Rob Rel Mindsets
Julie
7/3/2020
Load libraries
## ── Attaching packages ───────────────────## ✓ ggplot2 3.3.0 ✓ purrr 0.3.4
## ✓ tibble 3.0.1 ✓ dplyr 1.0.0
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ─── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Load data
## Parsed with column specification:
## cols(
## .default = col_double(),
## CaseNumber = col_character(),
## StudentType = col_character(),
## ProgramFilter = col_character(),
## YearFixed = col_character(),
## AgeFIXED = col_character(),
## DM_Year_T1 = col_character(),
## DM_Sex_T1 = col_character(),
## DM_Age_T1 = col_character(),
## DM_SES5_T1 = col_character(),
## ChildhoodSES_T1 = col_character(),
## DM_ChildhoodSES_T1 = col_character(),
## DM_CGS_T1 = col_character(),
## DM_SES3_T1 = col_character(),
## DM_SES2_T1 = col_character(),
## DM_Race3_T1 = col_character(),
## DM_Race4_T1 = col_character(),
## FSC1_T1 = col_character(),
## FSC2_T1 = col_character(),
## PosSlide_T1_1 = col_character(),
## Certain_T1 = col_character()
## # ... with 110 more columns
## )## See spec(...) for full column specifications.## Warning: 168 parsing failures.
## row col expected actual file
## 8 -- 2254 columns 326 columns './data/RAW_data.csv'
## 9 -- 2254 columns 1 columns './data/RAW_data.csv'
## 10 -- 2254 columns 1929 columns './data/RAW_data.csv'
## 16 -- 2254 columns 324 columns './data/RAW_data.csv'
## 17 -- 2254 columns 1931 columns './data/RAW_data.csv'
## ... ... ............ ............ .....................
## See problems(...) for more details.Clean data
# Take out rows that have open ended responses for some reason
data <- data %>%
mutate(CaseNumber = as.numeric(CaseNumber)) %>%
filter(!is.na(CaseNumber))## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion# Rename AVI items
data <- data %>%
rename(i_enthusiastic = HAP1_T2,
i_astonished = HA2_T2,
i_nervous = HAN3_T2,
i_dull = LAN1_T2,
i_quiet = LAN1_T2,
i_relaxed = LAP2_T2,
i_excited = HAP2_T2,
i_surprised = HA3_T2,
i_elated = HAP4_T2,
i_sleepy = LAN2_T2,
i_still = LA2_T2,
i_lonely =MAN2_T2,
i_strong = HAP3_T2,
i_passive = LA3_T2,
i_content = MAP3_T2,
i_sluggish = LAN3_T2,
i_inactive = AVI1_T2,
i_sad = MAN1_T2,
i_euphoric = AVI2_T2,
i_fearful = HAN1_T2,
i_happy = MAP1_T2,
i_idle = AVI3_T2,
i_calm = LAP1_T2,
i_unhappy = MAN3_T2,
i_hostile = HAN2_T2,
i_satisfied = MAP2_T2,
i_rested = AVI4_T2,
i_peaceful = LAP3_T2,
i_serene = LAP4_T2,
i_angry = AVI5_T2,
i_frustrated = AVI6_T2,
i_guilty = AVI7_T2,
i_jealous = AVI8_T2,
i_embarrassed = AVI9_T2,
i_regretful = AVI10_T2,
i_affectionate = AVI11_T2,
i_grateful = AVI12_T2,
i_curious = AVI13_T2,
i_passionate = AVI14_T2,
i_aroused = HA1_T2,
a_enthusiastic = actHAP1_T2,
a_astonished = actHA2_T2,
a_nervous = actHAN3_T2,
a_dull = actLAN1_T2,
a_quiet = actLAN1_T2,
a_relaxed = actLAP2_T2,
a_excited = actHAP2_T2,
a_surprised = actHA3_T2,
a_elated = actHAP4_T2,
a_sleepy = actLAN2_T2,
a_still = actLA2_T2,
a_lonely = actMAN2_T2,
a_strong = actHAP3_T2,
a_passive = actLA3_T2,
a_content = actMAP3_T2,
a_sluggish = actLAN3_T2,
a_inactive = actAVI1_T2,
a_sad = actMAN1_T2,
a_euphoric = actAVI2_T2,
a_fearful = actHAN1_T2,
a_happy = actMAP1_T2,
a_idle = actAVI3_T2,
a_calm = actLAP1_T2,
a_unhappy = actMAN3_T2,
a_hostile = actHAN2_T2,
a_satisfied = actMAP2_T2,
a_rested = actAVI4_T2,
a_peaceful = actLAP3_T2,
a_serene = actLAP4_T2,
a_angry = actAVI5_T2,
a_frustrated = actAVI6_T2,
a_guilty = actAVI7_T2,
a_jealous = actAVI8_T2,
a_embarrassed = actAVI9_T2,
a_regretful = actAVI10_T2,
a_affectionate = actAVI11_T2,
a_grateful = actAVI12_T2,
