CredentialForm1: Credential Form data
In LNIRT: LogNormal Response Time Item Response Theory Models
Description
Usage
Format
Details
References
Examples
Description
Responses and response time data from the credential data set of Cizek and Wollack (2016).
Usage
1
Format
A dataframe with 1636 rows and 610 variables.
Details
Variables:
EID: Examinee ID (character)
FormID: Test form name (character)
Flagged: 1/0 variable to indicate whether the test vendor suspects the examinee may have engaged in inappropriate behavior (numeric)
Pretest: Pretest item set assigned to candidate (numeric)
Attempt: Count of the attempt number for the candidate. A score of 1 indicates that candidate is a new, first-time examinee. Any examinee sitting for the exam for the fourth time or more is marked as 4+ (character)
Country: Country where candidate was educated (character)
StateCode: 2-digit code corresponding to the state in which the Candidate applied for licensure (numeric)
School_ID: 4-digit code corresponding to the particular institution in which the Candidate received his/her educational training (numeric)
Cent_id: 4-digit code corresponding to the particular testing center in which the Candidate sat for the exam (numeric)
Tot_time: The number of seconds testing (numeric)
iresp.1-170: item responses (1 to 4 or NA) for scored items 1 – 170 (numeric)
iresp.171-180: item responses (1 to 4 or NA) for 10 pilot items for pilot set 6 or 9 (numeric)
iresp.181-190: item responses (1 to 4 or NA) for 10 pilot items for pilot set 7 or 10 (numeric)
iresp.191-200: item responses (1 to 4 or NA) for 10 pilot items for pilot set 8 or 11 (numeric)
iraw.1-170: item correct score (1 or 0) for scored items 1 – 170 (numeric)
iraw.171-180: item correct score (1 or 0) for 10 pilot items for pilot set 6 or 9 (numeric)
iraw.181-190: item correct score (1 or 0) for 10 pilot items for pilot set 7 or 10 (numeric)
iraw.191-200: item correct score (1 or 0) for 10 pilot items for pilot set 8 or 11 (numeric)
idur.1-170: response time (in seconds) for scored items 1 – 170 (numeric)
idur.171-180: response time (in seconds) for 10 pilot items for pilot set 6 or 9 (numeric)
idur.181-190: response time (in seconds) for 10 pilot items for pilot set 7 or 10 (numeric)
idur.191-200: response time (in seconds) for 10 pilot items for pilot set 8 or 11 (numeric)
References
Cizek GJ, Wollack JA (eds.) (2016). Handbook of Quantitative Methods for Detecting Cheating on Tests. Routledge.
(Taylor&Francis)
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
## Not run:
###
### EXAMPLE APPLICATION CREDENTIAL FORM1 DATA CIZEK and WOLLACK (2016).
###
library(LNIRT)
data(CredentialForm1)
### DATA OBJECTS FOR LNIRT
### RA Data
Y <- as.matrix(CredentialForm1[c(which(colnames(CredentialForm1)=="iraw.1")
:which(colnames(CredentialForm1)=="iraw.170"))])
N <- nrow(Y)
### RT Data
RT<-as.matrix(CredentialForm1[c(which(colnames(CredentialForm1)=="idur.1")
:which(colnames(CredentialForm1)=="idur.170"))])
RT[RT==0]<-NA ## zero RTs are coded as missing values
RT<-log(RT) ## logarithmic transformation of RT
## RUN LNIRT MODEL 0
set.seed(12345) ## used to obtain the results reported in the paper ##
