## QUANTITATIVE DATA ANALYSIS

 SHE Level 5 SCQF Credit Points 15.00 ECTS Credit Points 7.50 Module Code MMG121386 Module Leader Vangelis Chiotis School Glasgow School for Business and Society Subject Finance and Accounting Trimesters A (September start) B (January start)

### Pre-Requisite Knowledge

Standard programme entry requirements or equivalent

### Summary of Content

The module will focus on introducing the student to the philosophy and application of statistical reasoning and statistical analysis methods for evidence analysis of quantitative data. Key practical skills incorporating the role and purpose of statistics as an analytical tool within a research project will be explored and developed. Emphasis will be placed on demonstrating how statistical analysis concepts and methods are necessary for the conversion of data into meaningful practical knowledge and the conversion of modelled relationships into meaningful practical tools. The module will also enhance the student's proficiency in statistical software (Minitab, IBM SPSS Statistics) for data presentation, analysis and interpretation.

### Syllabus

Inferential Analysis Elements Relation of study design to data analysis. Null and experimental hypotheses. Inferential evidence measure concepts. Significance level. Power. Sample size estimation. Choosing the right test. Analysis of Count Data Two-way contingency tables. Chi-square test of association. Measures of association. Two Sample Studies Paired and independent samples studies. Parametric and nonparametric tests. Point and interval estimation of treatment effect. Correlation Introduction to correlation. The concept of cause and effect. Parametric and nonparametric tests. Partial correlation. Linear Regression Model building. Model concepts and least squares estimation. Statistical validity checks. Practical validity of model fit. Diagnostic checking. Beyond linear modelling.

### Learning Outcomes

On completion of this module, the student should be able to:- Perform simple tests for association and interpret the results.- Perform an evidence analysis and interpret the outcomes using parametric and nonparametric hypothesis tests for two samples designs.- Perform a linear regression analysis and interpret the results from both statistical and practical perspectives.- Use statistical software to aid the presentation, analysis and evidence interpretation of quantitative data.- Critically evaluate the use and application of statistical methods within quantitative research.

### Teaching / Learning Strategy

The module will be presented using a range of teaching and learning strategies. These will include traditional interactive classroom sessions, IT sessions and distance learning elements using a Visual Learning Environment [VLE] (GCULearn) for enhancement of learning as appropriate. The learning strategies will build upon the students' maturity and their ability to reflect on their own learning experiences. Students will also be advised to consolidate their knowledge of the content via directed texts and articles, both paper based and electronic. Statistical software, such as Minitab and IBM SPSS Statistics, will be used throughout the module for evidence creation. Appropriate formative exercises will be used to consolidate the students' skills in terms of both presentation and analysis of results. This approach will provide the student with the opportunity to experience how a software package aids quantitative data presentation and analysis within research and to have feedback on their development of quantitative data analysis skills.

E-Book Sapsford, R. & Jupp, V. (2006) Data Collection and Analysis, 2 nd Ed. Sage. Texts De Vaus, D. A. (2002) Analyzing Social Science Data. Sage. Field, A. (2009) Discovering Statistics with SPSS, 3 rd Ed. Sage. Freund, J. E. (2001) Modern Elementary Statistics. 10 th Ed. Prentice Hall. McClave, J. T. & Sincich, T. (2009) Statistics, 11 th Ed. Prentice Hall . Neutens, J. J. & Rubinson, L. (2010) Research Techniques for the Health Sciences, 4 th Ed. Benjamin Cummings. Ott, R.L. & Longnecker, M. (2010) An Introduction to Statistical Methods and Data Analysis, 6 th Ed. Brooks/Cole. Polgar, S. D. & Thomas, S. A. (2008) Introduction to Research in the Health Sciences, 5th Ed. Churchill Livingstone. Taylor, S. (2007) Business Statistics for Non-mathematicians. Palgrave Macmillan. Web Resources http://www.open.ac.uk/infoskills-researchers/ http://open2.net/sciencetechnologynature/maths/menu_statistics.html http://www.stats.gla.ac.uk/steps/glossary/ http://davidmlane.com/hyperstat/index.html http://www.socr.ucla.edu/Applets.dir/ChoiceOfTest.html

### Transferrable Skills

Academic: Logical thinking, critical analysis, problem-solving, written and spoken communication, ability to use numerical data, computer literacy. Personal Development: Self-confidence, self-discipline, independence, ability to reflect, reliability, integrity, honesty. Enterprise or Business: Time management, to work in teams and leadership skills, independence.

### Module Structure

Activity Total Hours
Independent Learning (FT) 112.00
Lectures (PT) 10.00
Independent Learning (PT) 112.00
Assessment (FT) 20.00
Practicals (PT) 8.00
Lectures (FT) 10.00
Practicals (FT) 8.00
Assessment (PT) 20.00

### Assessment Methods

Component Duration Weighting Threshold Description
Coursework 1 n/a 100.00 50% Data analysis report (3000 words)