Rasch analysis can be applied to assessments in a wide range of
disciplines, including health studies, education, psychology, marketing,
economics and social sciences.
Many assessments in these disciplines involve a well defined
group of people responding to a set of items for assessment. Generally,
the responses to the items are scored 0, 1 (for two ordered categories);
or 0, 1, 2 (for three ordered categories); or 0, 1, 2, 3 (for four
ordered categories) and so on, to indicate increasing levels of a
response on some variable such as health status or academic achievement.
These responses are then added across items to give each person a total
score. This total score summarise the responses to all the items, and a
person with a higher total score than another one is deemed to show
more of the variable assessed. Summing the scores of the items to give a
single score for a person implies that the items are intended to
measure a single variable, often referred to as a unidimensional
variable.
The Rasch model is the only item response theory (IRT) model
in which the total score across items characterizes a person totally. It
is also the simplest of such models having the minimum of parameters
for the person (just one), and just one parameter corresponding to each
category of an item. This item parameter is generically referred to as a
threshold. There is just one in the case of a dichotomous item, two in
the case of three ordered categories, and so on.
1. What is Rasch Analysis?
What is a Rasch Analysis? The Rasch model, where the total
score summarizes completely a person's standing on a variable, arises
from a more fundamental requirement: that the comparison of two people
is independent of which items may be used within the set of items
assessing the same variable. Thus the Rasch model is taken as a
criterion for the structure of the responses, rather than a mere
statistical description of the responses. For example, the comparison of
the performance of two students' work marked by different graders
should be independent of the graders.
In this case it is considered that the researcher is
deliberately developing items that are valid for the purpose and that
meet the Rasch requirements of invariance of comparisons.
Analyzing data according to the Rasch model, that is,
conducting a Rasch analysis, gives a range of details for checking
whether or not adding the scores is justified in the data. This is
called the test of fit between the data and the model. If the invariance
of responses across different groups of people does not hold, then
taking the total score to characterize a person is not justified. Of
course, data never fit the model perfectly, and it is important to
consider the fit of data to the model with respect to the uses to be
made of the total scores. If the data do fit the model adequately for
the purpose, then the Rasch analysis also linearises the total score,
which is bounded by 0 and the maximum score on the items, into
measurements. The linearised value is the location of the person on the
unidimensional continuum - the value is called a parameter in the model
and there can be only one number in a unidimensional framework. This
parameter can then be used in analysis of variance and regression more
readily than the raw total.
2. Why undertake a Rasch analysis?
A researcher who is developing items of a test or
questionnaire intending to sum the scores on the items can use a Rasch
model analysis to check the degree to which this scoring and summing is
defensible in the data collected. For example, if two groups are to be
compared on the variable of interest (e.g. males and females), it is
important to demonstrate that the workings of the items is the same in
the two groups. Working in the same way permits interpreting the total
score as meaning the same in the two groups.
In checking how well the data fit the model, it is
important to be able to diagnose very quickly where the misfit is the
worst, and then proceed to try to understand this misfit in terms of the
construction of the items and the understanding of the variable in
terms of its theoretical development.
A very important part of the Rasch analysis from this
perspective is to be in dynamic and interactive control of an analysis
and to be able to follow the evidence to see where the responses may be
invalid.
3. Courses and Workshops available on Rasch Analysis
A workshop to introduce Rasch analysis. It will suit those
working in the measurement of outcomes in the health sciences, of
attitudinal data in the social sciences, or in educational testing. It
will take the form of hands-on practice in using the WINSTEPS software
package. At the end of the two days workshop students should understand
and be able to analyse data, using WINSTEPS.
The details of the workshop are as follows:
Course Title: Introductory Rasch Model Workshop: Applications in Survey Research and Educational Measurement
Date: 21 & 22 January 2014 (Tuesday & Wednesday)
Time: 8.30 a.m. - 5.30 p.m.
Venue: MPWS Training Centre, 63-1, 63-2,
Jalan Kajang Impian 1/11,Taman Kajang Impian, Seksyen 7, 43650 Bandar Baru Bangi, Selangor
Speaker: Dr. Akbariah Binti Mohd Mahdzir
Registration Fee: RM300 (early bird rate) / RM400 (normal rate)
Medium: English
