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Understanding Forms of Measurement
Using Quantitative Data in Research

This module provides a basic overview of quantitative research and how we collect data.  The objectives of the module are as follows:

  • To give a broad overview of how research questions might be answered through quantitative analysis

  • To introduce the vocabulary of quantitative analysis and specify the common terminology to be used in later modules. 

  • To introduce sources of data and concepts relating to how it may be possible to generalise results from samples of various kinds to the populations they are drawn from.

  • To discuss how variables are defined, what different types there are, and how this may influence how they are analysed.

  • To give some emphasis to certain ideas such as the nature of variability or the recognition of hierarchical units of analysis that are central to multilevel modelling

The aim of this module is to give an overall view of some the principles of effective data analysis. The focus is on how we summarise data to uncover patterns and relationships between variables, and how these relationships can begin to explain the values of the variables that we observe. Some key statistical vocabulary is introduced and concepts are illustrated by example. You will learn about the following: 

  • Why do we need to explore our data?

  • What questions might we ask?

  • Level of measurement and data exploration

Modules and Sample Analysis

The video modules and resources featured on this page provide additional information so as to better understand the practice of quantitative data analysis.  Please note some of these films require the Flash player plugin, for optimal viewing experience please create your free account with Centre for Multilevel Modeling. 

 

Download the quantitative data concepts and definitions below:

 

 

 

Download the introduction to different forms of measurement below:

 

 

 

Multilevel Structures
and Classifications

Multilevel modelling is designed to explore and analyse data that come from populations which have a complex structure. This module aims to introduce:

  • a range of multilevel structures and classifications and how they correspond to real-world situations, research designs, and/or social-science research problems

  • the different types of data frames associated with each structure and how subscripts are used to represent structure

  • targets of inference

  • the distinction between levels and variables, and fixed and random classifications

  • the notion that multilevel structures are likely to generate dependent, correlated data that requires explicit modelling

  • the difference between long and wide forms of data structures

  • the advantages, both technical and substantive, of using a multilevel model, and the disadvantages of not doing so.

Download the multilevel concepts and definitions below

 

 

 

Download the multilevel definitions and concepts below:

 

 

 

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