| Title: | Classical Test Theory Functions |
|---|---|
| Description: | A collection of common test and item analyses from a classical test theory (CTT) framework. Analyses can be applied to both dichotomous and polytomous data. Functions provide reliability analyses (alpha), item statistics, disctractor analyses, disattenuated correlations, scoring routines, and empirical ICCs. |
| Authors: | John T. Willse |
| Maintainer: | John T. Willse <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 2.3.3 |
| Built: | 2024-12-08 06:49:01 UTC |
| Source: | CRAN |
This package can be used to perform a variety of tasks and analyses associated with classical test theory (CTT): score multiple-choice responses, perform reliability analyses, conduct item analyses, and transform scores onto different scales.
| Package: | CTT |
| Type: | Package |
| Version: | 2.3.3 |
| Date: | 2018-09-11 |
| License: | GPL version 2 or newer |
The CTT package has the following functions: reliability, score, distractor.analysis, score.transform, spearman.brown, disattenuated.cor, subscales, polyserial.
John T. Willse <[email protected]>, Zhan Shu
Crocker, L. & Algina, J. (1986). Introduction to Classical & Modern Test Theory, New York: Harcourt Brace Jovanovich College Publishers.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometika, 16, 297-334.
Gulliksen, H. (1950). Theory of Mental Tests. New York: John Wiley & Sons, Inc.
Olsson, U., Drasgow, F. & Dorans, N. J. (1982). The Polyserial Correlation Coefficient. Psychometika, 47, 337-347.
This example data contains 20 unscored multiple-choice items that can be used with the CTT package.
data(CTTdata)data(CTTdata)
A data frame with 100 observations on the following 20 variables.
i1a character vector
i2a character vector
i3a character vector
i4a character vector
i5a character vector
i6a character vector
i7a character vector
i8a character vector
i9a character vector
i10a character vector
i11a character vector
i12a character vector
i13a character vector
i14a character vector
i15a character vector
i16a character vector
i17a character vector
i18a character vector
i19a character vector
i20a character vector
CTTkey
data(CTTdata)data(CTTdata)
This function produces empirical item characteristic curves.
cttICC(scores, itemVector, xlim, ylim, plotTitle, xlab, ylab, col = c("black","white"), colTheme, gDevice, file, ...)cttICC(scores, itemVector, xlim, ylim, plotTitle, xlab, ylab, col = c("black","white"), colTheme, gDevice, file, ...)
scores |
A total measure score, for creating expected mean values of the item. |
itemVector |
Observed item responses for the item ICC. |
xlim |
A vector overriding default limits for the x axis. |
ylim |
A vector overriding default limits for the y axis. |
plotTitle |
Controls the main plot title. |
xlab |
The label for the x axis. |
ylab |
The label for the y axis. |
col |
A vector of the colors to be used in the plot. The first color will be used for item labels. The second color will be used for shading the area of rejection. |
colTheme |
Four color themes ("cavaliers", "dukes", "spartans", "greys") are provided. If you provide a color theme, it will override the col paramater. |
gDevice |
Controls graphics device. Options are "screen" (default), "jpg", or "png". |
file |
The name of the output file if a device other than "screen" is chosen. |
... |
Additional parameters passed to the plot command. |
The function produces an item characterisic curve plot. The empirical ICC is created by calculating the item mean in between 2 and 20 bins. There must be at least 15 observations per bin, or a smaller number of bins is used.
John T. Willse
library(CTT)# Example data provided with package data(CTTdata) data(CTTkey) # Scores for each preson myScores <- score(CTTdata,CTTkey, output.scored=TRUE) # ICC for item 1 cttICC(myScores$score, myScores$scored[,1], colTheme="spartans", cex=1.5)library(CTT)# Example data provided with package data(CTTdata) data(CTTkey) # Scores for each preson myScores <- score(CTTdata,CTTkey, output.scored=TRUE) # ICC for item 1 cttICC(myScores$score, myScores$scored[,1], colTheme="spartans", cex=1.5)
This example data contains a key for the 20 unscored multiple-choice items found in CTTdata and can be used with the CTT package.
data(CTTkey)data(CTTkey)
The format is: chr [1:20] "D" "C" "A" "D" "D" "A" "D" "B" "D" "A" ...
