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: [email protected]: 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
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Using SPSS to explore
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Using SPSS to explore
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