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Global movement of Happiness ladder with Machine learning in R

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  • Introduction

    This blog is about world happiness ladder using the world happiness report data sets (Helliwell et. al., 2024). The basic objective is to demonstrate the use of panel data which is quite distinct from cross-sectional or time series data.

    Global happiness ladder

    Cross-sectional happiness ladder for 2023

    Fixed time, it is cross-sectional

    Times series vs. panel data visualisation

    Each line is a timeseries but together, it is panel data

    Mean global happiness lalder

    Cross-sectional mean happiness ladder for 2023

    Time is fixed, cross-sectional

    Times series vs. panel data visualisation

    Each line is a timeseires but together, they are panel data

    Factor analysis of panel global happiness ladder

    Parallel analysis suggests that the number of factors = 4 and the number of components = NA 
    
    Loadings:
    MR1 MR2 MR4 MR3
    Happiness 0.810
    GDP 0.899
    Support 0.761
    Life_Exp 0.882
    Freedom 0.651
    Positive 0.775
    Corruption -0.831
    Negative 0.550
    Year 0.487
    Generosity 0.440
    Regional
    MR1 MR2 MR4 MR3
    SS loadings 3.286 1.687 0.909 0.731
    Proportion Var 0.299 0.153 0.083 0.066
    Cumulative Var 0.299 0.452 0.535 0.601
    Values
    degree of freedom 17.00
    Chi-sq 132.48
    Chi-sq/df 7.79
    Harmonic sample size 2298.31
    Root Mean Square 0.02
    Probability of the empirical chi-sq 0.00
    Adjusted Root Mean Square 0.04
    Empirical BIC 0.90
    Sample size adjusted BIC 54.91
    fit (SSresidual vs SSoriginal values) 0.90
    fit applied to off diagonal elements 1.00
    SD of the residuals 0.02
    Number of factors extracted 4.00
    Number of observations 2363.00
    Value of the minimised function 0.16
    chi-sq based on the objective function 369.96
    p-value of observing the chi-sq 0.00
    chi-sq based on the objective function/df 6.73
    Null model 5.32
    df for null model 55.00
    chi-sq for null model 12542.24
    chi-sq for null model/df 228.04
    Tucker Lewis Index of factoring reliability 0.91
    RMSE Approximation 0.09
    RMSE Approximation-lower 0.09
    RMSE Approximation-upper 0.10
    RMSE Approximation-confidence interval 0.90
    RMSE Approximation-BIC 237.91
    RMSE Approximation-empirical BIC 291.92
    Mean item complexity 1.58
    Kaiser Meyer Olkin Measure of Sampling Adequacy 0.81
    Bartlett Chi 12542.24
    Barlett p-value 0.00
    Barlett df 55.00
    Barlett Chi/df 228.04
    lavaan 0.6-18 ended normally after 117 iterations
    Estimator ML
    Optimization method NLMINB
    Number of model parameters 24
    Used Total
    Number of observations 2098 2363
    Model Test User Model:
    Test statistic 2216.113
    Degrees of freedom 31
    P-value (Chi-square) 0.000
    Model Test Baseline Model:
    Test statistic 10727.135
    Degrees of freedom 45
    P-value 0.000
    User Model versus Baseline Model:
    Comparative Fit Index (CFI) 0.795
    Tucker-Lewis Index (TLI) 0.703
    Loglikelihood and Information Criteria:
    Loglikelihood user model (H0) -15445.704
    Loglikelihood unrestricted model (H1) -14337.648
    Akaike (AIC) 30939.408
    Bayesian (BIC) 31074.978
