Stick - codes, scores and frequencies
Code Score Frequency Proportion-All Proportion-Coded
unique_item 2.000 60 0.122 0.000
fishing 0.992 3 0.006 0.008
carve 0.989 4 0.008 0.011
digging 0.989 4 0.008 0.011
flag 0.987 5 0.010 0.013
marker 0.984 6 0.012 0.016
scratch 0.984 6 0.012 0.016
drum 0.979 8 0.016 0.021
hitting_balls 0.979 8 0.016 0.021
pointing 0.979 8 0.016 0.021
wand 0.971 11 0.022 0.029
holding_proping 0.963 14 0.028 0.037
play 0.963 14 0.028 0.037
building 0.957 16 0.033 0.043
making_it_into_a_weapon 0.952 18 0.037 0.048
food 0.941 22 0.045 0.059
drawing 0.939 23 0.047 0.061
reaching_moving_items 0.928 27 0.055 0.072
fetch_play 0.923 29 0.059 0.077
walking_stick 0.907 35 0.071 0.093
fire 0.888 42 0.085 0.112
hitting_poking 0.806 73 0.148 0.194
non_use 0.000 56 0.114 0.000
Shoe - codes, frequencies and scores
Code Score Frequency Proportion-All Proportion-Coded
unique_item 2.000 41 0.102 0.000
doorstopper 0.993 2 0.005 0.007
lunchbox 0.993 2 0.005 0.007
plant_in_it 0.993 2 0.005 0.007
use_for_an_experiment 0.990 3 0.007 0.010
bury_a_pet 0.987 4 0.010 0.013
paint_draw_write_on_it 0.987 4 0.010 0.013
throw_hit_someone 0.987 4 0.010 0.013
shadow_box 0.983 5 0.012 0.017
trap 0.983 5 0.012 0.017
hat 0.977 7 0.017 0.023
play_toy_specific 0.977 7 0.017 0.023
fire 0.957 13 0.032 0.043
art_craft 0.954 14 0.035 0.046
play_toy 0.954 7 0.017 0.046
diorama 0.944 17 0.042 0.056
project 0.940 18 0.045 0.060
pet_house 0.911 27 0.067 0.089
storage_specific 0.682 96 0.238 0.318
storage 0.467 65 0.161 0.533
non_use 0.000 60 0.149 0.000
Feather - codes, frequencies and scores
Code Score Frequency Proportion-All Proportion-Coded
unique_item 2.000 40 0.087 0.000
blow 0.995 2 0.004 0.005
brush 0.995 2 0.004 0.005
take_a_picture 0.995 2 0.004 0.005
trail 0.995 2 0.004 0.005
lighting 0.992 3 0.007 0.008
spreading_disease 0.992 3 0.007 0.008
bookmarker 0.989 4 0.009 0.011
dusting 0.989 4 0.009 0.011
paint 0.989 4 0.009 0.011
accessory_specific 0.986 5 0.011 0.014
fan 0.986 5 0.011 0.014
return_to_bird 0.986 5 0.011 0.014
scrapbook 0.986 5 0.011 0.014
sneeze 0.986 5 0.011 0.014
gift 0.984 6 0.013 0.016
fly 0.981 7 0.015 0.019
analyze 0.978 8 0.017 0.022
dreamcatcher 0.975 9 0.020 0.025
pillow 0.975 9 0.020 0.025
play 0.967 12 0.026 0.033
float 0.959 15 0.033 0.041
collection 0.954 17 0.037 0.046
pen 0.888 41 0.089 0.112
decoration 0.885 42 0.091 0.115
accessory 0.798 69 0.150 0.202
tickle 0.781 80 0.174 0.219
non_use 0.000 54 0.117 0.000
Paper - codes, frequencies and scores
Code Score Frequency Proportion-All Proportion-Coded
unique_item 2.000 28 0.056 0.000
bookmarker 0.995 2 0.004 0.005
funnel 0.995 2 0.004 0.005
instrument 0.995 2 0.004 0.005
kill_a_bug 0.995 2 0.004 0.005
machae 0.995 2 0.004 0.005
boat 0.992 3 0.006 0.008
telescope 0.992 3 0.006 0.008
play_specific 0.987 5 0.010 0.013
cover 0.985 6 0.012 0.015
napkin 0.985 6 0.012 0.015
confetti 0.980 8 0.016 0.020
papercut 0.977 9 0.018 0.023
kindling 0.975 10 0.020 0.025
play 0.934 26 0.052 0.066
plane 0.875 49 0.098 0.125
art_specific 0.870 51 0.102 0.130
art 0.840 12 0.024 0.160
draw_write 0.504 195 0.389 0.496
non_use 0.000 80 0.160 0.000
Bucket - codes, frequencies and scores
Code Score Frequency Proportion-All Proportion-Coded
unique_item 2.000 31 0.064 0.000
cyntrifical_force 0.995 2 0.004 0.005
floating_boat 0.995 2 0.004 0.005
painting 0.995 2 0.004 0.005
rain_water 0.995 2 0.004 0.005
sand_castle 0.995 2 0.004 0.005
boil 0.993 3 0.006 0.007
fill_a_hole 0.993 3 0.006 0.007
water_balloon 0.993 3 0.006 0.007
soak_your_feet 0.991 4 0.008 0.009
carry_water 0.989 5 0.010 0.011
drowning 0.989 5 0.010 0.011
freeze 0.989 5 0.010 0.011
spill 0.989 5 0.010 0.011
cooking 0.986 6 0.012 0.014
cool 0.984 7 0.014 0.016
play 0.984 7 0.014 0.016
storage 0.979 9 0.018 0.021
abc 0.973 12 0.025 0.027
wash_pet 0.966 15 0.031 0.034
aquarium_pet_house 0.959 18 0.037 0.041
put_out_fire 0.957 19 0.039 0.043
washing_sth_in_it 0.922 34 0.070 0.078
watering 0.906 41 0.084 0.094
throw_at_someone 0.854 64 0.131 0.146
drink 0.842 69 0.141 0.158
washing_cleaning 0.787 93 0.191 0.213
non_use 0.000 20 0.041 0.000


