HFS 219 Cornell University Issue Management as A Post Crisis Discipline Report

Course Development TeamHead of Programme
: Assoc Prof Chui Yoon Ping
Course Developer(s)
: Dr Ng Yuwen Stella
Technical Writer
: Emily Ko, ETP
©
2021 Singapore University of Social Sciences. All rights reserved.
No part of this material may be reproduced in any form or by any means without
permission in writing from the Educational Technology & Production, Singapore
University of Social Sciences.
ISBN 978-981-48-4757-5
Educational Technology & Production
Singapore University of Social Sciences
463 Clementi Road
Singapore 599494
How to cite this Study Guide (APA):
Ng, Y. S. (2021). HFS219 Human factors methods (study guide). Singapore: Singapore
University of Social Sciences.
Release V1.1
Build S1.0.5, T1.5.21
Table of Contents
Table of Contents
Course Guide
1. Welcome…………………………………………………………………………………………………… CG-2
2. Course Description and Aims…………………………………………………………………. CG-3
3. Learning Outcomes…………………………………………………………………………………. CG-5
4. Learning Material……………………………………………………………………………………. CG-6
5. Assessment Overview……………………………………………………………………………… CG-7
6. Course Schedule………………………………………………………………………………………. CG-9
7. Learning Mode………………………………………………………………………………………. CG-10
Study Unit 1: Introduction to HF Methods
Learning Outcomes……………………………………………………………………………………. SU1-2
Overview……………………………………………………………………………………………………. SU1-3
Chapter 1: Introduction to Human Factors Methods………………………………….. SU1-4
Summary……………………………………………………………………………………………………. SU1-9
Formative Assessment……………………………………………………………………………… SU1-10
Study Unit 2: Qualitative Methods
Learning Outcomes……………………………………………………………………………………. SU2-2
Overview……………………………………………………………………………………………………. SU2-3
Chapter 1: Qualitative Methods…………………………………………………………………. SU2-4
Summary………………………………………………………………………………………………….. SU2-14
i
Table of Contents
Formative Assessment……………………………………………………………………………… SU2-15
Study Unit 3: Experimental Design
Learning Outcomes……………………………………………………………………………………. SU3-2
Overview……………………………………………………………………………………………………. SU3-3
Chapter 1: Experimental Design…………………………………………………………………. SU3-4
Summary………………………………………………………………………………………………….. SU3-15
Formative Assessment……………………………………………………………………………… SU3-16
Study Unit 4: Task Analysis Methods
Learning Outcomes……………………………………………………………………………………. SU4-2
Overview……………………………………………………………………………………………………. SU4-3
Chapter 1: Task Analysis Methods……………………………………………………………… SU4-4
Summary………………………………………………………………………………………………….. SU4-18
Formative Assessment……………………………………………………………………………… SU4-19
Study Unit 5: Physical Measurements
Learning Outcomes……………………………………………………………………………………. SU5-2
Overview……………………………………………………………………………………………………. SU5-3
Chapter 1: Dexterity, Strength, Environmental Methods……………………………. SU5-4
Summary………………………………………………………………………………………………….. SU5-10
Formative Assessment……………………………………………………………………………… SU5-11
ii
Table of Contents
Study Unit 6: Cognitive Measurements
Learning Outcomes……………………………………………………………………………………. SU6-2
Overview……………………………………………………………………………………………………. SU6-3
Chapter 1: Mental Workload Measurement Methods…………………………………. SU6-4
Chapter 2: Situation Awareness and Team Assessment Methods……………… SU6-21
Summary………………………………………………………………………………………………….. SU6-27
Formative Assessment……………………………………………………………………………… SU6-28
iii
Table of Contents
iv
List of Figures
List of Figures
Figure 3.1 Population vs Sample…………………………………………………………………….. SU3-7
Figure 3.2 The further away from the centre the less similar the circumstances
and therefore external validity may be affected……………………………………………. SU3-13
v
List of Figures
vi
List of Lesson Recordings
List of Lesson Recordings
Task Analysis Methods…………………………………………………………………………………… SU4-6
Mental Workload Methods……………………………………………………………………………… SU6-6
Mental Workload Methods……………………………………………………………………………. SU6-17
vii
List of Lesson Recordings
viii
Course
Guide
Human Factors Methods
HFS219
Course Guide
1. Welcome
Welcome to the course HFS219 Human Factors Methods, a 5 credit unit (CU) course.
This Study Guide will be your personal learning resource to take you through the course
learning journey. The guide is divided into two main sections – the Course Guide and
Study Units.
The Course Guide describes the structure for the entire course and provides you with an
overview of the Study Units. It serves as a roadmap of the different learning components
within the course. This Course Guide contains important information regarding the
course learning outcomes, learning materials and resources, assessment breakdown and
additional course information.
CG-2
HFS219
Course Guide
2. Course Description and Aims
The course is designed to introduce the principles and concepts for the application of
human factors methods in the workplace or industry. This course will outline aspects
that are important in the application of appropriate method (in terms of the accuracy,
acceptability and appropriateness, cost benefit of the method, etc.) to address problems in
the real world.
The course will equip students with a broad perspective on the methods and tools
available and will discuss the strengths and limitations of the methods that are available.
Key areas of human factors methods will be covered that will include the following:
• methods to evaluate the design of equipments
• methods for evaluation of work ( individual as well as team)
• methods to evaluate other general aspects of work and organizational design.
For each of the key areas, available tools will be discussed and application encouraged in
practical problem based scenarios. In addition, students will be introduced to the concepts
of experimental design. Thus, as Human Factors & Safety practitioners, students will
be well versed in conducting research and investigations involving human subjects and
making sense of the data gathered.
Course Structure
This course is a 5-credit unit course presented over 6 weeks.
There are six Study Units in this course. The following provides an overview of each Study
Unit.
Study Unit 1 – Introduction to HF Methods
Introduction to the concepts of human factors methods. This unit sets the context for the
subsequent topics that will be covered.
CG-3
HFS219
Course Guide
Study Unit 2 – Qualitative Methods
Tools used for data collection for conducting human factor investigation/ study will be
introduced. Application examples as well as practical exercises to design surveys and
collect specific data regarding a system or scenario will be discussed. Focus areas include
Questionnaires, Observations, Interviews, Survey Methods, Case study, Participatory
design method.
Study Unit 3 – Experimental Design
This unit will introduce the fundamental concepts related to the design of experiments
such as validity and reliability, independent and dependant variables, hypothesis
formulation and testing, levels of significance. Practical exercise as well as lab project will
enable the students to practice and apply the concepts learnt to real world scenarios.
Study Unit 4 – Task Analysis Methods
Task analysis is fundamental human factors method and has widespread application for
the design and analysis of system performance including human performance evaluation,
error identification as well as usability studies. Some important methods of task analysis
will be discussed in this unit, for example – hierarchical task analysis, verbal protocols,
cognitive walkthrough, task decomposition, cognitive task analysis.
Study Unit 5 – Physical Measurements
This unit will cover the concepts and methods related to the measurement of human
physical capabilities such as dexterity and strength. Environmental measurement tools
that are commonly used by human factors professionals will also be introduced.
Study Unit 6 – Cognitive Measurements
This unit will cover the concepts and methods related to the measurement of human
mental workload and introduce the methods for evaluating Situational Awareness
(subjective and performance based measures).
CG-4
HFS219
Course Guide
3. Learning Outcomes
Knowledge & Understanding (Theory Component)
By the end of this course, you should be able to:
• Describe key principles of human factors methodology and have a good overview
the various human factors methods.
• Explain the various methods applicable to different scenarios encountered (e.g. task
analysis methods, interface design methods, mental workload methods, etc.).
• Examine the tools used for data collection for conducting human factor
investigations or studies.
• Illustrate the underlying concepts of statistical data analysis.
Key Skills (Practical Component)
By the end of this course, you should be able to:
• Apply appropriate methods that address problems in real world scenario
• Propose human factors studies and data analysis by using the tools/ methods
taught
CG-5
HFS219
Course Guide
4. Learning Material
The following is a list of the required learning materials to complete this course.
Required Textbook(s)
Neville, A. S., Paul, M. S., Guy, H. W., Chris, B., & Daniel, P. J. (2006). Human factors
methods: A practical guide for engineering and design. Aldershot: Ashgate.
CG-6
HFS219
Course Guide
5. Assessment Overview
The overall assessment weighting for this course is as follows:
Assessment
Description
Weight Allocation
Assignment 1
Online Quiz
10%
Assignment 2
Tutor-Marked Assignment
20%
Examination
Closed book exam
70%
TOTAL
100%
The following section provides important information regarding Assessments.
Continuous Assessment:
There will be continuous assessment in the form of an online quiz and a tutor-marked
assignment (TMA). In total, this continuous assessment will constitute 30 percent of
overall student assessment for this course. The two assignments are compulsory and are
non-substitutable. It is imperative that you read through your Assignment questions and
submission instructions before embarking on your Assignment.
Examination:
The final (2-hour) written exam will constitute the other 70 percent of overall student
assessment and will test the ability to marketing related concepts, theories and strategies
to particular situations commonly faced by marketing managers. All topics covered in the
course outline will be examinable. To prepare for the exam, you are advised to review
Specimen or Past Year Exam Papers available on Learning Management System.
CG-7
HFS219
Course Guide
Passing Mark:
To successfully pass the course, you must obtain a minimum passing mark of 40 percent.
That is, students must obtain at least a mark of 40 percent for the combined assessments
and also at least a mark of 40 percent for the final exam. For detailed information on the
Course grading policy, please refer to The Student Handbook (‘Award of Grades’ section
under Assessment and Examination Regulations). The Student Handbook is available
from the Student Portal.
Non-graded Learning Activities:
Activities for the purpose of self-learning are present in each study unit. These learning
activities are meant to enable you to assess your understanding and achievement of the
learning outcomes. The type of activities can be in the form of Quiz, Review Questions,
Application-Based Questions or similar. You are expected to complete the suggested
activities either independently and/or in groups.
