Module Information
Course Delivery
| Delivery Type | Delivery length / details |
|---|---|
| Lecture | 10 Hours. (10 x 1 hour lectures) |
| Practical | 10 Hours. (5 x 2 hour practical classes) |
| Seminars / Tutorials | 5 Hours. (5 x 1 hour group discussions) |
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | survey reports | 60% |
| Semester Assessment | coursework | 40% |
| Supplementary Exam | 2 Hours [practical examination, during which candidates may consult their notes (50%); survey report (50%).] open book, open note practical exam. | 100% |
Learning Outcomes
On completion of this module, a student should be able to:
1. implement the theory of finite sampling;
2. calculate sample sizes necessary to achieve predefined goals;
3. draw samples of appropriate kinds from various populations;
4. compile a questionnaire to obtain quality information;
5. collect, collate, present, analyse and interpret the data from a sample survey.
Brief description
This module combines the theory of sampling with the experience of planning and conducting a sample survey.
Aims
This module will give the student an appreciation of the value of statistical theory together with the difficulties involved in the practical application of these ideas. The student will gain experience in working as part of a team, planning and organising a sample survey, producing a questionnaire, handling and analysing real data and writing a report.
Content
2. FINITE SAMPLING THEORY: Theory of simple random sampling. Finite population corrections. Stratification, Quota, Cluster, Systematic and Multi-stage methods. Comparison of sampling designs for estimating means, totals, variances, proportions. Optimal sampling when total size or total cost is fixed.
3. PLANNING A SAMPLE SURVEY: Defining the problem, setting a time-schedule, deciding upon a suitable sampling scheme, compiling a questionnaire.
4. SOME PROBLEM AREAS: Target populations. Non-response. Surveying sensitive issues. Wildlife populations, elusive populations. Post-stratification.
5. DATA ANALYSIS: Checking for errors. Analysis of contingency tables, comparing proportions, distribution free (rank sum) methods, lucid presentation of results.
Reading List
Essential ReadingF R Joliffe (1986) Survey Design and Analysis Ellis Horwood Primo search
Students will be expected to browse through some readily available research papers. Primo search
Notes
This module is at CQFW Level 6