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Journal articles on the topic 'Hierarchical Linear Modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 June 2025

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1

Turner, John R. "Hierarchical Linear Modeling." Advances in Developing Human Resources 17, no. 1 (2014): 88–101. http://dx.doi.org/10.1177/1523422314559808.

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2

Park, Hee Sun. "Centering in Hierarchical Linear Modeling." Communication Methods and Measures 2, no. 4 (2008): 227–59. http://dx.doi.org/10.1080/19312450802310466.

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3

Ciarleglio, Maria M., and Robert W. Makuch. "Hierarchical linear modeling: An overview." Child Abuse & Neglect 31, no. 2 (2007): 91–98. http://dx.doi.org/10.1016/j.chiabu.2007.01.002.

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4

Teoh, Siau-Teng, and Salmi Mohd Isa. "Market Orientation and Salesperson’s Performance in a Hierarchical Linear Modeling Approach." International Academic Journal of Business Management 05, no. 02 (2018): 159–69. http://dx.doi.org/10.9756/iajbm/v5i2/1810030.

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5

Woltman, Heather, Andrea Feldstain, J. Christine MacKay, and Meredith Rocchi. "An introduction to hierarchical linear modeling." Tutorials in Quantitative Methods for Psychology 8, no. 1 (2012): 52–69. http://dx.doi.org/10.20982/tqmp.08.1.p052.

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6

Huta, Veronika. "When to Use Hierarchical Linear Modeling." Quantitative Methods for Psychology 10, no. 1 (2014): 13–28. http://dx.doi.org/10.20982/tqmp.10.1.p013.

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7

Schonfeld, Irvin Sam, and David Rindskopf. "Hierarchical Linear Modeling in Organizational Research." Organizational Research Methods 10, no. 3 (2007): 417–29. http://dx.doi.org/10.1177/1094428107300229.

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8

Todd, Samuel Y., T. Russell Crook, and Anthony G. Barilla. "Hierarchical Linear Modeling of Multilevel Data." Journal of Sport Management 19, no. 4 (2005): 387–403. http://dx.doi.org/10.1123/jsm.19.4.387.

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Most data involving organizations are hierarchical in nature and often contain variables measured at multiple levels of analysis. Hierarchical linear modeling (HLM) is a relatively new and innovative statistical method that organizational scientists have used to alleviate some common problems associated with multilevel data, thus advancing our understanding of organizations. This article presents a broad overview of HLM’s logic through an empirical analysis and outlines how its use can strengthen sport management research. For illustration purposes, we use both HLM and the traditional linear regression model to analyze how organizational and individual factors in Major League Baseball impact individual players’ salaries. A key implication is that, depending on the method, parameter estimates differ because of the multilevel data structure and, thus, findings differ. We explain these differences and conclude by presenting theoretical discussions from strategic management and consumer behavior to provide a potential research agenda for sport management scholars.
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9

Kumar, Naveen, Christina Mastrangelo, and Doug Montgomery. "Hierarchical modeling using generalized linear models." Quality and Reliability Engineering International 27, no. 6 (2011): 835–42. http://dx.doi.org/10.1002/qre.1176.

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10

Vecchio, Robert P. "Special Issue: Focus on Hierarchical Linear Modeling." Journal of Management 23, no. 6 (1997): 721. http://dx.doi.org/10.1177/014920639702300601.

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11

Warne, Russell T., Yan Li, E. Lisako J. McKyer, Rachel Condie, Cassandra S. Diep, and Peter S. Murano. "Managing Clustered Data Using Hierarchical Linear Modeling." Journal of Nutrition Education and Behavior 44, no. 3 (2012): 271–77. http://dx.doi.org/10.1016/j.jneb.2011.06.013.

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12

Wang, Jianjun. "Reasons for Hierarchical Linear Modeling: A Reminder." Journal of Experimental Education 68, no. 1 (1999): 89–93. http://dx.doi.org/10.1080/00220979909598496.

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13

Nagi, Naveena, and Jacob A. Abraham. "Hierarchical fault modeling for linear analog circuits." Analog Integrated Circuits and Signal Processing 10, no. 1-2 (1996): 89–99. http://dx.doi.org/10.1007/bf00713981.

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14

Loeb, Jane W. "Hierarchical Linear Modeling in Salary Equity Studies." New Directions for Institutional Research 2003, no. 117 (2003): 69–96. http://dx.doi.org/10.1002/ir.69.

