Questions tagged [multilevel-analysis]

Statistical methods appropriate for the analysis of data sets comprising several levels of hierarchy of units of analysis (e.g., students nested in classes nested in schools; observations nested in patients nested in hospitals). If you can refer to more specific models like mixed-model or glmm, please do so.

Overview

"Multilevel analysis is a general term referring to statistical methods appropriate for the analysis of data sets comprising several types of unit of analysis. The levels in the multilevel analysis are another name for the different types of unit of analysis. Each level of analysis will correspond to a population, so that multilevel studies will refer to several populations..."

-T.A.B. Snijders, Multilevel Analysis, p. 673-677 in M. Lewis-Beck, A.E. Bryman, and T.F. Liao (eds.), The SAGE Encyclopedia of Social Science Research Methods (Volume II). Sage, 2003.

Related tags

  • for linear multilevel models, or HLMs
  • for models with random intercepts
  • for models with random intercepts and slopes
  • for generalized linear mixed models (binary, ordinal, count response)
  • and for R implementations
  • for Stata implementation
  • for Bayesian models comprising several levels of hierarchy of priors and hyperpriors

Please use these tags to make your question more specific and easier to find.

164 questions
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Using predict in metafor when each author has multiple rows in the data

I'm running a meta-analysis where I'm interested in the effect of X on the effect of age on habitat use (raw mean values and variances) using the metafor package. An example of one of my models is: mod6 <- rma.mv( yi = Used_value, V =…
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How to setup prediction at week level in Mixed effect Models panel data

I am doing a research study where I am trying to design a Methodology to design Mixed effect regression models. I have sample CPG panel level data as below - enter image description here Can you please give an idea How to build a Mixed effect…
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How to impute a three-level dataset with ordered and continuous variables with non-normal distribution in R using `mi` in R

I am currently trying to impute missing entries in a three-level dataset using the mi-package. Currently, I am facing two issues with how to impute multilevel data correctly: Firstly, the variables at level two and three are imputed with different…
Pål Bjartan
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Error when using a multilevel regression (lme4)

I want to use a multilevel regression to analyse the effect of some independent variables on a dependent variable and use varying intercept and slope. My regression includes non-numeric independent variables which I want to use for the varying…
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Sample or population beta weights in R's lm() and lme()?

A journal is asking whether I report sample or population beta weights for my regressions. I am using the lm() function in base R and the lme() function from the nlme-package. Which kind of beta weight do they give? I was not able to find any…
mkks
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Extract ICC and AIC frin pooled multi-level modeling using lmer() and mice()

I am trying to run multi-level modeling using imputed dataset. imp <- mice(data, m= 20, method = "pmm") out1 <- with(data= imp, exp = lmer(issue.atti1 ~ age+gender+race+edu+pre1+pre2+group1+(1|state)) out1.pool <- pool(out1) summary(out1.pool) The…
noone
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Looping over multiple multilevel ordered logits, tidying the results, dropping one observation at a time

I'm trying to loop over multiple multilevel ordered logistic regressions with random intercepts on the country, dropping one observation at a time from the main dataset, while producing one massive, augmented tidy data frame with the results. Given…
Mike
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Create a time varying and time invariant contextual variables in r

I am attempting to do a longitudinal multilevel model using ESS survey data from 2002-2018. I want to look at the effects of macroeconomic performance on political trust. In order to do this I am using a 'within-between' approach which models a…
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Matching with multi-level multiple membership data

I am designing a within multi-level study in which my data has both nested and multiple-membership structure. The subjects are multimembers of V1 and also are nested in V2.The subjects are all from one year. When applying matching or weighting,…
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Stan - Why is imputation of bernoulli response variables in Stan resulting in biased inference?

I am trying to impute a binary response variable using a hierarchical model in Stan, but am getting biased estimates. The data consist of a number of sessions that each have a different prevalence (proportion viral shedding in this case) that is…
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Resampling multilevel data by group

I am trying to write a function that resamples names nested in groups. My function works for resampling without respect to groups, but I don't want to create samples of names that aren't in the same group. Here's the function, where x is a vector of…
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Is it meaningful to model mixed effect with center as random intercept when each center is given the same treatment?

I have 3 treatments (A, B, C) and 30 centers. Each center only administers one treatment to its participants. Example, center1 administers Treatment A to its participants, center2 administers Treatment B, and so on. Data on outcome and demographics…
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Get standard error for variances in random effects for multilevel model

How to calculate the standard error for variance in the multilevel model? m5 <- lmer(peakflow ~ pm25 + gender +age + (1| participant), data = mydata, REML = F) summary(m5) Random effects: Groups Name Variance Std.Dev. participant…
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Create a new Variable of values of another variable-multilevel regression

I am up to create a multilevel analysis (and I am a total newbie). In this analysis I want to test if a high value of a predictor( here:senseofhumor) (numeric value - transfered into "high","low","medium") would predict the (numeric)outcome more…
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Mediation package R - continuous treatment variable: How to define treatment and control values?

I am running a (multilevel) mediation analysis with a binary outcome variable, a continuous mediator variable (given in z-scores) and a continuous treatment variable (also given in z-scores) with the mediate() function in R. According to the…
Philip
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