ERIC Identifier: ED355249
Publication Date: 1992-12-00
Author: Bracey, Gerald - Rudner, Lawrence M.
Clearinghouse on Tests Measurement and Evaluation Washington DC.
Person-Fit Statistics: High Potential and Many Unanswered Questions. ERIC/TM Digest.
"Whenever we measure anything, whether in the physical, the biological, or
the social sciences, that measurement contains a certain amount of chance
error....Two sets of measurements of the same features of the same individuals
will never exactly duplicate each other....However, at the same time, repeated
measurements of a series of objects or individuals will ordinarily show some
So wrote Robert L. Thorndike in Lindquist's "Educational Measurement" (1951).
Traditionally, research into measurement error has dealt with whether or not the
test items fit. But over the last 15 or so years, we have seen mounting interest
in whether or not the people who answer the items fit. Most of the interest has
centered on people whose responses do not fit the typical pattern.
Attempts to systematically identify such people have led researchers to a
number of person-fit statistics, with names like caution index, norm conformity
index, individual consistency index, and optimal appropriate measurement. This
digest describes the need for person-fit statistics, summarizes the research on
their use, and identifies areas in need of further research.
THE NEED FOR PERSON-FIT STATISTICS
In presenting the need
for such statistics, Wright (1977), described several types of people whose
response patterns look askew: sleepers who get bored with a test and do poorly
on later items, fumblers who do poorly in the beginning because the test format
has confused them, plodders who never get to later items, guessers who take wild
stabs at the answer, and cheaters.
One can add to the list people who misalign their answer sheets or show
exceptional creativity in interpreting questions, people with high ability and
atypical schooling, people who do not speak English well, and people who are
conservative in their use of partial information.
While personality traits or response styles cause aberrant patterns, Harnisch
and Linn (1981) observed that the same number right on a test can mean very
different things. On a 20-item test, for example, a score of 10 right can be
obtained in 194,756 ways.
Harnisch and Linn also contend that finding aberrant response patterns is no
mere academic concern of the psychometrician. They argue that identifying groups
or individuals with such patterns can reveal groups with unusual instructional
histories or individuals whose test scores cannot be interpreted in standard
When Harnisch and Linn analyzed data from a state testing program, they found
that schools in different parts of the state had very different caution indices.
They suggest that this result could have been caused by curricula that didn't
match the test as well as curricula in other parts of the state. However, the
authors give no empirical evidence favoring their conclusion over several others
that could also explain the high caution indices.
Tatsuoka and Tatsuoka (1982) offer empirical evidence that patterns of
aberrant responses relate to differences in instruction. Two methods were used
to teach students addition in signed-number operations. The students then took a
test containing both addition and subtraction problems. While the mean
differences between the two groups were not significant, the person-fit indices
were. In another experiment, students given lessons using different conceptual
frameworks showed more aberrance than a group given consistent lesson
Frary (1982) has argued that unusual test-response patterns can identify at
least some types of test bias. Analysis of scores for properly fitting and
poorly fitting examinees, for example, removes some of the noise associated with
gross categorizations such as race or gender.
For the most part, however, person-fit statistics have not yet been applied
to many settings. Although the need has been documented and uses suggested, this
area has been largely one of potential, not actual, use.
APPLICATIONS OF PERSON-FIT STATISTICS
conducted one of the first systematic investigations of person-fit statistics.
Using computer-generated data modeling, a well-regarded test, and different
types of misfit, Rudner concluded that the statistics have great potential: They
can identify significant percentages of examinees with abnormal response
patterns. Further, accuracy increases significantly as response patterns become
Papers such as Rudner's have addressed theoretical and methodological
concerns about the nature, accuracy, and interchangeability of person-fit
statistics. Other researchers have addressed the frequency and amount of
abnormal response patterns and, therefore, the practical utility of person-fit
Frary (1980) calculated four different fit statistics on a large sample of
eighth graders who had taken a commercial achievement test battery. Along with
the moderate to strong intercorrelations mentioned earlier, Frary found that
blacks and females differed from whites and males on some tests. Overall,
females showed fewer aberrant responses than males, but racial differences
occurred in both directions. Among low-scoring students, the effects were
consistent: White and female students made fewer unusual choices.
