Research Methodology Data Sampling and Data Analysis

Course Objectives

At the end of the course the student will have the ability to:

  • Distinguish between different types of data;
  • Collect and organise different types of data in appropriate ways;
  • Understand the types of sampling methods and their relevance;
  • Design their own research instrument to collect research data;
  • Articulate what descriptive statistics can reveal;
  • Understand the fundamentals of inferential statistics;
  • Describe various styles of interpretation of data;
  • Compare the relative appropriateness of different analytical approaches for a specific study;
  • Apply one or more analytical approaches to the data collected;
  • State the ethical issues involved in using data in research;
  • Use a specified/selected program to perform basic data analysis;
  • Present the results of data analysis and make logical inferences.

Course Content

Introduction: Data and Sampling

  • What is data in the context of research?
  • Use of data:
  • Types of data:
    - Quantitative or Discrete data: nominal, ordinal; dichotomous
    - Qualitative or continuous data: interval data; ratio data
  • What is sampling?
  • Types of sampling
  • Population and sample
  • Sampling frame
  • Sample size

Probability and nonprobability sampling

  • Probability Sampling:
    - Simple random sampling, Systematic sampling, Stratified sampling, Cluster/Multi-stage sampling

Non-Probability Sampling:

  • Convenience sampling (accidental sampling / opportunity sampling), judgment sampling, purposive sampling, quota sampling
  • Sampling errors and biases, e.g. selection bias, random sampling error
  • Non-sampling errors
    - caused by data collection, processing or sample design, e.g. overcoverage, undercoverage, measurement error, processing error, non-response error

Case study discussion

Data Collection Methods

  • Research instrument
    - Validity
    - Reliability
  • Research instrument
    - Surveys
    - Interviews
  • Advantages and disadvantages of different research instruments
  • Structured, semi-structured and unstructured questions
  • Characteristics of good instrument design
  • Types of measurement scales, e.g. Likert scale
  • Capturing / coding various types of quantitative data
  • Good practices in managing qualitative data: systematic file naming; data tracking system; document transcription / translation procedures; realistic timelines
  • Ethical issues in data collection and data management, including accuracy, privacy, confidentiality
  • Case studies

Data Analysis

  • Data cleaning, missing data

Descriptive statistics

  • Measures of spread: mode, median, mean;
  • Measures of central tendency: standard deviation, variance, etc.

Inferential statistics

  • Purpose of inferential statistics
  • Estimation of parameters;
  • Hypothesis testing
  • Types of variables: independent variable; dependent variable

Correlation analysis

  • Regression analysis

Overview of selected software tool to perform data analysis

  • Presentation of results and inferences

Recommended Text:

Book Title -: Business Research Methods (11th Ed.)
Author/s -: Cooper, DR & Schindler, PS.
Publisher -: New York: McGraw-Hill, (2010)

Book Title -: Research methods for Business Students (5th Ed.)
Author/s -: Saunders, M.
Publisher -: Harlow : Pearson Education.(2012)

Syllabus