Please see manuscript for a long description of the following data. We will load the example data, and you can use the? with the dataset name to learn more about the data.
library(lrd)#>#> Attaching package: 'lrd'#> The following object is masked from 'package:base':#>#> kappadata("cued_recall_manuscript")head(cued_recall_manuscript)#> Sub.ID Trial_num Cue Target Answer#> 1 1 1 chlorination ideological ideological#> 2 1 2 bendy financial financial#> 3 1 3 topography editing editing#> 4 1 4 enquiry buzzing buzzing#> 5 1 5 draconian statistic statistic#> 6 1 6 speedball stopwatch stopwatch#?cued_recall_manuscriptScoring inlrd is case sensitive, so we will usetolower() to lower case all correct answers and participant answers.
cued_recall_manuscript$Target<-tolower(cued_recall_manuscript$Target)cued_recall_manuscript$Answer<-tolower(cued_recall_manuscript$Answer)You should define the following:
Note that the answer key can be in a separate dataframe, use something likeanswer_key$answer for the key argument andanswer_key$id_num for the trial number. Fill inanswer_key with your dataframe name and the column name for those columns after the$.
cued_output<-prop_correct_cued(data = cued_recall_manuscript,responses ="Answer",key ="Target",key.trial ="Trial_num",id ="Sub.ID",id.trial ="Trial_num",cutoff =1,flag =TRUE,group.by =NULL)str(cued_output)#> List of 2#> $ DF_Scored :'data.frame': 120 obs. of 7 variables:#> ..$ Trial.ID : int [1:120] 1 1 1 1 1 1 2 2 2 2 ...#> ..$ Sub.ID : int [1:120] 1 3 5 2 4 6 6 5 2 1 ...#> ..$ Cue : chr [1:120] "chlorination" "chlorination" "chlorination" "chlorination" ...#> ..$ Target : chr [1:120] "ideological" "ideological" "ideological" "ideological" ...#> ..$ Responses: chr [1:120] "ideological" "ideological" "ideological" "idological" ...#> ..$ Answer : chr [1:120] "ideological" "ideological" "ideological" "ideological" ...#> ..$ Scored : num [1:120] 1 1 1 1 1 0 0 0 1 1 ...#> $ DF_Participant:'data.frame': 6 obs. of 3 variables:#> ..$ Sub.ID : int [1:6] 1 2 3 4 5 6#> ..$ Proportion.Correct : num [1:6] 1 0.8 0.85 0.95 0.75 0.45#> ..$ Z.Score.Participant: num [1:6, 1] 1.026 0 0.256 0.769 -0.256 ...#> .. ..- attr(*, "scaled:center")= num 0.8#> .. ..- attr(*, "scaled:scale")= num 0.195We can useDF_Scored to see the original dataframe with our new scored column - also to check if our answer key and participant answers matched up correctly! TheDF_Participant can be used to view a participant level summary of the data. Last, if a grouping variable is used, we can useDF_Group to see that output.
