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Journal articles on the topic 'Multilevel Regression Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Muthén, Bengt, and Tihomir Asparouhov. "Multilevel regression mixture analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, no. 3 (2009): 639–57. http://dx.doi.org/10.1111/j.1467-985x.2009.00589.x.

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Maas, Cora J. M., and Joop J. Hox. "Robustness issues in multilevel regression analysis." Statistica Neerlandica 58, no. 2 (2004): 127–37. http://dx.doi.org/10.1046/j.0039-0402.2003.00252.x.

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3

Maldonado, Luis. "Data Analysis Using Regression and Multilevel/Hierarchical Models." Persona y Sociedad 26, no. 1 (2012): 191. http://dx.doi.org/10.53689/pys.v26i1.12.

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En cursos de métodos estadísticos avanzados, habitualmente son utilizados textos econométricos como principal referencia. Sin embargo, econometría es sólo una perspectiva a partir de la cual se puede aprender estadística. Una alternativa la ofrece la disciplina de la estadística misma, en donde conceptos tales como variabilidad de los coefi cientes, incerteza en torno a dicha variabilidad o simulación, son de uso común en la actualidad. Lamentablemente, la discusión en torno a dichos conceptos es frecuentemente planteada en términos altamente matemáticos, lo cual hace difícil el estudio de mét

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4

Wong, George Y., and William M. Mason. "The Hierarchical Logistic Regression Model for Multilevel Analysis." Journal of the American Statistical Association 80, no. 391 (1985): 513–24. http://dx.doi.org/10.1080/01621459.1985.10478148.

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5

Longford, N. T. "Regression analysis of multilevel data with measurement error." British Journal of Mathematical and Statistical Psychology 46, no. 2 (1993): 301–11. http://dx.doi.org/10.1111/j.2044-8317.1993.tb01018.x.

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Livingston, Michael. "Multilevel Analysis for Applied Research-It's Just Regression!" Drug and Alcohol Review 28, no. 1 (2009): 91–92. http://dx.doi.org/10.1111/j.1465-3362.2008.00013_5.x.

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Furlow, Carolyn F., and Phill Gagné. "Multilevel Analysis for Applied Research: It's Just Regression." Structural Equation Modeling: A Multidisciplinary Journal 15, no. 2 (2008): 370–75. http://dx.doi.org/10.1080/10705510801922670.

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Camilli, Gregory, Jimmy de la Torre, and Chia-Yi Chiu. "A Noncentral t Regression Model for Meta-Analysis." Journal of Educational and Behavioral Statistics 35, no. 2 (2010): 125–53. http://dx.doi.org/10.3102/1076998609346966.

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In this article, three multilevel models for meta-analysis are examined. Hedges and Olkin suggested that effect sizes follow a noncentral t distribution and proposed several approximate methods. Raudenbush and Bryk further refined this model; however, this procedure is based on a normal approximation. In the current research literature, this approximate procedure has not been compared to one based directly on the noncentral t distribution, which is the approach taken in this article. A multilevel model is presented, and estimation is carried out on a real data set using the Markov chain Monte

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De Leeuw, Jan, and Ita Kreft. "Random Coefficient Models for Multilevel Analysis." Journal of Educational Statistics 11, no. 1 (1986): 57–85. http://dx.doi.org/10.3102/10769986011001057.

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We propose a possible statistical model for both contextual analysis and slopes as outcomes analysis. These techniques have been used in multilevel analysis for quite some time, but a precise specification of the regression models has not been given before. We formalize them by proposing a random coefficient regression model, and we investigate its statistical properties in some detail. Various estimation methods are reviewed and applied to Dutch school-career example.

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Kobayashi, Yuichiro. "Identifying L2 Developmental Indices while Controlling for L1 Effects: A Multilevel Ordinal Logistic Regression Analysis." Journal of Pan-Pacific Association of Applied Linguistics 25, no. 2 (2021): 87–104. http://dx.doi.org/10.25256/paal.25.2.5.

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11

Hedeker, Donald, and Robert D. Gibbons. "A Random-Effects Ordinal Regression Model for Multilevel Analysis." Biometrics 50, no. 4 (1994): 933. http://dx.doi.org/10.2307/2533433.

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12

Austin, Peter C., and Juan Merlo. "Intermediate and advanced topics in multilevel logistic regression analysis." Statistics in Medicine 36, no. 20 (2017): 3257–77. http://dx.doi.org/10.1002/sim.7336.

