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Direct linkLudtke, Oliver; Marsh, Herbert W.; Robitzsch, Alexander; Trautwein, Ulrich – Psychological Methods, 2011
In multilevel modeling, group-level variables (L2) for assessing contextual effects are frequently generated by aggregating variables from a lower level (L1). A major problem of contextual analyses in the social sciences is that there is no error-free measurement of constructs. In the present article, 2 types of error occurring in multilevel data…
Descriptors: Simulation, Educational Psychology, Social Sciences, Measurement
Ludtke, Oliver; Marsh, Herbert W.; Robitzsch, Alexander; Trautwein, Ulrich; Asparouhov, Tihomir; Muthen, Bengt – Psychological Methods, 2008
In multilevel modeling (MLM), group-level (L2) characteristics are often measured by aggregating individual-level (L1) characteristics within each group so as to assess contextual effects (e.g., group-average effects of socioeconomic status, achievement, climate). Most previous applications have used a multilevel manifest covariate (MMC) approach,…
Descriptors: Statistical Analysis, Sampling, Context Effect, Simulation
Bauer, Daniel J.; Preacher, Kristopher J.; Gil, Karen M. – Psychological Methods, 2006
The authors propose new procedures for evaluating direct, indirect, and total effects in multilevel models when all relevant variables are measured at Level 1 and all effects are random. Formulas are provided for the mean and variance of the indirect and total effects and for the sampling variances of the average indirect and total effects.…
Descriptors: Testing, Models, Sampling, Context Effect
