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National Centre for Research Methods

National Centre for Research Methods

NCRM

Advanced Spatial Analysis for Researchers using ArcGIS

This two-day course covers advanced spatial analysis techniques using the industry-leading GIS software – ArcGIS.

Participants will learn about methods of spatial analysis, ArcGIS extensions, model builder and how to build web applications. Previous experience of ArcGIS is a prerequisite.

The course covers:

  • GeoDatabases
  • Manipulating coordinate systems
  • Advanced geoprocessing tools
  • Spatial analysis
  • Creating models using the ModelBuilder
  • ArcGIS extensions
  • Building and sharing web maps using webGIS

 

By the end of the course participants will:

  • Understand the benefits and creation of geodatabases
  • Be able to use advanced geoprocessing tools and ESRI extensions
  • Understand how to create a model
  • Be able to create web maps to share their data

 

Target Audience

Current users of ArcGIS who wish to expand their knowledge and practical skills in the more advanced aspects of ArcGIS software.

Participants must be confident users of ArcGIS and understand the following terms: geodatabases, feature layers, feature classes, shapefiles, metadata, attribute and spatial joining, geoprocessing, select by location and attributes (spatial/attribute queries) and must be aware of the difference between ArcMap, ArcGIS and ArcCatalog, ArcGIS online

StartEndPlaces LeftCourse Fee 
07/02/201808/02/2018[Read More]
Advances in Diary Method for Qualitative Researchers

Advances in Diary Method for Qualitative Researchers

This advanced training event will help you to use solicited diary method to gather qualitative information. You will learn about gathering, handling, analysing and reporting data sourced through solicited diary method, through real-life diary data, small group work, and mini lectures.

The event will cover the practicalities, as well as the methodological and ethical decisions involved in using solicited diary method, such as getting diaries back, providing financial incentives, protecting the well-being and privacy of diarists and their diaries during the data collection phase, and techniques for analyzing diary data, including visual diaries.  In particular, we will consider how diary method is used and modified with participants who may lack the capacity to write, remember events, or think clearly and logically, including for example young children, adults with learning disabilities, people with dementia, and people living in low income countries. 

StartEndPlaces LeftCourse Fee 
01/12/201701/12/2017[Read More]
Designing and Implementing Mobile Web Surveys

Designing and Implementing Mobile Web Surveys

Mobile devices (smartphones and tablets) are increasingly being used by respondents to complete Web surveys. This presents a number of design challenges for survey researchers. Smartphones also offer a number of added possibilities for survey designers, such as the use of GPS to track movement, apps to trigger measurement at set times (ecological momentary assessment), the possibility of capturing images, and other features. This course will focus on the design implications of the rise of mobile device use for survey research. The research evidence will be reviewed, and the various options for accommodating mobile Web users will be discussed. The challenges of using the enhanced features of mobile phones for general population surveys will also be reviewed. The course is focused on situations where respondents are using their own devices, i.e., the designer has little control over the device used. Participants are encouraged to bring their own example surveys to the course to discuss.

The course covers:

  • The rise in mobile Web use
  • Coverage and nonresponse implications of mobile Web
  • How to identify mobile Web users (user agent strings and paradata)
  • How Web surveys on a mobile device are different from those on a PC
  • How to design Web surveys to accommodate mobile users
  • Challenges and opportunities of exploiting the features of mobile surveys

 

By the end of the course participants will:

  • Understand the varieties and uses of paradata in Web surveys
  • Have the knowledge or tools to collect, process, and analyse paradata
  • Understand how to extract meaning from paradata
StartEndCourse Fee 
08/12/201708/12/2017[Read More]
NCRM

Experimental approaches for social research

Social questions are not traditionally studied using experimental methods; indeed, the lack of prominence given to the experimental method is one of the features often said to distinguish social science from the biological or physical sciences. However, there are approaches available to the social scientist that can fairly be described as experimental. These include the economic games of experimental economics, to the ‘experiments in the field’ of experimental ethnography, the natural experiments that have high evidential value in public health, and large randomized controlled trials increasingly used in public policy and development economics. This one-day course explores these various different types of experiment. It emphasises the practical and creative aspects of designing experiments, and the ethical and interpretative issues they throw up. It will draw examples from a wide range of research areas. The aim is to give participants a clear sense of what kinds of experimental options they might have, and how they could devise novel experimental approaches of their own, even at the small scale and with modest resources. Each session of the course will mix presentations, group discussions, and practical exercises

Some experience of quantitative data analysis

The course covers:

  • The various definitions of experiment and the ways experimental approaches have been used in social research
  • Relationships between experimental approaches and approaches of other kinds (e.g. surveys, ethnography, interviews)
  • Principles of experimental design
  • Ethical and interpretative issues arising from experimental research

 

By the end of the course participants will:

  • Understand what an experimental approach is and the options for experimental research available to them
  • Have worked through a number of key examples of experimental approaches to social science
  • Be equipped to design and critique experiments for themselves
  • Considered the ethical issues involved in experimental work
  • Have reflected on how data gained through experimental research might be related to those gathered in other ways
StartEndPlaces LeftCourse Fee 
20/11/201720/11/2017[Read More]
NCRM

Introduction to Latent Class Analysis

Latent Class Analysis (LCA) is a branch of the more General Latent Variable Modelling approach. It is typically used to classify subjects (such as individuals or countries) in groups that represent underlying patterns from the data. In addition to this application LCA provides a flexible framework that can be used in a wide range of contexts: in longitudinal studies (e.g., mixture latent growth models, hidden Markov chains), in evaluation of data quality (e.g., extreme response style, cross-cultural equivalence), non-parametric multilevel models, joint modelling for dealing with missing data.

