All Questions
3,081 questions
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30
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Should I conduct a multilevel for this or another analysis? Need help
I have three sources of data (teachers, parents and students) assessing students, in three waves. I want to assess all and see the differences between moments but then I also want to use variables for ...
0
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0
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19
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Differences-in-differences where the treatment affects the sensitivity to another variable
I have a case where agents respond to a variable $X$, such that
$$y_t = \beta^{P,T} X_t + \varepsilon_t $$
$\beta^{P, T}$ can differ for the treated ($T = 1$) and untreated groups, and also the post-...
-1
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0
answers
8
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what happens when a network has low closeness centrality - what happens to nodes on the periphery [closed]
I have thirty-nine nodes with closeness centrality scores but I do not know what to say about the nodes on the periphery who do not have scores.
3
votes
1
answer
46
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Calculate marginal effects for random effects model with two crossed random effects
I am trying to get effects marginal of two crossed random effects (using STAN or brms). I understand how to do it for a single random effect following McElreath's book and Kurtz's brms version of the ...
4
votes
2
answers
41
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Accounting for non-independence and autocorrelation in HGAM
I am currently trying to fit a HGAM to model differences in daily activity patterns of fish in two treatments. Data were collected with high-resolution telemetry, and I currently have estimates of ...
- 105
0
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0
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22
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Derive gamma-parameters from preset R^2 in mixed models
For a simulation study in R, I want to select the effect sizes according to a preset $R^2$.
Consider this two level random intercept mixed model, with one L1 predictor $X_{ij}$ and one L2 predictor $...
- 153
0
votes
0
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9
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Extracting individual level posterior class memebership probabilities in multilevel LCA
I am conducting a multilevel laten class analysis using the R package multilevLCA.
I have fitted the model using multiple steps (i.e. determining optimal number of classes as well as clusters). I now ...
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6
votes
1
answer
125
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Is there a way to forecast by subgroup without forecasting each subgroup separately?
I am trying to find an appropriate model to forecast the number of applications received at the end of a recruitment cycle based on previous recruitment cycles and the number of applications received ...
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0
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15
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Panel regression model with market share as dependent variable
The data set consists of different variables from different banks during 6 years. I want to understand how these variables correlate with market share. The problem is that market share is a zero-sum ...
7
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1
answer
69
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+50
Why don't we typically worry about stationarity in panel data models with fixed effects?
Why don't we typically worry about stationarity in panel data models with fixed effects?
In time series analysis, stationarity is often a crucial assumption. However, I've noticed that in applied ...
- 229
1
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0
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48
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Opposite results using Bayesian (STAN) vs Multilevel model (nlme). How is this possible?
My datasets contains the median wages and the cumulative installed wind-capacity for 4000 counties over a period of 20 years. The wages tend to rise over the period and the capacity tends to highly ...
- 11
0
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0
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23
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Fixed effect in cross-border discontinuity approach: include both county fixed effect and state border fixed effect?
The cross-border discontinuity approach in concern uses state-wide events that are introduced into states in a staggered manner and involves county-level GDP as the dependent variable. I am wondering ...
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0
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0
answers
18
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How to Forecast Sales for Sub-Locations Without Historical Proportion Data?
I have a time series dataset of total sales for a product in a store over time. This product is available in two different locations within the store: one stand near the checkout and another stand in ...
3
votes
1
answer
50
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Comparisions between REML and ML
I have two questions and really appreciate your answers.
If Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML) methods have the same fixed effects in a model, are the results from these ...
- 31
0
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0
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24
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Multilevel Model in R
I have data from a study in which 19 participants (9 males, 10 females) have each completed 4 jumping conditions (BW, 20, 25, 30) whilst I have measured joint level data for the hip, knee and ankle. I ...
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0
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1
answer
42
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CWC(M) in multilevel modeling
I am new to multilevel modeling and recently learned about CWC(M) by Zhang et al. (2009, https://journals.sagepub.com/doi/abs/10.1177/1094428108327450). I am running a multitlevel moderated mediation ...
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2
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1
answer
29
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Fixed Effects vs. Random Effects in Panel Data with a Time-Invariant Main Variable
I’m working with panel data and planning to use a fixed-effects model. However, my primary variable of interest is time-invariant, which I can't include in a fixed-effects model.
