The instructions and the Requirements you can find it in file named Assignment. Can you do it

Assignment: Analysis of multivariate data

(Indicative word length: 1500 words, excluding tables and graphs)

Using the dataset Adults (Assignment 2).sav from session 8, and based on material covered in sessions 7-9, write a report outlining the nature of the relationship between income (NetPay OR Rich) and class (NSSEC) as captured by the Quarterly Labour Force Survey. To undertake the analysis:

a) Select at least THREE factors (variables) that might be related to income, one of which MUST be class (NSSEC).

b) For each factor in turn, outline the nature of the basic relationship between that factor and income, making appropriate use of visualisation, correlation and regression (EITHER ordinary least-squares OR logistic).

c) Report on a multivariate regression model (ordinary least squares OR logistic) that simultaneously takes account of all of your chosen explanatory factors when predicting income.

In your write-up you should consider making reference to:

- Model fit
- The reliability of the model
- The statistical concept of ‘controlling for'

Particular care should be taken to ensure that the statistical techniques adopted match the data type used (continuous or categorical).

Additional marks will be awarded for presentational standard, so use of unedited statistical output should be avoided.

NOTE: Take great care in matching the income variable you chose (NetPay or Rich) to the type of correlation and regression you wish to undertake.

Synopsis of session contents: see all Session in detail to help you what we did ( you can find it in their files).

Session: An introduction to causal modelling

Contingency table analysis often begs the question of the degree of association and direction of cause and effect between the variables being studied. In this session, the basic measure of association between two variables (correlation) is introduced. The nature of causality is also discussed and the standard method for statistical analysis of causality (regression) is introduced. A practical session will allow hands-on practise in measuring the degree of association between variables and in predicting the value of one variable from the value of another.

References-see list of resources (above). In particular, see:

Field A (2009) Discovering Statistics using SPSS, London: SAGE. [Chapters 6 & 7]

Session: Logistic regression: an introduction

Logistic regression is a key social science tool, allowing the testing and development of social theory using survey data. A theoretical introduction to Logistic regression, with examples of its use in practise, is followed by a practical session in which logistic regression is used to tease meaning from a real social survey dataset.

References

Bewick V, Cheek L and Ball J (2005) ‘Statistics review 14: logistic regression',Critical Care, 9(1), 112-118.

Field A (2009) Discovering Statistics using SPSS, London: SAGE. [Chapter 8]

Menard S (1995) Applied logistic regression analysis. SAGE University Paper Series on Quantitative Applications in the Social Sciences, 07-106. SAGE, Thousand Oaks, CA.

Session: Logistic regression: going multivariate

This session extends the use of logistic regression from models comprising single predictor variables to models comprising multiple predictors. This raises modelling issues such as variable confounding, model robustness and identification of a ‘best' model. Practical solutions to these issues are rehearsed as part of the practical session.

References - as Session 8a

Academic integrity

The standard University regulations regarding academic integrity apply. These include the prohibition of any kind of plagiarism, collusion and data fabrication.

In addition, School of Environmental Science regulations prohibit students from submitting the same work for assessment more than once. Work will be judged to have been submitted more than once where comparison of two submissions by the same student, across one or more modules, shows significant areas of commonality (e.g. extensive paraphrasing or direct duplication of content from the original submission). Duplicate submissions will be awarded a mark of zero.
Resources

The following books and resources provide a useful introduction to the principles and practice of data analysis which will be covered during the module.

To check their availability in the library, search for the ENVS450 reading list on the library catalogue:

http://library.liv.ac.uk/search/v?SEARCH=envs450

Further details of books which are of particular relevance to specific weeks are provided below, and in addition further references will be provided during the course of the module.

For Statistics and SPSS the two best resources are:

Field, A. P. (2009) Discovering statistics using SPSS (and sex and drugs and rock 'n' roll), 3rd edition. London : SAGE, 2009.

NOTE: as the title implies, Field's style of writing will not be to everyone's taste. The book's comprehensive coverage might also feel overwhelming. Hence the list of alternative texts below.

Other recommended statistics texts:

Acton C., Miller R. et al., 2009. SPSS for Social Scientists, 2nd edition. London: Palgrave.
Balnaves, B., and Caputi, P., 2001. Introduction to Quantitative Research Methods: An investigative approach. London: Sage.
Bryman, A. 1992. Quantity and Quality in Social Research. London: Routledge.
Clegg, F. (1982) Simple Statistics. Cambridge: Cambridge University Press.
Dale, A. Fieldhouse, E., and Holdsworth, C. 2000. Analyzing Census Microdata. London: Arnold. Particularly Chapters 6 & 7.
de Vaus, D. A. 1991. Surveys in Social Research. London: Allen and Unwin.
Diamond I and Jefferies J (2001) Beginning statistics: an introduction for social scientists. SAGE, London.
ESDS (2010) Using SPSS for Windows (Copy on VITAL in Session 1 folder)
Foster, J.J. 2001 Data Analysis Using SPSS for Windows - New Edition: Versions 8-10. Sage Publications: London.
Marsh, C. 1988. Exploring Data. Polity: Cambridge.
Rose D. and Sullivan O. 1996 Introducing Data Analysis for Social Scientists, 2nd Ed. Open University Press: Buckingham.
Rowntree, D. (1991) Statistics without tears. London: Penguin.
Wright D B (1997) Understanding statistics: an introduction for the social sciences. SAGE, London.
See also the video guide ‘Using weights in social surveys: an introduction using Nesstar', available online via: http://www.ccsr.ac.uk/esds/video/weights/weights.html

For Qualitative Analysis recommended introductory texts include:

Bryman, A. & Burgess, R. G. 1994. Analyzing qualitative data. New York: Routledge.
Denzin, N. K., and Y. S. Lincoln. 1994. Handbook of Qualitative Research. London: Sage.
Hay, I (2010). Qualitative Research Methods in Human Geography. Oxford.
Mason, J. 2002. Qualitative Researching, 2nd edition. London: Sage.
Silverman, D. 1993. Interpreting Qualitative Data: Methods for Analysing Talk, text and Interaction. London: Sage.
---, ed. 1997. Qualitative Research: Theory, Method, and Practice. London: Sage.
Weitzman, E. A., and M. B. Miles. 1995. Computer Programs for Qualitative Data Analysis. London: Sage.

Attachment:- Adults (Assignment 2).rar