**Instructor:** Gary C. Ramseyer

**Office:** 451 Degarmo Hall

**Phone:** 309-438-7939

**Email:** gcramsey@ilstu.edu

**Web Site:** http://www.ilstu.edu/~gcramsey

**TEXT:**

- Blommers, P.J. and Forsyth, R.A. (1977). Elementary Statistical Methods in Psychology and Education (2nd ed.) Lanham, MD: University Press of America.
- Blommers, P.J. and Forsyth, R.A. (1977). Study Manual for Elementary Statistical Methods in Psychology and Education (2nd ed.) Lanham, MD: University Press of America.

**OBJECTIVES:**

This course is designed to give the student a basic knowledge and understanding of
the statistical method particularly as it pertains to research in the behavioral
sciences. Several reoccurring themes are emphasized that unify the subject matter.
At the end of the semester a student should be able to read and intelligently assess
much of the research literature in his or her own particular field. Also, at the end
of the semester a student should be able to apply the statistical techniques
presented in the course to his or her own research projects.

**METHOD OF INSTRUCTION:**

Discussion is the predominate mode of operation in this course. A majority of the
class time is spent in a discussion of the exercises in the study manual. The
instructor firmly believes that basic statistical skills are most effectively learned
by having the student work a wide variety of exercises and problems. These study
manual exercises are not handed in or graded but the results should be carefully
recorded for study purposes throughout the semester. Several additional exercises
are assigned each week and handed in for credit. Occasionally, the instuctor will
lecture on certain critical topics that are traditionally troublesome for many
students. Students are encouraged at all times to participate in class discussion,
to ask questions, or to simply release their pent up aggression against statistics
(Due to sanitation ordinances, the throwing of tomatoes is disallowed). The instuctor
endeavors to promote a relaxed, free-wheeling atmosphere in the classroom.

**EVALUATION:**

A student's grade in the course is based on his or her composite performance on
three multiple-choice, open-book examinations and the graded, hand-in exercises.
Each exam is equally weighted, noncomprehensive, and approximately 35-40 items
(points) in length. The student is allowed approximately two hours of working time for
each exam (except the first which is taken in class) and may utilize freely various
textbooks, class notes, and prepared summary sheets (the only exam aid ruled out is
a statistical consultant). An approximate letter grade will be assigned to each exam
based on comparing the exam's distribution with distributions from previous semesters
on parallel exams. Only points not letter grades are recorded and accumulated in the
grade book. The hand-in exercises account for approximately 25-30 points over the
semester or roughly the equivalent of a fourth exam. At the close of the semester,
the distribution of composite scores is compared with composite distributions
from previous semesters to arrive at a final letter grade for each student. Over
perhaps a ten-year period, the approximate distribution of final grades is as follows:
15% As, 30% Bs, 40% Cs, 10% Ds and 3% Fs. It should be emphasized, however,
that these are long-run average percentages over many semesters and that in any
given semester the percentage in a grade category may vary up or down depending
on the comparative magnitude of the composite scores. The instructor feels strongly
that this comparative system of grading is very fair and allows the student to earn a
grade commensurate with his or her achievement regardless of how peers perform in the
class. Classroom attendance is mandatory except for emergency situations.

**REQUIREMENT:**

A ten-digit scientific calculator.

**TOPICS:**

- Types of data.
- The frequency distribution and graphical techniques.
- Percentile ranks and percentiles.
- Symbolic representaion of data.
- Indices of location or central tendency.
- Measures of variability.
- Linear transformations and z-scores.
- Introduction to probability.
- The normal distribution.
- Introduction to sampling error theory.
- Testing a statistical hypothesis about a population mean or a population proportion.
- Testing a statistical hypothesis about the difference between two population means for both independent and related samples.
- Small sampling error theory and the t-statistic.
- Correlation.

RETURN to Courses Taught on Home Page of Gary Ramseyer.