output spss motivasi

Correlations

Notes

Output Created 28-SEP-2011 22:06:26Comments Input Filter <none>

Weight <none>Split File <none>N of Rows in Working Data File

40

Missing Value Handling

Definition of Missing User-defined missing values are treated as missing.

Cases Used Statistics for each pair of variables are based on all the cases with valid data for that pair.

Syntax CORRELATIONS /VARIABLES=VAR00001

VAR00002 VAR00003 VAR00004 VAR00005 VAR00006 VAR00007 VAR00008 VAR00009 VAR00010 VAR00011 VAR00012

VAR00013 VAR00014 VAR00015 VAR00016 VAR00017 VAR00018 VAR00019 VAR00020 VAR00021 VAR00022

/PRINT=TWOTAIL NOSIG /STATISTICS

DESCRIPTIVES XPROD /MISSING=PAIRWISE .

Resources Elapsed Time

0:00:00.02

Descriptive Statistics

Mean Std. Deviation Nitem x ke 1 3.7500 1.00639 40item x ke 2 4.0000 .96077 40item x ke 3 3.7250 .98677 40item x ke 4 3.9500 .81492 40item x ke 5 3.7500 .98058 40

item x ke 6 3.7500 .83972 40item x ke 7 3.8000 1.01779 40item x ke 8 3.5750 .78078 40item x ke 9 3.7000 1.15913 40item x ke 10 3.7000 1.04268 40item x ke 11 3.5250 .78406 40item x ke 12 3.5500 1.03651 40item x ke 13 3.6000 .98189 40item x ke 14 3.5750 .84391 40item x ke 15 3.8000 1.11401 40item x ke 16 3.5750 1.12973 40item x ke 17 3.4750 .98677 40item x ke 18 3.6500 1.14466 40item x ke 19 3.5500 .98580 40item x ke 20 3.8000 .96609 40item x ke 21 3.6250 .95239 40total x 77.4250 12.85997 40

Correlations

item x ke 1 item x ke 2 item x ke 3 item x ke 4 item x ke 5 item x ke 6 item x ke 7 item x ke 8 item x ke 9 item x ke 10 item x ke 11 item x ke 12 item x ke 13 item x ke 14 item x ke 15 item x ke 16 item x ke 17 item x ke 18 item x ke 19 item x ke 20 item x ke 21 total xitem x ke 1 Pearson

Correlation1 .610(**) .342(*) .328(*) .247 .379(*) .225 .383(*) .308 .318(*) .268 .307 .519(**) .174 .457(**) .378(*) .484(**) .456(**) .349(*) .264 .328(*) .623(**)

Sig. (2-tailed) .000 .031 .039 .125 .016 .162 .015 .053 .046 .094 .054 .001 .284 .003 .016 .002 .003 .027 .100 .039 .000Sum of Squares and Cross-products

39.500 23.000 13.250 10.500 9.500 12.500 9.000 11.750 14.000 13.000 8.250 12.500 20.000 5.750 20.000 16.750 18.750 20.500 13.500 10.000 12.250 314.250

Covariance 1.013 .590 .340 .269 .244 .321 .231 .301 .359 .333 .212 .321 .513 .147 .513 .429 .481 .526 .346 .256 .314 8.058N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40

item x ke 2 Pearson Correlation

.610(**) 1 .487(**) .458(**) .381(*) .540(**) .262 .342(*) .230 .307 .204 .257 .381(*) .190 .287 .331(*) .406(**) .396(*) .217 .331(*) .392(*) .606(**)


23.000 36.000 18.000 14.000 14.000 17.000 10.000 10.000 10.000 12.000 6.000 10.000 14.000 6.000 12.000 14.000 15.000 17.000 8.000 12.000 14.000 292.000

Covariance .590 .923 .462 .359 .359 .436 .256 .256 .256 .308 .154 .256 .359 .154 .308 .359 .385 .436 .205 .308 .359 7.487N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.342(*) .487(**) 1 .525(**) .431(**) .379(*) .276 .111 .419(**) .341(*) .357(*) .327(*) .386(*) .133 .229 .329(*) .111 .321(*) .186 .317(*) .351(*) .561(**)


13.250 18.000 37.975 16.450 16.250 12.250 10.800 3.325 18.700 13.700 10.775 13.050 14.600 4.325 9.800 14.325 4.225 14.150 7.050 11.800 12.875 277.675