CTTdata
data(CTTkey)data(CTTkey)
This function is used to calculate the disattentuated correlation between two measures given the corresponding test reliabilities.
disattenuated.cor(r.xy, r.xx, new.r.xx = 1)disattenuated.cor(r.xy, r.xx, new.r.xx = 1)
r.xy |
The correlation between test x and test y |
r.xx |
Each tests' reliability |
new.r.xx |
A new reliability for each test (optional) |
The data given in r.xy may be a single value or a matrix. A matrix is assumed to be a correlation matrix (square, symmetric).
The data given in r.xx should be a vector, with one reliability for each instrument involved in the correlation, r.xy.
The new.r.xx represents a new reliability for each measure. If these values are less than 1, the returned correlation is the value that would be expected with the new reliability.
If r.xy is a single value a single value is returned. If r.xy is a matrix then a matrix is returned with the reliabilities on the diagonal, the disattenuated correlations in the upper triangle and the original correlations in the lower triangle.
John T. Willse, Zhan Shu
Spearman, C. (1904). The proof and measurement of association between two things. American Journal of Psychology, 15, 72-101.
Gulliksen, H. (1950). Theory of Mental Tests. New York: John Wiley & Sons, Inc.
# r.xy=0.6, r.xx=0.7,r.yy=0.8 disattenuated.cor(0.6,c(0.7,0.8)) # if r.xy is a matrix: cor1 <- matrix(c(1.0000000, 0.24391288, 0.2812319, 0.05251050, 0.2439129, 1.00000000, 0.1652985, 0.08126448, 0.2812319, 0.16529850, 1.0000000, 0.27971630, 0.0525105, 0.08126448, 0.2797163, 1.00000000), byrow=TRUE, ncol=4) rxx1 <- c(0.8,0.8,0.81,0.9) # reliability of each test new.rxx1 <- c(0.9,0.97,0.8,0.7) # projected new reliability of those tests disattenuated.cor(cor1, rxx1, new.rxx1)# r.xy=0.6, r.xx=0.7,r.yy=0.8 disattenuated.cor(0.6,c(0.7,0.8)) # if r.xy is a matrix: cor1 <- matrix(c(1.0000000, 0.24391288, 0.2812319, 0.05251050, 0.2439129, 1.00000000, 0.1652985, 0.08126448, 0.2812319, 0.16529850, 1.0000000, 0.27971630, 0.0525105, 0.08126448, 0.2797163, 1.00000000), byrow=TRUE, ncol=4) rxx1 <- c(0.8,0.8,0.81,0.9) # reliability of each test new.rxx1 <- c(0.9,0.97,0.8,0.7) # projected new reliability of those tests disattenuated.cor(cor1, rxx1, new.rxx1)
This function is deprecated. Use distractorAnalysis for a more complete distractor analysis.
distractor.analysis(items, key, scores, p.table = FALSE, write.csv)distractor.analysis(items, key, scores, p.table = FALSE, write.csv)
items |
The unscored item response from a multiple-choice test |
key |
The answer key for the items |
scores |
An optional set of person scores associated with the item data. If scores are not provided (default) the scores are calculated using the item data and key. |
p.table |
If p.table=FALSE (the default) the function returns the counts of examinees who provide each answer. If p.table=TRUE the function returns the proportion of examinees who provide each answer. |
write.csv |
If the optional file name is provided the function will save a .csv file with the results. |
The scores are used to split respondents into terciles. The number (or proportion if p.table=TRUE) of examinees in each tercile giving each response is reported. The correct answer is indicated with an "*".
If p.table=F counts of respondents in each tercile who chose each answer is returned as a list of tables. Each item is a separate element in the list. If p.table=T the tables contain the proportion of respondents who chose each corresponding answer.
John T. Willse, Zhan Shu
Allen, M. J. & Yen, W. M. (1979). Introduction to Measurement Theory. Lon Grove, Illinois: Waveland Press, INC.
# Example data provided with package data(CTTdata) data(CTTkey) distractor.analysis(CTTdata,CTTkey) # Results provided in a .csv file. distractor.analysis(CTTdata,CTTkey,p.table=TRUE,write.csv="Hello.csv")# Example data provided with package data(CTTdata) data(CTTkey) distractor.analysis(CTTdata,CTTkey) # Results provided in a .csv file. distractor.analysis(CTTdata,CTTkey,p.table=TRUE,write.csv="Hello.csv")
This function provides a distractor analysis.
