Various Datasets and SPSS Syntax Files
1/12/06

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      "Foundations of Behavioral Statistics" datasets
            Jump to Table 9.2

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            Jump to Table 13.3





Holzinger & Swineford data and SPSS syntax

TITLE 'Holzinger & Swineford (1939) Data **Citation Below**' .
COMMENT ******************************************************.
COMMENT
    Holzinger, KJ, & Swineford, F (1939) A study in
factor analysis: The stability of a bi-factor solution (No 48)
Chicago, IL: University of Chicago (data on pp 81-91).
COMMENT ******************************************************.
SET BLANKS=SYSMIS UNDEFINED=WARN printback=listing .
DATA LIST
  FILE='c:\spsswin\HOLZINGR.DTA' FIXED RECORDS=2 TABLE
  /1 id 1-3 sex 4-4 ageyr 6-7 agemo 8-9 t1 11-12 t2 14-15
  t3 17-18 t4 20-21 t5 23-24 t6 26-27 t7 29-30 t8 32-33
  t9 35-36 t10 38-40 t11 42-44 t12 46-48 t13 50-52 t14 54-56
  t15 58-60 t16 62-64 t17 66-67 t18 69-70 t19 72-73 t20 74-76
  t21 78-79 /2
  t22 11-12 t23 14-15 t24 17-18 t25 20-21 t26 23-24 .
COMPUTE SCHOOL=1 .
IF (ID GT 200)SCHOOL=2 .
IF (ID GE 1 AND ID LE 85)GRADE=7 .
IF (ID GE 86 AND ID LE 168)GRADE=8 .
IF (ID GE 201 AND ID LE 281)GRADE=7 .
IF (ID GE 282 AND ID LE 351)GRADE=8  .
IF (ID GE 1 AND ID LE 44)TRACK=2 .
IF (ID GE 45 AND ID LE 85)TRACK=1 .
IF (ID GE 86 AND ID LE 129)TRACK=2 .
IF (ID GE 130)TRACK=1 .
PRINT FORMATS SCHOOL TO TRACK(F1.0) .
VALUE LABELS SCHOOL(1)PASTEUR (2) GRANT-WHITE/
  TRACK (1)JUNE PROMOTIONS (2)FEB PROMOTIONS/ .
VARIABLE LABELS
  T1 VISUAL PERCEPTION TEST FROM SPEARMAN VPT, PART III
  T2 CUBES, SIMPLIFICATION OF BRIGHAM'S SPATIAL RELATIONS TEST
  T3 PAPER FORM BOARD--SHAPES THAT CAN BE COMBINED TO FORM A TARGET
  T4 LOZENGES FROM THORNDIKE--SHAPES FLIPPED THEN IDENTIFY TARGET
  T5 GENERAL INFORMATION VERBAL TEST
  T6 PARAGRAPH COMPREHENSION TEST
  T7 SENTENCE COMPLETION TEST
  T8 WORD CLASSIFICATION--WHICH WORD NOT BELONG IN SET
  T9 WORD MEANING TEST
  T10 SPEEDED ADDITION TEST
  T11 SPEEDED CODE TEST--TRANSFORM SHAPES INTO ALPHA WITH CODE
  T12 SPEEDED COUNTING OF DOTS IN SHAPE
  T13 SPEEDED DISCRIM STRAIGHT AND CURVED CAPS
  T14 MEMORY OF TARGET WORDS
  T15 MEMORY OF TARGET NUMBERS
  T16 MEMORY OF TARGET SHAPES
  T17 MEMORY OF OBJECT-NUMBER ASSOCIATION TARGETS
  T18 MEMORY OF NUMBER-OBJECT ASSOCIATION TARGETS
  T19 MEMORY OF FIGURE-WORD ASSOCIATION TARGETS
  T20 DEDUCTIVE MATH ABILITY
  T21 MATH NUMBER PUZZLES
  T22 MATH WORD PROBLEM REASONING
  T23 COMPLETION OF A MATH NUMBER SERIES
  T24 WOODY-MCCALL MIXED MATH FUNDAMENTALS TEST
  T25 REVISION OF T3--PAPER FORM BOARD
  T26 FLAGS--POSSIBLE SUBSTITUTE FOR T4 LOZENGES .
SUBTITLE 'FACTOR 9 VARIABLES INTO 3 FACTORS ##############' .
execute .
FACTOR VARIABLES=T6 T7 T9 T10 T12 T13 T14 T15 T17/
  PRINT=ALL/PLOT=EIGEN/
  CRITERIA=FACTORS(3)/EXTRACTION=PC/ROTATION=VARIMAX/
  SAVE=REG(ALL FSCORE) .
subtitle 'CANONICAL CORRELATION ANALYSIS *****************'  .
manova t6 t7 t9 with t21 t22 t23 t15/
  print=signif(multiv eigen dimenr)/
  discrim=stan corr alpha(.999) .



