Stick - codes, frequencies and proportions
code count prop
fishing 3 0.008
carve 4 0.011
digging 4 0.011
flag 5 0.013
marker 6 0.016
scratch 6 0.016
drum 8 0.021
hitting_balls 8 0.021
pointing 8 0.021
wand 11 0.029
holding_proping 14 0.037
play 14 0.037
building 16 0.043
making_it_into_a_weapon 18 0.048
food 22 0.059
drawing 23 0.061
reaching_moving_items 27 0.072
fetch_play 29 0.077
walking_stick 35 0.093
fire 42 0.112
hitting_poking 73 0.194
Shoe - codes, frequencies and proportions
code count prop
doorstopper 2 0.005
get_new_shoes 2 0.005
lunchbox 2 0.005
plant_in_it 2 0.005
use_for_an_experiment 3 0.008
bury_a_pet 4 0.010
paint_draw_write_on_it 4 0.010
throw_hit_someone 4 0.010
shadow_box 5 0.013
trap 5 0.013
hat 7 0.018
play_toy 7 0.018
specific_play_toy 7 0.018
fire 13 0.034
art_craft 14 0.036
diorama 17 0.044
project 18 0.047
pet_house 27 0.070
store_objects 65 0.168
conventional_use 82 0.212
storage_active_purpose 96 0.249
Feather - codes, frequencies and proportions
code count prop
analyze 1 0.003
put_on_head 1 0.003
spreading_disease 1 0.003
trace 1 0.003
blow 2 0.005
brush 2 0.005
put_on_hat 2 0.005
spreading_diseases 2 0.005
take_a_picture 2 0.005
trail 2 0.005
lighting 3 0.008
bookmarker 4 0.011
dusting 4 0.011
paint 4 0.011
accessory_specific 5 0.014
fan 5 0.014
return_to_bird 5 0.014
scrapbook 5 0.014
sneeze 5 0.014
gift 6 0.016
analyze 7 0.019
fly 7 0.019
dreamcather 9 0.025
pillow 9 0.025
play 12 0.033
float 15 0.041
collection 17 0.046
pen 41 0.112
decoration 42 0.114
accessory 66 0.180
tickle 80 0.218
Paper - codes, frequencies and proportions
code count prop
bookmarker 2 0.005
funnel 2 0.005
instrument 2 0.005
kill_a_bug 2 0.005
machae 2 0.005
boat 3 0.008
telescope 3 0.008
play_specific 5 0.013
cover 6 0.015
napkin 6 0.015
confetti 8 0.020
papercut 9 0.023
kindling 10 0.025
art_generic 12 0.031
play_generic 26 0.066
plane 49 0.125
art_specific 51 0.130
draw_write 195 0.496
Bucket - codes, frequencies and proportions
code count prop
pot 1 0.002
water_ballon 1 0.002
cyntrifical_force 2 0.005
floating_boat 2 0.005
painting 2 0.005
rain_water 2 0.005
sand_castle 2 0.005
water_balloon 2 0.005
boil 3 0.007
fill_a_hole 3 0.007
wash_animals 3 0.007
washing 3 0.007
soak_your_feet 4 0.009
carry_water 5 0.011
drowning 5 0.011
freeze 5 0.011
spill 5 0.011
cooking 6 0.014
cool 7 0.016
play 7 0.016
storage 9 0.021
abc 12 0.027
wash_pet 12 0.027
aquarium_pet_house 18 0.041
put_out_fire 19 0.043
washing_sth_in_it 35 0.080
watering 41 0.094
throw_at_someone 64 0.146
drink 69 0.158
washing_cleaning 89 0.203


Descriptives for object scores
vars n mean sd median trimmed mad min max range skew kurtosis se
stickScores 1 89 5.205 1.407 5.551 5.255 1.467 1.795 8.662 6.867 -0.283 -0.309 0.149
shoeScores 2 89 4.614 1.580 4.487 4.547 1.390 1.539 9.619 8.080 0.432 0.325 0.167
featherScores 3 89 4.530 1.764 4.790 4.580 1.475 0.782 7.736 6.954 -0.376 -0.612 0.187
paperScores 4 89 3.780 1.529 3.860 3.802 1.483 0.000 7.374 7.374 -0.155 -0.365 0.162
bucketScores 5 89 5.056 1.383 5.304 5.141 1.324 1.651 7.781 6.130 -0.593 0.131 0.147
gauTotScore 6 89 23.184 5.587 24.095 23.378 4.260 10.860 33.848 22.988 -0.450 -0.432 0.592


Object score correlations

GAU Object Score Correlations
  stickScores shoeScores featherScores paperScores bucketScores gauTotScore
stickScores   0.416*** 0.438*** 0.322** 0.259* 0.660***
shoeScores     0.423*** 0.430*** 0.474*** 0.756***
featherScores       0.448*** 0.536*** 0.801***
paperScores         0.336** 0.701***
bucketScores           0.708***
gauTotScore            
Computed correlation used pearson-method with listwise-deletion. 