In this course you will receive an introduction to the essential topics of LCA such as: what is LCA, how to run models, how to choose between alternative models, how to classify observations, how to evaluate and predict classifications. You will also apply this knowledge to a number of more advanced models that look at the relationship between latent class variables and at longitudinal data.

The course covers:

  • Refresher of basic concepts in categorical analysis: (marginal) probability, odds ratios, logistic regression;
  • Basic concepts and assumptions of latent class analysis;
  • Introduction to Latent GOLD software;
  • Model fit evaluation: global, local and substantive evaluation;
  • Classification of cases;
  • Apply these concepts to a number of models looking at: predicting class membership, relationships between latent classes, hidden Markov chains.

 By the end of the course participants will:

  • Know what is Latent Class Analysis;
  • Be able to estimate and interpret results from Latent Class Analysis;
  • Be able to choose between alternative Latent Class Models;
  • Understand latent class classification and how to predict it;
  • Be able to investigate the relationship between latent class variables.
StartEndPlaces LeftCourse Fee 
16/11/201717/11/2017[Read More]
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Introduction to Longitudinal Structural Equation Modelling with R

Longitudinal data (data collected multiple times from the same cases) is becoming increasingly popular due to the important insights it can bring us. For example, it can be used to track how individuals change in time and what are the causes of change, it can also be used to understand causal relationships or used as part of impact evaluation. Unfortunately, traditional models such as OLS regression are not appropriate as multiple individuals are nested in different time points. For this reason specialised statistical models need to be learned.

 

Structural Equation Modelling (SEM) offers a flexible framework in which longitudinal data can be analysed. It offers a series of advantages compared to other approaches such as traditional multilevel models: the inclusion of multiple relationships (path analysis, mediation, etc.), the inclusion of measurement error, the estimation of change in measurement error, multi-group analysis, etc.

 

The course will cover some of the basics and more advanced models used in Longitudinal SEM using the lavaan package in R. In addition to the fact that the package is free and open source they also offer great flexibility, being able to estimate most of the models typically used in Longitudinal SEM.

 

The course covers:

  • Introduction to R and lavaan package;
  • Short discussion of the SEM framework;
  • Regression and path analysis in SEM;
  • Cross-lagged models;
  • Latent Growth Models;
  • Factor models and their identification;
  • Equivalence testing;
  • Second order cross-lagged and Latent Growth Models;

 

By the end of the course participants will:

  • Know what is SEM;
  • Be able to estimate and interpret results from a cross-lagged model;
  • Be able to estimate and interpret results from a Latent Growth Model;
  • Be able to estimate and interpret longitudinal equivalence testing;
  • Understand second order factors and how they can be used in Longitudinal SEM;

Pre-requisites

 

Intermediate level course.

Knowledge of regression analysis. Prior knowledge of R or SEM would be an advantage but not essential.

 

Preparatory Reading

 

For an introduction to Latent Class Analysis:

Enders, C. K. (2010). Applied Missing Data Analysis (1st ed.). New York: The Guilford Press.

 

Further reading

 

Using lavaan in R:

Beaujean, A. A. (2014). Latent Variable Modeling Using R: A Step-by-Step Guide. New York: Routledge.

 

StartEndPlaces LeftCourse Fee 
18/01/201819/01/2018[Read More]
NCRM

Introduction to Programming

In this course, we introduce the concept of a computer program. We start by considering what a computer program is and comparing it with how we think about accomplishing everyday tasks. We look at how far we can get with defining a computer program in words and pictures before we ever start writing code.

By relating the simple constructs we have used to describe tasks to specific programming constructs using the Python programming language we will build up a set of individual programming tasks which put together will constitute a complete program.

The constructs can of course be re-used to create any number of different programs to solve a variety of problems.

StartEndPlaces LeftCourse Fee 
22/11/201722/11/2017[Read More]
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Quant for Qual Researchers

This course is aimed at researchers and teachers who have previously mostly worked in the qualitative tradition of research, but wish to understand or begin to research in the quantitative tradition. It assumes no more than a lay knowledge of quantitative methods (such as surveys or polling) and will take participants on a journey from the methodological and epistemological foundations of quantitative methods, through design, sampling and principles of analysis. No prior statistical knowledge is required and the course will mostly follow a problem based learning approach.

 

The course covers:

  • The epistemological and methodological basis of quantitative methods and its challenges.
  • Basic research design and quantitative qualitative integration
  • Introduction to sampling
  • Introduction to questionnaire design and basic scaling
  • Secondary analysis of existing data
  • Introduction to basic analysis techniques and significance

Pre-requisites

 

Basic understanding of qualitative methods. Prior knowledge of SPSS is not required.