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13

Evans, Clare R., George Leckie, and Juan Merlo. "Multilevel versus single-level regression for the analysis of multilevel information: The case of quantitative intersectional analysis." Social Science & Medicine 245 (January 2020): 112499. http://dx.doi.org/10.1016/j.socscimed.2019.112499.

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Shi, Lei, and Mario Miguel Ojeda. "Local influence in multilevel regression for growth curves." Journal of Multivariate Analysis 91, no. 2 (2004): 282–304. http://dx.doi.org/10.1016/j.jmva.2003.08.007.

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Goldsmith, Jeff, Vadim Zipunnikov, and Jennifer Schrack. "Generalized multilevel function-on-scalar regression and principal component analysis." Biometrics 71, no. 2 (2015): 344–53. http://dx.doi.org/10.1111/biom.12278.

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Candia, Douglas Andabati, Patrick Guge Oloo Weke, Moses Mwangi Manene, and George Muhua. "A comparison of multilevel ordinal regression models in the analysis of police force ratings." Croatian review of economic, business and social statistics 10, no. 1 (2024): 1–12. http://dx.doi.org/10.62366/crebss.2024.1.001.

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In the literature several methods have been developed to model ordinal data while considering their natural ordering. However, this study sought to compare two possible link functions for the multilevel ordinal regression using males’ ratings of the police forces in Uganda as an outcome variable. Variables were obtained from the UNGBS database (Uganda National Governance Baseline Survey). The highest proportion of males rated the police as good (40.9%) followed by fair (24.96%), poor (19.1%), and lastly very good (15.1%). The multilevel ordered logistic regression model with both individual an

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Boucher, Matthew, Andrew Christian, Menachem Rafaelof, Kevin Shepherd, Stephen A. Rizzi, and James Stephenson. "Multilevel probit regression for 3-alternative forced choice audibility testing." Journal of the Acoustical Society of America 151, no. 4 (2022): A221. http://dx.doi.org/10.1121/10.0011117.

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A recent psychoacoustic test at NASA Langley generated a dataset of 3-alternative forced-choice responses for 40 subjects that measured the audibility of a tone complex in a shaped broadband masker. The task was completed by four subjects at a time in a small theatre-like environment using predetermined stimuli levels. These data were subject to 4 forms of probit regression: a “complete pooling” analysis in which all data from the test was fit with one curve, two forms of “no pooling” analyses in which subjects’ data were treated individually (using both packaged and custom software), and a “p

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18

Liang, Junjie, Yanting Wu, Dongkuan Xu, and Vasant G. Honavar. "Longitudinal Deep Kernel Gaussian Process Regression." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (2021): 8556–64. http://dx.doi.org/10.1609/aaai.v35i10.17038.

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Gaussian processes offer an attractive framework for predictive modeling from longitudinal data, \ie irregularly sampled, sparse observations from a set of individuals over time. However, such methods have two key shortcomings: (i) They rely on ad hoc heuristics or expensive trial and error to choose the effective kernels, and (ii) They fail to handle multilevel correlation structure in the data. We introduce Longitudinal deep kernel Gaussian process regression (L-DKGPR) to overcome these limitations by fully automating the discovery of complex multilevel correlation structure from longitudina

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Al-Balushi, Moza S., Mohammed S. Ahmed, and M. Mazharul Islam. "Determinants of Contraceptive Practice in Oman by Using Multilevel Modeling." Sultan Qaboos University Journal for Science [SQUJS] 20, no. 1 (2015): 83. http://dx.doi.org/10.24200/squjs.vol20iss1pp83-92.

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In this paper, multilevel logistic regression models are developed for examining the hierarchical effects of contraceptive use and its selected determinants in Oman using the 2008 Oman National Reproductive Health Survey (ONRHS). Comparison between single level and multilevel logistic regression models has been made to examine the plausibility of multilevel effects of contraceptive use. From the multilevel logistic regression model analysis, it was found that there is real multilevel variation among contraceptive users in Oman. The results indicate that a multilevel logistic regression model i

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Boucher, Matthew A., Siddhartha Krishnamurthy, Andrew W. Christian, and Stephen A. Rizzi. "Sound quality metric indicators of rotorcraft noise annoyance using multilevel analysis." Journal of the Acoustical Society of America 153, no. 2 (2023): 867–76. http://dx.doi.org/10.1121/10.0016888.