Covariance .340 .462 .974 .422 .417 .314 .277 .085 .479 .351 .276 .335 .374 .111 .251 .367 .108 .363 .181 .303 .330 7.120N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.328(*) .458(**) .525(**) 1 .529(**) .468(**) .328(*) .006 .228 .344(*) .283 .428(**) .391(*) .304 .469(**) .338(*) .285 .228 .163 .410(**) .504(**) .604(**)


10.500 14.000 16.450 25.900 16.500 12.500 10.600 .150 8.400 11.400 7.050 14.100 12.200 8.150 16.600 12.150 8.950 8.300 5.100 12.600 15.250 246.850

Covariance .269 .359 .422 .664 .423 .321 .272 .004 .215 .292 .181 .362 .313 .209 .426 .312 .229 .213 .131 .323 .391 6.329

N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40item x ke 5 Pearson

Correlation.247 .381(*) .431(**) .529(**) 1 .420(**) .462(**) .394(*) .248 .100 .442(**) .164 .533(**) .209 .305 .087 .258 .400(*) .411(**) .325(*) .529(**) .588(**)


9.500 14.000 16.250 16.500 37.500 13.500 18.000 11.750 11.000 4.000 13.250 6.500 20.000 6.750 13.000 3.750 9.750 17.500 15.500 12.000 19.250 289.250

Covariance .244 .359 .417 .423 .962 .346 .462 .301 .282 .103 .340 .167 .513 .173 .333 .096 .250 .449 .397 .308 .494 7.417N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.379(*) .540(**) .379(*) .468(**) .420(**) 1 .480(**) .538(**) .158 .381(*) .321(*) .133 .435(**) .389(*) .247 .426(**) .487(**) .307 .232 .442(**) .393(*) .635(**)


12.500 17.000 12.250 12.500 13.500 27.500 16.000 13.750 6.000 13.000 8.250 4.500 14.000 10.750 9.000 15.750 15.750 11.500 7.500 14.000 12.250 267.250

Covariance .321 .436 .314 .321 .346 .705 .410 .353 .154 .333 .212 .115 .359 .276 .231 .404 .404 .295 .192 .359 .314 6.853N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.225 .262 .276 .328(*) .462(**) .480(**) 1 .407(**) .317(*) .208 .488(**) .277 .354(*) .316(*) .235 .259 .352(*) .335(*) .419(**) .532(**) .423(**) .598(**)


9.000 10.000 10.800 10.600 18.000 16.000 40.400 12.600 14.600 8.600 15.200 11.400 13.800 10.600 10.400 11.600 13.800 15.200 16.400 20.400 16.000 305.400

Covariance .231 .256 .277 .272 .462 .410 1.036 .323 .374 .221 .390 .292 .354 .272 .267 .297 .354 .390 .421 .523 .410 7.831N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.383(*) .342(*) .111 .006 .394(*) .538(**) .407(**) 1 .309 .312 .332(*) .106 .408(**) .264 .224 .313(*) .302 .403(**) .311 .258 .332(*) .527(**)


11.750 10.000 3.325 .150 11.750 13.750 12.600 23.775 10.900 9.900 7.925 3.350 12.200 6.775 7.600 10.775 9.075 14.050 9.350 7.600 9.625 206.225

Covariance .301 .256 .085 .004 .301 .353 .323 .610 .279 .254 .203 .086 .313 .174 .195 .276 .233 .360 .240 .195 .247 5.288N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.308 .230 .419(**) .228 .248 .158 .317(*) .309 1 .645(**) .601(**) .440(**) .410(**) .338(*) .469(**) .429(**) .128 .421(**) .507(**) .334(*) .314(*) .642(**)


14.000 10.000 18.700 8.400 11.000 6.000 14.600 10.900 52.400 30.400 21.300 20.600 18.200 12.900 23.600 21.900 5.700 21.800 22.600 14.600 13.500 373.100

Covariance .359 .256 .479 .215 .282 .154 .374 .279 1.344 .779 .546 .528 .467 .331 .605 .562 .146 .559 .579 .374 .346 9.567N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.318(*) .307 .341(*) .344(*) .100 .381(*) .208 .312 .645(**) 1 .449(**) .394(*) .406(**) .463(**) .477(**) .607(**) .366(*) .404(**) .314(*) .346(*) .116 .641(**)


13.000 12.000 13.700 11.400 4.000 13.000 8.600 9.900 30.400 42.400 14.300 16.600 16.200 15.900 21.600 27.900 14.700 18.800 12.600 13.600 4.500 335.100

