# What is Path Analysis (for people who hate statistics)

Dr Pauline McGovern, CCSR

As the title shows, this is a session for non-statisticians. Path analysis is an advanced statistical technique and published articles can be incomprehensible to most people. Yet, in essence, it is not difficult to understand. Path analysis is basically a story with a series of events. It matches the way we commonly explain what happens.

## Example

Boss: Why were you late for work?

Me: Well it was raining and I ran back home to fetch my umbrella and missed my bus!

Here, we have: initial cause (rain); mediating circumstances (fetching umbrella, missing the bus); and final outcome (late for work).

After introducing path analysis, I will use a health related example from my own research. I will use three statistics (inevitable in an explanation of a statistical technique) but they are not complicated and I will explain them. The purpose of this session is not to teach you how to do path analysis but to explain how useful it is in research and to expose a largely unknown secret – statistics are not that difficult! Maybe, as a result, you may even consider using path analysis in your own work.

## Further reading

If you want to read more, try these suggestions:

• For an example of a health-related path analysis, read Honjo K., Tsutsumi A., Kawachi I., Kawakami N. (2006) What Accounts for the Relationship Between Social Class and Smoking Cessation? Results of a Path Analysis Social Science and Medicine 62:317-328 – this is not easy to read but look at the path model on p. 318 and try reading the Discussion that starts on p. 325.
• There are no simple books on path analysis but if you are interested to explore this technique further, look at Mertler C.A., Vannatta R.A. (2005) Advanced and Multivariate Statistical Methods: Practical Application and Interpretation California:Pyrczak Publishing

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