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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2018/2019

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DRPS : Course Catalogue : School of Social and Political Science : School (School of Social and Political Studies)

Undergraduate Course: Introduction to Statistics for Social Science- Summer School (SSPS08006)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 8 (Year 1 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryIntroducing basic statistical tools for Students in the With Quantitative Methods Degrees. This is a 2 week conversion course covering the same course content as the standard semester length version.

This course is the introduction to common quantitative techniques and software used in the social sciences. It is designed to meet the needs of students in the with Quantitative Methods degree programmes in SPS, and to provide them with a broad range of basic concepts and methods, which they will later use as the basis for intermediate and advanced quantitative techniques. The course is aimed at students who also study Sociology, Social Policy, Politics, and International Relations. As such, it will contain examples and applications relevant for all these disciplines.
Course description Academic Description:

This course is the introduction to common quantitative techniques and software used in the social and political sciences. It is designed to meet the needs of students in the ┐with Quantitative Methods┐ degree programmes in SPS, and to provide them with a broad range of basic concepts and methods, which they will later use as the basis for intermediate and advanced quantitative techniques. The course is aimed at students who also study Sociology, Social Policy, Politics, and International Relations. As such, it will contain examples and applications relevant for all these disciplines.

Drawing on real data examples, and incorporating hands-on manipulation of data, this course will follow a path leading students from univariate analysis exploring the distribution of a single variable through bivariate analysis examining the association between two variables to the point of conducting basic multivariate analysis which adds further variables. Students will be taught using the actual methodological and substantive research undertaken by academics in SPS.

Outline Content:

1 - Introduction: Why do quantitative methods?
Students will be introduced to key concepts and vocabulary used in quantitative methods in the social sciences and to some of the possibilities they offer. We will ask in particular what special role surveys play in social science research and why they are important for public policy. Two important tools will be introduced: existing datasets through the UK Data Service, and the SPSS software, which will be used throughout this course.

2 - The Research Process
This section describes the process that we follow when undertaking research.

3 - Levels of measurement, univariate statistics, central tendency, measures of dispersion, recoding variables, standardisation and graphs
This week students will learn about the distribution of a variable, its level of measurement, and its associated measures of central tendency and of dispersion. Students will learn how to recode and standardise variables and how to explore them statistically. Using SPSS you will learn how to produce frequency tables with statistics and graphs, how to use the Descriptives and Explore commands to generate statistics for each variable (such as the mean, the minimum and the variance).

4 - Bivariate analysis ┐ contingency tables (categorical variables)
This week focuses on the inter-relationship between two or more categorical variables using 2-way contingency tables. Students will learn the most common way of displaying and analysing social statistics in tables and about the best way of arranging tables. Students will also learn the most common statistics to measure the association between variables in a table.

5 - Trivariate analysis - Causality and the concept of control in a 3-way contingency table
This week of the course focuses on associations between categorical variables, building on the use of 2-way contingency tables by introducing 3-way contingency tables and the concept of control. Understanding the concept of control through the use of 3-way contingency tables (and supported by the use of measures of associations used for such tables) provides a visually clear introduction to the meaning of statistical control, and, through that, an introduction to ideas of causality, which will be invaluable for the rest of the with QM degree.

6 - Bivariate analysis for continuous variables ┐ simple linear regression
This week turns to continuous variables, focusing on Pearson┐s r (correlation) and simple linear regression. Correlation and regression are two useful techniques to explore a (linear) correlation between two (or more) interval or ratio variables. Simple linear regression results in numerical explanation of a dependent variable by one independent variable.

7 - Introduction to multiple linear regression
This week of the course continues with continuous variables by building on last week┐s simple linear regression. This week we will be learning how to carry out multiple linear regression which is used to estimate a linear relationship between a dependent variable and several independent variables.

8 - Linear regression continued: dummy variables and interactions
This week continues with multiple linear regression enabling students to learn this technique in depth, including solutions for situations in which the independent variables are not continuous (categorical) and when there might be an interaction between two of the independent variables.

9 - Regression diagnostics
This week students will learn how to check the ┐goodness of fit┐ of their models using regression diagnostic techniques.

10 - Inference and the logic of sampling
In this week students will be encouraged to consider the uses of quantitative methods in a wider social context. The issue of representativeness and inference will be discussed in relation to the process of producing survey knowledge and to sampling in surveys.

11 - Communicating quantitative analysis
This week students will learn about communicating research findings, data presentation and data visualisation, including graphic techniques.

Student Learning Experience:

The course is hands-on, taught through lectures and workshops in the computer lab. You will work through a lab book in the workshops, engage with other students to present statistical results as part of a group presentation, carry out your own analyses and write it up for assessment. The course is cross-discipline and open to students with backgrounds in social sciences and the humanities.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements While entry to this course normally requires a pass at B in Mathematics at SQA Higher or A-level, students with confidence in their level (high school equivalent) of mathematical knowledge will be considered for admission. Please contact the course convenor if would like to join the course but have any concerns about your current Mathematical knowledge being sufficient
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. A basic understanding of secondary data collection, access and management using statistics software package
  2. A basic understanding of univariate statistics: graphical skills, presenting and communicating data
  3. A basic understanding of bivariate statistics, including measures of association
  4. An understanding of inference and the logic of sampling, of the difference between association and causality, and the concept of control
  5. A basic understanding of multiple linear regression analysis
Reading List
D.M. Diez, C.D. Barr, & M.C. Etinkaya-Rundel (2013) OpenIntro Statistics (2nd edition),
http://www.openintro.org/stat/textbook.php
J. Pallant (2010 4th edition) SPSS Survival Manual, Maidenhead: Open UP
C. Marsh & J. Elliott (2008) Exploring Data (2nd edition), Cambridge: Polity
J. Fielding & N. Gilbert (2006) Understanding Social Statistics (2nd edition), London: Sage
D. Freedman et al. (various editions), Statistics, London: Norton
H. Blalock (various editions), Social Statistics, New York: McGraw-Hill
Additional Information
Graduate Attributes and Skills Not entered
Special Arrangements Students are expected to attend the whole 2 weeks, which covers the same material as the semester length version. 1 day is roughly equivalent to 1 week, with half the day given over to supervised lab sessions in place of the independent learning that would be expected during the 11 week version.
KeywordsNot entered
Contacts
Course organiserDr Morag Treanor
Tel: (0131 6)50 3918
Email: morag.treanor@ed.ac.uk
Course secretaryMr Daniel Jackson
Tel: (0131 6)50 3932
Email: Daniel.Jackson@ed.ac.uk
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