CG-8
HFS219
Course Guide
6. Course Schedule
To help monitor your study progress, you should pay special attention to your
Course Schedule. It contains study unit related activities including Assignments, Selfassessments, and Examinations. Please refer to the Course Timetable in the Student Portal
for the updated Course Schedule.
Note: You should always make it a point to check the Student Portal for any
announcements and latest updates.
CG-9
HFS219
Course Guide
7. Learning Mode
The learning process for this course is structured along the following lines of learning:
a.
Self-study guided by the study guide units. Independent study will require at
least 3 hours per week.
b.
Working on assignments, either individually or in groups.
c.
Classroom Seminar sessions (3 hours each session, 6 sessions in total).
iStudyGuide
You may be viewing the iStudyGuide version, which is the mobile version of the
Study Guide. The iStudyGuide is developed to enhance your learning experience with
interactive learning activities and engaging multimedia. Depending on the reader you are
using to view the iStudyGuide, you will be able to personalise your learning with digital
bookmarks, note-taking and highlight sections of the guide.
Interaction with Instructor and Fellow Students
Although flexible learning – learning at your own pace, space and time – is a hallmark
at SUSS, you are encouraged to engage your instructor and fellow students in online
discussion forums. Sharing of ideas through meaningful debates will help broaden your
learning and crystallise your thinking.
Academic Integrity
As a student of SUSS, it is expected that you adhere to the academic standards stipulated
in The Student Handbook, which contains important information regarding academic
policies, academic integrity and course administration. It is necessary that you read and
understand the information stipulated in the Student Handbook, prior to embarking on
the course.
CG-10
Study
Unit
Introduction to HF Methods
1
HFS219
Introduction to HF Methods
Learning Outcomes
By the end of this unit, you should be able to:
1.
Give an overview of the Human Factors methods
2.
Explain the relevance of the application of Human Factors methods / tools in
work design and human performance
SU1-2
HFS219
Introduction to HF Methods
Overview
This study unit provides an introduction to the Human Factors (HF) tools and techniques
that are available to researchers and HF practitioners.
SU1-3
HFS219
Introduction to HF Methods
Chapter 1: Introduction to Human Factors Methods
1.1 Introduction
Human Factors (HF) Methodology studies the different methods / tools that would be
used in the Human Factors studies.
Methods form a core part of Human Factors studies. The methods offer the HF practitioner
a structured approach to analyse and evaluate a problem related to system design or
human performance.
The methods and tools help the HF practitioner to:
• address real world problems within reasonably practicable means
• develop prototype solutions
• prioritise solutions based on the working circumstances and costs
• analyse and evaluate effects of changes and solutions that have been implemented.
Systems (used here in the context of hardware, i.e., equipment as well as software) are
made for use for humans by humans. In theory, we advocate that the system should be
designed with the user in mind (User Centric Design). However, the reality is that we are
often surrounded with poor design.
Poor design impacts performance and this in turn affects the productivity of an
organisation. Companies and organisations are keen to improve productivity and
performance and therefore an understanding of the methods used for evaluating
workplace systems, design and human performance is invaluable.
It is also important to note that the methods we choose will influence what we find from
any investigation. The methods used must produce findings that are valid, reliable and
meet the objectives of the investigation and also be safe to use.
While many methods are discussed in the subsequent chapters as standalone, often in the
real world many of these methods may need to be used together.
SU1-4
HFS219
Introduction to HF Methods
1.2 Classification of Methods
Methods can be classified as follows:
1.
Based on when the method is used in the context of the product life cycle:
Formative vs Summative Methods
2.
Based on objectivity of the user experience: Subjective vs Objective Methods
3.
Based on the type of data collected: Qualitative vs Quantitative Methods
4.
Based on where the methods are used: Laboratory vs Field Methods
1.2.1 Formative Methods vs Summative Methods
• Formative methods are more appropriately used during the development of the
product.
• Summative methods are more appropriately used to evaluate the finished product.
Example:
When engineers at a mobile phone factory test certain parts of the mobile phone’s
functionality (e.g., battery life, durability of microchips, strength and reliability of casing,
etc.) during the development of the mobile phone, they are conducting formative
evaluation. When the mobile phone is finally complete and tested before shipping out to
consumers, it is a form of summative evaluation.
Some methods may be appropriate for use in all phases of the life cycle of the product.
For example, usability testing is thought to be a summative method but there are many
advantages of using it as a formative method. Generally, the earlier in the lifecycle human
factors is applied, the greater the benefits in the long term.
SU1-5
HFS219
Introduction to HF Methods
1.2.2 Subjective Methods vs Objective Methods
• Subjective Methods: Users explain and detail their experiences about a product.
• Objective Methods: User experiences with the product are measured and
quantified.
Example:
Interviewing the users about their frustrations and problems using the new mobile phone
is a Subjective method, while data (e.g., battery usage, time spent on different apps, etc.)
collected from the mobile phone is an Objective method.
1.2.3 Qualitative Methods vs Quantitative Methods
Qualitative methods:
• used when collecting, analysing and interpreting data by observing what people
do and say.
• ways of collecting data which are concerned with describing meaning, rather than
with drawing statistical inferences. They provide an in-depth and rich description
of the phenomenon being observed. Data is in the form of words, pictures or objects.
It is often subjective − individuals’ interpretations of events are important, e.g., uses
participant observation, in-depth interviews, etc.
Quantitative methods:
• involve analysis of numerical data.
• focus on numbers and frequencies rather than on meaning and experience.
• provide information which is easy to analyse statistically and fairly reliable.
• are associated with the scientific and experimental approach and are often
criticised for not providing an in-depth description. Researcher uses tools, such
as questionnaires or equipment to collect numerical data. It is often quantitative –
requires precise measurement & analysis of target concepts, e.g., user surveys and
rating scales, questionnaires, etc.
SU1-6
HFS219
Introduction to HF Methods
Example: Case studies, interviews, and observational studies are qualitative methods,
while psychometric tests and rating scores are quantitative methods.
1.2.4 Laboratory Methods vs Field Methods
• Field-based methods are conducted in real place and real time scenarios. There
are advantages of realism in terms of task variables, environmental concerns and
subject characteristics but lack experimental control.
• Laboratory-based methods are used in laboratory setting for data collection. This
facilitates better experimental control for better data collection.
For theoretical studies, laboratory is preferred while for practical research questions, the
real world is a better setting. Often to combine the benefits of both laboratory and field
research, simulations of real world can be used. Such a simulation can be physical or
computer-based simulation.
Example: An engineer testing the GPS function on a mobile phone by simulating location
changes through a computer is using a Laboratory based method, while an engineer
testing the GPS function by driving around in car is using a Field-based method.
1.3 Classification of Research Studies
There are two basic types of research studies:
1.
Cross-sectional: Cross-sectional studies are used to gather information on a
population at a single point in time. An example of a cross-sectional survey
would be a questionnaire that collects data on how users feel about Internet
spam, as of September 2009.
2.
Longitudinal: Longitudinal studies gather data over a period of time. The
researcher may then analyse changes in the population and attempt to describe
and/or explain them. The three main types of longitudinal studies are:
a.
Trend: Trend studies focus on a particular population, which use
samples from different groups of people at different points in time and
scrutinised repeatedly. Trend studies do not have to be conducted by
SU1-7
HFS219
Introduction to HF Methods
just one researcher or research project since they may be conducted
over a long period of time. For example: A sample of millennials can be
studied prior to elections. Sometime after the election, another group
of millennials can be studied for the changes.
b.
Cohort: Cohort studies also focus on a particular population, sampled
and studied more than once. A cohort is a group of people that
experience the same type of event or characteristic within a certain time
period. For example: A group of people who were born in a particular
year.
c.
Panel: Panel studies allow the researcher to find out why changes in
the population are occurring, since they use the same sample of people
every time. That sample is called a panel. For example: The same group
of individuals can be interviewed on their views on technology use in
the year 2000 and 2015.
1.4 Existing Human Factors Methods
There are over two hundred Human Factors Methods and techniques available for use.
These could be broadly categorised as follows:
1.
Qualitative Data Collection Methods
2.
Task Analysis Methods
3.
Cognitive Task Analysis Methods
4.
Dexterity and Strength Measurement Tools
5.
Environmental Methods
6.
Mental Workload Assessment Methods
7.
Situation Awareness Measurement Methods
This course will cover the details and key concepts of the relevant methods used from the
above categories.
SU1-8
HFS219
Introduction to HF Methods
Summary
This study unit provided an overview of the human factors methods and how they are
commonly described in research papers. Research methods can be classified based on their
product life cycle, objectivity, type of data, and environment, while research studies can
be classified as cross-sectional or longitudinal, depending on the time period of the study.
SU1-9
HFS219
Introduction to HF Methods
Formative Assessment
1.
Mei Fen wants to find out about teenagers’ opinions on smoking. She designed a
questionnaire and distributed it to 100 secondary school students. Mei Fen used a
____________ research method in a ____________ research study.
a. subjective, cross-sectional
b. subjective, longitudinal
c. objective, cross-sectional
d. objective, longitudinal
2.
Su Fen wants to find out about how much teenagers know about the negative effects
of smoking. She designed a multiple-choice test and distributed it to 100 secondary
school students. Su Fen used a ____________ research method in a ____________
research study.
a. subjective, cross-sectional
b. subjective, longitudinal
c. objective, cross-sectional
d. objective, longitudinal
3.
Which of the following scenarios uses a formative method?
a. Studying how people’s attitudes towards workplace safety changed year-onyear from 2000 to 2020
b. Studying people’s attitude towards workplace safety in 2020.
c. Studying people’s attitude towards workplace safety in 2000.
d. Studying people’s attitude towards workplace safety in 2010.
4.
Which of the following studies uses a field method?
a. Observing pilots in a flight simulator.
b. Collecting data from drivers in a driving circuit.
SU1-10
HFS219
Introduction to HF Methods
c. Observing patients in a hospital.
d. Collecting data from participants using test equipment in a lab.
SU1-11
HFS219
Introduction to HF Methods
Solutions or Suggested Answers
Formative Assessment
1.
