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Analyzing Data
(Ch. 5, 10 & t-test
and Chi-square handouts)
* you may bring clean copies of the t-test
and chi-square handouts with you for use on exam. T-test
tables of critical values will be provided. BRING A
CALCULATOR
descriptive versus inferential
statistics
statistical hypothesis testing and logic behind it (what
does "significant" mean?)
null hypothesis/experimental hypothesis
t-test - be able to do problems like those on problem set
be able to INTERPRET t-test and draw correct conclusions
alpha and type I and Type II error
power and what increases power
interpreting non-significant results (e.g., power or error)
Chi-Square (be able to compute and interpret)
critical values/using tables (for t-test & Chi Square)
when to use t-test, chi-square, r (correlation)
Developmental and
quasi-experimental Designs (Brown et al. supplementary
reading)
(pre-experimental)
pretest-posttest designs (e.g., one-shot case study) - and
problems with these designsSpecial
problems associated with the study of development/change
developmental designs as quasi-experimental designs
Cross-sectional vs. longitudinal designs (and advantages and
disadvantages of each)
sequential designs
Cohort (and problems with cohort in developmental research)
Advanced Experimental and Correlational Designs (Ch. 7, 9,
12) :
levels and variables
questions to consider in designing studies (e.g., How many
variables/levels to test?)
two-group experimental design (experimental group/control
group)
random assignment vs correlated assignment (matched or
natural pairs, repeated measures)
Independent (between
groups) vs. correlated/repeated measures (within groups)
designs
Repeated measures
designs - Advantages and disadvantages of repeated measures
designs and when to use
Order effects, practice effects, carry-over effects
Ways to increase
design complexity and advantages of doing so
Factorial designs
main effects vs. interaction effects (and their
interpretation)
be able to draw and interpret 2X2 design results
advantages of factorial designs
use of F-test and ANOVA analyses
correlation coefficients/testing
for significance - be able to interpret
positive, negative (inverse) relationships
correlation vs. causation and third variables
partial correlations and how control for third variables
coefficient of determination
regression analysis/multiple
regression
multiple correlation coefficient (R) and percent variance accounted for (R2)
in regression analysis
outcome/predictor variables
factor analysis
**Note: be able to identify or evaluate a study's design
Short Essay
One
of the
following questions will be selected for the essay portion
of the exam.
1) How is it that developmental psychologists go about
studying development (or change)? Discuss the difficulties
associated with studying development and the advantages and
disadvantages of longitudinal and cross-sectional designs.
What role does cohort play in these designs and how does
cohort affect external or internal validity in these
designs? (Be sure to provide examples.)
2)
Discuss different ways that scientists increase the
complexity of designs (e.g., increased # of levels of a
variable, factorial designs) and the advantages and
disadvantages of doing so. As an example, design a factorial
study to test the effects of having a peanut butter sandwich
and/or orange juice for lunch on children's test
performance. If you conducted the study and found a main
effect for having the peanut butter sandwich and an
interaction between peanut butter and orange juice, what
might your data look like (draw and label a graph and/or
give hypothetical results in a table)? How is this design an
improvement over separate studies examining the effects of
peanut butter and orange juice? |
