detailed semester course outline for

BASOCW Research Methodology and Statistics (RMEST05)

(Designed for a 14–16 week semester structure, adaptable to hours/contact time)

Part A: Research Methodology

Module 1: Introduction to Research

• Definition, scope, and importance of research

• Types of research: basic vs. applied, qualitative vs. quantitative, exploratory vs. descriptive

• The research process and stages

Module 2: Research Problem and Literature Review

• Identifying and defining research problems

• Formulating research objectives, research questions, and hypotheses

• Conducting literature reviews (sources, referencing, citation styles)

• Avoiding plagiarism and ensuring academic integrity

Module 3: Research Design and Methodology

• Research design: experimental, descriptive, case study, survey, longitudinal, cross-sectional

• Sampling techniques: probability and non-probability sampling

• Data collection methods: questionnaires, interviews, focus groups, observation, secondary data

• Ethical considerations in research

Module 4: Qualitative Research Methods

• Nature and characteristics of qualitative research

• Case studies, ethnography, phenomenology, content analysis

• Coding and analyzing qualitative data

• Reliability and validity in qualitative research

Module 5: Quantitative Research Methods

• Nature and characteristics of quantitative research

• Surveys, experiments, and statistical sampling

• Measurement scales: nominal, ordinal, interval, ratio

• Reliability, validity, and measurement errors

Module 6: Writing Research Proposals

• Components of a research proposal: introduction, literature review, methodology, timeline, budget

• Presentation of a research proposal

• Peer review and feedback

Part B: Statistics for Research

Module 7: Introduction to Statistics

• Meaning, scope, and importance of statistics in research

• Data types: primary vs. secondary, qualitative vs. quantitative

• Levels of measurement and data coding

• Presentation of data: tables, charts, and graphs

Module 8: Descriptive Statistics

• Measures of central tendency: mean, median, mode

• Measures of dispersion: range, variance, standard deviation, coefficient of variation

• Skewness and kurtosis

Module 9: Probability and Sampling Distributions

• Basic concepts of probability

• Normal distribution, z-scores, and standard error

• Sampling distribution and the Central Limit Theorem

Module 10: Inferential Statistics

• Hypothesis testing: null and alternative hypotheses, significance levels

• Parametric tests: t-test, ANOVA

• Non-parametric tests: Chi-square test

• Correlation and regression analysis

Module 11: Statistical Tools and Data Analysis

• Introduction to statistical software (SPSS, STATA, Excel, or R basics)

• Data entry and cleaning

• Running descriptive and inferential statistics in software

• Interpretation and presentation of results

Module 12: Research Report Writing

• Structure of a research report/thesis (abstract, introduction, methodology, results, discussion, conclusion, references)

• Presentation of data (tables, graphs, figures)

• Referencing and citation (APA/Harvard style)

• Oral presentation of research findings

Teaching Methods

• Lectures and tutorials

• Practical workshops (statistical software training)

• Group discussions and presentations

• Case studies and field research practice

Assessment Methods

• Continuous assessment: assignments, class tests, proposal writing (40%)

• Mid-semester exam (20%)

• Final exam/project (40%)

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