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Creative approaches to qualitative researching

26 - 30 June 2017

Course content overview

This intermediate level course offers a hands-on introduction to creative approaches to doing qualitative research. The various stages of research will be covered, from data collection and analysis through to writing with qualitative data. We begin by introducing what we mean by doing qualitative research creatively, before moving on to consider 'facet methodology', an inventive orientation to researching the multidimensionality of everyday lives, which puts the researcher’s creativity and imagination at the heart of methodological practice. The course also explores some of the practical and ethical issues in using creative methods. Participants will be given a practical and hands-on introduction to a range of creative qualitative methods, including visual methods and ‘material methods’.  The course will also cover key principles in qualitative data analysis, and how these can be put into practice. Finally, we discuss practical and intellectual strategies for writing with qualitative data, and consider how it is possible to theorise, or write conceptually, with such data. The course includes several practical workshop exercises involving creating and analysing qualitative data, and participants will have the opportunity to work with their own data. As such, applicants will need to have qualitative data of some kind which they can bring with them to the summer school.

This course will:

  • Introduce students to creative methods both as an approach, and as a means of generating social science research data
  • Introduce students to a range of creative methods
  • Give students practical experience in the use of creative methods
  • Introduce students to analytical strategies appropriate to creative methods
  • Introduce students to strategies for writing with qualitative data

Course structure

Monday afternoon   -   Introduction to the week and to creative qualitative methods

Tuesday morning   -   Facet methodology

Tuesday afternoon   -   An introduction to visual methods / sketching workshop

Wednesday morning   -   Creative case studies: (i) object interview and (ii) everyday ethics

Wednesday afternooon   -   Creative case studies: (i) menthol workshop and (ii) internet forums

Thursday morning   -   Analysing creatively (to include student presentations)

Thursday afternoon   -   ANalysing creatively (to include student presentations)

Friday morning   -   Writing creatively

Course requirements

This is an intermediate course where participants will be expected to have a good understanding of the basic principles of qualitative methods and to have some experience of qualitative data collection and analysis. Ideally, participants would be in the position of having their own qualitative data to analyse. Participants will be invited to bring a small excerpt of their own qualitative data (eg text or pictures that can be brought in hard copy) for use in the participative workshop sections.

The number of places on this course is limited. Those applying to register on this course will be asked to submit a short statement outlining at what stage their research is and why they wish to attend this course.

Recommended reading

We would like you to read the following before the summer school if at all possible. We will refer you to lots more recommended reading during the week.

Back, L and Puwar, N (2013) (eds) Live Methods, Malden: Wiley-Blackwell (special issue of Sociological Review published both as usual and in a book format) - first two chapters (‘A manifesto for live methods’ and ‘Live sociology’.

Mason, J (2011) ‘Facet methodology: the case for an inventive research orientation’, Methodological Innovations Online. 6, 3, p. 75-92

Mason, J and  Davies, K. (2009) ‘Coming to our senses: a critical approach to sensory methodology’, Qualitative Research. 9, 5, p. 587-603. Available at: http://qrj.sagepub.com/cgi/doi/10.1177/1468794109343628.

Presenters

Sue Heath is a Professor of Sociology at the University of Manchester and Co-Director of the Morgan Centre for Research into Everyday Lives. In addition to her substantive interests in young adults' domestic and housing transitions and in shared housing across the lifecourse, she also has strong interests in research methodology, with particular interests in research ethics and innovative creative methods. She has published widely in these areas, including several methods textbooks.

Katherine Davies is a Lecturer in Sociology at the University of Sheffield and an Honorary Research Fellow in the Morgan Centre. Katherine's research focuses upon the complexities of personal relationships and the life course and she has a long standing interest in qualitatively driven methodological approaches which can capture the lived experience of everyday lives, times and relationships. Previous research has included a study investigating the social significance of family resemblances and a project researching how associations with friends, neighbours, colleagues and the like matter throughout the life course in both positive and negative ways, whilst Katherine’s PhD research explored how young people see themselves as ‘turning out’ in life, with a particular focus on the role of sibling relationships in shaping their ideas about their past, present and future selves.

