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Multilevel model

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</style><table class="sidebar nomobile nowraplinks hlist"><tbody><tr><td class="sidebar-pretitle">Part of a series on</td></tr><tr><th class="sidebar-title-with-pretitle"><a href="Regression_analysis" title="Regression analysis">Regression analysis</a></th></tr><tr><th class="sidebar-heading">

Models</th></tr><tr><td class="sidebar-content">

<ul><li><a href="Linear_regression" title="Linear regression">Linear regression</a></li>

<li><a href="Simple_linear_regression" title="Simple linear regression">Simple regression</a></li>

<li><a href="Polynomial_regression" title="Polynomial regression">Polynomial regression</a></li>

<li><a href="General_linear_model" title="General linear model">General linear model</a></li></ul></td>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Generalized_linear_model" title="Generalized linear model">Generalized linear model</a></li>

<li><a href="Vector_generalized_linear_model" title="Vector generalized linear model">Vector generalized linear model</a></li>

<li><a href="Discrete_choice" title="Discrete choice">Discrete choice</a></li>

<li><a href="Binomial_regression" title="Binomial regression">Binomial regression</a></li>

<li><a href="Binary_regression" title="Binary regression">Binary regression</a></li>

<li><a href="Logistic_regression" title="Logistic regression">Logistic regression</a></li>

<li><a href="Multinomial_logistic_regression" title="Multinomial logistic regression">Multinomial logistic regression</a></li>

<li><a href="Mixed_logit" title="Mixed logit">Mixed logit</a></li>

<li><a href="Probit_model" title="Probit model">Probit</a></li>

<li><a href="Multinomial_probit" title="Multinomial probit">Multinomial probit</a></li>

<li><a href="Ordered_logit" title="Ordered logit">Ordered logit</a></li>

<li><a href="Ordered_probit" class="mw-redirect" title="Ordered probit">Ordered probit</a></li>

<li><a href="Poisson_regression" title="Poisson regression">Poisson</a></li></ul></td>

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<ul>

<li><a href="Fixed_effects_model" title="Fixed effects model">Fixed effects</a></li>

<li><a href="Random_effects_model" title="Random effects model">Random effects</a></li>

<li><a href="Mixed_model" title="Mixed model">Linear mixed-effects model</a></li>

<li><a href="Nonlinear_mixed-effects_model" title="Nonlinear mixed-effects model">Nonlinear mixed-effects model</a></li></ul></td>

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<ul><li><a href="Nonlinear_regression" title="Nonlinear regression">Nonlinear regression</a></li>

<li><a href="Nonparametric_regression" title="Nonparametric regression">Nonparametric</a></li>

<li><a href="Semiparametric_regression" title="Semiparametric regression">Semiparametric</a></li>

<li><a href="Robust_regression" title="Robust regression">Robust</a></li>

<li><a href="Quantile_regression" title="Quantile regression">Quantile</a></li>

<li><a href="Isotonic_regression" title="Isotonic regression">Isotonic</a></li>

<li><a href="Principal_component_regression" title="Principal component regression">Principal components</a></li>

<li><a href="Least-angle_regression" title="Least-angle regression">Least angle</a></li>

<li><a href="Local_regression" title="Local regression">Local</a></li>

<li><a href="Segmented_regression" title="Segmented regression">Segmented</a></li></ul></td>

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<ul><li><a href="Errors-in-variables_models" class="mw-redirect" title="Errors-in-variables models">Errors-in-variables</a></li></ul></td>

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Estimation</th></tr><tr><td class="sidebar-content">

<ul><li><a href="Least_squares" title="Least squares">Least squares</a></li>

<li><a href="Linear_least_squares" title="Linear least squares">Linear</a></li>

<li><a href="Non-linear_least_squares" title="Non-linear least squares">Non-linear</a></li></ul></td>

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<ul><li><a href="Ordinary_least_squares" title="Ordinary least squares">Ordinary</a></li>

<li><a href="Weighted_least_squares" title="Weighted least squares">Weighted</a></li>

<li><a href="Generalized_least_squares" title="Generalized least squares">Generalized</a></li>

<li><a href="Generalized_estimating_equation" title="Generalized estimating equation">Generalized estimating equation</a></li></ul></td>

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<ul><li><a href="Partial_least_squares_regression" title="Partial least squares regression">Partial</a></li>

<li><a href="Total_least_squares" title="Total least squares">Total</a></li>

<li><a href="Non-negative_least_squares" title="Non-negative least squares">Non-negative</a></li>

<li><a href="Tikhonov_regularization" class="mw-redirect" title="Tikhonov regularization">Ridge regression</a></li>

<li><a href="Regularized_least_squares" title="Regularized least squares">Regularized</a></li></ul></td>

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<ul><li><a href="Least_absolute_deviations" title="Least absolute deviations">Least absolute deviations</a></li>

<li><a href="Iteratively_reweighted_least_squares" title="Iteratively reweighted least squares">Iteratively reweighted</a></li>

<li><a href="Bayesian_linear_regression" title="Bayesian linear regression">Bayesian</a></li>

<li><a href="Bayesian_multivariate_linear_regression" title="Bayesian multivariate linear regression">Bayesian multivariate</a></li>

<li><a href="Least-squares_spectral_analysis" title="Least-squares spectral analysis">Least-squares spectral analysis</a></li></ul></td>

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Background</th></tr><tr><td class="sidebar-content">

<ul><li><a href="Regression_validation" title="Regression validation">Regression validation</a></li>

<li><a href="Mean_and_predicted_response" class="mw-redirect" title="Mean and predicted response">Mean and predicted response</a></li>

<li><a href="Errors_and_residuals" title="Errors and residuals">Errors and residuals</a></li>

<li><a href="Goodness_of_fit" title="Goodness of fit">Goodness of fit</a></li>

<li><a href="Studentized_residual" title="Studentized residual">Studentized residual</a></li>

<li><a href="Gauss%E2%80%93Markov_theorem" title="Gauss–Markov theorem">Gauss–Markov theorem</a></li></ul></td>

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Multilevel models<a href="#cite_note-1">[a]</a> are <a href="Statistical_model" title="Statistical model">statistical models</a> of <a href="Parameter" title="Parameter">parameters</a> that vary at more than one level.<a href="#cite_note-Raud-2">[1]</a> An example could be a model of student performance that contains measures for individual students as well as measures for classrooms within which the students are grouped. These models can be seen as generalizations of <a href="Linear_model" title="Linear model">linear models</a> (in particular, <a href="Linear_regression" title="Linear regression">linear regression</a>), although they can also extend to non-linear models. These models became much more popular after sufficient computing power and software became available.<a href="#cite_note-Raud-2">[1]</a>

