IGROUP PRESENCE QUESTIONNAIRE (IPQ)

The Igroup Presence Questionnaire (IPQ) is a scale for measuring the sense of presence experienced in a virtual environment (VE). It has been constructed using a large pool of items and two survey waves with approximately 500 participants. It was originally constructed in German, but is now also available in English and Dutch.
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Common subjects

computer science | psychology | art | engineering | design | media sciences | architecture

Cities

weimar | jena | ulm | leipzig | bonn | hong kong | eindhoven | freiburg | london | dunedin | hull | oslo

links

it-resource
microvision.blogspot.com/

igroup presence questionnaire (IPQ) Sample Factor Analysis

spss syntax

SORT CASES BY study .
SPLIT FILE
  SEPARATE BY study .

FACTOR
  /VARIABLES  g1 sp1 sp2 sp3 sp4 sp5 inv1 inv2 inv3 inv4 real1 real2 real3 real4
  /MISSING pairwise
  /ANALYSIS  g1 sp1 sp2 sp3 sp4 sp5 inv1 inv2 inv3 inv4 real1 real2 real3 real4
  /PRINT UNIVARIATE INITIAL KMO EXTRACTION ROTATION FSCORE
  /FORMAT SORT BLANK(.10)
  /PLOT EIGEN ROTATION
  /CRITERIA FACTORS(3) ITERATE(25)
  /EXTRACTION PC
  /CRITERIA ITERATE(25) DELTA(0)
  /ROTATION OBLIMIN
  /METHOD=CORRELATION .

spss output

Descriptive Statistics

Descriptive Statistics(a)

Mean Std. Deviation Analysis N Missing N
G1 3,4898 1,8435 245 1
SP1 4,0293 2,0588 239 7
SP2 1,4813 1,6204 241 5
SP3 3,9835 1,8807 243 3
SP4 3,9794 1,8751 243 3
SP5 3,6557 1,8693 244 2
INV1 2,7500 1,7028 240 6
INV2 1,8797 1,8456 241 5
INV3 3,0123 1,7802 243 3
INV4 4,2387 1,6912 243 3
REAL1 2,9344 1,4698 244 2
REAL2 2,1440 1,7104 243 3
REAL3 1,7397 1,5387 242 4
REAL4 ,7388 1,2696 245 1
a STUDY = PQI

KMO and Bartlett's Test

KMO and Bartlett's Test(a)
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. ,864
Bartlett's Test of Sphericity Approx. Chi-Square 1094,179
df 91
Sig. ,000
a STUDY = PQI

Communalities

Communalities(a)

Initial Extraction
G1 1,000 ,677
SP1 1,000 ,351
SP2 1,000 ,478
SP3 1,000 ,577
SP4 1,000 ,733
SP5 1,000 ,685
INV1 1,000 ,556
INV2 1,000 ,589
INV3 1,000 ,612
INV4 1,000 ,536
REAL1 1,000 ,601
REAL2 1,000 ,502
REAL3 1,000 ,637
REAL4 1,000 ,463
Extraction Method: Principal Component Analysis.
a STUDY = PQI

Total Variance Explained

Total Variance Explained(b)

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings(a)
Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total
1 4,843 34,596 34,596 4,843 34,596 34,596 4,083
2 1,875 13,390 47,986 1,875 13,390 47,986 3,020
3 1,280 9,140 57,126 1,280 9,140 57,126 2,878
4 ,817 5,834 62,960



5 ,746 5,325 68,285



6 ,712 5,088 73,374



7 ,656 4,686 78,059



8 ,581 4,153 82,213



9 ,537 3,834 86,046



10 ,509 3,635 89,681



11 ,470 3,358 93,039



12 ,407 2,904 95,944



13 ,317 2,267 98,210



14 ,251 1,790 100,000



Extraction Method: Principal Component Analysis.
a When components are correlated, sums of squared loadings cannot be added to obtain a total variance.
b STUDY = PQI

Scree plot ; study= pqi

Scree plot ; study= pqi

Component Matrix

Component Matrix(a,b)

Component
1 2 3
G1 ,800 -,149 -,123
SP4 ,768 -,348 -,149
SP5 ,728 -,355 -,169
SP3 ,622 -,319 -,298
REAL1 -,616 ,158 -,444
SP2 -,571 ,253 ,298
SP1 ,551 -,118 -,183
REAL4 ,538 ,278 ,309
INV1 ,531 ,513 -,103
INV3 -,439 -,637 ,120
INV2 ,489 ,592
INV4 ,457 ,538 -,197
REAL3 ,481
,630
REAL2 ,495
,500
Extraction Method: Principal Component Analysis.
a 3 components extracted.
b STUDY = PQI

Pattern Matrix

Pattern Matrix(a,b)

Component
1 2 3
SP4 ,809
,137
SP5 ,801
,104
SP3 ,794

SP2 -,720

G1 ,688 ,154 ,153
SP1 ,548 ,110
INV3
-,802
INV2
,752
INV4
,726 -,109
INV1
,709
REAL3

,825
REAL2

,694
REAL1 -,203
-,683
REAL4
,399 ,472
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
a Rotation converged in 5 iterations.
b STUDY = PQI

Structure Matrix

Structure Matrix(a)