#Overallcued_output$DF_Scored#> Trial.ID Sub.ID Cue Target Responses Answer#> 1 1 1 chlorination ideological ideological ideological#> 2 1 3 chlorination ideological ideological ideological#> 3 1 5 chlorination ideological ideological ideological#> 4 1 2 chlorination ideological idological ideological#> 5 1 4 chlorination ideological ideologicel ideological#> 6 1 6 chlorination ideological ideological#> 7 2 6 bendy financial money financial#> 8 2 5 bendy financial money financial#> 9 2 2 bendy financial financial financial#> 10 2 1 bendy financial financial financial#> 11 2 3 bendy financial financial financial#> 12 2 4 bendy financial finenciel financial#> 13 3 5 topography editing editing editing#> 14 3 3 topography editing editting editing#> 15 3 6 topography editing editing editing#> 16 3 1 topography editing editing editing#> 17 3 4 topography editing editing editing#> 18 3 2 topography editing diting editing#> 19 4 5 enquiry buzzing buzzing buzzing#> 20 4 3 enquiry buzzing buzzing buzzing#> 21 4 6 enquiry buzzing buzzing buzzing#> 22 4 1 enquiry buzzing buzzing buzzing#> 23 4 4 enquiry buzzing buzzing buzzing#> 24 4 2 enquiry buzzing buzzing buzzing#> 25 5 5 draconian statistic statistic statistic#> 26 5 3 draconian statistic sttattisttic statistic#> 27 5 6 draconian statistic math statistic#> 28 5 1 draconian statistic statistic statistic#> 29 5 4 draconian statistic stetistic statistic#> 30 5 2 draconian statistic statistic statistic#> 31 6 3 speedball stopwatch sttopwattch stopwatch#> 32 6 4 speedball stopwatch stopwetch stopwatch#> 33 6 6 speedball stopwatch watch stopwatch#> 34 6 5 speedball stopwatch stopwatch stopwatch#> 35 6 2 speedball stopwatch stopwatch stopwatch#> 36 6 1 speedball stopwatch stopwatch stopwatch#> 37 7 1 valueless did did did#> 38 7 3 valueless did did did#> 39 7 5 valueless did done did#> 40 7 2 valueless did did did#> 41 7 4 valueless did did did#> 42 7 6 valueless did done did#> 43 8 6 grievous numerically numerically numerically#> 44 8 3 grievous numerically numerically numerically#> 45 8 5 grievous numerically numerically numerically#> 46 8 2 grievous numerically numrically numerically#> 47 8 1 grievous numerically numerically numerically#> 48 8 4 grievous numerically numericelly numerically#> 49 9 6 melatonin bloated bloated bloated#> 50 9 1 melatonin bloated bloated bloated#> 51 9 5 melatonin bloated bloated bloated#> 52 9 4 melatonin bloated bloeted bloated#> 53 9 3 melatonin bloated bloatted bloated#> 54 9 2 melatonin bloated bloatd bloated#> 55 10 6 dose domain area domain#> 56 10 5 dose domain area domain#> 57 10 4 dose domain domein domain#> 58 10 3 dose domain domain domain#> 59 10 2 dose domain domain domain#> 60 10 1 dose domain domain domain#> 61 11 6 dynastically steadily steadily#> 62 11 5 dynastically steadily steadily steadily#> 63 11 4 dynastically steadily steedily steadily#> 64 11 3 dynastically steadily stteadily steadily#> 65 11 2 dynastically steadily stadily steadily#> 66 11 1 dynastically steadily steadily steadily#> 67 12 5 staffer withdraw withdraw withdraw#> 68 12 4 staffer withdraw withdrew withdraw#> 69 12 3 staffer withdraw witthdraw withdraw#> 70 12 2 staffer withdraw withdraw withdraw#> 71 12 6 staffer withdraw withdraw withdraw#> 72 12 1 staffer withdraw withdraw withdraw#> 73 13 3 institutionalism