Brief Test of Attention
t-Test
Attention = t.test(cts_widenp13$Attention.1,cts_widenp13$Attention.3,paired=TRUE)
Attention
##
## Paired t-test
##
## data: cts_widenp13$Attention.1 and cts_widenp13$Attention.3
## t = 1.8908798, df = 421, p-value = 0.05932674
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.007305369234 0.376973615680
## sample estimates:
## mean of the differences
## 0.1848341232
Mixed Model Regression
Attn = lmer(Attention~Time + Age0 + PovStat + Race + Sex + (1|HNDid),data=DissData)
summary(Attn)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Attention ~ Time + Age0 + PovStat + Race + Sex + (1 | HNDid)
## Data: DissData
##
## REML criterion at convergence: 4499.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.66900117 -0.52650592 0.04977607 0.59716697 2.32604697
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 2.019611 1.421130
## Residual 2.040731 1.428542
## Number of obs: 1086, groups: HNDid, 664
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.996299442 0.401443103 704.474357227 22.40990 < 2.22e-16
## Time -0.130766727 0.046053767 520.505813922 -2.83944 0.00469606
## Age0 -0.026268358 0.007851739 636.474772366 -3.34555 0.00086949
## PovStatBelow -0.519896777 0.156889522 638.502023248 -3.31378 0.00097248
## RaceAfrAm -0.783490017 0.142973439 638.353464377 -5.47997 6.1289e-08
## SexMen -0.232035058 0.142994561 638.942501566 -1.62268 0.10515013
##
## Correlation of Fixed Effects:
## (Intr) Time Age0 PvSttB RcAfrA
## Time -0.242
## Age0 -0.917 -0.001
## PovStatBelw -0.254 0.015 0.150
## RaceAfrAm -0.215 -0.007 0.066 -0.058
## SexMen -0.115 0.019 -0.057 0.049 -0.076
California Verbal Learning Test A
t-Test
CVLtca = t.test(cts_widenp13$CVLtca.1,cts_widenp13$CVLtca.3,paired=TRUE)
CVLtca
##
## Paired t-test
##
## data: cts_widenp13$CVLtca.1 and cts_widenp13$CVLtca.3
## t = 19.435824, df = 435, p-value < 2.2204e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 5.459227255 6.687561736
## sample estimates:
## mean of the differences
## 6.073394495
Mixed Model Regression
CVL_listA = lmer(CVLtca~Time + Age0 + PovStat + Race + Sex + (1|HNDid),data=DissData)
summary(CVL_listA)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CVLtca ~ Time + Age0 + PovStat + Race + Sex + (1 | HNDid)
## Data: DissData
##
## REML criterion at convergence: 7391.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6537041 -0.4575660 0.0182753 0.4860896 2.8135714
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 30.32614 5.506918
## Residual 21.52922 4.639959
## Number of obs: 1116, groups: HNDid, 680
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 40.03879321 1.45492897 727.21886454 27.51941 < 2.22e-16
## Time -2.99605374 0.14921979 515.40470811 -20.07813 < 2.22e-16
## Age0 -0.19125783 0.02843451 663.32004637 -6.72626 3.7626e-11
## PovStatBelow -2.15432489 0.55743193 656.74738261 -3.86473 0.00012225
## RaceAfrAm -2.40306956 0.51479970 658.91571998 -4.66797 3.6867e-06
## SexMen -2.56358753 0.51424849 658.05532178 -4.98511 7.9286e-07
##
## Correlation of Fixed Effects:
## (Intr) Time Age0 PvSttB RcAfrA
## Time -0.219
## Age0 -0.922 -0.002
## PovStatBelw -0.258 0.009 0.149
## RaceAfrAm -0.239 0.002 0.088 -0.047
## SexMen -0.112 0.000 -0.057 0.053 -0.064
California Verbal Learning Test B
t-Test
CVLtcb = t.test(cts_widenp13$CVLtcb.1,cts_widenp13$CVLtcb.3,paired=TRUE)
CVLtcb
##
## Paired t-test
##
## data: cts_widenp13$CVLtcb.1 and cts_widenp13$CVLtcb.3
## t = 16.681488, df = 434, p-value < 2.2204e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.454072823 1.842478901
## sample estimates:
## mean of the differences
## 1.648275862
Mixed Model Regression