I have two questions:
...
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0
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0
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24
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Can I perform an IV regression where the first stage is estimated without fixed effects, but the second stage includes fixed effects?
I wish to perform an IV regression where the first stage is estimated without fixed effects, but the second stage is estimated with fixed effects. Is this possible? Are there any obvious issues with ...
0
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0
answers
12
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mice multilevel imputation: does specifying cluster variable ("-2" in predictor Matrix) without multilevel methods lead to cluster robust imputation?
In short: Are mice's imputations cluster robust when I only specify the cluster variable with "-2" in the predictor matrix but do not use multilevel models during imputation?
For clustered ...
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2
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1
answer
30
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How can REs adjust for confounding if they are required to be uncorrelated with the FEs in the model?
How can random effects adjust for confounding if they are required to be uncorrelated with the fixed effects (explanatory variables) in the model?
This question explains that including a variable in a ...
- 2,280
4
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1
answer
45
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How to Simulate a Multilevel Predictor Variable with Both L1 and L2 Variance Components?
I'm working on simulating multilevel data where I have a predictor variable measured at Level 1 (L1), which has both L1 and L2 variance components. For example, I want to simulate a socio-economic ...
- 153
1
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1
answer
40
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How to explain the totally different results from subsample analysis and interaction analysis
I'm running a panel data regression.
The main independent variable A is a treatment indicator. There is also another variable B that can split the sample into two online and offline observations.
...
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2
votes
1
answer
35
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Reporting Hierarchical Regression Results in Abstract
I did a hierarchical regression test in a social science study looking at how two variables (A and B) and their interaction term can predict variable C. My mentor told me to write in the abstract that ...
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2
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1
answer
114
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Difference-in-Difference with non-absorbing treatment
I have a difference-in-difference setup where treated units at one point in time can become control groups at a later point in time because there are multiple events that take place which are combined ...
- 387
0
votes
1
answer
33
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Mediation models require a sigma matrix that is symmetric
I'm trying to fit the following reproducible mediation model called final. But I get an error saying:
sigma must be a symmetric matrix
Could you please advise how ...
- 397
2
votes
0
answers
16
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Which estimator to choose for meta-analysis^ REML or CR2 with Wild Bootstrap?
I am following the following book: https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/multilevel-ma.html
I can't choose which estimator to choose: REML or CR2 with Wild Bootstrap.
Or maybe ...
- 21
3
votes
1
answer
61
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How can we simulate correlated random variables that vary at different levels in a multilevel/mixed effects setting?
I am very familiar with generating correlated random variables from a multivariate normal distribution.
This question is about doing that in a multilevel setting, where variables only vary at ...
- 65.8k
2
votes
1
answer
78
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General formula for mixed models
I'm trying to wrap my head around the general formula of mixed models and how it relates to the system of equations I'm used to.
The general formula read like this:
$$\mathbf{Y_{j}}=\mathbf{X_{j} \...
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4
votes
1
answer
39
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Outcome in mixed models - lower level or upper level?
I am learning about mixed models and I have a question regarding the outcomes that can be considered. If I have hierarchical data, do the outcomes that I can consider need to belong to the lower level?...
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8
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1
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471
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Power analysis for three-level multilevel models in R
For a study in a social science setting - where huge number of participants are not easily available - I'm trying to do a power analysis for a three-level multilevel design.
There are few packages ...
- 153
1
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1
answer
36
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Why does the coefficient lose significance when using IV independent variable and also including district fixed effects?
I am trying to examine the effect of a 2018 flood shock on labor force participation of male and female. However, the flood could not only be attributed to heavy rainfall but to other factors like ...
1
vote
1
answer
56
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Fixed effects vs. dummy variables in cross sectional data?
In panel data regression the term "fixed effects" is often used.
In cross sectional data regression the term "dummy variables" is often used.
Can I apply the concept of "fixed ...
1
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0
answers
15
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How to Set Up a Polynomial Multilevel Model
I have a modeling situation that I am not 100% sure how to approach. I have two independent variables, information and time, with time being a repeated measure. The dependent measure is difference. ...