Covariance .333 .308 .351 .292 .103 .333 .221 .254 .779 1.087 .367 .426 .415 .408 .554 .715 .377 .482 .323 .349 .115 8.592N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.268 .204 .357(*) .283 .442(**) .321(*) .488(**) .332(*) .601(**) .449(**) 1 .519(**) .580(**) .462(**) .329(*) .316(*) .167 .496(**) .479(**) .311 .270 .656(**)


8.250 6.000 10.775 7.050 13.250 8.250 15.200 7.925 21.300 14.300 23.975 16.450 17.400 11.925 11.200 10.925 5.025 17.350 14.450 9.200 7.875 258.075

Covariance .212 .154 .276 .181 .340 .212 .390 .203 .546 .367 .615 .422 .446 .306 .287 .280 .129 .445 .371 .236 .202 6.617N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.307 .257 .327(*) .428(**) .164 .133 .277 .106 .440(**) .394(*) .519(**) 1 .474(**) .421(**) .320(*) .424(**) .239 .512(**) .474(**) .215 .318(*) .598(**)


12.500 10.000 13.050 14.100 6.500 4.500 11.400 3.350 20.600 16.600 16.450 41.900 18.800 14.350 14.400 19.350 9.550 23.700 18.900 8.400 12.250 310.650

Covariance .321 .256 .335 .362 .167 .115 .292 .086 .528 .426 .422 1.074 .482 .368 .369 .496 .245 .608 .485 .215 .314 7.965N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.519(**) .381(*) .386(*) .391(*) .533(**) .435(**) .354(*) .408(**) .410(**) .406(**) .580(**) .474(**) 1 .532(**) .394(*) .351(*) .360(*) .716(**) .551(**) .373(*) .494(**) .763(**)


20.000 14.000 14.600 12.200 20.000 14.000 13.800 12.200 18.200 16.200 17.400 18.800 37.600 17.200 16.800 15.200 13.600 31.400 20.800 13.800 18.000 375.800

Covariance .513 .359 .374 .313 .513 .359 .354 .313 .467 .415 .446 .482 .964 .441 .431 .390 .349 .805 .533 .354 .462 9.636N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.174 .190 .133 .304 .209 .389(*) .316(*) .264 .338(*) .463(**) .462(**) .421(**) .532(**) 1 .316(*) .370(*) .064 .426(**) .473(**) .365(*) .275 .563(**)


5.750 6.000 4.325 8.150 6.750 10.750 10.600 6.775 12.900 15.900 11.925 14.350 17.200 27.775 11.600 13.775 2.075 16.050 15.350 11.600 8.625 238.225

Covariance .147 .154 .111 .209 .173 .276 .272 .174 .331 .408 .306 .368 .441 .712 .297 .353 .053 .412 .394 .297 .221 6.108N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.457(**) .287 .229 .469(**) .305 .247 .235 .224 .469(**) .477(**) .329(*) .320(*) .394(*) .316(*) 1 .501(**) .415(**) .426(**) .406(**) .462(**) .338(*) .645(**)


20.000 12.000 9.800 16.600 13.000 9.000 10.400 7.600 23.600 21.600 11.200 14.400 16.800 11.600 48.400 24.600 17.800 21.200 17.400 19.400 14.000 360.400

Covariance .513 .308 .251 .426 .333 .231 .267 .195 .605 .554 .287 .369 .431 .297 1.241 .631 .456 .544 .446 .497 .359 9.241N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.378(*) .331(*) .329(*) .338(*) .087 .426(**) .259 .313(*) .429(**) .607(**) .316(*) .424(**) .351(*) .370(*) .501(**) 1 .439(**) .477(**) .215 .437(**) .277 .645(**)


16.750 14.000 14.325 12.150 3.750 15.750 11.600 10.775 21.900 27.900 10.925 19.350 15.200 13.775 24.600 49.775 19.075 24.050 9.350 18.600 11.625 365.225

Covariance .429 .359 .367 .312 .096 .404 .297 .276 .562 .715 .280 .496 .390 .353 .631 1.276 .489 .617 .240 .477 .298 9.365N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.484(**) .406(**) .111 .285 .258 .487(**) .352(*) .302 .128 .366(*) .167 .239 .360(*) .064 .415(**) .439(**) 1 .514(**) .357(*) .398(*) .249 .568(**)