Multilevel models are particularly appropriate for research designs where data for participants are organized at more than one level (i.e., <a href="Nested_data" class="mw-redirect" title="Nested data">nested data</a>).<a href="#cite_note-Fidell-3">[2]</a> The units of analysis are usually individuals (at a lower level) who are nested within contextual/aggregate units (at a higher level).<a href="#cite_note-Luke-4">[3]</a> While the lowest level of data in multilevel models is usually an individual, repeated measurements of individuals may also be examined.<a href="#cite_note-Fidell-3">[2]</a><a href="#cite_note-Gomes2022-5">[4]</a> As such, multilevel models provide an alternative type of analysis for univariate or <a href="Multivariate_analysis" class="mw-redirect" title="Multivariate analysis">multivariate analysis</a> of <a href="Repeated_measures" class="mw-redirect" title="Repeated measures">repeated measures</a>. Individual differences in <a href="Growth_curve_(statistics)" title="Growth curve (statistics)">growth curves</a> may be examined.<a href="#cite_note-Fidell-3">[2]</a> Furthermore, multilevel models can be used as an alternative to <a href="ANCOVA" class="mw-redirect" title="ANCOVA">ANCOVA</a>, where scores on the dependent variable are adjusted for covariates (e.g. individual differences) before testing treatment differences.<a href="#cite_note-Cohen-6">[5]</a> Multilevel models are able to analyze these experiments without the assumptions of homogeneity-of-regression slopes that is required by ANCOVA.<a href="#cite_note-Fidell-3">[2]</a>

Multilevel models can be used on data with many levels, although 2-level models are the most common and the rest of this article deals only with these. The dependent variable must be examined at the lowest level of analysis.<a href="#cite_note-Raud-2">[1]</a>

<div class="mw-heading mw-heading2"><h2 id="Level_1_regression_equation">Level 1 regression equation</h2></div>

When there is a single level 1 independent variable, the level 1 model is

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<annotation encoding="application/x-tex">{\displaystyle Y_{ij}=\beta _{0j}+\beta _{1j}X_{ij}+e_{ij}}</annotation>

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<annotation encoding="application/x-tex">{\displaystyle Y_{ij}}</annotation>

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</math><img src="./6035187f7e2cee387277f07091bb7827e8e66818.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:2.828ex; height:2.843ex;" alt="{\displaystyle Y_{ij}}" loading="lazy"> refers to the score on the dependent variable for an individual observation at Level 1 (subscript i refers to individual case, subscript j refers to the group).</li>

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<annotation encoding="application/x-tex">{\displaystyle X_{ij}}</annotation>

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</math><img src="./5a3501695fc03e54ae7d33791d3fd08a5bfb9645.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:3.401ex; height:2.843ex;" alt="{\displaystyle X_{ij}}" loading="lazy"> refers to the Level 1 predictor.</li>

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<annotation encoding="application/x-tex">{\displaystyle \beta _{0j}}</annotation>

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<annotation encoding="application/x-tex">{\displaystyle \beta _{1j}}</annotation>

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<annotation encoding="application/x-tex">{\displaystyle e_{ij}\sim {\mathcal {N}}(0,\sigma _{1}^{2})}</annotation>

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At Level 1, both the intercepts and slopes in the groups can be either fixed (meaning that all groups have the same values, although in the real world this would be a rare occurrence), non-randomly varying (meaning that the intercepts and/or slopes are predictable from an independent variable at Level 2), or randomly varying (meaning that the intercepts and/or slopes are different in the different groups, and that each have their own overall mean and variance).<a href="#cite_note-Fidell-3">[2]</a><a href="#cite_note-Gomes2022-5">[4]</a>

When there are multiple level 1 independent variables, the model can be expanded by substituting vectors and matrices in the equation.

When the relationship between the response <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle Y_{ij}}">

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</math><img src="./5a3501695fc03e54ae7d33791d3fd08a5bfb9645.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:3.401ex; height:2.843ex;" alt="{\displaystyle X_{ij}}" loading="lazy"> can not be described by the linear relationship, then one can find some non linear functional relationship between the response and predictor, and extend the model to <a href="Nonlinear_mixed-effects_model" title="Nonlinear mixed-effects model">nonlinear mixed-effects model</a>. For example, when the response <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle Y_{ij}}">

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</math><img src="./6035187f7e2cee387277f07091bb7827e8e66818.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:2.828ex; height:2.843ex;" alt="{\displaystyle Y_{ij}}" loading="lazy"> is the cumulative infection trajectory of the <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle i}">

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<annotation encoding="application/x-tex">{\displaystyle j}</annotation>

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</math><img src="./2f461e54f5c093e92a55547b9764291390f0b5d0.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; margin-left: -0.027ex; width:0.985ex; height:2.509ex;" alt="{\displaystyle j}" loading="lazy">-th time points, then the ordered pair <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle (X_{ij},Y_{ij})}">

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<annotation encoding="application/x-tex">{\displaystyle (X_{ij},Y_{ij})}</annotation>

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</math><img src="./6c97d14ba4c0d73742216ce23e16c54db817b82f.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:9.072ex; height:3.009ex;" alt="{\displaystyle (X_{ij},Y_{ij})}" loading="lazy"> for each country may show a shape similar to <a href="Logistic_function" title="Logistic function">logistic function</a>.<a href="#cite_note-7">[6]</a><a href="#cite_note-ReferenceA-8">[7]</a>

<div class="mw-heading mw-heading2"><h2 id="Level_2_regression_equation">Level 2 regression equation</h2></div>

The dependent variables are the intercepts and the slopes for the independent variables at Level 1 in the groups of Level 2.

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<annotation encoding="application/x-tex">{\displaystyle u_{0j}\sim {\mathcal {N}}(0,\sigma _{2}^{2})}</annotation>

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</math><img src="./5e6ef1b5e4e31865fc7296e0c94d28b09601d50f.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:14.825ex; height:3.176ex;" alt="{\displaystyle u_{0j}\sim {\mathcal {N}}(0,\sigma _{2}^{2})}" loading="lazy">

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<annotation encoding="application/x-tex">{\displaystyle u_{1j}\sim {\mathcal {N}}(0,\sigma _{3}^{2})}</annotation>

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</math><img src="./78a5a4720498c23f6f389ed6f0ed36bb78a0547b.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:14.825ex; height:3.176ex;" alt="{\displaystyle u_{1j}\sim {\mathcal {N}}(0,\sigma _{3}^{2})}" loading="lazy">

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<annotation encoding="application/x-tex">{\displaystyle \beta _{0j}=\gamma _{00}+\gamma _{01}w_{j}+u_{0j}}</annotation>

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</math><img src="./6b59884083c7ac0e2f2dd2e35bcae419f413dc81.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:23.623ex; height:2.843ex;" alt="{\displaystyle \beta _{0j}=\gamma _{00}+\gamma _{01}w_{j}+u_{0j}}" loading="lazy">

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<annotation encoding="application/x-tex">{\displaystyle \beta _{1j}=\gamma _{10}+\gamma _{11}w_{j}+u_{1j}}</annotation>

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</math><img src="./6a88a6e4c284351c88a4d893455524fc07fb51b7.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:23.623ex; height:2.843ex;" alt="{\displaystyle \beta _{1j}=\gamma _{10}+\gamma _{11}w_{j}+u_{1j}}" loading="lazy">

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<annotation encoding="application/x-tex">{\displaystyle \gamma _{00}}</annotation>

