1. The building blocks of scientific research: types of research (analytical, descriptive, experimental, and qualitative), scientific parlance (hypothesis, concepts, operational definitions, and dependent/independent variables), sampling procedures, and measurement issues (reliability and validity).
2. Research and data collection methods: experimental research (developing hypotheses, independent/dependent variables, controls, sample selection, study designs, and experimental validity); descriptive research (questionnaires and interviews, case studies); qualitative research (characteristics, procedures, methods of data collection, data analysis, and internal/external validity).
3. The nature of research; scientific methods of enquiry, pure versus applied ways of problem solving. Developing the research problem; identifying a topic area, devising specific questions, discovering what is already known (reviewing the literature), determining feasible ways to answer the questions.
4. Ethics in research.
5. Introduction to data analysis software (SPSS for Windows). Establishing an SPSS database. Defining and transforming variables; data storage and retrieval.
6. Data analysis for descriptive and experimental research; descriptive statistics. Describing data; measures of variability, correlation and scatter plots. inferential statistics. Selecting an appropriate statistical test (parametric or non-parametric), and types of statistical tests (chi-square; t-tests; one-way ANOVA & post-hoc tests; Wilcoxon, Mann-Whitney U). Worked examples in SPSS. Repeated Measures ANOVA; Factorial ANOVA, Limits of agreement analysis for method comparison and test retest reliability. Worked examples in SPSS.
1. To expose students to the essential elements in the process of conducting sound scientific research.
2. To develop students’ skills in the key aspects of data handling and statistical analysis.