seminar spss di um

Seminar program: SPSS workshops

Date: 6-7/ 9 2014

Venue: Fakulti sains , UM

Data file – key in data

Key in the values

Copy and paste to all values column..

Missing value Data in complete- put the numbers that not uses in values –

For age- use 99 ( make sure they no use the number)

Filtering data

Frequencies -> variable

Gender

Frequency Percent Valid Percent

Cumulative

Percent

Valid Male 94 44.3 47.0 47.0

Female 106 50.0 53.0 100.0

Total 200 94.3 100.0

Missing System 12 5.7

Total 212 100.0

Missing data

Data ascending -> select and clear..

Mot1


Cumulative

Percent

Valid Never 1 .5 .5 .5

Very rarely - one or more a

year

2 1.0 1.0 1.5

Rarely - one a month 15 7.5 7.5 9.0

Often - sometimes a month 20 10.0 10.0 19.0

More than often - one a

week40 20.0 20.0 39.0

Very Often - more than one

a week81 40.5 40.5 79.5

Always - every day 40 20.0 20.0 99.5

7.00 1 .5 .5 100.0

Total 200 100.0 100.0

Got number 7 at data -> check back at data -> select variable-> find and replace ( ctrl + F)

Check back with questioners -> repair -> do flitering back

Mot4


Cumulative

Percent

Valid Never 4 2.0 2.0 2.0

Very rarely - one or more a

year4 2.0 2.0 4.0

Rarely - one a month 14 7.0 7.1 11.1

Often - sometimes a month 26 13.0 13.1 24.2

More than often - one a

week51 25.5 25.8 50.0

Very Often - more than one

a week62 31.0 31.3 81.3

Always - every day 36 18.0 18.2 99.5

7.00 1 .5 .5 100.0

Total 198 99.0 100.0

Missing 9.00 2 1.0

Total 200 100.0

Wrong data -> salah key in

Missing data _-> data hilang

ReliabilityMeasure something same

Alpha Cronbach – analysis by theme

Scale label

Reliability Statistics

Cronbach's

Alpha N of Items

.866 5

Good if more than 0.6

IF LESS THAN 0.6

See biggest value item at Cronbach’s Alpha item deleted -> delete that item -> analysis back

Alpha conbach= less than 0.6

Look at item statitics = deleted item with worse value

Validity boleh mengukur bahan yg diukur menggunakan instrument yg betul

Explanatory Factor Analysis = EFAPerbezaan dua pengukur yg hampir sama eg: stress and anxiety

Not confirm

Try and error

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .823

Bartlett's Test of Sphericity Approx. Chi-Square 1135.684

df 45

Sig. .000

More than 0.6 -> questionnaires acceptance -> proceed to CFA

Sig -> less than 0.05 significant

Component Matrixa

Component

1 2

Stress1 .638 .540

Stress2 .727 .524

Stress3 .730 .520

Stress4 .729 .443

Stress5 .272 .508

Anxiety1 .709 -.384

Anxiety2 .738 -.500

Anxiety3 .601 -.578

Anxiety4 .653 -.534

Anxiety5 .689 -.331

Extraction Method: Principal

Component Analysis.

a. 2 components extracted.

*nilai data stress n anxiety hampir sama-> mereka mungkin benda yg sama

Component Matrixa

Component

1 2

Stress1 .760 .370

Stress2 .835 .334

Stress3 .859 .274

Stress4 .838 .211

Stress5 .401 .348

Perfo1 -.471 .819

Perfo2 -.470 .830

Perfo3 -.412 .816


Component Analysis.


+ or - =measure the positive and negative thing there are different thing measure

Component 1 = stress lebih tinggi

Component 2 = perfo lebih tinggi

Component Matrixa

Component

1 2

Perfo1 .832 -.457

Perfo2 .822 -.487

Perfo3 .801 -.438

Reward1 .577 .623

Reward2 .574 .675

Reward3 .523 .736


Component Analysis.