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 3.507 1.664 3.450 3.420 1.458 0.000 8.467 8.467 0.494 0.248 0.176
featherScores 3 89 4.516 1.764 4.768 4.566 1.713 0.781 7.713 6.932 -0.364 -0.634 0.187
paperScores 4 89 3.763 1.525 3.860 3.788 1.479 0.000 7.374 7.374 -0.160 -0.342 0.162
bucketScores 5 89 5.047 1.398 5.281 5.118 1.238 1.641 7.780 6.140 -0.519 0.102 0.148
gauTotScore 6 89 22.038 5.649 23.009 22.190 4.405 9.990 34.082 24.092 -0.360 -0.457 0.599





We determined there were no ties in GAU Total Scores and thus tested for normality using the base R Kolmogorov-Smirnov test and failed to reject the null hypothesis that the scores are normally distributed (D = 0.1274, p = 0.1017)


Object score correlations

GAU Object Score Correlations
  stickScores shoeScores featherScores paperScores bucketScores gauTotScore
stickScores   0.380*** 0.437*** 0.321** 0.258* 0.648***
shoeScores     0.432*** 0.433*** 0.472*** 0.758***
featherScores       0.445*** 0.537*** 0.801***
paperScores         0.343*** 0.701***
bucketScores           0.711***
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   0.53 0.28 0.72   1
## shoeScores    0.66 0.43 0.57   1
## featherScores 0.75 0.57 0.43   1
## paperScores   0.59 0.35 0.65   1
## bucketScores  0.66 0.44 0.56   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  1.25 with Chi Square of  106.63
## The degrees of freedom for the model are 5  and the objective function was  0.07 
## 
## 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  3.92  with prob <  0.56 
## The total number of observations was  89  with MLE Chi Square =  6.24  with prob <  0.28 
## 
## Tucker Lewis Index of factoring reliability =  0.974
## RMSEA index =  0.058  and the 90 % confidence intervals are  NA 0.164
## BIC =  -16.2
## Fit based upon off diagonal values = 0.99
## 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



Correlation between GAU, CRA, and Experimental Condition
  GAU CRA RFL Condition
GAU   0.293** -0.095 0.152
CRA     -0.051 0.046
RFL       0.247*
Condition        
Computed correlation used pearson-method with listwise-deletion.


