# Qualitative Analysis Introduction

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Research Methods
Lecture 8: Quantitative Analysis:Introduction

Tutor:                   Contact:
Prof. A. Taleb-Bendiab   E-Mail:A.TalebBendiab@ljmu.ac.uk
Telephone: +44 (0)151 231 2284
Overview of the session

• Week 1: Introduction to Quantitative Analysis
• Week 2: Basic Statistics (using SPSS)
• Week 3: Statistical Testing (using SPSS)
Research Methods Review
• Qualitative Analysis
– Case studies
– Action research
– Thought experiments
– Non numerical
• Quantitative Analysis
– Numerical
– Experiments and surveys with numerical data
– Statistical techniques used to prove / disprove
hypothesis
Quantitative Analysis and Research
• Used extensively in the natural and social
sciences to study unpredictable complex
“natural” systems
– Behaviour of people, social environment and
nature
• Computers are predictable machines so why
use quantitative analysis?
– Increased complexity (e.g. the Internet – a
vast collection of computers)
– The “human factor”
– People form an important part of the loop in the
use of computers
– People are unpredictable, so we need to quantify
their interaction with computers
Quantitative Analysis Examples
• Analysis of computer network behaviour (traffic)
• Human computer interaction
– Human perception of computers
• Use interface design and assessment
– Making computers easier to use
• Extremes of quantitative analysis
– Highly theoretical numerical study (e.g. to
analyze computer network traffic patterns)
– Questionnaire / survey (e.g. to asses a software
application used in an organization)
• Either way, quantitative analysis, like all research,
calls for a plan or procedure
Quantitative Analysis Procedure

• The goal of quantitative analysis is to prove (or
disprove) a theory or hypothesis using
numerical data
• In general, this is not an easy task and calls for
a procedure as below:
1. State a hypothesis based on a “causal” relationship
2. Selection of an independent variable(s) (the cause)
and a dependent variable(s) (the effect) in the
relationship
3. Design of a controlled experiment or survey
4. Data collection
5. Data analysis using statistical methods (week 2)
6. Statistical testing to provide evidence that proves /
Theory

Hypothesis

Selection of Variables
and Measurements

Experiment /
Survey            Survey Design           Experiment
Design                                 Manipulate Variable
Questionnaire                              and Observe
Data collection

Data Analysis

Statistical Testing
Quantitative Analysis Procedure
1. The hypothesis
– Theories are very general and difficult to test
– Hypothesis considers a limited facet of a theory
– Hypothesis take the form of “causal” relationships
between dependent and independent variables
– Goal of the experiment:
(a) Prove a “causal” relationship between the dependent
and independent variables, or,
(b) Disprove that any relationship exists (the so-called
“null” hypothesis)
– Null hypothesis is usually a statement of “no
effect” or “no difference”
Quantitative Analysis Procedure
2. Selection of dependent and independent variables
and their scales of measurement
– Three different scales of measure
– Nominal (simply choose categories – male, female)
– Ordinal (choose categories that have an “ordered”
relationship – small, medium, large)
– Interval (measurement scale of equal interval –
length, time, cost, age)
– Causality relationships often occur as variations
– Variation of the independent variable causes
variation of the dependent variable
– Heavy smokers have a greater risk of poor health
than light smokers
Quantitative Analysis Procedure
3. Experiment / survey design
– Experiments and surveys are distinguished by the
role of the researcher
– Experiments
– The researcher can actively manipulate an aspect of the
setting in the laboratory or out in the field
– In practice the independent variable or cause may be
manipulated and the effect on the dependent variable then
recorded
– Surveys
– The researcher does not manipulate any relevant aspect or
variable but simply records values
– Experiments and Surveys can be combined
Quantitative Analysis Procedure
3. Experiment / survey design (contd.)
– Sampling of a subset of a population (see handout)
– Random or non-random sampling?
– Size of sample?
– Selection of control group as a point of comparison
– Mice in experimental group A are given drug X
– Mice in control group B are not
– In summary, much to do at the experiment / survey
design stage
– The success of the analysis depends on the design
– Often several different design may be found, which is
the best?
– Pilot studies can be used to evaluate different designs
Quantitative Analysis Procedure
4. Data collection
– Organization of data into a data matrix
– Rows for members of a sample
– Columns for measurements or variables for each member
– Use a statistical package (SPSS), spreadsheet or
database to store data
5. Data analysis
– Use of basic statistical measures to make sense of
data (week 2)
– Mean or average value, median or mid-way value and
standard deviation
– Visualization techniques, such as frequency distributions,
bar charts and box-plots reveal patterns in the data
Quantitative Analysis Procedure
5. Data analysis (contd.)
Frequency                             Normal distributions
1. Easy to deal with mean and
median values are in the
middle
2. Many biological growth
Variable                    lifecycles are described by a
normal distribution (plants,
flowers etc.)

Skewed or unbalanced distributions
1. Mean value is not obvious
2. statistical analysis is needed to
find the mean value
Quantitative Analysis Procedure
6. Testing the hypothesis
– Use of statistical “significance” tests to prove /
disprove hypothesis (week 3)
– Tests provide “court-room” evidence that our
hypothesis is true or false
– Statistics, unlike Mathematics can never give 100%
– Tests result in a probability or confidence factor
– Typically we may prove / disprove our hypothesis with a
probability or confidence factor of 0.95 (95%)
– Time permitting: re-running the experiment for a
second, third, fourth etc. time with different
samples can reinforce the results of the experiment
Relevance of Quantitative Analysis
• Quantitative analysis may be relevant to your
research topic
– Analysis of User Interfaces and HCI both often
use quantitative analysis techniques
– Multimedia and games often form the basis of the
a research experiment design
– Children learning via computers is often studied and
observed / measured using multimedia software or
playing computer-based interactive games
– Surveys to analyze impact (usefulness) of IT in
sectors of industry
– Computer security and network traffic
– Network traffic patterns apparent in security attacks
(crashing web servers at 1am on New Years Day)

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 views: 4 posted: 3/13/2012 language: pages: 15