Path Analysis

Definition

Path analysis is a statistical technique that is used to examine and test purported causal relationships among a set of variables. A causal relationship is directional in character, and occurs when one variable (e.g., amount of exercise) causes changes in another variable (e.g., physical fitness). The researcher specifies these relationships according to a theoretical model that is of interest to the researcher. The resulting path model and the results of the path analysis are usually then presented together in the form of a path diagram.

Although a path analysis makes causal inferences about how variables are related, correlational data are actually used to conduct the path analysis. In many instances, the results of the analysis provide information about the plausibility of the researcher's hypothesized model. But even if this information is not available, the path analysis provides estimates of the relative strengths of the causal effects and other associations among the variables in the model. These estimates are more useful to the extent that the researcher's specified model actually represents how the variables are truly related in the population of interest.

Variables in Path Analysis

Path analysis is a member of a more general type of statistical analysis known as structural equation modeling. The feature of path analysis that separates it from general structural equation modeling is that path analysis is limited to variables that are measured or observed, rather than latent. This means that each variable in a path analysis consists of a single set of numbers in a straightforward way. For example, extraversion would be considered a measured or observed variable if each person's level of extraversion was represented by a single number for that person, perhaps that person's score on an extraversion questionnaire. So the variable of extraversion as a whole would consist of one number for each person in the sample. Through certain statistical techniques, extraversion could be treated as a latent variable in a structural equation model by using several different measures simultaneously to represent each person's level of extraversion. But by definition, path analysis does not use latent variables.

Model Specification

The researcher must begin a path analysis by specifying the ways in which the variables of interest are thought to relate to one another. This is done based on theory and reasoning, and it is critical that the researcher specify the model thoughtfully. A key aspect of this process is deciding which particular variables causally affect other particular variables. A model in which exercise causes good health has a very different meaning than a model in which good health causes exercise. But in many instances, the numeric results of such alternative path analyses will reveal little or nothing about which model is closer to the truth. Because of this, there is no substitute for the researcher having a sound rationale for the form of the path model.

Path Diagrams

The path diagram is a visual display of the path model and the results of the path analysis. In path diagrams, measured variables are usually represented as squares or rectangles. A single-headed arrow (also known as a path or direct effect) drawn from one variable to another (say, from anxiety to attention seeking; see the standardized path diagram shown in Figure 1) means that a change in the value of anxiety is thought to tend to cause a change in the value of attention seeking (rather than vice versa). It is not necessary for the researcher to specify in advance whether increases in the first variable are thought to cause increases or decreases in the second variable. Mathematical algorithms will estimate both the magnitude of the effect and its positivity or negativity.

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