Course number: STAT 702 (395419) Course name: Research Methodology
Academic Year 2011 Course Convenor: Prof Gabriel TATI
Level: Postgraduate (Honours and Masters)
Course Objectives
This is an advanced course on Research Methods purposefully designed for
Postgraduate Students specialising in Applied Statistics, population studies
(MSc and Honours) business sciences. The course draws essentially upon quantitative data analysis and its embedment into research process as is discussed in most relevant textbooks. In order to be able to appreciate the kinds of analyses that are conducted in relation to such data and possibly analyse their own data (especially since students are required to carry out projects for Masters and PhD Thesis), an acquaintance with the wide range and interpretation of descriptive and some knowledge of inferential statistics is highly desirable for post-graduate students. The presentation makes extensive use of worked examples drawn from real-life situations to promote step-by-step understanding of the material presented. This will assist students in consolidating and acquiring problem-solving skills in research methods.
Study questions are placed at the end of each chapter. These study questions are intended to help the students master each topical section, and prepare for the tests and assignments.
Number of credits : 15
Course outcomes:
After completing this course, the students will be able to:
- Understand the significance and the major stages of research process.
- Design and implement their research project.
- Conduct quantitative data analysis with SPSS.
- Communicate their research findings in a scientific manner to target audiences.
Methods of assessment
Two class tests and a series of assignments are scheduled. The possibility of writing a third test will be discussed in accordance with the time allocated to the course. Additionally, the students will be requested to prepare and submit a research protocol at the end of the second term. Selected topics will be specified for Assignments. The guidelines for the preparation of the research proposal will be outlined in due course.
The percentage weighting for class test, assignment and research protocol is as follows: Test (40%), Assignment (10%), Research Protocol (15%) and a

written final exam (35%). The final exam will be written toward the end of June
2011 (a date will be communicated by end May 2011).
For this course, the recommended textbooks are:
1) Research Methodology (revised versions) Volumes 1 & 2 prepared by the Lecturer.
2) Business Research Methods.
Seventh Edition , by Donald R.Cooper
Pamela S.Schindler. McGraw-Hill Irwin.
3) Quantitative Research Methods for Professionals , 2007, by W. Paul
Vogt published by Pearson Eduycation Allyin Bacon (available at Van Schaik store, Parow Centre Mall, Shop 120)
4) The Practice of Social Science Research, 2010 Edition, by Earl Babbie et al, Oxford university press (available at Van Schaik store, Parow Centre,
Shop 120).
Available in the UWC Library, (Series 300-72)
Ref: 300.18 MOU Basic Concepts in the methodology of the social
Sciences by Johann Mouton and HC Marais. HSRC Series in Methodology
Series Editor: Johann Mouton. Read Part 1 and Part2.
Ref: 300-72 CRE Research Design, Second Edition by John W.Creswell
Read from Part 1 to Part 11
Ref 300-72 SPR Handbook of Research Methods by Natalie L Sproull
Read from chap 1 to chap 16.
Ref 300-72 HUY Methodology for the Social and Behavioural Sciences by G K Huysamen Read chap 1, 2, 3, 8, 9, 11, 12, 14, 15, 16.
Ref 300-72 SAR Social Research Second Edition by Sotirios Sarantakos
Read chap 1, 2, 3, 4, 5, 6, 10, 11, 14, 15, 16, 17, 18, 19).
Ref 300-72 BAL Social Research in Developing countries: Surveys and
Censuses in the Third World By Martin Balmer and Donald P.Warwick
Read section 1 to 7.

The Lecturer has prepared relevant course notes that strive to present, in a clear and concise way, the fundamentals of research methods. These notes are presented in two volumes that contain the material examined in part one and part two of the course. Volume one refers to part one and presents the fundamentals and principles of research methodology. The second volume refers to Part Two and presents some basic statistics and introductory applications of SPSS with window. To keep pace with the complexity of each topic presented, students are strongly advised to work out the study questions assigned to them. The two volumes are covered by copy rights.
It is by no means intended to cover exhaustively the various topics. The student should expand the course notes and deepen them by supplementary readings. The list of readings proposed below may serve as sources for valuable information on the topics discussed in class.
Thursday: 10h00 am – 13h00 p.m.
Friday: 9h00-12h00.
Venue: PG Lab (Statistics Department, 3rd floor, Science Building)
Terms: 2
1. Purpose and significance of Research in scientific enquiry
2. Logical framework of the scientific approach and its nature
3. Conceptual foundations of research
4. Theory and its place in the generation of scientific knowledge
5. Usefulness of research in scientific enquiry
6. Quantitative and qualitative methods
7. Major steps in the quantitative research process (overview)
8. The starting research question (broad perspective)
9. Exploring and refining the research question (s)
10. The research problem and the intellectual claim
11. Constructing a model of analysis
12. Research design and the Measurement problem