18.750 15.000 4.225 8.950 9.750 15.750 13.800 9.075 5.700 14.700 5.025 9.550 13.600 2.075 17.800 19.075 37.975 22.650 13.550 14.800 9.125 280.925

Covariance .481 .385 .108 .229 .250 .404 .354 .233 .146 .377 .129 .245 .349 .053 .456 .489 .974 .581 .347 .379 .234 7.203N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.456(**) .396(*) .321(*) .228 .400(*) .307 .335(*) .403(**) .421(**) .404(**) .496(**) .512(**) .716(**) .426(**) .426(**) .477(**) .514(**) 1 .516(**) .445(**) .394(*) .740(**)

Sig. (2-tailed) .003 .011 .043 .157 .011 .054 .035 .010 .007 .010 .001 .001 .000 .006 .006 .002 .001 .001 .004 .012 .000Sum of Squares and Cross-

20.500 17.000 14.150 8.300 17.500 11.500 15.200 14.050 21.800 18.800 17.350 23.700 31.400 16.050 21.200 24.050 22.650 51.100 22.700 19.200 16.750 424.950

products

Covariance .526 .436 .363 .213 .449 .295 .390 .360 .559 .482 .445 .608 .805 .412 .544 .617 .581 1.310 .582 .492 .429 10.896N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.349(*) .217 .186 .163 .411(**) .232 .419(**) .311 .507(**) .314(*) .479(**) .474(**) .551(**) .473(**) .406(**) .215 .357(*) .516(**) 1 .442(**) .335(*) .640(**)


13.500 8.000 7.050 5.100 15.500 7.500 16.400 9.350 22.600 12.600 14.450 18.900 20.800 15.350 17.400 9.350 13.550 22.700 37.900 16.400 12.250 316.650

Covariance .346 .205 .181 .131 .397 .192 .421 .240 .579 .323 .371 .485 .533 .394 .446 .240 .347 .582 .972 .421 .314 8.119N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.264 .331(*) .317(*) .410(**) .325(*) .442(**) .532(**) .258 .334(*) .346(*) .311 .215 .373(*) .365(*) .462(**) .437(**) .398(*) .445(**) .442(**) 1 .557(**) .653(**)


10.000 12.000 11.800 12.600 12.000 14.000 20.400 7.600 14.600 13.600 9.200 8.400 13.800 11.600 19.400 18.600 14.800 19.200 16.400 36.400 20.000 316.400

Covariance .256 .308 .303 .323 .308 .359 .523 .195 .374 .349 .236 .215 .354 .297 .497 .477 .379 .492 .421 .933 .513 8.113N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40


.328(*) .392(*) .351(*) .504(**) .529(**) .393(*) .423(**) .332(*) .314(*) .116 .270 .318(*) .494(**) .275 .338(*) .277 .249 .394(*) .335(*) .557(**) 1 .618(**)


12.250 14.000 12.875 15.250 19.250 12.250 16.000 9.625 13.500 4.500 7.875 12.250 18.000 8.625 14.000 11.625 9.125 16.750 12.250 20.000 35.375 295.375

Covariance .314 .359 .330 .391 .494 .314 .410 .247 .346 .115 .202 .314 .462 .221 .359 .298 .234 .429 .314 .513 .907 7.574N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40

total x Pearson Correlation

.623(**) .606(**) .561(**) .604(**) .588(**) .635(**) .598(**) .527(**) .642(**) .641(**) .656(**) .598(**) .763(**) .563(**) .645(**) .645(**) .568(**) .740(**) .640(**) .653(**) .618(**) 1

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 Sum of Squares and Cross-products

314.250 292.000 277.675 246.850 289.250 267.250 305.400 206.225 373.100 335.100 258.075 310.650 375.800 238.225 360.400 365.225 280.925 424.950 316.650 316.400 295.375 6449.775

Covariance 8.058 7.487 7.120 6.329 7.417 6.853 7.831 5.288 9.567 8.592 6.617 7.965 9.636 6.108 9.241 9.365 7.203 10.896 8.119 8.113 7.574 165.379N 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40

** Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).

>Error # 1. Command name: ú>The first word in the line is not recognized as an SPSS command.>This command not executed.

>Error # 1. Command name: ñ>The first word in the line is not recognized as an SPSS command.>This command not executed.

Reliability

Notes

Output Created 28-SEP-2011 22:17:09

Comments Input Filter <none>

Weight <none>Split File <none>N of Rows in Working Data File

40

Matrix Input

Missing Value Handling

Definition of Missing User-defined missing values are treated as missing.