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</math><img src="./80eab78fc673302df7fb8468463cc99f1baadae2.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:3.08ex; height:2.176ex;" alt="{\displaystyle \gamma _{00}}" loading="lazy"> refers to the overall intercept. This is the grand mean of the scores on the dependent variable across all the groups when all the predictors are equal to 0.</li>

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<annotation encoding="application/x-tex">{\displaystyle \gamma _{10}}</annotation>

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</math><img src="./ec33566980886cff7d269eb48973b4669824c85f.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:3.08ex; height:2.176ex;" alt="{\displaystyle \gamma _{10}}" loading="lazy"> refers to the average slope between the dependent variable and the Level 1 predictor.</li>

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<annotation encoding="application/x-tex">{\displaystyle w_{j}}</annotation>

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</math><img src="./326f4828cd2d8b281d5837f977d435d47450a191.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:2.574ex; height:2.343ex;" alt="{\displaystyle w_{j}}" loading="lazy"> refers to the Level 2 predictor.</li>

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<annotation encoding="application/x-tex">{\displaystyle \gamma _{01}}</annotation>

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</math><img src="./370da5cf717f0b318895afdb6ad23bfbf4744687.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:3.08ex; height:2.176ex;" alt="{\displaystyle \gamma _{01}}" loading="lazy"> and <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \gamma _{11}}">

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<annotation encoding="application/x-tex">{\displaystyle \gamma _{11}}</annotation>

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</math><img src="./2ce6c1d1ea661c5f2fefc531afd79915a021f6c0.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:3.08ex; height:2.176ex;" alt="{\displaystyle \gamma _{11}}" loading="lazy"> refer to the effect of the Level 2 predictor on the Level 1 intercept and slope respectively.</li>

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<annotation encoding="application/x-tex">{\displaystyle u_{0j}}</annotation>

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</math><img src="./f9d55a41095a0e723345d15a622863fce91abfc5.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:3.061ex; height:2.343ex;" alt="{\displaystyle u_{0j}}" loading="lazy"> refers to the deviation in group j from the overall intercept.</li>

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<annotation encoding="application/x-tex">{\displaystyle u_{1j}}</annotation>

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</math><img src="./b975778cf02253a2628610b0671850a2b5ecdf2e.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:3.061ex; height:2.343ex;" alt="{\displaystyle u_{1j}}" loading="lazy"> refers to the deviation in group j from the average slope between the dependent variable and the Level 1 predictor.</li></ul>

<div class="mw-heading mw-heading2"><h2 id="Types_of_models">Types of models</h2></div>

Before conducting a multilevel model analysis, a researcher must decide on several aspects, including which predictors are to be included in the analysis, if any. Second, the researcher must decide whether parameter values (i.e., the elements that will be estimated) will be fixed or random.<a href="#cite_note-Fidell-3">[2]</a><a href="#cite_note-Cohen-6">[5]</a><a href="#cite_note-Gomes2022-5">[4]</a> Fixed parameters are composed of a constant over all the groups, whereas a random parameter has a different value for each of the groups.<a href="#cite_note-Gomes2022-5">[4]</a> Additionally, the researcher must decide whether to employ a maximum likelihood estimation or a restricted maximum likelihood estimation type.<a href="#cite_note-Fidell-3">[2]</a>

<div class="mw-heading mw-heading3"><h3 id="Random_intercepts_model">Random intercepts model</h3></div>

A random intercepts model is a model in which intercepts are allowed to vary, and therefore, the scores on the dependent variable for each individual observation are predicted by the intercept that varies across groups.<a href="#cite_note-Cohen-6">[5]</a><a href="#cite_note-Garson-9">[8]</a><a href="#cite_note-Gomes2022-5">[4]</a> This model assumes that slopes are fixed (the same across different contexts). In addition, this model provides information about <a href="Intraclass_correlation" title="Intraclass correlation">intraclass correlations</a>, which are helpful in determining whether multilevel models are required in the first place.<a href="#cite_note-Fidell-3">[2]</a>

<div class="mw-heading mw-heading3"><h3 id="Random_slopes_model">Random slopes model</h3></div>

A random slopes model is a model in which slopes are allowed to vary according to a correlation matrix, and therefore, the slopes are different across grouping variable such as time or individuals. This model assumes that intercepts are fixed (the same across different contexts).<a href="#cite_note-Cohen-6">[5]</a>

<div class="mw-heading mw-heading3"><h3 id="Random_intercepts_and_slopes_model">Random intercepts and slopes model</h3></div>

A model that includes both random intercepts and random slopes is likely the most realistic type of model, although it is also the most complex. In this model, both intercepts and slopes are allowed to vary across groups, meaning that they are different in different contexts.<a href="#cite_note-Cohen-6">[5]</a>

<div class="mw-heading mw-heading3"><h3 id="Developing_a_multilevel_model">Developing a multilevel model</h3></div>

In order to conduct a multilevel model analysis, one would start with fixed coefficients (slopes and intercepts). One aspect would be allowed to vary at a time (that is, would be changed), and compared with the previous model in order to assess better model fit.<a href="#cite_note-Raud-2">[1]</a> There are three different questions that a researcher would ask in assessing a model. First, is it a good model? Second, is a more complex model better? Third, what contribution do individual predictors make to the model?

In order to assess models, different model fit statistics would be examined.<a href="#cite_note-Fidell-3">[2]</a> One such statistic is the chi-square <a href="Likelihood-ratio_test" title="Likelihood-ratio test">likelihood-ratio test</a>, which assesses the difference between models. The likelihood-ratio test can be employed for model building in general, for examining what happens when effects in a model are allowed to vary, and when testing a dummy-coded categorical variable as a single effect.<a href="#cite_note-Fidell-3">[2]</a> However, the test can only be used when models are <a href="Statistical_model#Nested_models" title="Statistical model">nested</a> (meaning that a more complex model includes all of the effects of a simpler model). When testing non-nested models, comparisons between models can be made using the <a href="Akaike_information_criterion" title="Akaike information criterion">Akaike information criterion</a> (AIC) or the <a href="Bayesian_information_criterion" title="Bayesian information criterion">Bayesian information criterion</a> (BIC), among others.<a href="#cite_note-Raud-2">[1]</a><a href="#cite_note-Fidell-3">[2]</a><a href="#cite_note-Cohen-6">[5]</a> See further <a href="Model_selection" title="Model selection">Model selection</a>.

<div class="mw-heading mw-heading2"><h2 id="Assumptions">Assumptions</h2></div>

Multilevel models have the same assumptions as other major general linear models (e.g., <a href="ANOVA" class="mw-redirect" title="ANOVA">ANOVA</a>, <a href="Linear_regression_model" class="mw-redirect" title="Linear regression model">regression</a>), but some of the assumptions are modified for the hierarchical nature of the design (i.e., nested data).

