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Short Learning Programme
on
Statistical Research Methods
Managers and researchers use questionnaires to conduct survey research to gather information (primary data) about their target populations. They need to know how to design appropriate questionnaires and to select relevant samples from the target populations. The collected data (samples) can now be used to make some inferences about the target population.

Purpose of the course:

At the end of the programme participants will be able to assist managers and researchers to design questionnaires and to conduct surveys to collect data. To draw appropriate samples from the populations of interest. To use sample information to make some inferences about the population parameters. To use computer software (SPSS, Excel, etc) to carry out calculations.

Admission requirements:

Learning assumed to be in place: 
Learners must be able to utilise data collection techniques, analyse and present their findings in a research report.

Course outcomes and assessment criteria :

Course outcomes and the associated assessment criteria: 

Study Unit

Outcomes

Assessment Criteria

  After completion of this course, participants will: Participant will be assessed on the following criteria:

MODULE 1  (US 262539):

UTILISE ALTERNATIVE METHODS TO COLLECT DATA 

At the end of the learning cycle, the learners will demonstrate knowledge and understanding of:

  • Data collection methods.
  • Principles of data collection.
  • Stages of planning a survey.
  • Design and evaluate questions for simple survey objectives. 
1.  Compare various data collection methods.
  1. Describe different data collection methods to compare their advantages and disadvantages. 
  2. Evaluate a specific data collection method used in a survey is a view of the survey objectives and resource constraint. 
  3. Select a data collection method for a survey is in view of the survey objectives and resource constraint. 
  4. Explain the difference between primary and secondary data sources to distinguish methods of data collection in each case. 
2.  Explain the basic principles of data collection.
  1. Explain the meaning of the term survey to distinguish it from other data collecting activities such as census and experiment. 
  2. Identify a given data collecting activity as a census, a survey, an experiment or an administrative data collection. 
  3. Describe the various stages of primary data collection to highlight the advantages and disadvantages of different approaches used at each stage. 
  4. Define the key functions of an enumerator to reflect the qualities of a good enumerator. 
  5. Designate the key functions of a supervisor to reflect the qualities of a good field supervisor.
3.  Explain the main stages in planning a survey.
  1. Outline the basic terms associated with surveys to prepare for survey planning activities. 
  2. State the main stages in planning a survey in order to reflect a comprehensive survey process. 
  3. Explain the importance of sensitivity to social profile in planning a survey to reflect the way it affects data collection. 
4.  Design and evaluate questions for simple survey objectives.
  1. Construct a few survey questions in order to obtain information relevant to the survey objectives. 
  2. Examine survey questions to assess their relevance to the survey objectives. 
  3. Modify survey questions in order to improve the chance of obtaining reliable responses. 

MODULE 2 (US 262559):

SELECT AND USE SAMPLING METHODS 

At the end of the learning cycle, the learners will demonstrate knowledge and understanding of:

  • Identify population,
  • Select sampling frame
  • Define different methods of sampling
  • Compare methods of sampling
  • Use different sampling methods.
  • Design sample for given survey objective and resource constraint.
1.  Identify population, select sampling frame, and potential errors.
  1. Discuss the relationship between a population and an associated sampling frame in order to determine the coverage. 
  2. Identify possible sampling and non-sampling errors in order to select the most suitable sampling frame. 
  3. Describe list frames and area-based frames in order to facilitate the selection of the most suitable frame. 
  4. Identify an appropriate population for a given context so as to facilitate the planning of a survey. 
  5. Compare sampling frames in order to select the best for a given context. 
2.  Define and compare different methods of sampling.
  1. Define probability and non-probability sampling methods and compared in order to justify the use of probability sampling. 
  2. Identify the sampling method used in a study in order to evaluate the validity of survey results. 
  3. Discuss the advantages and disadvantages of different sampling methods in a given context in order to select the best method. 
3.  Use different sampling methods.
  1. Describe the way a sampling method is used in a given context in order to assist the planning of field work. 
  2. Select a random sample in accordance with a given sampling method in order to implement a survey plan. 
  3. Use statistical software to select a random sample. 
4.  Design a sample for given survey objective and associated resource constraint.
  1. Select a suitable sampling method in order to realize the survey objective with minimal sampling error. 
  2. Calculate the sample size under given cost and error constraints. 
  3. Evaluate the sampling method used in a given study in order to determine the validity of the study results. 

MODULE 3  (US 10061):

PLAN FIELDWORK TO MEET REQUIRED DEADLINES AND BUDGET 

At the end of the learning cycle, the learners will demonstrate knowledge and understanding of:

  • A comprehensive understanding of techniques and methods for writing field instructions.
  • A basic understanding of techniques and methods for verbal and written communication.
  • An all rounded and generic understanding of the industry, the product and the industry role players
1.  Writing field instructions.
  1. Confirm that field instructions are clear, concise and specific to the original brief. 
  2. Insist that field instructions include training costs and trip/schedule details. 
  3. Write field instructions in the required format and agree on timeframes. 
2.  Selecting appropriate interviewers/moderators for the target population and interview complexity
  1. Reflect on respondent's response to different types of interviewers. 
  2. Study expertise in subject matter required by the interviewer. 
  3. Deliberate expertise required by measuring instrument. 
3.  Planning cost-effective field schedules.
  1. Confirm that field Schedule meets timing constraints. 
  2. Insist that field Schedule meets budget constraints. 
  3. Consider complexities of the sample when planning field Schedules. 

MODULE 4 (US 262557):

APPLY THE TECHNIQUES OF DATA PROCESSING 

At the end of the learning cycle, the learners will demonstrate knowledge and understanding of:

  • Code, enter, verify and validate data.
  • Check and edit data.
  • Impute missing values under supervision.
  • Transform the structure of data and tabulate the data.
1.  Code, enter, verify and validate data.
  1. Classify and code responses from an open question into numerical values. 
  2. Review the questionnaire to ensure that the minimum data required are reported. 
  3. Verify and validate data using a variety of methods to ensure the accuracy of data. 
  4. Capture data from paper questionnaires into an electronic file using the available means. 
  5. Construct frequency tables to validate data. 
  6. Discuss quality control measures in order to assess and evaluate the quality of data. 
2.  Check and edit data.
  1. State reasons for editing census and survey data to justify the editing stage of data processing. 
  2. Identify and describe types and sources of errors to reflect on what to look for during editing. 
  3. Explain and implement different types of edits for a given data set. 
  4. Discuss the effectiveness and limitations of the various types of edits to reflect on data quality issues. 
  5. Write computer commands to perform automatic data editing. 
  6. Use available software to perform micro and macro edits to identify errors in data files. 
  7. Discuss risks associated with editing to highlight the issues to be careful with.

3.  Transform the structure of data and tabulate the data.
  1. Transform the data structure used to input data for tabulation. 
  2. Construct appropriate tables to summarize data. 
  3. Change the data structure to one that is suitable for statistical packages. 
  4. Prepare data for storage and archiving

 

Assessment: 
Formative and Summative.
Method of assessment: 
Summative: Examination/ Case studies Formative: Class tests/Assignments

Additional information

L16 100 1
Mode of delivery: 
Contact
Target group: 
Managers and researchers.
Contact us
Name: 
Prof Hein Prinsloo
Telephone number: 
018 389 2387