Cases Used Statistics are based on all cases with valid data for all variables in the procedure.

Syntax RELIABILITY /VARIABLES=VAR00001

VAR00002 VAR00003 VAR00004 VAR00005 VAR00006 VAR00007 VAR00008 VAR00009 VAR00010 VAR00011 VAR00012

VAR00013 VAR00014 VAR00015 VAR00016 VAR00017 VAR00018 VAR00019 VAR00020 VAR00021

/FORMAT=NOLABELS

/SCALE(ALPHA)=ALL/MODEL=ALPHA /STATISTICS=DESCRIPTIVE

SCALE CORR COV /SUMMARY=TOTAL MEANS

VARIANCE COV CORR /ICC=MODEL(MIXED)

TYPE(CONSISTENCY) CIN=95 TESTVAL=0 .

Resources Elapsed Time

0:00:00.04

Memory Available 524288 bytesLargest Contiguous Area 524288 bytesWorkspace Required 6056 bytes

Warnings

The covariance matrix is calculated and used in the analysis.

Case Processing Summary

N %Cases Valid 40 100.0

Excluded(a) 0 .0Total 40 100.0

a Listwise deletion based on all variables in the procedure.

Reliability Statistics

Cronbach'sAlpha

Cronbach's AlphaBased on

StandardizedItems N of Items

.921 .922 21

Item Statistics

Mean Std. Deviation Nitem x ke 1 3.7500 1.00639 40item x ke 2 4.0000 .96077 40item x ke 3 3.7250 .98677 40item x ke 4 3.9500 .81492 40item x ke 5 3.7500 .98058 40item x ke 6 3.7500 .83972 40item x ke 7 3.8000 1.01779 40item x ke 8 3.5750 .78078 40item x ke 9 3.7000 1.15913 40item x ke 10 3.7000 1.04268 40item x ke 11 3.5250 .78406 40item x ke 12 3.5500 1.03651 40item x ke 13 3.6000 .98189 40item x ke 14 3.5750 .84391 40item x ke 15 3.8000 1.11401 40item x ke 16 3.5750 1.12973 40item x ke 17 3.4750 .98677 40item x ke 18 3.6500 1.14466 40item x ke 19 3.5500 .98580 40item x ke 20 3.8000 .96609 40item x ke 21 3.6250 .95239 40