out0 <- LNIRT(RT=RT,Y=Y,XG=5000,burnin=10,ident=2)
summary(out0)
## Check MCMC convergence
library(mcmcse)
##
## check several MCMC chains
##
## effective sample size and effective sample size
ess(out0$MCMC.Samples$Cov.Person.Ability.Speed[1001:5000]) ## effective sample size
mcse(out0$MCMC.Samples$Cov.Person.Ability.Speed[1001:5000]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Var.Person.Ability[1001:5000]) ## effective sample size
mcse(out0$MCMC.Samples$Var.Person.Ability[1001:5000]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Var.Person.Speed[1001:5000]) ## effective sample size
mcse(out0$MCMC.Samples$Var.Person.Speed[1001:5000]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Item.Discrimination[1001:5000,155]) ## effective sample size
mcse(out0$MCMC.Samples$Item.Discrimination[1001:5000,155]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Time.Discrimination[1001:5000,1]) ## effective sample size
mcse(out0$MCMC.Samples$Time.Discrimination[1001:5000,1]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Person.Ability[1001:5000,1]) ## effective sample size
mcse(out0$MCMC.Samples$Person.Ability[1001:5000,1]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Person.Speed[1001:5000,1]) ## effective sample size
mcse(out0$MCMC.Samples$Person.Speed[1001:5000,1]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
## Convergence Checks
library(coda)
summary(as.mcmc(out0$MCMC.Samples$Cov.Person.Ability.Speed[1001:5000]))
summary(as.mcmc(out0$MCMC.Samples$Var.Person.Ability[1001:5000]))
summary(as.mcmc(out0$MCMC.Samples$Var.Person.Speed[1001:5000]))
summary(as.mcmc(out0$MCMC.Samples$Item.Discrimination[1001:5000,155]))
summary(as.mcmc(out0$MCMC.Samples$Time.Discrimination[1001:5000,1]))
summary(as.mcmc(out0$MCMC.Samples$Person.Ability[1001:5000,1]))
summary(as.mcmc(out0$MCMC.Samples$Person.Speed[1001:5000,1]))
## check some chains on convergence
geweke.diag(as.mcmc(out0$MCMC.Samples$Cov.Person.Ability.Speed[500:5000]), frac1=0.1, frac2=0.5)
geweke.plot(as.mcmc(out0$MCMC.Samples$Cov.Person.Ability.Speed[500:5000]), frac1=0.1, frac2=0.5)
heidel.diag(as.mcmc(out0$MCMC.Samples$Cov.Person.Ability.Speed[500:5000], eps=0.1, pvalue=0.05))
geweke.diag(as.mcmc(out0$MCMC.Samples$Item.Discrimination[500:5000,155]), frac1=0.1, frac2=0.5)
geweke.plot(as.mcmc(out0$MCMC.Samples$Item.Discrimination[500:5000,155]), frac1=0.1, frac2=0.5)
heidel.diag(as.mcmc(out0$MCMC.Samples$Item.Discrimination[500:5000,155]), eps=0.1, pvalue=0.05)
geweke.diag(as.mcmc(out0$MCMC.Samples$Person.Ability[500:5000,1]), frac1=0.1, frac2=0.5)
geweke.plot(as.mcmc(out0$MCMC.Samples$Person.Ability[500:5000,1]), frac1=0.1, frac2=0.5)
heidel.diag(as.mcmc(out0$MCMC.Samples$Person.Ability[500:5000,1]), eps=0.1, pvalue=0.05)
## Item parameter estimates
min(apply(out0$MAB[500:5000,,1],2,mean))
max(apply(out0$MAB[500:5000,,1],2,mean))
min(apply(out0$MAB[500:5000,,2],2,mean))
max(apply(out0$MAB[500:5000,,2],2,mean))
min(apply(out0$MAB[500:5000,,3],2,mean))
max(apply(out0$MAB[500:5000,,3],2,mean))
min(apply(out0$MAB[500:5000,,4],2,mean))
max(apply(out0$MAB[500:5000,,4],2,mean))
