RES TR Youth

RESILIENCE Items taken from what Herb Marsh has identified as working as part of PERMA.

How quickly do you feel you return to normal after setbacks in your life
How much time does it generally take you to get back to normal when things go wrong in your life
Do you in general quickly get over and recover from significant life difficulties

RES.1 , RES.2 , RES.3

Two factors Consitency and preseverence

library(lavaan)

## This is lavaan 0.5-18
## lavaan is BETA software! Please report any bugs.

library(semPlot)
library(dplyr)

## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(GPArotation)
library(psych)
library(car)

## 
## Attaching package: 'car'
## 
## The following object is masked from 'package:psych':
## 
##     logit

library(ggplot2)

## 
## Attaching package: 'ggplot2'
## 
## The following object is masked from 'package:psych':
## 
##     %+%

library(GGally)

## 
## Attaching package: 'GGally'
## 
## The following object is masked from 'package:dplyr':
## 
##     nasa

all_surveys<-read.csv("allsurveysYT1.csv")
all_surveys<-tbl_df(all_surveys)
all_surveys

## Source: local data frame [1,160 x 124]
## 
##     X Dataset First.Name Last.Name Gender Age Grade ASDQII_1 ASDQII_2
## 1   1  ASCGW1    Charmie      Vang      2   5    10        6        6
## 2   2  ASCGW1    malorie    mauthe      2   4    10        5        5
## 3   3  ASCGW1        Roi     Stern      1   6    12        4        3
## 4   4  ASCGW1    Mattan   Yedidya       1   5    12        5        5
## 5   5  ASCGW1   Harrison Smithling      1   3     9        5        4
## 6   6  ASCGW1       Emma     Mason      2   3     7        5        5
## 7   7  ASCGW1       Dovi  Brackman      1   2     7        5        3
## 8   8  ASCGW1       Lexi Markowitz      2   5    10        4        4
## 9   9  ASCGW1    Yarden        Mor      2   3     9        6        5
## 10 10  ASCGW1     Amalia    Kamlet      2   4     9        5        1
## .. ..     ...        ...       ...    ... ...   ...      ...      ...
## Variables not shown: ASDQII_3 (int), ASDQII_4 (int), ASDQII_5 (int),
##   ASDQII_6 (int), ASDQII_7 (int), ASDQII_8 (int), ASDQII_9 (int),
##   ASDQII_10 (int), ASDQII_11 (int), ASDQII_12 (int), ASDQII_13 (int),
##   ASDQII_14 (int), ASDQII_15 (int), ASDQII_16 (int), ASDQII_17 (int),
##   ASDQII_18 (int), ASDQII_19 (int), ASDQII_20 (int), CPS_1 (int), CPS_2
##   (int), CPS_3 (int), CPS_4 (int), CPS_5 (int), CPS_6 (int), CPS_7 (int),
##   CPS_8 (int), CPS_9 (int), CPS_10 (int), MLQ_1 (int), MLQ_2 (int), MLQ_3
##   (int), MLQ_4 (int), MLQ_5 (int), MLQ_6 (int), MLQ_7 (int), MLQ_8 (int),
##   MLQ_9 (int), MLQ_10 (int), LET_1 (int), LET_2 (int), LET_3 (int), LET_4
##   (int), LET_5 (int), LET_6 (int), PWB_1 (int), PWB_2 (int), PWB_3 (int),
##   PWB_4 (int), PWB_5 (int), PWB_6 (int), PWB_7 (int), PWB_8 (int), PWB_9
##   (int), APSI_1 (int), APSI_2 (int), APSI_3 (int), APSI_4 (int), APSI_5
##   (int), APSI_6 (int), APSI_7 (int), APSI_8 (int), HAPPI.1_1 (int),
##   HAPPI.2_1 (int), HAPPI.3_1 (int), HAPPI.4_1 (int), RES.1 (int), RES.2
##   (int), RES.3 (int), SDQI_1 (int), SDQI_2 (int), SDQI_3 (int), SDQI_4
##   (int), SDQI_5 (int), SDQI_6 (int), SDQI_7 (int), SDQI_8 (int), SDQI_9
##   (int), SDQI_10 (int), LOT.R_1 (int), LOT.R_2 (int), LOT.R_3 (int),
##   LOT.R_4 (int), LOT.R_5 (int), LOT.R_6 (int), LOT.R_7 (int), LOT.R_8
##   (int), LOT.R_9 (int), LOT.R_10 (int), LS_1 (int), LS_2 (int), LS_3
##   (int), LS_4 (int), LS_5 (int), CPS1 (lgl), PNA_1 (int), PNA_2 (int),
##   PNA_3 (int), PNA_4 (int), PNA_5 (int), PNA_6 (int), PNA_7 (int), PNA_8
##   (int), PNA_9 (int), PNA_10 (int), PNA_11 (int), PNA_12 (int), PNA_13
##   (int), PNA_14 (int), PNA_15 (int), PNA_16 (int), PNA_17 (int), PNA_18
##   (int), PNA_19 (int), PNA_20 (int), Q46_11 (int)