The correlation between GAU and CRA scores was significant, (r = 0.293, p = 0.0056, 95% CI = [0.089, 0.473])



Descriptives for All Predictor Variables and Outcome
vars n mean sd median trimmed mad min max range skew kurtosis se
age 1 88 23.080 9.971 20.000 20.889 2.965 18.000 82.000 64.000 3.852 16.762 1.063
gender* 2 87 1.713 0.663 2.000 1.676 0.000 1.000 5.000 4.000 1.564 6.230 0.071
academic_class* 3 87 2.414 1.491 2.000 2.239 1.483 1.000 7.000 6.000 0.821 -0.264 0.160
gpa 4 87 5.690 1.845 6.000 5.873 1.483 1.000 8.000 7.000 -0.743 -0.118 0.198
race* 5 88 4.057 1.549 5.000 4.194 0.000 1.000 6.000 5.000 -0.938 -0.468 0.165
sex_orientation* 6 88 1.307 0.835 1.000 1.083 0.000 1.000 5.000 4.000 2.896 8.096 0.089
phq9 7 88 6.205 5.718 4.000 5.417 4.448 0.000 24.000 24.000 1.217 0.613 0.610
phqSI 8 88 0.273 0.601 0.000 0.139 0.000 0.000 3.000 3.000 2.325 5.255 0.064
gad7 9 88 5.489 4.384 4.500 5.028 3.706 0.000 19.000 19.000 1.045 0.799 0.467
sbq 10 88 6.080 3.312 5.000 5.583 2.965 3.000 16.000 13.000 1.134 0.514 0.353
sbqQ1 11 88 2.000 0.971 2.000 1.889 1.483 1.000 4.000 3.000 0.745 -0.416 0.103
sbqQ2 12 88 1.875 1.221 1.000 1.639 0.000 1.000 5.000 4.000 1.398 0.945 0.130
sbqQ3 13 88 1.409 0.672 1.000 1.278 0.000 1.000 3.000 2.000 1.340 0.433 0.072
sbqQ4 14 88 0.795 1.116 0.000 0.611 0.000 0.000 4.000 4.000 1.286 0.851 0.119
rfl 15 88 4.518 0.841 4.500 4.553 0.741 1.531 6.000 4.469 -0.672 1.204 0.090
rflCoping 16 88 4.667 0.987 4.714 4.744 0.847 1.429 6.000 4.571 -0.733 0.346 0.105
scs 17 88 30.318 10.857 28.000 29.042 10.378 18.000 69.000 51.000 1.083 1.057 1.157
gauTotScore 18 88 21.966 5.647 22.819 22.105 4.576 9.990 34.082 24.092 -0.339 -0.454 0.602
cra 19 88 9.955 4.490 10.000 10.042 4.448 0.000 19.000 19.000 -0.189 -0.751 0.479
condition* 20 88 1.523 0.502 2.000 1.528 0.000 1.000 2.000 1.000 -0.089 -2.015 0.054

Distribution of participants into Read and Walk conditions
percentage count
read 47.73 42
walk 52.27 46

Table 1 - Demographics and Variables Stratefied by Experimental Condition
Demographic Level Value read walk Stat PValue
age 23.080 (9.97) 25.524 (13.59) 20.848 (3.65) 5.053 0.027
gender male 35.6% (31) 36.6% (15) 34.8% (16) 2.773 0.597
female 60.9% (53) 56.5% (26) 65.9% (27)
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 5.489 (4.38) 6.238 (4.84) 4.804 (3.84) 2.386 0.126
sbq 6.080 (3.31) 6.952 (3.57) 5.283 (2.87) 5.894 0.017
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 0.795 (1.12) 1.095 (1.21) 0.522 (0.96) 6.143 0.015
rfl 4.518 (0.84) 4.302 (0.93) 4.716 (0.71) 5.604 0.020
rflCoping 4.667 (0.99) 4.398 (1.09) 4.913 (0.82) 6.349 0.014
scs 30.318 (10.86) 34.190 (11.72) 26.783 (8.72) 11.448 0.001
gauTotScore 21.966 (5.65) 21.071 (5.19) 22.783 (5.98) 2.040 0.157
cra 9.955 (4.49) 9.738 (4.36) 10.152 (4.65) 0.185 0.668