0011 1301 20 31 12 03 40 07 23 22 09 078 074 115 229 170 086 096 06 09 16 03 14
          34 05 24
0022 1307 32 21 12 17 34 05 12 22 09 087 084 125 285 184 085 100 12 12 10 -3 13
          21 01 12
0032 1301 27 21 12 15 20 03 07 12 03 075 049 078 159 170 085 095 01 05 06 -3 09
          18 07 20
0041 1302 32 31 16 24 42 08 18 21 17 069 065 106 175 181 080 091 05 03 10 -2 10
          22 06 19
0052 1202 29 19 12 07 37 08 16 25 18 085 063 126 213 187 099 104 15 14 14 29 15
          19 04 20
0062 1401 32 20 11 18 31 03 12 25 06 100 092 133 270 164 084 104 06 06 14 09 02
          16 10 22
0071 1201 17 24 12 08 40 10 24 32 20 108 065 124 175 121 071 078 04 03 05 18 10
          19 03 15
0082 1202 34 25 13 15 29 11 17 25 09 078 080 103 132 184 095 106 11 13 09 15 09
          22 18 24
0092 1300 27 23 11 12 29 08 23 19 19 104 052 093 265 184 091 105 18 06 11 12 15
          18 17 18
0112 1205 21 21 10 06 33 08 20 25 18 095 074 091 157 175 092 100 05 08 11 33 08
          25 08 16
0121 1202 22 23 13 16 38 06 14 22 11 086 060 114 155 173 086 107 09 09 13 10 09
          19 05 24
0131 1211 35 24 15 23 33 08 18 27 19 085 071 103 149 167 103 108 06 12 12 48 18
          32 09 25
0142 1207 34 18 15 33 33 08 16 31 16 135 068 104 211 166 092 103 08 12 18 21 13
          22 21 27
0152 1208 36 22 16 14 32 14 16 25 11 118 068 094 160 186 086 102 07 06 10 09 12
          19 04 23
0161 1206 35 23 13 29 54 15 22 27 21 092 064 087 211 168 084 102 05 04 08 36 15
          34 35 32
0172 1201 28 19 13 19 27 08 17 18 05 094 059 076 185 181 079 099 03 10 03 21 14
          21 16 25
0182 1411 26 19 18 12 35 06 16 24 09 085 058 133 189 164 084 100 13 12 07 05 05
          16 08 18
0191 1305 30 27 17 18 25 06 10 17 12 092 061 105 196 149 088 098 03 02 12 25 15
          15 15 19
0202 1208 34 21 14 32 62 13 21 34 26 090 094 097 207 171 074 107 03 02 14 48 23
          47 36 27
0212 1203 38 35 13 24 41 11 15 24 18 080 062 107 177 186 099 105 05 10 13 18 16
          38 26 22
0221 1410 35 32 17 16 22 05 10 20 04 060 044 092 194 169 096 096 08 07 10 54 15
          19 23 19
0231 1209 40 34 16 33 24 06 13 18 15 103 073 109 252 169 094 105 09 10 15 87 19
          27 29 28
0241 1211 30 25 16 15 37 10 23 25 22 080 062 092 180 166 083 101 08 04 15 27 13
          21 08 19
0252 1208 23 22 15 13 35 08 12 22 07 134 064 106 244 173 096 099 06 09 10  0 14
          20 05 23
0261 1203 34 22 12 10 45 07 15 27 09 108 060 092 149 154 092 106 09 05 07 30 13
          22 12 20
0271 1207 32 16 16 27 46 05 14 26 10 132 067 125 156 175 103 110 06 12 15 64 17
          24 21 29
0281 1208 33 21 16 36 18 08 09 20 05 095 067 102 163 190 097 106 12 10 20 48 14
          24 24 27
0292 1302 36 20 18 17 27 05 12 22 08 065 056 111 159 172 097 098 07 12 19 21 14
          17 12 16
0302 1205 28 24 14 34 31 06 12 21 14 118 090 117 310 196 094 101 10 12 14 10 13
          20 18 27
0312 1202 30 18 13 06 30 08 13 18 13 107 065 097 196 192 083 109 07 04 05 -3 11
          20 13 20
0332 1207 21 23 14 11 29 06 14 23 13 111 060 139 215 163 088 101 05 06 06 20 16
          14 12 25
0341 1600 18 24 17 02 29 05 12 18 07 047 031 073 121 159 084 092 03 03 04 01 01
          18 09 17
0352 1202 30 21 12 14 52 08 21 32 14 062 064 086 173 173 095 101 01 02 06 25 10
          22 19 23
0362 1203 25 24 12 19 44 10 17 25 13 124 086 087 203 182 092 096 14 10 14 11 15
          27 20 22
0382 1303 20 15 13 12 30 04 12 16 06 064 064 104 174 181 090 087 07 07 08 07 08
          07 03 13
0391 1310 29 21 14 04 32 05 19 23 10 072 042 075 177 176 087 087 03 04 11 27 17
          21 09 18
0401 1209 33 28 15 28 34 08 16 25 14 088 065 080 191 173 078 089 12 06 17 11 16
          39 27 22
0412 1208 23 18 12 18 35 09 12 24 12 094 050 070 183 154 096 087 03 18 08 24 12
          17 14 22
0422 1206 38 16 09 31 47 12 21 31 23 127 083 109 184 173 096 105 14 14 09 09 18
          20 24 24
0431 1205 35 31 19 20 58 09 21 28 22 074 069 090 176 172 079 105 03 06 14 09 11
          41 28 19
0442 1201 23 23 14 13 32 08 17 30 15 113 076 102 167 180 106 112 11 20 15 24 13
          22 27 25
0452 1306 19 20 10 10 39 05 17 19 11 065 056 106 172 155 077 097 06 05 14 24 10
          18 09 18
0462 1308 11 21 09 08 28 05 15 22 04 091 054 095 100 164 082 058 04 02 16 19 08
          25 01 15
0471 1408 25 21 14 15 27 06 12 22 10 091 038 112 240 178 100 103 06 07 10 27 13
          20 27 19
0481 1402 38 22 13 22 54 14 22 34 14 124 094 100 247 188 098 109 13 15 19 42 06
          27 19 25
0491 1307 36 22 14 36 27 09 17 23 09 111 076 100 180 180 100 100 07 06 04 21 06
          25 23 18
0501 1305 43 34 18 32 16 02 04 13 07 077 064 127 226 155 091 109 04 04 13 81 15
          39 33 24
0512 1306 19 19 09 11 30 08 13 23 09 141 048 160 196 191 093 096 08 07 08 09 12
          18 05 25
0521 1211 26 22 11 22 35 06 09 18 07 130 055 118 218 178 096 092 07 13 11 12 19
          18 13 21
0542 1302 27 25 17 09 40 11 22 31 15 059 066 096 199 184 068 102 02 15 14 -6 09
          29 18 17
0551 1307 33 23 19 30 16 05 09 20 01 054 053 101 198 170 094 095 07 05 09 28 13
          13 19 23
0561 1204 42 24 16 17 47 07 18 31 10 084 069 103 172 171 099 111 05 12 13 18 16
          18 20 15
0571 1507 23 30 13 26 08 02 06 15 01 075 050 084 194 171 085 106  0 03 09 27 14
          19 13 22
0581 1304 31 19 16 14 48 10 14 15 15 080 042 102 151 165 095 102 15 07 07 11 11
          25 23 23
0601 1208 30 24 13 33 46 06 18 32 15 096 076 096 159 162 088 111 17 11 18 39 18
          21 23 22
0621 1404 34 21 11 17 28 08 18 21 10 093 054 114 200 173 098 081 03 17 13 06 04
          36 19 25
0631 1602 24 19 13 26 25 05 12 19 12 096 071 105 203 179 105 104 05 12 16 05 13
          14 03 24
0641 1303 35 21 15 31 51 08 22 23 17 079 066 116 171 188 103 106 04 10 16 60 15
          10 23 21
0652 1400 23 26 08 16 27 03 10 17 09 078 074 091 174 165 081 095 10 11 13 -3 13
          17 07 26
0661 1503 25 23 14 14 41 04 12 18 07 081 054 123 142 159 084 095 03  0 09  0 10
          13 02 15
0672 1205 32 23 12 27 37 12 22 27 18 121 080 124 221 186 093 111 09 06 16 46 19
          35 31 24
0682 1209 24 24 12 29 44 08 20 28 15 113 060 107 172 180 091 110 10 12 10 36 23
          32 17 25
0692 1210 32 27 16 11 16 05 10 21 05 061 055 077 192 179 089 094 12 08 14 12 11
          25 19 23
0702 1311 32 20 15 10 47 10 18 28 17 127 064 109 210 177 099 105 09 10 15 45 16
          34 23 28
0712 1301 22 23 11 29 33 08 20 22 10 076 103 090 181 170 098 107 09 10 07 21 13
          27 06 15
0722 1211 39 24 13 20 54 11 18 30 21 131 089 108 227 177 103 106 12 05 04 03 21
          29 27 30
0731 1411 24 37 16 32 42 09 15 27 07 083 072 115 223 185 095 110 11 10 14 45 09
          26 19 20
0742 1300 28 23 15 29 24 08 11 25 13 089 055 122 153 176 083 099 15 16 08 -1 09
          21 12 26
0752 1309 17 20 12 07 39 09 10 24 11 157 065 114 141 190 101 107 14 13 15 12 13
          19 11 25
0761 1208 28 20 16 06 55 13 16 23 19 109 078 119 195 175 100 103 10 13 11 21 13
          19 06 19
0772 1402 04 18 11 06 22 06 12 16 07 083 068 110 166 177 084 098 07 03 14 -3 13
          25 06 22
0781 1303 26 37 14 27 22 04 09 26 08 086 051 115 186 173 104 109 07 13 10 36 17
          30 14 21
0791 1301 30 18 14 21 44 05 11 24 11 121 070 147 215 170 085 103 10 05 13 30 12
          15 11 25
0801 1307 30 24 09 26 51 10 23 31 19 080 078 092 154 186 099 110 17 12 17 31 10
          37 28 16
0812 1209 25 21 09 17 20 09 13 24 05 080 071 114 165 186 097 108 11 04 13 09 16
          22 30 25
0822 1309 23 21 12 19 23 05 09 21 07 072 086 115 168 136 088 101 05 11 18 09 15
          17 15 22
0831 1206 34 28 19 17 56 12 19 26 16 130 076 114 233 157 084 100 02 07 06 -6 18
          28 21 24
0851 1607 10 23 13 11 25  0 06 12 08 131 057 080 203 159 093 103 08 08 07 06 12
          24 12 19
0862 1407 38 22 18 33 26 12 13 22 13 128 079 147 207 185 104 112 19 10 13 42 21
          37 23 30
0871 1401 24 21 13 20 22 03 06 11 13 108 062 103 192 188 097 107 06 15 16 06 12
          19 10 31
0881 1411 27 24 10 14 29 05 08 20 11 135 079 129 217 183 094 106 24 13 19 10 15
          20 14 21
0891 1307 28 32 16 34 45 09 19 28 17 118 052 115 191 185 092 109 10 13 20 15 18
          27 31 25
0901 1405 29 33 06 18 18 01 09 18 04 123 066 104 208 179 095 098 09 08 16 24 13
          17 17 20
0912 1302 29 26 20 14 22 07 14 26 05 133 080 115 179 187 089 103 07 08 07 10 16
          26 23 22
0932 1407 18 21 12 05 34 03 06 21 07 114 054 097 165 161 081 091 22 12 06 07 04
          11 06 16
0942 1211 38 25 10 20 49 10 23 36 26 106 091 137 197 188 096 112 13 11 19 27 16
          43 32 30
0952 1308 33 28 12 23 23 08 16 23 06 094 075 108 215 183 098 107 11 17 12 21 17
          33 29 26
0962 1303 32 24 19 22 45 12 20 30 15 127 081 091 185 178 094 089 15 06 12 05 17
          25 23 26
0972 1311 20 29 14 26 09 04 05 22 04 082 065 111 176 191 096 105 17 08 15 12 16
          22 13 14
0982 1310 33 20 13 18 39 11 18 22 12 096 075 119 240 166 090 103 14 04 13 45 16
          21 24 23
0991 1310 24 26 16 21 24 08 11 10 13 153 067 150 196 180 095 098 16  0 15 27 17
          23 32 29
1002 1400 23 25 14 27 19 06 15 24 03 124 081 156 220 167 092 108 09 10 14 19 13
          26 18 18
1011 1504 31 19 15 17 36 03 08 14 11 082 062 128 206 168 078 096 04 09 19 28 12
          20 20 20
1021 1308 26 14 15 07 24 08 13 22 08 083 035 096 142 185 094 104 08 12 17 19 09
          17 23 24
1031 1410 25 22 08 07 50 14 19 27 15 095 048 107 136 184 094 099 11 08 08 30 13
          08 20 25
1041 1308 30 23 10 34 34 12 17 25 17 082 061 072 196 193 093 114 13 16 19 56 15
          30 07 21
1051 1304 26 19 15 04 48 10 23 19 20 127 077 117 152 171 080 097 05 13 08 06 10
          33 17 20
1062 1302 45 30 12 17 48 16 27 28 19 118 090 111 260 174 084 104 17 09 15 39 16
          41 30 28
1082 1311 30 15 14 11 26 05 11 21 10 128 061 122 166 156 074 079 10 10 13 17 09
          14 09 19
1091 1304 33 25 12 22 58 11 18 27 23 113 068 103 207 159 090 095 04 14 10 12 13
          24 28 20
1101 1503 37 15 15 35 29 08 13 24 07 098 063 106 225 173 089 104 13 09 16  0 12
          34 21 23
1112 1310 39 26 12 15 30 11 15 26 12 104 076 088 237 185 098 116 08 11 20 28 17
          26 15 25
1121 1303 26 31 17 15 48 13 18 28 13 135 072 125 148 162 081 105 11 04 13 36 19
          34 22 23
1131 1304 28 21 11 13 52 10 18 26 20 088 053 097 174 162 094 101 05 03 16 18 15
          24 17 15
1141 1503 41 36 25 35 56 08 23 26 35 069 062 124 217 178 099 111 08 09 14 37 19
          31 22 20
1152 1210 27 26 17 25 45 08 22 23 15 146 098 130 222 189 093 109 10 08 19 39 18
          28 23 28
1162 1305 41 26 14 06 49 16 23 31 22 171 088 114 187 172 092 109 05 16 09 29 17
          30 26 26
1172 1310 33 27 14 30 32 08 11 20 14 096 098 136 252 171 101 114 10 12 15 34 10
          14 15 20
1182 1405 38 22 14 13 55 17 22 26 28 131 080 128 271 178 083 113 11 05 15 06 18
          34 30 30
1192 1303 25 17 17 18 30 10 14 18 10 103 065 083 121 192 095 109 10 08 17 24 23
          38 20 25
1201 1304 34 37 13 35 38 11 19 31 21 075 044 078 175 185 098 111 10 12 10 27 13
          24 22 20
1212 1301 38 21 13 21 39 13 17 24 11 066 065 120 196 177 096 105 12 19 20 24 07
          43 22 19
1221 1304 37 25 15 34 40 15 14 27 13 105 074 107 196 167 088 099  0 14 20 69 18
          30 34 25
1232 1400 31 26 18 14 48 12 21 31 13 108 074 086 216 168 098 099 16 06 10 46 16
          18 16 27
1241 1405 25 30 15 31 20 03 08 22 06 082 066 079 228 178 091 111 12 10 16 50 13
          17 19 19
1252 1301 25 23 12 08 45 11 19 29 13 111 066 117 195 172 090 096 06 06 12 25 15
          23 21 24
1261 1600 31 23 15 20 37 08 19 27 19 122 064 137 180 180 082 096 11 15 17 54 15
          21 10 19
1271 1310 26 21 18 32 35 09 11 17 07 128 064 132 232 145 076 112 09 07 14 02 18
          22 24 28
1291 1304 21 29 15 27 45 07 09 29 08 085 057 104 186 180 098 108 05 10 13 18 11
          17 16 23
1301 1605 28 19 13 13 28 08 13 19 14 094 059 111 196 161 086 089 03 20 16 21 14
          21 05 22
1312 1404 29 24 13 12 36 08 17 28 09 134 103 128 247 174 094 101 20 10 15 33 22
          26 23 24
1321 1501 30 22 17 19 45 10 12 24 14 106 058 128 119 158 087 091 02 10 12 18 13
          28 16 21
1332 1404 45 28 19 34 37 12 26 31 25 086 083 152 234 182 090 113 14 16 20 54 19
          44 34 29
1342 1508 32 21 16 33 25 07 15 23 11 084 072 107 172 184 097 099 23 17 10 63 07
          26 06 19
1351 1405 37 27 13 33 29 10 20 24 20 115 071 138 230 172 090 110 12 14 11 66 15
          30 29 27
1362 1504 32 35 15 25 38 13 16 29 08 092 085 139 204 175 088 094 09 10 19 36 14
          30 14 18
1372 1311 26 28 17 08 38 13 22 25 21 118 066 113 177 158 074 093 11 08 12 39 14
          25 20 20
1382 1311 29 28 15 09 43 10 20 19 16 113 085 105 179 177 084 092 14 06 06 23 16
          32 18 25
1392 1304 36 30 17 26 41 09 24 32 17 076 076 102 208 188 103 107 18 14 10 30 15
          34 22 26
1401 1401 30 18 10 13 40 06 11 22 04 104 087 125 227 177 092 108 09 12 10 19 15
          13 20 22
1421 1311 25 32 16 35 45 13 25 33 22 105 083 156 252 168 082 104 02 01 19 18 20
          50 36 26
1432 1401 34 22 15 28 42 12 17 22 16 167 086 127 259 176 085 107 16 14 18 30 18
          23 11 25
1442 1503 30 25 15 14 19 04 08 17 04 113 049 120 173 165 088 092 05 06 07 03 03
          20 09 22
1452 1306 21 21 13 18 31 06 15 24 13 108 085 105 134 172 087 105 11 17 16-12 13
          26 05 27
1461 1407 44 32 13 21 44 08 20 31 17 079 079 120 222 168 097 098 09 12 11 17 13
          38 22 27
1471 1304 37 30 20 29 58 12 25 28 18 101 096 117 220 148 083 099 01 03 07 20 16
          26 24 21
1481 1310 29 21 13 36 45 13 23 20 20 077 066 102 196 173 093 096 07 06 12 44 13
          39 16 21
1491 1302 34 25 16 22 51 10 21 23 15 102 056 103 164 191 099 101 07 06 15 43 19
          22 16 26
1502 1402 33 27 17 36 45 07 18 33 18 144 093 131 248 191 088 110 17 05 18 42 18
          32 32 31
1512 1407 31 20 14 20 56 10 23 26 21 148 114 166 255 195 104 112 26 19 20 27 17
          34 32 30
1522 1400 19 15 11 12 41 10 23 32 16 139 081 105 224 171 087 112 10 04 16 13 23
          30 20 31
1532 1409 30 30 16 36 12 06 06 20 03 076 057 105 206 187 098 105 11 10 18 25 13
          16 09 22
1541 1500 43 36 17 31 40 11 21 33 33 088 079 142 208 162 086 112 09 05 13 34 20
          37 18 31
1552 1209 44 27 22 32 66 18 26 37 43 123 087 115 238 186 073 102 16 20 18 51 22
          49 39 33
1562 1508 12 22 14 05 31 08 20 16 06 055 064 112 215 170 083 084 13 10 14 12 11
          23 09 18
1572 1306 23 22 12 15 44 11 20 26 21 129 094 098 139 180 099 098 14 12 18 24 19
          13 18 27
1582 1308 25 24 13 10 44 12 20 31 18 160 108 125 227 188 086 101 18 14 20 09 13
          15 11 26
1592 1508 28 23 15 13 26 02 10 21 10 104 061 100 171 176 097 097 10 10 14 03 12
          18 15 21
1601 1404 35 19 18 21 55 17 22 33 29 082 070 126 197 178 082 100 06 15 17 42 22
          25 30 22
1621 1501 40 22 15 35 29 08 15 26 07 118 076 095 188 167 079 087 01 03 10  0 10
          19 05 23
1631 1311 41 29 15 34 60 15 23 26 34 130 089 151 222 184 102 109 12 13 16 35 23
          28 34 31
1641 1505 32 19 15 21 52 09 22 28 15 133 068 158 250 155 083 107 04 06 11 33 16
          36 09 21
1651 1401 29 20 19 19 46 09 17 28 17 124 081 150 195 146 092 091 09 17 13 24 19
          29 26 25
1662 1501 31 24 14 19 34 04 09 26 08 124 080 123 187 175 083 111 05 03 11 49 16
          26 05 27
1672 1507 38 27 16 09 21 09 10 27 10 108 069 112 149 189 099 106 11 09 16 39 18
          16 09 22