CVL_listB=lmer(CVLtcb~Time + Age0 + PovStat + Race + Sex + (1|HNDid),data=DissData)
summary(CVL_listB)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CVLtcb ~ Time + Age0 + PovStat + Race + Sex + (1 | HNDid)
## Data: DissData
##
## REML criterion at convergence: 4618.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.52819472 -0.57677728 -0.03778711 0.51976170 3.02911240
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 1.858942 1.363430
## Residual 2.137672 1.462078
## Number of obs: 1115, groups: HNDid, 680
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.479608428 0.396386829 743.127012832 21.39226
## Time -0.810263969 0.046395420 542.725692270 -17.46431
## Age0 -0.029795048 0.007685872 662.846706751 -3.87660
## PovStatBelow -0.463751318 0.150608104 655.530771317 -3.07919
## RaceAfrAm -0.767441416 0.139081620 657.487645810 -5.51792
## SexMen -0.359308758 0.138924985 656.520666669 -2.58635
## Pr(>|t|)
## (Intercept) < 2.22e-16
## Time < 2.22e-16
## Age0 0.00011647
## PovStatBelow 0.00216236
## RaceAfrAm 4.938e-08
## SexMen 0.00991391
##
## Correlation of Fixed Effects:
## (Intr) Time Age0 PvSttB RcAfrA
## Time -0.248
## Age0 -0.916 -0.003
## PovStatBelw -0.257 0.008 0.150
## RaceAfrAm -0.239 0.001 0.089 -0.046
## SexMen -0.112 -0.001 -0.056 0.053 -0.065
California Verbal Learning Test Learning Slope
t-Test
CVLbet = t.test(cts_widenp13$CVLbet.1,cts_widenp13$CVLbet.3,paired=TRUE)
CVLbet
##
## Paired t-test
##
## data: cts_widenp13$CVLbet.1 and cts_widenp13$CVLbet.3
## t = 0.59989275, df = 435, p-value = 0.5488901
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.09397595062 0.17654475796
## sample estimates:
## mean of the differences
## 0.04128440367
Mixed Model Regression
CVL_learn = lmer(CVLbet~Time + Age0 + PovStat + Race + Sex + (1|HNDid),data=DissData)
summary(CVL_learn)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: CVLbet ~ Time + Age0 + PovStat + Race + Sex + (1 | HNDid)
## Data: DissData
##
## REML criterion at convergence: 3435.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8551318 -0.6205495 0.0274793 0.6375800 3.1961604
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 0.1782288 0.4221716
## Residual 1.0770529 1.0378116
## Number of obs: 1116, groups: HNDid, 680
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.601686e+00 2.081104e-01 7.481387e+02 12.50147 < 2.22e-16
## Time -1.413392e-02 3.171352e-02 5.768089e+02 -0.44567 0.65599935
## Age0 -1.450664e-02 3.938868e-03 6.191657e+02 -3.68295 0.00025071
## PovStatBelow -1.292062e-04 7.695551e-02 6.071136e+02 -0.00168 0.99866093
## RaceAfrAm -2.279400e-02 7.115171e-02 6.112394e+02 -0.32036 0.74880666
## SexMen -2.725361e-01 7.104301e-02 6.095847e+02 -3.83621 0.00013796
##
## Correlation of Fixed Effects:
## (Intr) Time Age0 PvSttB RcAfrA
## Time -0.321
## Age0 -0.895 -0.002
## PovStatBelw -0.254 0.011 0.152
## RaceAfrAm -0.237 0.003 0.093 -0.044
## SexMen -0.108 -0.001 -0.055 0.051 -0.068
Digit Span Forward
t-Test
DigitSpanFwd = t.test(cts_widenp13$DigitSpanFwd.1,cts_widenp13$DigitSpanFwd.3,paired=TRUE)
DigitSpanFwd
##
## Paired t-test
##
## data: cts_widenp13$DigitSpanFwd.1 and cts_widenp13$DigitSpanFwd.3
## t = -0.94858957, df = 506, p-value = 0.3432822
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2180692256 0.0760573913
## sample estimates:
## mean of the differences
## -0.07100591716
Mixed Model Regression
