This document summarizes an SPSS workshop held on September 6-7, 2014 at the Faculty of Science, UM. It discusses various SPSS procedures like entering and cleaning data, checking for missing values, frequencies, descriptive statistics, reliability analysis, factor analysis, t-tests, ANOVA, and linear regression. Frequency tables are presented to analyze gender distribution and responses to motivation questions. Reliability analysis and factor analysis are conducted to assess scales. T-tests are used to compare depression, satisfaction, productivity, supervisor support, and coworker support between groups. ANOVA tests for differences in these variables between multiple ethnic groups.

1. Seminar program: SPSS workshops
Date: 6-7/ 9 2014
Venue: Fakulti sains , UM
Data file – key in data
Key in the values

2. 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

3. 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
Frequency Percent Valid Percent
Cumulative
Percent
Valid Never 1 .5 .5 .5
Very rarely - one or more a
year
2 1.0 1.0 1.5

4. 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
week
40 20.0 20.0 39.0
Very Often - more than one
a week
81 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
Frequency Percent Valid Percent
Cumulative
Percent
Valid Never 4 2.0 2.0 2.0
Very rarely - one or more a
year
4 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
week
51 25.5 25.8 50.0
Very Often - more than one
a week
62 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

5. Reliability
Measure something same
Alpha Cronbach – analysis by theme
Scale label

6. 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 = EFA
Perbezaan dua pengukur yg hampir sama eg: stress and anxiety

7. Not confirm
Try and error
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .823

8. 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

9. Perfo1 -.471 .819
Perfo2 -.470 .830
Perfo3 -.412 .816
Extraction Method: Principal
Component Analysis.
a. 2 components extracted.
+ 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
Extraction Method: Principal
Component Analysis.
a. 2 components extracted.
*terdapat perbezaan nilai yg ketara bermakna mereka kira benda yag berbeza
TO COMBINE SAME FACTORS -> TRY TO PUST IN TO FACTORS

10. 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
Extraction Method: Principal
Component Analysis.
a. 2 components extracted.
Selepas buat force factor masih belum dapat membezakan antara kedua2 boleh ubah.. jadi boleh
gabungkan kedua variable

11. Confirmatory Factor Analysis =CFA
Really confirm
Compute
Sebelum bt compute bt reliability dulu pastikan realibility ggo

12. Click paste -> syntax put out

13. Select  RUN ( green botton)
New variable motivation will appear
Reliability = must do at least 60
and validity = must do at least 100

14. normality of data
checking 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

15. To get quartiles

16. Data not normal
Test 1: check skweness and kartosis
Data 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

17. Skewness and kurtosis _ within -1 t- +1 ( normal)
Test 2: check the grapfh
Curves 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,

18. 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) =

19. 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

20. Test 2: build the grapf plot ( curve normal or not)
Test 3 : Q-Q plot are in same line ( normal)

21. 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

22. 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

23. New variable data will perform

24. Example independent sample t test
Hipotesis: 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

25. 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-value
SBP 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 Lower Systolic
blood
pressure
Equal variances
11.979 .001 9.051 500 .000 13.5995 1.5025 assumed
Equal variances
not assumed
8.033 154.580 .000 13.5995 1.6930

26. 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%

27. 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

28. Chi-Square Tests
Value df
Asymp. Sig. (2-
sided)
Pearson Chi-Square 34.476a 4 .000
Likelihood Ratio 35.614 4 .000

29. 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

30. Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
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

31. 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

33. 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
Within Groups 122.339 252 .485
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
Within Groups 138.636 241 .575
Total 144.932 244
coworker Between Groups 1.058 3 .353 1.439 .232
Within Groups 59.064 241 .245
Total 60.122 244
emotional Between Groups 3.989 3 1.330 2.406 .068
Within Groups 135.943 246 .553
Total 139.932 249
role Between Groups 2.622 3 .874 1.311 .272
Within Groups 159.403 239 .667
Total 162.025 242
commited Between Groups 1.348 3 .449 .930 .427
Within Groups 118.892 246 .483
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)

34. 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

35. 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

36. 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)

37. Eg: eggs and chicken. (have relation but don’t how come 1 st)
Regression
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1(C onstant) 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)

38. 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.

39. Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
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
a. Dependent Variable: depression
Result:
Emotional
demand most
contributed to
depression

40. Start with model 4 – will kick up one by one.
Model 4= coworker reject- sig value higher
Report:
Depression Depression model Mod depression

41. Insert independent variable then insert next—insert next independent variable – lastly put both
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
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)

42. Baron & Kenny (1986)
Assumption :
1) There must have relation between IV and DV
2) Iv significant to mediates
3) Mediation significant to DV
4) 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 DV
2) IV to mediates
3) Mediates to DV
mediates
dependent
variable
independet
variable

43. 4) Mediates to IV and DV
Test 1 : IV sig to DV
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 5.878 .434 13.544 .000
supervisor .482 .138 .221 3.486 .001
a. Dependent Variable: productivity
Test 2: IV to M
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 3.349 .597 5.609 .000
satisfaction .971 .143 .397 6.797 .000
a. Dependent Variable: productivity
Test 3 : M to DV
Test 4 : determine ( fully or partial mediation)
Partial= boleh pergi pada M and DV
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 2.805 .658 4.261 .000

44. satisfaction .875 .148 .360 5.926 .000
supervisor .309 .132 .142 2.335 .020
a. Dependent Variable: productivity
SOBEL TEST Mediation
 No signification for small simple size
Test 5
- Install directly to computer

45. 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 Res et all
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
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
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
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 2.805 .658 4.261 .000
satisfaction .875 .148 .360 5.926 .000

46. supervisor .309 .132 .142 2.335 .020
a. Dependent Variable: productivity
Report= z=3.36, SE=0.05, sig=<0.001
Sigficant there partical correlation
Exercise 1
Test 1 : IV to DV
comitment
emotional profomence
Coefficientsa

47. Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 7.757 .186 41.716 .000
emotional -.392 .145 -.172 -2.710 .007
a. Dependent Variable: productivity
=significant
Test 2 : IV to M
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
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
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 5.864 .403 14.539 .000
commited .571 .154 .232 3.710 .000
a. Dependent Variable: productivity
= significant
Test 4 : DV with M and IV

48. Determine: partially significant go sobel test
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 6.408 .460 13.939 .000
commited .495 .158 .200 3.142 .002
emotional -.343 .144 -.151 -2.373 .018
a. Dependent Variable: productivity
 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

49. Monte carlo
Test 1 : 1V to DV
comitment
coworker proformance
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 5.705 .685 8.332 .000
coworker .533 .221 .155 2.416 .016
a. Dependent Variable: productivity
=significant
Test 2: IV to M
Coefficientsa
Model Unstandardized Coefficients
Standardized
Coefficients t Sig.

50. B Std. Error Beta
1 (Constant) 2.545 .118 21.540 .000
commited .202 .045 .277 4.474 .000
a. Dependent Variable: coworker
= significant
Test 3 : M to DV
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 5.864 .403 14.539 .000
commited .571 .154 .232 3.710 .000
a. Dependent Variable: productivity
Test 4 : DV with IV and M
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 5.070 .718 7.057 .000
commited .460 .169 .181 2.722 .007
coworker .348 .228 .102 1.529 .128
a. Dependent Variable: productivity
Coworker = not significant more with productivity so it Fully Mediations
Test 5: test for monte carlo
http://www.quantpsy.org/medmc/medmc.htm

51. 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

52. 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

53. Test 1 : IV and DV
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 1.383 .039 35.066 .000
emotional .148 .031 .295 4.797 .000
a. Dependent Variable: depression
= significant
Test 2 : IV and M
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 4.303 .074 58.104 .000
emotional -.179 .058 -.192 -3.093 .002
a. Dependent Variable: satisfaction
satisfaction
emotional depression

54. =significant
Test 3 : M and DV
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 2.217 .135 16.474 .000
satisfaction -.165 .032 -.310 -5.135 .000
a. Dependent Variable: depression
= significant
Test 4 =
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 1.987 .144 13.775 .000
satisfaction -.141 .032 -.262 -4.342 .000
emotional .124 .030 .247 4.097 .000
a. Dependent Variable: depression
IV significant with DV= partial mediation
Test 5
Sobel test
= significant

55. Marte Carlo
Significant = because not content zero
Result : 95%, lower level= 0.007 , upper level = 0.04

56. 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

57. Standardize

58. new data appear
Compute Z IV and moderator
Eg: Z IV*ZM
Standardize
data
After compute Z iv
and Zm

59. Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
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

60. Not significant= because no cross between line
*no interaction effect between them
Exercise 3
supervisor
support
emosional
Test 1 : IV and DV
proformance

61. Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 5.878 .434 13.544 .000
supervisor .482 .138 .221 3.486 .001
a. Dependent Variable: productivity
Test 2 : M to DV
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 7.757 .186 41.716 .000
emotional -.392 .145 -.172 -2.710 .007
a. Dependent Variable: productivity
Test 3 : standardized

62. Test 4 : compute supervision and emos

63. Test 5 : get regreation zIV, z M , IV+M
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
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
a. Dependent Variable: productivity
Test 6 : go to excel
*No correlation = not significant = no interaction between to line = not interaction between supervision
support and emotional
-Tamat-