## Parallel analysis suggests that the number of factors =  1  and the number of components =  NA
## Factor Analysis using method =  minres
## Call: fa(r = gauScores[, c("stickScores", "shoeScores", "featherScores", 
##     "paperScores", "bucketScores")], nfactors = 1, rotate = "oblimin", 
##     oblique.scores = TRUE)
## Standardized loadings (pattern matrix) based upon correlation matrix
##                MR1   h2   u2 com
## stickScores   0.55 0.30 0.70   1
## shoeScores    0.66 0.44 0.56   1
## featherScores 0.75 0.56 0.44   1
## paperScores   0.59 0.35 0.65   1
## bucketScores  0.66 0.43 0.57   1
## 
##                 MR1
## SS loadings    2.07
## Proportion Var 0.41
## 
## Mean item complexity =  1
## Test of the hypothesis that 1 factor is sufficient.
## 
## The degrees of freedom for the null model are  10  and the objective function was  1.27 with Chi Square of  108.64
## The degrees of freedom for the model are 5  and the objective function was  0.09 
## 
## The root mean square of the residuals (RMSR) is  0.05 
## The df corrected root mean square of the residuals is  0.07 
## 
## The harmonic number of observations is  89 with the empirical chi square  4.75  with prob <  0.45 
## The total number of observations was  89  with MLE Chi Square =  7.85  with prob <  0.16 
## 
## Tucker Lewis Index of factoring reliability =  0.942
## RMSEA index =  0.084  and the 90 % confidence intervals are  NA 0.181
## BIC =  -14.6
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy             
##                                                 MR1
## Correlation of scores with factors             0.89
## Multiple R square of scores with factors       0.79
## Minimum correlation of possible factor scores  0.58




## Warning in chisq.test(chiData): Chi-squared approximation may be incorrect

## Warning in chisq.test(chiData): Chi-squared approximation may be incorrect

## Warning in chisq.test(chiData): Chi-squared approximation may be incorrect
Demographic Level Value read walk Stat PValue
age 23.114 (9.96) 25.524 (13.59) 20.913 (3.63) 4.918 0.029
gender male 36.8% (32) 36.6% (15) 37.0% (17) 2.847 0.584
female 59.8% (52) 56.5% (26) 63.4% (26)
decline 1.1% (1) 0.0% (0) 2.2% (1)
unknown 1.1% (1) 0.0% (0) 2.4% (1)
MTF 1.1% (1) 0.0% (0) 2.2% (1)
race Hispanic 13.6% (12) 19.0% (8) 9.5% (4) 5.513 0.357
BiMu 5.7% (5) 2.2% (1) 8.7% (4)
BAA 9.1% (8) 7.1% (3) 11.9% (5)
AAA 12.5% (11) 15.2% (7) 8.7% (4)
White 51.1% (45) 45.2% (19) 61.9% (26)
Other 8.0% (7) 8.7% (4) 6.5% (3)
sex_orientation heterosexual 85.2% (75) 83.3% (35) 87.0% (40) 4.828 0.305
homosexual 4.5% (4) 2.2% (1) 7.1% (3)
bisexual 6.8% (6) 9.5% (4) 4.3% (2)
queer 1.1% (1) 0.0% (0) 2.4% (1)
decline 2.3% (2) 4.8% (2) 0.0% (0)
phq9 6.205 (5.72) 7.476 (6.07) 5.043 (5.18) 4.117 0.046
phqSI 0.273 (0.60) 0.310 (0.68) 0.239 (0.52) 0.298 0.586
gad7 5.477 (4.39) 6.238 (4.84) 4.783 (3.85) 2.458 0.121
sbq 6.091 (3.30) 6.952 (3.57) 5.304 (2.86) 5.764 0.019
sbqQ1 2.000 (0.97) 2.286 (1.02) 1.739 (0.85) 7.477 0.008
sbqQ2 1.875 (1.22) 2.095 (1.36) 1.674 (1.06) 2.666 0.106
sbqQ3 1.409 (0.67) 1.476 (0.71) 1.348 (0.64) 0.800 0.374
sbqQ4 0.807 (1.11) 1.095 (1.21) 0.543 (0.96) 5.689 0.019
rfl 4.507 (0.84) 4.302 (0.93) 4.695 (0.71) 5.016 0.028
rflCoping 4.667 (0.99) 4.398 (1.09) 4.913 (0.82) 6.349 0.014
scs 30.216 (10.92) 34.190 (11.72) 26.587 (8.79) 11.985 0.001
gauTotScore 23.170 (5.62) 22.327 (5.15) 23.940 (5.96) 1.827 0.180

    gauTotScore
    B CI p
(Intercept)   19.59 16.53 – 22.65 <.001
condition (walk)   2.36 -0.06 – 4.77 .056
phq9   0.14 -0.10 – 0.39 .245
sbq   0.24 -0.19 – 0.67 .272
Observations   88
R2 / adj. R2   .080 / .047


    rfl
    B CI p
(Intercept)   5.09 4.68 – 5.51 <.001
condition (walk)   0.19 -0.14 – 0.52 .251
sbq   -0.09 -0.15 – -0.03 .003
phq9   -0.02 -0.06 – 0.01 .155
Observations   88
R2 / adj. R2   .247 / .221


    rflCoping
    B CI p
(Intercept)   5.45 4.99 – 5.92 <.001
condition (walk)   0.26 -0.11 – 0.63 .160
sbq   -0.15 -0.21 – -0.08 <.001
phq9   -0.00 -0.04 – 0.03 .834
Observations   88
R2 / adj. R2   .309 / .284


    scs
    B CI p
(Intercept)   19.42 15.76 – 23.08 <.001
condition (walk)   -3.42 -6.31 – -0.53 .021
sbq   1.02 0.50 – 1.53 <.001
phq9   1.03 0.74 – 1.32 <.001
Observations   88
R2 / adj. R2   .652 / .639