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It is hypothesized that sound quality metrics, particularly loudness, sharpness, tonality, impulsiveness, fluctuation strength, and roughness, could all be possible indicators of the reported annoyance to helicopter noise. To test this hypothesis, a psychoacoustic test was conducted in which subjects rated their annoyance levels to synthesized helicopter sounds. After controlling for loudness, a previous analysis using linear regression identified sharpness and tonality as important factors in predicting annoyance, followed by fluctuation strength. The current work focuses on multilevel regres

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21

Tokunaga, Shoji. "Three Topics on Statistical Analysis. Regression to the Mean, Multilevel Statistical Models, and Poisson Regression." Nihon Kyukyu Igakukai Zasshi 12, no. 7 (2001): 333–42. http://dx.doi.org/10.3893/jjaam.12.333.

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22

Ross, John A. "Multilevel Analysis for Applied Research: It’s Just Regression! (Robert Bickel, 2007)." Curriculum Inquiry 38, no. 4 (2008): 433–34. http://dx.doi.org/10.1111/j.1467-873x.2008.00418.x.

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23

Chen, Li, Jianhua Xuan, Rebecca B. Riggins, Yue Wang, Eric P. Hoffman, and Robert Clarke. "Multilevel support vector regression analysis to identify condition-specific regulatory networks." Bioinformatics 26, no. 11 (2010): 1416–22. http://dx.doi.org/10.1093/bioinformatics/btq144.

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Wong, May C. M., K. F. Lam, and Edward C. M. Lo. "Analysis of multilevel grouped survival data with time-varying regression coefficients." Statistics in Medicine 30, no. 3 (2010): 250–59. http://dx.doi.org/10.1002/sim.4094.

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Li, Lei, Matthew A. Rysavy, and Abhik Das. "Comments on “Intermediate and advanced topics in multilevel logistic regression analysis”." Statistics in Medicine 37, no. 19 (2018): 2902–6. http://dx.doi.org/10.1002/sim.7683.

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Ker, Hsiang Wei, Ying Haur Lee, T. C. Huang, and Ke Lin. "Preliminary Analysis of AASHO Road Test Rigid Pavement Data Using Modern Regression Techniques." Advanced Materials Research 723 (August 2013): 869–76. http://dx.doi.org/10.4028/www.scientific.net/amr.723.869.

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The normality assumptions with random errors and constant variance were often violated while analyzing multilevel pavement performance data using conventional regression techniques. Because of its hierarchical data structure, multilevel data are often analyzed using Linear Mixed-Effects (LME) models. The exploratory analysis, statistical modeling, and the examination of model-fit of LME models are more complicated than those of standard multiple regressions. A systematic modeling approach using visual-graphical techniques and LME models was proposed and demonstrated using the original AASHO ro

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27

Atoyebi, Samuel Babajide, and Titi Obilade. "Effect of Increasing Sample Size on Multi-Collinearity in Multilevel Non-Linear Model." Asian Journal of Probability and Statistics 26, no. 1 (2024): 104–28. http://dx.doi.org/10.9734/ajpas/2024/v26i1587.

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A multilevel logistic regression model demonstrating high correlations among predictor variables is susceptible to multi-collinearity. Multi-collinearity significantly impacts the robustness and interpretability of multilevel non-linear models. In multilevel non-linear models, the effects of multi-collinearity can be amplified, leading to distorted parameter estimates and inflated standard errors. This phenomenon contributes to an escalation in the variances of parameter estimates, thereby resulting in inaccurate inferences regarding the relationships between the response and explanatory facto

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DeLuca, Thomas A. "Instructional Spending Metrics: A Multilevel Analysis Using NCES Data." Journal of Education Finance 44, no. 1 (2018): 23–44. http://dx.doi.org/10.1353/jef.2018.a721594.

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abstract: Educational institutions operate within multiple hierarchical levels with each level typically influencing policies or outcomes at other levels within the hierarchy. For example, decisions made at the state level may influence decisions at the local education agency (LEA) level which may influence decisions at the school building level (as well in the other direction). Using multilevel regression models (or hierarchical linear modeling) and national data sets, this study looks at two common metrics used to describe instructional spending; per pupil dollars and percentage of total dol

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Kitabo, Cheru Atsmegiorgis, and Ehit Tesfu Damtie. "Bayesian Multilevel Analysis of Utilization of Antenatal Care Services in Ethiopia." Computational and Mathematical Methods in Medicine 2020 (July 4, 2020): 1–11. http://dx.doi.org/10.1155/2020/8749753.