plot(apply(out0$MAB[500:5000,,4],2,mean),(apply(RT,2,mean,na.rm=TRUE)))
### Explanatory Variables Test-takers
XFT <- data.frame(CredentialForm1[1:10],stringsAsFactors=TRUE) #Background Variables
XFT$Tot_time <- (XFT$Tot_time-mean(XFT$Tot_time))/sqrt(var(XFT$Tot_time))
## DUMMY CODING FOR CATEGORICAL PREDICTORS
## Pretest Groups
XFT$Pgroup <- matrix(0,ncol=2,nrow=N)
XFT$Pgroup[XFT$Pretest==6,1] <- -1
XFT$Pgroup[XFT$Pretest==6,2] <- -1
XFT$Pgroup[XFT$Pretest==7,1] <- 1
XFT$Pgroup[XFT$Pretest==8,2] <- 1
## Countries
XFT$Cgroup <- matrix(0,ncol=3,nrow=N)
XFT$Cgroup[XFT$Country=="USA",1] <- 1
XFT$Cgroup[XFT$Country=="Philippines",2] <- 1
XFT$Cgroup[XFT$Country=="India",3] <- 1
XFT$Cgroup[c(XFT$Country!="USA" & XFT$Country!="India" & XFT$Country!="Philippines"),1:3] <- -1
XA <- matrix(unlist(XFT[,c("Pgroup","Tot_time")]),ncol=3,nrow=N)
XT <- matrix(unlist(XFT[,c("Pgroup")]),ncol=2,nrow=N)
## RUN LNIRT MODEL 1 (Pretest and total test time)
## Include residual analysis
set.seed(12345) ## used to obtain the results reported in the paper ##
out1 <- LNIRT(RT=RT,Y=Y,XG=5000,XPA=XA,XPT=XT,residual=TRUE)
summary(out1)
######################################################################
### THIS PART IS NOT DISCUSSED IN THE PAPER ###
######################################################################
## RUN LNIRT MODEL 2 (Pretest and Country)
XA <- matrix(unlist(XFT[,c("Pgroup","Cgroup")]),ncol=5,nrow=N)
XT <- matrix(unlist(XFT[,c("Pgroup","Cgroup")]),ncol=5,nrow=N)
set.seed(12345) ##
out2 <- LNIRT(RT=RT,Y=Y,XG=5000,XPA=XA,XPT=XT)
summary(out2)
XA <- matrix(unlist(XFT[,c("Pgroup","Cgroup","Tot_time")]),ncol=6,nrow=N)
XT <- matrix(unlist(XFT[,c("Pgroup","Cgroup")]),ncol=5,nrow=N)
## RUN LNIRT MODEL 3
set.seed(12345) ##
out3 <- LNIRT(RT=RT,Y=Y,XG=5000,XPA=XA,XPT=XT)
summary(out3)
#########################################################################
#########################################################################
#########################################################################
######################################################################
### THIS PART IS DISCUSSED IN THE PAPER ###
######################################################################
## Subsection "Planned Missing By Design"
## Include pretest item data
MBDM<-matrix(rep(0,1636*200),nrow=1636,ncol=200)
MBDM[XFT$Pretest==6,171:180]<-1
MBDM[XFT$Pretest==7,181:190]<-1
MBDM[XFT$Pretest==8,191:200]<-1
MBDM[,1:170]<-1
Yt <- CredentialForm1[c(which(colnames(CredentialForm1)=="iraw.1")
:which(colnames(CredentialForm1)=="iraw.200"))]
## transform pretest data to numeric
Yt[,171:200] <- unlist(lapply(Yt[,171:200]
,function(x) as.numeric(x))) #warnings about NA can be ignored
Yt <- as.matrix(Yt,ncol=200,nrow=1636)
RTt <- (CredentialForm1[as.numeric(c(which(colnames(CredentialForm1)=="idur.1")
:which(colnames(CredentialForm1)=="idur.200")))])
RTt[,171:200] <- unlist(lapply(RTt[,171:200]
, function(x) as.numeric(as.character(x)))) #warnings about NA can be ignored
RTt[RTt==0] <- NA ## zero RTs are coded as missing values
RTt <- log(RTt) ## logarithmic transformation of RT
RTt <- as.matrix(RTt,ncol=200,nrow=1636)