a_curious = actAVI13_T2,
a_passionate = actAVI14_T2,
a_aroused = actHA1_T2,
idealrel_calm = IdealAffect_T1_1,
idealrel_happy = IdealAffect_T1_2,
idealrel_excited = IdealAffect_T1_3)
# Compute actual and ideal affect scores
data <- data %>%
rowwise() %>%
mutate(aHAP = mean(c(a_enthusiastic, a_excited, a_elated, a_euphoric), na.rm = T),
aLAP = mean(c(a_relaxed, a_calm, a_peaceful, a_serene), na.rm = T),
iHAP = mean(c(i_enthusiastic, i_excited, i_elated, i_euphoric), na.rm = T),
iLAP = mean(c(i_relaxed, i_calm, i_peaceful, i_serene)), na.rm = T)
# Extract variables of interest
data <- data %>%
dplyr::select(CaseNumber, StudentType, ProgramFilter, YearFixed, AgeFIXED, contains("DM_"), RelationshipFIT_T1, RelationshipDEVELOP_T1, RelationshipHAP_T1, PassionBinary_T1, RAS_T1, RASsingle_T1, Relationship_T1, ExLove_T1, Destiny_T2, Growth_T2, RAS_T3, RASsingle_T3, Destiny_T3, Growth_T3, Relationship_T3, aHAP, aLAP, iHAP, iLAP, idealrel_calm, idealrel_happy, idealrel_excited, RelationLength_T3_1_TEXT, RelationLength_T3_2_TEXT, RelStatus_T3, TightLoose_t1)
# Turn on program filter
data <- data %>%
filter(ProgramFilter == 1)
write.csv(data, file="./data/CLEANED_data.csv")Analyses:
See whether ideal HAP predicts relationship passion mindsets
##
## Call:
## lm(formula = RelationshipFIT_T1 ~ iHAP * Relationship_T1 + aHAP,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.07015 -0.39662 0.01654 0.72425 1.88393
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.62640 0.34638 10.470 <2e-16 ***
## iHAP 0.10828 0.11369 0.952 0.342
## Relationship_T1 0.35890 0.61054 0.588 0.557
## aHAP 0.09733 0.10998 0.885 0.377
## iHAP:Relationship_T1 -0.10779 0.18952 -0.569 0.570
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9573 on 178 degrees of freedom
## (848 observations deleted due to missingness)
## Multiple R-squared: 0.01902, Adjusted R-squared: -0.003027
## F-statistic: 0.8627 on 4 and 178 DF, p-value: 0.4875##
## Call:
## lm(formula = RelationshipDEVELOP_T1 ~ iHAP * Relationship_T1 +
## aHAP, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2307 -0.5190 0.0439 0.6042 1.8952
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.97882 0.35199 11.304 <2e-16 ***
## iHAP 0.14763 0.11554 1.278 0.203
## Relationship_T1 -0.11141 0.62044 -0.180 0.858
## aHAP -0.02167 0.11177 -0.194 0.847
## iHAP:Relationship_T1 0.10647 0.19259 0.553 0.581
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9728 on 178 degrees of freedom
## (848 observations deleted due to missingness)
## Multiple R-squared: 0.03415, Adjusted R-squared: 0.01245
## F-statistic: 1.573 on 4 and 178 DF, p-value: 0.1833##
## Call:
## lm(formula = RelationshipFIT_T1 ~ iLAP * Relationship_T1 + aLAP,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1675 -0.3984 -0.0132 0.7314 2.0341
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.72485 0.36385 12.986 <2e-16 ***
## iLAP -0.13602 0.10064 -1.352 0.178
## Relationship_T1 -0.57869 0.54257 -1.067 0.288
## aLAP -0.01577 0.09775 -0.161 0.872
## iLAP:Relationship_T1 0.18122 0.15459 1.172 0.243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9598 on 178 degrees of freedom
## (848 observations deleted due to missingness)
## Multiple R-squared: 0.01391, Adjusted R-squared: -0.008247
## F-statistic: 0.6278 on 4 and 178 DF, p-value: 0.6433##
## Call:
## lm(formula = RelationshipDEVELOP_T1 ~ iLAP * Relationship_T1 +
## aLAP, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3884 -0.5028 0.0060 0.6121 1.7520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.48158 0.36566 12.256 <2e-16 ***