*terdapat perbezaan nilai yg ketara bermakna mereka kira benda yag berbeza

TO COMBINE SAME FACTORS -> TRY TO PUST IN TO FACTORS

Component Matrixa

Component

1 2

ID .129 .357

Stress1 .629 -.549

Stress2 .721 -.523

Stress3 .722 -.528

Stress4 .722 -.456

Stress5 .264 -.512

Anxiety1 .712 .358

Anxiety2 .745 .482

Anxiety3 .610 .565

Anxiety4 .661 .517

Anxiety5 .694 .319


Component Analysis.


Selepas buat force factor masih belum dapat membezakan antara kedua2 boleh ubah.. jadi boleh gabungkan kedua variable

Confirmatory Factor Analysis =CFA Really confirm

ComputeSebelum bt compute bt reliability dulu pastikan realibility ggo

Click paste -> syntax put out

Select RUN ( green botton)

New variable motivation will appear

Reliability = must do at least 60

and validity = must do at least 100

normality of datachecking normality – graph, despcription statistic, formal statistical analaysis

to test normality of data*mesti sekurang-kurang 2 test berjaya conclude that normal or normal.

Dapatkan data normaliti

To get quartiles

Data not normal

Test 1: check skweness and kartosisData ini range value -1 and +1 + normal

Descriptives

Statistic Std. Error

Age Mean 34.85 .432

95% Confidence Interval for

Mean

Lower Bound 34.00

Upper Bound 35.70

5% Trimmed Mean 34.48

Median 33.00

Variance 93.521

Std. Deviation 9.671

Minimum 20

Maximum 59

Range 39

Interquartile Range 15

Skewness .530 .109

Kurtosis -.703 .218

Skewness and kurtosis _ within -1 t- +1 ( normal)

Test 2: check the grapfhCurves normal or not

Double click on graph – test for normal

*not normal distributor- skewed to right

Test 3: Q-Q plot

*no normal because point not straight line,

Test 4 : test of normality

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

Age .106 502 .000 .948 502 .000

a. Lilliefors Significance Correction

If n more than 100 look kat kolmo

Sig less than 0.05 not normal (significant data not normal)

Report : Data shown not normal -> report as median = age, median (Q1 , Q3) =

Statistics

Age

N Valid 502

Missing 0

Median 33.00

Percentiles 25 27.00

50 33.00

75 42.00

Data normal

Test 1 = check skewness and kurtosis, if in -1 dan +1 normal

Descriptives

Statistic Std. Error

Body mass index Mean 26.2081 .21896

95% Confidence Interval for

Mean

Lower Bound 25.7779

Upper Bound 26.6383

5% Trimmed Mean 25.9809

Median 25.8850

Variance 24.067

Std. Deviation 4.90586

Minimum 16.11

Maximum 43.83

Range 27.72

Interquartile Range 6.52

Skewness .648 .109

Kurtosis .779 .218

Test 2: build the grapf plot ( curve normal or not)

Test 3 : Q-Q plot are in same line ( normal)

Test 4 : N more than 100 look at Kol

Sig : >0.05, normal

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

Body mass index .040 502 .050 .973 502 .000

a. Lilliefors Significance Correction

Normal report

as normal distribution = mean +- S.D

Statistics

Age

N Valid 502

Missing 0

Percentiles 25 27.00

50 33.00

75 42.00

Change continues data to group

Continues data (eg: percentage, age) into group of data

Eg:

Low risk cvd (label 0)= cvd risk < 10%

High risk (label1) =cvd risk >10%

0 = reference no, low risk

1= higher risk, positive, predictor

New variable data will perform

Example independent sample t testHipotesis: high risk group has higher mean of SBP compared to low risk group

*two group same variable

Group- SBP high and low, Variable : risk group

Group Statistics

CVDgroup N Mean Std. Deviation Std. Error Mean

Systolic blood pressure high risk 112 135.009 16.4531 1.5547

low risk 390 121.409 13.2375 .6703

High risk Low risk T test p-valueSBP 135.09 ± 16.45 121.41+ 13.24 9.05 <0.001

Independent Samples Test

Levene's Test for

Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed)

