# QUANTITATIVE METHODS II - 2024/5

Module code: MAND031

## Module Overview

This unit builds on the Quantitative Methods I unit in semester 1. Students are exposed to (more) advanced methodological and statistical skills needed to understand and evaluate journal articles that use quantitative methods. This also enables students to have a basic understanding of the different methods, so they are able to choose the appropriate method needed for their research questions. Methods discussed include: logistic regressions, Poisson regression, log linear models, instrumental variables, multilevel analysis, longitudinal data analysis (time series and panel data) and other advanced techniques such as bayesian techniques, cluster analysis and social network analysis.

### Module provider

MASSARO Sebastiano (SBS)

### Module cap (Maximum number of students): N/A

Independent Learning Hours: 117

Seminar Hours: 5

Tutorial Hours: 6

Laboratory Hours: 22

Semester 2

N/A

## Module content

Indicative content:

Generalized Linear Models.

Generalized Linear Models: Logit Models.

Generalized Linear Models: Poisson Models.

Multilevel Models.

Simple Panel Data Methods.

Instrumental Variables. Two Stage Least Squares.

Mediation and Moderation.

Advanced Statistical techniques (Bayesian, SNA, Clustering, Deep Learning)

## Assessment pattern

Assessment type Unit of assessment Weighting
Coursework ASSESSMENT (AS.1)-EMPIRICAL RESEARCH PROJECT Pass/Fail

N/A

## Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their learning and achievement of the unit’s learning outcomes. Both the formative and summative assessments enable the achievement of the learning outcomes. The regular class participation and feedback enhance students’ learning and support their preparation and delivery of the two elements of summative assessment. These are outlined below.

Formative assessment

Formative assessment will comprise an ongoing feature of this unit. Students will actively engage in taught sessions and prepare for these via guided readings, discussion topics and other preparatory work issued by the instructor. These will be important for students and will facilitate their critical thinking and applied skills development, as well as enhance their learning and preparation for the three summative assessment elements.

The summative assessment for this unit consists of:

• Assessment 1. Empirical research project.

## Module aims

• To provide a firm grounding in the theory and practice of quantitative data analysis
• To focus on developing skills and knowledge in data management, visualisation and statistical modelling through the analysis of data sets.
• To provide training in a statistical software environment e.g. R, providing the tools for students to develop the skills to use software independently for quantitative analysis in dissertation research.

## Learning outcomes

 Attributes Developed 001 Understand and critically evaluate quantitative arguments and statistical analyses in business research KCPT 002 Perform a wide variety of data-related tasks in a chosen statistical software environment; KCPT 003 Create, manage and manipulate data sets KCPT 004 Design and produce professional and informative visualisations KCPT 005 Select appropriate statistical tests and models for making predictions and evaluating hypotheses, and apply these to data KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

## Methods of Teaching / Learning

The learning and teaching strategy is designed to support and achieve the learning outcomes. The learning and teaching methods include class contact sessions which are highly interactive in nature, class discussion, preparatory reading, verbal student presentations, student-led reviews of readings, practical activities, scenario discussion, and individual written assignments.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.