Kane and Brennan's person-fit statistics.
A.KB(matrix,
NA.method = "Pairwise", Save.MatImp = FALSE,
IP = NULL, IRT.PModel = "2PL", Ability = NULL, Ability.PModel = "ML",
mu = 0, sigma = 1)D.KB(matrix,
NA.method = "Pairwise", Save.MatImp = FALSE,
IP = NULL, IRT.PModel = "2PL", Ability = NULL, Ability.PModel = "ML",
mu = 0, sigma = 1)
E.KB(matrix,
NA.method = "Pairwise", Save.MatImp = FALSE,
IP = NULL, IRT.PModel = "2PL", Ability = NULL, Ability.PModel = "ML",
mu = 0, sigma = 1)
Data matrix of dichotomous item scores: Persons as rows, items as columns, item scores are either 0 or 1, missing values allowed.
Method to deal with missing values. The default is pairwise elimination ("Pairwise"). Alternatively, simple imputation methods are also available. The options available are "Hotdeck", "NPModel" (default), and "PModel".
Logical. Save (imputted) data matrix to file? Default is FALSE.
Matrix with previously estimated item parameters: One row per item, and three columns ([,1] item discrimination; [,2] item difficulty; [,3] lower-asymptote, also referred to as pseudo-guessing parameter).
In case no item parameters are available then IP=NULL.
Specify the IRT model to use in order to estimate the item parameters (only if IP=NULL). The options available are "1PL", "2PL" (default), and "3PL".
Vector with previoulsy estimated latent ability parameters, one per respondent, following the order of the row index of matrix.
In case no ability parameters are available then Ability=NULL.
Specify the method to use in order to estimate the latent ability parameters (only if Ability=NULL). The options available are "ML" (default), "BM", and "WL".
Mean of the apriori distribution. Only used when method="BM". Default is 0.
Standard deviation of the apriori distribution. Only used when method="BM". Default is 1.
An object of class "PerFit", which is a list with 12 elements:
A list of length \(N\) (number of respondents) with the values of the person-fit statistic.
The person-fit statistic used.
A message indicating whether perfect response vectors (all-0s or all-1s) were removed from the analysis.
Row indices of all-0s response vectors removed from the analysis (if applicable).
Row indices of all-1s response vectors removed from the analysis (if applicable).
The data matrix after imputation of missing values was performed (if applicable).
The number of response categories (2 in this case).
The parametric IRT model used in case NA.method="PModel", otherwise NULL.
The \(I\)x3 matrix of estimated item parameters in case NA.method="PModel", otherwise NULL.
The method used to estimate abilities in case NA.method="PModel", otherwise NULL.
The vector of \(N\) estimated ability parameters in case NA.method="PModel", otherwise NULL.
The imputation method used (if applicable).
Kane and Brennan (1980) discussed the agreement, disagreement, and dependability statistics. Assume that the items are ordered in increasing difficulty order (i.e., according to decreasing proportion-correct score). The agreement statistic for respondent \(n\) is $$A.KB=\sum_i{X_i}p_i,$$ where \(X_i\) is the 0-1 score of respondent \(n\) on item \(i\) and \(p_i\) is the proportion-correct score of item \(i\).
The disagreement statistic is $$D.KB = A.KB(max) - A.KB,$$ where \(A.KB(max)\) is the maximum value of A.KB given respondent \(n\)'s total score.
The dependability statistic is $$E.KB = A.KB / A.KB(max).$$
Small values of A.Kb and E.KB (i.e., in the left tail of the sampling distribution) are (potentially) indicative of aberrant response behavior. Large values of D.Kb (i.e., in the right tail of the sampling distribution) are (potentially) indicative of aberrant response behavior. These statistics are not computed for rows of matrix that consist of only 0s or only 1s (NA values are returned instead).
Missing values in matrix are dealt with by means of pairwise elimination by default. Alternatively, single imputation is also available. Three single imputation methods exist: Hotdeck imputation (NA.method = "Hotdeck"), nonparametric model imputation (NA.method = "NPModel"), and parametric model imputation (NA.method = "PModel"); see Zhang and Walker (2008).
Hotdeck imputation replaces missing responses of an examinee ('recipient') by item scores from the examinee which is closest to the recipient ('donor'), based on the recipient's nonmissing item scores. The similarity between nonmissing item scores of recipients and donors is based on the sum of absolute differences between the corresponding item scores. The donor's response pattern is deemed to be the most similar to the recipient's response pattern in the group, so item scores of the former are used to replace the corresponding missing values of the latter. When multiple donors are equidistant to a recipient, one donor is randomly drawn from the set of all donors.
The nonparametric model imputation method is similar to the hotdeck imputation, but item scores are generated from Bernoulli distributions with probabilities defined by donors with similar total score than the recipient (based on all items except the NAs).
The parametric model imputation method is similar to the hotdeck imputation, but item scores are generated from Bernoulli distributions with probabilities estimated by means of parametric IRT models (IRT.PModel = "1PL", "2PL", or "3PL"). Item parameters (IP) and ability parameters (Ability) may be provided for this purpose (otherwise the algorithm finds estimates for these parameters).
Kane, M. T., and Brennan, R. L. (1980) Agreement coefficients as indices of dependability for domain-referenced tests. Applied Psychological Measurement, 4(1), 105--126.
Karabatsos, G. (2003) Comparing the Aberrant Response Detection Performance of Thirty-Six Person-Fit Statistics. Applied Measurement In Education, 16(4), 277--298.
Meijer, R. R., and Sijtsma, K. (2001) Methodology review: Evaluating person fit. Applied Psychological Measurement, 25(2), 107--135.
Zhang, B., and Walker, C. M. (2008) Impact of missing data on person-model fit and person trait estimation. Applied Psychological Measurement, 32(6), 466--479.
# NOT RUN {
# Load the inadequacy scale data (dichotomous item scores):
data(InadequacyData)
# Compute the A.KB, D.KB, and E.KB scores:
A.out <- A.KB(InadequacyData); A.out
D.out <- D.KB(InadequacyData); D.out
E.out <- E.KB(InadequacyData); E.out
# }
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