Mean

Differenc

e

Std. Error

Difference

95% Confidence Interval of the Difference

Lower

Systolic

blood

pressure

Equal variances

assumed11.979 .001 9.051 500 .000 13.5995 1.5025

Equal variances

not assumed8.033 154.580 .000 13.5995 1.6930

if sig value <0.05= read t value on the top

if sig value > 0.05= read t value on low level

value 2 tailed ( 0.00 assume p=<000.1)

correlation

Correlations

Age Weight

Age Pearson Correlation 1 .107*

Sig. (2-tailed) .016

N 502 502

Weight Pearson Correlation .107* 1

Sig. (2-tailed) .016

Decimal point

3= p value

2 =

1= percentage

P=0.05

Probability of making Type 1 error is less than <5%

P= 0.001

Probability of making Type 1 error is less than <5%

N 502 502

*. Correlation is significant at the 0.05 level (2-tailed).

Relation have correlation but poor at level 0.05

r=0.107 ( p<0.05)

Chi-Square tests Test ddata for more then 2 varrable

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Pearson Chi-Square 34.476a 4 .000

Likelihood Ratio 35.614 4 .000

Linear-by-Linear Association 26.250 1 .000

N of Valid Cases 502

a. 4 cells (40.0%) have expected count less than 5. The minimum

expected count is .45.

Can take the Chi-square because 4 cells still not zero

Or less than 20%

Need to transform recode different variable group back

Analyze crosstab

Simple linear regation =menentukan faktor pekali

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) -20.660 2.351 -8.787 .000

Systolic blood pressure .230 .019 .481 12.270 .000

a. Dependent Variable: CVD Risk

Y= a + bx

Contant = -20.660 + 0.23 (SBP)

DAY 2 ( 7/9/2014)

1) Make reliability ( alpha less than 0.06 delete item)2) Compute data

T –test

Independent Samples Test

Levene's Test for Equality of Variances

F Sig. t df Sig. (2-tailed)

depression Equal variances assumed 2.340 .127 .255 231

Equal variances not assumed .248 187.272

satisfaction Equal variances assumed 1.338 .249 -2.430 236

Equal variances not assumed -2.431 222.029

productivity Equal variances assumed .677 .411 .028 228

Equal variances not assumed .027 205.604

supervisor Equal variances assumed .838 .361 -.795 227

Equal variances not assumed -.790 212.433

coworker Equal variances assumed .069 .793 -1.740 226

Equal variances not assumed -1.782 225.387

To determine the difference see the sig value

= >0.05 not sig

t=-2.43,df=236 (not significant)

Anova Untuk membezakan antara lebih dari 2 group

ANOVA

Sum of Squares df Mean Square F Sig.

depression Between Groups .154 3 .051 .362 .781

Within Groups 34.746 245 .142

Total 34.900 248

satisfaction Between Groups 2.550 3 .850 1.751 .157


Total 124.889 255

productivity Between Groups 13.585 3 4.528 1.591 .192

Within Groups 694.511 244 2.846

Total 708.096 247

supervisor Between Groups 6.296 3 2.099 3.648 .013


Total 144.932 244

coworker Between Groups 1.058 3 .353 1.439 .232


Total 60.122 244

emotional Between Groups 3.989 3 1.330 2.406 .068


Total 139.932 249

role Between Groups 2.622 3 .874 1.311 .272


Total 162.025 242

commited Between Groups 1.348 3 .449 .930 .427


Total 120.240 249

Supervisor = signifant because less than 0.05 ( terdapat perbezaan kumpulan)

Emotional= significant if sample saiz too small

Report= there are differences supervision support between group ethnics ( F= 3.64, df=3. Sig=0.05)

Test of Homogeneity of Variances

Levene Statistic df1 df2 Sig.

depression .939 3 245 .422

satisfaction .717 3 252 .543

productivity 3.368 3 244 .019

supervisor 2.664 3 241 .049

coworker .441 3 241 .724

emotional .670 3 246 .571

role 1.088 3 239 .355

commited .890 3 246 .447

Homogeneity = hope not significant (compare betweenin group)( normal distributor)

Not homogeneity= (not distribute normally)

Ankova

Tests of Between-Subjects Effects

Source Dependent Variable

Type III Sum of

Squares df Mean Square F Sig.