Inter-Item Correlation Matrix

item x ke 1 item x ke 2 item x ke 3 item x ke 4 item x ke 5 item x ke 6 item x ke 7 item x ke 8 item x ke 9 item x ke 10 item x ke 11 item x ke 12 item x ke 13 item x ke 14 item x ke 15 item x ke 16 item x ke 17 item x ke 18 item x ke 19 item x ke 20 item x ke 21item x ke 1 1.000 .610 .342 .328 .247 .379 .225 .383 .308 .318 .268 .307 .519 .174 .457 .378 .484 .456 .349 .264 .328item x ke 2 .610 1.000 .487 .458 .381 .540 .262 .342 .230 .307 .204 .257 .381 .190 .287 .331 .406 .396 .217 .331 .392item x ke 3 .342 .487 1.000 .525 .431 .379 .276 .111 .419 .341 .357 .327 .386 .133 .229 .329 .111 .321 .186 .317 .351item x ke 4 .328 .458 .525 1.000 .529 .468 .328 .006 .228 .344 .283 .428 .391 .304 .469 .338 .285 .228 .163 .410 .504item x ke 5 .247 .381 .431 .529 1.000 .420 .462 .394 .248 .100 .442 .164 .533 .209 .305 .087 .258 .400 .411 .325 .529item x ke 6 .379 .540 .379 .468 .420 1.000 .480 .538 .158 .381 .321 .133 .435 .389 .247 .426 .487 .307 .232 .442 .393item x ke 7 .225 .262 .276 .328 .462 .480 1.000 .407 .317 .208 .488 .277 .354 .316 .235 .259 .352 .335 .419 .532 .423item x ke 8 .383 .342 .111 .006 .394 .538 .407 1.000 .309 .312 .332 .106 .408 .264 .224 .313 .302 .403 .311 .258 .332item x ke 9 .308 .230 .419 .228 .248 .158 .317 .309 1.000 .645 .601 .440 .410 .338 .469 .429 .128 .421 .507 .334 .314item x ke 10 .318 .307 .341 .344 .100 .381 .208 .312 .645 1.000 .449 .394 .406 .463 .477 .607 .366 .404 .314 .346 .116item x ke 11 .268 .204 .357 .283 .442 .321 .488 .332 .601 .449 1.000 .519 .580 .462 .329 .316 .167 .496 .479 .311 .270item x ke 12 .307 .257 .327 .428 .164 .133 .277 .106 .440 .394 .519 1.000 .474 .421 .320 .424 .239 .512 .474 .215 .318item x ke 13 .519 .381 .386 .391 .533 .435 .354 .408 .410 .406 .580 .474 1.000 .532 .394 .351 .360 .716 .551 .373 .494item x ke 14 .174 .190 .133 .304 .209 .389 .316 .264 .338 .463 .462 .421 .532 1.000 .316 .370 .064 .426 .473 .365 .275item x ke 15 .457 .287 .229 .469 .305 .247 .235 .224 .469 .477 .329 .320 .394 .316 1.000 .501 .415 .426 .406 .462 .338item x ke 16 .378 .331 .329 .338 .087 .426 .259 .313 .429 .607 .316 .424 .351 .370 .501 1.000 .439 .477 .215 .437 .277item x ke 17 .484 .406 .111 .285 .258 .487 .352 .302 .128 .366 .167 .239 .360 .064 .415 .439 1.000 .514 .357 .398 .249item x ke 18 .456 .396 .321 .228 .400 .307 .335 .403 .421 .404 .496 .512 .716 .426 .426 .477 .514 1.000 .516 .445 .394item x ke 19 .349 .217 .186 .163 .411 .232 .419 .311 .507 .314 .479 .474 .551 .473 .406 .215 .357 .516 1.000 .442 .335item x ke 20 .264 .331 .317 .410 .325 .442 .532 .258 .334 .346 .311 .215 .373 .365 .462 .437 .398 .445 .442 1.000 .557item x ke 21 .328 .392 .351 .504 .529 .393 .423 .332 .314 .116 .270 .318 .494 .275 .338 .277 .249 .394 .335 .557 1.000


Inter-Item Covariance Matrix

item x ke 1 item x ke 2 item x ke 3 item x ke 4 item x ke 5 item x ke 6 item x ke 7 item x ke 8 item x ke 9 item x ke 10 item x ke 11 item x ke 12 item x ke 13 item x ke 14 item x ke 15 item x ke 16 item x ke 17 item x ke 18 item x ke 19 item x ke 20 item x ke 21item x ke 1 1.013 .590 .340 .269 .244 .321 .231 .301 .359 .333 .212 .321 .513 .147 .513 .429 .481 .526 .346 .256 .314item x ke 2 .590 .923 .462 .359 .359 .436 .256 .256 .256 .308 .154 .256 .359 .154 .308 .359 .385 .436 .205 .308 .359item x ke 3 .340 .462 .974 .422 .417 .314 .277 .085 .479 .351 .276 .335 .374 .111 .251 .367 .108 .363 .181 .303 .330item x ke 4 .269 .359 .422 .664 .423 .321 .272 .004 .215 .292 .181 .362 .313 .209 .426 .312 .229 .213 .131 .323 .391item x ke 5 .244 .359 .417 .423 .962 .346 .462 .301 .282 .103 .340 .167 .513 .173 .333 .096 .250 .449 .397 .308 .494item x ke 6 .321 .436 .314 .321 .346 .705 .410 .353 .154 .333 .212 .115 .359 .276 .231 .404 .404 .295 .192 .359 .314item x ke 7 .231 .256 .277 .272 .462 .410 1.036 .323 .374 .221 .390 .292 .354 .272 .267 .297 .354 .390 .421 .523 .410item x ke 8 .301 .256 .085 .004 .301 .353 .323 .610 .279 .254 .203 .086 .313 .174 .195 .276 .233 .360 .240 .195 .247item x ke 9 .359 .256 .479 .215 .282 .154 .374 .279 1.344 .779 .546 .528 .467 .331 .605 .562 .146 .559 .579 .374 .346item x ke 10 .333 .308 .351 .292 .103 .333 .221 .254 .779 1.087 .367 .426 .415 .408 .554 .715 .377 .482 .323 .349 .115item x ke 11 .212 .154 .276 .181 .340 .212 .390 .203 .546 .367 .615 .422 .446 .306 .287 .280 .129 .445 .371 .236 .202item x ke 12 .321 .256 .335 .362 .167 .115 .292 .086 .528 .426 .422 1.074 .482 .368 .369 .496 .245 .608 .485 .215 .314item x ke 13 .513 .359 .374 .313 .513 .359 .354 .313 .467 .415 .446 .482 .964 .441 .431 .390 .349 .805 .533 .354 .462item x ke 14 .147 .154 .111 .209 .173 .276 .272 .174 .331 .408 .306 .368 .441 .712 .297 .353 .053 .412 .394 .297 .221item x ke 15 .513 .308 .251 .426 .333 .231 .267 .195 .605 .554 .287 .369 .431 .297 1.241 .631 .456 .544 .446 .497 .359item x ke 16 .429 .359 .367 .312 .096 .404 .297 .276 .562 .715 .280 .496 .390 .353 .631 1.276 .489 .617 .240 .477 .298item x ke 17 .481 .385 .108 .229 .250 .404 .354 .233 .146 .377 .129 .245 .349 .053 .456 .489 .974 .581 .347 .379 .234item x ke 18 .526 .436 .363 .213 .449 .295 .390 .360 .559 .482 .445 .608 .805 .412 .544 .617 .581 1.310 .582 .492 .429item x ke 19 .346 .205 .181 .131 .397 .192 .421 .240 .579 .323 .371 .485 .533 .394 .446 .240 .347 .582 .972 .421 .314