Gaelle Aeby is a post-doctoral researcher at the Morgan Centre for Research into Everyday Lives. Her main research interests lie in personal networks and kin and non-kin relationships, youth, life trajectories, critical life transitions and their rituals, as well as institutions and welfare state regimes framing personal life. She also has a strong interest in innovative research designs combining quantitative and qualitative methods. She is currently conducting research on the recomposition of personal networks after intimate relationship breakdown and separation rituals in the UK and in Switzerland, funded by the Swiss National Science Foundation.

Helen Holmes is a Hallsworth Fellow in Sociology, based in the Sustainable Consumption Institute, and a member of the Morgan Centre for Research into Everyday Lives.  Her work focuses on the juncture of materiality, temporality and practice with her current project exploring this in relation to thrifty consumption and make do and mend.  She has a keen interest in creative methods, particularly object elicitation and visual methods.

Andy Balmer is Lecturer in Sociology at the University of Manchester and a member of the Morgan Centre. His research is focused on everyday life, science and technology and has explored such topics as lying and deception, dementia and care. He is currently conducting research into the experience of changes in the relationships of people living with dementia and their carers, alongside an ongoing project on the sociology of lying, as well as overseeing a further study of the potential for collaboration between natural and social scientists.

Rob Meckin’s research focuses on emerging biotechnologies and their implications for social change in terms of knowledge production and their relationships to other domains in society. These include the ways knowledge is constructed, interdisciplinary collaborations, academic-industry relationships, the meanings and activities of translating research and the material practices of laboratory work. He currently works on a large interdisciplinary project Responsible Research and Innovation at the Synthetic Biology Centre for Fine and Speciality Chemicals (http://synbiochem.co.uk/responsible-research-and-innovation/). 

Generalized Linear Models: a comprehensive system of analysis and graphics using R and the Rcommander

26 - 30 June 2017

Overview

This is a general course in data analysis using generalized linear models.  It is designed to provide a relatively complete course in data analysis for post-graduate students.  Analyses for many different types data are included; OLS, logistic, Poisson, proportional-odds and multinomial logit models, enabling a wide range of data to be modelled.  Graphical displays are extensively used, making the task of interpretation much simpler. 

A general approach is used which deals with data (coding and manipulation), the formulation of research hypotheses, the analysis process and the interpretation of results.  Participants will also learn about the use of contrast coding for categorical variables, interpreting and visualising interactions, regression diagnostics and data transformation and issues related to multicollinearity and variable selection.

The software package R is used in conjunction with the R-commander and the R-studio.  These packages provide a simple yet powerful system for data analysis.  No previous experience of using R is required for this course, nor is any previous experience of coding or using other statistical packages.

This course provides a number of practical sessions where participants are encouraged to analyse a variety of data and produce their own analyses.  Analyses may be conducted on the networked computers provided, or participants may use their own computers; the initial sessions cover setting up the software on lap-tops (all operating systems are allowed).

Course objectives

The main objective of this course is to provide a general method for modelling a wide range of data using regression-based techniques.  Participant will be able to select, run and interpret models for continuous, ordered and unordered data using modern graphical techniques. 

Course timetable

Day one

Afternoon - Introduction: A system of analysis

Day two

Morning - Data coding, manipulation and management; defining models: representing research questions

Afternoon - Analysis: An introduction to generalized linear models; Interpretation: using effect displays

Day three

Morning - Modelling continuous data; Contrast coding: dealing with categories explanatory variables

Afternoon - Modelling count data; Including and interpreting interactions.

Day four

Morning - Modelling categories (using logit models); Modelling ordered categorial variables (proportional odds models)

Afternoon - Modelling unordered categorical variables (multinomial logit models); Exercises modelling categorical variables

Day five

Morning - Model diagnostics and data transformations (Box-Cox and Box-Tidwell); Variable selection (strategies for dealing with collinearity using limited variable models and multimodel presentations)

Course presenters

The course will be presented by Graeme Hutcheson.

Graeme Hutcheson is a lecturer in the Manchester Institute of Education and has published extensively in the field of regression models and the analysis of social science data.

Prior or recommended knowledge/reading/skills

There are no pre-requisites for this course as instruction is provided for all techniques.  However, it will be of most use to those who are interested in modelling social science datasets (survey and quasi-experimental) and applying graphics to interpret these.