These findings raised the possibility of test bias. But using a knowledge
assurance statistic, Frary concluded that blacks and males did better than
whites and females when it came to correctly guessing items for which they had
only partial knowledge.
Doss (1981) applied a residual mean-square statistic from the computer
program RASCH in the PRIME system to a fifth-grade Chapter 1 setting, where
children were given the Iowa Tests of Basic Skills. He examined how removing the
poorest fitting 10%, 20%, and 30% of students affected the accuracy of pretest
predictions. While the N dropped substantially as he removed students,
prediction accuracy increased with each removal.
Although the improvement in accuracy is interesting, Doss's setting may not
have provided a meaningful testing situation. The test was badly matched to the
students abilities: Even though some (Doss doesn't say how many), took the
fourth-grade level of the battery, 25% scored at or below the chance level.
After the worst fitting 30% had been removed, only 13% of the Chapter 1 students
remained. Finally, the students showed losses from pretest to posttest. The
study does, nonetheless, point out another use of person fit statistics--to
objectively document whether the testing situation is meaningful.
Schmitt and Crocker (1984) investigated the relationship between scores on
the Test Anxiety Scale for Adolescents and person-fit. They used various indices
and the Metropolitan Achievement Tests in reading, mathematics, and science in
seventh and eighth grades. Students in the middle ability range showed no
relationship between test anxiety and person-fit indices. High-ability,
low-anxiety students showed greater misfit than high-ability, high-anxiety
students. At the low-ability end, the reverse was true: Low-ability, low-anxiety
students showed less misfit. The authors offer some conjectures on the findings
in terms of a Cognitive-Attentional Theory of Test Anxiety, but present no data
that might support their notions.
Two main questions remain for applying fit
statistics: (1) Are the statistics theoretically sound? (2) Will they help in
practical situations? Some people argue that these basic questions have been
answered; others contend that it's too soon to tell.
Person-fit statistics are a logical extension of popular measurement models
and thus are well grounded in statistical theory. They are atheoretical,
however, with theories of learning and cognition. For example, little research
has been done to explain why response patterns may be aberrant. While Tatsuoka
and Tatsuoka have looked at consistent errors made by students who apply the
wrong mathematics algorithms, causes for the aberrant response patterns listed
by Wright and others have not been systematically investigated.
Real questions exist about whether person-fit statistics will make any
difference in practical situations. When test takers are unlike the norming
group, will their response patterns be sufficiently atypical? How much of a
deviation in response patterns is normal for different subject areas in
different grades? Are fit statistics sensitive enough to detect strange response
patterns when they exist?
The research to date has shown that people with very strange response
patterns are indeed detected with few, if any, false identifications. Proponents
argue that this is enough to justify routine use of this statistical tool.
Doss, D.A. (1981) Will removing a few bad apples
save the barrel? Paper presented at the annual meeting of the American
Educational Research Association, Los Angeles, CA, April 13-17.
Drasgow, F., M. V. Levine, & M. E. McLaughlin. (1987). Detecting
inappropriate test scores with optimal and practical appropriateness indices.
Applied Psychological Measurement, 11(1), 59-79.
Frary, R.B. (1982) A comparison of person-fit measures. Paper presented at
the annual meeting of the American Educational Research Association, New York,
Harnisch, D. L., & Linn, R. L. (1981). Analysis of item response
patterns: Questionable test data and dissimilar curriculum practices. Journal of
Educational Measurement, 18(3), 133-146.
Levine, M., & Drasgow, F. (1982). Appropriateness measurement: Review,
critique, and validating studies. British Journal of Mathematical Statistical
Psychology, 35, 42-56.
Rudner, L. M. (1983). Individual assessment accuracy. Journal of Educational
Schmitt, A. P. and L. Crocker (1984), The relationship between test anxiety
and person fit measures. Paper presented at the annual meeting of the American
Educational Research Association, New Orleans, April 23-27.
Tatsuoka, K. K., & Tatsuoka, M. M. (1982). Detection of aberrant response
patterns and their effect on dimensionality. Journal of Educational Statistics,
Wright, B. D. (1977). Solving measurement problems with the Rasch model.
Journal of Educational Measurement, 14, 97-115.