Corrected Model supervisor 18.594a 15 1.240 2.223 .007

satisfaction 13.330b 15 .889 1.866 .028

Intercept supervisor 364.454 1 364.454 653.536 .000

satisfaction 542.281 1 542.281 1138.406 .000

ETHNIC supervisor 6.451 3 2.150 3.856 .010

satisfaction 2.461 3 .820 1.722 .163

EDU supervisor 8.384 5 1.677 3.007 .012

satisfaction 4.237 5 .847 1.779 .118

ETHNIC * EDU supervisor 4.603 7 .658 1.179 .316

satisfaction 4.572 7 .653 1.371 .219

Error supervisor 124.917 224 .558

satisfaction 106.703 224 .476

Total supervisor 2358.861 240

satisfaction 4184.556 240

Corrected Total supervisor 143.511 239

satisfaction 120.033 239

a. R Squared = .130 (Adjusted R Squared = .071)

b. R Squared = .111 (Adjusted R Squared = .052)

Significant=

Correlations

Rule of thumb-

Many factor contribute to 1 factor

If have correlation proceed to reggeration

Correlations

emotional depression supervisor coworker role

emotional Pearson Correlation 1 .295** -.100 -.232** .431**

Sig. (2-tailed) .000 .123 .000 .000

N 251 244 240 240 241

depression Pearson Correlation .295** 1 -.233** -.270** .278**

Sig. (2-tailed) .000 .000 .000 .000

N 244 250 239 239 238

supervisor Pearson Correlation -.100 -.233** 1 .397** -.168**

Sig. (2-tailed) .123 .000 .000 .010

N 240 239 246 238 235

coworker Pearson Correlation -.232** -.270** .397** 1 -.132*

Sig. (2-tailed) .000 .000 .000 .044

N 240 239 238 246 234

role Pearson Correlation .431** .278** -.168** -.132* 1

Sig. (2-tailed) .000 .000 .010 .044

N 241 238 235 234 244

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

>0.05 not significant = no coloration between to variable

Emo, sup, co, role p value <0.05 = significant= have relation between to depression

Cannot use correlation to test hypothesis because know the relation but don’t who come first (just perception)

Eg: eggs and chicken. (have relation but don’t how come 1 st)

Regression

Coefficientsa

Model


Standardized

Coefficients


1(Constant) 1.915 .181 10.600 .000

emotional .113 .035 .223 3.228 .001

role .081 .032 .171 2.503 .013

supervisor -.082 .034 -.156 -2.387 .018

coworker -.111 .054 -.137 -2.057 .041

a. Dependent Variable: depression

B= beta value

B = look at the – or + value ( hingher B value more strong contribute to depression)

Result : B= 0.11, s.e = 0.3

coworker = support if significant

More emotional demand more depress

Regression step wise

Kick people slowly.

To determine variable that less contributed to depression.

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 1.369 .041 33.640 .000

emotional .178 .032 .352 5.525 .000

2 (Constant) 1.732 .110 15.709 .000

emotional .163 .032 .323 5.151 .000

supervisor -.115 .033 -.221 -3.527 .001

3 (Constant) 1.629 .117 13.973 .000

emotional .127 .035 .251 3.674 .000

supervisor -.104 .033 -.200 -3.208 .002

role .080 .033 .170 2.465 .014

4 (Constant) 1.915 .181 10.600 .000

emotional .113 .035 .223 3.228 .001

supervisor -.082 .034 -.156 -2.387 .018

role .081 .032 .171 2.503 .013

coworker -.111 .054 -.137 -2.057 .041


Result:

Emotional demand most contributed to depression

Start with model 4 – will kick up one by one.

