to 30% of the grade. Lab
quizzes and lab work will be incorporated into each of the three
exams. Additionally two article critiques will contribute to 10% of
the final grade. A final course grade will be determined for each
student according to the following mastery levels:

90% mastery or above  -- A				
87% to 89% -- A-
84% to 86% -- B + 		
80% to 83% -- B		
77% to 79% -- B-
74% to 76% -- C +		
70% to 73% -- C		
67% to 69% -- C-
below 67 = D		
below 57 = F

Incomplete will be given only for a legitimate reason as outlined in
the university's Academic Guide, and only after a conference between
the instructor and the student. Throughout the course of this
section, you may contest every grade awarded to your exams or the
overall course performance within 48 hours of receiving such a grade.
Once this "statute of limitation" has passed, it is assumed that you
willingly accept the grade(s) assigned without further dispute.

Academic Honesty and Intellectual Integrity

According to P.72 of the Academic Handbook (June 1992 edition), each
faculty member has "a responsibility to foster the intellectual
honesty as well as the intellectual development of his/her
students."  In order to achieve these goals, each student enrolled in
this course is prohibited from engaging in any form of "cheating"
or "plagiarism."  Cheating is defined as "dishonesty of any kind with
respect to examination, course assignments, alteration of records, or
illegal possession of examinations"  (p. 72 of the Academic
Handbook).  "It is the responsibility of the student not only to
abstain from cheating but, in addition, to avoid the appearance of
cheating and to guard against making it possible for others to
cheat.  Any student who helps another student to cheat is as guilty
of cheating as the student he or she assists.  The student also
should do everything possible to induce respect for the examining
process and for honesty in the performance of assigned tasks in or
out of class." (p. 72 of Academic Handbook).

Plagiarism is defined as "offering the work of someone else as one's
own" (p. 72 of Academic Handbook).  "The language or ideas thus taken
from another may range from isolated formulas, sentences, or
paragraphs to entire articles copied from books, periodicals,
speeches, or the writings of other students.  The offering of
materials assembled or collected by others in the form of projects or
collections without acknowledgment also is considered plagiarism.
Any student who fails to give credit for ideas or materials taken
from another source is guilty of plagiarism." (p.72 of Academic
Evidence of student academic misconduct will result in (a) a lowered
course grade, (b) transfer out of this course, (c) dismissal from
student's academic unit, or (d) other disciplinary actions in
accordance with the guidelines outlined on p.73 of Academic Handbook.

Tips for Successful Learning in Y502 (or any statistics course)

For each hour in the class plan on spending 2-3 hours in
reviewing/previewing materials.

Establish a good study habit by following these steps:
Preview materials before coming to each class on Monday and Wednesday;
Review materials or recopy notes immediately after each lecture or
Talk about lecture / homework problems with study partner.

Always bring three things to class/lab:
The textbook by Kirk.
A functional calculator comparable to Casio model fx260.
All notes and handouts ever distributed in class; preferably already
organized in a three-ring notebook form.

Don’t be shy about asking questions, either directly in class or
through email. The instructor’s role is to help you learn and
understand the material. Asking questions gives the instructor a
chance to detect “problem” areas and try to facilitate the learning
process for you and for other students.

If you find it helpful, work closely with one or two of your
classmates and verbalize your understanding, such as the logic behind
hypothesis testing. But don’t become overly dependent on a classmate
or friend to answer your questions. Ultimately, learning is an
individual responsibility; there will be activities/situations in
which you will have to engage in alone.

About the exams, (a) forget about cramming the night or the week
before; this habit only immobilizes you and convinces you that
you “can’t do statistics”; (b) exams are always cumulative because of
the nature of materials tested; and (c) the textbook is not meant for
casual reading, it must be read slowly at least three or four times
before it makes sense.

Remember, this course is for you. I will try my best to answer your
questions or find the appropriate resources that can. We need to work
together to reach our goal -- your understanding of the material.

Reading lists and lecture notes can be found on Oncourse and should
be printed out by each student before each class. Each weeks lecture
notes will ordinarily be available the Sunday before class. Teaching
is a dynamic process and I almost always revise my notes relative to
the class’s needs.

Course materials are posted on Oncourse:
Login using your IU userID and password.

Additionally articles used for class can be found and printed from
the library electronic reserve: Password: edstats.


9/1 – 9/5	
Course orientation. Math background quiz. Review of fundamental
concepts (scales of measurement, variables, summation). Basics of
data organization: charts, graphs, distributions. 	
Ch. 1, 2

9/8 – 9/12
Central tendency, Variability (range, interquartile range, standard
deviation, variance). Box plot.  	
Ch. 3, 4

9/15 – 9/19
Probability, Rectangular distribution, Normal distribution, Standard
scores, Sampling distribution, Central limit theorem, Standard error.
Ch. 8, 9*

9/22 – 9/26
Ch. 5

9/29 – 10/3
Ch. 6

10/6 – 10/10
Exam 1 (Monday Oct 6th) / Flex Day

10/13 – 10/17
Hypothesis testing, One sample t-test, (one and two-tailed), Type I
and Type II error. Statistical Inference.Ch. 10

10/20 – 10/24
Hypothesis testing, á and p, power, confidence intervals, practical
Ch. 10

10/27 – 10/31
Student’s t-distribution, Degrees of freedom, One sample t-test
unknown population variance, t-test for a correlation.
Ch. 11

11/3 – 11/7
Two sample t-test independent sample, Two sample t-test dependent
sample. Exam 2 (Monday November 10th)
Ch. 12

11/10 – 11/14
Exam 2 (Monday November 10th) /Flex Day	

11/17 – 11/21
One-way Analysis of Variance. F- distribution, sums of squared
deviations, F-test, Tukey HSD post-hoc test.
Ch. 14

11/24 – 11/28
One-way Analysis of Variance.
Assumptions of ANOVA.

Thanksgiving recess no class 11/26.	
Ch. 14

12/1 – 12/5
Two-way (factorial) Analysis of Variance. Main effect, interaction
Ch. 15

12/8 – 12/12
Two-way (factorial) Analysis of Variance. Graphing and interpretation
of interactions. 	
Ch. 15

12/15 – 12/19
Final  Time / Place TBA 	
* Chapter 9 is the most important chapter in this statistics course.

Note: This is a tentative course schedule which is subject to change
without prior notice. Changes to the syllabus depend on the pace of
classroom instruction an students’ learning needs. Changes will be
communicated to each enrolled student via e-mail.