13. Measurement, validity and Reliability
14. Sampling and sample design.
15. The observation of facts (data collection).
16. Survey research (Cross-sectional design).
17. Analysis of information (data processing and analysis).
18. Interpretive framework (Discussion of findings and contribution to scientific knowledge)
19. Writing up research report.
20. Ethics in scientific research.
21 Writing a proposal proposal
22: Practical sessions and Assignments.
The Plan of the Course
Part One
The overall goal of Part One is to offer a comprehensive and systematic treatment of the scientific approach within the context of social science, with special reference to the major programmes taught in the department of
Statistics. Along this line, an emphasis is put on the linkages (relationships) between theory and practice. The underlying intention is to integrate research activities in an orderly framework so that the student can more easily comprehend the nature or social or business science research.
It is widely recognised that research, be it social of natural science, is a cyclical, self-correcting process consisting of seven major interrelated steps: definition of research problem, statement of the hypothesis, research design, measurement, data collection, data analysis, and generalisation. Theory is at the very core of these steps. In other words, theory both affects and is affected by each of them.
The logic underlying the presentation of the course material is to progress
(smoothly) from the conceptual and theoretical building components of the research process to data analysis and some computer applications. Such a progression is intended to offer students a comprehensive and systematic foundation for comprehending the breadth and depth of science research. The course is structured in chapters as follows:

The first and second Topic presents some general epistemological considerations underlying scientific. A synthetic examination is made for these issues related to the foundations of scientific knowledge, scientific knowledge, and the basic assumptions of the scientific approach. Logic in Algebra will be used as an illustration of deduction.
The third Topic is an examination of the conceptual foundations of research, followed by a discussion, Topic 4, of what a theory is (or put differently, what a theory is not) and its relationship, Topic 5, with the usefulness of scientific research outcomes. These topics also look into the issues relating to the concept formation, the roles and types of theories, models, variables, and the various sources for research problems and the construction of hypotheses.
Topic 6 examines the differences in the methodology used in qualitative and qualitative research perspectives.
Topic 7 is an overview of the major steps in the research process with a focus on the conceptualisation (theoretical construction) of the social reality under investigation.
Topic 8, 9 and 10 elaborates on ways in which the research question is broadly initiated and gradually refined to make it investigable. The statement of the research problem is an important step in research process, as it indicates the niche into which the researcher is contributing to the existing body of knowledge. Topic 8 and 9 are specifically concerned with the appropriate way of formulating such a statement from a theoretical perspective.
The modalities pertaining to research design and model construction are examined in details in Topic 11 and 12. The research design is an important stage in the investigation as it provides the logical framework for answering the research question (s), and that way, the logical model for inferring causal relations. Different types of research design will be reviewed in line with their appropriateness to social research. This chapter also examines issues relating to measurement, quantification, validity and reliability. The principles of sampling theory and the practical aspects of sampling designs are presented in the last sections of the chapter. Topic 11 focuses on the formulation of testable hypotheses for the research.
Topic 13, 14, 15 and 16 deal with various methods of data collection available to social and natural scientists. Scientific observation heavily relies on questionnaire construction which follows on theory or conceptual building. The construction of such a questionnaire is examined through the content of questions, types of questions, question format, and the sequence of questions. The chapter will also address some of the frequently encountered pitfalls in the course of questionnaire construction.
Data processing and analysis are discussed in Topic 17. Techniques in codebook construction, coding schemes and preparation of data for computer processing are examined in detail. The chapter also includes guidelines on

