Single–Factor Experiments with Fixed and Random Effects

2009 ◽  
pp. 113-158 ◽  
Author(s):  
Helge Toutenburg ◽  
Shalabh
Keyword(s):  
Random Effects ◽  
Single Factor ◽  
Fixed And Random Effects

scholarly journals Assessing the impacts of Airbnb listings on London house prices

2021 ◽  
pp. 239980832110018
Author(s):  
James Todd ◽  
Anwar Musah ◽  
James Cheshire
Keyword(s):  
Random Effects ◽  
Rapid Growth ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Positive Association ◽  
Sharing Economy ◽  
Local Context ◽  
Fixed And Random Effects ◽  
The World

Over the course of the last decade, sharing economy platforms have experienced significant growth within cities around the world. Airbnb, which is one of the largest and best-known platforms, provides the focus for this paper and offers a service that allows users to rent properties or spare rooms to guests. Its rapid growth has led to a growing discourse around the consequences of Airbnb rentals within the local context. The research within this paper focuses on determining impact on local housing prices within the inner London boroughs by constructing a longitudinal panel dataset, on which a fixed and random effects regression was conducted. The results indicate that there is a significant and modest positive association between the frequency of Airbnb and the house price per square metre in these boroughs.

scholarly journals Parametrizations, fixed and random effects

2017 ◽  
Vol 154 ◽  
pp. 162-176 ◽  
Author(s):  
Azzouz Dermoune ◽  
Cristian Preda
Keyword(s):  
Random Effects ◽  
Fixed And Random Effects

Meta-analysis of effect sizes reported at multiple time points: A multivariate approach

2012 ◽  
Vol 9 (5) ◽  
pp. 610-620 ◽  
Author(s):  
Thomas A Trikalinos ◽  
Ingram Olkin
Keyword(s):  
Random Effects ◽  
Treatment Effects ◽  
Multivariate Analyses ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Multiple Time ◽  
Time Points ◽  
Fixed And Random Effects ◽  
Multiple Time Points ◽  
Meta Analyses

Background Many comparative studies report results at multiple time points. Such data are correlated because they pertain to the same patients, but are typically meta-analyzed as separate quantitative syntheses at each time point, ignoring the correlations between time points. Purpose To develop a meta-analytic approach that estimates treatment effects at successive time points and takes account of the stochastic dependencies of those effects. Methods We present both fixed and random effects methods for multivariate meta-analysis of effect sizes reported at multiple time points. We provide formulas for calculating the covariance (and correlations) of the effect sizes at successive time points for four common metrics (log odds ratio, log risk ratio, risk difference, and arcsine difference) based on data reported in the primary studies. We work through an example of a meta-analysis of 17 randomized trials of radiotherapy and chemotherapy versus radiotherapy alone for the postoperative treatment of patients with malignant gliomas, where in each trial survival is assessed at 6, 12, 18, and 24 months post randomization. We also provide software code for the main analyses described in the article. Results We discuss the estimation of fixed and random effects models and explore five options for the structure of the covariance matrix of the random effects. In the example, we compare separate (univariate) meta-analyses at each of the four time points with joint analyses across all four time points using the proposed methods. Although results of univariate and multivariate analyses are generally similar in the example, there are small differences in the magnitude of the effect sizes and the corresponding standard errors. We also discuss conditional multivariate analyses where one compares treatment effects at later time points given observed data at earlier time points. Limitations Simulation and empirical studies are needed to clarify the gains of multivariate analyses compared with separate meta-analyses under a variety of conditions. Conclusions Data reported at multiple time points are multivariate in nature and are efficiently analyzed using multivariate methods. The latter are an attractive alternative or complement to performing separate meta-analyses.

scholarly journals Sars-Cov-2 in Argentina: Lockdown, Mobility, and Contagion

2020 ◽  
Author(s):  
Juan M.C. Larrosa
Keyword(s):  
Panel Data ◽  
Granger Causality ◽  
Random Effects ◽  
Social Costs ◽  
Political Discussion ◽  
Fixed And Random Effects ◽  
The World ◽  
Causality Tests ◽  
Granger Causality Tests ◽  
Over Time

AbstractThere is a debate in Argentina about the effectiveness of mandatory lockdown measures in containing COVID-19 that lasts five months making it one of the longest in the World. The population effort to comply the lockdown has been decreasing over time given the economic and social costs that it entails. We contributes by analyzing the Argentinian case through information of mobility and contagion given answers to recurrent questions on these topics. This paper aims to fill the gap in the literature by assessing the effects of lockdown measures and the regional relaxation on the numbers of rate of new infections. We also respond to issues of internal political discussion on regional contagion and the effect of marches and unexpected crowd events. We use pool, fixed and random effects panel data modeling and Granger causality tests identifying relations between mobility and contagion. Our results show that lockdown in Argentina has been effective in reducing the mobility but not in way that reduces the rate of contagion. Strict lockdown seems to be effective in short periods of time and by extend it without complementary measures loss effectiveness. Contagion rate seems to be discretely displaced in time and resurging amidst slowly increasing in mobility.

Analysis of aggregation, a worked example: numbers of ticks on red grouse chicks

Parasitology ◽  
2001 ◽  
Vol 122 (5) ◽  
pp. 563-569 ◽  
Author(s):  
D. A. ELSTON ◽  
R. MOSS ◽  
T. BOULINIER ◽  
C. ARROWSMITH ◽  
X. LAMBIN
Keyword(s):  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Negative Binomial ◽  
Generalized Linear Mixed Model ◽  
Red Grouse ◽  
Worked Example ◽  
Fixed And Random Effects ◽  
Individual Effects ◽  
Lagopus Lagopus Scoticus

The statistical aggregation of parasites among hosts is often described empirically by the negative binomial (Poisson-gamma) distribution. Alternatively, the Poisson-lognormal model can be used. This has the advantage that it can be fitted as a generalized linear mixed model, thereby quantifying the sources of aggregation in terms of both fixed and random effects. We give a worked example, assigning aggregation in the distribution of sheep ticksIxodes ricinuson red grouseLagopus lagopus scoticuschicks to temporal (year), spatial (altitude and location), brood and individual effects. Apparent aggregation among random individuals in random broods fell 8-fold when spatial and temporal effects had been accounted for.

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