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Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title	Quantitative Methods for Social Sciences 1
Module Code	LG602
School	School of Law & Government
Online Module Resources
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

Much of modern social science research relies on an analysis of data with a large number of observations. It is necessary to understand statistics if one is going to engage in much of the Politics/ IR literature, and good quantitative analyses can add to any theory testing. Indeed a general understanding of the principles of statistics can aid the qualitative researcher. This module aims to introduce students to quantitative statistical methods by immersing them in some of the most basic (and fundamental) building blocks of quantitative analyses.

Learning Outcomes

1. To read, evaluate and engage with a social scientific literature based on quantitative techniques
2. To apply the basic concepts of statistics to their research questions
3. Build a database relevant to their question
4. Use statistical software, Stata 10, to generate some results of summary and inference.

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	24	Weekly lectures and lab
Assignment	24	weekly assignments
Assignment	24	Research paper
Independent learning	55	data gathering, reading, practice
Total Workload: 127

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Types of data – nominal, categorical, ordinal and interval.
Concepts can be quantified differently - some lending themselves better to some ways than others - and which way these are measured will have an impact on what one can do with the data. It is therefore important to understand what way different variables measure different concepts..

Measurement – scales and indices.
This class introduces the basics of measurement. Any statistical analysis presupposes that the concepts are (and can be) measured reliably and validly; this class introduces these ideas, and how one might construct indices..

Introducing datasets for social science.
This class introduces students to some of the data resources available to academic social scientists, such as the Correlates of War dataset. Building on the previous class we will discuss the reliability and validity of certain indices. Looking at these data, we will discuss the levels of analysis..

Data summaries – centre and spread.
One of the main descriptive goals in social science is to portray the nature of a phenomenon. Basic descriptive statistics enable one to convey a great deal of information simply and accurately. Using the statistical software we will start to generate some results..

Graphing data.
One of the most efficient ways to convey large amounts of information and data is through graphs. Starting with a graph of the approach and retreat of Napoleon’s army to and from Moscow, we can see the uses and abuses of graphing, focussing particularly on the importance of reasonable scaling. Students will also generate their own graphs using Stata 10..

Sampling, estimation and statistical inference.
One of the factors that make statistics so powerful is that we can make reasonable inferences about some sampled data to the whole universe of cases. We will discuss sampling and inference based on an understanding of the Central Limit theorem..

Hypothesis testing.
The other reason statistical analyses are so powerful is their ability to between distinguish random variation and real trends. This class introduces the logic behind and operation of t-tests, which enables (for instance) one to see if two sets of numbers are ‘different’. This is demonstrated on real data using Stata 10..

Model Building – causality and association; correlations.
The validity of any such tests usually depends on reasonable model specification. In order to avoid spurious relationships it is vital that one understands and has a theoretically defensible causal model. Students will be reintroduced to the concepts of functions, and dependent and independent variables..

Cross tabulation.
Cross tabulation is one of the more common bivariate techniques for analysing categorical and ordinal data. It enables one to see if there is a relationship between two (or more) such variables. Students will be shown how to perform such tests and interpret the statistical output..

Regression analysis.
Regression analysis is the most common and useful multivariate techniques which enables to test certain causal models using most types of data. Students will be shown how to test models using regression analysis and interpret the results of the analyses..

Choosing the right technique.
One of the main problems researchers have in dealing with statistics is in knowing what technique to use in what circumstance. This class will give students a way to ascertain this, and in doing so will give an overview of some other techniques not dealt with in this module..

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Course Work Breakdown
Type	Description	% of total	Assessment Date
Assignment	weekly assignments	50%	n/a
Assignment	research paper	50%	n/a

Reassessment Requirement
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 1

Indicative Reading List