RES<-select(all_surveys, RES.1  , RES.2  , RES.3)
RES<- data.frame(apply(RES,2, as.numeric))
RES<-tbl_df(RES)
RES

## Source: local data frame [1,160 x 3]
## 
##    RES.1 RES.2 RES.3
## 1      8     8     8
## 2      8     9     9
## 3      7     7     7
## 4     11     9     9
## 5      8     7     7
## 6      8     9     8
## 7      7     6     7
## 8     12    11    11
## 9     10     8     9
## 10     4     5     6
## ..   ...   ...   ...

One factor Model

one.factor = 'RES =~ RES.1 + RES.2 + RES.3 
' 
one.fit=cfa(one.factor, data=RES, missing = "fiml", std.lv = T)

## Warning in lav_data_full(data = data, group = group, group.label = group.label, : lavaan WARNING: some cases are empty and will be removed:
##   17 22 23 24 28 29 43 45 78 79 80 81 84 85 86 87 93 94 95 110 111 112 116 121 122 123 124 125 128 129 130 131 133 135 137 138 140 147 151 152 155 156 162 166 169 170 171 172 173 174 176 177 179 180 183 184 186 187 188 189 192 194 195 197 200 202 203 204 207 208 210 212 214 215 217 220 222 223 224 226 227 228 229 230 234 238 240 243 245 246 247 249 252 255 256 265 266 267 268 270 271 274 275 280 281 282 284 286 287 289 291 292 298 300 304 309 310 311 312 315 316 317 320 322 325 327 330 333 334 336 339 340 344 348 350 351 352 354 355 357 360 361 362 364 365 366 367 368 369 370 371 372 373 374 375 376 377 379 380 381 384 385 386 389 390 397 398 399 400 401 402 403 404 405 406 407 408 410 416 417 418 419 420 421 422 423 424 425 427 428 429 430 431 432 434 436 444 445 446 447 448 452 453 454 455 456 457 459 460 462 463 464 465 467 468 470 472 473 474 475 476 478 481 482 485 486 490 491 493 495 539 540 541 542 543 544 545 546 547 548 549 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 662 679 687 723 756 760 782 783 784 785 809 810 829 903 904 906 907 909 910 911 1109 1110 1111 1112 1113 1114 1116 1117 1120 1121 1122 1125 1126 1127 1128 1129 1130 1131 1132 1134 1136 1137 1138 1139 1140 1142 1143 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1157 1158 1159 1160

summary(one.fit, standardized = TRUE, rsquare=TRUE)

## lavaan (0.5-18) converged normally after  17 iterations
## 
##                                                   Used       Total
##   Number of observations                           797        1160
## 
##   Number of missing patterns                         1
## 
##   Estimator                                         ML
##   Minimum Function Test Statistic                0.000
##   Degrees of freedom                                 0
##   Minimum Function Value               0.0000000000000
## 
## Parameter estimates:
## 
##   Information                                 Observed
##   Standard Errors                             Standard
## 
##                    Estimate  Std.err  Z-value  P(>|z|)   Std.lv  Std.all
## Latent variables:
##   RES =~
##     RES.1             2.300    0.078   29.437    0.000    2.300    0.860
##     RES.2             1.925    0.060   32.000    0.000    1.925    0.909
##     RES.3             1.930    0.066   29.409    0.000    1.930    0.860
## 
## Intercepts:
##     RES.1             7.794    0.095   82.307    0.000    7.794    2.915
##     RES.2             7.471    0.075   99.586    0.000    7.471    3.528
##     RES.3             7.404    0.080   93.118    0.000    7.404    3.298
##     RES               0.000                               0.000    0.000
## 
## Variances:
##     RES.1             1.859    0.135                      1.859    0.260
##     RES.2             0.779    0.079                      0.779    0.174
##     RES.3             1.316    0.095                      1.316    0.261
##     RES               1.000                               1.000    1.000
## 
## R-Square:
## 
##     RES.1             0.740
##     RES.2             0.826
##     RES.3             0.739

correl.1 = residuals(one.fit, type="cor")
correl.1

## $type
## [1] "cor.bollen"
## 
## $cor
##       RES.1 RES.2 RES.3
## RES.1 0                
## RES.2 0     0          
## RES.3 0     0     0    
## 
## $mean
## RES.1 RES.2 RES.3 
##     0     0     0

modindices(one.fit, sort. = TRUE, minimum.value = 3.84)