Recommended Reading:

Agresti, A. (1996).  An Introduction to Categorical Data Analysis.  Wiley.

Fox, J. and Weisberg, S. (2011).  An R companion to Applied Regression (second edition).  Sage Publications

Harrell, F. E. (2001).  Regression modelling strategies. Springer.

Hutcheson, G. & Sofroniou, N. (1999).  The multivariate social scientist.  Sage Publications.

Hutcheson, G. & Moutinho, L. (2008).  Statistical modelling for management.  Sage Publications.

Getting Started in R: an introduction to data analysis and visualisation

26 - 30 June 2017

Summary

R is an open source programming language and software environment for performing statistical calculations and creating data visualisations. It is rapidly becoming the tool of choice for data analysts with a growing number of employers seeking candidates with R programming skills.

This course will provide you with all the tools you need to get started analysing data in R. We will introduce the tidyverse, a collection of R packages created by Hadley Wickham and others which provides an intuitive framework for using R for data analysis. Students will learn the basics of R programming and how to use R for effective data analysis. Practical examples of data analysis on social science topics will be provided.

Course outline

1. R and the 'tidyverse'

This session will introduce R & RStudio and cover the basics of R programming and good coding practice. We will also discuss R packages and how to use them, with a particular focus on those that make up the 'tidyverse'. We also introduce R Markdown which will be used to report our analyses throughout the course.

2. Import and Tidy

Data scientists spend about 60% of their time cleaning and organizing data (CrowdFlower Data Science Report 2016: 6). This session will show you how to 'tidy' your data ready for analysis in R. In particular, we'll show you how to take data stored in a flat file, database, or web API, and load it into a dataframe in R. We will also talk about consistent data structures, and how to achieve them.

3. Transform

Together with importing and tidying, transforming data is one of the key element of data analysis. We will cover subsetting your data (to narrow your focus), creating new variables from existing ones, and calculating summary statistics.

4. Visualise

Data visualisation is what brings your data to life. This session will provide you with the skills and tools to create the perfect (static and interactive) visualisation for your data.

5. Bringing it all together

In this last session we review all we have learned on this course, and think about how we can bring it all together in dynamic outputs, such as interactive documents, plots, and Shiny applications.

Course objectives

After this course, users should be able to:

  • implement the basic operations of R;
  • read data in multiple forms;
  • clean, manipulate, explor and visualise data in R

Course tutors

The course will be taught by Dr Reka Solymosi and Dr Henry Partridge.

Dr Reka Solymosi is a lecturer in quantitative criminology at the University of Manchester in the United Kingdom. Before that she was a data analyst researching issues around transport crime and policing at Transport for London. Her research interests are around crowdsourced data collection, transport crime, and perception of crime and place. She uses R in both teaching and research, and co-runs the R at University of Manchester (RUM) group.

Dr Henry Partridge is a Research Associate in the Policy Evaluation and Reasearch Unit (PERU) based at Manchester Metropolitan University. He is currently involved in evaluation work on offender interventions for Interserve (Purple Futures), a Community Rehabilitation Company. Henry has strong research and analytical skills with particular expertise in R programming, data visualization, and spatial analysis. He co-runs the R user group at MMU.

Structural Equation Modelling with MPlus

26 - 30 June 2017

Overview

This course gives a hands on introduction to what is possible in a latent variable analysis framework using Mplus. Building up the different sides of latent variable modelling and structural equation modeling step by step, eight different types of analysis are tackled.

Regression, Path Analysis, Confirmatory Factor Analysis, Item Response Theory, Measurement modelling, Latent Class Analysis, Longitudinal Analysis and lastly, hybrids of these are all topics of the course covered in lectures and practical analysis in Mplus.

Bringing your own data and research questions is highly recommended!

Course objectives

  1. Distinguish and understand different types of latent variable analysis;
  2. Learn how to do basic and advanced structural equation modelling;
  3. Understand how to combine different techniques in one model; and
  4. Learn how to use Mplus

Course timetable

Day one

Afternoon - Regression

Day two

Morning - Path Analysis

Afternoon - Confirmatory Factor Analysis

Day three

Morning - Item Repsonse Theory

Afternoon - Measurement Modelling.

