Path Analysis
Path analysis represents an attempt to deal with causal types of relationships. Sewall Wright developed the Path analysis in 1930. It is useful for illustrating various issues in causal analysis.
Statistics Solutions is the country’s leader in statistical consulting and can assist with path analysis for your dissertation. Use the calendar below to schedule a free 30-minute consultation.
Researchers conduct path analysis diagrammatically using circles and arrows to represent causal relationships among variables. The ultimate goal is to predict the regression weight. Path analysis predicts the regression weight and then compares it to the observed correlation matrix. This type of analysis is also applicable in cases where the researcher wants to perform a goodness of fit test.
There are certain terminologies that are used in path analysis.
The exogenous variables in it are variables whose causes are outside of the model.
The endogenous variables are variables whose causes are inside the model.
The recursive model is a causal model that is unidirectional. It has a one-way causal flow. This model has no feedback loops or reciprocal effects. Here, variables cannot be both cause and effect at the same time.
The non recursive model in path analysis is a causal model with feedback loops and reciprocal effects.
The path coefficient is the standardized regression coefficient that predicts one variable from another.
Researchers often conduct it using the Analysis of Moment Structures (AMOS) module in SPSS. Other software, such as SAS and LISREL, can also perform it. According to Kline (1998), the sample size should be at least 10 times the number of parameters.
The best sample size should be 20 times the number of parameters in it.
Since path analysis is also a kind of statistical analysis, it also comes with several assumptions.
In path analysis, associations in the model should be linear, additive, and causal. The data used should follow an interval scale. To reduce volatility, error terms are assumed to be uncorrelated with variables and among themselves. It also assumes a one-way causal flow.
It does have some limitations.
Path analysis can very well evaluate, test or compute two or more than two types of causal hypotheses. However, the major limitation is that it cannot establish the direction of causality. The associations in the model should be linear, additive, and causal. The data used should follow an interval scale. To reduce volatility, error terms are assumed to be uncorrelated with variables and among themselves. It also assumes a one-way causal flow.nted by a single path.