quasi-experimental designs - westminster · quasi-experimental designs correlation and chi-square....
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Quasi-Experimental Designs
Correlation and Chi-Square
Quasi-
Almost, or approximately Not the real thing No manipulated Independent Variable
Why do Quasi-Experimental Research?
Hypothesized cause cannot be manipulatedImpossibleUnethical
Nature of relationship can be predictive Increase External Validity
External Validity
The ability to generalize from researchto other situationsto other respondentsto other forms of the variables under study
First Requirement for External Validity
Internal Validity
Threats to External Validity
Reactive or interaction effect of testing Selection x Treatment Interaction Reactive Effects of Experimental
Arrangements Multiple Treatment Interference
Pre-experimental Designs
Case StudiesSingle individualCovers many variablesOver time
Ideographic Research
Case studies Freud - Anna O. Allport - Letters from Jenny Watson - Little Albert Milner - H.M. Sacks - The Man Who Mistook His Wife
for a Hat
Presentations in Lab
Lab Presentations will be held in the A-V Classroom.
All A-V presentation materials should be placed on the R:Drive for our class and also on a portable device.
The only A-V materials needed are figures or tables with more than two means, OR photographic materials.
Threats to External Validity
Reactive or interaction effect of testingIn which a pretest might increase or decrease therespondent’s sensitivity or responsiveness to theexperimental variables and thus make the resultsobtained for a pretested population unrepresentativeof the effects of the experimental variable for the unpretested universe from which the experimentalrespondents were selected.
Pre-experimental Designs
SurveysMany individualsFocused variablesGoal is to represent a population
Term Project Papers
Research Reports are due on the R: Drive before midnight on Monday, December 14.
Nomothetic Research
MMPI Sex in America Guard Reserve Attitude Tracking Study Nielson Ratings
Threats to External Validity
Selection x Treatment Interaction
The interaction effects of selection biasesand the experimental variable.
Pre-experimental Designs
Static Group Comparison DesignsSubjects not Randomly Assigned
Most education researchMost therapy researchMost institutional research
Conclusions must be correlationalCannot support causation
Threats to External Validity
Reactive Effects of Experimental Arrangementswhich would preclude generalizations about the effect of the experimentalvariable upon persons being exposedto it in nonexperimental settings.
Measures of Association
Correlation- Parametricbetween 2 numeric (ordinal, interval or
ratio) scales Correlation - Non parametricbetween 2 category (nominal scales)
Measures of Association
Correlation- Parametric between 2 numeric (ordinal, interval or
ratio) scales Correlation - Non parametricbetween 2 category (nominal scales)
r
X2
Threats to External Validity
Multiple Treatment Interferencelikely to occur whenever multiple treatmentsare applied to the same respondents, because the effects of prior treatmentsare not usually erasable.
Pearson Product Moment Correlation
AssumptionsTwo variablesEach on at least an ordinal scaleWant to find the relationship between them
MagnitudeDirection
Pearson Product Moment Correlation
Null Hypothesis
H rx y0 0: , =
Pearson Product Moment Correlation
Alternate Hypothesis- Two Tailed
H rx y1 0: , >
Pearson Product Moment Correlation
Alternate Hypothesis- One Tailed
H rx y1 0: , >
H rx y1 0: , <OR
Pearson Product Moment Correlation
Formula
( )[ ] ( )[ ]r
N XY X Y
N X X N Y Y=
−
− −
∑∑∑∑∑ ∑∑2 2 2 2
Pearson Product Moment Correlation
Formula
( )[ ] ( )[ ]r
N XY X Y
N X X N Y Y=
−
− −
∑∑∑∑∑ ∑∑2 2 2 2
Pearson Product Moment Correlation
Example X Y3 284 322 212 203 254 256 37
Pearson Product Moment Correlation
Example
( )[ ] ( )[ ]r
N XY X Y
N X X N Y Y=
−
− −
∑∑∑∑∑ ∑∑2 2 2 2
Find the productsof the scores
C
X Y XY3 28 844 32 1282 21 422 20 403 25 75
Pearson Product Moment Correlation
Find the Sums of the Scores and Products
( )[ ] ( )[ ]r
N XY X Y
N X X N Y Y=
−
− −
∑∑∑∑∑ ∑∑2 2 2 2
CX Y XY
3 28 844 32 1282 21 422 20 403 25 754 25 1006 37 222
24 188 691
Pearson Product Moment Correlation
Substitute Values in Numerator
( )[ ] ( )[ ]r
N X X N Y Y=
−
− −∑∑ ∑∑7 691 24 188
2 2 2 2
( ) ( )( )
Pearson Product Moment Correlation
X Y X squared Y squared
3 28 9 7844 32 16 10242 21 4 4412 20 4 4003 25 9 6254 25 16 6256 37 36 1369
24 188 94 5268
Pearson Product Moment Correlation
Substitute Values in Denominator
[ ][ ]r = −
− −
7 691 24 188
7 94 24 7 5268 1882 2
( ) ( )( )
( ) ( ) ( ) ( )
Pearson Product Moment Correlation
Calculate
[ ][ ]r = −
− −
7 691 24 188
7 94 24 7 5268 1882 2
( ) ( )( )
( ) ( ) ( ) ( )
[ ][ ]r = −
− −4837 4512
658 576 36876 35344
Pearson Product Moment Correlation
Calculate
[ ][ ]r = −
− −
7 691 24 188
7 94 24 7 5268 1882 2
( ) ( )( )
( ) ( ) ( ) ( )
[ ][ ]r = −
− −4837 4512
658 576 36876 35344
r = 32582 1532( )( )
Pearson Product Moment Correlation
Calculate
[ ][ ]r = −
− −
7 691 24 188
7 94 24 7 5268 1882 2
( ) ( )( )
( ) ( ) ( ) ( )
[ ][ ]r = −
− −4837 4512
658 576 36876 35344
r = 32582 1532( )( ) r = = =
325125624
325354 435
917.
.
Pearson Product Moment Correlation
Interpretation Check Table to determine Significance
df = N-2Positive or NegativeDegree of relationshipr squared is amount of variance of X
accounted for by Yor vice versa
Chi-Square
We wish you a merry Chi-Square We wish you a merry Chi-Square We wish you a merry Chi-Square It’s so easy to do
Chi-Square
Measure of Association Test of Independence Between 2 Nominal Variables
Contingency Table
Y
X
Contingency Table
Y
X
O1,1
O2,1
O1,2
O2,2
R1
C1 C2 T
R2
Contingency Table
Y
X
O1,1
O2,1
O1,2
O2,2
R1
C1 C2 T
R2
Expected Frequencies are Computed this wayRow times Column, Divided by Grand.
E1,1
E2,1
E1,2
E2,2
Contingency Table
Y
X
23
53
17
7
40
76 24 100
60
Expected Frequencies are Computed this wayRow times Column, Divided by Grand.
E1,1
E2,1
E1,2
E2,2
Contingency Table
Y
X
23
53
17
7
40
76 24 100
60
Expected Frequencies are Computed this wayRow times Column, Divided by Grand.
(40*76)100
(60*76)100
(60*24)100
(40*24)100
Contingency Table
Y
X
23
53
17
7
40
76 24 100
60
To calculate Chi- Square O-EDivide by Expected and Sum them all up!
(40*76)100
(60*76)100
(60*24)100
(40*24)100