1682 1506 29 23 12 10 38 09 19 29 15 083 079 123 169 181 080 101 05 09 12 -6 10
          24 10 26
2011 1300 23 19 13 04 46 10 17 22 10 069 065 082 156 173 091 096 08 02 10 21 12
          17 11 17 13 25
2022 1110 33 22 12 17 43 08 17 30 10 065 060 098 195 174 081 106 09 15 17 33 12
          22 31 32 20 37
2031 1206 34 24 14 22 36 11 19 27 19 050 049 086 228 168 084 101 01 07 16 45 10
          43 21 18 19 40
2041 1111 29 23 12 09 38 09 19 25 11 114 059 103 144 130 084 101 10 15 14 25 21
          26 19 28 11 44
2051 1205 16 25 11 10 51 08 25 28 24 112 054 122 160 184 098 099 09 09 15 28 16
          35 21 25 10 28
2062 1206 30 25 12 20 42 10 23 28 18 094 084 113 201 188 086 116 10 10 16 36 14
          27 18 30 16 42
2082 1208 36 33 19 36 69 17 25 42 41 129 096 139 333 192 095 118 16 19 20 53 23
          45 40 34 20 48
2092 1111 28 25 10 09 35 10 18 29 11 096 083 095 174 168 097 113 05 06 16 26 17
          13 26 24 09 28
2102 1205 30 25 15 11 32 11 21 35 08 103 067 114 197 168 084 107 06 04 07 36 13
          18 26 27 17 38
2112 1205 20 25 13 06 39 09 21 27 16 089 049 101 178 181 074 097 07 09 15 07 06
          24 16 26 12 25
2121 1200 27 26 13 06 27 10 16 25 13 088 035 107 137 187 095 115 07 07 15 27 20
          36 27 24 17 37
2131 1210 32 21 16 08 27 01 07 29 11 103 062 136 154 173 100 106 03 18 04 45 18
          19 24 26 10 43
2141 1209 38 31 18 12 29 10 11 30 14 083 057 108 201 181 090 114 09 08 17 40 12
          37 23 27 17 47
2152 1208 17 21 07 06 35 05 10 11 10 099 065 087 147 166 094 090 10 06 14  0 09
          16 06 25 08 23
2161 1209 34 28 20 24 56 14 22 29 26 049 034 084 171 169 096 105 04 14 17 24 14
          21 31 25 23 37
2171 1301 25 31 12 18 37 07 12 18 11 065 053 080 125 186 097 113 09 08 18 42 13
          37 20 28 17 24
2182 1201 33 31 12 30 48 11 23 29 18 066 054 113 186 174 086 111 10 12 09 21 11
          38 24 30 18 46
2191 1301 40 23 15 20 65 10 23 23 35 072 086 104 222 174 088 102 07 11 18 30 18
          50 20 20 12 45
2202 1207 30 22 13 20 49 08 17 26 20 095 066 110 161 188 090 094 05 11 17 18 14
          31 16 33 18 28
2212 1201 36 28 19 22 59 13 24 33 36 082 079 103 205 193 098 117 17 14 20 32 16
          29 28 21 19 44
2221 1209 24 16 16 14 56 05 17 27 11 106 049 080 118 164 084 092 06 09 13 05 13
          28 08 21 12 28
2231 1208 40 26 17 26 56 13 24 39 25 088 069 121 225 191 095 105 11 16 18 54 20
          31 23 31 23 43
2242 1206 30 24 16 19 50 14 26 28 24 112 069 115 185 184 087 087 09 08 19 39 16
          38 25 32 16 38
2251 1400 42 27 17 27 25 07 13 17 04 126 115 200 236 198 094 116 21 19 20 47 24
          29 30 29 20 41
2262 1203 33 27 13 16 29 08 17 29 13 068 063 116 219 159 084 104 06 08 13 09 15
          23 06 21 15 35
2272 1204 32 22 12 15 39 09 20 26 17 074 079 118 191 180 079 093 03 07 14  0 08
          34 08 25 11 24
2281 1209 31 21 13 05 55 13 19 26 19 096 087 121 180 162 079 100 01 02 14 42 10
          25 05 29 16 37
2292 1407 27 23 11 04 24 09 11 24 07 055 068 118 178 173 092 102 03 11 14 06 07
          18 05 16 12 33
2301 1302 31 23 13 11 44 04 16 30 16 081 060 128 189 166 089 099 01 08 18 22 12
          07 21 25 18 40
2311 1205 31 34 13 03 49 09 19 26 17 051 088 092 182 179 091 100 02 07 19 21 17
          21 21 26 17 22
2322 1200 17 30 14 13 46 09 17 23 13 052 053 094 143 165 077 094 02 10 17 39 07
          27 06 28 18 33
2331 1200 30 20 14 13 51 09 20 30 21 108 068 113 180 181 090 111 09 08 19 68 15
          43 37 27 16 35
2341 1206 28 19 15 08 33 09 14 27 20 081 068 116 159 181 091 098 08 06 15 12 09
          27 13 29 15 31
2351 1302 19 22 14 08 18 05 12 21 04 050 053 072 162 178 089 100 03 04 12 02 06
          16 09 17 17 22
2361 1200 28 20 12 14 31 08 15 30 08 100 066 144 205 156 074 101 06 12 16 12 08
          27 18 29 18 32
2372 1210 38 25 13 13 41 09 23 31 15 056 067 130 198 178 096 096 08 09 17 36 08
          35 15 18 18 40
2382 1111 34 28 14 10 48 09 22 28 20 081 076 096 199 171 088 101 02 09 17 34 13
          28 20 24 15 28
2392 1308 18 22 09 05 20 02 04 10 02 030 019 061 112 156 085 090 03 05 06 02 02
          18 10 11 12 23
2402 1207 16 20 12 08 42 06 18 35 13 093 073 111 185 167 081 088 03 05 18 15 20
          28 04 30 16 33
2412 1204 18 30 13 17 52 19 24 36 33 092 083 108 148 175 101 103 09 03 14 30 21
          34 33 32 17 41
2422 1206 32 21 16 09 63 15 20 32 25 095 077 092 178 178 090 105 12 10 14 21 12
          31 28 26 19 41
2431 1206 34 21 15 09 47 09 23 24 24 058 031 091 173 159 076 101 01 08 15 21 11
          43 12 25 17 48
2441 1201 21 22 15 08 29 09 12 24 08 065 067 084 137 186 080 105  0 04 14 18 12
          24 13 21 13 43
2452 1209 28 20 14 14 37 08 18 29 10 061 059 100 214 189 087 100 04 04 13 03 11
          26 23 22 16 40
2462 1203 39 24 14 25 41 14 17 24 11 082 070 104 244 179 092 103 08 04 20 18 17
          28 22 27 16 39
2472 1204 32 26 14 10 47 11 23 27 23 062 039 083 143 173 090 104 07 07 20 54 15
          27 22 23 14 35
2482 1210 28 24 10 08 20 06 10 15 10 061 065 077 159 175 073 103 07 10 10 42 17
          18 01 26 12 38
2491 1205 24 20 16 22 44 11 19 32 18 071 060 094 154 186 100 105 07 07 11 38 15
          24 31 26 18 43
2502 1111 31 19 14 13 28 08 22 26 17 061 049 070 120 186 084 112 03 07 15 48 13
          28 18 26 15 34
2512 1208 29 26 13 25 53 10 22 32 18 085 072 128 202 185 087 111 04 08 09 33 16
          25 15 22 16 38
2521 1111 29 30 13 15 33 07 16 21 12 064 046 074 165 180 088 101 01 04 11 -1 09
          10 09 25 11 45
2531 1401 16 23 10 05 42 07 20 27 13 095 056 128 137 172 093 089 01 10 05 36 06
          15 06 21 05 13
2542 1204 25 25 15 26 32 05 07 24 05 046 077 072 121 176 095 108 08 08 17 30 14
          24 04 14 18 20
2561 1302 25 27 11 15 50 06 16 34 13 086 068 099 214 160 083 097 04 06 10 33 14
          28 19 21 13 39
2572 1203 37 31 15 16 54 14 28 31 20 088 079 114 223 173 088 106 05 06 16 36 12
          23 19 27 17 40
2582 1211 30 26 13 19 36 07 21 27 14 089 055 121 222 164 098 103 04 09 16 47 18
          16 24 32 16 32
2591 1105 29 24 16 10 45 09 21 28 17 100 069 121 206 181 085 106 09 07 08 60 17
          28 27 27 16 36
2602 1204 37 34 11 17 56 18 24 31 27 080 070 112 229 166 091 101 10 13 12 40 15
          38 30 29 14 47
2612 1200 28 20 14 04 30 08 18 25 06 073 074 084 161 189 089 106 10 05 15 35 16
          15 08 24 15 37
2621 1311 22 34 14 19 40 10 17 24 15 080 056 101 185 161 086 100 01 04 16 27 15
          19 11 25 19 35
2632 1204 38 21 14 18 56 18 26 37 31 120 069 109 205 179 084 103 05 07 11 87 15
          35 18 29 12 46
2641 1209 29 21 16 18 23 07 16 22 07 067 064 088 180 158 095 097 06 08 17 09 13
          36 27 20 18 43
2651 1210 25 25 18 30 48 09 16 36 16 106 067 127 175 177 088 102 04 14 20 28 17
          24 20 30 17 33
2662 1307 28 27 12 13 16 05 06 12 04 101 065 106 172 177 094 097 10 08 09 06 07
          12 02 19 16 32
2671 1302 23 20 16 18 29 06 12 25 06 043 056 100 145 151 073 095 10 03 08 09 04
          11 05 13 13 29
2682 1400 11 20 15 09 39 13 16 22 15 073 081 073 130 186 092 103 03 07 14 03 07
          14 05 16 11 28
2692 1211 45 37 16 29 32 10 16 30 16 073 069 101 220 178 084 113 04 13 19 70 15
          31 35 25 22 37
2702 1300 19 15 12 07 43 15 19 26 19 101 076 112 116 184 084 092 08 08 17 33 14
          21 18 28 14 19
2712 1208 41 21 10 11 41 09 16 28 14 124 095 119 220 185 083 103 09 11 19 09 14
          38 24 31 15 36
2721 1203 35 30 16 33 49 08 15 29 12 090 064 126 242 174 092 093 06 14 14 68 18
          27 33 21 17 42
2732 1200 34 24 21 16 46 09 14 24 10 089 098 116 236 171 096 105 07 10 13-12 20
          26 11 31 13 41
2741 1302 19 22 15 12 39 10 16 26 17 118 113 116 252 178 086 093 10 11 17 12 15
          21 11 23 12 19
2751 1207 25 21 13 15 40 08 17 25 17 068 042 082 170 145 078 095 03 17 16 13 07
          16 05 21 17 40
2761 1104 36 28 19 33 61 15 24 36 39 081 070 104 210 178 096 097 11 05 19 57 20
          43 32 29 19 48
2772 1400 19 23 10 06 31 07 13 19 19 068 056 097 149 174 094 096 01 01 09 -3 07
          21 01 20 11 25
2782 1209 27 21 12 07 34 09 19 27 22 088 062 109 191 171 096 096 04 02 15 33 08
          30 23 20 14 24
2792 1205 21 09 13 14 33 15 25 32 30 068 064 091 181 178 095 100 12 05 17 43 12
          31 19 27 14 46
2801 1209 27 22 11 32 35 08 13 26 13 056 056 075 177 171 081 107 03 11 18-12 14
          28 19 21 16 42
2812 1207 26 22 12 08 43 05 11 28 11 076 051 114 156 171 082 099 05 07 18 33 10
          27 24 22 17 27
2821 1402 20 27 18 15 41 09 19 28 11 130 088 105 241 195 095 115 01 13 16 48 15
          27 17 31 16 40
2832 1301 33 22 14 14 50 13 25 29 26 084 060 127 230 172 079 103 04 09 19 24 15
          26 30 30 17 46
2842 1211 38 26 19 07 39 16 25 28 15 109 072 111 200 182 092 108 11 15 20 45 18
          08 19 32 19 35
2852 1302 33 25 16 34 41 15 23 31 21 109 083 143 246 180 086 109 16 20 20 27 20
          33 22 33 14 45
2861 1306 36 29 17 16 55 09 23 29 23 149 092 156 247 182 084 106 11 15 19 48 20
          29 35 31 16 33
2871 1309 38 29 12 35 55 13 22 27 17 112 078 126 227 178 110 109 11 16 16 57 23
          48 15 30 14 48
2882 1302 31 25 10 28 54 10 25 34 27 103 064 098 168 165 089 104 11 07 14 30 19
          28 24 25 14 48
2892 1301 36 28 17 13 48 11 23 36 18 134 095 102 224 168 092 104 13 19 20 24 23
          43 22 31 18 46
2901 1308 31 26 16 29 58 11 26 37 12 085 074 099 189 198 104 110 07 15 17 42 18
          38 24 30 20 38
2912 1402 28 24 14 06 44 08 17 33 15 092 085 095 215 187 089 108 09 11 17 25 16
          31 19 32 15 31
2921 1308 38 37 15 28 61 10 19 33 19 110 085 135 199 166 082 095  0 19 16 39 19
          40 28 30 20 46
2931 1306 29 34 14 22 48 10 20 22 19 084 053 132 147 184 098 094 05 11 13 41 14
          27 24 28 14 35
2941 1307 35 24 16 15 60 12 20 30 23 092 090 125 232 196 083 106 11 09 18 54 22
          41 20 29 17 33
2952 1303 35 25 18 25 50 11 24 31 19 132 069 118 187 168 085 107 02 11 18 15 18
          27 20 22 16 40
2962 1210 23 21 13 03 54 09 21 24 12 133 065 111 235 182 090 102 12 11 20 24 16
          20 15 29 13 25
2972 1305 31 28 14 25 51 12 23 32 15 139 091 110 199 175 094 108 08 14 18 30 11
          30 06 26 15 45
2981 1406 51 26 23 34 84 18 26 43 38 133 110 161 249 195 112 119 15 20 20 71 24
          50 40 32 18 48
2992 1409 33 28 14 18 60 15 23 29 30 096 089 122 212 178 086 102 07 06 17 21 15
          29 31 30 18 40
3002 1511 21 26 13 17 64 12 16 23 33 118 069 112 132 180 086 101 08 13 12 34 12
          24 23 28 15 36
3022 1309 37 32 17 11 72 16 25 28 32 080 084 095 171 187 104 118 14 18 17 78 16
          50 27 33 15 45
3031 1301 37 20 15 21 36 08 19 33 19 078 045 101 179 174 086 113 05 13 17 40 21
          24 08 24 14 46
3041 1309 37 26 20 29 43 07 19 23 19 102 077 113 210 178 084 104 05 12 11 28 23
          27 23 28 08 29
3051 1307 39 26 15 24 78 17 26 35 30 115 101 136 266 190 101 119 06 04 08 54 11
          35 29 32 18 35
3062 1300 39 34 17 33 45 10 19 26 16 095 075 119 240 172 094 111 07 16 16 30 22
          35 33 28 18 37
3072 1308 18 16 06 04 55 10 21 29 19 068 042 080 125 194 088 103 15 12 14 15 03
          18 05 25 10 15
3082 1209 28 28 14 18 50 10 25 37 23 087 060 093 170 177 088 104 19 13 19 28 15
          23 13 24 20 45
3091 1410 29 23 16 09 38 06 24 27 09 100 063 182 227 169 088 097 06 10 14 33 19
          38 25 27 17 30
3102 1305 25 20 14 11 51 12 19 33 17 111 066 125 206 194 103 102 11 17 19 33 20
          35 14 29 17 39
3111 1304 29 33 16 11 57 13 23 31 26 078 061 103 198 173 079 107 07 09 13 62 13
          34 19 26 21 42
3122 1306 32 21 16 15 55 14 20 28 29 117 072 108 237 171 092 108 06 13 18 45 13
          18 24 31 15 40
3131 1306 30 21 14 21 51 12 24 33 27 096 069 113 206 183 096 107 15 11 20 84 14
          50 27 35 20 35
3142 1305 35 28 14 19 59 18 27 34 33 145 090 117 226 186 097 100 09 08 20 70 23
          44 39 33 15 44
3151 1201 16 34 13 32 60 14 22 29 24 099 058 108 215 188 079 101 05 11 10 48 21
          27 17 32 16 37
3162 1305 38 30 19 33 66 14 22 36 29 097 091 120 252 192 096 102 07 11 17 15 19
          43 36 30 17 33
3172 1302 26 25 12 07 41 10 15 25 13 115 078 129 183 158 095 113 09 13 20 15 17
          18 10 26 13 25
3182 1307 33 28 10 12 37 14 21 33 14 106 088 106 187 173 100 109 08 12 20 21 16
          35 15 27 10 37
3201 1503 28 27 20 32 48 10 19 30 17 069 049 130 198 179 095 105 03 05 14 42 12
          22 30 19 12 46
3212 1303 27 22 13 16 46 08 19 33 15 135 075 142 225 176 089 103 09 04 11 33 16
          19 23 26 17 46
3222 1404 26 29 17 10 53 08 19 29 13 085 091 109 220 181 086 109 07 12 14 45 15
          26 10 25 19 27
3231 1407 30 32 09 26 44 07 15 34 10 109 061 140 178 184 094 103 08 08 20 51 18
          27 20 29 15 48
3241 1303 22 23 17 08 40 04 13 25 17 092 054 081 150 174 087 102 06 03 19-18 12
          08 05 24 11 18
3251 1308 32 24 12 08 52 09 19 29 14 078 077 089 191 187 086 094 04 06 14 24 12
          30 21 18 17 40
3261 1406 31 23 12 13 37 12 19 21 15 082 084 108 182 180 090 108 04 12 15 40 12
          28 03 27 17 26
3271 1409 32 25 13 17 42 05 11 23 16 106 058 143 200 168 087 107 04 10 17 06 15
          30 13 29 13 45
3281 1604 40 21 15 15 39 08 18 24 13 084 067 126 215 180 111 100 10 17 10 28 14
          22 01 26 16 47
3291 1211 21 20 15 11 36 07 19 28 08 080 069 117 235 180 074 101 06 02 14 30 21
          18 15 23 16 47
3301 1301 31 25 15 26 58 15 24 34 23 055 052 080 154 171 085 102 04 06 14 50 14
          32 15 22 17 41
3312 1401 24 24 14 16 44 08 18 22 12 077 074 094 135 175 091 104 08 08 18 16 15
          35 06 29 11 46
3331 1403 41 25 20 15 42 12 21 32 19 064 068 094 189 177 079 105 02 05 11 03 09
          09 07 20 19 44
3342 1409 30 23 11 10 42 08 14 30 12 098 065 132 240 165 092 097 10 12 18 16 09
          17 22 25 18 24
3351 1601 35 20 14 16 55 10 16 26 10 080 078 105 205 171 085 102 05 03 13 15 17
          25 24 27 16 37
3361 1305 34 27 14 17 48 13 25 33 32 077 072 119 195 170 089 114 11 07 16 60 21
          39 34 24 16 37
3371 1403 25 22 13 13 43 07 16 25 11 135 061 126 204 159 076 097  0 03 10 03 18
          10 05 21 12 25
3382 1301 24 25 18 06 42 09 19 31 23 120 097 131 206 194 099 113 26 16 19 24 02
          37 19 28 18 44
3391 1404 36 26 18 17 53 10 19 26 10 056 046 115 180 174 099 098 10 07 12 30 11
          14 12 20 22 38
3401 1506 20 19 19 07 37 08 20 28 06 134 045 110 244 189 092 094 09 09 15 07 12
          13 07 26 15 41
3411 1411 28 24 12 09 31 04 12 27 11 096 072 115 174 172 085 105 07 08 18 33 21
          46 23 29 17 34
3422 1211 34 22 08 13 44 10 17 25 14 126 063 120 162 164 082 096 09 05 14 03 02
          29 23 33 15 29
3431 1403 34 21 17 19 51 10 18 28 14 078 062 103 228 173 099 104 01 08 14 24 14
          39 11 19 19 28
3442 1300 35 28 13 10 45 09 13 26 11 096 063 097 208 189 096 115 10 15 18 43 20
          39 27 20 17 36
3451 1303 37 26 15 24 49 09 17 31 20 070 058 085 204 189 092 112 07 09 18 39 12
          25 17 25 15 42
3461 1305 24 28 18 11 49 08 17 27 07 117 058 112 189 166 075 102 01 04 08 09 12
          32 12 20 16 36
3472 1410 18 24 14 13 31 07 16 23 07 106 054 121 219 162 087 104 02 03 14 28 15
          17 08 21 14 41
3482 1403 28 22 16 15 55 11 23 32 30 092 118 120 274 171 092 105 07 16 17 30 20
          28 18 30 16 32
3491 1402 26 27 14 04 48 11 18 33 14 117 049 124 158 181 087 102 09 04 13 10 15
          10 20 27 18 14
3511 1305 26 24 16 27 51 11 23 39 22 094 087 139 186 184 090 109 25 16 14 69 14
          39 31 29 16 45