Model 4= coworker reject- sig value higher

Report:

Depression Depression model Mod depression

Insert independent variable then insert next—insert next independent variable – lastly put both

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 5.777 .701 8.239 .000

coworker .507 .226 .147 2.245 .026

2 (Constant) 5.264 .719 7.324 .000

coworker .252 .243 .073 1.039 .300

supervisor .429 .162 .186 2.645 .009

a. Dependent Variable: productivity

Final model is model 1= coworker more contribute to productivity

Mediation ( model)

Baron & Kenny (1986)

Assumption :

1) There must have relation between IV and DV2) Iv significant to mediates3) Mediation significant to DV4) When M added in the model IV no longer significant to DV ( fully mediation) 5) If inclusion of M, the relationship between IV to DV ( partial mediation)

Hipotesis

1) IV to DV2) IV to mediates3) Mediates to DV

mediates

dependent variable

independet variable

4) Mediates to IV and DV

Test 1 : IV sig to DV

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 5.878 .434 13.544 .000

supervisor .482 .138 .221 3.486 .001


Test 2: IV to M

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 3.349 .597 5.609 .000

satisfaction .971 .143 .397 6.797 .000


Test 3 : M to DV

Test 4 : determine ( fully or partial mediation)

Partial= boleh pergi pada M and DV

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 2.805 .658 4.261 .000

satisfaction .875 .148 .360 5.926 .000

supervisor .309 .132 .142 2.335 .020


SOBEL TEST Mediation

No signification for small simple size

Test 5

- Install directly to computer

To get numbers

1. Run a regression analysis with the IV predicting the mediator. This will give a and sa.2. Run a regression analysis with the IV and mediator predicting the DV. This will give b and sb.

Note that sa and sb should never be negative.

To conduct the Sobel test

Details can be found in Baron and Kenny (1986), Sobel (1982), Goodman (1960), and MacKinnon, Warsi, and Dwyer (1995). Insert the a, b, sa, and sb into the cells below and this program will calculate the critical ratio as a test of whether the indirect effect of the IV on the DV via the mediator is significantly different from zero.

Input: Test statistic: Std. Error: p-value:

a Sobel test:

b Aroian test:

sa Goodman test:

sb

Alternatively, you can insert ta and tb into the cells below, where ta and tb are the t-test statistics for the difference between the a and b coefficients and zero. Results should be identical to the first test,

except for error due to rounding.

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 3.502 .178 19.637 .000

supervisor .202 .057 .221 3.545 .000

a. Dependent Variable: satisfaction

a= beta value

sa= standard error

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 2.805 .658 4.261 .000

satisfaction .875 .148 .360 5.926 .000

supervisor .309 .132 .142 2.335 .020

Reset all


Report= z=3.36, SE=0.05, sig=<0.001

Sigficant there partical correlation

Exercise 1

Test 1 : IV to DV

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

comitment

profomenceemotional

B Std. Error Beta

1 (Constant) 7.757 .186 41.716 .000

emotional -.392 .145 -.172 -2.710 .007


=significant

Test 2 : IV to M

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 2.664 .075 35.593 .000

emotional -.145 .059 -.156 -2.465 .014

a. Dependent Variable: commited

= significant

Test 3 : M to DV

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 5.864 .403 14.539 .000

commited .571 .154 .232 3.710 .000


= significant

Test 4 : DV with M and IV

Determine: partially significant go sobel test

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 6.408 .460 13.939 .000

commited .495 .158 .200 3.142 .002

emotional -.343 .144 -.151 -2.373 .018


Partially mediation = IV relation with M and DV

*if fully mediation= IV only relation with M but no DV anymore.