distractorAnalysis(items, key, scores, nGroups=4, defineGroups, multiKeySep="none", multiKeyScore=c("or","poly"), validResp, csvReport, pTable=TRUE, digits)distractorAnalysis(items, key, scores, nGroups=4, defineGroups, multiKeySep="none", multiKeyScore=c("or","poly"), validResp, csvReport, pTable=TRUE, digits)
items |
The unscored item response from a multiple-choice test |
key |
The answer key for the items |
scores |
An optional set of person scores associated with the item data. If scores are not provided (default) the scores are calculated using the item data and key. |
nGroups |
Determines the number of groups into which scores are discretized. For example, nGroups=4 (default) performs and analysis based on quartiles. |
defineGroups |
If provided, determines the quantile breakpoints for groups into which scores are discretized. For example, defineGroups=c(.27,.46,.27) performs and analysis with 3 quantiles and 27 percent of examinees in the top and the bottom groups. |
multiKeySep |
If a value other than "none" is provided (e.g., ","), the key and the raw items will be reviewed for the provided delimiter. Using this otion allows for multiple correct responses. |
multiKeyScore |
The first value controls how multiple keys are handled. If "or" any correct response results in score of 1. If "and" all responses must be correct. If the second value is "poly" the returned score is the sum of correct responses. If the second value is "dich" a maximum score of 1 is returned. If the respondent can only provide one response, use "or". If the responded can provide multiple responses and you use c("and", "poly") the score will be 0 or max score. |
validResp |
A list of vectors providing valid responses for the distractor tables. If no value is provided, valid responses are determined from the data and assumed to be the same across items. If "fromItem" is provided, values are determined from item responses and NOT assumed to be the same across items. |
csvReport |
If an optional file name is provided the function will save a .csv file with the results. |
pTable |
If pTable=FALSE the function returns the counts of examinees who provide each answer. If pTable=TRUE (default) the function returns the proportion of examinees who provide each answer. |
digits |
If digits (an integer) is provided, it specifies the number of decimals to which results will be rounded. |
The scores are used to split respondents into groups, with number determine by nGroups. The proportion (or number if pTable=FALSE) of examinees in each group giving each response is reported. The correct answer is indicated with an "*". Additional item statistics are provided. Descriptors of each item are returned as separate elements in a list.
correct |
An "*" indicates the correct response |
key |
The response option being described |
n |
The number of responsdents choosing that option |
rspP |
The proportion of respondents with that response |
pBis |
The point-biserial correlation between that reponse and the total score with that item removed |
discrim |
The upper proportion minus the lower proportion |
lower |
The proportion of respondents choosing that response that are from the lowest score group |
upper |
The proportion of respondents choosing that response that are from the highest score group |
John T. Willse
Allen, M. J. & Yen, W. M. (1979). Introduction to Measurement Theory. Lon Grove, Illinois: Waveland Press, INC.
# Example data provided with package data(CTTdata) data(CTTkey) distractorAnalysis(CTTdata,CTTkey) # Results provided in a .csv file. distractorAnalysis(CTTdata,CTTkey,csvReport="Hello.csv")# Example data provided with package data(CTTdata) data(CTTkey) distractorAnalysis(CTTdata,CTTkey) # Results provided in a .csv file. distractorAnalysis(CTTdata,CTTkey,csvReport="Hello.csv")
This function performs reliability analyses, providing coefficient alpha and classical item statistics. This function improves and replaces the function reliability from previous versions.
itemAnalysis(items, itemReport=TRUE, NA.Delete=TRUE, rBisML=FALSE, hardFlag, easyFlag, pBisFlag, bisFlag, flagStyle = c("X",""))itemAnalysis(items, itemReport=TRUE, NA.Delete=TRUE, rBisML=FALSE, hardFlag, easyFlag, pBisFlag, bisFlag, flagStyle = c("X",""))
items |
The scored response file with "0" (wrong) and "1" (correct) or Likert type data |
itemReport |
If itemReport=TRUE (the default) item analyses are conducted. The function will provide a dataframe containing item names, item means, item total correlations, and alpha if item is removed. |
NA.Delete |
If NA.Delete=TRUE (the default) records are deleted listwise if there are missing responses. If NA.Delete=FALSE all NA values are changed to 0s. |
rBisML |
A logical variable indicating whether the biserial correlation is calculated using a formal maximum likelihood estimator or an ad hoc estimator (default, speeds up analysis with many items). |
hardFlag |
If a numeric value is provided, a flag is added to itemReport for each item with a mean less than the value. itemReport=TRUE must also be set. |
easyFlag |
If a numeric value is provided, a flag is added to itemReport for each item with a mean greater than the value. itemReport=TRUE must also be set. |
pBisFlag |
If a numeric value is provided, a flag is added to itemReport for each item with a point-biserial correlation less than the value. itemReport=TRUE must also be set. |
bisFlag |
If a numeric value is provided, a flag is added to itemReport for each item with a biserial correlation less than the value. itemReport=TRUE must also be set. |
flagStyle |
Determines the values to be used for item flagging. Default uses an "X" when an item is flagged and "" when not. Any value, including booleans can be used. |
The input files must be scored files with "0" and "1" or numeric scales (e.g., Likert Type scales). Only basic scale information is returned to the screen. Use str() to view additional statistics that are available. If itemReport is used (preferred) item statistics are provided as part of a dataframe called itemReport. Use function reliability with option itemal (being phased out), for output pre 2.2.