<dl><dt>Linearity</dt></dl>

The assumption of linearity states that there is a rectilinear (straight-line, as opposed to non-linear or U-shaped) relationship between variables.<a href="#cite_note-Green-10">[9]</a> However, the model can be extended to nonlinear relationships.<a href="#cite_note-11">[10]</a> Particularly, when the mean part of the level 1 regression equation is replaced with a non-linear parametric function, then such a model framework is widely called the <a href="Nonlinear_mixed-effects_model" title="Nonlinear mixed-effects model">nonlinear mixed-effects model</a>.<a href="#cite_note-ReferenceA-8">[7]</a>

<dl><dt>Normality</dt></dl>

The assumption of normality states that the error terms at every level of the model are normally distributed.<a href="#cite_note-Green-10">[9]</a> However, most statistical software allows one to specify different distributions for the variance terms, such as a Poisson, binomial, logistic. The multilevel modelling approach can be used for all forms of Generalized Linear models.

<dl><dt>Homoscedasticity</dt></dl>

The assumption of <a href="Homoscedasticity" class="mw-redirect" title="Homoscedasticity">homoscedasticity</a>, also known as homogeneity of variance, assumes equality of population variances.<a href="#cite_note-Green-10">[9]</a> However, different variance-correlation matrix can be specified to account for this, and the heterogeneity of variance can itself be modeled.

<dl><dt>Independence of observations (No Autocorrelation of Model's Residuals)</dt></dl>

Independence is an assumption of general linear models, which states that cases are random samples from the population and that scores on the dependent variable are independent of each other.<a href="#cite_note-Green-10">[9]</a> One of the main purposes of multilevel models is to deal with cases where the assumption of independence is violated; multilevel models do, however, assume that 1) the level 1 and level 2 residuals are uncorrelated and 2) The errors (as measured by the residuals) at the highest level are uncorrelated.<a href="#cite_note-12">[11]</a>

<dl><dt>Orthogonality of regressors to random effects</dt></dl>

The regressors must not correlate with the random effects, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle u_{0j}}">

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</math><img src="./f9d55a41095a0e723345d15a622863fce91abfc5.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:3.061ex; height:2.343ex;" alt="{\displaystyle u_{0j}}" loading="lazy">. This assumption is testable but often ignored, rendering the estimator inconsistent.<a href="#cite_note-:0-13">[12]</a> If this assumption is violated, the random-effect must be modeled explicitly in the fixed part of the model, either by using dummy variables or including cluster means of all <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle X_{ij}}">

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</math><img src="./5a3501695fc03e54ae7d33791d3fd08a5bfb9645.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:3.401ex; height:2.843ex;" alt="{\displaystyle X_{ij}}" loading="lazy"> regressors.<a href="#cite_note-:0-13">[12]</a><a href="#cite_note-14">[13]</a><a href="#cite_note-15">[14]</a><a href="#cite_note-16">[15]</a> This assumption is probably the most important assumption the estimator makes, but one that is misunderstood by most applied researchers using these types of models.<a href="#cite_note-:0-13">[12]</a>

<div class="mw-heading mw-heading2"><h2 id="Statistical_tests">Statistical tests</h2></div>

The type of statistical tests that are employed in multilevel models depend on whether one is examining fixed effects or variance components. When examining fixed effects, the tests are compared with the standard error of the fixed effect, which results in a <a href="Z-test" title="Z-test">Z-test</a>.<a href="#cite_note-Cohen-6">[5]</a> A <a href="T-test" class="mw-redirect" title="T-test">t-test</a> can also be computed. When computing a t-test, it is important to keep in mind the degrees of freedom, which will depend on the level of the predictor (e.g., level 1 predictor or level 2 predictor).<a href="#cite_note-Cohen-6">[5]</a> For a level 1 predictor, the degrees of freedom are based on the number of level 1 predictors, the number of groups and the number of individual observations. For a level 2 predictor, the degrees of freedom are based on the number of level 2 predictors and the number of groups.<a href="#cite_note-Cohen-6">[5]</a>

<div class="mw-heading mw-heading2"><h2 id="Statistical_power">Statistical power</h2></div>

Statistical power for multilevel models differs depending on whether it is level 1 or level 2 effects that are being examined. Power for level 1 effects is dependent upon the number of individual observations, whereas the power for level 2 effects is dependent upon the number of groups.<a href="#cite_note-Lee-17">[16]</a> To conduct research with sufficient power, large sample sizes are required in multilevel models. However, the number of individual observations in groups is not as important as the number of groups in a study. In order to detect cross-level interactions, given that the group sizes are not too small, recommendations have been made that at least 20 groups are needed,<a href="#cite_note-Lee-17">[16]</a> although many fewer can be used if one is only interested in inference on the fixed effects and the random effects are control, or "nuisance", variables.<a href="#cite_note-Gomes2022-5">[4]</a> The issue of statistical power in multilevel models is complicated by the fact that power varies as a function of effect size and intraclass correlations, it differs for fixed effects versus random effects, and it changes depending on the number of groups and the number of individual observations per group.<a href="#cite_note-Lee-17">[16]</a>

<div class="mw-heading mw-heading2"><h2 id="Applications">Applications</h2></div>

<div class="mw-heading mw-heading3"><h3 id="Level">Level</h3></div>

The concept of level is the keystone of this approach. In an <a href="Educational_research" title="Educational research">educational research</a> example, the levels for a 2-level model might be

<ol><li>pupil</li>

<li>class</li></ol>

However, if one were studying multiple schools and multiple school districts, a 4-level model could include

<ol><li>pupil</li>

<li>class</li>

<li>school</li>

<li>district</li></ol>

The researcher must establish for each <a href="Variable_(mathematics)" title="Variable (mathematics)">variable</a> the level at which it was measured. In this example "test score" might be measured at pupil level, "teacher experience" at class level, "school funding" at school level, and "urban" at district level.

<div class="mw-heading mw-heading3"><h3 id="Example">Example</h3></div>

As a simple example, consider a basic linear regression model that predicts income as a function of age, class, gender and race. It might then be observed that income levels also vary depending on the city and state of residence. A simple way to incorporate this into the regression model would be to add an additional <a href="Independent_variable" class="mw-redirect" title="Independent variable">independent</a> <a href="Categorical_variable" title="Categorical variable">categorical variable</a> to account for the location (i.e. a set of additional binary predictors and associated regression coefficients, one per location). This would have the effect of shifting the mean income up or down—but it would still assume, for example, that the effect of race and gender on income is the same everywhere. In reality, this is unlikely to be the case—different local laws, different retirement policies, differences in level of racial prejudice, etc. are likely to cause all of the predictors to have different sorts of effects in different locales.

In other words, a simple linear regression model might, for example, predict that a given randomly sampled person in <a href="Seattle" title="Seattle">Seattle</a> would have an average yearly income $10,000 higher than a similar person in <a href="Mobile%2C_Alabama" title="Mobile, Alabama">Mobile, Alabama</a>. However, it would also predict, for example, that a white person might have an average income $7,000 above a black person, and a 65-year-old might have an income $3,000 below a 45-year-old, in both cases regardless of location. A multilevel model, however, would allow for different regression coefficients for each predictor in each location. Essentially, it would assume that people in a given location have correlated incomes generated by a single set of regression coefficients, whereas people in another location have incomes generated by a different set of coefficients. Meanwhile, the coefficients themselves are assumed to be correlated and generated from a single set of <a href="Hyperparameter_(Bayesian_statistics)" title="Hyperparameter (Bayesian statistics)">hyperparameters</a>. Additional levels are possible: For example, people might be grouped by cities, and the city-level regression coefficients grouped by state, and the state-level coefficients generated from a single hyper-hyperparameter.

