Last updated: 2022-11-09

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File	Version	Author	Date	Message
Rmd	67e1aac	knowlabUnimelb	2022-11-09	Publish data and analysis files

Asha Bartlett¹, Ami Eidels², and Daniel R. Little¹ ¹ The University of Melbourne, ² The University of Newcastle

Method

Participants

[NEED TO ADD A SUMMARY OF PARTICIPANT INFORMATION: HOW MANY TESTED, DEMOGRAPHIC INFORMATION, HOW WERE PARTICIPANTS REIMBURSED, HOW MANY ASSIGNED TO EACH CONDITION]

Design

[NEED TO ADD A DESCRIPTION OF THE EXPERIMENTAL DESIGN]

Data Cleaning

Subjects completed the experiment by clicking a link with the uniquely generated id code. Subjects were able to use the link multiple times; further, subjects were able to exit the experiment at any time. Consequently, the datafile contains partially completed data for some subjects which needed to be identified and removed.

Data Analysis

We first summarize performance by answering the following questions:

Task completions

How many tasks are completed on average?

[ADD ANALYSIS DESCRIPTION]

Average number of correctly completed tasks in each condition
phase	mean
untimed	3.989474
deadline	2.718310

[ADD DESCRIPTION OF ANALYSIS OF TASK COMPLETIONS]

Typing analysis

Typing data analysis
length	phase	acc	rt	wpm	totalTime
1	untimed	0.96	185	72	1035
2	untimed	0.96	206	72	2654
3	untimed	0.95	216	72	4511
4	untimed	0.94	222	71	6413
1	deadline	0.95	184	78	1056
2	deadline	0.94	197	78	2717
3	deadline	0.93	187	80	3996
4	deadline	0.93	204	76	5923

Typing data analysis - standard error
length	phase	rt	wpm	totalTime
1	untimed	4.7	1.06	35
2	untimed	4.3	1.06	62
3	untimed	4.4	1.05	127
4	untimed	4.2	1.05	165
1	deadline	9.8	0.65	61
2	deadline	3.5	0.73	75
3	deadline	2.9	0.67	89
4	deadline	2.5	0.70	92

ANOVA Table (type III tests)

$ANOVA Effect DFn DFd F p p<.05 ges 1 phase 1 36 0.56 4.6e-01 0.00044 2 length 3 108 13.27 1.9e-07 * 0.03000 3 phase:length 3 108 1.66 1.8e-01 0.00300

$Mauchly's Test for Sphericity Effect W p p<.05 1 length 0.36 1.1e-06 2 phase:length 0.50 2.4e-04

$Sphericity Corrections Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF] p[HF] 1 length 0.6 1.81, 65.01 2.8e-05 * 0.63 1.9, 68.24 1.9e-05 2 phase:length 0.7 2.11, 75.99 2.0e-01 0.75 2.25, 80.85 1.9e-01 p[HF]<.05 1 * 2

ANOVA Table (type III tests)

$ANOVA Effect DFn DFd F p p<.05 ges 1 phase 1 36 32.7 1.7e-06 * 0.066 2 length 3 108 7.9 8.7e-05 * 0.058 3 phase:length 3 108 1.5 2.2e-01 0.009

$Mauchly's Test for Sphericity Effect W p p<.05 1 length 0.68 0.019 * 2 phase:length 0.78 0.122

$Sphericity Corrections Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF] p[HF] 1 length 0.80 2.39, 85.96 0.00034 * 0.86 2.57, 92.49 0.00023 2 phase:length 0.85 2.55, 91.76 0.22900 0.92 2.76, 99.34 0.22600 p[HF]<.05 1 * 2

ANOVA Table (type III tests)

$ANOVA Effect DFn DFd F p p<.05 ges 1 phase 1 36 18.5 1.2e-04 * 5.0e-03 2 length 3 108 31.7 8.8e-15 * 2.2e-04 3 phase:length 3 108 7.8 9.2e-05 * 5.3e-05

$Mauchly's Test for Sphericity Effect W p p<.05 1 length 0.86 0.413
2 phase:length 0.76 0.096

$Sphericity Corrections Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF] p[HF] 1 length 0.92 2.77, 99.79 7.9e-14 * 1.01 3.03, 108.95 8.8e-15 2 phase:length 0.85 2.54, 91.57 2.5e-04 * 0.92 2.75, 99.12 1.6e-04 p[HF]<.05 1 2

ANOVA Table (type III tests)