Test 5 : Sobel test

Nilai a= ambil di 2

Nilai b amik dari test 4

Report= z=-2.12, SE=0.03 , sig=<0.05

significant

Monte carlo

Test 1 : 1V to DV

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 5.705 .685 8.332 .000

coworker .533 .221 .155 2.416 .016


=significant

Test 2: IV to M

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 2.545 .118 21.540 .000

commited .202 .045 .277 4.474 .000

comitment

proformancecoworker

a. Dependent Variable: coworker

= significant

Test 3 : M to DV

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 5.864 .403 14.539 .000

commited .571 .154 .232 3.710 .000


Test 4 : DV with IV and M

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 5.070 .718 7.057 .000

commited .460 .169 .181 2.722 .007

coworker .348 .228 .102 1.529 .128


Coworker = not significant more with productivity so it Fully Mediations

Test 5: test for monte carlo

http://www.quantpsy.org/medmc/medmc.htm

http://www.quantpsy.org/medmc/medmc.htm

Value a= form test 2

Value b = test 4

Sobet resut

Monte Carlo Result

– dapatan yg lebih tepat ( terutama pada sample yg skit)

Significant = if not content 0

Content zero if value = -ve and +ve

Result = 95% confident interval

Lower level = 0.04

Upper level = 0.03

Both positive value level = so it significant

Report = (95 % confident interval [CI], lower level, 0.04, upper level 0.03)

Exercise 2

Test 1 : IV and DV

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 1.383 .039 35.066 .000

emotional .148 .031 .295 4.797 .000


= significant

Test 2 : IV and M

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 4.303 .074 58.104 .000

emotional -.179 .058 -.192 -3.093 .002

a. Dependent Variable: satisfaction

satisfaction

depressionemotional

=significant

Test 3 : M and DV

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 2.217 .135 16.474 .000

satisfaction -.165 .032 -.310 -5.135 .000


= significant

Test 4 =

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 1.987 .144 13.775 .000

satisfaction -.141 .032 -.262 -4.342 .000

emotional .124 .030 .247 4.097 .000


IV significant with DV= partial mediation

Test 5

Sobel test

= significant

Marte Carlo

Significant = because not content zero

Result : 95%, lower level= 0.007 , upper level = 0.04

Moderation- Pembolehubah penyerderhana - Pembolehubah interaksi

Test 1 : IV to DV

Test 2 : M to DV

http://www.jeremydawson.co.uk/slopes.htm

test 3; standiziation for IV and moderator

insert IV and moderator

http://www.jeremydawson.co.uk/slopes.htm

Standardize

new data appear

Compute Z IV and moderator

Eg: Z IV*ZM

Standardize data

After compute Z iv and Zm

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) .963 .310 3.107 .002

supervisor .088 .063 .095 1.405 .161

coworker .418 .103 .299 4.070 .000

supXcow .052 .033 .107 1.556 .121

a. Dependent Variable: commited

Not significant between variable

Open : http://www.jeremydawson.co.uk/2-way_unstandardised.xls

IV

Moderator

IV + moderato

http://www.jeremydawson.co.uk/2-way_unstandardised.xls

Not significant= because no cross between line

*no interaction effect between them

Exercise 3

Test 1 : IV and DV

supervisor support

emosional

proformance

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 5.878 .434 13.544 .000

supervisor .482 .138 .221 3.486 .001


Test 2 : M to DV

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 7.757 .186 41.716 .000

emotional -.392 .145 -.172 -2.710 .007


Test 3 : standardized

Test 4 : compute supervision and emos

Test 5 : get regreation zIV, z M , IV+M

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 7.300 .106 68.681 .000

Zscore(supervisor) .390 .106 .232 3.669 .000

Zscore(emotional) -.323 .107 -.190 -3.010 .003

supXemo -.269 .090 -.190 -3.002 .003


Test 6 : go to excel

*No correlation = not significant = no interaction between to line = not interaction between supervision support and emotional

-Tamat-

seminar spss di um

Education

principalcomponent analysis

data hilang

numberfiltering data

confirmatory factor

graph test

grapfhcurves normal

normal distributor

dapatkan data normaliti