    Sample-size adjusted Bayesian (SABIC) 30998.727
    Root Mean Square Error of Approximation:
    RMSEA 0.183
    90 Percent confidence interval - lower 0.177
    90 Percent confidence interval - upper 0.190
    P-value H_0: RMSEA <= 0.050 0.000
    P-value H_0: RMSEA >= 0.080 1.000
    Standardized Root Mean Square Residual:
    SRMR 0.110
    Parameter Estimates:
    Standard errors Standard
    Information Expected
    Information saturated (h1) model Structured
    Latent Variables:
    Estimate Std.Err z-value P(>|z|)
    MR1 =~
    GDP 1.000
    Life_Exp 5.659 0.097 58.044 0.000
    Happiness 0.931 0.016 59.736 0.000
    Support 0.089 0.002 45.585 0.000
    MR2 =~
    Positive 1.000
    Freedom 1.858 0.076 24.518 0.000
    Generosity 0.857 0.058 14.877 0.000
    Regional 7.995 1.138 7.026 0.000
    MR4 =~
    Corruption 1.000
    MR3 =~
    Year 1.000
    Covariances:
    Estimate Std.Err z-value P(>|z|)
    MR1 ~~
    MR2 0.036 0.002 15.240 0.000
    MR4 -0.078 0.005 -16.353 0.000
    MR3 0.553 0.121 4.589 0.000
    MR2 ~~
    MR4 -0.007 0.000 -16.232 0.000
    MR3 0.084 0.009 9.636 0.000
    MR4 ~~
    MR3 -0.086 0.020 -4.320 0.000
    Variances:
    Estimate Std.Err z-value P(>|z|)
    .GDP 0.197 0.011 17.757 0.000
    .Life_Exp 12.638 0.506 24.994 0.000
    .Happiness 0.307 0.013 24.065 0.000
    .Support 0.006 0.000 28.956 0.000
    .Positive 0.007 0.000 26.406 0.000
    .Freedom 0.003 0.001 6.186 0.000
    .Generosity 0.023 0.001 31.520 0.000
    .Regional 10.537 0.327 32.245 0.000
    .Corruption 0.000
    .Year 0.000
    MR1 1.136 0.042 27.217 0.000
    MR2 0.005 0.000 14.435 0.000
    MR4 0.034 0.001 32.388 0.000
    MR3 24.675 0.762 32.388 0.000
    x
    npar 2.400000e+01
    fmin 5.281489e-01
    chisq 2.216113e+03
    df 3.100000e+01
    pvalue 0.000000e+00
    baseline.chisq 1.072713e+04
    baseline.df 4.500000e+01
    baseline.pvalue 0.000000e+00
    cfi 7.954423e-01
    tli 7.030614e-01
    nnfi 7.030614e-01
    rfi 7.001121e-01
    nfi 7.934106e-01
    pnfi 5.465717e-01
    ifi 7.957100e-01
    rni 7.954423e-01
    logl -1.544570e+04
    unrestricted.logl -1.433765e+04
    aic 3.093941e+04
    bic 3.107498e+04
    ntotal 2.098000e+03
    bic2 3.099873e+04
    rmsea 1.832962e-01
    rmsea.ci.lower 1.768656e-01
    rmsea.ci.upper 1.898093e-01
    rmsea.ci.level 9.000000e-01
    rmsea.pvalue 0.000000e+00
    rmsea.close.h0 5.000000e-02
    rmsea.notclose.pvalue 1.000000e+00
    rmsea.notclose.h0 8.000000e-02
    rmr 1.183338e+00
    rmr_nomean 1.183338e+00
    srmr 1.100957e-01
    srmr_bentler 1.100957e-01
    srmr_bentler_nomean 1.100957e-01
    crmr 1.217154e-01
    crmr_nomean 1.217154e-01
    srmr_mplus 1.100957e-01
    srmr_mplus_nomean 1.100957e-01
    cn_05 4.358775e+01
    cn_01 5.040973e+01
    gfi 8.167185e-01
    agfi 6.748232e-01
    pgfi 4.603323e-01
    mfi 5.940683e-01
    ecvi 1.079177e+00
    MR1 MR2
    alpha 0.4741262 0.0705956
    omega 0.8358631 0.0570646
    omega2 0.8358631 0.0570646
    omega3 0.8354815 0.0556176
    avevar 0.7454058 0.0295963

    The inter-connectivity between the latent variables and the various variables used to measure hapiiness.

    References

    Helliwell, J. F., Layard, R., Sachs, J. D., De Neve, J.-E., Aknin, L. B., & Wang, S. (Eds.). (2024). World Happiness Report 2024. University of Oxford: Wellbeing Research Centre.

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