Quantitative Data Analysis in Educational Research

Winter 2006
T Th 10-11:45


K. Téllez
ktellez@ucsc.edu
SS I 2
17
V: 459-2208

 

 

This course promotes introductory to intermediate-level knowledge of the concepts in quantitative research in educational settings.  Beginning with an exploration of the foundations of the measurement of human behavior, including a treatment of test theory and practice, students will learn the methods of data analysis and the general logic behind statistical inference as well as the use of alternative measures such as effect size.  The course addresses those methodologies most associated with the analysis of interval data but also examines the non-parametric alternatives available to educational researchers.  Students complete several comprehensive assignments and a project demonstrating a research design and analysis of their choice.  The pedagogical methods of the course include lectures, group tasks, and student presentations. 

The course's goal of an intermediate knowledge of quantitative methods includes the following themes/ideas:

a variety of quantitative research methodologies in educational research, as well as the strengths, weaknesses, and appropriate uses of those research methodologies; a range of substantive research problems and quantitative research paradigms in education; the distinctions among quantitative research paradigms in education, including classes of research questions, methods of collecting and analyzing evidence, theoretical assumptions, strengths, weaknesses, and the work of major proponents; the capacity to understand and critically analyze quantitative educational research; the understanding of the principles of descriptive and inferential statistics and their application in context of various research paradigms; an understanding of the role of quantitative research findings in the advancement of educational opportunities for all students. 

Primary Text

Shavelson, R.J.  (1996).   Statistical reasoning for the behavioral sciences (3rd Edition).  Boston: Allyn and Bacon

Secondary Required Text

Leech, N.L. Barrett, K.C., & Morgan G.A.  (2005).  SPSS for intermediate statistics (paper w/CD): Use and Interpretation, 2E.  Mahweh, NJ: Lawrence Erlbaum. 

Required Reading

Thompson, B. (1996). AERA editorial policies regarding statistical significance testing: Three suggested reforms. Educational Researcher, 25(2), 26-30.

Recommend texts and readings:

American Psychological Association   Publication Manual. 
Behrens, J. T., & Smith, M. L (1996). Data and data analysis. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 945-989). New York: Macmillan.
Einspruch, E. L. (1998). An introductory guide to SPSS for Windows. Thousand Oaks, CA: Sage.
Pedhazur, E. J., & Schmelkin, L. P. (1991). Measurement, design, & analysis: An integrated approach. Hillsdale, NJ: Erlbaum.
Rumberger and Willms  Ed Eval and Policy Analysis

Tatsuoka, M. M. (1992). Statistical methods. In M. C. Alkin (Ed.), Encyclopedia of educational research (Vol. 4, pp. 1275-1303). New York: Macmillan.
Vogt, W.P.  (1998).  Dictionary of statistics and methodology (2nd Edition).  Newbury Park, CA: Sage. 

Other resources:

D. Howells's U of Vermont Website

Datasets:

1. Big CRT Math/Reading


Dateek

Topic

Reading

Assignment Due

Misc.

TH Jan. 5

I.              Course Overview and Expectations

II.            General Introduction to the Role of Quantitative Research in Education. 

None

   
TU Jan. 10 a)   Research Design in the Behavioral Sciences. Shavelson, Ch 1, 2    
TH Jan. 12 b)   Statistics in Context: Research on Teacher Expectancy Leech et al., Ch 1    

TU Jan. 17

a)   Frequency Distributions.
b)   Measures of Central Tendency and Variability.

Shavelson, Ch 3,4,5
Leech et al., Ch 2

   

TH Jan. 19

c)   The Normal Distribution.
d)   Test Theory, Reliability and Validity

Leech et al., Ch 4

Assignment 1 due. 

 

TU Jan. 24

a)   Joint Distributions and Correlation Coefficients.
b)    Linear Regression.

Shavelson, Ch 6,7

   

TH Jan. 26

a)   Statistical Inference: By Intuition. Shavelson, Ch 8    

TU Jan. 31

b)   Probability Theory and Mathematical Distributions. Shavelson, Ch 9    

TH Feb. 2

a)   Statistical Inference Using the Normal Distribution. Shavelson, Ch 10 Assignment 2 due  

TU Feb. 7

b)   Decisions, Error, and Power.
c)    Statistical Significance vs. Effect Size

Shavelson, Ch 10,11

Thompson, B. (1996). AERA editorial policies regarding statistical significance testing: Three suggested reforms. Educational Researcher, 25(2), 26-30.

   

TH Feb. 9

a)   t-tests for Case I and Case II Research I

Shavelson, Ch 12

Find the Excel File, Effect Size Calculator here

TU Feb. 14

a)   t-tests for Case I and Case II Research II

Shavelson, Ch 12

 

TH Feb. 16

b)    One-Way Analysis of Variance I

Shavelson, Ch 13

Assignment 3 due  

TU Feb. 21

b)    One-Way Analysis of Variance I

Shavelson, Ch 13

Assignment 4due

 

TH Feb. 23

c)    Factorial Analysis of Variance.

Shavelson, Ch 13,14

Leech et al., Ch 8

   
TU Feb. 28 Group Work None    

TH March 2

a)   Analysis of Covariance. Shavelson, Ch 17    

TU March 7

Repeated Measures Designs Leech et al., Ch 9    

TH March 9

a)   Multiple Regression Analysis I

Shavelson, Ch 18

   

TU March 14

a)   Multiple Regression Analysis II Leech et al., Ch 6    

TH March 16

a)   Chi Square Tests.
b)   Nonparametric Tests.

Projects Due

Shavelson, Ch 19,20    

Course requirements:

1.   Regular Attendance

2.   Five Assignments (25% of total grade)

 Evaluation criteria: Accuracy and comprehensiveness

3.   Research Summaries (25% of total grade)

 Choose a research theme (e.g., mathematics achievement) and locate 4 research papers using quantitative strategies. Summarize the findings of these studies paying most attention to the research design and analysis.

4.   Group Project (20% of total grade)

The group project task is essentially similar to the course project, except that your task is only to provide an analysis of a dataset. Please include a description of your purposes for the analysis, all relevant data analysis, and a brief description of the results.

5.   A Course Project (30% of total grade)

The course project invites students to apply their skills of quantitative data analysis to a dataset using one of the methods studied in the course.  The course instructor will guide students to various datasets applicable to an assignment of interest to the student.  Mock data can also be used if appropriate.  The course project must include (a) a brief introduction to the content of the study, (b) a, (c) rationale for the method chosen, (c) rationale for the statistical test chosen, (d) results and analysis, (e) brief implications of the results.  Please conform to the writing style and conventions suggested by the APA. 

Evaluation criteria: Accuracy and comprehensiveness, correct and accurate analysis and interpretation of data, quality of writing.