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9.1: Two-level regression analysis for a continuous dependent variable with a random intercept (part a) |
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9.1: Two-level regression analysis for a continuous dependent variable with a random intercept (part b) |
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9.2: Two-level regression analysis for a continuous dependent variable with a random slope (part a) |
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9.2: Two-level regression analysis for a continuous dependent variable with a random slope (part b) |
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9.2: Two-level regression analysis for a continuous dependent variable with a random slope (part c) |
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9.3: Two-level path analysis with a continuous and a categorical dependent variable |
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9.4: Two-level path analysis with a continuous, a categorical, and a cluster-level observed dependent variable |
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9.5: Two-level path analysis with continuous dependent variables and random slopes |
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9.6: Two-level CFA with continuous factor indicators and covariates |
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9.7: Two-level CFA with categorical factor indicators and covariates |
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9.8: Two-level CFA with continuous factor indicators, covariates, and random slopes |
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9.9: Two-level SEM with categorical factor indicators on the within level and cluster-level continuous observed and random intercept factor indicators on the between level |
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9.10: Two-level SEM with continuous factor indicators and a random slope for a factor |
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9.11: Two-level multiple group CFA with continuous factor indicators |
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9.12: Two-level growth model for a continuous outcome (three-level analysis) |
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9.13: Two-level growth model for a categorical outcome (three-level analysis) |
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9.14: Two-level growth model for a continuous outcome (three-level analysis) with variation on both the within and between levels for a random slope of a time-varying covariate |
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9.15: Two-level multiple indicator growth model with categorical outcomes (three-level analysis) |
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9.16: Linear growth model for a continuous outcome with time-invariant and time-varying covariates carried out as a two-level growth model using the DATA WIDETOLONG command |
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9.17: Two-level growth model for a count outcome using a zero-inflated Poisson model (three-level analysis) |
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9.18: Two-level continuous-time survival analysis using Cox regression with a random intercept |
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9.19: Two-level mimic model with continuous factor indicators, random factor loadings, two covariates on within, and one covariate on between with equal loadings across levels (part 1) |
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9.19: Two-level mimic model with continuous factor indicators, random factor loadings, two covariates on within, and one covariate on between with equal loadings across levels (part 2) |
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9.19: Two-level mimic model with continuous factor indicators, random factor loadings, two covariates on within, and one covariate on between with equal loadings across levels (part 3) |
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9.20: Three-level regression for a continuous dependent variable |
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9.21: Three-level path analysis with a continuous and a categorical dependent variable |
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9.22: Three-level MIMIC model with continuous factor indicators, two covariates on within, one covariate on between level 2, one covariate on between level 3 with random slopes on both within and between level 2 |
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9.23: Three-level growth model with a continuous outcome and one covariate on each of the three levels |
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9.24:Regression for a continuous dependent variable using cross-classified data |
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9.25: Path analysis with continuous dependent variables using cross-classified data |
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9.26: IRT with random binary items using cross-classified data |
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9.27: Multiple indicator growth model with random intercepts and factor loadings using cross-classified data |
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