Transcript EPSY 640 Teaching Approach

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 2:003:30, or by appt
718B Harrington 845-1808 / fax: 862-1256
email: [email protected]
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 antidiscrimination 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 8451637.
SYLLABUS
Grades:
Midterm
Final
Projects
and Reviews
Homework
25%
30%
25%
20%
A: 90-100%
B: 80 - 89%
C: 70 - 79%
D: 60 - 69%
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
Structural path
models
Confirmatory Exploratory
MANIFEST
Factor analysis
Canonical analyss/
MANOVA
Discriminant
Analysis
True Score Theory
Validity
(concurrent/
predictive)
HLM
Reliability
(generalizability)
Multiple
regression
GLM
ATI
ANOVA
Distributional
Characteristics:
Multinormal
Poisson
Censored
Ordinal
Categorical
ANCOVA
2 group
t-test
IRT
bivariate
partial
correlation correlation
logistic models
Causal (Grizzle et al)
Loglinear
Models
Associational (Holland,et al)
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
z
Valid N (listwise)
N
Statistic
400
400
Minimum
Statistic
.00
Maximum
Statistic
1.00
Mean
Statistic
.5119
Std.
Deviation
Statistic
.28880
Skewness
Statistic Std. Error
-.079
.122
Kurtosis
Statistic Std. Error
-1.264
.243
DISTRIBUTIONS
• Normal: theoretically
important, found in
most science
measurements
Descriptive Statistics
yy
Valid N (listwise)
N
Statistic
400
400
Minimum
Statistic
-3.41
Maximum
Statistic
3.24
Mean
Statistic
-.0929
Std.
Deviation
Statistic
1.01158
Skewness
Statistic
Std. Error
.031
.122
Kurtosis
Statistic
Std. Error
.175
.243
DISTRIBUTIONS
• Poisson: useful for
distributions where
most observations are
similar, a few rare
ones differ
Descriptive Statistics
y
Valid N (listwise)
N
Statistic
400
400
Minimum
Statistic
.00
Maximum
Statistic
16.00
Mean
Statistic
3.1950
Std.
Deviation
Statistic
2.09235
Skewness
Statistic Std. Error
1.607
.122
Kurtosis
Statistic Std. Error
6.225
.243
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 measuresquare 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
• Social Stress almost completely mediates
the relationship between anxiety and
depression
.54
.09
DEP
DEP
ANX
ANX
.65
.69
SS
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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