QUANTITATIVE DATA ANALYSIS

SHE Level 5
SCQF Credit Points 15.00
ECTS Credit Points 7.50
Module Code MMX224423
Module Leader Thulani Moyo
School Glasgow School for Business and Society
Subject Economics
Trimesters
  • B (January start)
  • A (September start)

Summary of Content

The module is a sister module to the Introduction to Quantitative Research Methods module and aims to: -566d8 Further develop your understanding of statistical concepts and principles and your awareness of how such can provide practical and contextual analysis and interpretation as required in quantitative research. d8 Develop your software usage skills through practical formative examples and the practical module assessment d8 Further promote "hands-on undertaking" of data analysis through practically focused formative examples. d8 Further advance your communication skills through the preparation of written analysis reports related to -360 o practical formative examples and -360 o a practical summative assessment

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:- Use SPSS statistical analysis package, and to show how to use SPSS to analyse data.- Further understand of the statistical concepts taught in the Introduction to Quantitative Research Methods module. - Undertake formative examples and receive relevant feedback on your analysis report to help prepare for the module assignment.- Gain the teaching and practical experience required to undertake the Coursework assignment.- Help understand SPSS operation for later use in your own research where quantitative data could require evidence analysis and practical interpretation.

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 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.

Indicative Reading

-356 Becker, S., Bryman, A. & Ferguson, H. (Eds.). (2012). Understanding Research for Social Policy and Practice, 2nd Edition. 2. Bryman, A. (2012) Social Research Methods, 4th Edition 3. Byrne, D. (2011) Applying Social Science 4. Fielding, J. L., & Gilbert, G. N. (2006). Understanding Social Statistics. 5. Swift, L, Piff, S (2014) Quantitative methods for Business, Management and Finance 6. Creswell, J (2013) Research Design (International Student Edition): Quantitative, Qualitative and Mixed Methods Approaches. 7. Pallant, J (2013) SPSS Survival Manual: A Step by Step Guide to SPSS 8. Field, A (2013) Discovering Statistics using IBM SPSS Statistics 9. Bryman, Alan. (2004). Social Research Methods, 2nd edition. 10. Seale, Clive (ed.). (2004). Social Research Methods De Vaus, D. A. (2002) Analyzing Social Science Data. Sage. Field, A. (2009) Discovering Statistics with SPSS, 3rd Ed. Sage. Freund, J. E. (2001) Modern Elementary Statistics. 10th Ed. Prentice Hall. McClave, J. T. & Sincich, T. (2009) Statistics, 11th Ed. Prentice Hall. Neutens, J. J. & Rubinson, L. (2010) Research Techniques for the Health Sciences, 4th Ed. Benjamin Cummings. Ott, R.L. & Longnecker, M. (2010) An Introduction to Statistical Methods and Data Analysis, 6th 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. E-Book Sapsford, R. & Jupp, V. (2006) Data Collection and Analysis, 2nd Ed. Sage. 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
Assessment (FT) 20.00
Lectures (FT) 2.00
Practicals (FT) 16.00
Independent Learning (FT) 112.00

Assessment Methods

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