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Analyzing Data
(Ch. 11 & t-test
and Chi-square handouts)
* you may bring copies of the t-test
and chi-square handouts (links below) with you for use on exam (please
make sure that your copies do not have notes not related to
these tests). 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
reject vs. fail to reject (& why we don't accept the null)
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
*be able to do problems like those on problem set #2
Developmental and
quasi-experimental Designs (Ch. 13,
Supplementary Reading)
(non-experimental)
pretest-posttest designs (e.g., case study) - and
problems with these designs
Special
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)
Cohort and problems with cohort in developmental research
(and how related to
internal and external validity)
Advanced Experimental and Correlational Designs (Ch. 7, 10,
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 for factorial designs
correlation coefficients/testing
for significance - be able to interpret
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?
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