PerFit (version 1.2)

flagged.resp: Find (potentially) aberrant response patterns

Description

Find which respondents in the sample were flagged by the specified person-fit statistic.

Usage

flagged.resp(matrix, scores = TRUE, ord = TRUE, x, method = "Quantile", 
 Qlvl = 0.05, Blvl = 0.05, Breps = 1000, UDlvl = NA)

Arguments

matrix

A data matrix of item scores (persons as rows, items as columns). This is the same data matrix used to compute the PerFit object x.

scores

Logical: Should item scores of flagged respondents be shown in the output? Default is TRUE.

ord

Logical: Should items be ordered in increasing order of difficulty (i.e., in decreasing proportion-correct order)? Default is TRUE. Only used if scores=TRUE.

x

Object of class "PerFit".

method

Method required to compute the cutoff score: "Quantile", "Bootstrap", "UserDefined". Default is "Quantile".

Qlvl

Quantile level (value between 0 and 1), only used when method="Quantile". Default is 0.05.

Blvl

Bootstrap level (value between 0 and 1), only used when method="Bootstrap". Default is 0.05.

Breps

Number of bootstrap resamples, only used when method="Bootstrap". Default is 1000.

UDlvl

User-defined level, only used when method="UserDefined".

Value

• If scores=FALSE the output is a matrix with two columns:
• FlaggedID
  Row index identifying the flagged respondents.
• PFscores
  The values of the person-fit statistic.
• If scores=TRUE the output is a list with two elements:
• Scores
  Matrix with columns: FlaggedID, item scores (It**), and PFscores.
• MeanItemValue
  The items' mean value (which is nothing more than the proportion-correct for dichotomous items).

Details

This function finds the respondents in the dataset that were flagged by the person-fit statistic. This statistic is specified by means of the "PerFit" class object x. The cutoff score is internally computed, for which the user may define method, Qlvl, Blvl, Breps, and UDlvl accordingly (see cutoff). If scores=TRUE then the respondents' item scores will be shown in the output, either in the original item order (ord=FALSE) or in increasing difficulty order (ord=TRUE).

See Also

cutoff, plot.PerFit, PRFplot

Examples

# Load the inadequacy scale data (dichotomous item scores):
data(InadequacyData);

# As an example, compute the Ht person-fit scores:
Ht.PF <- Ht(InadequacyData);
Ht.PF$PFscores

# Determine which respondents were flagged by Ht for the quantile-based 1% cutoff:
flagged.resp(InadequacyData,x=Ht.PF,Qlvl=.01);