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In sub-Saharan Africa, 72% of pregnant women received an antenatal care visit at least once in their pregnancy period. Ethiopia has one of the highest rates of maternal mortality in sub-Saharan African countries. So, this high maternal mortality levels remain a major public health problem. According to EDHS, 2016, the antenatal care (ANC), delivery care (DC), and postnatal care (PNC) were 62%, 73%, and 13%, respectively, indicating that ANC is in a low level. The main objective of this study was to examine the factors that affect the utilization of antenatal care services in Ethiopia using Bay

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Tesema, Getayeneh Antehunegn, Zemenu Tadesse Tessema, Dessie Abebaw Angaw, Koku Sisay Tamirat, and Achamyeleh Birhanu Teshale. "Geographic weighted regression analysis of hot spots of anemia and its associated factors among children aged 6–59 months in Ethiopia: A geographic weighted regression analysis and multilevel robust Poisson regression analysis." PLOS ONE 16, no. 11 (2021): e0259147. http://dx.doi.org/10.1371/journal.pone.0259147.

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Introduction Anemia among children aged 6–59 months remains a major public health problem in low-and high-income countries including Ethiopia. Anemia is associated with significant consequences on the health of children such as under-five morbidity and mortality, increased risk of infection, and poor academic performance. The prevalence of anemia in Ethiopia has varied across areas. Therefore, this study aimed to investigate the geographic weighted regression analysis of anemia and its associated factors among children aged 6–59 months in Ethiopia. Methods This study was based on the 2016 Ethi

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Park, Il-Su, and Jun-Tae Han. "Study of effect on the obesity status using multilevel logistic regression analysis." Journal of the Korean Data And Information Science Society 30, no. 1 (2019): 205–17. http://dx.doi.org/10.7465/jkdi.2019.30.1.205.

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32

Boucher, Matthew, Siddhartha Krishnamurthy, Andrew Christian, and Stephen A. Rizzi. "Sound quality metric indicators of rotorcraft noise annoyance using multilevel regression analysis." Journal of the Acoustical Society of America 145, no. 3 (2019): 1899. http://dx.doi.org/10.1121/1.5101884.

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Alvarez-Cortes, Sara, Joan Bartrina-Rapesta, and Joan Serra-Sagrista. "Multilevel Split Regression Wavelet Analysis for Lossless Compression of Remote Sensing Data." IEEE Geoscience and Remote Sensing Letters 15, no. 10 (2018): 1540–44. http://dx.doi.org/10.1109/lgrs.2018.2850938.

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Jamshidi, Laleh, Lies Declercq, Belén Fernández-Castilla, et al. "Multilevel meta-analysis of multiple regression coefficients from single-case experimental studies." Behavior Research Methods 52, no. 5 (2020): 2008–19. http://dx.doi.org/10.3758/s13428-020-01380-w.

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Harbord, Roger M., and Penny Whiting. "Metandi: Meta-analysis of Diagnostic Accuracy Using Hierarchical Logistic Regression." Stata Journal: Promoting communications on statistics and Stata 9, no. 2 (2009): 211–29. http://dx.doi.org/10.1177/1536867x0900900203.

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Meta-analysis of diagnostic test accuracy presents many challenges. Even in the simplest case, when the data are summarized by a 2 x 2 table from each study, a statistically rigorous analysis requires hierarchical (multilevel) models that respect the binomial data structure, such as hierarchical logistic regression. We present a Stata package, metandi, to facilitate the fitting of such models in Stata. The commands display the results in two alternative parameterizations and produce a customizable plot. metandi requires either Stata 10 or above (which has the new command xtmelogit), or Stata 8

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Onifade, Eyitayo, Jodi Petersen, Timothy S. Bynum, and William S. Davidson. "Multilevel Recidivism Prediction." Criminal Justice and Behavior 38, no. 8 (2011): 840–53. http://dx.doi.org/10.1177/0093854811407026.

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Risk assessments such as the Youth Level of Service/Case Management Inventory (YLS/CMI) that predict delinquency outcomes based on proximal risk factors may benefit from an incorporation of distal risk factors in their prediction models. This study utilized a juvenile probationer sample and block group SES data in exploring the differential predictive validity of the YLS/CMI with youth of similar person-centered risk levels from different criminogenic neighborhood types. The study entailed an exploratory factor analysis of block group socioeconomic variables, which were used in a cluster analy

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Ula, Rahmatul, Risnawati Ibnas, Khalilah Nurfadilah, M. Ichsan Nawawi, and Asfar Asfar. "Penerapan model regresi multilevel untuk data ketepatan waktu lulus mahasiswa." Majalah Ilmiah Matematika dan Statistika 23, no. 1 (2023): 96. http://dx.doi.org/10.19184/mims.v23i1.34479.

