EPSY 640 INTRODUCTION

TEXAS A&M UNIVERSITY

SYLLABUS• Available at http://www.coe.tamu.edu/~vwillson/

Victor L. Willson, Professor Office: M 3-5, T, R 2:00-3:30, or by appt 718B Harrington 845-1808 / fax: 862-1256

email: v-willson@tamu.eduTexts: Glass, G. V, & Hopkins, K. D. (1996). Statistical Methods in Education and Psychology. Boston: Allyn & Bacon.

Cohen, J., Cohen, P., West, S., & Aiken, L. (2003). Applied Multiple Regression/Correlation for the Behavioral Sciences, 3rd Ed. Mahwah, NJ: Erlbaum

SYLLABUS

Students with Special NeedsThe Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that allstudents with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact the Office of Support Services for Students with Disabilities in Room 126 of the Student Services Building. The telephone number is 845-1637.

SYLLABUS

Grades: Midterm 25% A: 90-100% Final 30% B: 80 - 89%

Projects C: 70 - 79% and Reviews 25% D: 60 - 69%

Homework 20% F: < 60%

SYLLABUS

Note: The handouts and web-based files used in this course are copyrighted. By “handouts” I mean all materials generated for this class, which includes but is not limited to syllabi, quizzes, exams, lab problems, in-class materials, review sheets, and additional problem sets, in paper or electronic form. Because these materials are copyrighted, you do not have the right to copy the handouts unless I expressly grant permission.

As commonly defined, plagiarism consists of passing off as one’s own ideas, words, writings, etc. which belong to another. In accordance with this definition, you are committing plagiarism if you copy the work of another person and turn it in as your own, even if you should have the permission of that person. Plagiarism is one of the worst academic sins, for the plagiarist destroys the trust among colleagues, without which research cannot be safely communicated.

If you have any questions regarding plagiarism, please consult the latest issue of the Texas A&M University Student Rules, under the section “Scholastic Dishonesty”

Teaching Approach: Presentation Modes

• Symbolic- mathematical symbolic representations of concepts eg. y=b1x + b0

• Geometric- geometry of selected concepts such as correlation as a Venn diagram

• Graphical- two dimensional graphs (or 3 dimensional projections in a few cases) for concepts eg. correlation plots

• Tabular- data tables, summary tables of information/concepts

Presentation Modes

• Each major concept will be represented in at least two modes, most in 3 or 4

• The required texts provide only some of the modes

• Some unpublished chapters provided by me provide additional resources for these modes

OVERVIEW OF QUANTITATIVE METHODS

• Quantitative methods have developed over the last 125 years

• Different disciplines independently developed similar, complementary procedures

• Psychology and Sociology: Latent variable (factor analysis), measurement error, path analysis, Structural equation models (SEM)

• Agriculture, Biology: Manifest (observed) variable analyses (ANOVA, MANOVA, regression), discriminant analysis, multilevel modeling

STRUCTURAL EQUATION MODELS (SEM)

LATENT MANIFEST

Factor analysis Structural path models Confirmatory Exploratory Canonical analyss/

MANOVA

Discriminant True Score Theory Analysis GLM Validity Reliability Multiple ATI ANOVA (concurrent/ (generalizability) regression predictive) ANCOVA 2 group t-test IRT bivariate partial correlation correlation logistic models Causal (Grizzle et al)

Loglinear Models Associational (Holland,et al)

HLM Distributional Characteristics: Multinormal Poisson Censored Ordinal Categorical

Estimation Methods: OLS ML EM Bayesian

EXPLORING DATA

• Level of measurement: nominal, ordinal, interval or ratio- determines methods of quantitative analysis

• Theory: presence or absence determines modeling approach

• Exploratory approaches generally lack much theory to focus the analyses

EXPLORING DATA

DESCRIPTIVE– DISTRIBUTION OF SCORES- what is the shape (4

moments: mean, variance, skewness, kurtosis)– CENTRAL TENDENCY: mean median mode– VARIATION: range, variance, standard deviation,