Mei Fen wants to find out about teenagers’ opinions on smoking. She designed a
questionnaire and distributed it to 100 secondary school students. Mei Fen used a
____________ research method in a ____________ research study.
a.
subjective, cross-sectional
Correct. Questionnaires are subjective methods and data is only collected
at one point in time; hence it is a cross-sectional study.
b.
subjective, longitudinal
Incorrect. Data was not collected over a period of time, so it is not
longitudinal.
c.
objective, cross-sectional
Incorrect. Questionnaires are not objective methods.
d.
objective, longitudinal
Incorrect. See explanation for options b and c.
2.
Su Fen wants to find out about how much teenagers know about the negative effects
of smoking. She designed a multiple-choice test and distributed it to 100 secondary
school students. Su Fen used a ____________ research method in a ____________
research study.
a.
subjective, cross-sectional
Incorrect. Multiple-choice questions have clear right and wrong answers, so
they are objective methods.
b.
subjective, longitudinal
SU1-12
HFS219
Introduction to HF Methods
Incorrect. Data was not collected over a period of time, so it is not
longitudinal.
c.
objective, cross-sectional
Correct. Multiple-choice questions have clear right and wrong answers, so
they are objective methods. Data is only collected at one point in time; hence
it is a cross-sectional study.
d.
objective, longitudinal
Incorrect. See explanation for option b above.
3.
Which of the following scenarios uses a formative method?
a.
Studying how people’s attitudes towards workplace safety changed year-onyear from 2000 to 2020
Correct. Data is collected every year from 2000 to 2020.
b.
Studying people’s attitude towards workplace safety in 2020.
Incorrect. Data is only collected once in 2020.
c.
Studying people’s attitude towards workplace safety in 2000.
Incorrect. Data is only collected once in 2000.
d.
Studying people’s attitude towards workplace safety in 2010.
Incorrect. Data is only collected once in 2010.
4.
Which of the following studies uses a field method?
a.
Observing pilots in a flight simulator.
Incorrect. This is a laboratory method.
b.
Collecting data from drivers in a driving circuit.
Incorrect. This is a laboratory method.
c.
Observing patients in a hospital.
SU1-13
HFS219
Introduction to HF Methods
Correct. This is a field method.
d.
Collecting data from participants using test equipment in a lab.
Incorrect. This is a laboratory method.
SU1-14
Study
Unit
Qualitative Methods
2
HFS219
Qualitative Methods
Learning Outcomes
By the end of this unit, you should be able to:
1.
Describe the tools used for data collection for conducting human factor
investigations or studies
2.
Identify the considerations for selecting an appropriate method
3.
Apply the principles to design surveys and collect specific data regarding a
system or scenario
SU2-2
HFS219
Qualitative Methods
Overview
This study unit focuses on the qualitative data collection methods that are available to
researchers and HF practitioners.
SU2-3
HFS219
Qualitative Methods
Chapter 1: Qualitative Methods
An accurate representation of the system or activity under analysis is an important and
necessary foundation for any analysis efforts. When we want to design a new system, the
first step is to review the description of the current or analogous system to see if there
are any inaccuracies within the description that could potentially hinder the design and
development effort. Data collection methods are used to collect the relevant information
that is used to provide a description of the system or activity under analysis.
1.1 Introduction
Qualitative methods are used when collecting, analysing and interpreting data by
observing what people do and say. Qualitative methods are ways of collecting data, which
are concerned with describing meaning rather than with drawing statistical inferences.
They provide an in-depth and rich description of the phenomenon being observed. Data is
in the form of words, pictures or objects. It is often subjective − individuals’ interpretation
of events is important, e.g., uses participant observation, in-depth interviews, etc.
1.2 Interviews
Interviews can be used to collect a wide variety of data ranging from user perception to
reactions, to usability and error related data. They are usually conducted on a one-to-one
basis by the interviewer.
The data generated from the interview is huge and specific to the topic being discussed.
1.2.1 Procedure
1.
Define interview objective and type of interview to be conducted
2.
Develop the questions such that data can be easily gathered and analysed
SU2-4
HFS219
Qualitative Methods
3.
Pilot the interview and redesign if changes needed including style of interview
(structured, semi-structured or unstructured), questioning style, rewording and
adding/removing questions
4.
Select participants – choose appropriate sample from the population with
varying levels of expertise if applicable
5.
Conduct and record the interviews – follow guidelines and appropriate
communication style for conducting the interview
6.
Transcribe the data – lengthy process of going through notes or recording and
transcribing the interview sessions
7.
Gather the date – look for specific data that was “expected” and then move onto
glean out the extra or “unexpected” data
8.
Data analysis – Look for themes, patterns using content analysis. If any numerical
data such as frequency of occurrences can be found then a statistical data analysis
can also be conducted.
1.2.2 Advantages
1.
Flexible technique with wide application
2.
Interviewer can direct the analysis
3.
Can be used to elicit data regarding non-observable components of the task
1.2.3 Disadvantages
1.
Data analysis is time consuming
2.
Limited reliability
3.
The interpretation of data can be subject to bias
Activity 2.1
Role-play an interview with a user of the latest iPhone.
SU2-5
HFS219
Qualitative Methods
Define interview objective, question development, select appropriate participants,
conduct interview, transcribe data, data analysis.
1.3 Focus Groups
While many interviews concentrate on one-to-one elicitation of information, group
discussion can provide an efficient means of canvassing consensus and opinions from
several people. Ideally, the focus group would contain around five people with similar
background. The discussion could be managed by a facilitator, who introduces the topics
and facilitates the discussion.
1.4 Questionnaires
Questionnaires are very flexible means of quickly collecting large amounts of data from
large participant groups. They have been used in many forms to collect data, such as mail
surveys, online surveys, direct interview, etc.
Questionnaires can be used throughout the design process to evaluate design concepts
and prototypes, to probe users’ ideas and reactions to evaluate existing systems.
Some types of questions in the questionnaires:
1.
Rating Scale: Subjective data on the participant’s opinion is rated using a scale.
For example – Rate this portable device on a scale on 1 to 10 where 1 is poor and 10
is excellent.
2.
Paired Associated: Participants are asked to choose between two options. For
example – Do you like Device A or Device B?
3.
Ranking: Participants are asked to assign a numerical ranking based on certain
factors. For example – Numerically rank the list of portable devices in ascending /
descending order based on preference.
4.
Multiple Choice Questions: The participant is given several options and he can
choose more than one option.
SU2-6
HFS219
Qualitative Methods
5.
Open Ended Questions: These are used to get participants’ opinions where they
can write anything they wish and also elaborate on their answers.
6.
Closed Questions: Participants must choose from specific responses, typically
Yes or No or A or B.
7.
Probing Questions: These are normally used after an open-ended or closed
question to gather more specific data regarding the interviewee’s previous
answer. Typical probing question would be “why do you think you do not like Device
A?”
Questionnaires can be used early in the design process to get users’ opinions on an idea
or also as follow-up for feedback on the design.
It is important to consider the method of administering the questionnaires prior to framing
the questions. The methods to consider are:
• Interviews
• Email/Mailed Questionnaires
• Telephonic Interviews
• Online Questionnaires
Apart from the clarity and non-ambiguity of the questions, other factors are also important
for a well-designed questionnaire. They are:
• Ordering and grouping of the questions in meaningful sections
• Visual structuring of the content, formatting, no. of pages, colours used, etc.
• Avoiding too many open-ended questions if there is no opportunity to probe
further, e.g., in a mailed/online questionnaire
• Time taken to complete the questionnaire – too much time can discourage the
participants and the answers could be skewed after a certain no. of questions.
1.4.1 Advantages
1.
Flexible technique with wide application
2.
A number of established HF questionnaire methods already exists
SU2-7
HFS219
Qualitative Methods
3.
Easy to use
4.
Requires minimal training
1.4.2 Disadvantages
1.
Data analysis is time consuming
2.
Subject to bias
3.
Questionnaire development is time consuming and requires a large amount of
effort on behalf of the analyst
Activity 2.2
Design a questionnaire to survey the opinions of people using the latest iPhone.
Define study objectives. Define participants, construct questionnaire, administer the
questionnaire, data analysis, and follow up.
1.5 Observations
This method is used to monitor an individual or group of individuals while completing a
task and document the findings. The data gathered is mostly regarding the physical and
verbal aspects of a task or scenario. Five different types of information can be elicited from
observational methods. These are:
• sequence of activities
• duration of activities
• frequency of activities
• fraction of time spent in different states
• spatial movement
SU2-8
HFS219
Qualitative Methods
The observational methods can be best used when time constraints are not present. It is a
great way to elicit information about the operating environment and the observer is able
to observe the steps the subject goes through.
There are three main types of observations:
1.
Overt Observation: The observers will be watching the participants but will not
interfere at all but their presence is known.
2.
Covert Observation: The participants are not aware of the observer’s presence.
3.
Participant Observation: Usually taken over a large period of time with a group
of participants of similar characteristics.
1.5.1 Advantages
1.
Observational data provides real-life insight into the activity performed in
complex systems.
2.
Can have wide application.
3.
Provides objective information.
4.
Interaction in the operation environment can be studied.
1.5.2 Disadvantages
1.
The method is prone to biases. When people know they are being watched, they
may elicit different behaviour.
2.
Time consuming and may be expensive (to set up recording equipment).
3.
Non-observable part of task for example (the thought behind the action) is not
captured in the analysis.
1.6 Ethnography
Ethnography is a method of observational study, which involves the field observation of
the subjects in their natural setting. It is a qualitative observational method widely used
in anthropology studies.
SU2-9
HFS219
Qualitative Methods
The observer is called the ethnographer who becomes involved in the everyday activities
of the people he is studying. The ethnographer not only describes the activities and
practices he observes, but also attempts to interpret the context and meaning of the
activity. The data gathered is often presented in a written format as a document.
Key guiding principles of Ethnography:
1.
Natural setting: To learn about a world that you do not understand, you must
encounter it first hand.
2.
Holism: To study how a particular behaviour fits into the larger whole / context.