nItem |
The number of items |
nPerson |
The sample size used in calculating the values |
alpha |
Crobach's alpha |
scaleMean |
Average total sum socre |
scaleSD |
Standard deviation of total sum score |
itemReport |
Returned if itemReport = TRUE. Returns a data frame with key item analysis results: item mean (itemMean), point-biserial (pBis), biserial (bis), Cronbach's alpha if item removed, and any item flags indicated in the function call. |
John T. Willse
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometika, 16, 297-334.
score
# Scored input (data frame is preferred) x<-data.frame(matrix(c(0,0,0,0,0, 0,0,0,0,0, 0,0,0,0,1, 0,0,0,1,1, 0,0,1,1,1, 0,1,1,1,1, 1,1,1,1,1, 1,0,1,1,1, 0,0,0,1,1, 0,1,1,1,1),nrow=10,ncol=5,byrow=TRUE, dimnames=list(c(paste("P",c(1:10),sep="")),c(paste("I",c(1:5),sep=""))))) itemAnalysis(x) # To see an item report with flags. iA <- itemAnalysis(x, hardFlag=.25, pBisFlag=.15) iA$itemReport # To see more item statisitics str(itemAnalysis(x))# Scored input (data frame is preferred) x<-data.frame(matrix(c(0,0,0,0,0, 0,0,0,0,0, 0,0,0,0,1, 0,0,0,1,1, 0,0,1,1,1, 0,1,1,1,1, 1,1,1,1,1, 1,0,1,1,1, 0,0,0,1,1, 0,1,1,1,1),nrow=10,ncol=5,byrow=TRUE, dimnames=list(c(paste("P",c(1:10),sep="")),c(paste("I",c(1:5),sep=""))))) itemAnalysis(x) # To see an item report with flags. iA <- itemAnalysis(x, hardFlag=.25, pBisFlag=.15) iA$itemReport # To see more item statisitics str(itemAnalysis(x))
This function calculates polyserial correlations using either an ad hoc or ML estimator.
polyserial(x, y, ml = TRUE)polyserial(x, y, ml = TRUE)
x |
A continuous variable. |
y |
An ordinal variable with at least two categories. |
ml |
A logical variable indicating whether to use a formal maximum likelihood estimator (default) or an ad hoc estimator. |
The variables should be numeric. The function returns NA is y has only one category.
Returns the polyserial correlation.
John T. Willse
Olsson, U., Drasgow, F. & Dorans, N. J. (1982). The Polyserial Correlation Coefficient. Psychometika, 47, 337-347.
x <- rnorm(500, 50,5) y <- x + rnorm(500,0,2) x <- x + rnorm(500,0,2) cor(x,y) y <- ifelse(y>50,1,0) cor(x,y) polyserial(x,y, ml=FALSE) polyserial(x,y)x <- rnorm(500, 50,5) y <- x + rnorm(500,0,2) x <- x + rnorm(500,0,2) cor(x,y) y <- ifelse(y>50,1,0) cor(x,y) polyserial(x,y, ml=FALSE) polyserial(x,y)
This function performs reliability analyses, providing coefficient alpha and item statistics.
reliability(items, itemal = TRUE, NA.Delete = TRUE, ml = TRUE)reliability(items, itemal = TRUE, NA.Delete = TRUE, ml = TRUE)
items |
The scored response file with "0" (wrong) and "1" (correct) or Likert type data |
itemal |
If itemal=FALSE (the default) no item analyses are conducted. If itemal=TRUE, the function will provide item means, item total correlations, and alpha if item is removed. |
NA.Delete |
If NA.Delete=TRUE (the default) records are deleted listwise if there are missing responses. If NA.Delete=FALSE all NA values are changed to 0s. |
ml |
A logical variable indicating whether the biserial correlation is calculated using a formal maximum likelihood estimator (default) or an ad hoc estimator. |
The input files must be scored files with "0" and "1" or numeric scales (e.g., Likert Type scales). Only basic scale information is returned to the screen. Use str() to view additional statistics that are available.