Multilevel models are a subclass of <a href="Hierarchical_Bayesian_model" class="mw-redirect" title="Hierarchical Bayesian model">hierarchical Bayesian models</a>, which are general models with multiple levels of <a href="Random_variable" title="Random variable">random variables</a> and arbitrary relationships among the different variables. Multilevel analysis has been extended to include multilevel <a href="Structural_equation_modeling" title="Structural equation modeling">structural equation modeling</a>, multilevel <a href="Latent_class_model" title="Latent class model">latent class modeling</a>, and other more general models.

Multilevel models have been used in education research or geographical research, to estimate separately the variance between pupils within the same school, and the variance between schools. In psychological applications, the multiple levels are items in an instrument, individuals, and families. In sociological applications, multilevel models are used to examine individuals embedded within regions or countries. In <a href="Industrial_and_organizational_psychology" title="Industrial and organizational psychology">organizational psychology</a> research, data from individuals must often be nested within teams or other functional units. They are often used in ecological research as well under the more general term <a href="Mixed_model" title="Mixed model">mixed models</a>.<a href="#cite_note-Gomes2022-5">[4]</a>

Different covariables may be relevant on different levels. They can be used for longitudinal studies, as with growth studies, to separate changes within one individual and differences between individuals.

Cross-level interactions may also be of substantive interest; for example, when a slope is allowed to vary randomly, a level-2 predictor may be included in the slope formula for the level-1 covariate. For example, one may estimate the interaction of race and neighborhood to obtain an estimate of the interaction between an individual's characteristics and the social context.

<div class="mw-heading mw-heading3"><h3 id="Applications_to_longitudinal_(repeated_measures)_data">Applications to longitudinal (repeated measures) data</h3></div>

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</style><div role="note" class="hatnote navigation-not-searchable">Further information: <a href="Multilevel_Modeling_for_Repeated_Measures" class="mw-redirect" title="Multilevel Modeling for Repeated Measures">Multilevel Modeling for Repeated Measures</a></div>

<div class="mw-heading mw-heading2"><h2 id="Alternative_ways_of_analyzing_hierarchical_data">Alternative ways of analyzing hierarchical data</h2></div>

There are several alternative ways of analyzing hierarchical data, although most of them have some problems. First, traditional statistical techniques can be used. One could disaggregate higher-order variables to the individual level, and thus conduct an analysis on this individual level (for example, assign class variables to the individual level). The problem with this approach is that it would violate the assumption of independence, and thus could bias our results. This is known as atomistic fallacy.<a href="#cite_note-Hox-18">[17]</a> Another way to analyze the data using traditional statistical approaches is to aggregate individual level variables to higher-order variables and then to conduct an analysis on this higher level. The problem with this approach is that it discards all within-group information (because it takes the average of the individual level variables). As much as 80–90% of the variance could be wasted, and the relationship between aggregated variables is inflated, and thus distorted.<a href="#cite_note-Bryk-19">[18]</a> This is known as <a href="Ecological_fallacy" title="Ecological fallacy">ecological fallacy</a>, and statistically, this type of analysis results in decreased power in addition to the loss of information.<a href="#cite_note-Fidell-3">[2]</a>

Another way to analyze hierarchical data would be through a random-coefficients model. This model assumes that each group has a different regression model—with its own intercept and slope.<a href="#cite_note-Cohen-6">[5]</a> Because groups are sampled, the model assumes that the intercepts and slopes are also randomly sampled from a population of group intercepts and slopes. This allows for an analysis in which one can assume that slopes are fixed but intercepts are allowed to vary.<a href="#cite_note-Cohen-6">[5]</a> However this presents a problem, as individual components are independent but group components are independent between groups, but dependent within groups. This also allows for an analysis in which the slopes are random; however, the correlations of the error terms (disturbances) are dependent on the values of the individual-level variables.<a href="#cite_note-Cohen-6">[5]</a> Thus, the problem with using a random-coefficients model in order to analyze hierarchical data is that it is still not possible to incorporate higher order variables.

<div class="mw-heading mw-heading2"><h2 id="Error_terms">Error terms</h2></div>

Multilevel models have two error terms, which are also known as disturbances. The individual components are all independent, but there are also group components, which are independent between groups but correlated within groups. However, variance components can differ, as some groups are more homogeneous than others.<a href="#cite_note-Bryk-19">[18]</a>

<div class="mw-heading mw-heading2"><h2 id="Bayesian_nonlinear_mixed-effects_model">Bayesian nonlinear mixed-effects model</h2></div>

Multilevel modeling is frequently used in diverse applications and it can be formulated by the Bayesian framework. Particularly, Bayesian nonlinear mixed-effects models have recently received significant attention. A basic version of the Bayesian nonlinear mixed-effects models is represented as the following three-stage:

Stage 1: Individual-Level Model

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Stage 2: Population Model

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<annotation encoding="application/x-tex">{\displaystyle {\begin{aligned}&\theta _{li}=\alpha _{l}+\sum _{b=1}^{P}\beta _{lb}x_{ib}+\eta _{li},\\{\phantom {spacer}}\\&\eta _{li}\sim N(0,\omega _{l}^{2}),\\{\phantom {spacer}}\\&i=1,\ldots ,N,\,l=1,\ldots ,K.\end{aligned}}}</annotation>

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Stage 3: Prior

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<annotation encoding="application/x-tex">{\displaystyle {\begin{aligned}&\sigma ^{2}\sim \pi (\sigma ^{2}),\\{\phantom {spacer}}\\&\alpha _{l}\sim \pi (\alpha _{l}),\\{\phantom {spacer}}\\&(\beta _{l1},\ldots ,\beta _{lb},\ldots ,\beta _{lP})\sim \pi (\beta _{l1},\ldots ,\beta _{lb},\ldots ,\beta _{lP}),\\{\phantom {spacer}}\\&\omega _{l}^{2}\sim \pi (\omega _{l}^{2}),\\{\phantom {spacer}}\\&l=1,\ldots ,K.\end{aligned}}}</annotation>

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</math><img src="./7b18ca6d8dffb2c48ccb8e36279eb09007bb9f78.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -13.338ex; width:54.541ex; height:27.843ex;" alt="{\displaystyle {\begin{aligned}&\sigma ^{2}\sim \pi (\sigma ^{2}),\\{\phantom {spacer}}\\&\alpha _{l}\sim \pi (\alpha _{l}),\\{\phantom {spacer}}\\&(\beta _{l1},\ldots ,\beta _{lb},\ldots ,\beta _{lP})\sim \pi (\beta _{l1},\ldots ,\beta _{lb},\ldots ,\beta _{lP}),\\{\phantom {spacer}}\\&\omega _{l}^{2}\sim \pi (\omega _{l}^{2}),\\{\phantom {spacer}}\\&l=1,\ldots ,K.\end{aligned}}}" loading="lazy">