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15

Guo, Shenyang. "Analyzing grouped data with hierarchical linear modeling." Children and Youth Services Review 27, no. 6 (2005): 637–52. http://dx.doi.org/10.1016/j.childyouth.2004.11.017.

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16

Zhu, Yada, Jingrui He, and Rick Lawrence. "Hierarchical Modeling with Tensor Inputs." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1233–39. http://dx.doi.org/10.1609/aaai.v26i1.8283.

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In many real applications, the input data are naturally expressed as tensors, such as virtual metrology in semiconductor manufacturing, face recognition and gait recognition in computer vision, etc. In this paper, we propose a general optimization framework for dealing with tensor inputs. Most existing methods for supervised tensor learning use only rank-one weight tensors in the linear model and cannot readily incorporate domain knowledge. In our framework, we obtain the weight tensor in a hierarchical way — we first approximate it by a low-rank tensor, and then estimate the low-rank approximation using the prior knowledge from various sources, e.g., different domain experts. This is motivated by wafer quality prediction in semiconductor manufacturing. Furthermore, we propose an effective algorithm named H-MOTE for solving this framework, which is guaranteed to converge. The time complexity of H-MOTE is linear with respect to the number of examples as well as the size of the weight tensor. Experimental results show the superiority of H-MOTE over state-of-the-art techniques on both synthetic and real data sets.
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17

McNeish, Daniel, Laura M. Stapleton, and Rebecca D. Silverman. "On the unnecessary ubiquity of hierarchical linear modeling." Psychological Methods 22, no. 1 (2017): 114–40. http://dx.doi.org/10.1037/met0000078.

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18

Nezlek, John B., and Linda E. Zyzniewski. "Using hierarchical linear modeling to analyze grouped data." Group Dynamics: Theory, Research, and Practice 2, no. 4 (1998): 313–20. http://dx.doi.org/10.1037/1089-2699.2.4.313.

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19

Osgood, D. Wayne, and Gail L. Smith. "Applying Hierarchical Linear Modeling to Extended Longitudinal Evaluations." Evaluation Review 19, no. 1 (1995): 3–38. http://dx.doi.org/10.1177/0193841x9501900101.

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20

Putnam-Hornstein, Emily. "Hierarchical linear modeling: Applications to social work research." Journal of Social Work 13, no. 6 (2012): 599–615. http://dx.doi.org/10.1177/1468017312459985.

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21

Tate, Richard L., and Keenan A. Pituch. "Multivariate Hierarchical Linear Modeling in Randomized Field Experiments." Journal of Experimental Education 75, no. 4 (2007): 317–37. http://dx.doi.org/10.3200/jexe.75.4.317-338.

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22

Sosa, Juan, and Jeimy-Paola Aristizabal. "Some Developments in Bayesian Hierarchical Linear Regression Modeling." Revista Colombiana de Estadística 45, no. 2 (2022): 231–55. http://dx.doi.org/10.15446/rce.v45n2.98988.

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Considering the flexibility and applicability of Bayesian modeling, in this work we revise the main characteristics of two hierarchical models in a regression setting. We study the full probabilistic structure of the models along with the full conditional distribution for each model parameter. Under our hierarchical extensions, we allow the mean of the second stage of the model to have a linear dependency on a set of covariates. The Gibbs sampling algorithms used to obtain samples when fitting the models are fully described and derived. In addition, we consider a case study in which the plant size is characterized as a function of nitrogen soil concentration and a grouping factor (farm).
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23

Ahuja, Kabir, Vidhisha Balachandran, Madhur Panwar, et al. "Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers." Transactions of the Association for Computational Linguistics 13 (February 12, 2024): 121–41. https://doi.org/10.1162/tacl_a_00733.

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Abstract Transformers trained on natural language data have been shown to exhibit hierarchical generalization without explicitly encoding any structural bias. In this work, we investigate sources of inductive bias in transformer models and their training that could cause such preference for hierarchical generalization. We extensively experiment with transformers trained on five synthetic, controlled datasets using several training objectives and show that, while objectives such as sequence-to-sequence modeling, classification, etc., often fail to lead to hierarchical generalization, the language modeling objective consistently leads to transformers generalizing hierarchically. We then study how different generalization behaviors emerge during the training by conducting pruning experiments that reveal the joint existence of subnetworks within the model implementing different generalizations. Finally, we take a Bayesian perspective to understand transformers’ preference for hierarchical generalization: We establish a correlation between whether transformers generalize hierarchically on a dataset and if the simplest explanation of that dataset is provided by a hierarchical grammar compared to regular grammars exhibiting linear generalization. Overall, our work presents new insights on the origins of hierarchical generalization in transformers and provides a theoretical framework for studying generalization in language models.
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24

Sibthorp, Jim, Erin Witter, Mary Wells, Gary Ellis, and Judith Voelkl. "Hierarchical Linear Modeling in Park, Recreation, and Tourism Research." Journal of Leisure Research 36, no. 1 (2004): 89–100. http://dx.doi.org/10.1080/00222216.2004.11950012.

