quasi-experimental designs - westminster · quasi-experimental designs correlation and chi-square....
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Quasi-Experimental Designs
Correlation and Chi-Square
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Quasi-
Almost, or approximately Not the real thing No manipulated Independent Variable
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Why do Quasi-Experimental Research?
Hypothesized cause cannot be manipulatedImpossibleUnethical
Nature of relationship can be predictive Increase External Validity
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External Validity
The ability to generalize from researchto other situationsto other respondentsto other forms of the variables under study
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First Requirement for External Validity
Internal Validity
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Threats to External Validity
Reactive or interaction effect of testing Selection x Treatment Interaction Reactive Effects of Experimental
Arrangements Multiple Treatment Interference
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Pre-experimental Designs
Case StudiesSingle individualCovers many variablesOver time
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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
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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.
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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.
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Pre-experimental Designs
SurveysMany individualsFocused variablesGoal is to represent a population
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Term Project Papers
Research Reports are due on the R: Drive before midnight on Monday, December 14.
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Nomothetic Research
MMPI Sex in America Guard Reserve Attitude Tracking Study Nielson Ratings
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Threats to External Validity
Selection x Treatment Interaction
The interaction effects of selection biasesand the experimental variable.
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Pre-experimental Designs
Static Group Comparison DesignsSubjects not Randomly Assigned
Most education researchMost therapy researchMost institutional research
Conclusions must be correlationalCannot support causation
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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.
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Measures of Association
Correlation- Parametricbetween 2 numeric (ordinal, interval or
ratio) scales Correlation - Non parametricbetween 2 category (nominal scales)
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Measures of Association
Correlation- Parametric between 2 numeric (ordinal, interval or
ratio) scales Correlation - Non parametricbetween 2 category (nominal scales)
r
X2
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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.
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Pearson Product Moment Correlation
AssumptionsTwo variablesEach on at least an ordinal scaleWant to find the relationship between them
MagnitudeDirection
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Pearson Product Moment Correlation
Null Hypothesis
H rx y0 0: , =
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Pearson Product Moment Correlation
Alternate Hypothesis- Two Tailed
H rx y1 0: , >
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Pearson Product Moment Correlation
Alternate Hypothesis- One Tailed
H rx y1 0: , >
H rx y1 0: , <OR
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Pearson Product Moment Correlation
Formula
( )[ ] ( )[ ]r
N XY X Y
N X X N Y Y=
−
− −
∑∑∑∑∑ ∑∑2 2 2 2
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Pearson Product Moment Correlation
Formula
( )[ ] ( )[ ]r
N XY X Y
N X X N Y Y=
−
− −
∑∑∑∑∑ ∑∑2 2 2 2
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Pearson Product Moment Correlation
Example X Y3 284 322 212 203 254 256 37
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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
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C
X Y XY3 28 844 32 1282 21 422 20 403 25 75
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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
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CX Y XY
3 28 844 32 1282 21 422 20 403 25 754 25 1006 37 222
24 188 691
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Pearson Product Moment Correlation
Substitute Values in Numerator
( )[ ] ( )[ ]r
N X X N Y Y=
−
− −∑∑ ∑∑7 691 24 188
2 2 2 2
( ) ( )( )
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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
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Pearson Product Moment Correlation
Substitute Values in Denominator
[ ][ ]r = −
− −
7 691 24 188
7 94 24 7 5268 1882 2
( ) ( )( )
( ) ( ) ( ) ( )
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Pearson Product Moment Correlation
Calculate
[ ][ ]r = −
− −
7 691 24 188
7 94 24 7 5268 1882 2
( ) ( )( )
( ) ( ) ( ) ( )
[ ][ ]r = −
− −4837 4512
658 576 36876 35344
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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( )( )
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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.
.
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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
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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
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Chi-Square
Measure of Association Test of Independence Between 2 Nominal Variables
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Contingency Table
Y
X
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Contingency Table
Y
X
O1,1
O2,1
O1,2
O2,2
R1
C1 C2 T
R2
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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
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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
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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
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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