EFA/CFA Book data and SPSS syntax
LibQUAL+TM Data

001 2 8 7 5 5 3 2 3 3 6 5 5 7
002 2 5 7 5 5 4 5 5 6 3 4 3 6
003 2 6 5 5 6 5 3 5 3 3 5 4 7
004 2 5 5 4 6 4 4 4 4 2 1 5 4
005 2 5 5 5 5 5 4 6 2 4 4 5 7
006 2 7 7 7 8 7 8 7 6 6 8 7 5
007 2 8 8 7 7 6 7 6 7 6 5 6 5
008 2 7 7 7 7 5 3 3 6 5 4 5 8
009 2 1 3 1 1 1 1 1 1 1 1 1 1
010 2 9 9 9 8 5 7 5 2 7 6 7 7
011 2 9 9 9 7 7 9 9 7 4 6 5 9
012 2 4 3 3 5 6 5 4 5 2 5 1 4
013 2 6 6 5 7 7 7 7 7 6 5 6 7
014 2 7 7 2 7 2 3 2 3 6 6 5 9
015 2 9 9 9 9 7 5 4 5 4 1 1 4
016 2 9 9 9 9 5 5 5 7 6 7 6 6
017 2 8 8 9 9 7 6 6 8 6 8 9 9
018 2 8 5 8 5 4 8 5 8 4 3 4 7
019 2 9 9 9 9 6 7 6 6 7 9 9 9
020 2 8 7 7 9 5 6 6 4 5 6 2 9
021 2 9 9 8 9 9 9 9 8 8 6 6 7
022 2 7 7 7 8 7 6 7 6 7 8 8 7
023 2 7 8 7 6 5 6 5 6 6 5 7 7
024 2 8 9 7 8 5 3 4 5 9 9 7 7
025 2 8 8 8 8 6 6 6 7 9 6 9 9
026 2 8 9 9 9 6 6 6 6 8 8 9 8
027 2 8 8 8 8 8 7 8 8 8 9 8 7
028 2 9 9 9 7 6 7 7 6 9 9 6 9
029 2 6 5 6 5 5 6 6 5 4 5 4 5
030 2 8 8 7 8 7 8 8 7 8 7 6 7
031 2 7 7 7 8 8 7 8 7 7 6 8 8
032 2 1 1 2 1 1 1 1 1 6 6 5 9
033 2 8 8 8 8 8 7 7 8 6 1 6 7
034 2 8 8 6 7 5 6 5 6 8 9 8 7
035 2 8 8 8 5 7 4 8 5 6 6 6 7
036 2 8 8 8 8 5 5 6 5 7 7 7 8
037 2 6 4 5 6 7 7 7 8 5 5 4 4
038 2 5 4 5 3 3 4 3 4 6 7 3 3
039 2 5 7 6 7 7 5 6 7 6 6 5 5
040 2 7 7 7 8 6 7 7 7 7 6 8 8
041 2 6 8 7 7 8 7 7 7 7 9 7 7
042 2 7 8 8 5 6 6 6 6 6 5 5 6
043 2 8 8 7 7 5 6 5 6 6 7 6 7
044 2 7 7 5 7 4 5 4 4 8 7 6 8
045 2 9 9 9 9 8 9 8 8 9 7 7 9
046 2 7 7 7 7 7 7 7 7 7 7 7 1
047 2 7 5 7 6 4 7 5 4 7 9 6 7
048 2 1 3 3 1 4 5 4 4 9 6 7 7
049 2 7 8 6 7 8 8 8 8 7 6 7 7
050 2 8 8 7 8 8 7 7 8 7 8 7 7
051 2 9 8 8 8 6 4 6 8 6 7 6 7
052 2 9 9 8 9 8 7 8 7 8 7 9 9
053 2 6 6 5 6 8 4 6 7 7 7 6 7
054 2 8 8 8 4 5 4 5 8 7 9 6 7
055 2 8 8 7 9 2 2 2 1 5 2 6 8
056 2 8 8 6 8 3 3 5 6 6 5 4 7
057 2 9 9 9 9 2 2 2 2 8 9 9 9
058 2 8 8 8 8 7 7 8 8 8 8 8 8
059 2 6 5 6 7 6 7 6 7 5 6 6 7
060 2 8 9 7 7 6 5 6 6 7 7 6 7
061 2 7 7 7 7 7 7 7 8 7 7 7 7
062 2 6 7 7 6 4 4 5 7 6 3 7 3
063 2 8 8 7 8 8 6 7 9 7 7 7 8
064 2 8 7 8 8 4 4 4 3 8 2 6 4
065 2 8 9 8 7 7 7 7 7 6 6 7 7
066 2 8 8 9 8 8 8 8 8 7 9 6 7
067 2 7 7 7 7 7 7 6 8 7 6 7 8
068 2 6 3 7 9 4 3 3 3 9 4 5 5
069 2 9 8 8 9 8 8 7 8 6 7 6 9
070 2 6 6 6 5 4 4 4 4 5 2 5 6
071 2 8 8 9 8 7 7 7 7 8 6 7 8
072 2 8 8 8 8 7 6 8 7 7 7 7 8
073 2 9 9 7 8 4 5 6 7 6 7 6 7
074 2 8 8 8 8 6 6 6 6 8 8 8 8
075 2 8 8 8 7 8 7 8 7 6 8 8 9
076 2 8 8 7 8 4 5 6 8 8 5 6 7
077 2 6 6 6 6 7 7 5 6 6 5 3 7
078 2 7 5 5 4 3 5 5 6 2 2 6 8
079 2 4 4 5 8 7 8 8 8 6 4 6 9
080 2 9 9 8 7 8 1 5 9 9 6 9 9
081 2 8 5 8 7 3 1 6 1 7 7 6 9
082 2 9 7 7 9 9 8 7 9 8 8 8 7
083 2 8 8 8 8 6 4 7 7 7 9 6 6
084 2 7 7 7 7 6 6 6 7 6 7 6 8
085 2 4 2 3 4 1 4 1 1 1 1 1 1
086 2 6 7 5 6 5 6 6 6 5 6 6 7
087 2 6 6 7 7 8 5 6 9 6 7 6 7
088 2 8 8 7 8 6 5 7 7 8 5 6 9
089 2 9 9 9 9 9 6 9 9 8 9 9 7
090 2 5 5 5 5 3 3 4 4 7 5 5 7
091 2 8 8 8 8 7 7 7 7 6 9 7 9
092 2 5 5 4 5 7 7 7 7 7 6 7 5
093 2 8 8 8 8 6 6 6 6 4 4 6 7
094 2 8 8 9 8 8 6 8 8 9 5 7 9
095 2 8 7 7 7 8 6 7 6 7 7 7 8
096 2 8 8 8 8 7 7 7 6 7 5 7 8
097 2 7 8 7 8 7 5 8 5 7 8 7 7
098 2 4 5 2 1 5 1 2 2 1 2 6 2
099 2 8 8 1 8 3 5 8 3 4 2 1 7
100 2 9 8 8 9 7 4 9 8 9 8 9 9
101 3 7 7 7 7 6 7 6 6 7 6 6 8
102 3 8 8 6 5 4 4 4 7 6 7 5 8
103 3 5 5 5 5 5 7 5 6 4 5 5 5
104 3 9 9 9 9 6 6 6 7 5 6 9 7
105 3 9 9 8 8 5 3 5 6 8 6 6 8
106 3 6 7 5 7 6 5 5 6 6 7 8 4
107 3 8 6 9 7 6 8 8 7 5 5 6 7
108 3 8 7 8 8 6 4 6 7 6 5 6 7
109 3 6 6 6 5 5 5 5 4 6 7 5 8
110 3 8 7 7 8 6 8 4 6 6 8 7 7
111 3 9 8 9 9 9 9 9 9 8 5 8 9
112 3 7 7 8 7 6 8 7 5 6 6 7 5
113 3 7 7 7 6 6 6 6 6 4 6 7 6
114 3 7 7 7 8 1 2 2 1 7 7 8 3
115 3 7 7 8 7 7 7 7 7 7 5 6 4
116 3 8 9 8 8 6 5 6 9 4 3 5 7
117 3 5 4 5 5 7 7 5 5 1 3 5 3
118 3 9 9 8 9 7 7 7 9 7 6 8 9
119 3 8 8 8 8 7 7 7 8 6 7 8 8
120 3 7 6 8 8 7 6 7 6 7 8 7 5
121 3 8 8 8 9 8 8 8 8 4 7 6 7
122 3 8 7 8 8 5 4 6 5 6 7 6 7
123 3 5 3 4 5 2 2 2 2 2 2 2 7
124 3 9 9 9 9 8 8 8 8 6 7 6 9
125 3 8 8 7 9 3 3 3 5 4 5 6 8
126 3 9 9 9 9 9 8 8 9 6 7 8 8
127 3 9 9 8 9 8 8 8 8 8 7 8 9
128 3 9 9 7 9 7 7 7 6 8 7 8 7
129 3 3 3 7 3 7 5 6 3 7 3 6 6
130 3 9 9 8 9 6 7 7 7 7 7 8 8
131 3 8 8 6 7 3 5 4 5 8 6 8 8
132 3 5 5 5 5 2 3 3 2 6 6 5 5
133 3 7 6 8 8 5 5 5 6 5 4 5 8
134 3 8 8 9 8 6 6 6 7 8 7 8 8
135 3 8 8 9 8 8 9 8 8 7 3 7 7
136 3 7 6 8 8 4 4 4 6 8 6 7 8
137 3 8 8 8 8 2 2 2 2 1 1 6 4
138 3 8 7 8 9 6 7 5 7 6 8 6 6
139 3 7 8 8 7 6 7 6 7 4 8 8 9
140 3 9 9 7 8 7 8 7 8 5 6 8 8
141 3 8 7 7 8 6 7 8 7 7 6 6 7
142 3 8 7 6 7 6 6 6 6 7 2 5 8
143 3 7 7 7 5 3 2 5 3 9 3 7 4
144 3 8 8 8 8 9 8 8 8 8 8 8 7
145 3 8 7 8 7 3 5 5 5 6 8 5 6
146 3 7 7 6 7 6 5 6 7 7 5 7 6
147 3 8 8 7 8 5 6 5 6 6 3 5 3
148 3 5 7 5 7 6 6 6 6 5 5 6 7
149 3 9 9 9 9 7 7 7 7 9 9 9 7
150 3 8 8 7 8 5 5 5 7 1 5 6 8
151 3 8 5 8 8 8 8 8 8 7 2 6 6
152 3 9 9 9 9 5 6 5 9 9 8 8 5
153 3 9 9 8 9 8 8 8 9 8 9 8 8
154 3 8 8 8 8 7 7 7 8 8 8 7 7
155 3 6 5 5 5 3 2 3 2 1 2 5 3
156 3 5 5 3 5 5 5 5 7 5 3 7 7
157 3 7 6 7 7 3 3 4 4 3 6 7 4
158 3 7 6 6 7 5 5 7 6 7 4 8 6
159 3 9 8 9 9 6 6 6 7 7 9 8 9
160 3 7 7 7 7 6 6 6 2 8 8 6 7
161 3 8 8 3 7 5 5 5 4 7 5 6 7
162 3 8 8 8 8 8 8 8 9 7 3 7 2
163 3 9 9 9 9 1 1 1 1 6 5 6 9
164 3 9 9 9 9 6 6 7 6 6 3 8 7
165 3 7 7 6 7 6 7 6 6 6 7 5 6
166 3 9 5 9 8 9 9 9 3 9 8 9 7
167 3 9 7 9 7 8 7 8 7 5 7 6 7
168 3 5 3 6 3 3 2 3 5 6 2 2 2
169 3 9 8 7 7 7 7 7 9 7 8 7 6
170 3 9 9 7 9 1 1 3 2 4 3 8 5
171 3 8 8 7 7 5 7 7 7 7 4 4 7
172 3 6 7 5 6 6 6 6 6 4 5 6 4
173 3 9 8 9 8 9 9 9 9 6 7 6 7
174 3 9 9 8 9 2 5 5 6 7 5 8 7
175 3 6 6 7 8 6 5 6 5 6 7 7 6
176 3 8 8 5 9 7 3 3 3 5 7 8 7
177 3 9 9 8 9 9 8 9 9 7 6 6 5
178 3 8 8 7 8 3 3 7 4 5 5 5 7
179 3 7 7 7 8 5 5 5 6 6 6 7 7
180 3 8 8 8 7 4 4 4 7 5 8 8 7
181 3 8 8 8 9 5 8 5 6 8 8 6 7
182 3 8 8 8 8 6 6 6 6 7 8 8 7
183 3 9 9 9 9 8 8 8 8 9 7 7 8
184 3 9 7 5 7 5 6 5 5 4 5 3 8
185 3 9 9 9 9 8 7 8 7 5 7 9 9
186 3 6 7 5 4 5 5 5 3 5 5 5 5
187 3 9 8 8 9 7 6 7 7 6 7 7 7
188 3 9 9 9 9 8 8 8 8 6 6 7 8
189 3 6 5 6 7 1 2 1 2 2 4 6 5
190 3 9 9 9 9 8 7 7 9 8 9 9 9
191 3 9 9 9 9 4 5 4 4 4 5 6 9
192 3 9 9 8 7 3 3 3 5 5 7 7 9
193 3 9 9 6 9 8 5 5 9 4 9 8 7
194 3 8 7 6 6 7 8 7 9 6 8 9 8
195 3 6 6 5 6 7 6 7 8 6 7 9 6
196 3 6 6 4 5 5 4 5 5 6 4 2 6
197 3 9 9 9 9 9 6 7 9 6 6 6 7
198 3 6 5 5 5 3 5 3 3 3 3 6 7
199 3 6 4 4 6 8 8 8 8 8 7 8 6
200 3 4 4 7 4 2 1 2 2 2 3 6 8
Note. The first column reports case ID numbers. The second column represents ROLETYPE ("2" = graduate students; "3" = faculty). The next 12 variables (PER1 to PER12) are the 12 rating criteria presented in the book in Table 3.1.



