Handbook of Research Methods and Applications in Spatially Integrated Social Science
Show Less

Handbook of Research Methods and Applications in Spatially Integrated Social Science

  • Handbooks of Research Methods and Applications series

Edited by Robert Stimson

The chapters in this book provide coverage of the theoretical underpinnings and methodologies that typify research using a Spatially Integrated Social Science (SISS) approach. This insightful Handbook is intended chiefly as a primer for students and budding researchers who wish to investigate social, economic and behavioural phenomena by giving explicit consideration to the roles of space and place. The majority of chapters provide an emphasis on demonstrating applications of methods, tools and techniques that are used in SISS research, including long-established and relatively new approaches.
Buy Book in Print
Show Summary Details
You do not have access to this content

Chapter 22: Modelling effects of intervening variables using path analysis

Rod McCrea

Extract

Path analysis models the effects of independent variables on dependent variables via intervening or mediating variables. As such, it models pathways by which variables affect each other. As a simple example, we may expect physical proximity to services and facilities to positively impact on level of satisfaction with one’s neighbourhood via satisfaction with access to services and facilities. So, a path analysis would model the direct effects of, say, physical proximity to services and facilities on satisfaction with access to services and facilities, and of satisfaction with that access on overall neighbourhood satisfaction, as well as the indirect effects of physical proximity to services and facilities on neighbourhood satisfaction. However, other variables also affect neighbourhood satisfaction, both directly and indirectly, and so including these would lead to a more complicated path analysis. Other names associated with path analysis are analysis of covariance structures, causal analysis or modelling, simultaneous equation modelling and structural equation modelling, all of which can be used to do path analysis. Figure 22.1 shows path analysis in its simplest form. The independent or exogenous variable (x) predicts the intervening or mediating variable (y) which in turn predicts the dependent or endogenous variable (z). This approach models a path of effects, usually representing a hypothesized process where x leads to y leads to z. The independent variables (x) are called exogenous variables because they are determined outside the system.

You are not authenticated to view the full text of this chapter or article.

Elgaronline requires a subscription or purchase to access the full text of books or journals. Please login through your library system or with your personal username and password on the homepage.

Non-subscribers can freely search the site, view abstracts/ extracts and download selected front matter and introductory chapters for personal use.

Your library may not have purchased all subject areas. If you are authenticated and think you should have access to this title, please contact your librarian.


Further information

or login to access all content.