Perbedaan ANOVA dengan MANOVA ( What is your ccomment ? )

(1)

(2)

Perbedaan ANOVA dengan MANOVA

( What is your ccomment ? )

ANOVA

MANOVA

Jenis

kelamin

Tempat

tinggal

IPK

mahasisw

a

Jenis

kelamin

Tempat

tinggal

IPK

mahasisw

a

Lama

studi

mahasisw

(3)

Make your own model !

(4)

t-test vs. ANOVA vs.

MANOVA

Test

# of IVs

# of DVs

t-test

One

ANOVA

Multiple

One

(5)

Kinds of research questions (1)



Main efects of IVs



Holding all else constant, are mean diferences

in

the composite DV

among groups at

diferent leveels of an IV larger than expected by

chance?



Interaction among IVs



Holding all else constant, does change in the DV

oveer leveels of one IV depend on the leveel of

another IV?



Importance of DVs



Which of the DVs are changed and which are

(6)

Kinds of research questions (2)



Parameter estimates



After removeal of the efects of coveariate(s), what are

the means adjusted for particular DV(s)?



Specifc comparisons and trend analysis



If an interaction or main efect for an IV with more

than 2 leveels is signifcant, which leveels of main efect

or cells of interaction are diferent from which others?



Strength of association



If an interaction or main efect for an IV is signifcant,

what proportion of veariance of the linear combination

of DV scores is explained by the IV?



Efects of Coveariates



To what degree does a coveariate adjust the composite

(7)

MANOVA/ MANCOVA

SPSS Example

IVs

DVs

(8)

Analyze



GLM



Multivariate

DVs

(9)

Example of MANOVA (1)

Efect of training ( V) on satisfaction with

the system and performance (DVs)

Group means

Training

Satisfaction

Performance

Control

4.2

4.9

Face-to-face

training

7.9

7.0

Online training

6.0

6.9

(10)

MANOVA test statistics

Between-Subjects Factors 25 22 23 1.0 2.0 3.0 Training Group N

All four multivariate statistics indicate that training is

significantly related to the interrelationship between

satisfaction and performance.

Multivariate Testsc

.872 223.996a _2.000 _66.000 _.000

.128 223.996a 2.000 66.000 .000 6.788 223.996a 2.000 66.000 .000 6.788 223.996a 2.000 66.000 .000 .183 3.376 4.000 134.000 .011 .820 3.434a 4.000 132.000 .010 .215 3.489 4.000 130.000 .010 .193 6.458b 2.000 67.000 .003 Pillai's Trace

Wilks' Lambda Hotelling's Trace Roy's Largest Root Pillai's Trace Wilks' Lambda Hotelling's Trace Roy's Largest Root Effect

Intercept

Training Group

Value F Hypothesis df Error df Sig.

Exact statistic a.

The statistic is an upper bound on F that yields a lower bound on the significance level. b.

(11)

ANOVAs on the efect of training

on satisfaction and performance

Tests of Between-Subjects Effects

157.537a 2 78.768 3.437 .038

66.382b 2 33.191 5.267 .008

2542.512 1 2542.512 110.952 .000

2713.380 1 2713.380 430.590 .000

157.537 2 78.768 3.437 .038

66.382 2 33.191 5.267 .008

1535.335 67 22.915

422.203 67 6.302

4177.000 70 3167.000 70 1692.871 69 488.586 69 Dependent Variable SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE Source Corrected Model Intercept Training Group Error Total Corrected Total

Type III Sum

of Squares df Mean Square F Sig.

R Squared = .093 (Adjusted R Squared = .066) a.

R Squared = .136 (Adjusted R Squared = .110) b.