Day four

Morning - Latent Class Analysis

Afternoon - Longitudinal Modelling

Day five

Morning - Model Building

Course presenters

The course will be presented by Nick Shryane and Bram Vanhoutte.

Prior or recommended knowledge/reading/skills

Basic statistical knowledge (variance, mean, …) and experience in using regression models (linear and logistic)

Some experience in working with syntax is helpful but not absolutely necessary.

Introduction to social network analysis using UCINET and Netdraw

3 - 7 July 2017

Overview

This is an introductory course, covering the concepts, methods and data analysis techniques of social network analysis. The course is based on the book "Analyzing Social Networks" by Borgatti et al. (Sage) and all participants will be issued with a copy of the book. The course begins with a general introduction to the distinct goals and perspectives of social network analysis, followed by a practical discussion of network data, covering issues of collection, validity, visualization, and mathematical/computer representation. We then take up the methods of detection and description of structural properties, such as centrality, cohesion, subgroups and positional analysis techniques. This is a hands on course largely based around the use of UCINET software, and will give participants experience of analyzing real social network data using the techniques covered in the workshop. No prior knowledge of social network analysis is assumed for this course.

Course objectives

The course will:

  1. Introduce the idea of Social Network Analysis
  2. Explain how to describe and visualise networks using specialist software (UCINET)
  3. Explain key concepts of Social Network Analysis (e.g. Cohesion, Brokerage).
  4. Provide hands-on training to use software to investigate social network structure

Course timetable

Day one

Introduction to Social Network Analysis, terminology and the software UCINET/Netdraw.  Chapters 1 and 2

Day two

Morning - Collecting social network data and research design. Chapters 3 and 4

Afternoon - Data management and visualisation. Chapters 5 and 7

Day three

Morning - Multivariate techniques and whole networks. Chapters 6 and 9.

Afternoon - Centrality and ego networks. Chapters 10 and 15.

Day four

Morning - Equivalence and core-periphery. Chapter 12

Afternoon - Subgroups and two-mode networks. Chapters 11 and 13

Day five

Morning - Testing hypothesis and large networks. Chapters 8 and 14.

Chapter numbers refer to the book "Analyzing Social Networks) by Borgatti et al. (Sage).  Timetable is subject to change.

Course presenters

The course will be presented by Martin EverettNick Crossley and Elisa Bellotti.

Martin Everett holds a Chair in Social Network Analysis in the School of Social Sciences at the University of Manchester. After he was awarded his DPhil in Oxford, he worked at East London, Westminster and Greenwich universities. He joined the University of Manchester in 2009,  where he helped co-found the Mitchell centre for social network analysis. Martin is a co-author of the software package UCINET and has made significant contributions to methods for social network analysis.

Nick Crossley is professor of sociology at the University of Manchester. His main work using social network analysis has focused upon music worlds, social movements and covert networks. He has also written extensively about 'relational sociology', a theoretical position which advocates a focus upon networks in sociology. His most recent book is Networks of Sound, Style and Subversion: the Punk and Post-Punk Worlds of Manchester, London, Liverpool and Sheffield, 1975-1976 (Manchester University Press).

Prior or recommended knowledge/reading/skills

None required but it would be useful to read Scott, J (2000) Social Network Analysis: A Handbook. Sage.

Software to be used

UCINET and Netdraw. It is useful for participants to bring their own laptops running windows (Macs will need to have a PC emulator) and to have downloaded the software in advance. This can be done for a free period of time from Analytictech website.

Statistical analysis of social networks

3 - 7 July 2017

Summary

This is an introduction to statistical analysis of networks. While no strict prerequisites are assumed, you might find it helpful to have some basic knowledge of social network analysis beforehand. To benefit fully from the course requires a basic knowledge of standard statistical methods, such regression analysis. The course aims to give a basic understanding of and working handle on drawing inference for structure and attributes, both cross-sectionally as well as longitudinally. A fundamental notion of the course will be how the structure of observed graphs relate to various forms of random graphs. This will be developed in the context of non-parametric approaches and elaborated to analysis of networks using exponential random graph models (ERGM) and stochastic actor-oriented models. The main focus will be on explaining structure but an outlook to explaining individual-level outcomes will be provided.

