In this workshop we present a range of practical tips and guidelines on how to design, field, and analyse the more commonly used surveys. The initial focus is on how to setup and field a study. A variety of different questions and scales, including some unorthodox and novel ones, will be presented to give an appreciation of what is possible. Some of the topics covered will be line vs discrete scales, the effect of colour, optimal discrete/LIKERT scales, etc. Then we will present on basic analysis of common question types and reporting. We will discuss the pros and cons of common analyses (e.g. linear vs ordinal regression). The material is software agnostic and can be applied in any software.
In this workshop we build on the information from Surveys 1. We explore topics including questionnaire validation and index creation using methods such as Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) using Structural Equation Modelling (SEM), and Conjoint models such as Choice modelling. The material is software agnostic and can be applied in any software.
In multivariate statistics we simultaneously model and estimate variability in more than one variable often in order to examine the relationship between variables. In this workshop we examine the key aspects of moving from univariate to multivariate analysis, and the situations and scenarios where multivariate analysis is typically applied. We will focus on practical application of concepts through examples.
In this workshop we provide a theoretical and practical introduction to meta-analysis as part of a systematic review. We examine the process of performing a meta-analysis, in particular focussing on key statistical concepts such as heterogeneity and fixed and random effects modelling. We will discuss the available choices of statistical software and show you worked examples using the metafor package in R. A basic knowledge of R software is desirable, but not necessary, since you are not expected to produce and run your own code during the workshop.
Survival analysis is used when you want to measure the time elapsed up to when a specified event occurs. It is commonly used in studies where subjects are followed until death occurs, hence the name. In this workshop we will introduce some key concepts pertaining to survival analysis, including censoring of cases, the survival function, and the hazard ratio estimator. The Kaplan Meier survival curve will be explained through a worked example and the technique of Cox proportional hazards regression will be introduced using the same example dataset. You will be provided with software code in SPSS and R to reproduce the analysis presented in the workshop.
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