Focus on the relationship between the different aspects being observed.
3.
Descriptive: The ethnographer describes how people behave rather than how
they ought to behave.
4.
Member’s point of view: Understanding other people’s behaviour from their
point of view.
Ethnographers use observation as a method as well as observations coupled with
interviews. Data can be recorded manually (note taking) or video recording with audio
equipment can be used. Collecting and reading about relevant cultural artefacts, e.g.,
rituals, procedures, size, appearance, items of importance, etc. can also be used along with
interviews and observations.
Data analysis involves forming patterns and themes that emerge, highlighting key events
that are of interest, creating maps, charts, diagrams and photographs to represent findings.
1.7 Survey Methods
Surveys are used extensively to assess attitudes and characteristics of a wide range of
subjects, for example, from the quality of user-system interfaces to user behaviour. Data
are usually collected through the use of questionnaires, although sometimes interview
methods may be used. Surveys can use qualitative (e.g., ask open-ended questions) or
quantitative (e.g., use forced-choice questions) measures.
There are two basic types of surveys:
SU2-10
HFS219
Qualitative Methods
1.
Cross-sectional surveys: Cross-sectional surveys are used to gather information
on a population at a single point in time. An example of a cross-sectional survey
would be a questionnaire that collects data on how users feel about Internet
spam, as of September 2009.
2.
Longitudinal surveys: Longitudinal surveys gather data over a period of time.
The researcher may then analyse changes in the population and attempt to
describe and/or explain them. The three main types of longitudinal surveys are:
a.
Trend studies: Trend studies focus on a particular population, which
uses samples from different groups of people at different points in time
and scrutinised repeatedly. Trend studies do not have to be conducted
by just one researcher or research project since they may be conducted
over a long period of time. For example: A sample of millennials can be
studied prior to elections. Sometime after the election another group of
millennials can be studied for the changes.
b.
Cohort studies: Cohort studies also focus on a particular population,
sampled and studied more than once. A cohort is a group of people that
experience the same type of event or characteristic within a certain time
period. For example: A group of people who were born on a particular
year.
c.
Panel studies: Panel studies allow the researcher to find out why
changes in the population are occurring, since they use the same sample
of people every time. That sample is called a panel. For example:
The same group of individuals can be interviewed on their views on
technology use in the year 2000 and 2015.
1.8 Case Studies
Case study method is an invaluable tool to facilitate understanding of a complex issue
or object and can help to validate information that is already known through previous
research. Case studies emphasise detailed contextual analysis of a limited number of
events or conditions and their relationships.
SU2-11
HFS219
Qualitative Methods
Researchers have used the case study research method for many years across a variety of
disciplines. Social scientists, in particular, have made wide use of this qualitative research
method to examine real-life situations and provide the basis for the application of ideas
and extension of methods.
Case study methodology is used when the following applies:
• When the study needs to answer “how” and “why” questions.
• The researcher is unable to manipulate the behaviour of the actors/users involved.
• There is a need to cover contextual conditions that could be relevant to the
phenomenon under study.
• The boundaries between phenomenon and context are not clear.
1.8.1 Procedure
1.
Decide on the broader topic
2.
Select the particular phenomenon of interest
3.
Develop the research questions for investigating individual cases and their
contexts
4.
Collect raw data from multiple sources such as interviews, observations or
articles and existing files
5.
Group, classify and edit the data
6.
Perform triangulation of observations and form interpretations
7.
Write a report with the selected interpretation
SU2-12
HFS219
Qualitative Methods
Read
Qualitative Case Study Methodology: Study Design and Implementation for Novice
Researchers (Baxter & Jack, 2008)
http://www.nova.edu/ssss/QR/QR13-4/baxter.pdf
1.9 Participatory Design Methods
In participatory design, end-users are invited to cooperate with researchers and developers
during an innovation process. Potentially, they participate during several stages of an
innovation process: for example, during the initial exploration and problem definition (to
help define the problem and to focus ideas for solution) and during development (to help
evaluate proposed solutions).
Participatory design can be seen as an effort to involve the end users into the world of
design and development. However, users have limited decision-making powers on the
final product. Their role is more consultative.
Read
Human Factors Methods: A Practical Guide for Engineering and Design, Chapters 1
and 2
SU2-13
HFS219
Qualitative Methods
Summary
This study unit provided an overview of the qualitative research methods that are
commonly used in human factors. These qualitative methods include questionnaires,
interviews, focus group discussions, and many more.
SU2-14
HFS219
Qualitative Methods
Formative Assessment
1.
Mei Fen wants to collect data from a large group containing 10,000 participants.
Which one of the following data collection methods would you advise her to use?
a. Questionnaires
b. Focus group discussions
c. Interviews
d. User observation
2.
Su Fen wants to collect data from a small group of experts in a domain that she is
not familiar. Which one of the following data collection methods would you advise
her to use?
a. Questionnaires
b. Focus group discussions
c. Interviews
d. User observation
3.
Which of the following is an open-ended question?
a. How would you describe the design of the latest iPhone?
b. Do you like to use iPhone or Android phones?
c. On a scale of 0 to 10, how likely are you going to buy the latest iPhone?
d. Are you planning to buy the latest iPhone?
4.
Which of the following is an ethnographic study?
a. Observing pilots in a flight simulator.
b. Getting pilots to answer a questionnaire on the usability of the dashboard
design.
c. Observing car mechanics in a workshop.
d. Interviewing car mechanics on their work environment.
SU2-15
HFS219
Qualitative Methods
Solutions or Suggested Answers
Formative Assessment
1.
Mei Fen wants to collect data from a large group containing 10,000 participants.
Which one of the following data collection methods would you advise her to use?
a.
Questionnaires
Correct. Questionnaires are easy to use and efficient for collecting data
from a large group of people.
b.
Focus group discussions
Incorrect. Collecting data from 10,000 via focus groups will be too timeconsuming.
c.
Interviews
Incorrect. Collecting data from 10,000 via interviews will be too timeconsuming.
d.
User observation
Incorrect. Collecting data from 10,000 via observations will be too timeconsuming.
2.
Su Fen wants to collect data from a small group of experts in a domain that she is
not familiar. Which one of the following data collection methods would you advise
her to use?
a.
Questionnaires
Incorrect. It is difficult for Su Fen to design a good questionnaire if she is not
familiar with the domain.
b.
Focus group discussions
SU2-16
HFS219
Qualitative Methods
Correct. Using a focus group discussion allows the experts to question each
other based on their own expertise in the domain and is likely to be most
insightful for Su Fen.
c.
Interviews
Incorrect. Su Fen might not know what questions to ask if she is not familiar
with the domain.
d.
User observation
Incorrect. Su Fen might not know what is important to observe if she is not
familiar with the domain.
3.
Which of the following is an open-ended question?
a.
How would you describe the design of the latest iPhone?
Correct. This is an open-ended question.
b.
Do you like to use iPhone or Android phones?
Incorrect. This is a paired associated question.
c.
On a scale of 0 to 10, how likely are you going to buy the latest iPhone?
Incorrect. This is a rating scale question.
d.
Are you planning to buy the latest iPhone?
Incorrect. This is a closed question.
4.
Which of the following is an ethnographic study?
a.
Observing pilots in a flight simulator.
Incorrect. The pilots are not in their natural environment.
b.
Getting pilots to answer a questionnaire on the usability of the dashboard
design.
Incorrect. Ethnography is a type of observational study.
c.
Observing car mechanics in a workshop.
SU2-17
HFS219
Qualitative Methods
Correct. The car mechanics are in their natural environment.
d.
Interviewing car mechanics on their work environment.
Incorrect. Ethnography is a type of observational study.
SU2-18
Study
Unit
Experimental Design
3
HFS219
Experimental Design
Learning Outcomes
By the end of this unit, you should be able to:
1.
Explain the important concepts related to experimental design
2.
Apply the concepts to design an experiment that will address problems in the
real world scenario
SU3-2
HFS219
Experimental Design
Overview
This study unit provides an introduction to the basic concepts for an understanding of
research and research design (or the designing of experiments) in order to identify and
evaluate a problem and then put in place measures to help address the problem.
SU3-3
HFS219
Experimental Design
Chapter 1: Experimental Design
1.1 Introduction
Research Methods can be categorised into 3 broad groups:
• Descriptive Research describes the attributes of the population. These are often
carried out to assess the magnitude and scope of the problem before solutions are
suggested.
• Experimental Research tests the effect of a variable on the behaviour. This study
unit is dedicated to explaining experimental research.
• Evaluative Research is done to assess the effect of product / system or the goodness
of the product / system and makes recommendations for improvement based on
the information collected.
1.2 Terminology
Pre-test – To check whether the groups are different before the manipulation starts.
Post-test – Measurement of the effect(s).
Control Group – A control group is a group not receiving a manipulation that is being
done for the experiment group.
Treatment / Experiment Group – A treatment group is a group receiving the manipulation
that is being tested.
Double-Blind Experiment – Neither the researcher, nor the participants, know which is
the control group. This is done to minimise bias in the experiment.
Pilot Study – A pilot study is conducted before conducting the real experiment. This is
to verify and ensure that the experiment measures what it should. Errors, which could
potentially destroy the experiment, are often found during this process and addressed.
SU3-4
HFS219
Experimental Design
1.3 Designing and Conducting Experiments
An experiment is typically carried out by manipulating a variable. This variable is called
the independent variable, affecting the treatment group.
The effect that the researcher is interested in, i.e., the dependent variable(s), is measured.
It is a good idea to have a control group on whom no manipulation is done. This will enable
the researcher to compare the two groups.
Subjects should be randomly assigned to the treatment group and control group.
The aim of an experiment is to draw a conclusion, together with other observations.
Identifying and controlling non-experimental factors (confounding variables), which the
researcher does not want to influence the effects, are crucial to drawing a valid conclusion.
Different research designs have different attributes. The research design is the structure of
any scientific work. It gives direction and systematises the research.
The method you choose will affect your results and how you conclude the findings.
1.4 Components of an Experiment
Experiments help to understand phenomenon and they can be done to increase our
understanding about a topic / issue.