DigitSpanFwd = lmer(DigitSpanFwd~Time + Age0 + PovStat + Race + Sex + (1|HNDid),data=DissData)
summary(DigitSpanFwd)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DigitSpanFwd ~ Time + Age0 + PovStat + Race + Sex + (1 | HNDid)
## Data: DissData
##
## REML criterion at convergence: 4970.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.35125440 -0.48522793 -0.05872993 0.43987535 2.85364994
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 3.510214 1.873557
## Residual 1.416577 1.190200
## Number of obs: 1199, groups: HNDid, 692
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.384516018 0.444133403 717.037148836 21.12995 < 2.22e-16
## Time 0.038509921 0.036472070 557.063216595 1.05587 0.29148335
## Age0 -0.031857081 0.008825491 685.245148413 -3.60967 0.00032894
## PovStatBelow -0.682821257 0.174029128 690.428739375 -3.92360 9.5962e-05
## RaceAfrAm -0.781789697 0.160327412 686.579762724 -4.87621 1.3449e-06
## SexMen 0.304994102 0.160177405 685.936481208 1.90410 0.05731507
##
## Correlation of Fixed Effects:
## (Intr) Time Age0 PvSttB RcAfrA
## Time -0.155
## Age0 -0.934 0.001
## PovStatBelw -0.248 0.002 0.137
## RaceAfrAm -0.236 -0.021 0.084 -0.043
## SexMen -0.112 0.004 -0.062 0.047 -0.062
Digit Span Backward
t-Test
DigitSpanBck = t.test(cts_widenp13$DigitSpanBck.1,cts_widenp13$DigitSpanBck.3,paired=TRUE)
DigitSpanBck
##
## Paired t-test
##
## data: cts_widenp13$DigitSpanBck.1 and cts_widenp13$DigitSpanBck.3
## t = -2.2917537, df = 498, p-value = 0.02233633
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.33126336690 -0.02545005995
## sample estimates:
## mean of the differences
## -0.1783567134
Mixed Model Regression
DigitSpanBck = lmer(DigitSpanBck~Time + Age0 + PovStat + Race + Sex + (1|HNDid),data=DissData)
summary(DigitSpanBck)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DigitSpanBck ~ Time + Age0 + PovStat + Race + Sex + (1 | HNDid)
## Data: DissData
##
## REML criterion at convergence: 4920.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1527793 -0.4984362 -0.0621020 0.4241946 3.5032170
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 3.107264 1.762743
## Residual 1.528972 1.236516
## Number of obs: 1190, groups: HNDid, 691
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.793596963 0.428420087 707.504941994 18.19148 < 2.22e-16
## Time 0.062669964 0.038003106 544.685532352 1.64907 0.09970890
## Age0 -0.031038427 0.008496836 671.439396298 -3.65294 0.00027946
## PovStatBelow -0.649914575 0.167723832 674.823325315 -3.87491 0.00011707
## RaceAfrAm -1.086606250 0.154332738 671.602952348 -7.04067 4.7402e-12
## SexMen 0.260674726 0.154127882 671.122054155 1.69129 0.09124596
##
## Correlation of Fixed Effects:
## (Intr) Time Age0 PvSttB RcAfrA
## Time -0.166
## Age0 -0.932 0.000
## PovStatBelw -0.245 -0.001 0.135
## RaceAfrAm -0.235 -0.025 0.084 -0.043
## SexMen -0.113 0.008 -0.061 0.045 -0.064
Trail Making Test Part A
t-Test
TrailsA = t.test(cts_widenp13$TrailsAtestSec.1,cts_widenp13$TrailsAtestSec.3,paired=TRUE)
TrailsA
##
## Paired t-test
##
## data: cts_widenp13$TrailsAtestSec.1 and cts_widenp13$TrailsAtestSec.3
## t = -2.7165715, df = 562, p-value = 0.006799382
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -12.064351219 -1.939201179
## sample estimates:
## mean of the differences
## -7.001776199
Mixed Model Regression
TrailsA = lmer(TrailsAtestSec~Time + Age0 + PovStat + Race + Sex + (1|HNDid),data=DissData)
summary(TrailsA)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TrailsAtestSec ~ Time + Age0 + PovStat + Race + Sex + (1 | HNDid)