# To fit the model, item discrimination parameters are restricted to one.
alpha1<-rep(1,200) ### Pre-defined item discrimination parameters
## RUN LNIRT MODEL 4
set.seed(12345) ## used to obtain the results reported in the paper ##
out4 <- LNIRT(RT=RTt,Y=Yt,XG=5000,alpha=alpha1,MBDY=MBDM,MBDT=MBDM)
summary(out4)
### Subsection "Model-Fit Analysis"
### Return to output of out1
#report fit results
summary(out1)
## estimated average residual variance
mean(out1$Msigma2[500:5000,])
#recoding of number of zero attempts
XFT$Attempt[XFT$Attempt==0] <- 1
## explain heterogeneity in person-fit statistics RA and RT
summary(lm(out1$PFl ~ as.factor(XFT$Attempt)+(XFT$Cgroup)+(XFT$Pgroup)))
summary(lm(out1$lZPT ~ as.factor(XFT$Attempt)+(XFT$Cgroup)+(XFT$Pgroup)))
### overview plot of person fit RA versus person-fit RT per country
dev.new()
plot(out1$PFl,out1$lZPT,xlab="Person-fit Statistic RA",ylab="Person-fit Statistic RT",
col="black",cex=.5,bty="l",xlim=c(-3,3)
, ylim=c(0,500),cex.main=.8,cex.axis=.7,cex.lab=.8,pch=15)
## US
set1 <- which(XFT$Country=="USA")
points(out1$PFl[set1],out1$lZPT[set1],col="blue",pch=10,cex=.5)
## India
set2 <- which(XFT$Country=="India")
points(out1$PFl[set2],out1$lZPT[set2],col="red",pch=13,cex=.5)
## Philippines
set3 <- which(XFT$Country=="Philippines")
points(out1$PFl[set3],out1$lZPT[set3],col="green",pch=16,cex=.5)
abline(h = qchisq(.95, df= 170),lty = 2,col="red")
abline(v = qnorm(.95),lty = 2,col="red")
legend(-3,500,c("India","US","Philippines","Other"),
col=c("red","blue","green","black"),pch = c(13,10,16,15), bg = "gray95",cex=.7)
###################################################################################
###################################################################################
###################################################################################
## End(Not run)
LNIRT documentation built on Jan. 20, 2021, 1:05 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Format Details References Examples
Description
Responses and response time data from the credential data set of Cizek and Wollack (2016).
Usage
1 |
Format
A dataframe with 1636 rows and 610 variables.
Details
Variables:
EID: Examinee ID (character)
FormID: Test form name (character)
Flagged: 1/0 variable to indicate whether the test vendor suspects the examinee may have engaged in inappropriate behavior (numeric)
Pretest: Pretest item set assigned to candidate (numeric)
Attempt: Count of the attempt number for the candidate. A score of 1 indicates that candidate is a new, first-time examinee. Any examinee sitting for the exam for the fourth time or more is marked as 4+ (character)
Country: Country where candidate was educated (character)
StateCode: 2-digit code corresponding to the state in which the Candidate applied for licensure (numeric)
School_ID: 4-digit code corresponding to the particular institution in which the Candidate received his/her educational training (numeric)
Cent_id: 4-digit code corresponding to the particular testing center in which the Candidate sat for the exam (numeric)
Tot_time: The number of seconds testing (numeric)
iresp.1-170: item responses (1 to 4 or NA) for scored items 1 – 170 (numeric)
iresp.171-180: item responses (1 to 4 or NA) for 10 pilot items for pilot set 6 or 9 (numeric)
iresp.181-190: item responses (1 to 4 or NA) for 10 pilot items for pilot set 7 or 10 (numeric)
iresp.191-200: item responses (1 to 4 or NA) for 10 pilot items for pilot set 8 or 11 (numeric)
iraw.1-170: item correct score (1 or 0) for scored items 1 – 170 (numeric)
iraw.171-180: item correct score (1 or 0) for 10 pilot items for pilot set 6 or 9 (numeric)
iraw.181-190: item correct score (1 or 0) for 10 pilot items for pilot set 7 or 10 (numeric)
iraw.191-200: item correct score (1 or 0) for 10 pilot items for pilot set 8 or 11 (numeric)
idur.1-170: response time (in seconds) for scored items 1 – 170 (numeric)
idur.171-180: response time (in seconds) for 10 pilot items for pilot set 6 or 9 (numeric)
idur.181-190: response time (in seconds) for 10 pilot items for pilot set 7 or 10 (numeric)
idur.191-200: response time (in seconds) for 10 pilot items for pilot set 8 or 11 (numeric)
References
Cizek GJ, Wollack JA (eds.) (2016). Handbook of Quantitative Methods for Detecting Cheating on Tests. Routledge. (Taylor&Francis)
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | ## Not run:
###
### EXAMPLE APPLICATION CREDENTIAL FORM1 DATA CIZEK and WOLLACK (2016).
###
library(LNIRT)
data(CredentialForm1)
### DATA OBJECTS FOR LNIRT
### RA Data
Y <- as.matrix(CredentialForm1[c(which(colnames(CredentialForm1)=="iraw.1")
:which(colnames(CredentialForm1)=="iraw.170"))])
N <- nrow(Y)
### RT Data
RT<-as.matrix(CredentialForm1[c(which(colnames(CredentialForm1)=="idur.1")
:which(colnames(CredentialForm1)=="idur.170"))])
RT[RT==0]<-NA ## zero RTs are coded as missing values
RT<-log(RT) ## logarithmic transformation of RT
## RUN LNIRT MODEL 0
set.seed(12345) ## used to obtain the results reported in the paper ##
out0 <- LNIRT(RT=RT,Y=Y,XG=5000,burnin=10,ident=2)
summary(out0)
## Check MCMC convergence
library(mcmcse)
##
## check several MCMC chains
##
## effective sample size and effective sample size
ess(out0$MCMC.Samples$Cov.Person.Ability.Speed[1001:5000]) ## effective sample size
mcse(out0$MCMC.Samples$Cov.Person.Ability.Speed[1001:5000]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Var.Person.Ability[1001:5000]) ## effective sample size
mcse(out0$MCMC.Samples$Var.Person.Ability[1001:5000]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Var.Person.Speed[1001:5000]) ## effective sample size
mcse(out0$MCMC.Samples$Var.Person.Speed[1001:5000]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Item.Discrimination[1001:5000,155]) ## effective sample size
mcse(out0$MCMC.Samples$Item.Discrimination[1001:5000,155]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Time.Discrimination[1001:5000,1]) ## effective sample size
mcse(out0$MCMC.Samples$Time.Discrimination[1001:5000,1]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Person.Ability[1001:5000,1]) ## effective sample size
mcse(out0$MCMC.Samples$Person.Ability[1001:5000,1]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
ess(out0$MCMC.Samples$Person.Speed[1001:5000,1]) ## effective sample size
mcse(out0$MCMC.Samples$Person.Speed[1001:5000,1]
, size = 100, g = NULL,method = "bm", warn = FALSE) #standard error
## Convergence Checks
library(coda)
summary(as.mcmc(out0$MCMC.Samples$Cov.Person.Ability.Speed[1001:5000]))
summary(as.mcmc(out0$MCMC.Samples$Var.Person.Ability[1001:5000]))
summary(as.mcmc(out0$MCMC.Samples$Var.Person.Speed[1001:5000]))
summary(as.mcmc(out0$MCMC.Samples$Item.Discrimination[1001:5000,155]))
summary(as.mcmc(out0$MCMC.Samples$Time.Discrimination[1001:5000,1]))
summary(as.mcmc(out0$MCMC.Samples$Person.Ability[1001:5000,1]))
summary(as.mcmc(out0$MCMC.Samples$Person.Speed[1001:5000,1]))
## check some chains on convergence
geweke.diag(as.mcmc(out0$MCMC.Samples$Cov.Person.Ability.Speed[500:5000]), frac1=0.1, frac2=0.5)
geweke.plot(as.mcmc(out0$MCMC.Samples$Cov.Person.Ability.Speed[500:5000]), frac1=0.1, frac2=0.5)
heidel.diag(as.mcmc(out0$MCMC.Samples$Cov.Person.Ability.Speed[500:5000], eps=0.1, pvalue=0.05))
geweke.diag(as.mcmc(out0$MCMC.Samples$Item.Discrimination[500:5000,155]), frac1=0.1, frac2=0.5)