SET PRINTBACK=LISTING .
COMMENT  Type in the pattern matrix that is the target as
   matrix "A"; Then type in the pattern matrix to be
   rotated to 'best fit' position with the target as
   matrix "B"; Type in a matrix "N_A" consisting of all
   zeroes with the same number or rows as "A" and "B";
   Type in a square matrix of all zeroes with the number
   of rows and columns equal to the number of orthogonal
   factors in "A" and "B" .
MATRIX .
COMMENT  100 Grad Students, Varimax Pattern Matrix .
COMPUTE A =
{ .92134,  .18690,  .18737 ;
  .84248,  .21945,  .25625 ;
  .75606,  .28211,  .35335 ;
  .80837,  .28360,  .19958 ;
  .22292,  .84662,  .26935 ;
  .17110,  .85348,  .03794 ;
  .29969,  .82875,  .22423 ;
  .22454,  .80378,  .26075 ;
  .30781,  .13580,  .81976 ;
  .15795,  .23077,  .80478 ;
  .27222,  .20840,  .80207 } .
COMMENT  100 Faculty, Varimax Pattern Matrix .
COMPUTE B =
{ .20284,  .89659,  .18675 ;
  .10828,  .87403,  .21537 ;
  .23051,  .73176,  .16198 ;
  .16361,  .87285,  .24099 ;
  .90807,  .12646,  .21722 ;
  .89654,  .13470,  .21639 ;
  .90832,  .17788,  .19870 ;
  .79337,  .32388,  .14477 ;
  .33664,  .10530,  .66769 ;
  .18185,  .22943,  .79192 ;
  .11897,  .33942,  .71967 } .
COMPUTE N_A =
{ .0 ;
  .0 ;
  .0 ;
  .0 ;
  .0 ;
  .0 ;
  .0 ;
  .0 ;
  .0 ;
  .0 ;
  .0 } .
COMPUTE DIAG_M =
{ .0, .0, .0 ;
  .0, .0, .0 ;
  .0, .0, .0 } .
COMPUTE N_B=N_A .
PRINT A /
  FORMAT='F8.2' /
  TITLE='First Pattern Matrix (Target)' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 /
  CLABELS=Fact_I, Fact_II, Fact_III / .
COMPUTE A_N=A .
-LOOP #I=1 TO NROW(A) .
+ LOOP #J=1 TO NCOL(A) .
COMPUTE A_N(#I,#J)=A(#I,#J) ** 2 .
+ END LOOP .
-END LOOP .
PRINT A_N /
  FORMAT='F8.4' /
  TITLE='First Pattern Matrix (Target) Squared' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 /
  CLABELS=Fact_I, Fact_II, Fact_III / .
-LOOP #J=1 TO NCOL(A) .
+ LOOP #I=1 TO NROW(A) .
COMPUTE N_A(#I)=A_N(#I,#J) + N_A(#I) .
+ END LOOP .
-END LOOP .
PRINT N_A /
  FORMAT='F8.3' /
  TITLE='Row Sum of Squares for First Pattern Matrix' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 / .
LOOP #I=1 TO NROW(A) .
COMPUTE N_A(#I) = 1.0 / (N_A(#I) ** .5) .
END LOOP .
PRINT N_A /
  FORMAT='F8.3' /
  TITLE='Normalization Factor for Rows' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 / .
-LOOP #J=1 TO NCOL(A) .
+ LOOP #I=1 TO NROW(A) .
COMPUTE A_N(#I,#J)=A(#I,#J) * N_A(#I) .
+ END LOOP .
-END LOOP .
PRINT A_N /
  FORMAT='F8.4' /
  TITLE='First Pattern Matrix (Target) Normalized' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 /
  CLABELS=Fact_I, Fact_II, Fact_III / .
PRINT B /
  FORMAT='F8.2' /
  TITLE='Second Pattern Matrix' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 /
  CLABELS=Fact_I, Fact_II, Fact_III / .
COMPUTE B_N=B .
-LOOP #I=1 TO NROW(B) .
+ LOOP #J=1 TO NCOL(B) .
COMPUTE B_N(#I,#J)=B(#I,#J) ** 2 .
+ END LOOP .
-END LOOP .
PRINT B_N /
  FORMAT='F8.4' /
  TITLE='Second Pattern Matrix Squared' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 /
  CLABELS=Fact_I, Fact_II, Fact_III / .
-LOOP #J=1 TO NCOL(B) .
+ LOOP #I=1 TO NROW(B) .
COMPUTE N_B(#I)=B_N(#I,#J) + N_B(#I) .
+ END LOOP .
-END LOOP .
PRINT N_B /
  FORMAT='F8.3' /
  TITLE='Row Sum of Squares for Second Pattern Matrix' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 / .
LOOP #I=1 TO NROW(B) .
COMPUTE N_B(#I) = 1.0 / (N_B(#I) ** .5) .
END LOOP .
PRINT N_B /
  FORMAT='F8.3' /
  TITLE='Normalization Factor for Rows' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 / .
-LOOP #J=1 TO NCOL(B) .
+ LOOP #I=1 TO NROW(B) .
COMPUTE B_N(#I,#J)=B(#I,#J) * N_B(#I) .
+ END LOOP .
-END LOOP .
PRINT B_N /
  FORMAT='F8.4' /
  TITLE='Second Pattern Matrix Normalized' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 /
  CLABELS=Fact_I, Fact_II, Fact_III / .
COMPUTE A_T=TRANSPOS(A_N) .
PRINT A_T /
  FORMAT='F8.2' /
  TITLE='A_N Transpose' /
  SPACE=4 /
  RLABELS=Fact_I, Fact_II, Fact_III /
  CLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 / .
COMPUTE B_T=TRANSPOS(B_N) .
PRINT B_T /
  FORMAT='F8.2' /
  TITLE='B_N Transpose' /
  SPACE=4 /
  RLABELS=Fact_I, Fact_II, Fact_III /
  CLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 / .
COMPUTE RI=A_T * B_N .
PRINT RI /
  FORMAT='F8.3' /
  TITLE='A_N Transpose times B_N' /
  SPACE=4 /
  RLABELS=Fact_I, Fact_II, Fact_III /
  CLABELS=Fact_I, Fact_II, Fact_III / .
COMPUTE RI_T=TRANSPOS(RI) .
PRINT RI_T /
  FORMAT='F8.3' /
  TITLE='Transpose of (A_N Transpose times B_N)' /
  SPACE=4 /
  RLABELS=Fact_I, Fact_II, Fact_III /
  CLABELS=Fact_I, Fact_II, Fact_III / .
COMPUTE QUAD=RI * RI_T .
PRINT QUAD /
  FORMAT='F8.3' /
  TITLE='A_N Trans * B_N * Trans of (A_N Trans * B_N)' /
  SPACE=4 /
  RLABELS=Fact_I, Fact_II, Fact_III /
  CLABELS=Fact_I, Fact_II, Fact_III / .
CALL EIGEN(QUAD, EIGVEC, EIG) .
PRINT EIG /
  FORMAT='F8.3' /
  TITLE='Eigenvalues of QUAD' /
  SPACE=4 /
  RLABELS=Fact_I, Fact_II, Fact_III / .
PRINT EIGVEC /
  FORMAT='F8.3' /
  TITLE='Eigenvectors of QUAD' /
  SPACE=4 /
  RLABELS=ONE, TWO /
  CLABELS=Fact_I, Fact_II, Fact_III / .
-LOOP #I=1 TO NROW(QUAD) .
+ LOOP #J=1 TO NROW(QUAD) .
COMPUTE EIGVEC(#I,#J)=EIGVEC(#I,#J) * (EIG(#J) ** .5) .
+ END LOOP .
-END LOOP .
PRINT EIGVEC /
  FORMAT='F8.3' /
  TITLE='Pattern Coefficients of QUAD' /
  SPACE=4 /
  RLABELS=ONE, TWO /
  CLABELS=Fact_I, Fact_II, Fact_III / .
LOOP I=1 TO NROW(EIG) .
COMPUTE EIG(I)=EIG(I) ** -1.5 .
END LOOP .
PRINT EIG /
  FORMAT='F8.3' /
  TITLE='Eigenvalues raised to -1.5' /
  SPACE=4 /
  RLABELS=Fact_I, Fact_II, Fact_III / .
CALL SETDIAG(DIAG_M,EIG) .
PRINT DIAG_M /
  FORMAT='F8.3' /
  TITLE='Diagonal Matrix (Eigenvalues raised to -1.5)' /
  SPACE=4 /
  CLABELS=Fact_I, Fact_II, Fact_III /
  RLABELS=Fact_I, Fact_II, Fact_III / .
COMPUTE VEC_T=TRANSPOS(EIGVEC) .
PRINT VEC_T /
  FORMAT='F8.3' /
  TITLE='Transpose of Eigenvectors' /
  SPACE=4 /
  RLABELS=Fact_I, Fact_II, Fact_III /
  CLABELS=ONE, TWO / .
COMPUTE D=RI_T * EIGVEC .
PRINT D /
  FORMAT='F9.3' /
  TITLE='D= trans (trans A times B) times Eigenvectors' /
  SPACE=4 /
  RLABELS=Fact_I, Fact_II, Fact_III /
  CLABELS=Fact_I, Fact_II, Fact_III / .
-LOOP J=1 TO NCOL(A) .
COMPUTE EE=EIG(J) .
+ LOOP I=1 TO NCOL(A) .
COMPUTE D(I,J)=D(I,J) * EE .
+ END LOOP .
-END LOOP .
PRINT D /
  FORMAT='F9.3' /
  TITLE='D = D times Eigenvalues ** -1.5' /
  SPACE=4 /
  RLABELS=Fact_I, Fact_II, Fact_III /
  CLABELS=Fact_I, Fact_II, Fact_III / .
COMPUTE D_T=TRANSPOS(D) .
PRINT D_T /
  FORMAT='F9.3' /
  TITLE='D transposed' /
  SPACE=4 /
  RLABELS=Fact_I, Fact_II, Fact_III /
  CLABELS=Fact_I, Fact_II, Fact_III / .
COMPUTE C=EIGVEC * D_T .
PRINT C /
  FORMAT='F9.3' /
  TITLE='Factor Correlations (Cosines)' /
  SPACE=4 /
  RLABELS=Fact_Ia, Fact_IIa, Fact_IIIa /
  CLABELS=Fact_Ib, Fact_IIb, Fact_IIIb / .
COMPUTE C=D * VEC_T .
COMPUTE B_ROT=B * C .
PRINT B_ROT /
  FORMAT='F8.3' /
  TITLE='B rotated to Best-Fit with A' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 /
  CLABELS=Fact_I, Fact_II, Fact_III / .
COMPUTE BROT_N=B_ROT .
-LOOP #I=1 TO NROW(A) .
+ LOOP #J=1 TO NCOL(A) .
COMPUTE BROT_N(#I,#J)=B_ROT(#I,#J) ** 2 .
+ END LOOP .
COMPUTE N_A(#I)= .0 .
-END LOOP .
PRINT BROT_N /
  FORMAT='F8.4' /
  TITLE='Best Fit Pattern Matrix (Target) Squared' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 /
  CLABELS=Fact_I, Fact_II, Fact_III / .
-LOOP #J=1 TO NCOL(A) .
+ LOOP #I=1 TO NROW(A) .
COMPUTE N_A(#I)=BROT_N(#I,#J) + N_A(#I) .
+ END LOOP .
-END LOOP .
PRINT N_A /
  FORMAT='F8.3' /
  TITLE='Row Sum of Squares for Best Fit Matrix' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 / .
LOOP #I=1 TO NROW(A) .
COMPUTE N_A(#I) = 1.0 / (N_A(#I) ** .5) .
END LOOP .
PRINT N_A /
  FORMAT='F8.3' /
  TITLE='Normalization Factor for Rows' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 / .
-LOOP #J=1 TO NCOL(A) .
+ LOOP #I=1 TO NROW(A) .
COMPUTE BROT_N(#I,#J)=B_ROT(#I,#J) * N_A(#I) .
+ END LOOP .
-END LOOP .
PRINT BROT_N /
  FORMAT='F8.4' /
  TITLE='Best Fit Pattern Matrix (Target) Normalized' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 /
  CLABELS=Fact_I, Fact_II, Fact_III / .
COMPUTE BROTN_T=TRANSPOS(BROT_N) .
COMPUTE T_M=A_N * BROTN_T .
COMPUTE TEST=DIAG(T_M) .
PRINT TEST /
  FORMAT='F8.3' /
  TITLE='Test Vector Cosines for Variables' /
  SPACE=4 /
  RLABELS=Per1, Per2, Per3, Per4, Per5, Per6,
  Per7, Per8, Per9, Per10, Per11 / .
END MATRIX .






Linda Zientek's Bootstrap EFA syntax

The program requires TWO syntax files. The first file calls the second one.

COMMENT Bootstrap Factor Analysis Program I.
COMMENT by Linda Reichwein Zientek and Bruce Thompson.
COMMENT The assistance of Raynald Levesque with the
algorithm for adding the bootstrapped files is
gratefully acknowledged.

COMMENT Highlighted portions of the program will need
to be changed according to the data set:
the number of factors, and variables.
COMMENT Once the changes have been made, copy and paste
the program into SPSS and run.
COMMENT This program is set up to run 3 factors and
calls a second SPSS syntax file.