nItem |
The number of items |
nPerson |
The sample size used in calculating the values |
alpha |
Crobach's alpha |
scaleMean |
Average total sum socre |
scaleSD |
Standard deviation of total sum score |
alphaIfDeleted |
Cronbach's alpha if the corresponding item were deleted |
pBis |
The item total correlation, with the item's contribution removed from the total |
bis |
The item total biserial (or polyserial) correlation, with the item's contribution removed from the total' |
itemMean |
Average of each item |
John T. Willse, Zhan Shu
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometika, 16, 297-334.
# Scored input (data frame is preferred) x<-data.frame(matrix(c(0,0,0,0,0, 0,0,0,0,0, 0,0,0,0,1, 0,0,0,1,1, 0,0,1,1,1, 0,1,1,1,1, 1,1,1,1,1, 1,0,1,1,1, 0,0,0,1,1, 0,1,1,1,1),nrow=10,ncol=5,byrow=TRUE, dimnames=list(c(paste("P",c(1:10),sep="")),c(paste("I",c(1:5),sep=""))))) reliability(x, itemal=TRUE) # To see more item statisitics str(reliability(x,itemal=TRUE))# Scored input (data frame is preferred) x<-data.frame(matrix(c(0,0,0,0,0, 0,0,0,0,0, 0,0,0,0,1, 0,0,0,1,1, 0,0,1,1,1, 0,1,1,1,1, 1,1,1,1,1, 1,0,1,1,1, 0,0,0,1,1, 0,1,1,1,1),nrow=10,ncol=5,byrow=TRUE, dimnames=list(c(paste("P",c(1:10),sep="")),c(paste("I",c(1:5),sep=""))))) reliability(x, itemal=TRUE) # To see more item statisitics str(reliability(x,itemal=TRUE))
This function can score multiple choice item responses. This function can also call and return results from function reliability.
score(items,key,output.scored=FALSE,ID=NA,rel=FALSE,multiKeySep="none", multiKeyScore=c("or","poly"))score(items,key,output.scored=FALSE,ID=NA,rel=FALSE,multiKeySep="none", multiKeyScore=c("or","poly"))
items |
The item responses to be scored |
key |
The answer key |
output.scored |
If output.scored=FALSE (the default) only a vector of scores is returned. If output.scored=TRUE a matrix containing scored items is returned. |
ID |
If respondent IDs are provided scores are labeled appropriately. |
rel |
If rel=TRUE, the function will call the function reliability and provide that output as well. |
multiKeySep |
If a value other than "none" is provided (e.g., ","), the key and the raw items will be reviewed for the provided delimiter. Using this otion allows for multiple correct responses. |
multiKeyScore |
The first value controls how multiple keys are handled. If "or" any correct response results in score of 1. If "and" all responses must be correct. If the second value is "poly" the returned score is the sum of correct responses. If the second value is "dich" a maximum score of 1 is returned. If the respondent can only provide one response, use "or". If the responded can provide multiple responses and you use c("and", "poly") the score will be 0 or max score. |
John T. Willse
reliability
# Example data provided with package data(CTTdata) data(CTTkey) # Scores for each preson score(CTTdata,CTTkey) # Scores, scored file, and relibility score(CTTdata,CTTkey,output.scored=TRUE,rel=TRUE)# Example data provided with package data(CTTdata) data(CTTkey) # Scores for each preson score(CTTdata,CTTkey) # Scores, scored file, and relibility score(CTTdata,CTTkey,output.scored=TRUE,rel=TRUE)
The function transforms the score metric by setting new scales' mean, standard deviation, and normalizing the distribution.
score.transform(scores, mu.new = 0, sd.new = 1, normalize = FALSE)score.transform(scores, mu.new = 0, sd.new = 1, normalize = FALSE)
scores |
Vector for examinee scores |
mu.new |
Desired mean of the scale |
sd.new |
Desired standard deviation of scales |
normalize |
If normailize=True, the score will be normalized applying the inverse of the cumulative distribution function of the normal distribution to the respondents percentile score. |
The function returns a list with two vectors: new.scores is the transformed score and p.scores is the percentile rank of every examinee. If normalize=TRUE than percentile scores are used to create a roughly normal distribution by applying an inverse cumulative normal distribution function to the p.scores.
