Advanced Quantitative Methods - SPY00002M

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  • Department: Social Policy and Social Work
  • Module co-ordinator: Dr. Naomi Finch
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2018-19

Module will run

Occurrence Teaching cycle
A Spring Term 2018-19

Module aims

The module is designed to provide research training in quantitative methods for students doing post graduate research degrees in social policy and related disciplines. It aims to help students to develop practical skills in data analysis, using a standard statistical package (SPSS) and to give students an understanding of statistical theory.

Students will learn how to interpret critically the results of their quantitative analyses and to write up results in a clear and easily understood form. The module is also designed to help students to go on themselves to learn more specialised techniques as required in any further research work they undertake.

Module learning outcomes

By the end of this module students should become familiar with some common statistical techniques that are most widely used in social sciences research; and have developed an ability to choose and conduct appropriate statistical analysis using SPSS. Students would be able to grasp the relevant knowledge and theory, and develop research skills as detailed below:

Knowledge and Theory

  • Understand the assumptions behind statistical techniques
  • Ability to select and apply appropriate statistical techniques for testing hypotheses
  • Familiar with a range of statistical techniques and ability to choose and implement appropriate statistical techniques for research
  • Ability to interpret statistical outputs and think through the meaning and implications of statistical findings within a public and social policy context
  • To build on this module and be able to go on to learn more specialised statistical techniques

Research Skills

  • Ability to carry out statistical analyses using SPSS
  • Ability to use syntax files to record and programme statistical analysis
  • Ability to manage and analyse large cross-sectional survey datasets
  • Ability to generate, interpret and present statistics and charts to describe the distributional characteristics of variables
  • Ability to explore the relationships between two or more variables
  • Write up statistical findings appropriately

Assessment

Task Length % of module mark
Essay/coursework
Essay
N/A 100

Special assessment rules

None

Reassessment

Task Length % of module mark
Essay/coursework
Essay
N/A 100

Module feedback

Information currently unavailable

Indicative reading

Recommended readings for each week are listed below. Other useful references including some more specific and advanced techniques and web resources can be seen in the Resource list on the Module VLE.

Week 2:

Clegg, F. (1983) Simple Statistics: A Course Book for the Social Sciences. Cambridge: CUP.

Read chapter 4 The normal distribution.

Read chapter 5 Probability.

Field, A. (2009) Discovering Statistics Using SPSS, 3rd Ed. London: Sage.

Read chapter 2.

Hinton, P.R. (2004) Statistics Explained 2nd Ed. London: Routledge.

Read chapter 4 Introduction to hypothesis testing.

Read chapter 9 Significance, error and power.

Norusis, M. J. (2011) IBM SPSS Statistics 19 Guide To Data Analysis. Upper Saddle River, N.J.: Pearson Education.

Read chapter 2 An introductory tour of IBM SPSS Statistics.

Read chapter 4 Counting responses.

Read chapter 5 Computing descriptive statistics.

British Household Panel Survey website - http://www.iser.essex.ac.uk/bhps (accessed on 05.01.2015)

Understanding Society website - http://www.understandingsociety.ac.uk/ (accessed on 05.01.2015)

Week 3: HYPOTHESIS OF DIFFERENCE

Clegg, F. (1983) Simple Statistics: A Course Book for the Social Sciences. Cambridge: CUP.

Read chapter 6 What are statistical tests all about.

Read chapter 9 Simple statistical tests.

Read chapter 11 Two parametric tests.

Field, A. (2009) Discovering Statistics Using SPSS, 3rd Ed. London: Sage.

Read chapter 9 Comparing two means.

Read chapter 10 Comparing several means: ANOVA.

Norusis, M. J. (2011) IBM SPSS Statistics 19 Guide To Data Analysis. Upper Saddle River, N.J.: Pearson Education.

Read chapter 14 Testing a hypothesis about two independent means.

Read chapter 15 One-Way Analysis of Variance.

Week 4: HYPOTHESIS OF ASSOCIATION

Clegg, F. (1983) Simple Statistics: A Course Book for the Social Sciences. Cambridge: CUP.

Read chapter 15 Correlation.

Hinton, P.R. (2004) Statistics Explained 2nd Ed. London: Routledge.

Read chapter 19 Analysing frequency data: chi-square.

Norusis, M. J. (2011) IBM SPSS Statistics 19 Guide To Data Analysis. Upper Saddle River, N.J.: Pearson Education.

