路径分析vs线性模型：为何选路径分析而非广义线性（或非线性）模型？

Great question—this is a super common point of confusion when picking between traditional regression frameworks and structural equation modeling (SEM)/path analysis. Let’s break down the key scenarios where path analysis is the better tool:

• Directly test direct, indirect, and total effects
  Standard GLMs only give you the total effect of a predictor on an outcome. Path analysis lets you explicitly model and quantify mediation or moderation pathways that your theory predicts. For example: if you think education affects income both directly and indirectly via work experience, a GLM can only tell you how education correlates with income overall. Path analysis will split that into the direct effect (education → income) and the indirect effect (education → experience → income), which is critical if your research focuses on how variables interact, not just that they do.

• Validate a priori theoretical frameworks (instead of data-dredging)
  GLMs with selection criteria (like stepwise regression) are data-driven—they pick variables that "fit" the data best, which can lead to overfitting and results that don’t align with your actual research theory. Path analysis starts with a theoretical model you’ve built based on existing literature or hypotheses, then tests whether the data supports that specific structure. For instance, if your theory says A → B → C, path analysis lets you verify if that chain exists, rather than letting the data randomly suggest A and C are directly related without B in the picture.

• Handle multiple dependent variables and latent constructs
  GLMs are limited to one dependent variable per model. Path analysis (and full SEM) lets you model relationships between multiple outcomes at once—say, how a single predictor affects both mental health and physical health, and how those two outcomes relate to each other. Additionally, if you’re working with latent variables (like "self-esteem" or "social support," which can’t be measured with one question), path analysis (via SEM) lets you combine multiple indicators into a single latent construct, accounting for measurement error in a way GLMs can’t.

• Account for measurement error in observed variables
  GLMs assume your predictors and outcomes are measured perfectly, which is almost never true in real-world research. Path analysis lets you explicitly include measurement error terms for your observed variables, leading to more accurate coefficient estimates. For example, if you’re using a 5-item scale to measure stress, path analysis can model the error in each item, whereas a GLM would treat the scale score as a perfect measure—biasing your results.

• Assess overall model fit, not just individual coefficients
  GLMs focus on individual p-values and R² to judge "goodness of fit." Path analysis gives you a suite of holistic fit metrics (like CFI, RMSEA, or χ² tests) that tell you how well your entire theoretical model matches the data. This is huge because a GLM might have significant individual coefficients, but the overall model could still contradict your core theory. Path analysis lets you test the structure of your hypotheses, not just isolated relationships.

Of course, there are cases where a GLM with model selection makes sense—like if you’re in an exploratory phase with no clear theory, or working with small sample sizes (path analysis needs larger datasets to estimate all parameters reliably). But when your goal is to test causal pathways, validate theoretical models, or unpack complex variable relationships, path analysis is irreplaceable.

内容的提问来源于stack exchange，提问作者emmy