Undergraduate Course: Introduction to Statistics for Social Science (SSPS08008)
Course Outline
| School | School of Social and Political Science |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Introducing basic statistical tools for Students taking "...With Quantitative Methods" degrees.
This course is the introduction to statistical data analysis, 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. Students during the course will learn how to use common statistical packages to analyse secondary datasets and address interesting research questions.
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| Course description |
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.
Outline Content:
1 - Why quantitative methods? Introduction to data and SPSS
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 discuss surveys and secondary data and their role in social science research. 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 ¿ Data exploration, manipulation and visualisation with SPSS
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 also learn how to recode variables and how to explore them statistically. Using SPSS students will produce frequency tables with statistics and graphs and summarise and explore a variable.
3 ¿ Bivariate analysis ¿ categorical variables
This week focuses on the relationship between two 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 categorical variables. 3-way contingency tables are introduced together with the concepts of statistical control and causality.
4 - Bivariate analysis ¿ continuous variables
Up until now learning has focussed on categorical variables. 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. Students will create scatterplots and interpret correlations and regressions outcomes.
5 ¿ The notion of Statistical Inference
Students will be introduced to the concept of statistical inference, the difference between population and sample and the Central Limit Theorem. This week students will be encouraged to consider the uses of quantitative methods in a wider social context. We will discuss research questions, formulate and test research hypotheses.
6 ¿ Multiple Linear regression
This week builds on simple linear regression. 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.
7 ¿ Regression Diagnostics
This week students will learn how to check the ¿goodness of fit¿ of their models using regression diagnostic techniques. Running a regression model is not enough. Students will learn how to study the validity of their model using a set of measures, called the regression diagnostics.
8 ¿ Regressions with dummy variables and interaction terms
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 - Logistic Regression
Students will learn how to run a regression model with a binary dependent variable. We have learned in the previous weeks how to run regressions for continuous dependent variables and this week we extend this knowledge running a model with a dichotomous dependent variable.
10 ¿ Introduction to other statistical packages. Presenting the research findings.
SPSS has been used throughout this course. This week students will be briefly introduced to other statistical packages used by social scientists; STATA and R. This will provide the students with more options on which tool to use for their future research. Students will also learn how to present their research findings in sophisticated reports and about ways of data presentation.
Student Learning Experience:
The course is hands-on, taught through lectures and workshops. Students will engage with other students to present statistical results as part of a group presentation, carry out their 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.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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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
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| Academic year 2018/19, Not available to visiting students (SS1)
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Quota: 56 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Seminar/Tutorial Hours 20,
Formative Assessment Hours 1,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
165 )
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| Assessment (Further Info) |
Written Exam
60 %,
Coursework
0 %,
Practical Exam
40 %
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| Additional Information (Assessment) |
Course assessment is based on:
- Assessment 1 - a multiple choice in-class test to be held mid-semester. This is worth 30% of the final mark.
- Assessment 2 - a group presentation worth 20% of the mark.
- Assessment 3 - a three-week take home project with an upper limit of 2500 words worth 50% of the final mark.
Assessment weighting:
Multiple Choice In-Class Test 30%
Take Home Project 50%
Group presentation 20%
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| Feedback |
You will be given feedback on assessment 1 - a multiple choice in-class test to be held mid-semester that is worth 30% of the final mark.
You will be given feedback on assessment 2 - a group presentation that is worth 20% of the mark.
The feedback to the first two assignments will help students to undertake assessment 3 - a three-week take home project with an upper limit of 2500 words worth 50% of the final mark.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- A basic understanding of secondary data collection, access and management using statistics software package
- A basic understanding of univariate statistics: graphical skills, presenting and communicating data
- A basic understanding of bivariate statistics, including measures of association
- An understanding of inference and the logic of sampling, of the difference between association and causality, and the concept of control
- A basic understanding of multiple linear regression analysis
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Reading List
Reading List
Field, Andy (2013) Discovering Statistics using IBM SPSS Statistics, Fourth Edition, Sage: London
Fielding, Jane & Gilbert, Nigel (2006) Understanding Social Statistics, 2nd edition, Sage: London
Macinnes, John (2017) An Introduction to Secondary Data Analysis with IBM SPSS Statistics, Sage: London
Field, Andy (2016) An Adventure in Statistics. The Reality Enigma, Sage: London
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Eloi Ribe
Tel:
Email: Eloi.Ribe@ed.ac.uk |
Course secretary | Mr Euan Morse
Tel: 0131 (6)51 1137
Email: emorse@ed.ac.uk |
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