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Multilevel logistic regression is one of the alternatives to solving a problem that has a nested data structure like the student data in Alauddin in 2016. The data indicates that students are nested in each different study program. This condition allows the students in the same study program tend to have similar characteristics. The study aims to gain a student graduating model of punctuality using multilevel regression analysis and recognize factors that have a significant impact on student graduating time. Based on our research, we find the best model that fits the data to be the random inte

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Saikia, Munmi. "Location Determinants of Indian Multinationals: A Multilevel Analysis." Global Business Review 21, no. 5 (2019): 1200–1217. http://dx.doi.org/10.1177/0972150919857014.

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The study investigates a simultaneous act of country-level determinants and firm-level heterogeneity on location distribution of the new wave of outward foreign direct investment (OFDI) from India. Indian firms are nested within host countries. Therefore, the current study employs mixed multilevel linear regression model to analyse multilevel and longitudinal data. Using firm-to-destination data, the study finds that location distribution of Indian multinationals is simultaneously driven by firm-level heterogeneity and country-level determinants. The study shows that location determinants of t

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Jin, Ying, and Hershel Eason. "DIF Analysis with Multilevel Data: A Simulation Study Using the Latent Variable Approach." Journal of Educational Issues 2, no. 2 (2016): 290. http://dx.doi.org/10.5296/jei.v2i2.10045.

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<p>The effects of mean ability difference (MAD) and short tests on the performance of various DIF methods have been studied extensively in previous simulation studies. Their effects, however, have not been studied under multilevel data structure. MAD was frequently observed in large-scale cross-country comparison studies where the primary sampling units were more likely to be clusters (<em>e.g.</em>, schools). With short tests, regular DIF methods under MAD-present conditions might suffer from inflated type I error rate due to low reliability of test scores, which would adver

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40

Intarut, Nirun, Parinya Srihatrai, and Narongsak Chantawang. "Prevalence of multimorbidity in Thailand: a multilevel analysis of a population-based survey." International Journal Of Community Medicine And Public Health 8, no. 8 (2021): 3750. http://dx.doi.org/10.18203/2394-6040.ijcmph20212760.

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Background: The objective of the study was to estimate the prevalence of multimorbidity and to analyze the factors related to multimorbidity using multilevel analysis.Methods: The data from the 2017 National Health and Welfare Survey was used. In total, 27,960 families and 65,781 participants were analyzed. Multilevel logistic regression analysis with 2-levels was performed to assess independent risk factors for the multimorbidity.Results: Of 14,353 participants analyzed, 20.4% (95% confidence interval (CI): 20.1, 20.7) of those showed multimorbidity. 59% were females; 74.4% were 56-66 years,

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Dey, Rakhi, Susmita Rani Dey, Meem Haque, et al. "Mapping the prevalence and covariates associated with home delivery in Bangladesh: A multilevel regression analysis." PLOS ONE 19, no. 11 (2024): e0313606. http://dx.doi.org/10.1371/journal.pone.0313606.

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Introduction Bangladesh has made an intense effort to improve maternal healthcare facilities including facility delivery, but the number of home deliveries is still very high. Therefore, this study aims to find out district-wise prevalence and determine the individual and community-level predictors of home delivery among women in Bangladesh. Methods Data were derived from the Multiple Indicator Cluster Survey (MICS) 2019, a nationwide cross-sectional survey in Bangladesh. A final sample of 9,166 (weighted) women who gave birth in the two years preceding the survey were included in this study.

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Lebedev, Ilya. "Application of Multilevel Models in Classification and Regression Problems." Informatics and Automation 22, no. 3 (2023): 487–510. http://dx.doi.org/10.15622/ia.22.3.1.

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There is a constant need to create methods for improving the quality indicators of information processing. In most practical cases, the ranges of target variables and predictors are formed under the influence of external and internal factors. Phenomena such as concept drift cause the model to lose its completeness and accuracy over time. The purpose of the work is to improve the processing data samples quality based on multi-level models for classification and regression problems. A two-level data processing architecture is proposed. At the lower level, the analysis of incoming information flo

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Kim, Changhoon, yunjin Sohn, and Maengseok Noh. "Multilevel analysis for tuberculosis mortality during treatment." Korean Data Analysis Society 25, no. 2 (2023): 573–83. http://dx.doi.org/10.37727/jkdas.2023.25.2.573.

