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RESEARCH STRATEGIES
Experimental strategies are not determined solely
by potential weaknesses – EVERY study has some
weakness.
Rather, the difference between true and non or
quasi experiments is determined by the amount of
control over assignment to groups (between
subjects) or order of conditions (within subjects)
RESEARCH STRATEGIES
Correlational designs are NOT between subject
designs with discrete categories or groups you are
comparing!• This is probably ex post facto or differential groups
design
Not every design that compares two variables is
correlational!
BETWEEN VS. WITHIN
Between • DIFFERENT individuals give data point at each
level of the IV. Therefore, you are comparing the average scores of one group of people to the average scores of (a) DIFFERENT group(s) of people
Within• The SAME individuals participate and give a data
point at each condition of the experiment (or each level of the IV).
• Thus, you are comparing an individual’s score under one condition to their own scores in a different condition(s)
VARIABLES
If you are looking at the effects of more than one variable on another
variable (DV), then you have multiple IVs (i.e.. a factorial design)
If one IV is between and another is within, you have a mixed factorial
design
You are then probably looking at main effects of IVs PLUS their
interactions• i.e. does the DV at one level of one IV depend on the level of another IV• E.g. do girls perform better with fewer classmates in a classroom while
boys perform better with more classmates in a classroom? (interaction with number of classmates (IV 1) and gender (IV 2)
INTERACTIONS CONT.
If both males and females do better (and about the
same amount better) with fewer classmates then
gender and number of classmates do not interact
However, if the improvement is much greater for
one gender than the other, there may be an
interaction
MAIN EFFECTS
If males generally perform better than females (or vice versa), but
class size does not effect performance, there is a main effect of
gender but not class size and no interaction
If both genders do equally well, but both do better in smaller
classes, then there is a main effect of class size, but not gender
If males do better than females but both do better in smaller
classes, there are main effects of both gender and class size but no
interaction
There can be main effects and no interaction AND interactions
without main effects
0
20
40
60
80
100
dressy casual sloppyMales
Females
0
20
40
60
80
100
dressy casual sloppyMales
Females
40
60
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sloppy casual dressy
malesfemales
SEEING INTERACTIONS
• Is there an interaction?
• What numbers do we
compare to see if there is
a main effect of:• Gender?• Class size?
• Are there main effects?
Small large
males 80 78
females 70 69
Small large
males 85 72
females 80 68
Small large
males 84 81
females 83 71
SEEING INTERACTIONS
• What numbers
would we place in
the missing cell to
create• An interaction?• No interaction?
Small large
males 80 78
females 70
Small large
males 85 72
females 68
Small large
males 84
females 83 71
1. DIFFERENTIAL RESEARCH DESIGN (NE)
Also called ex post facto research
Compares pre-existing groups defined by
participant variable
E.g. shyness scores from single child vs. child with
siblings
Existence and description of relationships
Similar to correlational design but different data
and analysis
2. POSTTEST-ONLY NON-EQUIVALENT CONTROL GROUP
DESIGN (NE)Also called static group comparison
Applied settings
Measure effectiveness of treatment with pre-existing
participants
Similar but nonequivalent participants used as control
condition
X O Exp. GrpO Control
3. P R E T E S T – P O S T T E S T N O N -E Q U I VA L E N T CONTROL GROUP
DESIGN (QE)
Stronger version of posttest only designBoth control (C) and experimental (E) groups measured prior to treatment and again after E group receives treatmentShows if groups are similar on the DV before manipulation of IV Also controls for time related changes in DV indep. of IVReduces threat of both assignment bias and time related threats
O X O Exp. Grp. O O Control
1 . O N E - G R O U P P R E T E S T – P O S T T E S T D E S I G N ( N E )
One pre and one post-test measurement
E.g. voter’s confidence in electoral candidate
before and after televised debate
O X O
2. TIME SERIES DESIGN (QE)
Treatment is manipulated by researcher
Series of observations for each participant before
and after treatment or event
E.g. Measures of stress weekly for 2 months
preceding and following introduction of
aromatherapy in workplace
O O O X O O O
3. I N T E R R U P T E D T I M E SE R I E S D E SI G N ( Q E )
Treatment is NOT manipulated by researcherE.g. Depression measured monthly for 3 months before and after ChristmasWorks with predictable event like decriminalizing marijuanaFor unpredictable events like Katrina, rely on archival dataCan see trends in data before treatmentCan observe long-term changes following treatmentBut other changes can coincide with treatment
• E.g. cold weather/snowfall and Christmas
4. E Q UI VA L E N T T I M E – SA M P L E S D E SI G N ( Q E )
Treatment is repeatedly administered and removed during
series of observations
E.g. introducing music in the workplace – turning it on and off
and measuring worker concentration at regular intervals weekly
O O O X O N O X O
Best used when treatment effect is expected to be temporary
Hard to determine causality if treatment effect is permanent
CORRELATIONAL STUDIES
Simply measures 2 variables [usually two scores (X and Y) from
same individual] or scores on 1 variable between 2 related individuals
Criterion (Y) and Predictor (X) variables
Degree and nature of relationship• descriptive or predictive
Correlation coefficients+1.00 to -1.00
No attempt to explain relationship
No attempt to manipulate or control variables