1. Tutor: Prof. A. Taleb-Bendiab Contact: E-Mail:A.TalebBendiab@ljmu.ac.uk Telephone: +44 (0)151 231 2284 CMPDLLM002 Research Methods Lecture 8: Quantitative Analysis:Introduction

2. Overview of the session Week 1: Introduction to Quantitative Analysis Week 2: Basic Statistics (using SPSS) Week 3: Statistical Testing (using SPSS)

3. 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

4. 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

5. 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

6. 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 / disproves the hypothesis (week 3)

7. Theory Hypothesis Selection of Variables and Measurements Data collection Data Analysis Statistical Testing Survey Design Questionnaire Experiment / Survey Design Experiment Manipulate Variable and Observe

8. 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”

9. 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

10. 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

11. 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

12. 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

13. Quantitative Analysis Procedure 5. Data analysis (contd.) Normal distributions 1.Easy to deal with mean and median values are in the middle 2.Many biological growth 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 Variable Frequency

14. 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

15. 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)