Component
1 2 3
SP4 ,847 ,229 ,434
SP5 ,821 ,201 ,394
G1 ,793 ,399 ,457
SP3 ,756 ,178 ,224
SP2 -,686 -,197 -,184
SP1 ,583 ,275 ,244
INV3 -,186 -,780 -,149
INV2 ,209 ,762 ,274
INV1 ,307 ,740 ,234
INV4 ,263 ,724 ,113
REAL3 ,256 ,149 ,795
REAL1 -,456 -,208 -,753
REAL2 ,305 ,184 ,708
REAL4 ,270 ,513 ,564
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
a STUDY = PQI

Component Correlation Matrix

Component Correlation Matrix(a)
Component 1 2 3
1 1,000 ,299 ,383
2 ,299 1,000 ,260
3 ,383 ,260 1,000
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
a STUDY = PQI

STUDY = PQII

Descriptive Statistics

Descriptive Statistics(a)

Mean Std. Deviation Analysis N Missing N
G1 3,3780 1,7384 291 5
SP1 3,0483 2,0235 290 6
SP2 1,9252 1,5896 294 2
SP3 3,2389 1,8406 293 3
SP4 3,7372 1,7069 293 3
SP5 3,7897 1,6367 290 6
INV1 3,0952 1,6372 294 2
INV2 2,3966 1,9748 295 1
INV3 3,2158 1,7697 292 4
INV4 3,8938 1,7874 292 4
REAL1 2,9623 1,5472 292 4
REAL2 2,1241 1,7084 290 6
REAL3 2,3176 1,6531 296 0
REAL4 ,9761 1,3908 293 3
a STUDY = PQII

KMO and Bartlett's Test

KMO and Bartlett's Test(a)
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. ,899
Bartlett's Test of Sphericity Approx. Chi-Square 1349,410
df 91
Sig. ,000
a STUDY = PQII

Communalities

Communalities(a)

Initial Extraction
G1 1,000 ,616
SP1 1,000 ,399
SP2 1,000 ,426
SP3 1,000 ,552
SP4 1,000 ,603
SP5 1,000 ,662
INV1 1,000 ,431
INV2 1,000 ,722
INV3 1,000 ,635
INV4 1,000 ,570
REAL1 1,000 ,537
REAL2 1,000 ,538
REAL3 1,000 ,569
REAL4 1,000 ,665
Extraction Method: Principal Component Analysis.
a STUDY = PQII

Total Variance Explained

Total Variance Explained(b)

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings(a)
Component Total % of Variance Cumulative % Total % of Variance Cumulative % Total
1 5,394 38,532 38,532 5,394 38,532 38,532 4,103
2 1,311 9,365 47,896 1,311 9,365 47,896 3,449
3 1,220 8,711 56,607 1,220 8,711 56,607 3,225
4 ,901 6,438 63,045



5 ,731 5,225 68,270



6 ,672 4,803 73,073



7 ,603 4,306 77,378



8 ,585 4,175 81,553



9 ,541 3,863 85,416



10 ,504 3,597 89,013



11 ,469 3,351 92,363



12 ,376 2,686 95,050



13 ,372 2,656 97,705



14 ,321 2,295 100,000



Extraction Method: Principal Component Analysis.
a When components are correlated, sums of squared loadings cannot be added to obtain a total variance.
b STUDY = PQII

Scree plot ; study= pqii

Scree plot ; study= pqii

Component Matrix

Component Matrix(a,b)

Component
1 2 3
G1 ,779

SP5 ,764 -,203 -,193
SP4 ,754 -,120 -,141
INV4 ,691 ,254 -,166
INV2 ,639 ,559
SP3 ,624 -,288 -,281
REAL1 -,609 ,314 -,260
REAL3 ,587 -,201 ,429
SP1 ,574 -,221 -,144
REAL2 ,567
,461
INV3 -,555 -,511 ,257
INV1 ,493 ,433
SP2 -,471 ,332 ,306
REAL4 ,468 ,195 ,639
Extraction Method: Principal Component Analysis.
a 3 components extracted.
b STUDY = PQII

Pattern Matrix

Pattern Matrix(a,b)

Component
1 2 3
SP3 ,734

SP2 -,696

SP5 ,695 ,162 ,102
SP4 ,598 ,225 ,138
SP1 ,572

G1 ,521 ,234 ,257
INV2
,821
INV3
-,802 ,154
INV1
,633
INV4 ,304 ,577
REAL4 -,277 ,201 ,796
REAL2

,687
REAL3 ,209
,684
REAL1 -,409 ,138 -,535
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
a Rotation converged in 8 iterations.
b STUDY = PQII

Structure Matrix

Structure Matrix(a)

Component
1 2 3
SP5 ,789 ,443 ,407
SP3 ,741 ,297 ,254
SP4 ,727 ,484 ,431
G1 ,695 ,507 ,525
SP2 -,643 -,160 -,133
SP1 ,623 ,287 ,312
INV2 ,298 ,845 ,373
INV3 -,326 -,783 -,160
INV4 ,519 ,695 ,340
INV1 ,226 ,652 ,294
REAL4
,381 ,767
REAL3 ,425 ,229 ,730
REAL2 ,335 ,306 ,728
REAL1 -,551 -,193 -,633
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
a STUDY = PQII

Component Correlation Matrix

Component Correlation Matrix(a)
Component 1 2 3
1 1,000 ,353 ,358
2 ,353 1,000 ,348
3 ,358 ,348 1,000
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
a STUDY = PQII