## iLAP 0.11233 0.10114 1.111 0.2682
## Relationship_T1 -0.31971 0.54528 -0.586 0.5584
## aLAP -0.17148 0.09823 -1.746 0.0826 .
## iLAP:Relationship_T1 0.16825 0.15536 1.083 0.2803
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9645 on 178 degrees of freedom
## (848 observations deleted due to missingness)
## Multiple R-squared: 0.05044, Adjusted R-squared: 0.0291
## F-statistic: 2.364 on 4 and 178 DF, p-value: 0.05484See whether ideal affect (HAP, LAP) predicts wanting congruent emotion (excitement, calm) in relationship
#HAP --> excitement in rel
lm(data = data, idealrel_excited ~ iHAP * Relationship_T1 + aHAP) %>% summary##
## Call:
## lm(formula = idealrel_excited ~ iHAP * Relationship_T1 + aHAP,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -64.620 -12.528 3.924 14.011 36.782
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.098 7.451 6.321 2.05e-09 ***
## iHAP 9.290 2.430 3.822 0.000183 ***
## Relationship_T1 7.890 13.009 0.606 0.544977
## aHAP -1.231 2.334 -0.527 0.598641
## iHAP:Relationship_T1 -2.629 4.033 -0.652 0.515416
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.31 on 177 degrees of freedom
## (849 observations deleted due to missingness)
## Multiple R-squared: 0.1008, Adjusted R-squared: 0.08051
## F-statistic: 4.962 on 4 and 177 DF, p-value: 0.0008162#LAP --> excitement in rel
lm(data = data, idealrel_excited ~ iLAP * Relationship_T1 + aLAP) %>% summary##
## Call:
## lm(formula = idealrel_excited ~ iLAP * Relationship_T1 + aLAP,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -73.579 -12.008 3.754 15.830 30.484
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 66.9307 8.0195 8.346 1.96e-14 ***
## iLAP 3.6301 2.2190 1.636 0.104
## Relationship_T1 -0.9676 11.9586 -0.081 0.936
## aLAP -2.1526 2.1545 -0.999 0.319
## iLAP:Relationship_T1 0.1873 3.4079 0.055 0.956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.15 on 177 degrees of freedom
## (849 observations deleted due to missingness)
## Multiple R-squared: 0.02452, Adjusted R-squared: 0.002479
## F-statistic: 1.112 on 4 and 177 DF, p-value: 0.3522##
## Call:
## lm(formula = idealrel_calm ~ iHAP * Relationship_T1 + aHAP, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.904 -16.375 3.078 18.411 35.627
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 68.346 7.985 8.559 5.32e-15 ***
## iHAP 3.274 2.605 1.257 0.210
## Relationship_T1 -11.638 13.941 -0.835 0.405
## aHAP -1.419 2.501 -0.567 0.571
## iHAP:Relationship_T1 2.687 4.322 0.622 0.535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.76 on 177 degrees of freedom
## (849 observations deleted due to missingness)
## Multiple R-squared: 0.02603, Adjusted R-squared: 0.004021
## F-statistic: 1.183 on 4 and 177 DF, p-value: 0.3201##
## Call:
## lm(formula = idealrel_calm ~ iLAP * Relationship_T1 + aLAP, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -72.447 -13.296 2.865 16.966 36.935
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60.637 8.028 7.554 2.17e-12 ***
## iLAP 6.074 2.221 2.735 0.00688 **
## Relationship_T1 -6.875 11.971 -0.574 0.56646
## aLAP -2.266 2.157 -1.051 0.29480
## iLAP:Relationship_T1 1.184 3.411 0.347 0.72894
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.17 on 177 degrees of freedom
## (849 observations deleted due to missingness)
## Multiple R-squared: 0.07812, Adjusted R-squared: 0.05728
## F-statistic: 3.75 on 4 and 177 DF, p-value: 0.005918##
## Call:
## lm(formula = idealrel_happy ~ iHAP * Relationship_T1 + aHAP,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -71.865 -7.195 5.444 13.711 22.222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 75.2298 6.8033 11.058 < 2e-16 ***