Read chapter 17 Comparing observed and expected counts.

Week 5: SIMPLE LINEAR REGRESSION

Field, A. (2009) Discovering Statistics Using SPSS, 3rd Ed. London: Sage.

Read chapter 7 Regression (Section 7.2-7.4)

Norusis, M. J. (2011) IBM SPSS Statistics 19 Guide To Data Analysis. Upper Saddle River, N.J.: Pearson Education.

Read chapter 20 Linear regression and correlation.

Week 6: MULTIPLE REGRESSION

Field, A. (2009) Discovering Statistics Using SPSS, 3rd Ed. London: Sage.

Read chapter 7 Regression (Section 7.5-7.11).

Norusis, M. J. (2011) IBM SPSS Statistics 19 Guide To Data Analysis. Upper

Saddle River, N.J.: Pearson Education.

Read chapter 23 Building multiple regression models.

Read chapter 24 Multiple regression diagnostics.

Week 7: LOGISTIC REGRESSION

Field, A. (2009) Discovering Statistics Using SPSS, 3rd Ed. London: Sage.

Read chapter 8 Logistic regression.

Pampel, F.C. (2000) Logistic Regression: A Primer. London: Sage.

Read chapter 1 The logic of logistic regression.

Read chapter 2: Interpreting logistic regression coefficients.

Read chapter 3: Estimation and model fit. Module Website

Week 8: FACTOR ANALYSIS

Field, A (2013) Discovering statistics using IBM SPSS statistics: and sex and drugs and rock 'n' roll. (4th Ed.) London: Sage.

Read chapter 17: Exploratory Factor Analysis

Pedhazur, E., & Schmelkin, L. (1991) Measurement, design and analysis: an intergrated approach. NJ: Erlbaum.

Read chapter 22: Exploratory Factor Analysis

Tabachnick, B. G. & Fidell, L.S. (2014) Using Multivariate Statistics (6th Ed.). Essex: Pearson Education Limited.

Read chapter 13: Principal Components and Factor Analysis

Week 10: TECHNIQUE APPLICATION AND PRESENTING DATA

Papers to read for the lecture / seminar

OLS (linear) regression

Rose, J; Hewitt, B and Baxter, J (2011) Women and part-time employment: Easing or squeezing time pressure? In Journal of Sociology

http://jos.sagepub.com/content/early/2011/11/04/1440783311419907

Fitzpatrick, T; Abonyi, S and Kelly, I (2012) Factors Associated with Perceived Time Pressure among Employed Mothers and Fathers in Psychology Vol.3, No.2, 165-174 www.scirp.org/journal/PaperInformation.aspx?paperID=17371

Lee, Y and Bhargava, V (2009) Leisure Time: Do Married and Single Individuals Spend It Differently? In Family and Consumer Sciences Research Journal Volume 32, Issue 3, Article first published online: 2 JUL 2009, Article first published online: 2 JUL 2009

http://onlinelibrary.wiley.com/doi/10.1177/1077727X03261631/pdf

Logistic regression

Yoshida, A (2011) Dads who do Diapers: Factors Affecting Care of Young Children by Fathers in Journal of Family Issues 2012 33: 451 originally published online 5 August 2011

http://jfi.sagepub.com/content/33/4/451.full.pdf+html

Geisler, E (2012) How Policy Matters: Germanys Parental Leave Benefit Reform and Fathers Behaviour 1999-2009, MPIDR Working Paper WP 2012-021 July 2012

www.demogr.mpg.de/papers/working/wp-2012-021.pdf

Bernhardt, E; Noack, T; Lyngstad, T (2008) Shared Housework in Norway and Sweden: advancing the gender revolution in Journal of European Social Policy 18:275

http://www.suda.su.se/yaps/YAPS_WP/EQUAL_WP_2.pdf

OLS and logistic regression

Robson, K (2003) Peer Alienation: Predictors in childhood and outcomes in adulthood, ISER Working Papers Number 2003-21

https://www.iser.essex.ac.uk/files/iser_working_papers/2003-21.pdf

Evertsson, M (2006) The reproduction of gender: housework and attitudes towards gender equality in the home among Swedish boys and girls in Journal of Sociology, Volume 57, Issue 3

http://www.yale.edu/ciqle/PUBLICATIONS/EvertssonBJOS(57.3).pdf



The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.