EPSY 640 INTRODUCTION TEXAS A&M UNIVERSITY

SYLLABUS Available at Victor L. Willson, Professor Office: M 3-5, T, R 2:00- 3:30, or by appt 718B Harrington / fax: Texts: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 Needs The 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

SYLLABUS Grades: Midterm 25%A: % Final 30%B: % Projects C: % and Reviews 25%D: % 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=b 1 x + b 0 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 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

DISTRIBUTIONS Normal: theoretically important, found in most science measurements

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

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: (X 75 – x 25 )/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

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 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

Using SPSS to explore