geweke.plot(as.mcmc(out0$MCMC.Samples$Item.Discrimination[500:5000,155]), frac1=0.1, frac2=0.5)
heidel.diag(as.mcmc(out0$MCMC.Samples$Item.Discrimination[500:5000,155]), eps=0.1, pvalue=0.05)
geweke.diag(as.mcmc(out0$MCMC.Samples$Person.Ability[500:5000,1]), frac1=0.1, frac2=0.5)
geweke.plot(as.mcmc(out0$MCMC.Samples$Person.Ability[500:5000,1]), frac1=0.1, frac2=0.5)
heidel.diag(as.mcmc(out0$MCMC.Samples$Person.Ability[500:5000,1]), eps=0.1, pvalue=0.05)
## Item parameter estimates
min(apply(out0$MAB[500:5000,,1],2,mean))
max(apply(out0$MAB[500:5000,,1],2,mean))
min(apply(out0$MAB[500:5000,,2],2,mean))
max(apply(out0$MAB[500:5000,,2],2,mean))
min(apply(out0$MAB[500:5000,,3],2,mean))
max(apply(out0$MAB[500:5000,,3],2,mean))
min(apply(out0$MAB[500:5000,,4],2,mean))
max(apply(out0$MAB[500:5000,,4],2,mean))
plot(apply(out0$MAB[500:5000,,4],2,mean),(apply(RT,2,mean,na.rm=TRUE)))
### Explanatory Variables Test-takers
XFT <- data.frame(CredentialForm1[1:10],stringsAsFactors=TRUE) #Background Variables
XFT$Tot_time <- (XFT$Tot_time-mean(XFT$Tot_time))/sqrt(var(XFT$Tot_time))
## DUMMY CODING FOR CATEGORICAL PREDICTORS
## Pretest Groups
XFT$Pgroup <- matrix(0,ncol=2,nrow=N)
XFT$Pgroup[XFT$Pretest==6,1] <- -1
XFT$Pgroup[XFT$Pretest==6,2] <- -1
XFT$Pgroup[XFT$Pretest==7,1] <- 1
XFT$Pgroup[XFT$Pretest==8,2] <- 1
## Countries
XFT$Cgroup <- matrix(0,ncol=3,nrow=N)
XFT$Cgroup[XFT$Country=="USA",1] <- 1
XFT$Cgroup[XFT$Country=="Philippines",2] <- 1
XFT$Cgroup[XFT$Country=="India",3] <- 1
XFT$Cgroup[c(XFT$Country!="USA" & XFT$Country!="India" & XFT$Country!="Philippines"),1:3] <- -1
XA <- matrix(unlist(XFT[,c("Pgroup","Tot_time")]),ncol=3,nrow=N)
XT <- matrix(unlist(XFT[,c("Pgroup")]),ncol=2,nrow=N)
## RUN LNIRT MODEL 1 (Pretest and total test time)
## Include residual analysis
set.seed(12345) ## used to obtain the results reported in the paper ##
out1 <- LNIRT(RT=RT,Y=Y,XG=5000,XPA=XA,XPT=XT,residual=TRUE)
summary(out1)
######################################################################
### THIS PART IS NOT DISCUSSED IN THE PAPER ###
######################################################################
## RUN LNIRT MODEL 2 (Pretest and Country)
XA <- matrix(unlist(XFT[,c("Pgroup","Cgroup")]),ncol=5,nrow=N)
XT <- matrix(unlist(XFT[,c("Pgroup","Cgroup")]),ncol=5,nrow=N)
set.seed(12345) ##
out2 <- LNIRT(RT=RT,Y=Y,XG=5000,XPA=XA,XPT=XT)
summary(out2)
XA <- matrix(unlist(XFT[,c("Pgroup","Cgroup","Tot_time")]),ncol=6,nrow=N)
XT <- matrix(unlist(XFT[,c("Pgroup","Cgroup")]),ncol=5,nrow=N)
## RUN LNIRT MODEL 3
set.seed(12345) ##
out3 <- LNIRT(RT=RT,Y=Y,XG=5000,XPA=XA,XPT=XT)
summary(out3)
#########################################################################
#########################################################################
#########################################################################
######################################################################