## iHAP 6.4064 2.2192 2.887 0.00438 **
## Relationship_T1 1.6663 11.8784 0.140 0.88860
## aHAP -3.8580 2.1308 -1.811 0.07190 .
## iHAP:Relationship_T1 -0.7007 3.6827 -0.190 0.84931
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.54 on 177 degrees of freedom
## (849 observations deleted due to missingness)
## Multiple R-squared: 0.05341, Adjusted R-squared: 0.03202
## F-statistic: 2.497 on 4 and 177 DF, p-value: 0.04447##
## Call:
## lm(formula = idealrel_happy ~ iLAP * Relationship_T1 + aLAP,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.159 -7.399 5.299 13.110 26.758
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 82.306 6.963 11.821 <2e-16 ***
## iLAP 4.765 1.927 2.473 0.0143 *
## Relationship_T1 -4.464 10.383 -0.430 0.6678
## aLAP -4.667 1.871 -2.495 0.0135 *
## iLAP:Relationship_T1 1.194 2.959 0.404 0.6869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.37 on 177 degrees of freedom
## (849 observations deleted due to missingness)
## Multiple R-squared: 0.07148, Adjusted R-squared: 0.0505
## F-statistic: 3.407 on 4 and 177 DF, p-value: 0.01033#Visualize:
ggplot(data, aes(x = iHAP, y = idealrel_excited)) +
geom_point() +
geom_smooth(method="lm")## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 849 rows containing non-finite values (stat_smooth).## Warning: Removed 849 rows containing missing values (geom_point).
## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 849 rows containing non-finite values (stat_smooth).
## Warning: Removed 849 rows containing missing values (geom_point).
Seeing what predicts CHANGE in RAS:
# First, to make sure the Wave 3 relationships are the same as Wave 1, filter those in Wave 3 who report rel of longer than 1 year (since gap between Wave 1 and 3 seems to be about a year):
data_cont_rel <- data %>%
mutate(RelationLength_T3_1_TEXT = as.numeric(as.character(RelationLength_T3_1_TEXT))) %>%
mutate(RelationLength_T3_2_TEXT = as.numeric(as.character(RelationLength_T3_2_TEXT))) %>%
filter((RelationLength_T3_1_TEXT >= 1) | (RelationLength_T3_1_TEXT == 0 & RelationLength_T3_2_TEXT >= 12) | (is.na(RelationLength_T3_1_TEXT) & RelationLength_T3_2_TEXT >= 12))# Ideal affect (iHAP and iLAP) alone does not predict change (even with interaction term):
lm(data = data, RAS_T3 ~ iHAP * iLAP + RAS_T1 + aHAP + aLAP) %>% summary##
## Call:
## lm(formula = RAS_T3 ~ iHAP * iLAP + RAS_T1 + aHAP + aLAP, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.74090 -0.25797 0.01052 0.24782 0.54724
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42440 1.62551 0.261 0.796165
