Understanding and Using Statistics
||Dr Seda Erdem
||Lectures (x 18), Practical Labs/Workshops (x 18) and Presentations (x4)
||A project (60% made up of a presentation 20% and a research paper 40%), plus a final examination (40%)
By the end of the module, students should be able to conduct, interpret and appropriately report statistical analysis related to their research projects to a publishable standard. Students should be able to understand and evaluate:
- The where, why and how of data collection;
- Displaying and interpreting the data;
- Different sampling distributions and hypothesis testing used for various research questions;
- Linear and multiple regressions and interpretation of analysis results;
- Analysis of variance; and
In addition to the above objectives, successful students will be able to undertake other advanced modules in their degrees. As the module provides an overview of a wide range of quantitative methods, for those Management School students not taking further quantitative methods module, it can act as a standalone module.
The learning outcomes of this module are:
- To develop rigorous research questions and design high quality, ethically guided research projects to answer those questions.
- To design and carry out a literature review with critical discussion of the quality, reliability and validity of a range of sources.
- To critically appreciate the value of quantitative methods and how quantitative research projects may be designed. Collect and analyse data using quantitative methods, drawing valid conclusions.
- To apply the above knowledge, skills and techniques to research problems in business, management and related areas (e.g. economics/policy research).
- To develop, present and communicate arguments logically in written and verbal form.
- Work independently and manage time effectively.
By the end of the module, students should be able to understand and have skills on:
- Sampling and measurement techniques: The application, strengths and weaknesses of different sampling and measurement techniques in various contexts. Collecting and coding data. Identification of incorrect applications from real-world examples.
- Basics of descriptive statistics: The use of descriptive statistics. The ways of displaying data. Measures of central tendency and variability. Application to real data.
- Sampling distributions and hypothesis testing: Introduction to various sampling distributions. Introduction to inferential statistics methods of confidence intervals and significance tests. Differences in various tests. Power of a test, and its calculation. Application to real data.
- Regression and correlation: Fundamentals of linear- and multi-regression. Hypothesis testing in regression. Application to real data. Presenting and interpreting regression results. Regression versus correlation. How to use statistical packages (e.g., SPSS) to perform regression analysis. Writing a research report.
- Analysis of Variance (ANOVA): The general approach and logic of ANOVA. Multiple comparison procedures. The fundamentals of one-way and multi-way analysis. Application to real data. The use of statistical software (e.g., SPSS) for ANOVA. Writing a research report.
- Factor Analysis: The general approach and logic of Factor Analysis. The use of Factor Analysis in management, economics, behavioural science and related areas. Performing Factor Analysis using SPSS. Application to real data. Interpreting the analysis results. Writing a research report.
This module information is representative of what is included in the module in a given year. Details of actual reading, lectures and coursework may vary year to year and will be available at the beginning of the semester.