COMMENT Call Data Set.
COMMENT Variables.
COMMENT Number of loops.
COMMENT Corresponds to the number of factors. If more or
less than 4 factors then add or subtract.
COMMENT variables to the target matrix and means and
standard deviations.
COMMENT Calling Program II.
set mxloop=50000 results=none highres=off cache 100000
  mprint=off journal=off.
 
set workspace= 100000 compressed=on printback=none.

data list file='c:\spsswin\holzinger.dta' FIXED RECORDS=2
  /1 id 1-3 sex 4-4 ageyr 6-7
  agemo 8-9 t1 11-12 t2 14-15 t3 17-18 t4 20-21
  t5 23-24 t6 26-27 t7 29-30 t8 32-33 t9 35-36
  t10 38-40 t11 42-44 t12 46-48 t13 50-52 t14 54-56
  t15 58-60 t16 62-64 t17 66-67 t18 69-70 t19 72-73
  t20 74-76 t21 78-79 /2 t22 11-12 t23 14-15
  t24 17-18 t25 20-21 t26 23-24 .
save outfile='c:\holz.sav'/
  keep= T6 T7 T9 T10 T12 T13 T14 T15 T17 . 
execute .

FACTOR 
 /MATRIX=OUT (FAC='c:\bootfac.sav') 
 /VARIABLES T6 T7 T9 T10 T12 T13 T14 T15 T17 
 /MISSING listwise
 /ANALYSIS  T6 T7 T9 T10 T12 T13 T14 T15 T17 
 /Print UNIVARIATE CORRELATION EXTRACTION ROTATION
 /plot eigen
 /FORMAT Sort /CRITERIA FACTORS 3 ITERATE(25) 
 /EXTRACTION PC /CRITERIA ITERATE(25) 
 /ROTATION VARIMAX
 /METHOD=CORRELATION
 /PRINT=extraction rotation. 

get file='c:\holz.sav'.
numeric seqnum(f1)  .
leave seqnum.
compute seqnum=sum(seqnum,1).
leave seqnum.
execute.
save outfile='c:\holz.sav'.

COMMENT CREATE TARGET MATRIX.
COMMENT bs and var001 etc correspond to the factor .
COMMENT Be sure to Add Commas between Abs(var00n)

get file='c:\bootfac.sav'.
FLIP
  VARIABLES=  T6 T7 T9 T10 T12 T13 T14 T15 T17 . 

compute b1=0.
IF (ABS(var001)=max (ABS(var001), abs(var002)  ,
 abs(var003) ) ) b1 = 1 .
IF (b1=1 and var001<0) b1=-1.

compute b2=0.
IF (ABS(var002)=max (ABS(var001), abs(var002)   ,
 abs(var003)  ) ) b2 = 1.
IF (b2=1 and var002<0) b2=-1.

compute b3=0.
IF (ABS(var003)=max (abs(var001), abs(var002)  ,
 abs(var003) ) ) b3 = 1.
IF (b3=1 and var003<0) b3=-1.

COMMENT IF four factors, make the following adjustments:
COMMENT. compute b4=0.
COMMENT IF (ABS(var004)=max (abs(var001), abs(var002)  ,
abs(var003) , abs(var004) ) ) b4 = 1.
COMMENT IF (b4=1 and var004<0) b4=-1.

EXECUTE .

numeric seqnum(f1)  .
leave seqnum.
compute seqnum=sum(seqnum,1).
leave seqnum.
execute.
save /outfile='c:\b1.sav'.

COMMENT Be sure to save the Program II to the correct drive.
COMMENT Following algorithm concatenating bootstrap results
contributed by Raynald Levesque.

*//////////////////.
DEFINE !boot (nb=!TOKENS(1))

!DO !cnt=1 !TO !nb
INCLUDE 'c:\bfa_2.sps'.

!IF (!cnt=1) !THEN
GET FILE='C:\brotorig.SAV'.
!ELSE
ADD FILES FILE='c:\Tbrotorig.SAV'
     /FILE='C:\brotorig.SAV'.
!IFEND
SAVE OUTFILE='c:\Tbrotorig.SAV'.
!IF (!cnt=1) !THEN
GET FILE='C:\eigenvorig.SAV'.
!ELSE
ADD FILES FILE='c:\Teigenvorig.SAV'
     /FILE='C:\eigenvorig.SAV'.
!IFEND
SAVE OUTFILE='c:\Teigenvorig.SAV'.
!DOEND
!ENDDEFINE.
*//////////////////.

*The following macro call will do nb number of resampling.
SET MPRINT=yes.
!boot nb=10   .
SET MPRINT=no.

COMMENT Mean Bootstrap Results for Factor I.
COMMENT If var1000 corresponds to 1000 loops.
COMMENT If for example 10 loops are run,
then change var1000 to var010.

get file='c:\Tbrotorig.sav'.
select if (seqnum=1).
rename variables  col1=col01 col2=col02 col3=col03
 col4=col04 col5=col05 col6=col06 col7=col07
 col8=col08 col9=col09.
flip variables=col01 to col09.
compute mfac1=mean(var001 to var010).
compute sdfac1=sd(var001 to var010).
compute t_fac1=mfac1/sdfac1.
execute.
save outfile='c:\mfac1.sav'.

COMMENT Mean Bootstrap Results for Factor II.

get file='c:\Tbrotorig.sav'.
select if (seqnum=2).
rename variables  col1=col01 col2=col02 col3=col03
 col4=col04 col5=col05 col6=col06 col7=col07
 col8=col08 col9=col09. 
flip variables=col01 to col09.
compute mfac2=mean(var001 to var010).
compute sdfac2=sd(var001 to var010).
compute t_fac2=mfac2/sdfac2.
execute.
save outfile='c:\mfac2.sav'.

COMMENT Mean Bootstrap Results for Factor III.
COMMENT If more than three factors add the highlighted
section and change seqnum to the corresponding factor.

get file='c:\Tbrotorig.sav'.
select if (seqnum=3).
rename variables  col1=col01 col2=col02 col3=col03
 col4=col04 col5=col05 col6=col06 col7=col07
 col8=col08 col9=col09.
flip variables=col01 to col09.
compute mfac3=mean(var001 to var010).
compute sdfac3=sd(var001 to var010).
compute t_fac3=mfac3/sdfac3.
execute.
save outfile='c:\mfac3.sav'.

COMMENT Mean Bootstrap Results for Eigenvalues.

get file='c:\Teigenvorig.sav'.
rename variables  col1=col01 col2=col02 col3=col03
 col4=col04 col5=col05 col6=col06 col7=col07
 col8=col08 col9=col09.
flip variables=col01 to col09  .
compute meigenv=mean(var001 to var010).
compute sdeigenv=sd(var001 to var010).
compute t_eigen=meigenv/sdeigenv.
execute.
save outfile='c:\eigenv.sav'.

COMMENT If more than three factors then for each
additional factor add (file='c:\mfacnumber.sav')
between file mfac3 and c:eigenv.
COMMENT Then add the corresponding mean sd and t_scores
for each factor after t_fac3.

sort cases by case_lbl.
match files 
file='c:\mfac1.sav'  / 
file='c:\mfac2.sav' / 
file='c:\mfac3.sav' / 
file='c:\eigenv.sav' / 
by case_lbl / 
keep=mfac1 sdfac1 t_fac1 mfac2 sdfac2 t_fac2 mfac3
 sdfac3 t_fac3 meigenv sdeigenv t_eigen.
execute.



COMMENT This program will be called by Program I.

COMMENT Variable Set  Number of Cases  Number of Variables.
COMMENT Variables correspond to the number of factors. 
COMMENT Change these according to the given format.
COMMENT NOTE Commas exist between Fact_n

set mxloop=50000 results=none highres=off cache 100000
 compression = on mprint=off journal=off.
set printback=none workspace=40000.
get file='c:\holz.sav'.

COMMENT Resample with Replacement.
input program.

loop #i=1 to  301 .
compute seqnum=trunc(uniform(  301  ))+1.
end case.
end loop.
end file.
end input program.

sort cases by seqnum.
match files file=* /tables='c:\holz.sav'/by seqnum.
execute.
save outfile='c:\fact.sav'.

FACTOR 
 /MATRIX=OUT (FAC='c:\bootfac10.sav') 
 /VARIABLES T6 T7 T9 T10 T12 T13 T14 T15 T17 
 /MISSING listwise
 /ANALYSIS T6 T7 T9 T10 T12 T13 T14 T15 T17 
  /PRINT INITIAL EXTRACTION ROTATION
  /CRITERIA FACTORS(3) ITERATE(25)
  /EXTRACTION PC
  /CRITERIA ITERATE(25)
  /ROTATION VARIMAX
  /METHOD=CORRELATION . 

get file='c:\fact.sav'.
correlations variables= T6 T7 T9 T10 T12 T13 T14 T15 T17/
  matrix=out('c:\corr2.sav').
get file='c:\corr2.sav'.
SORT CASES BY rowtype_ (A) .
FILTER OFF.
use 1 thru 9 .
EXECUTE .
flip variables= T6 T7 T9 T10 T12 T13 T14 T15 T17 .

MATRIX.
get m /variables=var001 to var009 .
print m.
CALL EIGEN(m,A,B).
print B.
COMPUTE B_T=TRANSPOS(B) .
save B_T /outfile='c:\eigenvorig.sav'.
END MATRIX.

get file='c:\b1.sav'.
get file='c:\bootfac10.sav'.
FLIP
  VARIABLES= T6 T7 T9 T10 T12 T13 T14 T15 T17 .

RENAME VARIABLES var001=FACT_1 var002=FACT_2 var003=FACT_3 .
numeric seqnum(f1)  .
leave seqnum.
compute seqnum=sum(seqnum,1).
leave seqnum.
execute.
sort cases by seqnum.
match files file=* /tables='c:\b1.sav'/by seqnum.
execute.

MATRIX .
GET A/VARIABLES=b1 b2   b3.
GET B/variables= FACT_1 FACT_2   FACT_3.
print B.
print A.

COMMENT PROCRUSTEAN ROTATION BY BRUCE THOMPSON.

COMPUTE N_A =make(9,1,0).
print N_A.
COMPUTE DIAG_M =make(3,3,0).
PRINT DIAG_M.
COMPUTE N_B=N_A .
PRINT A /
  FORMAT='F8.2' /
  TITLE='First Pattern Matrix (Target)' /
  SPACE=4/
  RLABELS= T6 T7 T9 T10 T12 T13 T14 T15 T17  /
  CLABELS=Fact_I, Fact_II    , Fact_III / .
COMPUTE A_N=A .
- LOOP #I=1 TO NROW(A) .
-   LOOP #J=1 TO NCOL(A) .
-      COMPUTE A_N(#I,#J)=A(#I,#J) ** 2 .
-   END LOOP .
-END LOOP .

PRINT A_N /
 FORMAT='F8.4' /
 TITLE='First Pattern Matrix (Target) Squared' /
 SPACE=4 /
 RLABELS= T6 T7 T9 T10 T12 T13 T14 T15 T17     /
 CLABELS=Fact_I, Fact_II   , Fact_III / .
-LOOP #J=1 TO NCOL(A) .
+ LOOP #I=1 TO NROW(A) .
COMPUTE N_A(#I)=A_N(#I,#J) + N_A(#I) .
+ END LOOP .
-END LOOP .
PRINT N_A /
 FORMAT='F8.3' /
 TITLE='Row Sum of Squares for First Pattern Matrix' /
 SPACE=4 /
 RLABELS= T6 T7 T9 T10 T12 T13 T14 T15 T17      / .

LOOP #I=1 TO NROW(A) .
- COMPUTE N_A(#I) = 1.0 / (N_A(#I) ** .5) .
END LOOP .
PRINT N_A /
 FORMAT='F8.3' /
 TITLE='Normalization Factor for Rows' /
 SPACE=4 /
 RLABELS= T6 T7 T9 T10 T12 T13 T14 T15 T17 / .
LOOP #J=1 TO NCOL(A) .
+ LOOP #I=1 TO NROW(A) .
COMPUTE A_N(#I,#J)=A(#I,#J) * N_A(#I) .
+ END LOOP .
END LOOP .

PRINT A_N /
 FORMAT='F8.4' /
 TITLE='First Pattern Matrix (Target) Normalized' /
 SPACE=4 /
 RLABELS= T6 T7 T9 T10 T12 T13 T14 T15 T17 /
 CLABELS=Fact_I, Fact_II   , Fact_III / .
PRINT B /
 FORMAT='F8.2' /
 TITLE='Second Pattern Matrix' /
 SPACE=4 /
 RLABELS=  T6 T7 T9 T10 T12 T13 T14 T15 T17 /
 CLABELS=Fact_I, Fact_II   , Fact_III / .
COMPUTE B_N=B .
-LOOP #I=1 TO NROW(B) .
+ LOOP #J=1 TO NCOL(B) .
COMPUTE B_N(#I,#J)=B(#I,#J) ** 2 .
+ END LOOP .
-END LOOP .

PRINT B_N /
 FORMAT='F8.4' /
 TITLE='Second Pattern Matrix Squared' /
 SPACE=4 /
 RLABELS=  T6 T7 T9 T10 T12 T13 T14 T15 T17 /
 CLABELS=Fact_I, Fact_II   , Fact_III / .
-LOOP #J=1 TO NCOL(B) .
+ LOOP #I=1 TO NROW(B) .
COMPUTE N_B(#I)=B_N(#I,#J) + N_B(#I) .
+ END LOOP .
-END LOOP .
PRINT N_B /
 FORMAT='F8.3' /
 TITLE='Row Sum of Squares for Second Pattern Matrix' /
 SPACE=4 /
 RLABELS=  T6 T7 T9 T10 T12 T13 T14 T15 T17 / .

LOOP #I=1 TO NROW(B) .
- COMPUTE N_B(#I) = 1.0 / (N_B(#I) ** .5) .
END LOOP .
PRINT N_B / FORMAT='F8.3' /
 TITLE='Normalization Factor for Rows' /
 SPACE=4 /
 RLABELS=   T6 T7 T9 T10 T12 T13 T14 T15 T17 / .

LOOP #J=1 TO NCOL(B) .
+ LOOP #I=1 TO NROW(B) .
COMPUTE B_N(#I,#J)=B(#I,#J) * N_B(#I) .
+ END LOOP .
END LOOP .

PRINT B_N / FORMAT='F8.4' /
 TITLE='Second Pattern Matrix Normalized' /
 SPACE=4 /
 RLABELS=  T6 T7 T9 T10 T12 T13 T14 T15 T17 /
 CLABELS=Fact_I, Fact_II  , Fact_III / .