Supplemental tests for randomization differences using Wilcox version of Kolmogorov-Smirnov test accounting for ties
predictor test_statistic critical_value p_value
PHQ-9 0.3509 0.2898 0.0062
GAD-7 0.1522 0.2898 0.6087
SBQ-R 0.2930 0.2898 0.0343


    gauTotScore
    B CI std. Beta CI p
(Intercept)   18.18 15.12 – 21.24     <.001
condition (walk)   2.51 0.09 – 4.92 0.22 0.01 – 0.43 .042
phq9   0.16 -0.09 – 0.40 0.16 -0.09 – 0.40 .206
sbq   0.25 -0.18 – 0.67 0.14 -0.10 – 0.39 .255
Observations   88
R2 / adj. R2   .090 / .057
F-statistics   2.758*


    cra
    B CI std. Beta CI p
(Intercept)   9.25 6.75 – 11.74     <.001
condition (walk)   0.42 -1.55 – 2.39 0.05 -0.17 – 0.26 .671
phq9   -0.17 -0.37 – 0.03 -0.22 -0.47 – 0.03 .091
sbq   0.26 -0.09 – 0.61 0.19 -0.07 – 0.44 .149
Observations   88
R2 / adj. R2   .040 / .006
F-statistics   1.181 


##            Test stat Pr(>|t|)
## condition         NA       NA
## phq9          -0.003    0.997
## sbq           -1.636    0.106
## Tukey test    -1.748    0.081
## Warning in mmps(...): Interactions and/or factors skipped


    rfl
    B CI std. Beta CI p
(Intercept)   5.10 4.69 – 5.51     <.001
condition (walk)   0.21 -0.12 – 0.53 0.12 -0.07 – 0.31 .210
phq9   -0.02 -0.06 – 0.01 -0.17 -0.39 – 0.05 .139
sbq   -0.09 -0.15 – -0.03 -0.35 -0.57 – -0.13 .003
Observations   88
R2 / adj. R2   .260 / .233
F-statistics   9.816***


    scs
    B CI std. Beta CI p
(Intercept)   19.58 15.91 – 23.26     <.001
condition (walk)   -3.25 -6.15 – -0.34 -0.15 -0.28 – -0.02 .029
phq9   1.02 0.73 – 1.32 0.54 0.39 – 0.69 <.001
sbq   1.00 0.49 – 1.51 0.31 0.15 – 0.46 <.001
Observations   88
R2 / adj. R2   .644 / .632
F-statistics   50.722***


Comparing model of scs with phq+sbq only to condition+phq9+sbq

Analysis of Variance Table
Res.Df RSS Df Sum of Sq F Pr(>F)
84 3648 NA NA NA NA
85 3862 -1 -214.8 4.947 0.02881

Diagnostic plots for regressions predicting gau scores and scs scores

##            Test stat Pr(>|t|)
## condition         NA       NA
## phq9           0.466    0.642
## sbq           -1.632    0.106
## Tukey test    -0.102    0.919

Thanks to the following packages to make this analysis possible

(R Core Team 2016a), (R Core Team 2016b), (Wickham and Francois 2016), (Wickham 2007), (Revelle 2016), (Wickham 2009), (Lüdecke 2016), (Mirai Solutions GmbH 2016), (Vannoy "5/16/2016"), (Francois 2014), (Xie 2016), (Daróczi and Tsegelskyi 2015), (Fox and Weisberg 2011)

Bibliography

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Fox, John, and Sanford Weisberg. 2011. An R Companion to Applied Regression. Second. Thousand Oaks CA: Sage. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion.

Francois, Romain. 2014. Bibtex: Bibtex Parser. https://CRAN.R-project.org/package=bibtex.

Lüdecke, Daniel. 2016. SjPlot: Data Visualization for Statistics in Social Science. https://CRAN.R-project.org/package=sjPlot.

Mirai Solutions GmbH. 2016. XLConnect: Excel Connector for R. https://CRAN.R-project.org/package=XLConnect.

R Core Team. 2016a. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

———. 2016b. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Revelle, William. 2016. Psych: Procedures for Psychological, Psychometric, and Personality Research. Evanston, Illinois: Northwestern University. https://CRAN.R-project.org/package=psych.

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Wickham, Hadley. 2007. “Reshaping Data with the reshape Package.” Journal of Statistical Software 21 (12): 1–20. http://www.jstatsoft.org/v21/i12/.

———. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Wickham, Hadley, and Romain Francois. 2016. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.

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