Experimental Design is a systematic and scientific approach to research in which the
researcher manipulates one or more variables, and controls and measures any change in
other variables.
Designing an experiment is fundamental to addressing a research question. This section
will introduce the foundations of research methods (both quantitative and qualitative)
particularly because the way a study is designed greatly influences the subsequent data
analysis and interpretation.
SU3-5
HFS219
Experimental Design
1.4.1 Hypothesis
Hypotheses are predictions about what the examination of appropriately collected data
will show. It is the researcher’s tentative explanation predicting the main results of the
experiment. It is often supported by theory, research or personal experience and states the
predicted results from variables presented in the purpose statement. In an experiment,
the hypothesis should be stated briefly and clearly. It should express relationship between
two variables – independent and dependent variables – and should be testable.
Independent Variable (IV) – These are variables that are being manipulated by the
researcher. There can be 3 types of independent variables:
1.
Task related – size of control, type of display, work rest cycles
2.
Environmental – changes in noise, ventilation
3.
Subject related – height, age, experience
Dependant Variable (DV) – These are variables that are being measured to assess the effect
of the independent variable. These can be grouped as follows based on the data collected:
1.
Physical characteristics – height, weight
2.
Performance data – reaction times, strength, memory
3.
Subjective data – opinion, rating, preferences
4.
Physiological measures – temperature, heart rate, blink rate
Confounding Variable – These are variables that the researcher has not accounted for
but may have affected the experiment. If the confounding variables are not taken into
consideration during the experiment, then the conclusions drawn from the experiment
may be unreliable. Another important reason for conducting an experiment under
laboratory condition is to try to control confounding variables as much as possible.
Null Hypothesis – No relationship exists between variables.
Alternative Hypothesis states a specific relationship between the variables.
The null hypothesis is presumed to be true unless data from the experiment produces
overwhelming evidence to the contrary.
SU3-6
HFS219
Experimental Design
1.4.2 Hypothesis Testing
HYPOTHESIS TESTING is the process of choosing between two competing claims.
Everyday, we make hypothesis tests in our choice of what to eat for breakfast, what clothes
to wear, etc.
What distinguishes statistical hypothesis testing from everyday variety is the use of
statistical measures in the statement of the hypothesis, the collection of sample data and the
use of sample data in a well-defined decision making process.
Population is a group of objects / subjects about which conclusions are to be drawn.
A sample is a portion or subset of objects / subjects drawn from the population. Sample
is viewed as a miniature population. Therefore, conclusions drawn from a sample of the
population can enable us to draw conclusions about a population.
Figure 3.1 Population vs Sample
Sampling or choosing the subjects for the study is critical to the validity of the study.
Representative Sample – Data is collected from a sample of people (subjects) representative
of all relevant aspects of the population of interest. To obtain a representative sample, the
subjects are to be selected randomly from the population.
Sample Size is important. It should be enough (in numbers) to be able to collect data
reliably for assessing the effects of the independent variable. Too few subjects may lead to
incorrect conclusion that the independent variable has no effect on the dependant variable.
SU3-7
HFS219
Experimental Design
Too many subjects may incorrectly magnify the tiny effects that the independent variable
may have on the dependant variable, which may be of no practical significance.
Sampling groups correctly is especially important when we have more than one condition
in the experiment. One group often serves as a control group, whilst others are tested
under the experimental conditions.
Nature and Purpose of Statistical Inference
Inference means the drawing of conclusions from data.
Statistical Inference can be defined as the drawing of conclusions from quantitative or
qualitative information using the methods of statistics to describe and arrange the
data and to test suitable hypotheses. In statistics, the investigator starts from specific
observations (data) to induce (estimate) the general relationships between variables.
Variable is something that can vary and therefore can have many different values or
categories. These can be measured during the experiment. Examples: distance, time, heart
rate, errors, etc. The important characteristic of a variable is its ability to be precisely
measured.
Numeric Variable
Variables that denote measurable quantity. Data collected is quantitative.
a.
Continuous Variable: Those variables that can be measured very precisely are said
to be continuous as they can take any value within a given range.
Example of a continuous variable could be time – we could measure time as 5
minutes or more precisely as 5 min 09 sec.
b.
Discrete Variable: Discrete variables can take on only certain discrete values within
the range. Example: reporting of number of errors in a workplace.
Categorical Variable
Variables that can be described only within categories or characteristics. Data collected is
qualitative.
SU3-8
HFS219
Experimental Design
a.
Ordinal Variable: Takes value that can be ranked higher or lower than another.
Example: exam grades.
b.
Nominal Variable: Takes value that cannot be sequenced or ordered logically.
Example: gender, eye colour.
P Value
This is the probability that the difference as large as what has been observed is due to
chance alone.
The p value is obtained from calculating one of the standard statistical tests.
False Positives and False Negatives
Science is based on the following set of principles:
1.
Previous experience serves as the basis for developing hypotheses.
2.
Hypotheses serve as the basis for developing predictions.
3.
Predictions must be subjected to experimental or observational testing.
4.
If the predictions are consistent with the data, they are retained, but if they are
inconsistent with the data, they are rejected or modified.
There are two possible types of errors:
False Positive Error or Type 1 error or Alpha Error
This is when the investigator asserts that the data support a hypothesis when in fact the
hypothesis is false.
False Negative Error or Type 2 Error or Beta Error
This is when the investigator asserts that the data do not support the hypothesis when in
fact the hypothesis is true.
1.4.3 Statistical Testing and Inference
Steps of Statistical Testing
Analysing data using statistical tests usually includes the following basic steps:
SU3-9
HFS219
Experimental Design
1.
Developing the null and alternative hypotheses
2.
Establishing an appropriate alpha level
3.
Performing a suitable test of statistical significance on appropriately collected
data
4.
Comparing the p value from the test with the alpha level
5.
Rejecting or failing to reject the null hypothesis
Developing Null Hypothesis and Alternative Hypothesis
The null hypothesis states that there is no real (true) difference between the means (or
proportions) of the groups being compared (or that there is no real association between
two continuous variables).
If the data are not consistent with a hypothesis, the hypothesis should be rejected and the
alternative hypothesis accepted instead.
Establish alpha level
Before doing any calculations to test the null hypothesis, the investigator must establish a
criterion called the alpha level, which is the highest risk of making a false positive error
that the investigator is willing to accept. The level of alpha is commonly set at α = 0.05.
This says that the investigator is willing to run a 5% risk (but no more) of being in error
when rejecting the null hypothesis.
Perform test of statistical significance
The investigator now performs a suitable statistical test of significance on appropriately
collected data to obtain the p value for the data.
Compare p value obtained with alpha
After the p value is obtained, it is compared with the alpha level previously chosen.
Reject or Fail to Reject the Null Hypothesis
If the p value is found to be greater than the alpha level, the investigator fails to reject the
null hypothesis.
SU3-10
HFS219
Experimental Design
Standard Deviation and Standard Error
Because most research is done on samples, rather than on complete populations, we need
to have some idea of how close the mean of our study sample is likely to come to the real
world mean, i.e., the mean in the underlying population from which the sample came.
The mean of the distribution is the average of the data values that is used to derive the
central tendency of the data.
The variance of the distribution is the expectation of the squared deviation from the mean
and the standard deviation is the root of variance.
Standard Error
The distribution of means is also a normal (Gaussian) distribution, with its own mean and
standard deviation.
The standard deviation of the distribution of means is called standard error.
Standard Error (SE) = SD / Square root of N
The larger the sample size, the smaller the standard error, and the better the estimate of
the population mean.
Confidence Intervals
Mean +/- 1.96 SE
This is the 95% Confidence Interval, which is the range of values in which the investigator
can be 95% confident that the true mean of the underlying population falls.
Degrees of Freedom
The term “degrees of freedom” refers to the number of observations that are free to vary. For
simplicity, the degrees of freedom for any test are considered to be the total sample size –
1 degree of freedom for each mean that is calculated.
SU3-11
HFS219
Experimental Design
For example, the general formula for the degrees of freedom for Student’s two-group ttest is N1 + N2 – 2, where N1 is the sample size in the first group and N2 is the sample
size in the second group.
1.4.4 Reliability and Validity
Reliability
This is the measure of the dependability and consistency of the results of the experiments
over time, i.e., repeated testing gives similar results.
Validity
Does the dependant variable measure what we intend to measure in the experiment?
For example, is heart rate a good way to measure mental stress?
Internal validity
The truth about inferences regarding cause-effect or causal relationship. In simple terms,
it means that there is evidence to say that what you did in the experiment caused what
you observed.
DO (change) “A” —————- causes ————— SEE (effect) “B”
The key question in internal validity is whether the observed change can be attributed to
the intervention (i.e., cause) and not to the other possible causes (i.e., the alternate cause).
Internal validity relates only to a specific problem therefore cannot be generalised.
External validity
This refers to the degree to which the conclusions of your experiment would hold true for
other persons in other places at other times.
External validity is related to generalising the outcome of the experiment.
SU3-12
HFS219
Experimental Design
Figure 3.2 The further away from the centre the less similar the
circumstances and therefore external validity may be affected
Activity 3.1
Here is a layout to help plan your experimental designs.
1.
What is the question/problem you want to investigate? What is your testable
hypothesis? What is/are your expected outcome(s) if your hypothesis is
supported?
2.
What DATA do you need to obtain in order to test your hypothesis?
Indicate units of the actual measurements and also how the data would be
summarised and/or normalised (i.e., mean ± SD, %, mm/sec, etc.).
3.
What is/are the possible approach(es) or METHOD(S) to answer the
question(s)? Give some thought here as to what kind of statistical analysis
you will perform. It is prudent to make sure you can analyse the data later
using routine statistics.
4.
What is/are your CONTROL(S) for the variable(s) being tested? What will
each control tell you?
5.
What is “one data unit” in your experiment? How many measurements,
observations, trials would be required for each level of assessment? Consider
how long it takes to get one observation and how much time you have.
SU3-13
HFS219
Experimental Design
6.