$ANOVA Effect DFn DFd F p p<.05 ges 1 phase 1 36 6.6e-04 9.8e-01 6.8e-07 2 length 3 108 3.0e+02 7.2e-52 * 6.8e-01 3 phase:length 3 108 1.6e+00 1.9e-01 3.0e-03

$Mauchly's Test for Sphericity Effect W p p<.05 1 length 0.08 2.2e-17 2 phase:length 0.38 2.4e-06

$Sphericity Corrections Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF] p[HF] 1 length 0.43 1.28, 45.97 1.7e-23 * 0.43 1.3, 46.91 6.2e-24 2 phase:length 0.65 1.95, 70.36 2.1e-01 0.69 2.07, 74.35 2.1e-01 p[HF]<.05 1 * 2

Reward Rate

[ADD DESCRIPTION]

ANOVA Table (type III tests)

$ANOVA Effect DFn DFd F p p<.05 ges 1 phase 1 36 0.18 6.7e-01 8.4e-05 2 difficulty 3 108 327.13 5.0e-54 * 7.1e-01 3 phase:difficulty 3 108 1.04 3.8e-01 1.0e-03

$Mauchly's Test for Sphericity Effect W p p<.05 1 difficulty 0.022 9.8e-27 2 phase:difficulty 0.245 2.4e-09

$Sphericity Corrections Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF] p[HF] 1 difficulty 0.39 1.16, 41.85 1.9e-22 * 0.39 1.18, 42.38 1.0e-22 2 phase:difficulty 0.61 1.84, 66.28 3.5e-01 0.64 1.94, 69.68 3.6e-01 p[HF]<.05 1 * 2

Pairwise comparisons using t tests with pooled SD

data: rrdata$rewardRate and paste(rrdata$difficulty, rrdata$phase)

            long deadline long untimed med deadline med untimed

long untimed 1 - - -
med deadline 6e-12 2e-10 - -
med untimed 1e-10 2e-09 1 -
short deadline <2e-16 <2e-16 7e-06 7e-07
short untimed <2e-16 <2e-16 2e-05 2e-06
v.long deadline 3e-04 2e-05 <2e-16 <2e-16
v.long untimed 3e-04 2e-05 <2e-16 <2e-16
short deadline short untimed v.long deadline long untimed - - -
med deadline - - -
med untimed - - -
short deadline - - -
short untimed 1 - -
v.long deadline <2e-16 <2e-16 -
v.long untimed <2e-16 <2e-16 1

P value adjustment method: bonferroni

Optimality in each condition

What is the proportion of easy, medium, hard, and very hard tasks selected first, second, third or fourth? [ADD DESCRIPTION]

Do the marginal distributions differ from uniformity?

We tested whether the marginal distributions were different from uniformly random selection using the fact that the mean rank is distributed according to a $\chi^2$ distribution with the following test-statistic: \[\chi^2 = \frac{12N}{k(k+1)}\sum_{j=1}^k \left(m_j - \frac{k+1}{2} \right)^2\] see (Marden, 1995).

Chi2 test of uniformity
phase	chi2	df	p
untimed	23	3	0
deadline	259	3	0

[ADD DESCRIPTION]

We compared the location conditions and phases using chi-2 analysis.

Pearson’s chi-squared test
	Comparison	chi2	df	p
X-squared	Untimed vs Deadline	162	15	0

How optimal were responses?

Comparison against Easy to Hard order

How consistent were responses with an easy to hard ordering?

Wordle-Clue Score Analysis

How motivated are participants to get the Wordle-Clue guess correct?

Wordle guesses are scored out of 10. A match-in-place is scored 2; a match-out-of-place is scored 1. The final score for each trial is the sum across all letters.

Average Wordle Scores (Max Score = 10)
phase	score
untimed	6.7
deadline	4.7

        Df Sum Sq Mean Sq F value  Pr(>F)

phase 1 75.9 75.9 40.2 1.6e-08 *** Residuals 74 139.7 1.9
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

What is the correlation between the Wordle-Clue score and the distance from the optimal schedule? From the easy-hard schedule?

This test indicates whether participants who are more motivated to score highly on the wordle test are also more likely to selection optimal schedules. This would be indicated by a significant correlation (high wordle score coupled with a low average distance). We also test the same wordle score compared to the easy-hard schedule.

