This summer school helps you apply Factor Analysis to make a scale or index using statistical software. There is a part focus on mixed-methods research contexts. We cover these topics: – factor analysis using confirmatory methods, latent factor analysis within structural equation modelling; methods of using qualitative data to strengthen an argument and make the analysis rigorous and transparent. The school offers unique new training, developed specifically for this outlet.
This one-week event involves 28 hours of contact time of which about 5-6 hours are computer practicals led by the experienced tutor, Wendy Olsen, using SPSS and STATA.
The computer practicals for factor analysis include applications of SPSS AMOS which has a graphical interface (nice pathway diagrams), STATA SEM.
There is 7/16 overlap with a mixed-methods training stream so that you can see how interviews or discourse analysis fit well with factor anlaysis of attitudes. This also helps with comparative cross-country studies of attitudes and norms.
The summer school provides a good underpinning to your statistical and survey research.
The course involves lectures, active learning and a project. Each day up to two lectures and one ‘lectorial’ occur. A lectorial is active learning led from the front with guided small group work. The project is individually done and will lead to the creation of a poster display with hot links. Participants may want to bring their own laptops (but it’s optional).
The organisation of the course involves lectures, active learning and a project. Each day up to two lectures and one ‘lectorial’ occur. A lectorial is active learning led from the front with guided small group work. The project is individually done and will lead to the creation of a poster display with hot links. Participants may want to bring their own laptops (but it’s optional).
The aims of the course are:
To examine seminal papers using mixed methods and discuss rigour in comparative research.
To introduce the idea of measurement error and measurement models, and contrast confirmatory with exploratory factor analysis.
To use STATA and SPSS AMOS, and some students may use MPLUS. Both STATA and SPSS AMOS have graphical windows for planning a factor analysis model.
To examine latent factor histograms and scattergrams, and interpret them from sociological and social-theory angles.
To apply factor analysis.
To Practice making presentations using students’ own data and well-constructed logical arguments.
To practice debating-format and/or panel discussion about knowledge construction.
This course will be presented by Wendy Olsen.
Wendy Olsen joined Manchester University in 2002 and is Professor of Socio-Economics. She worked till 2014 both for the Institute for Development Policy and Management (IDPM) and in the Discipline of Social Statistics. She is Director of the MSc in Social Research Methods & Statistics degree programme in Social Sciences (http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=24892). She has previously taught sociology, development economics, and research methodology. She teaches statistics and PhD research methodology as well as computerised qualitative data analysis, the comparative method, the case-study method, and topics in political economy (e.g. child labour in India). She has release from some of her teaching duties due to research projects (see . She is fostering the use of mixed-methods research among statistical and other researchers.
Students will gain most if they already tried regression or used microdata once or twice before. They should already be familiar with SPSS or STATA but not necessarily both. Full guidance will be given about using the software. Sample programmes will be supplied, making it easier to use the software.
Crompton, R. and Harris, F. (1998) 'Explaining women's employment patterns: 'orientations to work' revisited.' British Journal of Sociology, 49, 1, 118-149.
Crompton, R., M. Brockmann and C. Lyonette. 2005. "Attitudes, Women's Employment and the Domestic Division Of Labour: A Cross-National Analysis in Two Waves." Work, Employment and Society 19(2):211-231.
Fuller, B., Caspary, G., Kagan, S.L., Gauthier, C., Huang, D.S.C., Carroll, J. and McCarthy, J. (2002). 'Does maternal employment influence poor children's social development?' Early Childhood Research Quarterly 17: 470-497.
Hoffman, D.M and L.S. Fidell. 1979. "Characteristics of Androgynous, Undifferentiated, Masculine, and Feminine Middle-Class Women." Sex Roles 5(6).
Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (2005). Multivariate Data Analysis. New Jersey: Prentice-Hall.
Loehlin, J. C. (2004). Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis, 4th ed. NY: Psychology Press.
Muthén, B. (1984). 'A General, Structural Equation Model with Dichotomous, Ordered Categorical, and Continuous Latent Factors'. Psychometrika 49.
Kaplan, D. (2008). Structural Equation Modeling: Foundations and Extensions. London: Sage.
Booking is now open for the Summer School 2017.
To pay be credit card please visit our e-store
To pay by invoice (institutions only) please complete the booking form on the methods@manchester website and email a copy of a Purchase Order to email@example.com
If you are based at the University of Manchester and your fee is being paid by your department please complete the booking form and contact us to arrange an internal journal transfer.