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Tuberculosis is an infectious disease with social determinants and is associated with individual and regional factors. In this study, multilevel logistic regression analysis was used to identify regional factors affecting mortality during treatment considering the individual level and regional level factors together in the early period (2012-2015) when the Korean tuberculosis PPM cooperation system was introduced. When PPM general hospitals, public health centers, and clinics were selected as treatment institutions, there was no difference in mortality according to regional PPM support rates.

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Muhammad Ismail, Kinza Yousfani, Khushboo Ishaq, Muhammad Junaid, and Asia Ameer Khan. "Income-Expenditure Dynamics across Districts in Khyber Pakhtunkhwa: A Multilevel Statistical Approach." Critical Review of Social Sciences Studies 2, no. 2 (2024): 672–88. http://dx.doi.org/10.59075/r7102k18.

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Financial development in emerging economies, such as Pakistan, often leads to disparities in wealth, causing long-term financial and social instabilities. Dealing with these imbalances requires a holistic approach that incorporates financial expansion and equal distribution of wealth. This study assesses the relationship between earnings and expenses within multiple districts in Khyber Pakhtunkhwa, Pakistan, using a multilevel regression model to gain a localized understanding of wealth disparities and consumption patterns. The Tehsil Municipal Administration in Peshawar and the Bureau of Stat

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Budi, Susetyo. "Identifying the effects of financial literacy on student achievement in mathematics." World Journal of Advanced Research and Reviews 21, no. 1 (2024): 733–45. https://doi.org/10.5281/zenodo.13220823.

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Quality education is critical to produce a quality generation of the country. However, the quality of Indonesian education is still lagging behind other countries. An analysis is needed to identify student and school factors that affect student achievement. In this study, the student factors were financial literacy ability and parents' educational background. Meanwhile, the school factors were accreditation scores. The data used in this study is the data of Indonesian Student Competency Assessment (AKSI) which is an education monitoring program through a survey using the Multistage Probability

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Erfina Agustin Hidayat and Nusar Hajarisman. "Aplikasi Regresi Logistik Ordinal Multilevel untuk Pemodelan Huruf Mutu Mata Kuliah Statistika Dasar Mahasiswa Universitas Islam Bandung Tahun 2019/2020." Bandung Conference Series: Statistics 3, no. 2 (2023): 302–12. http://dx.doi.org/10.29313/bcss.v3i2.8096.

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Abstract. Multilevel ordinal logistic regression analysis is a regression analysis for discrete-scale responses, especially with a hierarchical ordinal scale. The hierarchical structure indicates that the data analyzed comes from several levels, where lower levels are nested in higher levels. This article will discuss the application of multilevel ordinal logistic regression using the Maximum Likelihood Estimation (MLE) method in the field of education, namely regarding the quality letter of the Basic Statistics course of 2019 Bandung Islamic University students at the Faculty of Engineering,

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Kim, Sun-Hee, and Kwang-Soo Lee. "Comparing Risk-adjusted In-hospital Mortality for Craniotomies : Logistic Regression versus Multilevel Analysis." Korean Journal of Health Service Management 9, no. 2 (2015): 81–88. http://dx.doi.org/10.12811/kshsm.2015.9.2.081.

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Conijn, Judith M., Wilco H. M. Emons, Marcel A. L. M. van Assen, and Klaas Sijtsma. "On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis." Multivariate Behavioral Research 46, no. 2 (2011): 365–88. http://dx.doi.org/10.1080/00273171.2010.546733.

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Krämer, Walter. "Andrew Gelman and Jennifer Hill: Data analysis using regression and multilevel/hierarchical models." Statistical Papers 52, no. 3 (2010): 741–42. http://dx.doi.org/10.1007/s00362-010-0311-z.

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Jin, Shan, Nazeem Muhajarine, Jennifer Cushon, and Hyun J. Lim. "Factors Associated with Childhood Depression in Saskatoon Students: A Multilevel Analysis." Canadian Journal of Community Mental Health 32, no. 1 (2013): 29–42. http://dx.doi.org/10.7870/cjcmh-2013-004.

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Abstract:

This study examined links between depression and multilevel factors among children from Saskatoon elementary schools. A total of 4,200 students participated in the Saskatoon Student Health Survey conducted in 2008–9. Covariates included demographics and family structure, relationships, physical activity, bullying experiences, and school refusal behaviours. A multilevel logistic regression model was used to examine the impact of individual-level and school-level (contextual) factors. The study revealed that depression disparity existed among schools, and students’ school refusal behaviours such

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