COMPUTE A_T=TRANSPOS(A_N) .
PRINT A_T / FORMAT='F8.2' /
 TITLE='A_N Transpose' /
 SPACE=4 /
 RLABELS=Fact_I, Fact_II  , Fact_III /
 CLABELS=  T6 T7 T9 T10 T12 T13 T14 T15 T17 / .

COMPUTE B_T=TRANSPOS(B_N) .
PRINT B_T / FORMAT='F8.2' /
 TITLE='B_N Transpose' /
 SPACE=4 /
 RLABELS=Fact_I, Fact_II  , Fact_III /
 CLABELS=  T6 T7 T9 T10 T12 T13 T14 T15 T17 / .

COMPUTE RI=A_T * B_N .
PRINT RI / FORMAT='F8.3' /
 TITLE='A_N Transpose times B_N' /
 SPACE=4 /
 RLABELS=Fact_I, Fact_II  , Fact_III /
 CLABELS=Fact_I, Fact_II   , Fact_III / .
COMPUTE RI_T=TRANSPOS(RI) .
PRINT RI_T / FORMAT='F8.3' /
 TITLE='Transpose of (A_N Transpose times B_N)' /
 SPACE=4 /
 RLABELS=Fact_I, Fact_II  , Fact_III /
 CLABELS=Fact_I, Fact_II  , Fact_III / .

COMPUTE QUAD=RI * RI_T .
PRINT QUAD / FORMAT='F8.3' /
 TITLE='A_N Trans * B_N * Trans of (A_N Trans * B_N)' /
 SPACE=2 /
 RLABELS=Fact_I, Fact_II, Fact_III /
 CLABELS=Fact_I, Fact_II, Fact_III / .
CALL EIGEN(QUAD, EIGVEC, EIG) .
PRINT EIG / FORMAT='F8.3' /
 TITLE='Eigenvalues of QUAD' /
 SPACE=4 /
 RLABELS=Fact_I, Fact_II, Fact_III / .

PRINT EIGVEC / FORMAT='F8.3' /
 TITLE='Eigenvectors of QUAD' /
 SPACE=4 /
 RLABELS=ONE, TWO   , THREE /
 CLABELS=Fact_I, Fact_II   , Fact_III / .
-LOOP #I=1 TO NROW(QUAD) .
+ LOOP #J=1 TO NROW(QUAD) .
COMPUTE EIGVEC(#I,#J)=EIGVEC(#I,#J) * (EIG(#J) ** .5) .
+ END LOOP .
-END LOOP .

PRINT EIGVEC / FORMAT='F8.3' /
 TITLE='Pattern Coefficients of QUAD' /
 SPACE=4 /
 RLABELS=ONE, TWO  , THREE/
 CLABELS=Fact_I, Fact_II, Fact_III / .

LOOP I=1 TO NROW(EIG) .
- COMPUTE EIG(I)=EIG(I) ** -1.5 .
END LOOP .
PRINT EIG / FORMAT='F8.3' /
 TITLE='Eigenvalues raised to -1.5' /
 SPACE=4 /
 RLABELS=Fact_I, Fact_II, Fact_III / .

CALL SETDIAG(DIAG_M,EIG) .
PRINT DIAG_M / FORMAT='F8.3' /
 TITLE='Diagonal Matrix (Eigenvalues raised to -1.5)' /
 SPACE=4 /
 CLABELS=Fact_I, Fact_II, Fact_III /
 RLABELS=Fact_I, Fact_II, Fact_III / .

COMPUTE VEC_T=TRANSPOS(EIGVEC) .
PRINT VEC_T / FORMAT='F8.3' /
 TITLE='Transpose of Eigenvectors' /
 SPACE=4 /
 RLABELS=Fact_I, Fact_II, Fact_III /
 CLABELS=ONE, TWO, THREE / .

COMPUTE D=RI_T * EIGVEC .
PRINT D / FORMAT='F9.3' /
 TITLE='D= trans (trans A times B) times Eigenvectors' /
 SPACE=4 /
 RLABELS=Fact_I, Fact_II, Fact_III /
 CLABELS=Fact_I, Fact_I, Fact_III / .

LOOP J=1 TO NCOL(A) .
- COMPUTE EE=EIG(J) .
- LOOP I=1 TO NCOL(A) .
-   COMPUTE D(I,J)=D(I,J) * EE .
- END LOOP .
END LOOP .
PRINT D / FORMAT='F9.3' /
 TITLE='D = D times Eigenvalues ** -1.5' /
 SPACE=4 /
 RLABELS=Fact_I, Fact_II, Fact_III /
 CLABELS=Fact_I, Fact_II, Fact_III / .

COMPUTE D_T=TRANSPOS(D) .
PRINT D_T / FORMAT='F9.3' /
 TITLE='D transposed' /
 SPACE=4 /
 RLABELS=Fact_I, Fact_II, Fact_III /
 CLABELS=Fact_I, Fact_II, Fact_III / .

COMPUTE C=EIGVEC * D_T .
PRINT C / FORMAT='F9.3' /
 TITLE='Factor Correlations (Cosines)' /
 SPACE=4 / RLABELS=Fact_Ia, Fact_IIa, Fact_IIIa/
 CLABELS=Fact_Ib, Fact_IIb, Fact_IIIb/ .
COMPUTE C=D * VEC_T .
COMPUTE B_ROT=B * C .
PRINT B_ROT / FORMAT='F8.3' /
 TITLE='B rotated to Best-Fit with A' /
 SPACE=2 / RLABELS= T6 T7 T9 T10 T12 T13 T14 T15 T17/
 CLABELS=Fact_I, Fact_II, Fact_III / .

COMPUTE BROT_N=B_ROT .
LOOP #I=1 TO NROW(A) .
-  LOOP #J=1 TO NCOL(A) .
-     COMPUTE BROT_N(#I,#J)=B_ROT(#I,#J) ** 2 .
-  END LOOP .
COMPUTE N_A(#I)= .0 .
END LOOP .

PRINT BROT_N / FORMAT='F8.4' /
 TITLE='Best Fit Pattern Matrix (Target) Squared' /
 SPACE=4 / RLABELS= T6 T7 T9 T10 T12 T13 T14 T15 T17 /
 CLABELS=Fact_I, Fact_II, Fact_III .
-LOOP #J=1 TO NCOL(A) .
+ LOOP #I=1 TO NROW(A) .
COMPUTE N_A(#I)=BROT_N(#I,#J) + N_A(#I) .
+ END LOOP .
-END LOOP .

PRINT N_A / FORMAT='F8.3' /
 TITLE='Row Sum of Squares for Best Fit Matrix' /
 SPACE=4 / RLABELS=  T6 T7 T9 T10 T12 T13 T14 T15 T17/ .
LOOP #I=1 TO NROW(A) .
- COMPUTE N_A(#I) = 1.0 / (N_A(#I) ** .5) .
END LOOP .
PRINT N_A / FORMAT='F8.3' /
 TITLE='Normalization Factor for Rows' /
 SPACE=4 / RLABELS=  T6 T7 T9 T10 T12 T13 T14 T15 T17/ .
-LOOP #J=1 TO NCOL(A) .
+ LOOP #I=1 TO NROW(A) .
COMPUTE BROT_N(#I,#J)=B_ROT(#I,#J) * N_A(#I) .
+ END LOOP .
-END LOOP .
PRINT BROT_N / FORMAT='F8.4' /
 TITLE='Best Fit Pattern Matrix (Target) Normalized' /
 SPACE=4 /  RLABELS=  T6 T7 T9 T10 T12 T13 T14 T15 T17/
 CLABELS=Fact_I, Fact_II, Fact_III/ .

COMPUTE BROTN_T=TRANSPOS(BROT_N) .
COMPUTE T_M=A_N * BROTN_T .
COMPUTE TEST=DIAG(T_M) .
PRINT TEST / FORMAT='F8.3' /
 TITLE='Test Vector Cosines for Variables' /
 SPACE=4 / RLABELS=  T6 T7 T9 T10 T12 T13 T14 T15 T17 / .
SAVE BROTN_T /OUTFILE='C:\brotorig.SAV'.
END MATRIX .

get file='c:\brotorig.sav'.
numeric seqnum(f1) .
leave seqnum.
compute seqnum=sum(seqnum,1).
leave seqnum.
execute.
save outfile='c:\brotorig.sav'.



Table 9.2 & Figure 9.1

set printback=listing .
data list file='c:\stat_bk2\cross.dat' records=1 /1
  case 1-2 y 4-6 (1) x1 8-9 x2 11-12 inv_grp 14 .
list variables=y x1 x2 inv_grp/cases=99/format=numbered .
descriptives variables=all .
correlations variables=y x1 x2/
  statistics=all .
regression variables=y x1 x2/
  dependendent=y/enter x1 x2 .

select if (inv_grp eq 1) .
compute x1_1000=x1 * 1000. .
compute x2_1000=x2 * 1000. .
descriptives variables=x1 x2 x1_1000 x2_1000 .
regression variables=y x1 x2/
  dependendent=y/enter x1 x2 .

data list file='c:\stat_bk2\cross.dat' records=1 /1
  case 1-2 y 4-6 (1) x1 8-9 x2 11-12 inv_grp 14 .
select if (inv_grp eq 2) .
compute x1_1000=x1 * 1000. .
compute x2_1000=x2 * 1000. .
descriptives variables=x1 x2 x1_1000 x2_1000 .
regression variables=y x1 x2/
  dependendent=y/enter x1 x2 .

DATA LIST FILE='C:\STAT_BK2\CROSS.DAT' RECORDS=1 /1
  CASE 1-2 Y 4-6 (1) X1 8-9 X2 11-12 INV_GRP 14 .
IF (INV_GRP EQ 1)ZX1_1 = (X1 - 9.62500) / 6.50137 .
IF (INV_GRP EQ 1)ZX2_1 = (X2 - 5.37500) / 4.30739 .
IF (INV_GRP EQ 2)ZX1_2 = (X1 - 11.57143) / 4.79086 .
IF (INV_GRP EQ 2)ZX2_2 = (X2 - 4.00000) / 2.70801 .
VARIABLE LABELS
  ZX1_1 'z score version of X1, subgroup 1'
  ZX2_1 'z score version of X2, subgroup 1'
  ZX1_2 'z score version of X1, subgroup 2'
  ZX2_2 'z score version of X2, subgroup 2' .
DESCRIPTIVES VARIABLES=ZX1_1 ZX2_1 ZX1_2 ZX2_2 . 
COMPUTE YHAT11=(.719003 * ZX1_1) + (.085253 * ZX2_1) .
COMPUTE YHAT22=(.920505 * ZX1_2) + (.078289 * ZX2_2) .
COMPUTE YHAT21=(.719003 * ZX1_2) + (.085253 * ZX2_2) .
COMPUTE YHAT12=(.920505 * ZX1_1) + (.078289 * ZX2_1) .
VARIABLE LABELS
  YHAT11 'Subgroup 1^s data, subgroup 1^s weights'
  YHAT22 'Subgroup 2^s data, subgroup 2^s weights'
  YHAT21 'Subgroup 2^s data, subgroup 1^s weights'
  YHAT12 'Subgroup 1^s data, subgroup 2^s weights' .
CORRELATIONS VARIABLES=Y YHAT11 YHAT22 YHAT21 YHAT12/
  STATISTICS=ALL .

 1 2.0  0  9 1
 2 2.0  2  6 1
 3 2.2  4  3 2
 4 2.6  6  1 1
 5 3.2  8  0 2
 6 4.0 10  0 1
 7 5.0 12  1 2
 8 6.2 14  3 1
 9 9.8 16  6 2
10 7.6 18 13 1
11 6.3 11  4 1
12 8.9 17  5 2
13 7.7 15  6 2
14 2.7 16  7 1
15 3.3  9  7 2



Table 9.14

data list file='c:\stat_bk2\drinks.dta' records=1/1
  blood 1-4 (2) lbs 6-8 drinks 10 .
list variables=all/cases=9999/format=numbered .
descriptives variables=all .
COMPUTE d_lbs = lbs - 170 .
COMPUTE d_drinks = drinks - 4 .
COMPUTE lbsXdrin = d_lbs * d_drinks .
LIST VARIABLES=ALL/CASES=9999/FORMAT=NUMBERED .
CORRELATIONS VARIABLES=ALL/STATISTICS=ALL .
REGRESSION VARIABLES=ALL/DEPENDENT=blood/
  ENTER lbs drinks .
REGRESSION VARIABLES=ALL/DEPENDENT=blood/
  ENTER d_lbs d_drinks/ENTER lbsXdrin .

if(lbs lt 170) w_grp=1 .
if(lbs gt 170) w_grp=2 .
if(drinks lt 4) dr_grp=1 .
if(drinks gt 4) dr_grp=2 .
formats w_grp dr_grp (F2) .
list variables=blood lbs drinks w_grp dr_grp/cases=99/
  format=numbered .
anova blood by w_grp(1,2) dr_grp(1,2)/
  statistics=all .

compute dwi = 0 .
if (blood gt .08)dwi = 1 .
descriptives variables=all .
frequencies variables=dwi .
if (dwi eq 0) logit = ln((1 - .53125)/.53125) .
if (dwi eq 1) logit = ln (.53125 / (1 - .53125)) .
regression variables=logit lbs drinks/dependent=logit/
  enter lbs drinks .

LOGISTIC REGRESSION dwi
  /METHOD=ENTER drinks lbs
  /SAVE PRED
  /PRINT=CORR ITER(1)
  /CRITERIA PIN(.05) POUT(.10) ITERATE(50) .