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25

Ogrodniczuk, John S., William E. Piper, and Anthony S. Joyce. "Investigating Follow-Up Outcome Change Using Hierarchical Linear Modeling." Psychotherapy Research 11, no. 1 (2001): 13–28. http://dx.doi.org/10.1093/ptr/11.1.13.

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26

KOZBELT, AARON. "Hierarchical Linear Modeling of Creative Artists' Problem Solving Behaviors*." Journal of Creative Behavior 42, no. 3 (2008): 181–200. http://dx.doi.org/10.1002/j.2162-6057.2008.tb01294.x.

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27

Miyazaki, Yasuo, and Kimberly S. Maier. "Johnson–Neyman Type Technique in Hierarchical Linear Models." Journal of Educational and Behavioral Statistics 30, no. 3 (2005): 233–59. http://dx.doi.org/10.3102/10769986030003233.

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In hierarchical linear models we often find that group indicator variables at the cluster level are significant predictors for the regression slopes. When this is the case, the average relationship between the outcome and a key independent variable are different from group to group. In these settings, a question such as “what range of the independent variable is the difference in the outcome variable statistically significant among groups?” naturally arises. The Johnson–Neyman (J-N) technique answers this kind of question in the analysis of covariance (ANCOVA) settings. In the hierarchical modeling context, the F test, which is widely used in ANCOVA, cannot be applied because the assumption of homogeneity of variance within cluster units is violated. Instead, the approximate Wald test can be used to determine the region of significance. To illustrate the application of the J-N technique in the context of hierarchical linear modeling, an example from research in education is provided.
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28

Popp, Michael, and Wolfgang Mathis. "Embedded order reduction of hierarchical systems within an mCCM framework." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 4 (2018): 1535–44. http://dx.doi.org/10.1108/compel-09-2017-0375.

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Purpose The purpose of this paper is to present the embedding of linear and nonlinear order reduction methods in a theoretical framework for handling hierarchically interconnected dynamical systems. Design/methodology/approach Based on the component connection modeling (CCM), a modified framework called mCCM for describing interconnected dynamic systems especially with hierarchical structures is introduced and used for order reduction purposes. The balanced truncation method for linear systems and the trajectory piecewise linear reduction scheme are used for the order reduction of systems described within the mCCM framework. Findings It is shown that order reduction methods can be embedded into the context of interconnected dynamical systems with the benefit of having a further degree of freedom in form of the hierarchical level, on which the order reduction is performed. Originality/value The aspect of hierarchy within system descriptions is exploited for order reduction purposes. This distinguishes the presented approach from common methods, which already start with single large-scale systems without explicitly considering hierarchical structures.
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29

Wendorf, Craig A. "Comparisons of Structural Equation Modeling and Hierarchical Linear Modeling Approaches to Couples' Data." Structural Equation Modeling: A Multidisciplinary Journal 9, no. 1 (2002): 126–40. http://dx.doi.org/10.1207/s15328007sem0901_7.

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30

Yonehara, Aki. "Needs Assessment for Literacy Development by Hierarchical Generalized Linear Modeling." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 40, no. 2 (2013): 123–34. http://dx.doi.org/10.2333/jbhmk.40.123.

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31

Gage, Nicholas A., and Timothy J. Lewis. "Hierarchical Linear Modeling Meta-Analysis of Single-Subject Design Research." Journal of Special Education 48, no. 1 (2012): 3–16. http://dx.doi.org/10.1177/0022466912443894.

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32

Chen, Chih-Kai, and Yung-Teen Chiu. "Application of hierarchical linear growth modeling on social capital competitiveness." Journal of Statistics and Management Systems 15, no. 4-5 (2012): 415–34. http://dx.doi.org/10.1080/09720510.2012.10701634.

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33

Kim, Tae Kuen, Phyllis Solomon, and Karen A. Zurlo. "Applying Hierarchical Linear Modeling (HLM) to Social Work Administration Research." Administration in Social Work 33, no. 3 (2009): 262–77. http://dx.doi.org/10.1080/03643100902987739.

