Online methods training

CLOSER Learning Hub
CLOSER

The Learning Hub is an online teaching resource has been produced by CLOSER to introduce longitudinal studies to non-experts in both academic and policy settings. The Learning Hub has information and resources to help you explore longitudinal studies and get started using the data. As you progress through the different sections of the site, you can test their knowledge using interactive quizzes.

Topics covered include:

  • Introduction to longitudinal research: types of longitudinal studies and using longitudinal data for research
  • Study design: sampling, data collection instruments, methods of data collection and sweep implementation
  • Analysis: getting started and exploring the data, general linear regression, logistic regression and multinomial logistic regression

An overview of how longitudinal research can be used by researchers, policymakers and practitioners to increase our understanding of mental health and wellbeing is also available in the ‘explore by topic’ section. Case studies of longitudinal research are also provided, covering the research questions asked, what data were used, key findings and implications for policy.

 

UK Data Service Data Skills Modules
UK Data Service 

These introductory level interactive modules are designed for users who want to get to grips with key aspects of survey, longitudinal and aggregate data. The three modules can be conducted in your own time and you are able to dip in and out when needed. The modules give an introduction to key aspects of the data using short instructional videos, interactive quizzes and activities using open access software where possible.

Longitudinal data module units include:

  • Unit 1: What are longitudinal data?
  • Unit 2: Use cases
  • Unit 3: Downloading data & getting started
  • Unit 4: Preparing to work with the data
  • Unit 5: Analysing the data

 

NCRM Online Learning Resources
National Centre for Research Methods

NCRM has many online resources that cover a wide range of research methodologies and are intended to help people interested in social science research methods. They follow a standard format and each includes purposely-created video tutorials and supporting materials such as presentation slides and video transcripts along with related datasets, recommended readings and links to related research publications and resources.

Topics covered include:

  • Survey data quality and total survey error
  • Multilevel models: random coefficient and intercept models
  • Structural Equation Modelling (SEM): what it is and what it isn't
  • Biosocial research framework

 

LEMMA Online Multilevel Modelling Course
University of Bristol Centre for Multilevel Modelling

The LEMMA (Learning Environment for Multilevel Methodology and Applications) online multilevel modelling course contains a set of graduated modules starting from an introduction to quantitative research progressing to multilevel modelling of continuous and binary data. All modules have a concept component, and most modules have practical lessons - instructions on how to carry out analyses in MLwiN, R and Stata. Interactive quiz questions are included throughout the modules to self-test your understanding.

Course modules include:

  • Multiple regression
  • Multilevel structures & classifications
  • Introduction to multilevel modelling
  • Application 1: The use of performance indicators in education
  • Regression models for binary responses
  • Multilevel models for binary responses
  • Multilevel modelling in practice: Research questions, data preparation and analysis
  • Single-level and multilevel models for ordinal responses
  • Single-level and Multilevel Models for Nominal Responses
  • Three-level multilevel models
  • Cross-classified multilevel models
  • Multiple membership multilevel models
  • Missing data
  • Multilevel Modelling of Repeated Measures Data

 

Introduction to Growth Curve Modeling
Curran-Bauer Analytics

Growth curve models go by a variety of names (multilevel models, mixed effects models, latent curve models) but share a common focus on individual change over time. In this series of videos, Patrick Bauer introduces growth curve modeling and provides an overview of many options for analyzing repeated measures data

Materials from an introductory workshop "Introduction to Growth Curve Modeling: An Overview and Recommendations for Practice" are also available. These materials include slides from the workshop and the data on Patrick describes in the videos, as well as scripts for fitting linear growth models to this data in SAS, SPSS, Stata, and Mplus.

Video episodes include:

  • What is growth curve modeling?
  • The coding of time
  • A multilevel modeling framework
  • A structural equation modeling framework
  • Nonlinear trajectories
  • Time-invariant covariates
  • Time-varying covariates
  • Multivariate growth
  • Choosing between multilevel and structural equation approaches 

 

Mplus Short Course Online Resources
Mplus

Mplus has made presentation videos and slides available from a range of short courses on different topics. 

Topics covered include:

  • Advanced factor analysis and structural equation modeling with continuous outcomes
  • Advanced regression analysis, IRT, factor analysis and structural equation modeling with categorical, censored, and count outcomes
  • Introductory and intermediate growth modeling
  • Advanced growth modeling, missing data analysis, and survival analysis
  • Categorical latent variable modeling with cross-sectional and longitudinal data
  • Multilevel modeling of cross-sectional and longitudinal data
  • Bayesian analysis using Mplus
  • Regression and mediation analysis
  • Introductory, intermediate and advanced dynamic structural equation modeling 

 

RADIANCE: Rigorous training in longitudinal data science
University College London

RADIANCE offers online training that aims to enhance knowledge, self-confidence and expertise among researchers who want to utilize complex longitudinal biosocial data. 

Topics covered include:

  • Data stewardship: Principles of responsible data science; data governance, ethics and confidentiality; data quality assessment, accessibility, linkage; questions and targets of estimation; and statistical software, data description and visualization
  • Analysis: Principles of statistical inference and modelling; data dimension reduction and classification; multiple imputation of missing data‚Äč; analysis of longitudinal and time to event data; machine learning methods; and causal inference
  • Reproducibility: Interpretation and triangulation; and open science, code sharing, and collaborations 

The training program caters to a range of career stages as each topic is delivered at three levels of difficulty. Data Science Basics includes videos that are accessible online and give brief introductions to concepts and core skills. Data Science Intermediate consists of recorded lectures and live online sessions with practical tutorials. Data Science Advanced Courses are online live lectures and tutorials.

 

DATAMIND Courses
DATAMIND, the Health Data Research Hub for Mental Health

DATAMIND offers short courses to support anyone from a participant to a researcher working with or interested in health data, especially mental health data.

The courses offered include:

  • The Data Literacy Short Course (51 minutes), created by the McPin Foundation and DATAMIND with the help of the DATAMIND Super Research Advisory Group. The course covers how the NHS uses and stores healthcare data, patient and citizen rights, how researchers use health data to learn more about health conditions and treatments, and the possible risks and benefits of using data.
  • A Practical View on Code Sharing and Co-Developing (16 minutes), developed by DATAMIND, the UK Health Data Research Hub for Mental Health, and the University of York through Project SPORE. The course covers various aspects of code sharing, including why you should consider sharing your code, when to share it, with whom to share it, how to do it effectively, licensing and intellectual property considerations, documenting your code, creating contributing guidelines, ensuring discoverability, and code maintenance.
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