item x ke 20 .256 .308 .303 .323 .308 .359 .523 .195 .374 .349 .236 .215 .354 .297 .497 .477 .379 .492 .421 .933 .513item x ke 21 .314 .359 .330 .391 .494 .314 .410 .247 .346 .115 .202 .314 .462 .221 .359 .298 .234 .429 .314 .513 .907


Summary Item Statistics

Mean Minimum Maximum RangeMaximum /Minimum Variance N of Items

Item Means 3.687 3.475 4.000 .525 1.151 .019 21Item Variances .966 .610 1.344 .734 2.204 .047 21Inter-Item Covariances .345 .004 .805 .801 209.333 .017 21Inter-Item Correlations .360 .006 .716 .710 118.507 .014 21


Item-Total Statistics

Scale Mean ifItem Deleted

Scale Variance ifItem Deleted

Corrected Item-Total

Correlation

SquaredMultiple

Correlation

Cronbach'sAlpha if Item

Deleteditem x ke 1 73.6750 150.276 .571 .631 .918item x ke 2 73.4250 151.328 .555 .587 .918item x ke 3 73.7000 152.113 .505 .595 .919item x ke 4 73.4750 153.384 .561 .807 .918item x ke 5 73.6750 151.507 .535 .766 .918item x ke 6 73.6750 152.379 .593 .747 .917item x ke 7 73.6250 150.753 .544 .550 .918item x ke 8 73.8500 155.413 .481 .665 .919item x ke 9 73.7250 147.589 .584 .736 .917item x ke 10 73.7250 149.281 .589 .738 .917item x ke 11 73.9000 152.759 .619 .664 .917item x ke 12 73.8750 150.522 .542 .702 .918item x ke 13 73.8250 147.071 .728 .743 .914item x ke 14 73.8500 153.874 .515 .656 .919item x ke 15 73.6250 148.138 .590 .583 .917item x ke 16 73.8500 147.926 .589 .602 .917item x ke 17 73.9500 151.946 .512 .716 .919item x ke 18 73.7750 144.897 .696 .744 .915item x ke 19 73.8750 150.112 .592 .704 .917item x ke 20 73.6250 150.087 .607 .643 .917item x ke 21 73.8000 151.138 .569 .621 .918

Scale Statistics

Mean Variance Std. Deviation N of Items

77.4250 165.379 12.85997 21

Intraclass Correlation Coefficient

Intraclass

Correlation(a)

95% Confidence Interval F Test with True Value 0

Lower Bound Upper Bound Value df1 df2 SigSingle Measures .357(b) .260 .489 12.681 39.0 780 .000Average Measures .921(c) .881 .953 12.681 39.0 780 .000

Two-way mixed effects model where people effects are random and measures effects are fixed.a Type C intraclass correlation coefficients using a consistency definition-the between-measure variance is excluded from the denominator variance.b The estimator is the same, whether the interaction effect is present or not.c This estimate is computed assuming the interaction effect is absent, because it is not estimable otherwise.

output spss motivasi

Documents