Education | Statistical Methods Applied to Education
P501 | 5881 | Joanne Peng


This is a course designed primarily for graduate students who need an
introduction to statistical methods for basic data analysis in
education or social sciences. Topics covered in this course include an
introduction to description of variables, measures of central
tendency, variability, hypothesis testing, correlation techniques,
comparisons of means (t-test and one-way analysis of variance), and
analysis of categorical data. Emphasis is placed on theoretical and
computational skills.

Objectives

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

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 by SPSS
software under Windows.

Textbooks

Hinkle, D.E., Wiersma, W. &  Jurs, S. G. (1998). Applied Statistics
for the Behavioral Sciences (4th ed.,), Boston, MA: Houghton Mifflin.

Hinkle, D.E., Zoski, K. W. & Cox, J. R. (1998). Applying Statistical
Concepts Workbook, Boston, MA: Houghton Mifflin.

SPSS Inc. (latest). SPSS Base 10.0: Application Guide. Chicago, IL:
SPSS Co.

Assignments, Exams, and Labs

For each topic covered in this course, practice problems and readings
taken from Hinkle, Wiersma, & Jurs will be assigned.  Students are
expected to be up-to-date on the readings since class time is
structured around in-depth understanding of statistical concepts and
practice. Practice problems are not graded because answers will be
provided for you.  You will be required instead to take three in-class
exams.  The specific contents of and instructions on exams will be
announced later in class.

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 analyze data by using the SPSS software.
Thus, a limited prior knowledge of computers is assumed.  My e-mail
address is PENG; the GA assigned to this course is Woong Lim, his
e-mail address is WLIM.  Woong's office phone and office are ED 4008,
856-8585.

The attendance of each lab session is optional; but but repeated
absences are known to researchers and students to affect learning.
Activities that typically take place in the labs include, but are not
limited to: (a) clarification of previous lectures, (b) answering
questions related to practice problems, assigned readings, or any
administrative aspect of the course, and (c) instruction on basic SPSS
command language and execution.  The first lab is scheduled for next
Wednesday (9/6), from 5:30 pm to 6:30 pm in Room 2015.

Grading System

Student's performance in this course will be evaluated based on the
required exams.  The first two exams contribute 60% (or 30% each)
toward the final grade.  The final exam contributes 40% toward the
final grade.   A final course grade, expressed in letters, will be
determined for each student according to the following mastery levels:

85% mastery or above  -- A
80% to 84% -- A-
75% to 79% -- B +
70% to 74% -- B
65% to 69% -- B-
60% to 64% -- C +
55% to 59% -- C
50% to 54% -- C-

The letter grades should be interpreted according to the School of
Education grading policy as follows:

A     Outstanding achievement..
A-    Excellent achievement.
B+   Very good achievement.
B     Good achievement
B-    Fair achievement
C+   Not wholly satisfactory achievement.
C     Marginal achievement.
C-    Unsatisfactory achievement.

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 Handbook).

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.

Points to Ponder

1.  Nothing is easier than being busy.

2.  Nothing is harder than staying focused.

3.  Priorities are not what you do but who you are.

4.  The word for learning in Greek is manqnw [manthan]—to increase
one's knowledge or to be increased in knowledge, frequently by inquiry
or observation.  This Greek word is akin to  which denotes "a
disciple," a follower of the master by his/her example and by the
application of the knowledge gained from the master.  Thus, "learning"
implies a process of becoming mathematical (inclined to learn, to be
alert, or to ascertain) and the act of practicing what is learned so
as to be a different person.

5.  With this definition for "learning," I therefore interpret
"teaching" to mean guiding and nurturing a learner in becoming
mathematical.

Tips for Successful Learning in Y502 (or any stats. Course)

1.  For each hour in the class or the lab, always plan on spending
three hours in reviewing/previewing materials.

2.  Establish a good study habit by following these steps:

I.  Preview materials before coming to each class;
II.  On Mondays and Wednesdays, come to classes prepared; and
III.  Review materials immediately after each lecture or lab.

3.  Always bring three things to class/lab:

A.  The textbook by HWJ.
B.  A functional calculator comparable to Casio models fx250, SL200,
TI 25 or Sharp EL-531 lb.
C.  All notes and handouts ever distributed in class; preferably
already organized in a three-ring notebook form.

4.  Don't be shy about asking questions, either directly 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.

5.  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.

6.  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 at least three or four times before it
makes sense.

Warning: Taking this course may change your intellectual life forever!

P501 Lecture No. and Dates

Week The week of    Monday    Wednesday    Comments
1    8/28 (M)        1           2

2    9/4  (M)        3           4        Class meets on Monday (9/4)

3    9/11 (M)        5           6

4    9/18 (M)        7           8 lab    Wed. lab (9/20) will
9        be used for lecture.

5    9/25 (M)  [Help Session]  1st Exam   No class on Monday (9/25)

6    10/2 (M)        10          11

7    10/9 (M)        12          13

8    10/16 (M)       14          15

9    10/23 (M)       16          17

10    10/30 (M)       18          19

11    11/6 (M)        20        2nd Exam

12    11/13 (M)       21          22

13    11/20 (M)                             No class on Wednesday
(11/22)

14    11/27 (M)       24          25

15    12/4 (M)        26     Wrap Up & Course
Evaluation

Schedule for P501 (Section 5881)

Week       Topics
Readings in HWJ

#1  Orientation to the course                           Chapters 1, 2
Review of fundamental concepts

#2  Review of percentile, percentile rank, and ogive        Chapter 3
Central tendency, variation, and standardized scores    Chapter 3

#3  Normal distribution                                     Chapter 4
Correlational indices                                   Chapter 5

#4  Sampling distributions and sampling procedures          Chapter 7

#5  Hypothesis testing and one-sample t-test                Chapter 8
Point and interval estimations                          Chapter 9

TEST #1   September 27th (W) Place to be announced.         Ch. 1-5, 7

#6 & 7 Hypothesis testing and interval estimation for       Chapter 11
comparison of two-sample means

#8  Hypothesis testing and interval estimation for          Chapter 14
comparison of k-sample means

#9 & 10 Comparison procedures of means                      Chapter 15

#11 Hypothesis testing & interval estimation of             Chapter 10
one-sample proportion

TEST #2 November 8th (W) Place to be announced.       Ch. 8-9,11,14-15
					
#12 Hypothesis testing and interval estimation of equality  Chapter 12
between two-sample proportions

#13 & 14 Chi-square tests for frequencies                   Chapter 21

#15 Other nonparametric tests                               Chapter 22

Final Exam, Time/Place to be announced later. Ch. 10,12, 21-22

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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 and students' learning. They will be
communicated to each enrolled student via the electronic mail.