Outline the step-by-step procedure you will use to obtain a single
measurement or observation, and be sure to specify all the quantitative
parameters (how much, how long, when, what level, etc.) and the equipment
used for each step. This must be precise and clear enough that anyone can
do it with a consistent level of accuracy and complete enough for anyone to
replicate your experiment with comparable equipment.
7.
How will your data be summarised, analysed, and presented? Show relevant
calculations (e.g., normalisation of data, etc.) and indicate the statistical tests
you’ll employ. For graphic presentation, indicate the type of graph and the
variables to be plotted.
8.
State any assumptions you are making in doing this experiment and justify
them, i.e., explain your rationale for making them.
SU3-14
HFS219
Experimental Design
Summary
This study unit provided an overview of the structure of experiments and the important
concepts in experimental design such as variable types, hypothesis testing, and basic
statistical knowledge. In experimental research, a good experimental design is critical to
ensure the validity and reliability of the results of the experiment.
SU3-15
HFS219
Experimental Design
Formative Assessment
1.
Mei Fen wants to test if workers inhaling chemical fumes over a long period of time
will give birth to children with developmental issues. Identify the dependent variable
in this scenario.
a. Extent of developmental issues in children
b. Number of days the workers were subjected to chemical fumes
c. Age of factory workers
d. Gender of factory workers
2.
Su Fen conducted a study several times and found that it always produces the same
results. Su Fen’s study is said to have ____________.
a. external validity
b. internal validity
c. reliability
d. construct validity
3.
Which of the following is a descriptive research?
a. The HFS219 lecturer collects data on the shoe sizes of students in her class.
b. The HFS219 lecturer wants to find out if it is more likely for a student with
bigger shoe size to score higher at the exams.
c. The HFS219 lecturer wants to find out if shoe sizes affect exam scores.
d. The HFS219 lecture wants to find out if it is more likely for a student with
smaller shoe size to score higher at the exams.
4.
Which of the following is an ordinal variable?
a. Weight in kilograms
b. Colours – red, orange, yellow, green, blue, violet
c. Age group – children, youth, adults, seniors
SU3-16
HFS219
Experimental Design
d. Height in centimetres
SU3-17
HFS219
Experimental Design
Solutions or Suggested Answers
Formative Assessment
1.
Mei Fen wants to test if workers inhaling chemical fumes over a long period of time
will give birth to children with developmental issues. Identify the dependent variable
in this scenario.
a.
Extent of developmental issues in children
Correct. In this experiment, Mei Fen is testing if the extent of development
issues is dependent on workers inhaling chemical fumes.
b.
Number of days the workers were subjected to chemical fumes
Incorrect. This is the independent variable.
c.
Age of factory workers
Incorrect. This could be a confounding variable.
d.
Gender of factory workers
Incorrect. This could be a confounding variable.
2.
Su Fen conducted a study several times and found that it always produces the same
results. Su Fen’s study is said to have ____________.
a.
external validity
Incorrect. External validity refers to the degree to which the conclusions of
your experiment would hold true for other persons in other places at other
times.
b.
internal validity
Incorrect. Internal validity refers to validity within the scope of the
experiment − whether the observed change can be attributed to the
SU3-18
HFS219
Experimental Design
intervention (i.e., cause) and not to the other possible causes (i.e., the alternate
cause).
c.
reliability
Correct. Reliability is the measure of the dependability and consistency of
the results of the experiments over time.
d.
construct validity
Incorrect. Construct validity refers to how well the test is constructed to
measure what it is designed to measure.
3.
Which of the following is a descriptive research?
a.
The HFS219 lecturer collects data on the shoe sizes of students in her class.
Correct. This is a descriptive research.
b.
The HFS219 lecturer wants to find out if it is more likely for a student with
bigger shoe size to score higher at the exams.
Incorrect. This is an evaluative research.
c.
The HFS219 lecturer wants to find out if shoe sizes affect exam scores.
Incorrect. This is an experimental research.
d.
The HFS219 lecture wants to find out if it is more likely for a student with
smaller shoe size to score higher at the exams.
Incorrect. This is an evaluative research.
4.
Which of the following is an ordinal variable?
a.
Weight in kilograms
Incorrect. This is a numerical variable.
b.
Colours – red, orange, yellow, green, blue, violet
Incorrect. This is a nominal variable.
c.
Age group – children, youth, adults, seniors
SU3-19
HFS219
Experimental Design
Correct. This is an ordinal variable.
d.
Height in centimetres
Incorrect. This is a numerical variable.
SU3-20
Study
Unit
Task Analysis Methods
4
HFS219
Task Analysis Methods
Learning Outcomes
By the end of this unit, you should be able to:
1.
Explain the principles and various methods used for task analysis
2.
Apply the principles to conduct task analysis and collect specific data, regarding
a system or scenario
SU4-2
HFS219
Task Analysis Methods
Overview
This study unit provides an introduction to task analysis methods that are commonly used
by HF practitioners. Task analysis has widespread application for the design and analysis
of system performance including human performance evaluation, error identification as
well as usability studies. Some important methods of task analysis will be discussed in
this unit, for example, hierarchical task analysis, verbal protocols, cognitive walkthrough,
task decomposition, cognitive task analysis.
SU4-3
HFS219
Task Analysis Methods
Chapter 1: Task Analysis Methods
1.1 Introduction
Task analysis methods are used to describe the task being performed. The simplest of the
analysis can be done by breaking up the task into its subtasks/ subcomponents, noting
the sequence of operation and the equipment required at each stage.
By studying the task, the researcher aims to get a thorough understanding of the task and
therefore capture what is important in a task performance. The researcher gathers data
with respect to both physical (actions) as well as cognitive (thought process) for analyses.
This is useful aid in the design and development of the system. Task analysis is also useful
for the planning operations, i.e., function allocation.
Key components in the task analysis are:
1.
Identifying tasks that are to be studied
2.
Collecting and understanding the data about the task (Flow Charts, Standard
Operating Procedures, Manuals, etc.)
3.
Documentation of the analysed task (word or diagrams)
Task analysis methods are popular as they are used often in Human Factors analysis, for
example, for evaluation of performance or usability or for error identification.
The key advantages are their usefulness and flexibility of use in different situations.
The main disadvantages of task analysis methods are their intensive time and resource
requirements as well as the reliability of the results. It is also important to note that two
researchers may interpret the task under study in two different ways.
1.1.1 Applications of Task Analysis
Task analysis is extensively used to:
1.
design systems
SU4-4
HFS219
Task Analysis Methods
2.
build training programmes
3.
analyse safety requirements
Fields of application include aviation, traffic control, nuclear petrochemical domains,
military applications, etc.
Although there are many different task analysis methods, this study unit will focus only
on some of the key methods as listed below:
1.
Hierarchical Task Analysis (HTA)
2.
Task Decomposition
3.
Goals, Operators, Methods and Selection Rules (GOMS)
4.
Verbal Protocol Analysis (VPA)
5.
Applied Cognitive Task Analysis (ACTA)
6.
Cognitive Walkthrough (CW)
7.
Critical Decision Method (CDM)
8.
Critical Incident Technique (CIT)
1.2 General Task Analysis Methods
1.2.1 Hierarchical Task Analysis (HTA)
HTA is a method of task analysis that involves breaking down the task into its
subcomponents by preserving the hierarchy / sequence of events.
For example:
Job – Duty – Task – Subtask – Activity
OR
Goals – Sub-goals − Operations − Plans
The purpose of the hierarchical breakdown is to provide a logical description of the
various activities that constitute a job and the various tools that are used in accomplishing
the activities.
SU4-5
HFS219
Task Analysis Methods
Advantages
1.
Requires minimal training and easy to implement.
2.
Can be applied widely to study any task in any domain.
3.
HTA results in a vivid capture of the task activity under study.
4.
HTA helps to identify critical task components which are subsequently used as
important information to conduct further human factor analysis, for example,
human error identification methods, workload assessment, allocation of job
functions.
Disadvantages
1.
HTA provides mostly descriptive information.
2.
It does not capture the cognitive components (thoughts and decisions making
process) of the task being performed.
3.
It is a time intensive method.
4.
The reliability of the method is limited as different researchers may describe the
same task differently and likewise the same researcher may produce a different
task description for the same task on different occasion.
For more information on HTA, watch the video below:
Lesson Recording
Task Analysis Methods
Activity 4.1
Identify a task and do a hierarchical task analysis.
1.
Describe the purpose of analysis
2.
Break down task into subcomponents
SU4-6
HFS219
Task Analysis Methods
3.
Design a presentation format for the analysis of task
4.
Can you use your analysis to suggest improvements in task performance or
equipment design?
1.2.2 Task Decomposition
It is a procedure for producing an ordered list of all the tasks that people will do in a
system. It includes operator information requirements, equipment used by the operators,
evaluations and decisions that must be made, task times, errors made, operator actions
and environmental conditions. The categories for decomposing the task can be chosen by
the researcher based on the requirements of the analysis.
Purpose of the task decomposition is to gather detailed information about a particular
task.
The resulting product can be used to:
1.
Estimate time and effort required to perform the task
2.
Determine the manning, skills and training requirements
3.
Determine interface requirements
4.
Provide input to do review of the system or define the specifications of the system
Tools required: Pen and paper, Audio and video equipment if possible. The analysis can
be presented with Microsoft Word or Power point or Excel.
Procedure
1.
Hierarchical task analysis for step by step description of the task.
2.
Creating task descriptions to give the researcher enough information about the
exact requirements of the task in terms of time, resources and man power.
3.
Perform task decomposition based on the requirements of the analysis. There
are several task decomposition categories, for example, task description, task
function, sequence of activity, skill required for a task, manning requirements
SU4-7
HFS219
Task Analysis Methods
for a task, controls used, etc. Information about the task decomposition
categories is collected by observation, system documentation, procedures and
training manuals, discussion with operators and designers. Other data collection
methods like interviews, questionnaires and walkthrough analysis can also be
done.
4.
Data collected is presented as a task decomposition output table. (Refer to pages
64, 65 of Human Factors Methods)
Advantages
1.