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1
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1
answer
35
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Year fixed effects vs dropping one year dummy
I am running regressions with year fixed effects, but one of my covariates is age, which is perfectly collinear with year. I’ve noticed that some papers include year dummies but drop one year to allow ...
- 563
1
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1
answer
56
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Multi-level modelling?
In an instructional study, I have pretest and post-test measures of writing quality--no control condition. There are 110 students nested in 10 classes. I have pretest measures of spelling skill and ...
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1
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0
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62
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Prediction Intervals With Hierarchical Regression Model
I'm reading this data analysis book by Gelman and Hill and am trying to understand predictions with hierarchical models. On page 273 they are demonstrating making new predictions for an already ...
- 133
1
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1
answer
93
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Why Random effects and Fixed effects produce the same results?
I have conducted an experiment with 40 participants and 16 treatment combinations (balanced data with 640 data points). The 16 treatment combinations are based on 5 binary variables (the main effects ...
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0
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0
answers
37
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Control variable on same level as fixed effect
There is a panel dataset with daily observations over two years and a control variable that changes on a monthly level and is the same for all units, so for all units within a month it takes the same ...
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1
vote
1
answer
114
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PanelOLS - R Squared
I'm using the PanelOLS package in Python to estimate a fixed-effects model and have noticed that it provides four different R-squared values: rsquared_within, ...
- 363
2
votes
1
answer
52
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Inflated fixed effects in mixed-effects logistic regression model
I have a dataset with multiple observations per ID and a binary outcome. I am trying to fit a mixed-effects logistic regression, however, the fixed effect estimate of the intercept is extremely large ...
- 123
1
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0
answers
56
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Error in lmerTest: The random-effects parameters and the residual variance (or scale parameter) are probably unidentifiable
I know that there have been similar questions before, but I dont still get it.
I would like to estimate a multilevel model with repeated measures in R using the package “lmerTest”. The model ...
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2
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0
answers
19
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Hierarchical models where the hierarchy structure depends on a latent variable
I am having trouble formulating a hierarchical model for the purpose of Bayesian inference in the case where the actual hierarchical structure depends on a latent variable. I am wondering if this is ...
- 131
2
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0
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39
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Can I Perform a Micro Synthetic Control Analysis with Different Aggregation Levels for Treatment and Control Groups?
I am conducting an analysis using the microsynth package in R to evaluate the impact of increased police presence on various outcome measures obtained from an official survey. My treatment areas ...
- 121
0
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0
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11
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Fitting nonlinear Bayesian regression with a summation term in brms
I'm trying to fit parameters for a Holling type II curve for multiple prey items. This takes the form:
$$
\frac{dP_i}{dt} = \frac{a_iP_i}{1 +\sum_j{a_jh_jP_j}}
$$
where $P_i$ is density of prey ...
- 358
1
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1
answer
26
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Setting predictor variables with 3-levels in multilevel mode
I am working with a random intercept multilevel modeling.
I want to predict general health based on survey data. The survey uses nested data set on three levels: individual, county, and state.
I am ...
- 183
1
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0
answers
16
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Unit-invariant treatments in panel data
Beginner with a beginner question about two-way fixed effects. I'm using R, if that's relevant.
Like the user in this question, I'm interested in estimating how time-varying (but unit-invariant) macro ...
- 111
3
votes
1
answer
48
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Mixed Model: Translation from mathematical notation to R's lmer() - package: lmerTest
The following data should be generated and fitted to a mixed model (for further simulation studies):
$y$: outcome of clinical study (effect of medication)
indiv individuals = 20
repl replicate ...
- 95
1
vote
2
answers
35
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About adding random effects in Multilevel (HLM) analysis
I am doing regression analysis in HLM. I am wondering whether random effects should be added in this process.
Let me ask a question using a famous example. LV1 is a student and LV2 is a school. LV1 ...
2
votes
1
answer
35
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Experimental condition with multilevel model
I am working with a survey experiment. The data is set at three levels: individual, county, and state.
The experimental condition was randomized at the individual level 1. That is, some individuals in ...
- 183
0
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0
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27
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Proper Difference-in-Difference Model for Time Variant Groups
Take the following example... I have two areas: Area A and Area B. Area A are individuals in a geographic area who are exposed to a health intervention. The health intervention is applied to the ...
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