Here, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle y_{ij}}">

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</math><img src="./a30d2b1b4d1c7261af28c383ab3542c420e8dc23.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:2.835ex; height:2.009ex;" alt="{\displaystyle x_{ib}}" loading="lazy"> is the <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle b}">

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</math><img src="./f11423fbb2e967f986e36804a8ae4271734917c3.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:0.998ex; height:2.176ex;" alt="{\displaystyle b}" loading="lazy">-th covariate of the <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle i}">

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</math><img src="./add78d8608ad86e54951b8c8bd6c8d8416533d20.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:0.802ex; height:2.176ex;" alt="{\displaystyle i}" loading="lazy">-th subject. Parameters involved in the model are written in Greek letters. <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle f(t;\theta _{1},\ldots ,\theta _{K})}">

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</math><img src="./7dc90a5afc9a8a37a858ba966d299f1b5cc7b27e.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:15.068ex; height:2.843ex;" alt="{\displaystyle f(t;\theta _{1},\ldots ,\theta _{K})}" loading="lazy"> is a known function parameterized by the <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle K}">

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</math><img src="./2b76fce82a62ed5461908f0dc8f037de4e3686b0.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.066ex; height:2.176ex;" alt="{\displaystyle K}" loading="lazy">-dimensional vector <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle (\theta _{1},\ldots ,\theta _{K})}">

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</math><img src="./2e6821b2c59c9d8e6b7d011cee289badbdc92e1c.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:11.916ex; height:2.843ex;" alt="{\displaystyle (\theta _{1},\ldots ,\theta _{K})}" loading="lazy">. Typically, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle f}">

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</math><img src="./132e57acb643253e7810ee9702d9581f159a1c61.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:1.279ex; height:2.509ex;" alt="{\displaystyle f}" loading="lazy"> is a `nonlinear' function and describes the temporal trajectory of individuals. In the model, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \epsilon _{ij}}">

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</math><img src="./4e85834c4fe6c3f61e115a18433e23c93e6eb44f.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:2.421ex; height:2.343ex;" alt="{\displaystyle \epsilon _{ij}}" loading="lazy"> and <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \eta _{li}}">

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</math><img src="./725bbd45a3979050200eb94ff55b5844d0b7252f.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:2.445ex; height:2.176ex;" alt="{\displaystyle \eta _{li}}" loading="lazy"> describe within-individual variability and between-individual variability, respectively. If Stage 3: Prior is not considered, then the model reduces to a frequentist nonlinear mixed-effect model.

A central task in the application of the Bayesian nonlinear mixed-effect models is to evaluate the posterior density:

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<annotation encoding="application/x-tex">{\displaystyle \pi (\{\theta _{li}\}_{i=1,l=1}^{N,K},\sigma ^{2},\{\alpha _{l}\}_{l=1}^{K},\{\beta _{lb}\}_{l=1,b=1}^{K,P},\{\omega _{l}\}_{l=1}^{K}|\{y_{ij}\}_{i=1,j=1}^{N,M_{i}})}</annotation>

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</math><img src="./e73b71337c2ae71d95fdd9d582502eb8de34ca77.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.338ex; width:57.868ex; height:3.843ex;" alt="{\displaystyle \pi (\{\theta _{li}\}_{i=1,l=1}^{N,K},\sigma ^{2},\{\alpha _{l}\}_{l=1}^{K},\{\beta _{lb}\}_{l=1,b=1}^{K,P},\{\omega _{l}\}_{l=1}^{K}|\{y_{ij}\}_{i=1,j=1}^{N,M_{i}})}" loading="lazy">

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<annotation encoding="application/x-tex">{\displaystyle \propto \pi (\{y_{ij}\}_{i=1,j=1}^{N,M_{i}},\{\theta _{li}\}_{i=1,l=1}^{N,K},\sigma ^{2},\{\alpha _{l}\}_{l=1}^{K},\{\beta _{lb}\}_{l=1,b=1}^{K,P},\{\omega _{l}\}_{l=1}^{K})}</annotation>

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</math><img src="./9b3e7ee4d3e371f0c7954319050cbe8d785e8fa1.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.338ex; width:60.709ex; height:3.843ex;" alt="{\displaystyle \propto \pi (\{y_{ij}\}_{i=1,j=1}^{N,M_{i}},\{\theta _{li}\}_{i=1,l=1}^{N,K},\sigma ^{2},\{\alpha _{l}\}_{l=1}^{K},\{\beta _{lb}\}_{l=1,b=1}^{K,P},\{\omega _{l}\}_{l=1}^{K})}" loading="lazy">

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<mtext>Stage 1: Individual-Level Model</mtext>

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<mtext>Stage 2: Population Model</mtext>

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<mtext>Stage 3: Prior</mtext>

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<annotation encoding="application/x-tex">{\displaystyle {\begin{aligned}=&~\left.{\pi (\{y_{ij}\}_{i=1,j=1}^{N,M_{i}}|\{\theta _{li}\}_{i=1,l=1}^{N,K},\sigma ^{2})}\right\}{\text{Stage 1: Individual-Level Model}}\\{\phantom {spacer}}\\\times &~\left.{\pi (\{\theta _{li}\}_{i=1,l=1}^{N,K}|\{\alpha _{l}\}_{l=1}^{K},\{\beta _{lb}\}_{l=1,b=1}^{K,P},\{\omega _{l}\}_{l=1}^{K})}\right\}{\text{Stage 2: Population Model}}\\{\phantom {spacer}}\\\times &~\left.{p(\sigma ^{2},\{\alpha _{l}\}_{l=1}^{K},\{\beta _{lb}\}_{l=1,b=1}^{K,P},\{\omega _{l}\}_{l=1}^{K})}\right\}{\text{Stage 3: Prior}}\end{aligned}}}</annotation>

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</math><img src="./bf35bd2a293ea48971f4fdc93b612ea229ce4bb0.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -9.671ex; width:79.141ex; height:20.509ex;" alt="{\displaystyle {\begin{aligned}=&~\left.{\pi (\{y_{ij}\}_{i=1,j=1}^{N,M_{i}}|\{\theta _{li}\}_{i=1,l=1}^{N,K},\sigma ^{2})}\right\}{\text{Stage 1: Individual-Level Model}}\\{\phantom {spacer}}\\\times &~\left.{\pi (\{\theta _{li}\}_{i=1,l=1}^{N,K}|\{\alpha _{l}\}_{l=1}^{K},\{\beta _{lb}\}_{l=1,b=1}^{K,P},\{\omega _{l}\}_{l=1}^{K})}\right\}{\text{Stage 2: Population Model}}\\{\phantom {spacer}}\\\times &~\left.{p(\sigma ^{2},\{\alpha _{l}\}_{l=1}^{K},\{\beta _{lb}\}_{l=1,b=1}^{K,P},\{\omega _{l}\}_{l=1}^{K})}\right\}{\text{Stage 3: Prior}}\end{aligned}}}" loading="lazy">

