Prof. Larry H. Ludlow
617-552-4221
Off. Hrs: Mon 2-4, Tue 3-5, Wed 1-3, by appt.
e-mail: Ludlow@bc.edu
Professor's Folder: Ludlow

Graduate Assistant: Ms. Tiffany Cooper
She is responsible for grading all assignments, instruction in SPSS, lectures when I am out of town, and is your first resource for clarification of statistics material.
Office--outside my office (336C).

Introduction
This course is usually a continuation of material presented in ED/PY 468 - Statistics I. As such it is assumed that you are competent in the area of descriptive statistics. It is usually assumed that you know how to run the SPSS statistical package on a Macintosh or IBM (PC) computer. Since many of you have not had ED/PY 468 here nor have you necessarily been exposed to the Mac or PC systems and SPSS software, a review of fundamental statistical material necessary for this course will be offered, and a laboratory based primer in computing will be provided. You will, however, be expected to quickly come up to a fairly reasonable proficiency on the computer.

The basic purpose of the course is to teach skills necessary to conduct various forms of inferential statistical analysis that most students will employ in their dissertation research. My emphasis is on the correct application of statistical techniques, the interpretation of the results, and the write-up of the problem and results in a style that is practical, complete, and concise. In addition to the lectures considerable time is spent on computer output interpretation. Your skills will be developed through regular statistical computing exercises. A major component of these exercises is the demonstration, on your part, of individual initiative and resourcefulness in accomplishing the tasks.

Required Texts:

Hinkle, Wiersma & Jurs (1998). Applied Statistics for the Behavioral Sciences.
(4th ed). Houghton Mifflin.

SPSS 10.0 Guide to Data Analysis, 2002.

Course Requirements:

1. Reading assignments for each class
2. Statistics and Computer exercises
3. Final examination

Policy:

a) Late papers: all assignments are due on time.

b) Redone papers: requests to redo an assignment will be granted with the understanding that a redone paper will not be rescored.

c) Papers will be word processed with an equation editor whenever possible.

d) The steps taken to arrive at a hand calculated solution must be shown. Solutions presented without the accompanying steps will be scored as incorrect.

e) There is no classroom lecture period assigned specifically for review prior to the final.

f) The final is comprised of about 20% true/false, multiple-choice questions that are in closed-book format. About 80% of the other questions on the exams are open book problems. The final exam is cumulative.

Course Outline:

A. First Review of Descriptive Statistics (Chaps. 1-3)
1. Scales of measurement
2. Graphs/plots
3. Central tendency
4. Variance
Handout Review Assignment-due Sept 17th
SPSS Computing Review and Assignment (Tiffany's responsibility)

B. Second review of descriptive statistics (Chaps 4-6)
1. Normal distribution and standard scores
2. Covariance, correlation
3. Regression

C. Statistical inference and continuous variables (Chap 7)
1. Rules of probability
2. Principles of statistical inference

a. Central limit theorem
b. Sampling distributions
c. Standard error of the mean
d. Sampling designs

D. Introduction to Hypothesis Testing (Chap. 8)
1. Hypotheses

Null and Alternatives

2. Statistical significance

Alpha levels
One-tail and two-tail tests
Type I and Type II error

E. One sample mean tests (Chap. 8-9)

1. z and t-tests
2. Degrees of freedom
3. Point estimates and confidence intervals
ASSIGNMENT

F. Inferences about the differences between means (Chap. 11, 13)
1. Independent groups

Homogeneity of variance
Confidence Intervals

2. Dependent groups
3. Power (p309-328)

Factors influencing power
Effect sizes

T-TEST OUTPUT EXAMPLE
ASSIGNMENT

G. Inferences concerning proportions (Chap. 10, 12, 21)
1. One-sample case (p238-247)
2. Two-sample case (p294-298)
3. Chi-square distribution (p574-590)

a. goodness-of-fit
expected frequencies
b. test of association
c. standardized residuals

CHI-SQUARE OUTPUT EXAMPLE
ASSIGNMENT

H. Inferences concerning correlations (Chap. 10, 12)
1. Test of a single r (p229-238)

Fisher's z transformation

2. Differences between pairs of r's (p290-294)

Confidence intervals

I. Inferences concerning regression (Chaps. 17-18 and handout)
1. Simple linear regression

Statistical significance

2. Multiple regression model

Significance of model vs. significance of predictors
R-squared vs. adjusted r-squared
Residual analysis

3. Partial correlation
CORRELATION and REGRESSION OUTPUT EXAMPLE
ASSIGNMENT

J. One way ANOVA (Chap. 14)

1. Model

Multiple t-test problem (inflated alpha problem)

2. Assumptions
3. F distribution
4. Power and sample size (some of Chap. 13)

K. ANOVA multiple comparison procedures (Chap 15)
1. Planned comparisons (a priori)

Contrasts

2. Post-hoc comparisons (a posteriori)

Per comparison (comparisonwise) error
Family-wise (experimentwise) error

ANOVA OUTPUT EXAMPLE
ASSIGNMENT

L. Factorial ANOVA designs (Chap 16)
1. Assumptions
2. Models
3. Main effects
4. Interactions
5. Planned comparisons
FINAL


Statistical Resources

Edwards, A. L. Statistical Analysis in Psychology and Education .

Edwards, A. L. Multiple Regression and the Analysis of Variance and Covariance.

Ferguson, G. A. Statistical Analysis in Psychology and Education.

Glass & Hopkins. Statistical Methods in Education and Psychology.

Guilford, J.P. and Fruchter, B. Fundamental Statistics in Psychology and Education

Hayes, W. L. Statistics.

Hopkins, K.D., Glass, G.V. and Hopkins, B.R. Basic Statistics for the Behavioral Sciences.

Huiteman, B.H. The Analysis of Covariance and Alternatives.

Keppel, G. Design and Analysis: A Researcher's Handbook.

McNemar, Q. Psychological Statistics .

Pagano, R. R. Understanding Statistics in the Behavioral Sciences.

Runyon, R.P. and Haber, A. Fundamentals of Behavioral Statistics.

Shavelson, R. J. Statistical Reasoning for the Social Sciences.

Siegel, S. Nonparametric Statistics for the Behavioral Sciences.

Thorndike, R. M. Correlational Procedure for Research.

Winer, B.J. Statistical Principle in Experimental Design