We specified multilevel regression models for each country separately.
Multilevel regression models were used for data analysis.
Descriptive statistics and a series of multilevel regression models were used.
Then, this study constructed multilevel regression models with walking activity as the dependent variable.
Multilevel regression models examined in 2015 the independent and joint associations of the three environmental measures with change in waist circumference, accounting for socio-demographic covariates.
Multilevel regression models confirm that spatial access to pedestrians has differing effects on retail sales according to the type of retail sector.
To compare the use of conventional statistical models with multilevel regression models in volume outcome analyses of surgical procedures in an empirical case study.
In multilevel regression models, profiles were compared on accelerometer-measured moderate-to-vigorous PA (MVPA) and sedentary time and self-reported PA, adjusting for covariates and clustering.
As Hox ([2010]) suggested, using multilevel regression models without taking into account the clustering of data in schools often leads to biased estimates, since achievement variables often have substantial variance at the school level.
Using a national dataset, this study investigates the inequalities in physical functional status, depressive symptoms, and self-rated health among groups of people with different socioeconomic status (SES) through the use of multilevel regression models.
Our multilevel regression models verify the positive effects of higher access to primary destinations, such as neighborhood retail stores, schools, cultural facilities, and available public transit along the local street network on walking volume.