 

Preparatory Reading

 

Desirable.

Williams, M (2003)  Making Sense of Social Research. London: Sage.

De Vaus, D (2013) Surveys in Social Research. 6th edition. London: Allen & Unwin.

 

By the end of the course participants will:

  • Understand the reasoning underlying quantitative methods and their role in a pluralist approach to research
  • Learn about basic design issues and how these inform methodological choices
  • Be introduced to basic sampling decisions and techniques
  • Learn how to go about designing a questionnaire and measure through simple scales
  • Be aware of the possibilities of using existing data to answer research questions
  • Learn about univariate and bivariate analysis
  • Understand the concepts of central tendency and dispersion
  • Learn how to use some simple descriptive statistics
StartEndPlaces LeftCourse Fee 
09/01/201811/01/2018[Read More]
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Quant for Qual Researchers FOC

This course is aimed at researchers and teachers who have previously mostly worked in the qualitative tradition of research, but wish to understand or begin to research in the quantitative tradition. It assumes no more than a lay knowledge of quantitative methods (such as surveys or polling) and will take participants on a journey from the methodological and epistemological foundations of quantitative methods, through design, sampling and principles of analysis. No prior statistical knowledge is required and the course will mostly follow a problem based learning approach.

 

The course covers:

  • The epistemological and methodological basis of quantitative methods and its challenges.
  • Basic research design and quantitative qualitative integration
  • Introduction to sampling
  • Introduction to questionnaire design and basic scaling
  • Secondary analysis of existing data
  • Introduction to basic analysis techniques and significance

Pre-requisites

 

Basic understanding of qualitative methods. Prior knowledge of SPSS is not required.

 

Preparatory Reading

 

Desirable.

Williams, M (2003)  Making Sense of Social Research. London: Sage.

De Vaus, D (2013) Surveys in Social Research. 6th edition. London: Allen & Unwin.

 

By the end of the course participants will:

  • Understand the reasoning underlying quantitative methods and their role in a pluralist approach to research
  • Learn about basic design issues and how these inform methodological choices
  • Be introduced to basic sampling decisions and techniques
  • Learn how to go about designing a questionnaire and measure through simple scales
  • Be aware of the possibilities of using existing data to answer research questions
  • Learn about univariate and bivariate analysis
  • Understand the concepts of central tendency and dispersion
  • Learn how to use some simple descriptive statistics
StartEndPlaces LeftCourse Fee 
09/01/201811/01/2018[Read More]
NCRM

Using Creative Research Methods

This course will outline creative research methods and show you how to use them appropriately at every stage of the research process. The course assumes that you have a good working knowledge of conventional research methods, and builds on that knowledge by introducing arts-based methods, research using technology, mixed methods, and transformative research frameworks such as participatory and activist research. Any or all of these techniques can be used alongside more conventional research methods and are often particularly useful when addressing more complex research questions. In the afternoon you will have the opportunity to try applying these methods in practice. Attention will be paid to ethical issues throughout. The day will include plenty of practical advice and tips on using creative methods in research.

 

The course covers:

  • Arts-based methods
  • Research using technology
  • Mixed methods
  • Transformative research frameworks

 

By the end of the course participants will:

  •  Have a good level of knowledge of creative research methods
  •  Understand how to use creative methods alongside more traditional methods
  •  Understand when to use creative methods in research
  • Know how creative methods can add value to funding bids

 

This course will be relevant for researchers from the third sector, public services (e.g. health, criminal justice, social care, education, local or national government), and those who work in independent research organisations or academia. It is an intermediate level course and attendees will need a good working knowledge of traditional research methods.

StartEndPlaces LeftCourse Fee 
15/03/201815/03/2018[Read More]
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Web Survey Paradata

Paradata are the data automatically generated when respondents answer Web surveys. There are many different kinds of Web survey paradata, including email tracking tools, user agent strings (to identify devices used), and server-side and client-side paradata providing information on things like response times, mouse-clicks, scrolling behaviour, and so on. This course will provide participants with an overview of the different types of Web paradata, and how they can be collected, managed, and analysed to provide useful information on data quality and nonresponse in Web surveys, leading to design improvements.

 

The course covers:

  • Different types of Web paradata and their uses
  • Capturing paradata in Web surveys
  • Processing Web survey paradata
  • Analysing paradata

 

By the end of the course participants will:

  • Understand the varieties and uses of paradata in Web surveys
  • Have the knowledge or tools to collect, process, and analyse paradata
  • Understand how to extract meaning from paradata
    • Introduction and overview
    • What do we mean by paradata: client-side versus server-side paradata
    • Classification of different types of paradata
    • Capturing paradata in Web surveys: conceptual and practical issues
    • How paradata can be used: examples of use
    • Processing Web paradata: concept and practical issues
    • Analysing Web paradata: conceptual and practical issues
    • Combining paradata with other sources of information
    • Using paradata to improve online instruments

     

    Detailed programme to follow
StartEndCourse Fee 
07/12/201707/12/2017[Read More]

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