Research Design - Data Analysis

Comprehensive introduction to analysing quantitative and qualitative data, includes step by step instructions, examples and templates.

Extensive discussion on qualitative and quantitative analysis measures with step by step instructions.

An exploration of quantitative and qualitative data analysis software programs

This module introduces you to some of the concepts required to interpret statistics published in journal articles and other literature, as well as to a range of ways you can analyse your own data using statistics.

Covers fundamental statistical concepts for clinical researchers, including why we use significance testing, how to interpret confidence intervals, and p-values, how sample size and variability affects results, why bias and confounding factors are important considerations in designing studies, and when to seek statistical support.

This course is designed to cover the general role of a biostatistician, the process of interacting with biostatisticians, how to get prepared before meeting them, and understand the common languages used in research and statistics.

Click on ‘Learn more’, then on the RET Program website, click on ‘log in’, sign up as a new member with your details. When you come to ‘Institution’ in the drop-down box, click on ‘@research’, you will then be asked to click on your health service in the drop-down box. In the next box where it asks for the @Research Access Code, enter ‘atResearch’. Registering this way will provide free access to the RET Program courses, courtesy of @research.

This seminar provides an overview of how to perform sample size calculations for different research study designs and demonstrates how to use free “PS” software. It also covers issues specific to clinical trials and assists researchers to identify situations where the best course of action is to consult a statistician.

To accompany the Rapid Critical Appraisal of Scientific Literature seminar, this seminar tackles critical appraisal from a statistical literacy point of view based on the 2013 Nature paper by Sutherland et al. It uses examples from the medical literature to “help non-scientists interrogate advisers and grasp the limitations of evidence” and will indicate when it is time to consult the statisticians. These tips are highly relevant for those looking for a refresher in statistical literacy or struggling to understand the seemingly unlimited sources of bias and confounding.

An explanation and tool for calculating norm-based T-scores to aid in the interpretation and understanding of clinical and research data in the context of the general Australian population and an Australian population with cancer.

Originally called Statistical Package for Social Sciences, now known as SPSS. Course Contents: Welcome, Versions, Editions, & Modules, Taking a Look, Sample Data, Graph board Templates, Bar Charts, Histograms, Scatterplots, Frequencies, Descriptives, Explore, Labels & Definitions, Entering Data, Importing Data, Hierarchical Clustering, Factor Analysis, Regression, Next Steps.

This module provides an introduction to using the statistical software SPSS. The module covers the key ways you can use the software to analyse quantitative data, and uses some small data sets as examples. The idea is that you can then apply these same concepts to any data of your own, regardless of your discipline or the amount of data you have.

This online seminar covers the following topics: Stata as a data analysis software package, Navigating Stata, data impact, Exporting data, data visualisation, Data management, Basic statistical analysis.

The purpose of this guide is to facilitate the first manipulations under STATA. As a general rule, these
manipulations are illustrated using screenshots from STATA 14 but these illustrations remain valid for
the other versions of STATA.

With the help of this document you will be able to learn how to download a database from an Excel
format or to create variables and data directly in STATA, modify them, label them.

The process of coding qualitative data is an important part of the analytical process of analysing qualitative research. When you generate data from qualitative methods such as semi-structured interviews, qualitative coding allows you to interpret, organise, and structure your observations and interpretations into meaningful theories. Coding in qualitative research allows you to be reflexive, critical, and rigorous with your findings.

Resources on qualitative coding analyses, featuring major coding applications.

Extensive discussion on the why, how, what, when of coding qualitative data.

This document details how to set up a project in NVivo. In particular, it covers how to import different types
of materials into the project (including Word, PDF, audio, video and picture files saved on your computer;
survey data; web content; emails; and references and PDFs from EndNote), and how to store information
about cases and files in the project using case and file classifications

This document looks at how to organise project materials together according to specific themes and topics by
coding into codes; how to use mind maps to visualise the layout of codes; and how to conduct simple
searches.

25 short Youtube clips covering NVivo 12 including: Getting started, Adding files, File classifications, Autocode by speakername, Transcription, Coding, Viewing coding, Reviewing codes, Coding techniques, Making notes, Search folders, Sets, Text search query, Query options, Word frequency query, Matrix query, Cross tab query, Framework matrix, Mind maps, Project maps, Concept maps, Comparison diagrams.

The guide will enable you to start your analysis with MAXQDA. It focuses on the actual usage of the program, covering: Introduction, Interface, Code your data, Analyse your data, Conduct mixed methods analysis.

Data extraction guide for complex meta-analysis, primarily aimed at systematic reviewers preparing data for meta-analysis.