## Data: DissData
##
## REML criterion at convergence: 13113
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5984918 -0.2396807 -0.1028936 0.0673114 12.2795893
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 240.8849 15.52047
## Residual 1708.9405 41.33933
## Number of obs: 1262, groups: HNDid, 699
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.0677531 7.6045258 855.6481266 -0.53491 0.5928496
## Time 3.2988046 1.1719446 716.5422827 2.81481 0.0050145
## Age0 0.5926617 0.1457051 734.2284742 4.06754 5.2652e-05
## PovStatBelow 1.9101803 2.8670011 742.6314370 0.66626 0.5054492
## RaceAfrAm 7.1551958 2.6398265 731.3756600 2.71048 0.0068761
## SexMen 3.4325153 2.6339474 728.8978267 1.30318 0.1929238
##
## Correlation of Fixed Effects:
## (Intr) Time Age0 PvSttB RcAfrA
## Time -0.300
## Age0 -0.902 -0.004
## PovStatBelw -0.237 -0.004 0.141
## RaceAfrAm -0.223 -0.021 0.087 -0.058
## SexMen -0.106 0.008 -0.063 0.044 -0.069
Trail Making Test Part B
t-Test
TrailsB = t.test(cts_widenp13$TrailsBtestSec.1,cts_widenp13$TrailsBtestSec.3,paired=TRUE)
TrailsB
##
## Paired t-test
##
## data: cts_widenp13$TrailsBtestSec.1 and cts_widenp13$TrailsBtestSec.3
## t = -1.1841996, df = 561, p-value = 0.2368355
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -15.204589816 3.766867396
## sample estimates:
## mean of the differences
## -5.71886121
Mixed Model Regression
TrailsB = lmer(TrailsBtestSec~Time + Age0 + PovStat + Race + Sex + (1|HNDid),data=DissData)
## Error in eval(predvars, data, env): object 'TrailsBtestSec' not found
summary(TrailsB)
## Length Class Mode
## statistic 1 -none- numeric
## parameter 1 -none- numeric
## p.value 1 -none- numeric
## conf.int 2 -none- numeric
## estimate 1 -none- numeric
## null.value 1 -none- numeric
## alternative 1 -none- character
## method 1 -none- character
## data.name 1 -none- character
Word Fluency
t-Test
FluencyWord = t.test(cts_widenp13$FluencyWord.1,cts_widenp13$FluencyWord.3,paired=TRUE)
FluencyWord
##
## Paired t-test
##
## data: cts_widenp13$FluencyWord.1 and cts_widenp13$FluencyWord.3
## t = -0.2468344, df = 561, p-value = 0.8051267
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3984683477 0.3095003761
## sample estimates:
## mean of the differences
## -0.04448398577
Mixed Model Regression
FluencyWord = lmer(FluencyWord~Time + Age0 + PovStat + Race + Sex + (1|HNDid),data=DissData)
summary(FluencyWord)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: FluencyWord ~ Time + Age0 + PovStat + Race + Sex + (1 | HNDid)
## Data: DissData
##
## REML criterion at convergence: 7471.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.55689162 -0.53495207 -0.01874509 0.45378766 2.52112813
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 20.082616 4.481363
## Residual 9.113221 3.018811
## Number of obs: 1260, groups: HNDid, 698
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 24.27434051 1.06903533 731.06396141 22.70677 < 2.22e-16
## Time 0.04063451 0.08840089 601.88469256 0.45966 0.645925
## Age0 -0.08871606 0.02121410 694.73513161 -4.18194 3.2603e-05
## PovStatBelow -1.36447456 0.41631237 699.42134851 -3.27753 0.001099
## RaceAfrAm -1.79757579 0.38436706 692.58277945 -4.67672 3.5054e-06
## SexMen 1.70465626 0.38405821 691.11061760 4.43854 1.0540e-05
##
## Correlation of Fixed Effects:
## (Intr) Time Age0 PvSttB RcAfrA
## Time -0.159
## Age0 -0.933 -0.003
## PovStatBelw -0.243 -0.010 0.135
## RaceAfrAm -0.235 -0.010 0.084 -0.047
## SexMen -0.109 0.003 -0.062 0.049 -0.066