beside beside beside#> 74 13 6 institutionalism beside beside beside#> 75 13 5 institutionalism beside beside beside#> 76 13 2 institutionalism beside bsid beside#> 77 13 4 institutionalism beside beside beside#> 78 13 1 institutionalism beside beside beside#> 79 14 1 dollhouse doodle doodle doodle#> 80 14 3 dollhouse doodle doodle doodle#> 81 14 5 dollhouse doodle draw doodle#> 82 14 2 dollhouse doodle doodl doodle#> 83 14 4 dollhouse doodle doodle doodle#> 84 14 6 dollhouse doodle draw doodle#> 85 15 6 bolero membrane membrane membrane#> 86 15 5 bolero membrane membrane membrane#> 87 15 2 bolero membrane mmbran membrane#> 88 15 1 bolero membrane membrane membrane#> 89 15 3 bolero membrane membrane membrane#> 90 15 4 bolero membrane membrene membrane#> 91 16 5 soulless unofficially unofficially unofficially#> 92 16 3 soulless unofficially unofficially unofficially#> 93 16 6 soulless unofficially unofficially#> 94 16 1 soulless unofficially unofficially unofficially#> 95 16 4 soulless unofficially unofficielly unofficially#> 96 16 2 soulless unofficially unofficially unofficially#> 97 17 5 uncurled vibration vibration vibration#> 98 17 3 uncurled vibration vibrattion vibration#> 99 17 6 uncurled vibration vibration vibration#> 100 17 1 uncurled vibration vibration vibration#> 101 17 4 uncurled vibration vibretion vibration#> 102 17 2 uncurled vibration vibration vibration#> 103 18 5 giveaway permitted permitted permitted#> 104 18 3 giveaway permitted permitttted permitted#> 105 18 6 giveaway permitted granted permitted#> 106 18 1 giveaway permitted permitted permitted#> 107 18 4 giveaway permitted permitted permitted#> 108 18 2 giveaway permitted prmittd permitted#> 109 19 3 origination sleek sleek sleek#> 110 19 4 origination sleek sleek sleek#> 111 19 6 origination sleek shiny sleek#> 112 19 5 origination sleek shiny sleek#> 113 19 2 origination sleek slk sleek#> 114 19 1 origination sleek sleek sleek#> 115 20 1 iconology ignorance ignorance ignorance#> 116 20 3 iconology ignorance ignorance ignorance#> 117 20 5 iconology ignorance ignorance ignorance#> 118 20 2 iconology ignorance ignoranc ignorance#> 119 20 4 iconology ignorance ignorence ignorance#> 120 20 6 iconology ignorance ignorance ignorance#> Scored#> 1 1#> 2 1#> 3 1#> 4 1#> 5 1#> 6 0#> 7 0#> 8 0#> 9 1#> 10 1#> 11 1#> 12 0#> 13 1#> 14 1#> 15 1#> 16 1#> 17 1#> 18 1#> 19 1#> 20 1#> 21 1#> 22 1#> 23 1#> 24 1#> 25 1#> 26 0#> 27 0#> 28 1#> 29 1#> 30 1#> 31 0#> 32 1#> 33 0#> 34 1#> 35 1#> 36 1#> 37 1#> 38 1#> 39 0#> 40 1#> 41 1#> 42 0#> 43 1#> 44 1#> 45 1#> 46 1#> 47 1#> 48 1#> 49 1#> 50 1#> 51 1#> 52 1#> 53 1#> 54 1#> 55 0#> 56 0#> 57 1#> 58 1#> 59 1#> 60 1#> 61 0#> 62 1#> 63 1#> 64 1#> 65 1#> 66 1#> 67 1#> 68 1#> 69 1#> 70 1#> 71 1#> 72 1#> 73 1#> 74 1#> 75 1#> 76 0#> 77 1#> 78 1#> 79 1#> 80 1#> 81 0#> 82 1#> 83 1#> 84 0#> 85 1#> 86 1#> 87 0#> 88 1#> 89 1#> 90 1#> 91 1#> 92 1#> 93 0#> 94 1#> 95 1#> 96 1#> 97 1#> 98 1#> 99 1#> 100 1#> 101 1#> 102 1#> 103 1#> 104 0#> 105 0#> 106 1#> 107 1#> 108 0#> 109 1#> 110 1#> 111 0#> 112 0#> 113 0#> 114 1#> 115 1#> 116 1#> 117 1#> 118 1#> 119 1#> 120 1#Participantcued_output$DF_Participant#> Sub.ID Proportion.Correct Z.Score.Participant#> 1 1 1.00 1.0259784#> 2 2 0.80 0.0000000#> 3 3 0.85 0.2564946#> 4 4 0.95 0.7694838#> 5 5 0.75 -0.2564946#> 6 6 0.45 -1.7954621