## iHAP 0.20806 0.49698 0.419 0.679052
## iLAP 0.05905 0.40676 0.145 0.885735
## RAS_T1 0.80265 0.21473 3.738 0.000968 ***
## aHAP 0.15780 0.14065 1.122 0.272539
## aLAP -0.25480 0.12752 -1.998 0.056694 .
## iHAP:iLAP -0.03538 0.12608 -0.281 0.781312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3475 on 25 degrees of freedom
## (999 observations deleted due to missingness)
## Multiple R-squared: 0.5264, Adjusted R-squared: 0.4127
## F-statistic: 4.631 on 6 and 25 DF, p-value: 0.002712# JUST relationship develop vs. fit mindsets predicting change in relationship satisfaction
lm(data = data, RAS_T3 ~ RelationshipFIT_T1 + RelationshipDEVELOP_T1 + RAS_T1) %>% summary##
## Call:
## lm(formula = RAS_T3 ~ RelationshipFIT_T1 + RelationshipDEVELOP_T1 +
## RAS_T1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.15265 -0.24524 0.02014 0.27838 0.90091
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.27414 0.33671 3.784 0.000252 ***
## RelationshipFIT_T1 0.10009 0.04277 2.340 0.021081 *
## RelationshipDEVELOP_T1 -0.02745 0.04643 -0.591 0.555651
## RAS_T1 0.54945 0.07031 7.814 3.55e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3787 on 110 degrees of freedom
## (917 observations deleted due to missingness)
## Multiple R-squared: 0.4111, Adjusted R-squared: 0.395
## F-statistic: 25.59 on 3 and 110 DF, p-value: 1.234e-12##
## Call:
## lm(formula = RAS_T3 ~ PassionBinary_T1 + RAS_T1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.15251 -0.20874 0.02699 0.29126 0.81692
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.64287 0.28653 5.734 8.59e-08 ***
## PassionBinary_T1 -0.10107 0.07204 -1.403 0.163
## RAS_T1 0.57699 0.06927 8.329 2.39e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3831 on 111 degrees of freedom
## (917 observations deleted due to missingness)
## Multiple R-squared: 0.3921, Adjusted R-squared: 0.3811
## F-statistic: 35.79 on 2 and 111 DF, p-value: 1.008e-12# Once iHAP is included, effect flips(!)
lm(data = data, RAS_T3 ~ RelationshipFIT_T1 + RelationshipDEVELOP_T1 + iHAP + RAS_T1 + aHAP) %>% summary##
## Call:
## lm(formula = RAS_T3 ~ RelationshipFIT_T1 + RelationshipDEVELOP_T1 +
## iHAP + RAS_T1 + aHAP, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45223 -0.25943 -0.00709 0.23628 0.60778
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.36776 0.69187 -0.532 0.59956
## RelationshipFIT_T1 0.08591 0.06712 1.280 0.21187
## RelationshipDEVELOP_T1 0.23214 0.09023 2.573 0.01615 *
## iHAP -0.01511 0.11251 -0.134 0.89419
## RAS_T1 0.61413 0.17232 3.564 0.00144 **
## aHAP 0.10049 0.09887 1.016 0.31881
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.326 on 26 degrees of freedom
## (999 observations deleted due to missingness)
## Multiple R-squared: 0.5666, Adjusted R-squared: 0.4833
## F-statistic: 6.799 on 5 and 26 DF, p-value: 0.0003558Above may be due to low power (since predictor (ideal affect) is only half of sample AND only people in relationships). So instead, try looking at a variable in time 1:
# Ideal relationship emotions do not predict RAS change:
lm(data = data, RAS_T3 ~ idealrel_excited + idealrel_calm + idealrel_happy + RAS_T1) %>% summary##
## Call:
## lm(formula = RAS_T3 ~ idealrel_excited + idealrel_calm + idealrel_happy +
## RAS_T1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.21296 -0.20810 0.02788 0.28945 0.86224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.399e+00 3.815e-01 3.666 0.000431 ***
## idealrel_excited 4.079e-04 2.977e-03 0.137 0.891358
## idealrel_calm -9.038e-05 2.344e-03 -0.039 0.969328
## idealrel_happy 1.035e-03 3.204e-03 0.323 0.747552
## RAS_T1 5.723e-01 8.367e-02 6.840 1.19e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3986 on 84 degrees of freedom
## (942 observations deleted due to missingness)
## Multiple R-squared: 0.3648, Adjusted R-squared: 0.3345
## F-statistic: 12.06 on 4 and 84 DF, p-value: 8.614e-08Important moderator may be duration of relationship:
# Relationship develop vs. fit mindsets
lm(data = data, RAS_T3 ~ RelationshipFIT_T1 * RelationLength_T3_1_TEXT + RAS_T1) %>% summary##