data exploring by ways of simple and cross-tabulation, associations and relationships between variables, and summarizing.
Topic 18 is concerned with the interpretation of results. Statistical results may provide the quantitative framework for supporting or refuting the research hypotheses. The limits of statistics with regard to the interpretation of research findings will be underscored in the presentation of the topic.
Topic 19 is devoted to the writing of research report. A report must clearly provide detailed accounts on way in which the research was carried out from the start research question to the interpretation of results. There is no sense in conducting research if the results obtained are not released to the scientific community (for a critical and objective evaluation).
Ethical concerns in research are examined in Topic 20. Any research involving human or animals must comply with ethics. The discussion will revolve around ways of ensuring the rights and the welfare of the research participants, including the right to privacy.
Topic 21 presents the requirements for a convincing proposal for research.
The last part of the volume 1 contains a step-bystep exposé on how to use
Excel to conduct data querying, summarise data via pivot tables and charts, and conduct advanced statistical analysis (with the data-analysis add-in module). It also contains a series of exercises mostly taken from previous test and exam papers written by students from 2005 to 2010.
Part Two
Part 2 of the course is designed for those with no previous strong knowledge of quantitative data analysis, and includes inferential statistics. The emphasis throughout this part two is on understanding the underlying principles of data analysis, and on bringing these out with reference to simple but realistic worked samples using the statistical software package SPSS. The teaching deals exclusively with cross-sectional data. The presentation of the material is organised around four integrated components. The first component consists of substantive chapters presenting the basic principles in statistical measuring. In the second component, key concepts are presented at the end of each chapter, and students should make sure that they understand their meaning, this component also include multiple choice questions at the end of each chapter to be used by students as a small test as a way of assisting in the absorption of some of the main ideas and/or statistical techniques contained in that chapter. The third component consists of substantial assignments involving data analysis tasks on the computer at the end of the chapters. These assignments combine the computing skills which students need to undertake specific pieces of analysis, with an understanding of the statistical procedures presented in the relevant chapters. Assignments also include writing a brief report on each analysis, which will give them practice at

writing up results. The fourth and final component presents computing instructions after the presentation of each chapter. This presentation is primarily concerned with getting across the main points that students need to know about doing data analysis using SPSS on a PC. The basic information about relevant SPSS procedures are presented first, and students only need to follow those instructions to do data analysis. Part two is structured around six practical sessions.
Session one: Introductory Notes on Data Collection
. Underlying principles of data analysis
. Levels of measurement of variables
Key concepts to discuss include: data analysis, socio-logic, experiment, variance, dependent and independent variable, operationalisation, levels of measurement, validity and reliability.
Session two: Data Collection and Preparation
. Principles of data gathering
. Principles of data preparation and coding for analyses
. Principles of data entry
. Creation of data files in SPSS (basic file handling procedures, description of different types of files in SPSS)
Key concepts to discuss include:
Questionnaire constructing (structured/unstructured questions)
Data coding
Pre-coding
Code book/code frame
Raw data file
Session three: Descriptive Data Analysis
. Univariate and bivariate frequency distributions
. Description of measures of central tendency
. Description of simple indices for different levels of measurement of variables
. Description of the properties of the Normal Distribution
Key concepts will include
Frequency distribution, descriptive statistics, bar charts, histograms, central tendency, measures of dispersion, normal distribution.
S ession four. Univariate and bivariate Descriptive Statistics Using SPSS
. Introductory principles of analysis using SPSS
. Description of the logical sequence of SPSS commands
. Production of univariate and bivariate descriptive statistics using SPSS.
Session five: Bivariate Analysis for categorical variables and interval variables.
. Setting up and interpreting cross-tabulated data
. Assessing association between cross-tabulated variables
. Standardised measures of association
. Simple measures of association for different levels of measurement.

. Purpose and use of the regression line in simple linear regression.
. Computational procedures of a regression line, including a and b coefficients
. Meaning and importance of the residual variation
. Principles of correlation analysis, including calculation of r 2 .
. Assumptions underpinning regression analysis.
Key concepts t o discuss include: bivariate analysis, cross-tabulation, marginal frequencies, model of no association (statistical independence), standardised measures of association, proportionate reduction of errors, simple linear regression, scatter plot, regression line, residual variance, regression coefficients, correlation analysis.
Session 6: Inferential Statistics and the generalisation from Sample to
Population
. Purpose of statistical significance testing
. Description of the distribution of repeated sample means
. The Confidence interval and the null hypothesis
. Interpretation of a t-test for the difference between two sample means
. Interpretation of test for the difference between two sample proportions
. Extension of the t-test to ANOVA (one way and two-way classification)
. Calculation and interpretation of a χ 2 test.
. Assumptions behind, and limitations of, statistical significance tests.
Key concepts to discuss include : inferential statistics, distribution of repeated sample means, standard error of the mean, null hypothesis, statistical significance, t-test, dichotomous variable, normal approximation to the binomial distribution, ANOVA, continuity correction, chisquare (χ 2 ) tests, scientifically/non-scientific samples, non-parametric statistics, type I/type II errors.