[1] -0.36

Pearson's product-moment correlation

data: corrDataSet$wordleScore and corrDataSet$d t = -3, df = 74, p-value = 0.002 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.54 -0.14 sample estimates: cor -0.36

[1] -0.069

Pearson's product-moment correlation

data: corrDataSet$wordleScore[corrDataSet$phase == “untimed”] and corrDataSet$d[corrDataSet$phase == “untimed”] t = -0.4, df = 36, p-value = 0.7 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.38 0.26 sample estimates: cor -0.069

[1] -0.71

Pearson's product-moment correlation

data: corrDataSet$wordleScore[corrDataSet$phase == “deadline”] and corrDataSet$d[corrDataSet$phase == “deadline”] t = -6, df = 36, p-value = 5e-07 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.84 -0.51 sample estimates: cor -0.71

[1] 0.36

Pearson's product-moment correlation

data: corrDataSet$wordleScore and corrDataSet$eh_d t = 3, df = 74, p-value = 0.002 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.14 0.54 sample estimates: cor 0.36

sessionInfo()

R version 4.1.3 (2022-03-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

locale:
[1] LC_COLLATE=English_Australia.1252  LC_CTYPE=English_Australia.1252   
[3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C                      
[5] LC_TIME=English_Australia.1252    

attached base packages:
[1] stats4    grid      stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] pmr_1.2.5.1       jpeg_0.1-9        rstatix_0.7.0     lme4_1.1-29      
 [5] Matrix_1.4-0      png_0.1-7         reshape2_1.4.4    knitr_1.38       
 [9] hrbrthemes_0.8.0  english_1.2-6     gtools_3.9.2      DescTools_0.99.45
[13] forcats_0.5.1     stringr_1.4.0     dplyr_1.0.8       purrr_0.3.4      
[17] readr_2.1.2       tidyr_1.2.0       tibble_3.1.6      ggplot2_3.3.5    
[21] tidyverse_1.3.1   workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] minqa_1.2.4       colorspace_2.0-3  ellipsis_0.3.2    class_7.3-20     
 [5] rprojroot_2.0.3   fs_1.5.2          gld_2.6.5         rstudioapi_0.13  
 [9] proxy_0.4-27      farver_2.1.0      fansi_1.0.3       mvtnorm_1.1-3    
[13] lubridate_1.8.0   xml2_1.3.3        splines_4.1.3     extrafont_0.18   
[17] rootSolve_1.8.2.3 jsonlite_1.8.0    nloptr_2.0.0      broom_0.8.0      
[21] Rttf2pt1_1.3.10   dbplyr_2.1.1      compiler_4.1.3    httr_1.4.2       
[25] backports_1.4.1   assertthat_0.2.1  fastmap_1.1.0     cli_3.2.0        
[29] later_1.3.0       htmltools_0.5.2   tools_4.1.3       gtable_0.3.0     
[33] glue_1.6.2        lmom_2.9          Rcpp_1.0.8.3      carData_3.0-5    
[37] cellranger_1.1.0  jquerylib_0.1.4   vctrs_0.4.1       nlme_3.1-155     
[41] extrafontdb_1.0   xfun_0.30         ps_1.6.0          rvest_1.0.2      
[45] lifecycle_1.0.1   getPass_0.2-2     MASS_7.3-55       scales_1.2.0     
[49] hms_1.1.1         promises_1.2.0.1  expm_0.999-6      yaml_2.3.5       
[53] Exact_3.1         gdtools_0.2.4     sass_0.4.1        stringi_1.7.6    
[57] highr_0.9         e1071_1.7-11      boot_1.3-28       rlang_1.0.2      
[61] pkgconfig_2.0.3   systemfonts_1.0.4 evaluate_0.15     lattice_0.20-45  
[65] labeling_0.4.2    processx_3.5.3    tidyselect_1.1.2  plyr_1.8.7       
[69] magrittr_2.0.3    R6_2.5.1          generics_0.1.2    DBI_1.1.2        
[73] pillar_1.7.0      haven_2.5.0       whisker_0.4       withr_2.5.0      
[77] abind_1.4-5       modelr_0.1.8      crayon_1.5.1      car_3.0-12       
[81] utf8_1.2.2        tzdb_0.3.0        rmarkdown_2.13    readxl_1.4.0     
[85] data.table_1.14.2 callr_3.7.0       git2r_0.30.1      reprex_2.0.1     
[89] digest_0.6.29     httpuv_1.6.5      munsell_0.5.0     bslib_0.3.1

2022 Schedule RT - Experiment 8d: Labelled Word List Typing, 4 Tasks - Wordle Reward - Keyboard

knowlabUnimelb

2022-04-10

Method

Participants

Design

Data Analysis

Task completions

Typing analysis

Reward Rate

Optimality in each condition

Comparison against Easy to Hard order

Wordle-Clue Score Analysis