Task decomposition is a flexible method with widespread application. The
researcher can choose the focus of the task decomposition; therefore any aspect
of the task can be studied including the cognitive components, for example,
decision-making process.
2.
Provides a very detailed analysis of the task (including information about the
possible errors and consequences of those errors).
Disadvantages
1.
Time intensive method and laborious.
Activity 4.2
In Activity 1, you conducted a hierarchical task analysis. Use that information for this
activity.
Identify a task and choose the task decomposition category (for example, nature of
the task, potential errors and error consequences).
Present your data as a task decomposition table.
SU4-8
HFS219
Task Analysis Methods
1.2.3 Goals, Operators, Methods and Selection Rules (GOMS)
It is a method used to define the user’s goals, decompose these goals into sub-goals and
demonstrate how the goals are achieved through user interaction.
It is used widely in Human Computer Interaction (HCI) studies to provide a description
of human performance in terms of user goals, operators, methods and selection rules for
existing systems or designs.
1.
Goals describe the end outcome (what users want to achieve). Goals can be
broken into sub-goals.
2.
Operators are physical or cognitive actions that are performed to get to the goal.
3.
Methods describe the procedures for accomplishing a goal in terms of operators
and sub-goals. There can be more than one method available to accomplish a
single goal.
4.
Selection rules are used to describe when a user would select a certain method
over the others.
Tools required: Pen and paper
Procedure
1.
User’s top-level goals are defined.
• Example: Set the clock
2.
Goals are decomposed to a set of sub-goals.
• Goal: Set the clock
◦ Sub-Goal 1 – Set Hour
◦ Sub-Goal 2 – Set Minute
3.
Operators are defined – In the example below, we have physical and cognitive
operators.
• Reach button
• Hold button
• Release button
SU4-9
HFS219
Task Analysis Methods
• Clickon button
• Decide: If then
4.
Methods are described – This is done for all goals and sub-goals until they are
exhausted and cannot be further broken down.
• Method for goal: Set the clock
◦ Step 1: Hold the Time button
◦ Step 2: Accomplish goal: Set-Hour
◦ Step 3: Accomplish goal: Set-Minute
◦ Step 4: Release TIME button
◦ Step 5: Return with goal accomplished
• Method for sub-goal: Set-Digit
◦ Step 1: ClickOn button
◦ Step 2: Decide: If target = current , then return with
goal accomplished
◦ Step 3: Goto 1
5.
Selection rules are described.
• Selection rule for sub-goal: Set-Hour
◦ If target HOUR is = 5 hours from current HOUR, then
Accomplish Goal: Click&Hold HOUR
Advantages
1.
It provides a hierarchical description of task activity.
2.
It gives the researcher an insight into the different ways the goals can be achieved;
therefore it is a useful aid to the system designers to compare between systems.
Disadvantages
1.
Difficult to learn and apply
SU4-10
HFS219
Task Analysis Methods
2.
Time consuming
3.
Limited usage − for human computer interaction studies
1.2.4 Verbal Protocol Analysis (VPA)
Verbal Protocol Analysis is a method that is used to elicit verbal description of the
processes (cognitive and physical) from individual performing a task. The operator is
asked to “think aloud” as he is performing the task under analysis. The outcome of the
VPA is a written transcript of the operator behaviour. VPA is often used in cognitive
research and behaviour analysis. It also has widespread application for mental workload
assessment, situational awareness measurement and task analysis.
Tools required: Audiovisual recording equipment. Specialised software is available for
encoding the transcript such as Observer, Wordstation, and General Enquirer.
Procedure
1.
Define the scenario under analysis.
2.
Give clear instruction to the participants about what is required of them during
the analysis. The participants are to verbalise continuously about their thought
process.
3.
Begin scenario and record (audiovisual recording is preferred over audio).
4.
Transcribe verbal data to written format (excel spreadsheet is used).
5.
Verbal transcript it categorised and coded. 5 categories of coding are used
– words, word senses, phrases, sentences and themes. Computer software
packages are available to aid in encoding the data (refer to Human Factors
Methods pages 59, 60, 61 for examples).
6.
Data analysis
Advantages
1.
Data obtained is very rich in information.
2.
Verbalisation of the steps gives a genuine insight into cognitive processes,
especially if data is obtained from domain experts.
SU4-11
HFS219
3.
Task Analysis Methods
Simple to conduct the VPA.
Disadvantages
1.
Data analysis is time consuming and laborious.
2.
It is difficult to accurately verbalise the cognitive behaviour.
3.
Verbalisation may interfere with the performance of the primary task.
4.
There may be participant bias and this may affect the outcome of the analysis.
1.3 Cognitive Task Analysis (CTA) Methods
Cognitive Task Analysis methods are used to analyse and represent the knowledge and
cognitive activities which individuals utilise to perform complex tasks. It is a descriptive
method as it documents the individual’s thoughts and decision-making process.
CTA methods focus primarily on how people function in cognitively demanding domains.
They are most useful in developing training programmes, developing performance
measures, and developing selection criteria for very demanding jobs. They may also
provide insights into creating effective decision support systems and other software
systems to aid in the performance of critical and complex tasks.
The following CTA methods will be described in detail:
• Applied Cognitive Task Analysis (ACTA)
• Cognitive Walkthrough (CW)
• Critical Decision Method (CDM)
• Critical Incident Technique (CIT)
1.3.1 Applied Cognitive Task Analysis (ACTA)
Applied Cognitive Task Analysis is a method for performing cognitive task analysis. It is
a streamlined method of CTA and consists of 3 interview methods that help the analyst
to extract meaningful information about cognitive task demands and skills required for
the task. In using ACTA, information is translated into improved training scenarios or
SU4-12
HFS219
Task Analysis Methods
improved design interfaces. Critical cognitive elements are elicited from subject matter
experts (SME’s) using ACTA techniques.
Procedure includes a series of three structured interviews.
1.
The first interview generates the Task Diagram, which provides a broad
overview of the task and highlights difficult cognitive portions of the task that
should be probed further.
2.
This is followed by a Knowledge Audit, which surveys the aspects of expertise
required for a specific task or subtask.
3.
Finally, in the Simulation Interview, the cognitive processes of experts are probed
within the context of a specific scenario on the job.
4.
The output of the process is a Cognitive Demands Table, which presents the
results so they can be applied to a specific project. The cognitive demands table
helps in finding the common themes in the data and conflicting information
given by multiple SME.
a.
Cognitive Demand Table includes the following:
• Difficult cognitive element
• Explains why a task is difficult
• Common errors
• Critical cues and strategies
(For more details on the procedure, refer to pages 88, 89, 90 of Human Factors Methods.)
Tools required: Pen and paper, Audio and video recording equipment
Advantages
1.
Provides the researcher with a set of probes.
2.
ACTA has been used extensively for analysing cognitive difficulties as well as
cognitive skills in task performance and offers a structured approach to cognitive
task analysis.
SU4-13
HFS219
Task Analysis Methods
Disadvantages
1.
Requires great skill for the researcher to maximise the benefit from the analysis.
2.
Time intensive and laborious including training time to learn ACTA as well as
data analysis.
Consistency and hence reliability of the method are questionable.
1.3.2 Cognitive Walkthrough (CW)
Cognitive walkthrough analyses the mental processes during the task performance. It is
also a way of testing usability when there are no appropriate subjects available.
It has been used primarily for the evaluation of human computer interaction specifically
user interface usability and ease of learning of the interface. Cognitive walkthrough is
applied to the design and evaluation of complex systems, for example, fighter aircraft
cockpit.
Cognitive walkthrough involves the analyst walking through each user action involved
in task step. It is performed by an analyst; therefore it does not require real users. It can
be performed at any stage of the design cycle.
5 key features of a cognitive walkthrough:
1.
It is performed by an analyst and reflects analyst’s judgement. It is not based on
data from user testing.
2.
Examines the specific user tasks rather than global aspects of user interface.
3.
Analyses the correct sequence of actions but one must verify what the correct
sequence of actions is.
4.
Analyst must consider the knowledge of the users that influences the mental
processes.
5.
May help to identify the likely trouble spots in an interface and suggest a reason
for the trouble.
Tools required: Pen and paper, Audio and video recording equipment
SU4-14
HFS219
Task Analysis Methods
Procedure
Preparation Phase
1.
Select tasks and create task descriptions – HTA can be used for creating task
descriptions for the selected tasks.
2.
Determine the correct sequence of actions.
3.
Identify user groups for the user interface.
4.
Identify and record user’s initial goals – what goals the user has at the start of
the task.
Evaluation Phase
5.
Analyse the interaction between user and the interface – Analyst to walkthrough
each task, applying the criteria determined in the steps above.
The cognitive walkthrough evaluation concentrates on the following key aspects:
• The relationship between required and actual goals the user has
• Problems in selecting and executing an action
• Changing goals due to action execution and system response
(Refer to pages 97, 98 of Human Factors Methods for examples on CW.)
Advantages
1.
CW can be performed at any stage of the design cycle from early design process
to system evaluation for redesign.
2.
CW can help to predict errors.
3.
It presents a structured approach to user interface analysis.
Disadvantages
1.
Requires great skill on behalf of the analyst for the method to achieve its full
potential.
2.
It may be time consuming for more complex tasks.
3.
Requires further validity and reliability testing.
SU4-15
HFS219
Task Analysis Methods
1.3.3 Critical Decision Method (CDM)
Critical Decision Method (CDM) is a semi-structured interview method that uses cognitive
probes in order to elicit information regarding expert decision-making. CDM normally
is used in analysing non-routine incidents such as emergency incidents or highly
challenging incidents in which the expert’s skills are applied.
Advantages
1.
Once known, CDM can be quick and east to apply.
2.
CDM can be used in many domains.
Disadvantages
1.
It is always not easy to gain access to the subject matter experts who are the key
for this method to be used.
2.
Reliance on verbal reports of the expert introduces bias related to facts being
misrepresented.
1.3.4 Critical Incident Technique (CIT)
Critical Incident Technique (CIT) is a method that is used retrospectively to analyse
operator decision-making. The operator is asked to recall and discuss a specific incident
that was of particular importance in some context of task performance.