The participant will be provided with several hands-on exercises, applying the approaches to a suite of real world data sets. We will use the stand-alone graphical user interface package MPNet and R. In R we will learn how to use the packages ‘sna’, ‘statnet’, and ‘RSiena’. No familiarity with R is assumed but preparatory exercises will be provided ahead of the course.

Literature we will draw on includes:

Lusher, D., Koskinen, J., Robins, G., (2013). Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, Cambridge University Press, NY.

Snijders, Tom A. B., Gerhard G. van de Bunt, and Christian E.G. Steglich. 2010. “Introduction to stochastic actor-based models for network dynamics.” Social Networks 32:44-60.

MPNet can be downloaded from MelNet

Course objectives

The course will:

  1. Introduce how statistical evidence relates to social networks
  2. Explain how to draw inference about key network mechanisms from observations
  3. Provide hands-on training to use software to investigate
    • social network structure
    • tie-formation in cross-sectional data
    • tie-formation in longitudinal data
    • take into account network dependencies between individuals

Course timetable

Day one

Introduction to working with networks in R

Day two

Morning – Subgraphs and null distributions and ERGM rationale

Afternoon – ERGMs and dependence

Day three

Morning – ERGM: Issues and technicalities

Afternoon – SAOM: introduction to longitudinal modelling

Day four

Morning – SAOM: introduction to longitudinal modelling

Afternoon – Extensions and further issues

Day five

Morning – Influence, contagion, and outlook to further issues.

Timetable is subject to change.

Course tutors

The course will be taught by Dr Johan Koskinen

Integrated Mixed-Methods Research including QCA

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 four topics –

  • mixed methods data management;
  • qualitative comparative analysis (QCA) and the comparative method;
  • fuzzy set analysis of pathways of causality; 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 tutors, Wendy Olsen and Steph Thomson, based on previous experiences with similar kinds of materials. The computer practicals for QCA include applications of NVIVO, fsQCA, SPSS, and Excel software.  We have a partial overlap with the Factor Analysis Mixed Methods Stream.  The Factor Analysis students are using SPSS AMOS, STATA, and MPLUS software as well as learning about multiple methods for gaining original knowledge. There is 7/16 overlap of the two streams (7 sessions out of 16).

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).

Course objectives

The aims of the course are:

  • To examine seminal papers using mixed methods and discuss rigour in comparative research. To learn more about Qualitative Comparative Analysis (QCA).
  • To experience in practical settings how to use NVIVO for systematic data handling. 
  • To introduce Boolean algebra and Venn diagrams. 
  • To apply QCA ideas to personal projects, either using data offered in the course or the data you bring to the course.  We show you how to use Excel software and fsQCA freeware.
  • To examine fuzzy set histograms and scattergrams. 
  • To link NVIVO with SPSS for qualitative+demographic or survey data. 
  • To Practice making presentations using students’ own data and well-constructed logical arguments.
  • To practice debating-format and/or panel discussion about knowledge construction.

Course team

This course will be presented by Wendy Olsen and Stephanie Thomson.

Stephanie Thomson is a Postdoctoral Research Fellow in Oxford University (2015-).  Her current work focusses on how secondary schools in England are deemed similar to one another in official discourses and the development of an alternative, typological account.

Prior to this, she was a Research Associate at the University of Manchester working on the Social Policy in a Cold Climate (SPCC) research programme (led by PI Ruth Lupton).  Stephanie contributed to the assessment of the changes in educational policy (both for school-age children and those post-16) and the associated effects.  

Her ESRC-funded doctoral work explored how parents helped their children with primary-school mathematics.  As part of this work, she used a case-based method – Qualitative Comparative Analysis (QCA) – to assess whether particular configurations of child-level factors and parental help led to high achievement in mathematics.  Her interest in methods has been sustained since then. She helped create teaching materials for ‘Core Mathematics’.

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.

Course requirements

No prior knowledge is required.  The course is framed at the Masters/ PhD level of achievement and aims to help researchers who wish to publish their own original findings in their later careers.

Guidance on preparatory readings will be available some days ahead of the event.

Background is held at Compasss in the Bibliography and working papers areas.

