Facilitation skills offer immediate, practical benefits to any group process. As facilitator, your role is to set the agenda, encourage participation, and guide the pace of the meeting. Use these Dale Carnegie Training® strategies to help make your meeting a success. You can provide handouts to focus the discussion and give attendees a place to record their ideas. If you print handouts with three slides per page, PowerPoint will automatically include blank lines for your meeting participants to take notes.
I am going to add a note
Begin with a strong opening, relevant to the topic and your audience. Appeal to your audience’s interests. Your energy and attitude set the tone and provide momentum for the meeting. Facilitation is a process that helps people communicate and work together. Establish an open environment conducive to problem solving and change.
Bridge to your key topic. State the purpose and importance of your meeting. As facilitator, you are a catalyst for change. Ask questions. Coach participants. Encourage participation. Record comments. Thank participants for their individual contributions, ideas, and insights. You can use PowerPoint’s Meeting Minder to record comments during the meeting.
Set clear goals for the meeting. Clarify the problem to be solved. Get agreement from the group on the agenda, the problem and the process. As facilitator, guide, inspire, and support participation from all attendees. Solicit personal experiences. Ask for evidence to back up points. Make every participant feel they are valuable with something to contribute.
Once you have defined the problem, move to solution-finding. Encourage participants to be open-minded, think creatively, and suggest solutions. Respect diverse opinions. Diversity strengthens problem-solving and adds substance to discussions. Record ideas. Consider all suggestions. Keep a positive attitude throughout the process. Bring all participants into the process. List and prioritize options. Use the group process to determine the solution.
Now that you have a solution, outline your action plan. Using the group process, agree upon an orderly direction to reach your goals. List the action steps. You can use the Meeting Minder to record action items for meeting participants. Identify resources needed. Assign responsibilities. Establish methods of accountability. Define a time line. Identify costs. Define current resources. Also, consider what is needed for success. Training? Specific expertise? Additional information? Special materials? Additional resources? Management buy-in? Proper planning prior to action leads to goal achievement.
Close your meeting with a brief summary. Thank participants for their input. Review action plans, assignments, and accountability measures. Thank the group for following the process, working together as a team, and committing to problem solving and goal achievement. If you used Meeting Minder to keep track of action items, PowerPoint will automatically add a slide with the action items to the end of your presentation. Use this slide to review the action items and get buy-in from participants. Facilitation is leadership in action. Close your presentation with a strong comment to reinforce the success of the team. Empower the group to work toward goal achievement. Apply effective facilitation skills and you will have productive, profitable meetings.
Item Analysis: Classical and Beyond
Classical and Beyond
Measurement Theory and Item Analysis
Heriot Watt University
12th February 2003.
Why is item analysis relevant?
Item analysis provides a way of measuring
the quality of questions - seeing how
appropriate they were for the candidates
and how well they measured their ability.
It also provides a way of re-using items
over and over again in different tests with
prior knowledge of how they are going to
What kinds of item analysis
Classical Latent Trait Models
RaschItem Response theory
IRT1 IRT2 IRT3 IRT4
Classical analysis is the easiest and most widely used
form of analysis. The statistics can be computed by
generic statistical packages (or at a push by hand)
and need no specialist software.
Analysis is performed on the test as a whole rather
than on the item and although item statistics can be
generated, they apply only to that group of students
on that collection of items
Classical test analysis assumes that any
test score is comprised of a “true” value,
plus randomised error. Crucially it
assumes that this error is normally
distributed; uncorrelated with true score
and the mean of the error is zero.
xobs = xtrue + G(0, σerr)
The difficulty of a (1 mark) question in
classical analysis is simply the proportion
of people who answered the question
incorrectly. For multiple mark questions,
it is the average mark expressed as a
Given on a scale of 0-1, the higher the
proportion the greater the difficulty
The discrimination of an item is the
(Pearson) correlation between the
average item mark and the average total
Being a correlation it can vary from –1 to
+1 with higher values indicating
(desirable) high discrimination.
Reliability is a measure of how well the test “holds
together”. For practical reasons, internal
consistency estimates are the easiest to obtain
which indicate the extent to which each item
correlates with every other item.
This is measured on a scale of 0-1. The greater
the number the higher the reliability.
Latent Trait Models
• Classical analysis has the test (not the item as its
basis. Although the statistics generated are often
generalised to similar students taking a similar test;
they only really apply to those students taking that
• Latent trait models aim to look beyond that at the
underlying traits which are producing the test
performance. They are measured at item level and
provide sample-free measurement
Latent Trait Models
• Latent trait models have been around since the
1940s, but were not widely used until the 1960s.
Although theoretically possible, it is practically
unfeasible to use these without specialist software.
• They aim to measure the underlying ability (or trait)
which is producing the test performance rather than
measuring performance per se.
• This leads to them being sample-free. As the
statistics are not dependant on the test situation
which generated them, they can be used more
Item Response Theory
Mathematically, Rasch is identical to the most basic
IRT model (IRT1), however there are some
important differences which makes it a more viable
proposition for practical testing
• In Rasch the model is superior. Data which does
not fit the model is discarded.
• Rasch does not permit abilities to be estimated for
extreme items and persons.
• Rasch eschews the use of bayesian priors to assist
IRT - the generalised model
( ) )(
ag = gradient of the ICC at the point θ
bg = the ability level at which ag is maximised
cg = probability of low candidates correctly
IRT - Item Characteristic Curves
•An ICC is a plot of
the candidates ability
over the probability of
question. The higher
the ability the higher
the chance that they
will respond correctly.
c - intercept
a - gradient
b - ability at max (a)
IRT - About the Parameters
• Although there is no “correct” difficulty for
any one item, it is clearly desirable that
the difficulty of the test is centred around
the average ability of the candidates.
• The higher the “b” parameter the more
difficult the question - note that this is
inversely proportionate to the probability
of the question being answered correctly.
IRT - About the Parameters
• In IRT (unlike Rasch) maximal
discrimination is sought. Thus the higher
the “a” parameter the more desirable the
• Note however that differences in the
discrimination of questions can lead to
differences in the difficulties of questions
across the ability range.
IRT - About the Parameters
• A high “c” parameter suggests that
candidates with very little ability may
choose the correct answer.
• This is rarely a valid parameter outwith
multiple choice testing…and the value
should not vary excessively from the
reciprocal of the number of choices.
IRT - Parameter Estimation
• Before being used (in an item bank or for
measurement) items must first be calibrated. That
is their parameters must be estimated.
• There are two main procedures - Joint Maximal
Likelihood and Marginal Maximal Likelihood. JML
is most common for IRT1 and 2, while MML is used
more frequently for IRT3.
• Bayesian estimation and estimated bounds may be
imposed on the data to avoid one parameter
degrading, or high discrimation items being over
Resources - Classical Analysis
• Standard statistical packages (Excel; SPSS; SAS)
• ITEMAN (available from www.assess.com)
Matlock-Hetzel (1997) Basic Concepts in Item and Test
Analysis available at www.ericae.net/ft/tamu/Espy.htm
Resources - IRT
• BILOG (available at www.assess.com)
• Xcalibre available at www.assess.com
• Lord (1980) Applications of Item Response Theory
to Practical Testing Problems
• Baker, Frank (2001). The Basics of Item Response
Theory - available at http://ericae.net/irt/baker/