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Qualitative Analysis Introduction

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					CMPDLLM002
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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