The panel on the right displays Bayesian research cycle using Bayesian nonlinear mixed-effects model.<a href="#cite_note-Repeated_Measurement_Data_2201-20">[19]</a> A research cycle using the Bayesian nonlinear mixed-effects model comprises two steps: (a) standard research cycle and (b) Bayesian-specific workflow. Standard research cycle involves literature review, defining a problem and specifying the research question and hypothesis. Bayesian-specific workflow comprises three sub-steps: (b)–(i) formalizing prior distributions based on background knowledge and prior elicitation; (b)–(ii) determining the likelihood function based on a nonlinear function <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle f}">

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<annotation encoding="application/x-tex">{\displaystyle f}</annotation>

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</math><img src="./132e57acb643253e7810ee9702d9581f159a1c61.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:1.279ex; height:2.509ex;" alt="{\displaystyle f}" loading="lazy">; and (b)–(iii) making a posterior inference. The resulting posterior inference can be used to start a new research cycle.

<ul><li><a href="Hyperparameter_(Bayesian_statistics)" title="Hyperparameter (Bayesian statistics)">Hyperparameter</a></li>

<li><a href="Mixed-design_analysis_of_variance" title="Mixed-design analysis of variance">Mixed-design analysis of variance</a></li>

<li><a href="Multiscale_modeling" title="Multiscale modeling">Multiscale modeling</a></li>

<li><a href="Random_effects_model" title="Random effects model">Random effects model</a></li>

<li><a href="Nonlinear_mixed-effects_model" title="Nonlinear mixed-effects model">Nonlinear mixed-effects model</a></li>

<li><a href="Bayesian_hierarchical_modeling" title="Bayesian hierarchical modeling">Bayesian hierarchical modeling</a></li>

<li><a href="Restricted_randomization" title="Restricted randomization">Restricted randomization</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="Notes">Notes</h2></div>

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<li id="cite_note-1"><a href="#cite_ref-1">^</a> also known as hierarchical linear models, linear mixed-effect models, mixed models, nested data models, random coefficient, random-effects models, random parameter models, or split-plot designs

</li>

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<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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<li id="cite_note-7"><a href="#cite_ref-7">^</a> <cite id="CITEREFLeeLeiMallick2020" class="citation journal cs1">Lee, Se Yoon; Lei, Bowen; Mallick, Bani (2020). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390340">"Estimation of COVID-19 spread curves integrating global data and borrowing information"</a>. PLOS ONE. 15 (7): e0236860. <a href="ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<a rel="nofollow" class="external text" href="https://arxiv.org/abs/2005.00662">2005.00662</a>. <a href="Bibcode_(identifier)" class="mw-redirect" title="Bibcode (identifier)">Bibcode</a>:<a rel="nofollow" class="external text" href="https://ui.adsabs.harvard.edu/abs/2020PLoSO..1536860L">2020PLoSO..1536860L</a>. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1371%2Fjournal.pone.0236860">10.1371/journal.pone.0236860</a>. <a href="PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390340">7390340</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/32726361">32726361</a>.</cite>

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<li id="cite_note-Garson-9"><a href="#cite_ref-Garson_9-0">^</a> <cite id="CITEREFeditor2012" class="citation book cs1">editor, G. David Garson (10 April 2012). Hierarchical linear modeling : guide and applications. Thousand Oaks, Calif.: Sage Publications. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-1-4129-9885-7</bdi>.</cite> <code class="cs1-code">{{cite book}}</code>: <code class="cs1-code">|last=</code> has generic name (help)

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<li id="cite_note-Green-10">^ <a href="#cite_ref-Green_10-0">a</a> <a href="#cite_ref-Green_10-1">b</a> <a href="#cite_ref-Green_10-2">c</a> <a href="#cite_ref-Green_10-3">d</a> <cite id="CITEREFSalkind2004" class="citation book cs1">Salkind, Samuel B. Green, Neil J. (2004). <a rel="nofollow" class="external text" href="https://archive.org/details/usingspssforwind00samu">Using SPSS for Windows and Macintosh : analyzing and understanding data</a> (4th ed.). Upper Saddle River, NJ: Pearson Education. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-13-146597-8</bdi>.</cite><code class="cs1-code">{{cite book}}</code>: CS1 maint: multiple names: authors list (link)

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<li id="cite_note-11"><a href="#cite_ref-11">^</a> <cite id="CITEREFGoldstein1991" class="citation journal cs1">Goldstein, Harvey (1991). "Nonlinear Multilevel Models, with an Application to Discrete Response Data". Biometrika. 78 (1): 45–51. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1093%2Fbiomet%2F78.1.45">10.1093/biomet/78.1.45</a>. <a href="JSTOR_(identifier)" class="mw-redirect" title="JSTOR (identifier)">JSTOR</a> <a rel="nofollow" class="external text" href="https://www.jstor.org/stable/2336894">2336894</a>.</cite>

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<li id="cite_note-12"><a href="#cite_ref-12">^</a> <cite id="CITEREFATS_Statistical_Consulting_Group" class="citation web cs1">ATS Statistical Consulting Group. <a rel="nofollow" class="external text" href="https://web.archive.org/web/20101231163641/http://www.ats.ucla.edu/stat/hlm/seminars/hlm6/outline_hlm_seminar.pdf">"Introduction to Multilevel Modeling Using HLM 6"</a> (PDF). Archived from <a rel="nofollow" class="external text" href="http://www.ats.ucla.edu/stat/hlm/seminars/hlm6/outline_hlm_seminar.pdf">the original</a> (PDF) on 31 December 2010.</cite>

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<li id="cite_note-:0-13">^ <a href="#cite_ref-:0_13-0">a</a> <a href="#cite_ref-:0_13-1">b</a> <a href="#cite_ref-:0_13-2">c</a> <cite id="CITEREFAntonakisBastardozRönkkö2021" class="citation journal cs1">Antonakis, John; Bastardoz, Nicolas; Rönkkö, Mikko (2021). <a rel="nofollow" class="external text" href="https://jyx.jyu.fi/bitstream/123456789/66704/2/Antonakisym.pdf">"On Ignoring the Random Effects Assumption in Multilevel Models: Review, Critique, and Recommendations"</a> (PDF). Organizational Research Methods. 24 (2): 443–483. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1177%2F1094428119877457">10.1177/1094428119877457</a>. <a href="ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/1094-4281">1094-4281</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:210355362">210355362</a>.</cite>

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<li id="cite_note-14"><a href="#cite_ref-14">^</a> <cite id="CITEREFMcNeishKelley2019" class="citation journal cs1">McNeish, Daniel; Kelley, Ken (2019). <a rel="nofollow" class="external text" href="http://doi.apa.org/getdoi.cfm?doi=10.1037/met0000182">"Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations"</a>. Psychological Methods. 24 (1): 20–35. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1037%2Fmet0000182">10.1037/met0000182</a>. <a href="ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/1939-1463">1939-1463</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/29863377">29863377</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:44145669">44145669</a>.</cite>