## Call:
## lm(formula = RAS_T3 ~ RelationshipFIT_T1 * RelationLength_T3_1_TEXT +
## RAS_T1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.14644 -0.21003 0.00342 0.25653 0.98862
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.073994 0.427493 2.512
## RelationshipFIT_T1 0.098442 0.069679 1.413
## RelationLength_T3_1_TEXT 0.023419 0.061212 0.383
## RAS_T1 0.560775 0.081582 6.874
## RelationshipFIT_T1:RelationLength_T3_1_TEXT -0.002225 0.013207 -0.168
## Pr(>|t|)
## (Intercept) 0.0139 *
## RelationshipFIT_T1 0.1614
## RelationLength_T3_1_TEXT 0.7030
## RAS_T1 9.82e-10 ***
## RelationshipFIT_T1:RelationLength_T3_1_TEXT 0.8666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3859 on 85 degrees of freedom
## (941 observations deleted due to missingness)
## Multiple R-squared: 0.3976, Adjusted R-squared: 0.3692
## F-statistic: 14.02 on 4 and 85 DF, p-value: 7.929e-09##
## Call:
## lm(formula = RAS_T3 ~ RelationshipDEVELOP_T1 * RelationLength_T3_1_TEXT +
## RAS_T1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.18408 -0.19127 0.01489 0.28801 0.98582
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.276659 0.456221 2.798
## RelationshipDEVELOP_T1 0.035744 0.078987 0.453
## RelationLength_T3_1_TEXT 0.040846 0.073355 0.557
## RAS_T1 0.577217 0.082918 6.961
## RelationshipDEVELOP_T1:RelationLength_T3_1_TEXT -0.005758 0.015510 -0.371
## Pr(>|t|)
## (Intercept) 0.00635 **
## RelationshipDEVELOP_T1 0.65204
## RelationLength_T3_1_TEXT 0.57911
## RAS_T1 6.62e-10 ***
## RelationshipDEVELOP_T1:RelationLength_T3_1_TEXT 0.71137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3927 on 85 degrees of freedom
## (941 observations deleted due to missingness)
## Multiple R-squared: 0.3763, Adjusted R-squared: 0.3469
## F-statistic: 12.82 on 4 and 85 DF, p-value: 3.297e-08# Ideal rel excited:
lm(data = data, RAS_T3 ~ idealrel_excited * RelationLength_T3_1_TEXT + RAS_T1) %>% summary##
## Call:
## lm(formula = RAS_T3 ~ idealrel_excited * RelationLength_T3_1_TEXT +
## RAS_T1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.16955 -0.21406 0.00456 0.28338 0.89458
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.1013952 0.4034880 2.730
## idealrel_excited 0.0040276 0.0034084 1.182
## RelationLength_T3_1_TEXT 0.1033644 0.0631718 1.636
## RAS_T1 0.5800734 0.0815299 7.115
## idealrel_excited:RelationLength_T3_1_TEXT -0.0010699 0.0007422 -1.441
## Pr(>|t|)
## (Intercept) 0.00772 **
## idealrel_excited 0.24068
## RelationLength_T3_1_TEXT 0.10553
## RAS_T1 3.47e-10 ***
## idealrel_excited:RelationLength_T3_1_TEXT 0.15317
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3903 on 84 degrees of freedom
## (942 observations deleted due to missingness)
## Multiple R-squared: 0.3911, Adjusted R-squared: 0.3621
## F-statistic: 13.49 on 4 and 84 DF, p-value: 1.559e-08# Ideal rel calm:
lm(data = data, RAS_T3 ~ idealrel_calm * RelationLength_T3_1_TEXT + RAS_T1) %>% summary##
## Call:
## lm(formula = RAS_T3 ~ idealrel_calm * RelationLength_T3_1_TEXT +
## RAS_T1, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.15096 -0.19070 0.00915 0.29826 0.93234
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.346e+00 3.600e-01 3.739 0.000336
## idealrel_calm 1.211e-03 2.960e-03 0.409 0.683463
## RelationLength_T3_1_TEXT 1.578e-02 2.564e-02 0.615 0.539913
## RAS_T1 5.771e-01 8.353e-02 6.909 8.76e-10
## idealrel_calm:RelationLength_T3_1_TEXT -5.670e-06 3.482e-04 -0.016 0.987046
##
## (Intercept) ***
## idealrel_calm
## RelationLength_T3_1_TEXT
## RAS_T1 ***
## idealrel_calm:RelationLength_T3_1_TEXT
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3945 on 84 degrees of freedom
## (942 observations deleted due to missingness)
## Multiple R-squared: 0.3777, Adjusted R-squared: 0.3481
## F-statistic: 12.75 on 4 and 84 DF, p-value: 3.76e-08