{"id":5419,"date":"2023-12-01T12:00:46","date_gmt":"2023-12-01T11:00:46","guid":{"rendered":"https:\/\/blogs.kcl.ac.uk\/editlab\/?p=5419"},"modified":"2023-12-04T15:24:50","modified_gmt":"2023-12-04T14:24:50","slug":"x-for-cross-lag","status":"publish","type":"post","link":"https:\/\/blogs.kcl.ac.uk\/editlab\/2023\/12\/01\/x-for-cross-lag\/","title":{"rendered":"X for Cross Lag"},"content":{"rendered":"

Researchers interested in longitudinal processes can use a range of statistical models to examine how variables are associated over time. One approach often used to examine reciprocal – or bidirectional – associations between variables is the cross-lagged panel model.<\/em><\/h4>\n
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Genevieve, Postdoctoral Researcher<\/p><\/div>\n

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What is a cross-lagged panel model?<\/b><\/p>\n

The cross-lagged panel model (also called longitudinal autoregressive models) is a statistical technique applied to longitudinal data to examine reciprocal or bidirectional associations (i.e., the \u2018cross-lagged\u2019 paths) between variables over time. Cross-lagged panel models are tested using structural equation modelling, a broad and flexible statistical tool that allows examining multiple hypotheses simultaneously using different types of variables (for example, binary, ordinal, continuous; Adachi & Willoughby, 2015; Kline, 2023; Selig & Little, 2012).\u00a0<\/span><\/p>\n

By using the cross-lagged panel model, researchers can examine the direction of association between variables and determine whether there are reciprocal influences or bidirectional associations between them. Additionally, this model allows for a more nuanced understanding of how <\/span>changes in one variable affect changes in another over time<\/b>, which can be particularly useful when examining complex phenomena such as behaviour change or the development of psychological states.<\/span><\/p>\n

To conduct a cross-lagged panel model, researchers need to collect longitudinal data for at least two variables measured at two different time points. Let\u2019s consider Figure 1 to illustrate the main features of the model.\u00a0<\/span><\/p>\n

Specifically, the model examines whether one variable is prospectively associated with change in another variable (paths d) by accounting for three elements: 1) baseline levels in both variables (indicated by B), 2) stability over time (paths c), and 3) baseline or previous associations between the variables (correlation a). The model can also be extended to include multiple variables at multiple time points (for example, Figure 2).\u00a0<\/span><\/p>\n\n\t\t