Recommended reading

QCA Seminal article: Lam and E. Ostrom (2010), “Analyzing the dynamic complexity of development interventions: lessons from an irrigation experiment in Nepal, Policy Science, 43:1, pp. 1-25. DOI 10.1007/s11077-009-9082-6 . 

OR

QCA Seminal article on national policy regimes: Hudson and Kühner, 2009, “Towards productive welfare? A comparative analysis of 23 OECD countries “, Journal of European Social Policy 2009, 19-34. DOI: 10.1177/0958928708098522.

Byrne, D., and C. Ragin, eds. (2009), Handbook of Case-Centred Research Methods, London: Sage.

Olsen, W.K. (2012) Data Collection:  Key Trends and Methods in Social Research, London:  Sage .

Ragin, C. C. (1987). The Comparative Method: Moving beyond qualitative and quantitative strategies. Berkeley ; Los Angeles ; London, University of California Press.

Ragin, C. C. (2000). Fuzzy-Set Social Science. Chicago; London, University of Chicago Press. Ragin, C. C. and H. S. Becker, Eds. (1992). What is a Case? Exploring the foundations of social  inquiry. Cambridge [England]; New York, NY, USA, Cambridge University Press.

Ragin, C.C. (2008). Redesigning Social Inquiry: Set relations in social research. Chicago: Chicago University Press.

Rihoux, B., & Ragin, C. C., eds., (2009). Configurational Comparative Methods. Qualitative Comparative Analysis (QCA) and related techniques (Applied Social Research Methods). Thousand Oaks and London: Sage.

Factor Analysis for Integrated Mixed Methods Research

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).

Course objectives

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.

Course team

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.

Course requirements

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.  

Recommended reading

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).

Basic texts

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.

Intermediate text

Kaplan, D. (2008). Structural Equation Modeling: Foundations and Extensions. London: Sage.

Big Data Analysis for Social Scientists masterclass

4 - 5 July 2017

Overview

This course, presented by Robert Ackland, introduces participants to the collection and analysis of socially-generated 'big data' using the R statistical software, VOSON (for collecting WWW hyperlinks and text content) and Gephi network visualisation software. The main emphasis is on applying social network analysis and quantitative text analysis to data from social media and the WWW. The course will also provide an opportunity for participants to learn how these data and techniques are being used in social science research.

Course objectives

The course will cover:

  • R and RStudio refresher
  • Collecting YouTube video comment data, Facebook fan page data, and Twitter data with SocialMediaLab R package
  • Basic social network analysis in R/igraph (graph visualisation, core node- and network-level metrics, network clustering, constructing two-mode networks)
  • Basic text analysis in R (building a corpus, descriptive analysis, wordclouds)
  • Collecting WWW hyperlink and website text content with VOSON
  • Introduction to Gephi for network visualisation

Course presenter

The masterclass will be presented by Robert Ackland.

Assoc. Prof. Robert Ackland has a joint appointment in the School of Sociology and the Centre for Social Research and Methods at the Australian National University (ANU). He was awarded his PhD in economics from the ANU in 2001, and he has been researching online social and organisational networks since 2002. He leads the Virtual Observatory for the Study of Online Networks Lab (http://vosonlab.net) which was established in 2005 and is advancing the social science of the Internet by conducting research, developing research tools, and providing research training. Robert established the Social Science of the Internet specialisation in the ANU's Master of Social Research in 2008, and his book Web Social Science: Concepts, Data and Tools for Social Scientists in the Digital Age (SAGE) was published in July 2013. He created the VOSON software for hyperlink network construction and analysis, which has been publicly available since 2006 and has been used by around 2500 researchers worldwide.

Prior or recommended knowledge/reading/skills

It is preferable that students have had some exposure to social network analysis and quantitative text analysis. Experience with R or other programming languages is also desirable, but the course includes a brief R and RStudio 'refresher'.

Booking a place

  • To book a place on one of the above courses, please use our booking form.
  • You can review details on fees in the fees and attendance section. The course fee is for a single course, and includes 28 hours of face-to-face teaching over five days, and lunch on four days.
  • Once your booking has been processed please visit the e-store to pay by credit or debit card.
  • 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.