John T. Willse, Zhan Shu
# Example data provided with package data(CTTdata) data(CTTkey) # Data scored to demonstrate function scores <- score(CTTdata,CTTkey)$score # obtain the scores # the targeted mean=3, standard deviation=1 score.transform(scores,3,1) # the score should be transformed by normalized precentile score.transform(scores,3,1,TRUE)# Example data provided with package data(CTTdata) data(CTTkey) # Data scored to demonstrate function scores <- score(CTTdata,CTTkey)$score # obtain the scores # the targeted mean=3, standard deviation=1 score.transform(scores,3,1) # the score should be transformed by normalized precentile score.transform(scores,3,1,TRUE)
This function calculates either a predicted reliability for a measure given the original reliability and a new test length, or the function calculates the required test length to achieve a desired level of reliability.
spearman.brown(r.xx, input = 2, n.or.r = "n")spearman.brown(r.xx, input = 2, n.or.r = "n")
r.xx |
The original relibility |
input |
The new test length or a desired level of reliability, depending on n.or.r |
n.or.r |
If n.or.r="n", the function will return a new reliability; if n.or.r="r", the function will return the factor by which the test length must change to achieve a desired level of reliability. |
If n.or.r="n", the function will return a new reliability and input should be the factor by which the test length is to be changed. If n.or.r="r", the function will return the factor by which the test length must change to achieve a desired level of reliability (provided in input).
John Willse, Zhan Shu
Spearman, C. (1910). Correlation calculated with faulty data. British Journal of Psychology, 3,
271-295.
Brown,W. (1910). Some experimental results in the correlation of mental abilities. British Journal
of Psychology, 3, 296-322.
# old relibility is 0.6, if the measure is lengthened # by a factor of 2, the relibility of new test is: spearman.brown(0.6,2,"n") # old relibility is 0.5, if we want a new measure to # be 0.8, the new test length is: spearman.brown(0.5, 0.8, "r")# old relibility is 0.6, if the measure is lengthened # by a factor of 2, the relibility of new test is: spearman.brown(0.6,2,"n") # old relibility is 0.5, if we want a new measure to # be 0.8, the new test length is: spearman.brown(0.5, 0.8, "r")
This convenience function is provided to facilitate extracting subscales from a single set of item responses.
subscales(items, scales, scale.names = NA, score.items = FALSE, check.reliability = FALSE, key=NA)subscales(items, scales, scale.names = NA, score.items = FALSE, check.reliability = FALSE, key=NA)
items |
The item response (scored or not) |
scales |
A design matrix, with items represented in rows and separate subscales represented in columns. An item may appear in more than one subscale. |
scale.names |
Optional vector of names for the subscales |
score.items |
If responses are not scored, they may be scored using score.items=TRUE (key must be provided) |
check.reliability |
If check.reliability=TRUE, the reliability for each subscale will be calculted |
key |
Optional key, required only if score.scales=TRUE. |
This function provides an easy way to create new datasets from a single set of item responses. This function is also a front end for score and reliability, enabling the item responses to be partitioned into separate scales, scored, and reliability analyses performed using this one function.
A list is returned. Results for each subscale (i.e., column in the scales matrix) are provided as sparate objects in that list.
score |
Each examinee's score on the associated subscale |
reliablity |
Reliability results (if requested) for the associated subscale |
scored |
The scored item responses (if required) for each respondent for the associated subscale |
John Willse, Zhan Shu
reliability, score
# Example data included with package data(CTTdata) data(CTTkey) # design matrix q <- matrix(c(1,0, 1,0, 1,0, 1,0, 1,0, 1,0, 1,0, 1,0, 1,0, 1,1, 0,1, 0,1, 0,1, 0,1, 0,1, 0,1, 0,1, 0,1, 0,1, 0,1), ncol=2, byrow=TRUE) subscales(CTTdata,q,c("T1","T2"),TRUE,TRUE,CTTkey)# Example data included with package data(CTTdata) data(CTTkey) # design matrix q <- matrix(c(1,0, 1,0, 1,0, 1,0, 1,0, 1,0, 1,0, 1,0, 1,0, 1,1, 0,1, 0,1, 0,1, 0,1, 0,1, 0,1, 0,1, 0,1, 0,1, 0,1), ncol=2, byrow=TRUE) subscales(CTTdata,q,c("T1","T2"),TRUE,TRUE,CTTkey)