RMR (root mean residual=square root of squared residuals/errors of fit

– MEDIATION: change in correlation due to intervening variable; complete or partial

– MODERATION: change in value of correlation due to membership in different group

DISTRIBUTIONS

• Uniform: equal number of cases for each value of variable

Descriptive Statistics

400 .00 1.00 .5119 .28880 -.079 .122 -1.264 .243

400

z

Valid N (listwise)

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error

N Minimum Maximum Mean Std.Deviation

Skewness Kurtosis

DISTRIBUTIONS

• Normal: theoretically important, found in most science measurements

Descriptive Statistics

400 -3.41 3.24 -.0929 1.01158 .031 .122 .175 .243

400

yy

Valid N (listwise)

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error

N Minimum Maximum Mean Std.Deviation

Skewness Kurtosis

DISTRIBUTIONS

• Poisson: useful for distributions where most observations are similar, a few rare ones differ

Descriptive Statistics

400 .00 16.00 3.1950 2.09235 1.607 .122 6.225 .243

400

y

Valid N (listwise)

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error

N Minimum Maximum Mean Std.Deviation

Skewness Kurtosis

CENTRAL TENDENCY

• Mean (average) is widely used because it is statistically helpful, sensitive to extreme scores (may be good or bad)

• Median used for non-symmetric distributions (eg. Poisson had mean of 3.2, median of 3.0

• Mode, rarely useful for statistical purposes

Variation

• Range: Max score – Min score• Semi-interquartile range: (X75 – x25)/2• eg for Poisson, SIR = (4-2)/2• Standard deviation: “average” distance of

scores from the mean; square root of squared distances from the mean divided by the number of scores

• Variance: area or squared measure- square of standard deviation

Standard Deviation

SD SD

Variance

SD

Mediation

• Suppose Anxiety predicts Depression in teenagers, r = .54

• Suppose Anxiety also predicts Social Stress, r = .686

• Now when Social Stress predicts Depression in conjunction with Anxiety, the partial correlation of Anxiety to Depression drops to .09, the relationship of Social Stress to Depression is .651

Mediation

ANX

SS

DEP

ANX

DEP.54 .09

.69

.65

• Social Stress almost completely mediates the relationship between anxiety and depression

MODERATION

• Suppose Aggression predicts Achievement: correlation is .5 for 400 students

• Break groups into Anglos (200) and African-Americans (200); recalculate correlation for each group

• Anglo r = 0.6, African-American r = 0.2• We say ethnicity moderates the

relationship

Using SPSS to explore• Graphical- use GRAPHS/INTERACTIVE to

examine distributions

yes

no

Using SPSS to explore

no yes

Using SPSS to explore

0.00

yes

0.001.00 0.00

0.00

0.00

0.00

0.00

0.00 0.000.00

0.00

0.00

0.00

0.00 0.00

0.00

0.00

0.00

1.00

0.00

0.000.00

0.00

0.00

1.00

0.00

0.00

0.00

1.00

0.00

1.00

0.00

0.00

0.00

0.000.00

1.00

1.00

1.000.00

1.000.000.00

0.00

1.00

0.00

0.00

1.00

0.00

0.00

0.00

0.00

0.00

1.00

0.00

1.00

0.00

0.00

1.00

1.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.000.00

0.00

0.000.00

0.00

0.000.00

0.00

1.00

0.00

0.000.00

0.00

1.00

0.00

0.00

0.00

0.00

0.000.00

0.00

0.00

1.00 0.00

1.000.00

0.00

1.00

0.00

1.001.00

0.00

0.00

0.001.001.00

0.00

0.00

0.000.00

0.00

0.00

0.00

0.00

0.000.00

1.00

0.00

0.00

0.000.00

1.00

0.00

0.00

0.000.001.00

0.000.000.00

0.00

no