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Short Learning Programme
on
Data Analysis
Managers and researchers need to be familiar with the techniques for analysing numerical data. They need to know when and how to apply them and to interpret statistical results. Calculations are carried out by using statistical software packages such as SPSS, Excel, etc.

Purpose of the course:

To assist managers and researchers in recognising the types of data to be analysed statistically. To use relevant statistical techniques to analyse numerical data. To develop charts/graphs for displaying numerical data. To use a computer software to carry out calculations.

Admission requirements:

Learning assumed to be in place: 
Basic excel will be of an advantage.

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 246515)

ANALYSE PUBLISHING RESEARCH DATA 

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

  • Selecting and using appropriate analysis tools.
  • Finding relationships between data parts.
  • Recording final analysis for accessibility by decision-makers.
1.  Select and use appropriate analysis tools.

ASSESSMENT CRITERION 1 

Identify the purpose of analysis within given specifications. 

ASSESSMENT CRITERION 2 

Select analysis tools for their relevance to specific projects or the organisational requirements. 

ASSESSMENT CRITERION 3 

Brake down given data into logical parts or facts by applying selected analysis tools. 

2.  Find relationships between data parts.

ASSESSMENT CRITERION 1 

Compare data parts based on the purpose of analysis. 

ASSESSMENT CRITERION 2 

Identify differences between data parts are based on the purpose of analysis. 

ASSESSMENT CRITERION 3 

Find correlations and conjunctions between data parts are based on the purpose of analysis. 

ASSESSMENT CRITERION 4 

Identify trends in the data based on the purpose of analysis. 

ASSESSMENT CRITERION 5 

Contextualise findings are within the purpose of analysis and organisational requirements. 

3.  Record final analysis for accessibility by decision-makers.

ASSESSMENT CRITERION 1 

Complete he analysis is within the agreed time frames and budgets. 

ASSESSMENT CRITERION 2 

State and record defensible assumptions. 

ASSESSMENT CRITERION 3 

Formulate conclusions are in accessible formats. 

ASSESSMENT CRITERION 4 

Make recommendations relevant to organisational requirements are made. 

MODULE 2  (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.
  • Basic principles of data collection.
  • Surveys
  • Questionnaires.
  • Simple survey objectives
1.  Compare various data collection methods.

ASSESSMENT CRITERION 1 

Describe different data collection methods to compare their advantages and disadvantages. 

ASSESSMENT CRITERION 2 

Evaluate a specific data collection method used in a survey in view of the survey objectives and resource constraint.

ASSESSMENT CRITERION 3 

Select a data collection method for a survey in view of the survey objectives and resource constraint. 

ASSESSMENT CRITERION 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.

ASSESSMENT CRITERION 1 

Explain the term survey to distinguish it from other data collecting activities such as census and experiment. 

ASSESSMENT CRITERION 2 

Identify a given data collecting activity as a census, a survey, an experiment or an administrative data collection

ASSESSMENT CRITERION 3

Describe the various stages of primary data collection to highlight the advantages and disadvantages of different approaches used at each stage. 

ASSESSMENT CRITERION 4 

Describe the key functions of an enumerator to reflect the qualities of a good enumerator.

ASSESSMENT CRITERION 5 

Describe the key functions of a supervisor to reflect the qualities of a good field supervisor. 

3.  Explain the main stages in planning a survey.

ASSESSMENT CRITERION 1 

Define the basic terms associated with surveys to prepare for survey planning activities.

ASSESSMENT CRITERION 2

Identify and define basic terms not limited to population, sample, sampling units and sampling frame.

ASSESSMENT CRITERION 3

Describe the main stages in planning a survey in order to reflect a comprehensive survey process. 

ASSESSMENT CRITERION 4

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.

ASSESSMENT CRITERION 1 

Construct a few survey questions in order to obtain information relevant to the survey objectives. 

ASSESSMENT CRITERION 2 

Examine survey questions critically to assess their relevance to the survey objectives. 

ASSESSMENT CRITERION 3 

Modify survey questions in order to improve the chance of obtaining reliable responses. 

MODULE 3 (US 262538)

USE STATISTICAL METHODS TO ANALYSE DATA 

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

  • Use graphical methods to explore and present data.
  • Calculate and interpret sample statistics.
  • Fit and interpret linear regression models.
  • Perform chi-square tests.
  • Perform non-parametric tests.
1.  Use graphical methods to explore and present data.

ASSESSMENT CRITERION 1 

Produce graphical and tabular representations to explore data in view of the purpose of the investigation. 

ASSESSMENT CRITERION 2 

Interpret graphical and tabular representations in order to draw preliminary conclusions. 

ASSESSMENT CRITERION 3 

Utilise graphical and tabular representations are to detect outliers in the data. 

ASSESSMENT CRITERION 4 

Use graphical and tabular representations are to present results. 

2.  Calculate and interpret sample statistics.

ASSESSMENT CRITERION 1 

Calculate different and appropriate numerical summary statistics to explore the behaviour of the data. 

ASSESSMENT CRITERION 2 

Interpret the calculated values of summary statistics in terms of the survey objectives. 

ASSESSMENT CRITERION 3 

Select a numerical summary statistic in order to make comparison between data. 

ASSESSMENT CRITERION 4 

Explain the limitations of the summary statistics in order to assist their interpretation. 

ASSESSMENT CRITERION 5 

Calculate Pearson's product moment correlation coefficient to examine the strength of relationship between variables. 

3.  Fit and interpret linear regression models.

ASSESSMENT CRITERION 1 

Construct a scatter plot using a computer software to explore the relationship between variables. 

ASSESSMENT CRITERION 2 

Interpret a scatter plot in order to decide whether a linear relationship exists between variables. 

ASSESSMENT CRITERION 3 

Calculate least squares estimates using computer software in order to fit a regression line. 

ASSESSMENT CRITERION 4 

Perform residual analysis to determine validity of the normality assumption. 

4.  Perform chi-square tests.

ASSESSMENT CRITERION 1 

State appropriate null and alternative hypotheses for a given problem. 

ASSESSMENT CRITERION 2 

Organize data into a contingency table to prepare for a chi-square test. 

ASSESSMENT CRITERION 3 

Perform a chi-square test in using computer software to test for independence or homogeneity.

ASSESSMENT CRITERION 4 

Interpret a p-value from the test in order to draw conclusions about the study problem. 

ASSESSMENT CRITERION 5 

State assumptions of the chi-square test in order to evaluate the validity of the test.

5.  Perform non-parametric tests.

ASSESSMENT CRITERION 1 

Select and perform appropriate non-parametric test for testing a hypothesis. 

ASSESSMENT CRITERION 2 

Discuss the limitations of the specific non-parametric test in order to assess the validity of the test. 

ASSESSMENT CRITERION 3 

State the assumptions of a non-parametric test so as to make correct use of the test. 

ASSESSMENT CRITERION 4 

Compare non-parametric tests to facilitate choice of an appropriate test for use in a given context. 

ASSESSMENT CRITERION 5 

Calculate Spearman's rank correlation coefficient  to examine the strength of relationship between variables. 

 

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

Additional information

Mode of delivery: 
Contact
Target group: 
Managers and researchers.
Contact us
Name: 
Prof Jan Meyer
Telephone number: 
018 389 2073