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EDEP 601: Introduction to Quantitative Methods (3 credits)

Introduction to quantitative methods in behavioral sciences. Review of elementary statistical methods. Introduction to general linear model as principle of data analysis. (Meets EdD common core inquiry methods requirement or elective.) Pre: EDEP 429 or consent. (Cross-listed as PSY 610 and SW 651)


Modified: February 18, 2006

Basic Texts:

Pedhazur, E. J., & Schmelkin, L. P. (1991). Measurement, design, and analysis: An integrated approach. Hillsdale, NJ: Erlbaum.

Highly Recommended Reading:

Abelson, R. P. (1995). Statistics as principled argument. Hillsdale, NJ: Erlbaum.

Ward, J. H., Jr., & Jennings, E. (1973). Introduction to linear models. [Originally published by Prentice-Hall, now available from The Institute for Job and Occupational Analysis (IJOA), 10010 San Pedro, Suite 440, San Antonio, Texas 78216]

Other References:

Darlington, R. B. (1990). Regression and linear models. New York: McGraw-Hill.

Hays, W. L. (1994). Statistics (5th ed.). Orlando, FL: Harcourt Brace.

Judd, C. M., & McClelland, G. H. (1989). Statistical analysis: A model comparison approach. Orlando, FL: Harcourt Brace Janovich. [Their Book]; [Their Course]

Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). Orlando, FL: Harcourt Brace.

Wickens, T. D. (1995). The geometry of multivariate statistics. Hillsdale, NJ: Erlbaum.

References for Review:

Ferguson, G. A., & Takane, Y. (1989). Statistical analysis in psychology and education (6th ed.). New York: McGraw-Hill.

Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology (3rd ed.). Needham Heights, MA: Allyn & Bacon.



Data Sets and SAS Control Files


Online Resources: (barely scratch the surface of what's available on the Internet)

The student is strongly encouraged to make use of these Internet resouces/links as a way to reinforce one's understanding of basic statistical concepts.

James Algina's pages (syllabi, SAS and SPSS code, papers, etc.) [Very good stuff!!]

Multimedia

Relevant Text Online

Online Articles

Glossaries

Guidelines

Calculators and Online Applications

Statistical Packages

Downloadable Software

Data Set Libraries

Courses

Discussion Lists

Miscellanea


Course Outline:

A. The Nature of Theory in the Behavioral/Social Sciences

B. Quantitative Modeling: Regarding Variance/Covariance Structures

1. The Measurement Model (relations btwn latents and indicators)

a. Dimensionalizing the domain of inquiry

b. The concept of measurement error

2. The Structural Model (relations btwn latents or btwn indictors)

a. Relations among dimensions

b. The concept of sampling error

C. Review of the Logic of Statistical Inference (EDEP 429)

(See 'H' below)

1. Sampling Error and Sampling Distributions (e.g., t, F, chi-square)

2. The Null Hypothesis and Expected Values

3. Data and Observed Values

4. Comparing Observed and Expected Values

a. Appropriate test statistics

b. "Power" in statistical inference

D. The Concept of Covariance/Correlation

1. Correlation as Standardized Covariance

2. Interpretation of Correlation

a. Strength and direction of relationship

b. Components of variance / Partitioning sums of squares

E. The General Linear Model (Ordinary Least Squares, OLS)

1. The "Simple" Linear Model and the Concept of Least Squares

2. Linear Models with Multiple Explanatory Variables

F. Qualitative vs Quantitative Variables in Linear Models

1. Coding the Information from Categorical and Ordered Variables

2. Coding Schemes for Categorical Variables

a. Dummy coding

b. Contrast coding

3. The Concept of Interaction and Its Representation in a Linear Model

4. Polynomial Regression: Representation of Curvilinear Relationships

G. Orthogonality, Independence, and Multicollinearity in Linear Models

1. Manipulated and Measured Variables

2. Implications of Unequal, Disproportionate n's and Correlation between X's

3. Partial and Semi-Partial (Part) Correlation: Components of Variance Revisited

H. Testing Hypotheses within the Context of Linear Models

1. More Complex Models (Null Hypothesis Allowed to be False)

2. Less Complex Models (Null Hypothesis Forced to be True)

3. General Form of the F-ratio for Comparing Linear Models


If you have difficulty with any of the concepts presented in the text or lectures, please consult one or more of the links above or the syllabus for one or more of the courses (with associated links) on my home page. If you are unable to satisfactorily resolve any questions by consulting the appropriate links, please make an appointment with me at your earliest convenience.


Evaluation and assignment of grades


Questions or comments to: daniel@hawaii.edu

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