(12)

Example of MANOVA (2)

Efect of training and job ( Vs) on satisfaction

with the system and performance (DVs)

Group means

Control

Face-to-face

training

Online

IT

Satisfaction

5.9

8.2

6.9

Performance

6.0

6.9

8.0

Non-IT

(13)

MANOVA test statistics

Multivariate Testsc

.878 225.868a 2.000 63.000 .000

.122 225.868a 2.000 63.000 .000

7.170 225.868a 2.000 63.000 .000

.124 2.116 4.000 128.000 .083

.877 2.134a 4.000 126.000 .080

.139 2.150 4.000 124.000 .079

.128 4.100b 2.000 64.000 .021

.109 3.853a 2.000 63.000 .026

.891 3.853a 2.000 63.000 .026

.122 3.853a 2.000 63.000 .026

.077 1.287 4.000 128.000 .279

.923 1.292a 4.000 126.000 .277

.084 1.296 4.000 124.000 .275

.083 2.643b _2.000 _64.000 _.079

Pillai's Trace Wilks' Lambda Hotelling's Trace

Roy's Largest Root Pillai's Trace

Wilks' Lambda Hotelling's Trace

Roy's Largest Root Effect

Intercept

Training Group

Job

Training Group * Gender

Exact statistic a.

Design: Intercept+RACE+GENDER+RACE * GENDER c.

(14)

ANOVAs on the efect of training

and job on satisfaction and

performance

Tests of Between-Subjects Effects

220.589a 5 44.118 1.918 .104 133.718b 5 26.744 4.823 .001 2278.580 1 2278.580 99.050 .000 2383.114 1 2383.114 429.792 .000 109.195 2 54.598 2.373 .101 32.250 2 16.125 2.908 .062 51.586 1 51.586 2.242 .139 38.677 1 38.677 6.975 .010 12.162 2 6.081 .264 .769 29.151 2 14.576 2.629 .080 1472.283 64 23.004

354.868 64 5.545 4177.000 70 3167.000 70 1692.871 69 488.586 69 Dependent Variable SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE Source Corrected Model Intercept Training Group Job

Training Group * Job

Error Total

Corrected Total

Type III Sum

(15)

Example of MANCOVA

Efect of training on satisfaction and performance,

controlling for computer self-efcacy and computer

anxiety

Training group means

Control

Face-to-face

training

Online

training

Satisfaction

4.2

7.9

6.0

Performance

4.9

7.0

6.9

Computer

self-efficacy

3.5

4.5

3.6

Computer

anxiety

(16)

MANCOVA test statistics

Multivariate Testsc

.188 7.409a 2.000 64.000 .001

.812 7.409a 2.000 64.000 .001

.232 7.409a 2.000 64.000 .001

.468 28.195a 2.000 64.000 .000

.532 28.195a 2.000 64.000 .000

.881 28.195a 2.000 64.000 .000

.068 2.333a 2.000 64.000 .105

.932 2.333a 2.000 64.000 .105

.073 2.333a 2.000 64.000 .105

.157 2.774 4.000 130.000 .030

.844 2.840a 4.000 128.000 .027

.184 2.903 4.000 126.000 .024

.178 5.797b 2.000 65.000 .005

Pillai's Trace Wilks' Lambda Hotelling's Trace

Roy's Largest Root Effect Intercept Computer Self-efficacy Computer Anxiety Training

Exact statistic a.

Design: Intercept+Computer Self-efficacy +Computer anxiety+training c.

?

(17)

nterpretation



Coveariates



Computer

self-eficacy

is

multiveariate

signifcant, but computer anxiety is not. (There

was an adjustment in the group means on the

DVs due to diferences in computer

self-eficacy but no signifcant adjustment of group

means due to diferences in computer anxiety).



Main efect of training



Training is signifcantly multiveariate related to

(18)

ANOVAs output

Tests of Between-Subjects Effects

995.326a 4 248.832 23.187 .000

117.825b 4 29.456 5.164 .001

4.920 1 4.920 .459 .501

85.587 1 85.587 15.005 .000

614.328 1 614.328 57.246 .000

6.631 1 6.631 1.163 .285

8.738 1 8.738 .814 .370

24.577 1 24.577 4.309 .042

44.072 2 22.036 2.053 .137

51.832 2 25.916 4.543 .014

697.545 65 10.731

370.761 65 5.704

4177.000 70 3167.000 70 1692.871 69 488.586 69 Dependent Variable SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE SATISFACTION PERFORMANCE Source Corrected Model Intercept Computer Self-efficacy Computer Anxiety Training Error Total Corrected Total

Type III Sum

(19)

nterpretation



Coveariates



If a coveariate is signifcantly related to a DV, it

means that the training group means on the DV

were signifcantly adjusted due to diferences

on the coveariates.



Main efect of IV



No signifcant diferences in satisfaction are

found.



There are signifcant diferences in mean