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9.1: Two-level regression analysis for a continuous dependent variable with a random intercept (part a) |
ex9.1a |
ex9.1a.inp |
ex9.1a.dat |
mcex9.1a |
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9.1: Two-level regression analysis for a continuous dependent variable with a random intercept (part b) |
ex9.1b |
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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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N/A |
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9.2: Two-level regression analysis for a continuous dependent variable with a random slope (part c) |
ex9.2c |
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9.3: Two-level path analysis with a continuous and a categorical dependent variable |
ex9.3 |
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ex9.3.dat |
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9.4: Two-level path analysis with a continuous, a categorical, and a cluster-level observed dependent variable |
ex9.4 |
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9.5: Two-level path analysis with continuous dependent variables and random slopes |
ex9.5 |
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9.6: Two-level CFA with continuous factor indicators and covariates |
ex9.6 |
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ex9.6.dat |
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9.7: Two-level CFA with categorical factor indicators and covariates |
ex9.7 |
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ex9.7.dat |
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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 |
ex9.9 |
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ex9.9.dat |
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9.10: Two-level SEM with continuous factor indicators and a random slope for a factor |
ex9.10 |
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ex9.10.dat |
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9.11: Two-level multiple group CFA with continuous factor indicators |
ex9.11 |
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ex9.11.dat |
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9.12: Two-level growth model for a continuous outcome (three-level analysis) |
ex9.12 |
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9.13: Two-level growth model for a categorical outcome (three-level analysis) |
ex9.13 |
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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 |
ex9.14 |
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ex9.14.dat |
mcex9.14 |
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9.15: Two-level multiple indicator growth model with categorical outcomes (three-level analysis) |
ex9.15 |
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mcex9.15 |
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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 |
ex9.16 |
ex9.16.inp |
ex9.16.dat |
N/A |
N/A |

9.17: Two-level growth model for a count outcome using a zero-inflated Poisson model (three-level analysis) |
ex9.17 |
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9.18: Two-level continuous-time survival analysis using Cox regression with a random intercept |
ex9.18 |
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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) |
ex9.19part2 |
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ex9.19.dat |
N/A |
N/A |

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) |
ex9.19part3 |
ex9.19part3.inp |
ex9.19.dat |
N/A |
N/A |

9.20: Three-level regression for a continuous dependent variable |
ex9.20 |
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ex9.20.dat |
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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 |
ex9.22 |
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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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9.28: Two-level regression analysis for a continuous dependent variable with a random intercept and a random residual variance |
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9.29: Two-level confirmatory factor analysis (CFA) with continuous factor indicators, covariates, and a factor with a random residual variance |
ex9.29 |
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9.30: Two-level time series analysis with a univariate first-order autoregressive AR(1) model for a continuous dependent variable with a random intercept, random AR(1) slope, and random residual variance |
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9.30: Two-level time series analysis with a univariate first-order autoregressive AR(1) model for a continuous dependent variable with a random intercept, random AR(1) and AR(2) slope, and random residual variance (part b) |
ex9.30b |
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ex9.30.dat |
none |
none |

9.31: Two-level time series analysis with a univariate first-order autoregressive AR(1) model for a continuous dependent variable with a covariate, random intercept, random AR(1) slope, random slope, and random residual variance |
ex9.31 |
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9.32: Two-level time series analysis with a bivariate cross- lagged model for continuous dependent variables with random intercepts and random slopes |
ex9.32 |
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9.32: Two-level time series analysis with a bivariate cross- lagged model for continuous dependent variables with random intercepts and random slopes and random residual variances and covariance (part b) |
ex9.32b |
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ex9.32.dat |
none |
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9.33: Two-level time series analysis with a first-order autoregressive AR(1) factor analysis model for a single continuous indicator and measurement error |
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9.33: Two-level, single-indicator measurement error model analyzed as a two-level ARIMA(1,0,1) = ARMA(1,1) with a random AR(1) (part b) |
ex9.33b |
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ex9.33.dat |
none |
none |

9.34: Two-level time series analysis with a first-order autoregressive AR(1) confirmatory factor analysis (CFA) model for continuous factor indicators with random intercepts, a random AR(1) slope, and a random residual variance |
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9.35: Two-level time series analysis with a first-order autoregressive AR(1) IRT model for binary factor indicators with random thresholds, a random AR(1) slope, and a random residual variance |
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9.36: Two-level time series analysis with a bivariate cross-lagged model for two factors and continuous factor indicators with random intercepts and random slopes |
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9.37: Two-level time series analysis with a univariate first-order autoregressive AR(1) model for a continuous dependent variable with a covariate, linear trend, random slopes, and a random residual variance |
ex9.37 |
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mcex9.37 |
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9.38: Cross-classified time series analysis with a univariate first-order autoregressive AR(1) model for a continuous dependent variable with a covariate, random intercept, and random slope |
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9.38: Cross-classified time series analysis with a univariate first-order autoregressive AR(1) model for a continuous dependent variable with a covariate, random intercept, and random slope (part b) |
ex9.38b |
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9.39: Cross-classified time series analysis with a univariate first-order autoregressive AR(1) model for a continuous dependent variable with a covariate, linear trend, and random slope |
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9.39: Cross-classified time series analysis with a univariate first-order autoregressive AR(1) model for a continuous dependent variable with a covariate, linear trend, and random slope (part b) |
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none |
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9.40: Cross-classified time series analysis with a first-order autoregressive AR(1) confirmatory factor analysis (CFA) model for continuous factor indicators with random intercepts and a factor varying across both subjects and time |
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9.40: Cross-classified time series analysis with a first-order autoregressive AR(1) confirmatory factor analysis (CFA) model for continuous factor indicators with random intercepts, random factor loadings, and a factor varying across both subjects and time (part 2) |
ex9.40 (part 2) |
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ex9.40.dat (part 2) |
mcex9.27 |
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