## [1] lhs      op       rhs      mi       epc      sepc.lv  sepc.all sepc.nox
## <0 rows> (or 0-length row.names)

fitmeasures(one.fit)

##                npar                fmin               chisq 
##               9.000               0.000               0.000 
##                  df              pvalue      baseline.chisq 
##               0.000                  NA            1596.740 
##         baseline.df     baseline.pvalue                 cfi 
##               3.000               0.000               1.000 
##                 tli                nnfi                 rfi 
##               1.000               1.000               1.000 
##                 nfi                pnfi                 ifi 
##               1.000               0.000               1.000 
##                 rni                logl   unrestricted.logl 
##               1.000           -4620.531           -4620.531 
##                 aic                 bic              ntotal 
##            9259.062            9301.190             797.000 
##                bic2               rmsea      rmsea.ci.lower 
##            9272.610               0.000               0.000 
##      rmsea.ci.upper        rmsea.pvalue                 rmr 
##               0.000               1.000               0.000 
##          rmr_nomean                srmr        srmr_bentler 
##               0.000               0.000               0.000 
## srmr_bentler_nomean         srmr_bollen  srmr_bollen_nomean 
##               0.000               0.000               0.000 
##          srmr_mplus   srmr_mplus_nomean               cn_05 
##               0.000               0.000               1.000 
##               cn_01                 gfi                agfi 
##               1.000               1.000               1.000 
##                pgfi                 mfi                ecvi 
##               0.000               1.000                  NA


Target Rotation

$loadings

Loadings:

MR1

1 0.860

2 0.909

3 0.860

MR1

SS loadings 2.305

Proportion Var 0.768

$

NULL

$TLI

[1] -Inf

$

NULL

Factor Analysis using method = minres

Call: fa(r = RES_cor, nfactors = 1, n.obs = 1160, rotate = “TargetQ”,

Target = Targ_key)

Standardized loadings (pattern matrix) based upon correlation matrix

MR1 h2 u2 com

1 0.86 0.74 0.26 1

2 0.91 0.83 0.17 1

3 0.86 0.74 0.26 1

MR1

SS loadings 2.31

Proportion Var 0.77

Mean item complexity = 1

Test of the hypothesis that 1 factor is sufficient.

The degrees of freedom for the null model are 3 and the objective function was 2 with Chi Square of 2318.31

The degrees of freedom for the model are 0 and the objective function was 0

The root mean square of the residuals (RMSR) is 0

The df corrected root mean square of the residuals is NA

The harmonic number of observations is 1160 with the empirical chi square 0 with prob < NA

The total number of observations was 1160 with MLE Chi Square = 0 with prob < NA

Tucker Lewis Index of factoring reliability = -Inf

Fit based upon off diagonal values = 1

Measures of factor score adequacy

MR1

Correlation of scores with factors 0.96

Multiple R square of scores with factors 0.91

Minimum correlation of possible factor scores 0.83

``` CFI

1-((out_targetQ$STATISTIC - out_targetQ$dof)/(out_targetQ$null.chisq- out_targetQ$null.dof))

## [1] 1

RES TR Youth

Levi Brackman

28 July 2015

$loadings

Loadings:

MR1

1 0.860

2 0.909

3 0.860

MR1

SS loadings 2.305

Proportion Var 0.768

$

NULL

$TLI

[1] -Inf

$

NULL

Factor Analysis using method = minres

Call: fa(r = RES_cor, nfactors = 1, n.obs = 1160, rotate = “TargetQ”,

Target = Targ_key)

Standardized loadings (pattern matrix) based upon correlation matrix

MR1 h2 u2 com

1 0.86 0.74 0.26 1

2 0.91 0.83 0.17 1

3 0.86 0.74 0.26 1

MR1

SS loadings 2.31

Proportion Var 0.77

Mean item complexity = 1

Test of the hypothesis that 1 factor is sufficient.

The degrees of freedom for the null model are 3 and the objective function was 2 with Chi Square of 2318.31

The degrees of freedom for the model are 0 and the objective function was 0

The root mean square of the residuals (RMSR) is 0

The df corrected root mean square of the residuals is NA

The harmonic number of observations is 1160 with the empirical chi square 0 with prob < NA

The total number of observations was 1160 with MLE Chi Square = 0 with prob < NA

Tucker Lewis Index of factoring reliability = -Inf

Fit based upon off diagonal values = 1

Measures of factor score adequacy

MR1

Correlation of scores with factors 0.96

Multiple R square of scores with factors 0.91

Minimum correlation of possible factor scores 0.83