3-7 July 2017
This summer school strand approaches mixed methods from the viewpoint that methods can be integrated not separated at the analysis stage. It focuses on the use of case-studies and the case-study comparative method in mixed-methods research contexts. The content focuses on the topics:
- mixed methods data management;
- factor analysis using both confirmatory methods, and latent factor analysis within structural equation modelling; and
- 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, in several of these areas. 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, based on previous experiences with similar kinds of materials. The computer practicals for factor analysis include applications of SPSS AMOS which has a graphical interface (nice pathway diagrams), STATA which from version 15 also has such an interface, and Excel software. There is 7/16 overlap of a comparative research stream (“QCA and Fuzzy Sets”) with this Factor Analysis mixed-methods stream (7 sessions out of 16). Thus, you will meet people who also use qualitative research and do comparative projects. Your knowledge of epistemology and realist philosophy of science will grow, giving a good underpinning to your statistical and survey research.
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.
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