quasi-experimental designs - westminster · quasi-experimental designs correlation and chi-square....

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.


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.


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


Selection x Treatment Interaction

The interaction effects of selection biasesand the experimental variable.


Static Group Comparison DesignsSubjects not Randomly Assigned

Most education researchMost therapy researchMost institutional research

Conclusions must be correlationalCannot support causation


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


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


Null Hypothesis

H rx y0 0: , =


Alternate Hypothesis- Two Tailed

H rx y1 0: , >


Alternate Hypothesis- One Tailed

H rx y1 0: , >

H rx y1 0: , <OR


Formula

( )[ ] ( )[ ]r

N XY X Y

N X X N Y Y=

−

− −

∑∑∑∑∑ ∑∑2 2 2 2


Example X Y3 284 322 212 203 254 256 37


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


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


Substitute Values in Numerator

( )[ ] ( )[ ]r

N X X N Y Y=

−

− −∑∑ ∑∑7 691 24 188

2 2 2 2

( ) ( )( )


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


Substitute Values in Denominator

[ ][ ]r = −

− −

7 691 24 188

7 94 24 7 5268 1882 2

( ) ( )( )

( ) ( ) ( ) ( )


Calculate

[ ][ ]r = −

− −

7 691 24 188

7 94 24 7 5268 1882 2

( ) ( )( )

( ) ( ) ( ) ( )

[ ][ ]r = −

− −4837 4512

658 576 36876 35344


Calculate

[ ][ ]r = −

− −

7 691 24 188

7 94 24 7 5268 1882 2

( ) ( )( )

( ) ( ) ( ) ( )

[ ][ ]r = −

− −4837 4512

658 576 36876 35344

r = 32582 1532( )( )


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.

.


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


E1,1

E2,1

E1,2

E2,2

Contingency Table

Y

X

23

53

17

7

40

76 24 100

60


(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

quasi-experimental designs - westminster · quasi-experimental designs correlation and chi-square....

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