Search
Search type

School of Social Sciences

Students outside a lecture theatre at The University of Manchester
BAEcon Economics
Learn how the social sciences can help you to understand today's world.

BAEcon Economics / Course details

Year of entry: 2018

Course unit details:
Research Design & Statistical Inference

Unit code SOST20031
Credit rating 20
Unit level Level 2
Teaching period(s) Semester 1
Offered by Social Statistics
Available as a free choice unit? Yes

Overview

This module provides an introduction to the concepts underpinning statistical inference using the examples of a paper on the 'Mozart effect' and the controversial book 'The Bell Curve: Intelligence and Class Structure in American Life'. In the process of understanding and critiquing this paper and book, the students will learn about the key processes of statistics on collecting data, summarizing data, and interpreting data.

The students will collect their own data by listening to Mozart, pop music and silence while answering standardised cognitive ability tests. They will explore their data and examine if they scored higher on the IQ test when listening to Mozart. They will learn to visualise their results using Excel. These data analysis skills are highly in demand in the labour market.

Students will also learn the importance of sampling and designing appropriate studies to investigate the 'Mozart effect' and 'The Bell Curve'. In order to understand the key meta-analysis paper on the Mozart effect, the students will learn about probability distributions, hypothesis testing, confidence intervals and evaluating evidence for the null hypothesis.

Lectures (10 x 2 hr lectures) ; Tutorials (10 x 1 hr tutorials)

1: The 'Mozart Effect paper': discussion and critique. Tutorial: Student's own data on testing the Mozart Effect.
2. Did listening to Mozart make me smarter? Exploratory Data Analysis: Examining Distributions of data from tutorial 1. Tutorial: Calculating means and standard deviations of student's own IQ test data.
3. How do I interpret tables and charts from my data on the Mozart effect? Reading Tables and Examining Relationships between data from tutorial 1. Tutorial: Drawing scatterplots and barcharts in Excel (Computer Lab; GRADED).
4. Can I design a better study of the Mozart effect? Sampling and designing alternative studies on the Mozart effect. Tutorial: Designing a social experiment.
5. Probability, tossing coins and making inferences. Introduction to probability. Tutorial: Coin tossing, rolling dice and finding out the guilty coin/dice (FORMATIVE assessment).
6. The bell curve in Intelligence. Introduction to probability distributions. Tutorial: Using the standard normal table, using data from tutorial 1 (Computer lab, GRADED).
7. Debates around racial differences in intelligence. Confidence Intervals for the population mean. Tutorial: Interpreting confidence intervals.
8. Developing alternative hypotheses on the Mozart effect. Testing the hypothesis 'listening to Mozart makes people smarter'. Tutorial: The null hypothesis on the Mozart effect.
9. Making statistical inferences from your own IQ test data. Relating Hypotheses tests and confidence intervals. Tutorial: Evidence for the null hypothesis (GRADED).
10. Mozart effect'Shmozart effect: A meta-analysis. Tutorial: Principles behind a meta-analysis.

Aims

The unit aims to:

(i) To explore some of the debates surrounding making inferences from intelligence tests.
(ii) To develop the students' understanding of the concepts underpinning statistical inference.
(iii) To introduce students to the principles of collecting data, summarizing data, and interpreting data.
(iv) To explore the importance of sampling and designing appropriate studies in answering research questions
(v) To enable students to develop and test evidence for their own hypotheses.

Learning outcomes

Student should/will (please delete as appropriate) be able to

Knowledge and Understanding: An understanding of the evidence and debates regarding claims made by academics and the media.

Intellectual skills: Skills and an understanding of good practices in evaluating evidence and data and assessing robustness. Develop critical skills in evaluating data and methods through lectures, practicals, group work and independent reading.

Practical skills: Skills in using social statistics and practical experience of data analysis including using Excel and on-line statistical resources. Skills in evaluating evidence and scientific claims.

Transferable skills and personal qualities: Critical data analysis and evaluation skills. Social statistics and data analysis skills are highly in demand in the labour market. The group work will also aid the student in development of their communication and team working skills.

Teaching and learning methods

The course will involve: lectures, tutorial work and lab classes. Extensive use will be made of on-line statistical resource 'Statistical Reasoning' from the Open Learning Initiative, which will be woven into each lecture and tutorial.

The lecture component will provide the theoretical and methodological framework for statistical inference, while the tutorials will be problem based to enable student learning on to analyse quantitative data. Practical sessions will give students hands some experience in data analysis and interpretation and using appropriate Excel for data manipulation. Such skills are highly transferable.

The emphasis is on using the general online statistical resource to answering the question on the 'Mozart effect'. Students will use their own data to try to answer this research question.

Assessment methods

Students will be assessed on 3 x written exercises based on tutorials (30%) and 1 x 2 hour exam (length to be confirmed) (70%)

Students will also be required to submit a non-assesed assignment partway through the course for formative feedback.

Feedback methods

 

All Social Statistics courses include both formative feedback – which lets you know how you’re getting on and what you could do to improve – and summative feedback – which gives you a mark for your assessed work.
 

Recommended reading

Key readings:
De Vaus, D.A. (2002) Surveys in Social Research (5th edition) London: Routledge
Simpson, L. Dorling, D. Editors (1999) Statistics in Society. London: Edward Arnold.

Background:
Gould, Stephen Jay (1996). The mismeasure of man: Revised and expanded. New York: W. W. Norton. (Original work published 1981)
Heckman, James J. (1995). "Lessons from the Bell Curve". Journal of Political Economy 103 (5): 1091-1120.
Herrnstein, R. & Murray (1994) The Bell Curve: Intelligence and Class Structure in American Life. New York: Free Press.
F.H. Rauscher, G.L. Shaw, K.N. Ky (1993) Music and spatial task performance
Nature, 365, p. 611
Pietschnig, J., Voracek, M., & Formann, A.K. (2010). Mozart effect'Shmozart effect: A meta-analysis. Intelligence, 38(3), 314-323.

Key online resource:
Statistical Reasoning from the Open Learning Initiative
https://oli.cmu.edu/jcourse/lms/students/syllabus.do?section=b886ee7e80020ca6001afe324755a448

Study hours

Scheduled activity hours
Lectures 20
Practical classes & workshops 10
Independent study hours
Independent study 170

Teaching staff

Staff member Role
Alan Marshall Unit coordinator

Return to course details