0.04 100 1
0.11 100 3
0.19 100 5
0.26 100 7
0.03 120 1
0.09 120 3
0.16 120 5
0.22 120 7
0.03 140 1
0.08 140 3
0.13 140 5
0.19 140 7
0.02 160 1
0.07 160 3
0.12 160 5
0.16 160 7
0.02 180 1
0.06 180 3
0.11 180 5
0.15 180 7
0.02 200 1
0.06 200 3
0.09 200 5
0.13 200 7
0.02 220 1
0.05 220 3
0.09 220 5
0.12 220 7
0.02 240 1
0.05 240 3
0.08 240 5
0.11 240 7



Table 12.1

set printback=listing .
data list file='c:\spsswin\tucker.dat' records=1 /1
  dv 1-2 level 4 .
compute a1 = 0 .
compute a2 = 0 .
compute a3 = 0 .
compute a4 = 0 .
compute a5 = 0 .
if (level eq 1) a1 = -1 .
if (level eq 2) a1 = 1 .
if (a1 ne 0) a2 = -1 .
if (level eq 3) a2 = 2 .
if (a2 ne 0) a3 = -1 .
if (level eq 4) a3 = 3 .
if (a3 ne 0) a4 = -1 .
if (level eq 5) a4 = 4 .
if (a4 ne 0) a5 = -1 .
if (level eq 6) a5 = 5 .
print formats a1 to a5 (F3) .
variable labels
  a1 'Freshmen vs Sophomores'
  a2 'Juniors vs Freshmen,Sophomores'
  a3 'Seniors vs Fr,So,Jrs'
  a4 'Grad Studs vs Undergrad'
  a5 'Doc Studs vs All Others' .
value labels
  a1 0 'not tested' -1 'mean 1' 1 'mean 2'/
  a2 0 'not tested' -1 'mean 1' 2 'mean 2'/
  a3 0 'not tested' -1 'mean 1' 3 'mean 2'/
  a4 0 'not tested' -1 'mean 1' 4 'mean 2'/
  a5 0 'not tested' -1 'mean 1' 5 'mean 2' .
list variables=dv to a5/cases=99999/format=numbered .
correlations variables=dv a1 to a5 .
regression variables=level a1 to a5/
  dependent=level/enter a1 to a5 .
oneway dv by level(1,6)/ranges=tukey/ranges=scheffe/
  statistics=all .
regression variables=dv a1 to a5/
  dependent=dv/enter a1/enter a2/enter a3/
  enter a4/enter a5 .
descriptives variables=dv a1 to a5/save .
list variables=zdv za1 to za5/cases=99/format=numbered .
descriptives variables=zdv za1 to za5 .
compute yhat=(-.031270 * za1) + (-.018054 * za2)
  + (-.012766 * za3) + (-.009889 * za4)
  + (.839693 * za5) .
compute e = zdv - yhat .
print formats yhat e (F8.5) .
list variables=dv zdv yhat e/cases=99999/format=numbered .
correlations variables=zdv yhat e/statistics=all .
oneway yhat by level(1,6)/statistics=all .



Table 13.1

  1  7 0 1  0.478  0.522  0.917  -0.087      0.3 = % smoke
  2  7 0 1  0.478  0.522  0.917  -0.087
  3  7 0 1  0.478  0.522  0.917  -0.087 0.446666 = % femal
  4  7 0 1  0.478  0.522  0.917  -0.087
  5  7 0 1  0.478  0.522  0.917  -0.087
  6  7 0 1  0.478  0.522  0.917  -0.087
  7  7 0 1  0.478  0.522  0.917  -0.087
  8  7 0 1  0.478  0.522  0.917  -0.087
  9  7 0 1  0.478  0.522  0.917  -0.087
 10  7 0 1  0.478  0.522  0.917  -0.087
 11  7 0 1  0.478  0.522  0.917  -0.087
 12  7 0 0  0.522  0.478  1.091   0.087
 13  7 0 0  0.522  0.478  1.091   0.087
 14  7 0 0  0.522  0.478  1.091   0.087
 15  7 0 0  0.522  0.478  1.091   0.087
 16  7 0 0  0.522  0.478  1.091   0.087
 17  7 0 0  0.522  0.478  1.091   0.087
 18  7 0 0  0.522  0.478  1.091   0.087
 19  7 0 0  0.522  0.478  1.091   0.087
 20  7 0 0  0.522  0.478  1.091   0.087
 21  7 0 0  0.522  0.478  1.091   0.087
 22  7 0 0  0.522  0.478  1.091   0.087
 23  7 0 0  0.522  0.478  1.091   0.087
 24 10 0 1  0.467  0.533  0.875  -0.134
 25 10 0 1  0.467  0.533  0.875  -0.134
 26 10 0 1  0.467  0.533  0.875  -0.134
 27 10 0 1  0.467  0.533  0.875  -0.134
 28 10 0 1  0.467  0.533  0.875  -0.134
 29 10 0 1  0.467  0.533  0.875  -0.134
 30 10 0 1  0.467  0.533  0.875  -0.134
 31 10 0 0  0.533  0.467  1.143   0.134
 32 10 0 0  0.533  0.467  1.143   0.134
 33 10 0 0  0.533  0.467  1.143   0.134
 34 10 0 0  0.533  0.467  1.143   0.134
 35 10 0 0  0.533  0.467  1.143   0.134
 36 10 0 0  0.533  0.467  1.143   0.134
 37 10 0 0  0.533  0.467  1.143   0.134
 38 10 0 0  0.533  0.467  1.143   0.134
 39 10 1 1  0.267  0.733  0.364  -1.012
 40 10 1 1  0.267  0.733  0.364  -1.012
 41 10 1 1  0.267  0.733  0.364  -1.012
 42 10 1 1  0.267  0.733  0.364  -1.012
 43 10 1 0  0.733  0.267  2.750   1.012
 44 10 1 0  0.733  0.267  2.750   1.012
 45 10 1 0  0.733  0.267  2.750   1.012
 46 10 1 0  0.733  0.267  2.750   1.012
 47 10 1 0  0.733  0.267  2.750   1.012
 48 10 1 0  0.733  0.267  2.750   1.012
 49 10 1 0  0.733  0.267  2.750   1.012
 50 10 1 0  0.733  0.267  2.750   1.012
 51 10 1 0  0.733  0.267  2.750   1.012
 52 10 1 0  0.733  0.267  2.750   1.012
 53 10 1 0  0.733  0.267  2.750   1.012
 54 12 0 1  0.400  0.600  0.667  -0.405
 55 12 0 1  0.400  0.600  0.667  -0.405
 56 12 0 1  0.400  0.600  0.667  -0.405
 57 12 0 1  0.400  0.600  0.667  -0.405
 58 12 0 1  0.400  0.600  0.667  -0.405
 59 12 0 1  0.400  0.600  0.667  -0.405
 60 12 0 0  0.600  0.400  1.500   0.405
 61 12 0 0  0.600  0.400  1.500   0.405
 62 12 0 0  0.600  0.400  1.500   0.405
 63 12 0 0  0.600  0.400  1.500   0.405
 64 12 0 0  0.600  0.400  1.500   0.405
 65 12 0 0  0.600  0.400  1.500   0.405
 66 12 0 0  0.600  0.400  1.500   0.405
 67 12 0 0  0.600  0.400  1.500   0.405
 68 12 0 0  0.600  0.400  1.500   0.405
 69 12 1 1  0.267  0.733  0.364  -1.012
 70 12 1 1  0.267  0.733  0.364  -1.012
 71 12 1 1  0.267  0.733  0.364  -1.012
 72 12 1 1  0.267  0.733  0.364  -1.012
 73 12 1 0  0.733  0.267  2.750   1.012
 74 12 1 0  0.733  0.267  2.750   1.012
 75 12 1 0  0.733  0.267  2.750   1.012
 76 12 1 0  0.733  0.267  2.750   1.012
 77 12 1 0  0.733  0.267  2.750   1.012
 78 12 1 0  0.733  0.267  2.750   1.012
 79 12 1 0  0.733  0.267  2.750   1.012
 80 12 1 0  0.733  0.267  2.750   1.012
 81 12 1 0  0.733  0.267  2.750   1.012
 82 12 1 0  0.733  0.267  2.750   1.012
 83 12 1 0  0.733  0.267  2.750   1.012
 84 16 0 1  0.267  0.733  0.364  -1.012
 85 16 0 1  0.267  0.733  0.364  -1.012
 86 16 0 1  0.267  0.733  0.364  -1.012
 87 16 0 1  0.267  0.733  0.364  -1.012
 88 16 0 0  0.733  0.267  2.750   1.012
 89 16 0 0  0.733  0.267  2.750   1.012
 90 16 0 0  0.733  0.267  2.750   1.012
 91 16 0 0  0.733  0.267  2.750   1.012
 92 16 0 0  0.733  0.267  2.750   1.012
 93 16 0 0  0.733  0.267  2.750   1.012
 94 16 0 0  0.733  0.267  2.750   1.012
 95 16 0 0  0.733  0.267  2.750   1.012
 96 16 0 0  0.733  0.267  2.750   1.012
 97 16 0 0  0.733  0.267  2.750   1.012
 98 16 0 0  0.733  0.267  2.750   1.012
 99 16 1 1  0.133  0.867  0.154  -1.872
100 16 1 1  0.133  0.867  0.154  -1.872
101 16 1 0  0.867  0.133  6.500   1.872
102 16 1 0  0.867  0.133  6.500   1.872
103 16 1 0  0.867  0.133  6.500   1.872
104 16 1 0  0.867  0.133  6.500   1.872
105 16 1 0  0.867  0.133  6.500   1.872
106 16 1 0  0.867  0.133  6.500   1.872
107 16 1 0  0.867  0.133  6.500   1.872
108 16 1 0  0.867  0.133  6.500   1.872
109 16 1 0  0.867  0.133  6.500   1.872
110 16 1 0  0.867  0.133  6.500   1.872
111 16 1 0  0.867  0.133  6.500   1.872
112 16 1 0  0.867  0.133  6.500   1.872
113 16 1 0  0.867  0.133  6.500   1.872
114 20 0 1  0.267  0.733  0.364  -1.012
115 20 0 1  0.267  0.733  0.364  -1.012
116 20 0 1  0.267  0.733  0.364  -1.012
117 20 0 1  0.267  0.733  0.364  -1.012
118 20 0 0  0.733  0.267  2.750   1.012
119 20 0 0  0.733  0.267  2.750   1.012
120 20 0 0  0.733  0.267  2.750   1.012
121 20 0 0  0.733  0.267  2.750   1.012
122 20 0 0  0.733  0.267  2.750   1.012
123 20 0 0  0.733  0.267  2.750   1.012
124 20 0 0  0.733  0.267  2.750   1.012
125 20 0 0  0.733  0.267  2.750   1.012
126 20 0 0  0.733  0.267  2.750   1.012
127 20 0 0  0.733  0.267  2.750   1.012
128 20 0 0  0.733  0.267  2.750   1.012
129 20 1 1  0.067  0.933  0.071  -2.639
130 20 1 0  0.933  0.067 14.000   2.639
131 20 1 0  0.933  0.067 14.000   2.639
132 20 1 0  0.933  0.067 14.000   2.639
133 20 1 0  0.933  0.067 14.000   2.639
134 20 1 0  0.933  0.067 14.000   2.639
135 20 1 0  0.933  0.067 14.000   2.639
136 20 1 0  0.933  0.067 14.000   2.639
137 20 1 0  0.933  0.067 14.000   2.639
138 20 1 0  0.933  0.067 14.000   2.639
139 20 1 0  0.933  0.067 14.000   2.639
140 20 1 0  0.933  0.067 14.000   2.639
141 20 1 0  0.933  0.067 14.000   2.639
142 20 1 0  0.933  0.067 14.000   2.639
143 20 1 0  0.933  0.067 14.000   2.639
144 24 1 1  0.200  0.800  0.250  -1.386
145 24 1 0  0.800  0.200  4.000   1.386
146 24 1 0  0.800  0.200  4.000   1.386
147 24 1 0  0.800  0.200  4.000   1.386
148 24 1 0  0.800  0.200  4.000   1.386
149 33 1 1  0.500  0.500  1.000   0.000
150 33 1 0  0.500  0.500  1.000   0.000



Table 13.3

set printback=listing .
data list file='c:\stat_bk2\reye.dat' records=1/1
  id 1-3 reye_s 5 aspirin 7 gender 9 .
value labels
  reye_s 0 'no' 1 'yes'/aspirin 0 'no' 1 'yes'/
  gender 0 'female' 1 'male' .
list variables=all/cases=999 .
frequencies variables=reye_s to gender .
crosstabs tables=reye_s by aspirin/
  reye_s by aspirin by gender/statistics=all .
compute constant = reye_s .
loglinear reye_s (0,1) aspirin (0,1) gender (0,1)
  WITH constant/print=all/
  design=constant .
loglinear reye_s (0,1) aspirin (0,1) gender (0,1)/
  print=all/
  design=reye_s /
  design=aspirin /
  design=gender /
  design=reye_s, aspirin /
  design=reye_s, gender /
  design=aspirin, gender /
  design=reye_s, aspirin, gender /
  design=reye_s, aspirin, reye_s by aspirin /
  design=reye_s, gender, reye_s by gender /
  design=aspirin, gender, aspirin by gender /
  design=gender, reye_s, aspirin, reye_s by aspirin /
  design=aspirin, reye_s, gender, reye_s by gender /
  design=reye_s, aspirin, gender, aspirin by gender /
  design=reye_s, aspirin, gender,
     reye_s by aspirin, reye_s by gender /
  design=reye_s, aspirin, gender,
     reye_s by aspirin, aspirin by gender /
  design=reye_s, aspirin, gender,
     reye_s by gender, aspirin by gender /
  design=reye_s, aspirin, gender,
     reye_s by aspirin, reye_s by gender,
     aspirin by gender /
  design=reye_s, aspirin, gender,
   reye_s by aspirin, reye_s by gender, aspirin by gender,
   reye_s by aspirin by gender .

  1 1 1 0
  2 1 1 0
  3 1 1 0
  4 1 1 0
  5 1 1 0
  6 1 1 0
  7 1 1 0
  8 1 1 0
  9 1 1 0
 10 1 1 0
 11 1 1 0
 12 1 1 0
 13 1 1 0
 14 1 1 0
 15 1 1 0
 16 1 1 0
 17 1 1 0
 18 1 1 0
 19 1 1 0
 20 1 1 0
 21 1 1 0
 22 1 1 0
 23 1 1 0
 24 1 1 0
 25 1 1 1
 26 1 1 1
 27 1 1 1
 28 1 1 1
 29 1 1 1
 30 1 1 1
 31 1 1 1
 32 1 1 1
 33 1 1 1
 34 1 1 1
 35 1 1 1
 36 1 1 1
 37 1 1 1
 38 1 1 1
 39 1 1 1
 40 1 1 1
 41 1 1 1
 42 1 1 1
 43 1 1 1
 44 1 1 1
 45 1 1 1
 46 1 1 1
 47 1 1 1
 48 1 1 1
 49 1 0 0
 50 1 0 1
 51 0 1 0
 52 0 1 0
 53 0 1 0
 54 0 1 0
 55 0 1 0
 56 0 1 0
 57 0 1 0
 58 0 1 0
 59 0 1 0
 60 0 1 0
 61 0 1 0
 62 0 1 0
 63 0 1 0
 64 0 1 0
 65 0 1 0
 66 0 1 0
 67 0 1 0
 68 0 1 0
 69 0 1 0
 70 0 1 0
 71 0 1 0
 72 0 1 0
 73 0 1 0
 74 0 1 0
 75 0 1 1
 76 0 1 1
 77 0 1 1
 78 0 1 1
 79 0 1 1
 80 0 1 1
 81 0 1 1
 82 0 1 1
 83 0 1 1
 84 0 1 1
 85 0 1 1
 86 0 1 1
 87 0 0 0
 88 0 0 0
 89 0 0 0
 90 0 0 0
 91 0 0 0
 92 0 0 0
 93 0 0 0
 94 0 0 0
 95 0 0 0
 96 0 0 1
 97 0 0 1
 98 0 0 1
 99 0 0 1
100 0 0 1