### THIS PART IS DISCUSSED IN THE PAPER ###
######################################################################
## Subsection "Planned Missing By Design"
## Include pretest item data
MBDM<-matrix(rep(0,1636*200),nrow=1636,ncol=200)
MBDM[XFT$Pretest==6,171:180]<-1
MBDM[XFT$Pretest==7,181:190]<-1
MBDM[XFT$Pretest==8,191:200]<-1
MBDM[,1:170]<-1
Yt <- CredentialForm1[c(which(colnames(CredentialForm1)=="iraw.1")
:which(colnames(CredentialForm1)=="iraw.200"))]
## transform pretest data to numeric
Yt[,171:200] <- unlist(lapply(Yt[,171:200]
,function(x) as.numeric(x))) #warnings about NA can be ignored
Yt <- as.matrix(Yt,ncol=200,nrow=1636)
RTt <- (CredentialForm1[as.numeric(c(which(colnames(CredentialForm1)=="idur.1")
:which(colnames(CredentialForm1)=="idur.200")))])
RTt[,171:200] <- unlist(lapply(RTt[,171:200]
, function(x) as.numeric(as.character(x)))) #warnings about NA can be ignored
RTt[RTt==0] <- NA ## zero RTs are coded as missing values
RTt <- log(RTt) ## logarithmic transformation of RT
RTt <- as.matrix(RTt,ncol=200,nrow=1636)
# To fit the model, item discrimination parameters are restricted to one.
alpha1<-rep(1,200) ### Pre-defined item discrimination parameters
## RUN LNIRT MODEL 4
set.seed(12345) ## used to obtain the results reported in the paper ##
out4 <- LNIRT(RT=RTt,Y=Yt,XG=5000,alpha=alpha1,MBDY=MBDM,MBDT=MBDM)
summary(out4)
### Subsection "Model-Fit Analysis"
### Return to output of out1
#report fit results
summary(out1)
## estimated average residual variance
mean(out1$Msigma2[500:5000,])
#recoding of number of zero attempts
XFT$Attempt[XFT$Attempt==0] <- 1
## explain heterogeneity in person-fit statistics RA and RT
summary(lm(out1$PFl ~ as.factor(XFT$Attempt)+(XFT$Cgroup)+(XFT$Pgroup)))
summary(lm(out1$lZPT ~ as.factor(XFT$Attempt)+(XFT$Cgroup)+(XFT$Pgroup)))
### overview plot of person fit RA versus person-fit RT per country
dev.new()
plot(out1$PFl,out1$lZPT,xlab="Person-fit Statistic RA",ylab="Person-fit Statistic RT",
col="black",cex=.5,bty="l",xlim=c(-3,3)
, ylim=c(0,500),cex.main=.8,cex.axis=.7,cex.lab=.8,pch=15)
## US
set1 <- which(XFT$Country=="USA")
points(out1$PFl[set1],out1$lZPT[set1],col="blue",pch=10,cex=.5)
## India
set2 <- which(XFT$Country=="India")
points(out1$PFl[set2],out1$lZPT[set2],col="red",pch=13,cex=.5)
## Philippines
set3 <- which(XFT$Country=="Philippines")
points(out1$PFl[set3],out1$lZPT[set3],col="green",pch=16,cex=.5)
abline(h = qchisq(.95, df= 170),lty = 2,col="red")
abline(v = qnorm(.95),lty = 2,col="red")
legend(-3,500,c("India","US","Philippines","Other"),
col=c("red","blue","green","black"),pch = c(13,10,16,15), bg = "gray95",cex=.7)
###################################################################################
###################################################################################
###################################################################################
## End(Not run)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.