2022 Schedule RT - Experiment 8e: Labelled Word List Typing, 4 Tasks - Points Reward
knowlabUnimelb
2022-04-10
Last updated: 2022-11-09
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Knit directory: SCHEDULING/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 67e1aac | knowlabUnimelb | 2022-11-09 | Publish data and analysis files |
Daniel R. Little1 and Ami Eidels2 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]
| phase | mean |
|---|---|
| untimed | 4.365217 |
| deadline | 3.811412 |
[ADD DESCRIPTION OF ANALYSIS OF TASK COMPLETIONS]
Typing analysis
| length | phase | acc | rt | wpm | totalTime |
|---|---|---|---|---|---|
| 1 | untimed | 0.95 | 218 | 63 | 1236 |
| 2 | untimed | 0.94 | 248 | 61 | 3546 |
| 3 | untimed | NA | 254 | 62 | 5233 |
| 4 | untimed | 0.94 | 259 | 62 | 7265 |
| 1 | deadline | 0.94 | 198 | 65 | 1144 |
| 2 | deadline | 0.93 | 227 | 65 | 3121 |
| 3 | deadline | NA | 241 | 65 | 5167 |
| 4 | deadline | 0.92 | 241 | 65 | 7064 |
Df Sum Sq Mean Sq F value Pr(>F) phase 1 6.71e+05 670931 72.5 <2e-16 Residuals 10892
1.01e+08 9259
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1 Df Sum Sq Mean Sq F value Pr(>F)
phase 1 0.8 0.841 86.5 <2e-16 Residuals 10890 105.8
0.010
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1 2 observations deleted due to missingness Df Sum Sq Mean
Sq F value Pr(>F)
phase 1 20573 20573 69.4 <2e-16 ** Residuals 10892 3229579
297
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1 Df Sum Sq Mean Sq F value Pr(>F)
phase 1 4.54e+07 45409612 4 0.046 Residuals 10892 1.24e+11
11357177
— Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05
‘.’ 0.1 ’ ’ 1
Reward Rate
[ADD DESCRIPTION]
ANOVA
Table (type III tests)
$ANOVA Effect DFn DFd F p p<.05 ges 1 phase 1 68 0.70 4.1e-01 0.00030 2 difficulty 3 204 298.33 2.6e-74 * 0.26700 3 phase:difficulty 3 204 0.71 5.5e-01 0.00069
$Mauchly's Test for Sphericity Effect W p p<.05 1
difficulty 0.30 9.8e-16 2 phase:difficulty 0.47 9.4e-10
$Sphericity Corrections Effect GGe DF[GG] p[GG]
p[GG]<.05 HFe DF[HF] 1 difficulty 0.59 1.77, 120.43 5.4e-45 * 0.60
1.81, 123.42 2 phase:difficulty 0.69 2.06, 140.34 5.0e-01 0.71 2.13,
144.8 p[HF] p[HF]<.05 1 4.8e-46 * 2 5.0e-01
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).
| phase | chi2 | df | p |
|---|---|---|---|
| untimed | 37 | 3 | 0 |
| deadline | 577 | 3 | 0 |
[ADD DESCRIPTION]
We compared the location conditions and phases using chi-2 analysis.
| Comparison | chi2 | df | p | |
|---|---|---|---|---|
| X-squared | Untimed vs Deadline | 134 | 15 | 0 |
- How optimal were responses?
Comparison against Easy to Hard order
- How consistent were responses with an easy to hard ordering?
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):
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[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