Module title: Statistical Research Methods

SCQF level: 11:
SCQF credit value: 10.00
ECTS credit value: 5

Module code: SOE11621
Module leader: Robert Raeside
School The Business School
Subject area group: Management
Prerequisites

None.

2018/9, Trimester 2, Face-to-Face,
Occurrence: 002
Primary mode of delivery: Face-to-Face
Location of delivery: CRAIGLOCKHAR
Partner:
Member of staff responsible for delivering module: Robert Raeside
Module Organiser:


Learning, Teaching and Assessment (LTA) Approach:
The LTA approach is rooted in the premise that the development of a sound knowledge of the role of statistical methods to the collection, analysis and modelling of data to support decision-making and research. The module will be facilitated by two research-active academics with inputs from research projects and industry collaborations. The module will be run in workshop format over two days and be based on a participative and learner-focused pedagogical approach – where you will be encouraged to participate in hands on data analysis and knowledge discovery. Underpinning business and industrial research from both public and private sectors will support and refresh the module content.
The assessments are summative and will include an investigation using inferential statistics to investigate key research questions from a questionnaire created dataset and a more complex study of linked data in which statistical modelling will be applied.


Formative Assessment:
.

Summative Assessment:
.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Tutorial 16
Face To Face Guided independent study 84
Total Study Hours100
Expected Total Study Hours for Module100


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Report 40 1,2,3 15 HOURS= 0, WORDS= 1500
Report 60 4,5 15 HOURS= 0, WORDS= 1500
Component 1 subtotal: 40
Component 2 subtotal: 60
Module subtotal: 100

Description of module content:

In this module you will learn a scientific approach to organise and interrogate data. Emphasis is given to the presentation of data to facilitate understanding. In the module the concepts of using statistical inference to make judgement and inform decision-making is explained. You will learn about how to simplify complex data and use data to segment subjects in to homogeneous groups. To module concludes with an introduction to statistical modelling using linear regression and an overview is given of general linear models. These will be implemented in either the R package or SPSS.

Exploring and describing data,
Statistical inference – parametric methods t – tests and Analysis of Variance
Statistical presentation of data.
Multivariate methods – exploratory factor analysis and segmentation methods (Hierarchical clustering and K means).
Statistical Modelling – OLS regression, general linear models – Poisson, Logistic, Probit and Torbit models
Diagnosing model fits

Learning Outcomes for module:

Learning Outcomes of the module
LO1: Ability to organise, summarise and understand messages from data
LO2: How to assess the significant between groups
LO3: To communicate effectively using data
LO4: Apply methods of data reduction and of classification to research data
LO5: To construct and diagnose statistical models

Indicative References and Reading List - URL:

Please contact your Module Leader for details
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