Registry
Module Specifications
Archived Version 2011 - 2012
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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. | |||||||||||||||||||||||||||||||||||||||||
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 |
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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. | |||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources 879, websire, UCLA ATS, 0, resources to help you learn stata, http://www.ats.ucla.edu/stat/stata/, | |||||||||||||||||||||||||||||||||||||||||
Programme or List of Programmes |
ALPD | PhD |
ALPM | MA |
ALPT | PhD-track |
ARPD | PhD |
ARPM | MSc |
ARPT | PhD-track |
BSPD | PhD |
BSPT | PhD-track |
CSPD | PhD |
CSPM | Master of Arts |
CSPT | PhD-track |
ELPD | PhD |
ELPM | MA by Research |
ELPT | PhD-Track |
ESPD | PhD |
ESPM | MA |
ESPT | PhD-track |
HGPD | PhD |
HGPM | MA |
HGPT | PhD-track |
LGLM | LLM |
LGPD | PhD |
LGPM | MA |
LGPT | PhD-track |
MPBS | MPhil (Business) |
MPLG | MPhil (Law & Government) |
TPPD | PhD |
TPPM | Master of Arts |
TPPT | PhD-track |
- See the module specification for LG602 in 2003 - 2004
- See the module specification for LG602 in 2004 - 2005
- See the module specification for LG602 in 2005 - 2006
- See the module specification for LG602 in 2006 - 2007
- See the module specification for LG602 in 2007 - 2008
- See the module specification for LG602 in 2008 - 2009
- See the module specification for LG602 in 2009 - 2010
- See the module specification for LG602 in 2010 - 2011
- See the module specification for LG602 in 2011 - 2012
- See the module specification for LG602 in 2012 - 2013
- See the module specification for LG602 in 2013 - 2014
- See the module specification for LG602 in 2014 - 2015
- See the module specification for LG602 in 2015 - 2016
- See the module specification for LG602 in 2016 - 2017
- See the module specification for LG602 in 2017 - 2018
- See the module specification for LG602 in 2018 - 2019
- See the module specification for LG602 in 2019 - 2020
- See the module specification for LG602 in 2020 - 2021
- See the module specification for LG602 in 2021 - 2022
- See the module specification for LG602 in 2022 - 2023
- See the module specification for the current year