Table of Contents

Handbook of Research Methods and Applications in Spatially Integrated Social Science

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.

Chapter 22: Modelling effects of intervening variables using path analysis

Rod McCrea

Subjects: economics and finance, regional economics, geography, economic geography, environmental geography, human geography, research methods in geography, research methods, research methods in economics, research methods in geography, urban and regional studies, regional economics, research methods in urban and regional studies


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.

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