The goal of the CIT is to obtain a complete account of the operator’s solution plan as
well as the factors that influenced the design of the plan. A risk associated with using the
technique is that knowledge elicited may be idiosyncratic or atypical, so it is usually used
in combination with other methods.
Tools required: Pen and paper, Audio and video recording equipment
Procedure
1.
Select incident to be analysed.
SU4-16
HFS219
Task Analysis Methods
2.
Gather data and record account of the incident − It involves the use of interviews
to facilitate operator recall of critical events or incidents, including action and
decisions made by themselves and colleagues and the reasons why they made
them. The analyst uses a set of probes to elicit relevant information about the
participant’s thought process during the scenario.
3.
Construct an accurate incident timeline to give a clear picture of the event.
4.
Select specific incident points to be further analysed.
5.
Probe selected incident points.
Advantages
1.
The CIT can be used to elicit specific information regarding decision making in
complex systems.
2.
Flexible method with very high face validity.
3.
Can be used extensively in wide variety of domains.
Disadvantages
1.
Reliability is questionable.
2.
Problems associated with the recalling of past events such as memory
degradation.
3.
Interviewers should be very experienced in being able to elicit the required
information.
Operators may not wish to recall events in which their performance is under scrutiny.
Read
Human Factors Methods: A Practical Guide for Engineering and Design, Chapters 3
and 4
SU4-17
HFS219
Task Analysis Methods
Summary
This study unit provided an overview of four general task analysis methods and four
cognitive task analysis methods that are commonly used by human factors researchers.
Task analysis methods help researchers to break down a task in a structured and
systematic way. The most fundamental and widely used task analysis method is the
Hierarchical Task Analysis.
SU4-18
HFS219
Task Analysis Methods
Formative Assessment
1.
Mei Fen wants to study the decision-making process of pilots and her professor
suggested that she use a “think aloud” approach. Which of the following methods
uses a “think aloud” approach?
a. Verbal protocol analysis
b. Case study
c. Questionnaire
d. GOMS
2.
Su Fen is using a task analysis method that required her to construct a timeline. Which
of the following task analysis methods is she using?
a. Hierarchical Task Analysis
b. Applied Cognitive Task Analysis
c. Critical Incident Technique
d. Cognitive Walkthrough
3.
Which of the following is a retrospective method?
a. Critical Incident Technique
b. Cognitive Walkthrough
c. GOMS
d. Hierarchical Task Analysis
4.
Which of the following methods requires subject method experts?
a. Critical Incident Technique
b. Hierarchical Task Analysis
c. Critical Decision Method
d. Verbal Protocol Analysis
SU4-19
HFS219
Task Analysis Methods
Solutions or Suggested Answers
Formative Assessment
1.
Mei Fen wants to study the decision-making process of pilots and her professor
suggested that she use a “think aloud” approach. Which of the following methods
uses a “think aloud” approach?
a.
Verbal protocol analysis
Correct. Verbal Protocol Analysis is a method that is used to elicit verbal
description of the processes (cognitive and physical) from individual
performing a task.
b.
Case study
Incorrect. Case study is not a task analysis method.
c.
Questionnaire
Incorrect. Questionnaire is not a task analysis method.
d.
GOMS
Incorrect. GOMS is a method used to define the user’s goals, decompose
these goals into sub-goals and demonstrate how the goals are achieved
through user interaction.
2.
Su Fen is using a task analysis method that required her to construct a timeline. Which
of the following task analysis methods is she using?
a.
Hierarchical Task Analysis
Incorrect. HTA does not require the construction of a timeline.
b.
Applied Cognitive Task Analysis
Incorrect. ACTA does not require the construction of a timeline.
c.
Critical Incident Technique
SU4-20
HFS219
Task Analysis Methods
Correct. CIT is a method that requires individuals to recall the timeline of
events that led to a specific incident and discuss a specific incident and
discuss it in the context of task performance.
d.
Cognitive Walkthrough
Incorrect. Cognitive Walkthrough does not require the construction of a
timeline.
3.
Which of the following is a retrospective method?
a.
Critical Incident Technique
Correct. CIT is a method that is used retrospectively to analyze operator
decision-making.
b.
Cognitive Walkthrough
Incorrect. Cognitive Walkthrough is not a retrospective method.
c.
GOMS
Incorrect. GOMS is not a retrospective method.
d.
Hierarchical Task Analysis
Incorrect. HTA is not a retrospective method.
4.
Which of the following methods requires subject method experts?
a.
Critical Incident Technique
Incorrect. CIT does not require subject matter experts.
b.
Hierarchical Task Analysis
Incorrect. HTA does not require subject matter experts.
c.
Critical Decision Method
Correct. CDM requires subject matter experts.
d.
Verbal Protocol Analysis
SU4-21
HFS219
Task Analysis Methods
Incorrect. VPA does not require subject matter experts.
SU4-22
Study
Unit
Physical Measurements
5
HFS219
Physical Measurements
Learning Outcomes
By the end of this unit, you should be able to:
1.
Explain the principles and various methods used for assessment of dexterity,
strength, and environmental conditions
2.
Apply the principles and describe the tools used to collect data regarding
dexterity, strength, and environmental conditions
SU5-2
HFS219
Physical Measurements
Overview
This unit will cover the concepts and methods related to the measurement of human
physical capabilities such as dexterity and strength. Environmental measurement tools
that are commonly used by human factors professionals will also be introduced.
SU5-3
HFS219
Physical Measurements
Chapter 1: Dexterity, Strength, Environmental Methods
1.1 Dexterity
The musculoskeletal structure of the human body gives us the ability to perform a wide
range of physical activities – from minute tasks such as threading a needle that uses few
muscles in our hands, to heavy actions such as somersaults that require the use of various
muscles in the whole body.
Basic types of body movements:
• Flexion: Movement that results in a decrease in angle at a joint, e.g., bending the
elbow.
• Extension: Movement that results in an increase in angle at a joint, e.g., straightening
the elbow. Moving the joint beyond its normal extended position is known as
hyperextension.
• Abduction: Movement away from the midline of the body in a lateral plane, e.g.,
raising the leg sideways.
• Adduction: Movement towards the midline of the body in a lateral plane, e.g.,
moving one leg from the side towards the other leg.
1.1.1 Control of Motor Responses
HF practitioners are generally interested in purposeful motor responses, i.e., responses
that are intentionally executed by a person to achieve a goal. With practice, humans are
able to improve the control of their muscles and gain precision in activating the right
muscles with the right amount of force at the right time to achieve their goals. This is
known as skill.
To better understand human performance and motor skills, motor movements are
categorised into several major classes:
• Discrete movements: single movement to a stationary target, e.g., pressing a button
SU5-4
HFS219
Physical Measurements
• Repetitive movements: repeated movements to a stationary target or targets, e.g.,
hammering a nail
• Sequential movements: multiple discrete movements to targets that are spaced out,
e.g., typing on a keyboard
• Continuous movements: movements that require adjustment of strength or position,
e.g., turning a steering wheel
• Static positioning movements: controlling muscles to stay stationary (absence of
movement), e.g., maintaining a yoga position
Speed and accuracy are commonly used to study human motor skills.
1.1.2 Speed of Movements
Time is a common and convenient measurement tool for movement speed. Total response
time can be separated into two parts – reaction time and movement time. For example, in
a 100-metre sprint, the time that the athlete takes to react to the gunshot and start pushing
off is considered reaction time and the time that he takes to move to the end of the 100metre mark is then considered movement time.
Reaction Time
• Simple reaction time: Time taken to respond when there is only one stimulus and one
response required, e.g., reacting to a gunshot for 100-metre sprint
• Choice reaction time: Time taken to respond when there are multiple stimuli and
each requires a different response, e.g., a badminton player deciding to hit the
shuttlecock far or drop it near the net, depending on the position of his opponent
Based on Hick-Hyman Law, reaction time increases with the increase in the number of
choices. Besides choices, there are other factors that could affect reaction time such as:
• Mode of stimulus
• Expectancy
• Age
• Stimulus-response compatibility
SU5-5
HFS219
Physical Measurements
• Practice
• Type of movement
Movement Time
Fitt’s Law explains how movement time is affected by the distance moved and the
required precision of reaching the end target.
MT = a + b log2(2D/W)
where
MT = Movement Time
a, b = Empirically derived constants
D = Distance from starting point to end point
W = Width of end target
1.1.3 Accuracy of Movements
Accuracy of movements are of great importance to HF practitioners, sometimes more so
than the speed of movements. Accuracy can be affected by:
• Personal factors
• Target location
• Distance and speed of movement
• Continuous control and tremour
• Static muscular control
1.2 Strength
Strength is usually associated with specific muscles or groups of muscles and is assessed
by measuring the amount of force that these muscles exert on an external object. Static
strength such as grip strength and torso lifting strength is commonly assessed in human
factors. Although assessment of dynamic strength is also useful, it is difficult to assess due
SU5-6
HFS219
Physical Measurements
to the confounding effects of acceleration and complex interactions of multiple muscles
over several joints.
Strength varies widely across people. In general, strength of females ranges about 35 to 85
percent of males depending on the muscle groups that are used. Age also has an effect on
strength, peaking at about 25 to 35 years old.
Some commonly used strength instruments include:
• Hand dynamometer (Grip strength)
• Spring dynamometer (Grip strength)
• Pinch gauge (Pinch strength)
• Back-leg dynamometer (Arm lift, leg lift, shoulder lift, seated torso pull)
1.2.1 NIOSH Lifting Equation
A large proportion of back injuries can be attributed to lifting tasks. NIOSH has developed
an equation that derives the recommended weight limit for lifting task so as to lower the
risks of back injuries.
RWL = LC x HM x VM x DM x AM x FM x CM
where
RWL = Recommended Weight Limit
LC = Load Constant (23kg)
HM = Horizontal Multiplier
VM = Vertical Multiplier
DM = Distance Multiplier
AM = Asymmetry Multiplier
FM = Frequ…