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<li id="cite_note-15"><a href="#cite_ref-15">^</a> <cite id="CITEREFBlieseSchepkerEssmanPloyhart2020" class="citation journal cs1">Bliese, Paul D.; Schepker, Donald J.; Essman, Spenser M.; Ployhart, Robert E. (2020). <a rel="nofollow" class="external text" href="http://journals.sagepub.com/doi/10.1177/0149206319868016">"Bridging Methodological Divides Between Macro- and Microresearch: Endogeneity and Methods for Panel Data"</a>. Journal of Management. 46 (1): 70–99. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1177%2F0149206319868016">10.1177/0149206319868016</a>. <a href="ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/0149-2063">0149-2063</a>. <a href="S2CID_(identifier)" class="mw-redirect" title="S2CID (identifier)">S2CID</a> <a rel="nofollow" class="external text" href="https://api.semanticscholar.org/CorpusID:202288849">202288849</a>.</cite>

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<li id="cite_note-16"><a href="#cite_ref-16">^</a> <cite id="CITEREFWooldridge2010" class="citation book cs1">Wooldridge, Jeffrey M. (1 October 2010). <a rel="nofollow" class="external text" href="https://books.google.com/books?id=hSs3AgAAQBAJ&dq=info:T5fz2cmyyF8J:scholar.google.com&pg=PP1">Econometric Analysis of Cross Section and Panel Data, second edition</a>. MIT Press. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-262-29679-3</bdi>.</cite>

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<li id="cite_note-Lee-17">^ <a href="#cite_ref-Lee_17-0">a</a> <a href="#cite_ref-Lee_17-1">b</a> <a href="#cite_ref-Lee_17-2">c</a> <cite id="CITEREFLeeuw1998" class="citation book cs1">Leeuw, Ita Kreft, Jan de (1998). Introducing multilevel modeling (Repr. ed.). London: Sage Publications Ltd. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-7619-5141-4</bdi>.</cite><code class="cs1-code">{{cite book}}</code>: CS1 maint: multiple names: authors list (link)

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<li id="cite_note-Bryk-19">^ <a href="#cite_ref-Bryk_19-0">a</a> <a href="#cite_ref-Bryk_19-1">b</a> <cite id="CITEREFBrykRaudenbush,_Stephen_W.1988" class="citation journal cs1">Bryk, Anthony S.; Raudenbush, Stephen W. (1 January 1988). "Heterogeneity of variance in experimental studies: A challenge to conventional interpretations". Psychological Bulletin. 104 (3): 396–404. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1037%2F0033-2909.104.3.396">10.1037/0033-2909.104.3.396</a>.</cite>

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<li id="cite_note-Repeated_Measurement_Data_2201-20">^ <a href="#cite_ref-Repeated_Measurement_Data_2201_20-0">a</a> <a href="#cite_ref-Repeated_Measurement_Data_2201_20-1">b</a> <cite id="CITEREFLee2022" class="citation journal cs1">Lee, Se Yoon (2022). <a rel="nofollow" class="external text" href="https://doi.org/10.3390%2Fmath10060898">"Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications"</a>. Mathematics. 10 (6): 898. <a href="ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<a rel="nofollow" class="external text" href="https://arxiv.org/abs/2201.12430">2201.12430</a>. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.3390%2Fmath10060898">10.3390/math10060898</a>.</cite>

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</ol></div></div>

<div class="mw-heading mw-heading2"><h2 id="Further_reading">Further reading</h2></div>

<ul><li><cite id="CITEREFGelmanHill2007" class="citation book cs1"><a href="Andrew_Gelman" title="Andrew Gelman">Gelman, A.</a>; Hill, J. (2007). <a rel="nofollow" class="external text" href="https://books.google.com/books?id=lV3DIdV0F9AC&pg=PA235">Data Analysis Using Regression and Multilevel/Hierarchical Models</a>. New York: Cambridge University Press. pp. 235–299. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-521-68689-1</bdi>.</cite></li>

<li><cite id="CITEREFGoldstein2011" class="citation book cs1">Goldstein, H. (2011). <a rel="nofollow" class="external text" href="https://books.google.com/books?id=mdwt7ibSGUYC">Multilevel Statistical Models</a> (4th ed.). London: Wiley. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-470-74865-7</bdi>.</cite></li>

<li><cite id="CITEREFHedekerGibbons2012" class="citation book cs1">Hedeker, D.; Gibbons, R. D. (2012). Longitudinal Data Analysis (2nd ed.). New York: Wiley. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-470-88918-3</bdi>.</cite></li>

<li><cite id="CITEREFHox2010" class="citation book cs1"><a href="Joop_Hox" title="Joop Hox">Hox, J. J.</a> (2010). <a rel="nofollow" class="external text" href="https://books.google.com/books?id=jLPHBQAAQBAJ">Multilevel Analysis: Techniques and Applications</a> (2nd ed.). New York: Routledge. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-1-84872-845-5</bdi>.</cite></li>

<li><cite id="CITEREFRaudenbushBryk2002" class="citation book cs1">Raudenbush, S. W.; Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Thousand Oaks, CA: Sage.</cite> This concentrates on education.</li>

<li><cite id="CITEREFSnijdersBosker2011" class="citation book cs1">Snijders, T. A. B.; Bosker, R. J. (2011). <a rel="nofollow" class="external text" href="https://books.google.com/books?id=N1BQvcomDdQC">Multilevel Analysis: an Introduction to Basic and Advanced Multilevel Modeling</a> (2nd ed.). London: Sage. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>9781446254332</bdi>.</cite></li>

<li><cite id="CITEREFSwamyTavlas2001" class="citation book cs1"><a href="P._A._V._B._Swamy" title="P. A. V. B. Swamy">Swamy, P. A. V. B.</a>; Tavlas, George S. (2001). "Random Coefficient Models". In Baltagi, Badi H. (ed.). A Companion to Theoretical Econometrics. Oxford: Blackwell. pp. 410–429. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-631-21254-6</bdi>.</cite></li>

<li><cite id="CITEREFVerbekeMolenberghs2013" class="citation book cs1">Verbeke, G.; Molenberghs, G. (2013). Linear Mixed Models for Longitudinal Data. Springer.</cite> Includes <a href="SAS_(software)" title="SAS (software)">SAS</a> code</li>

<li><cite id="CITEREFGomes2022" class="citation journal cs1">Gomes, Dylan G.E. (20 January 2022). <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8784019">"Should I use fixed effects or random effects when I have fewer than five levels of a grouping factor in a mixed-effects model?"</a>. PeerJ. 10: e12794. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.7717%2Fpeerj.12794">10.7717/peerj.12794</a>. <a href="PMC_(identifier)" class="mw-redirect" title="PMC (identifier)">PMC</a> <a rel="nofollow" class="external text" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8784019">8784019</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/35116198">35116198</a>.</cite></li></ul>

<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>

<ul><li><a rel="nofollow" class="external text" href="http://www.cmm.bristol.ac.uk">Centre for Multilevel Modelling</a></li></ul>

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