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34

Otani, Koichiro, B. Joon Kim, Brian Waterman, Sarah Boslaugh, W. Dean Klinkenberg, and W. Claiborne Dunagan. "Patient Satisfaction and Organizational Impact: A Hierarchical Linear Modeling Approach." Health Marketing Quarterly 29, no. 3 (2012): 256–69. http://dx.doi.org/10.1080/07359683.2012.705724.

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35

Ma, Li, and Qing Qu. "Differentiation in leader–member exchange: A hierarchical linear modeling approach." Leadership Quarterly 21, no. 5 (2010): 733–44. http://dx.doi.org/10.1016/j.leaqua.2010.07.004.

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36

Putnam-Hornstein, Emily, and Terry V. Shaw. "Foster care reunification: An exploration of non-linear hierarchical modeling." Children and Youth Services Review 33, no. 5 (2011): 705–14. http://dx.doi.org/10.1016/j.childyouth.2010.11.010.

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37

Coryn, Chris L. S. "Using Hierarchical Linear Modeling for Proformative Evaluation: A Case Example." Journal of MultiDisciplinary Evaluation 4, no. 7 (2007): 53–60. http://dx.doi.org/10.56645/jmde.v4i7.10.

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38

Deadrick, Diana L., Nathan Bennett, and Craig J. Russell. "Using Hierarchical Linear Modeling to Examine Dynamic Performance Criteria Over Time." Journal of Management 23, no. 6 (1997): 745–57. http://dx.doi.org/10.1177/014920639702300603.

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The selection literature has long debated the theoretical and practical significance of dynamic criteria. Recent research has begun to explore the nature of individual performance over time. This study contributes to this body of research through a hierarchical linear modeling analysis of dynamic criteria. The purpose of this study was to investigate the role of ability in explaining initial job performance, as well as the rate of improvement-or performance trend-among a sample of 408 sewing machine operators over a 24 week period. The results of a hierarchical linear modeling analysis suggest that ability measures are differentially related to initial performance and performance improvement trend.
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39

Nelson, Tyler, Joon Jin Song, Yoo-Mi Chin, and James D. Stamey. "Bayesian Correction for Misclassification in Multilevel Count Data Models." Computational and Mathematical Methods in Medicine 2018 (2018): 1–6. http://dx.doi.org/10.1155/2018/3212351.

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Covariate misclassification is well known to yield biased estimates in single level regression models. The impact on hierarchical count models has been less studied. A fully Bayesian approach to modeling both the misclassified covariate and the hierarchical response is proposed. Models with a single diagnostic test and with multiple diagnostic tests are considered. Simulation studies show the ability of the proposed model to appropriately account for the misclassification by reducing bias and improving performance of interval estimators. A real data example further demonstrated the consequences of ignoring the misclassification. Ignoring misclassification yielded a model that indicated there was a significant, positive impact on the number of children of females who observed spousal abuse between their parents. When the misclassification was accounted for, the relationship switched to negative, but not significant. Ignoring misclassification in standard linear and generalized linear models is well known to lead to biased results. We provide an approach to extend misclassification modeling to the important area of hierarchical generalized linear models.
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40

Vaughn, Allison A., Matthew Bergman, and Barry Fass-Holmes. "Nonresident Undergraduates’ Performance in English Writing Classes— Hierarchical Linear Modeling Analysis." Journal of International Students 5, no. 4 (2015): 319–33. http://dx.doi.org/10.32674/jis.v5i4.398.

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Do undergraduates whose native language is not English have writing deficiencies leading to academic struggles? The present study showed that the answer to this question was “no” at an American West Coast public university. This university’s nonresident undergraduates on average earned B- to B+ in their colleges’ English intensive-writing programs’ classes, C in community college English classes, and term grade point averages between 2.5 (C+ to B-) and 3.2 (B) in the fall term of the five most recent academic years. Hierarchical linear modeling analyses showed that the predictors with the largest effect sizes were English writing programs and class level; however, each predictor accounted for less than 25% of the total variance.
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41

Pernet, Cyril R., Nicolas Chauveau, Carl Gaspar, and Guillaume A. Rousselet. "LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data." Computational Intelligence and Neuroscience 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/831409.

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Magnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses.
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42

Mitchell, Sid, E. Michael Loovis, and Stephen A. Butterfield. "A Case for Using Hierarchical Linear Modeling in Exercise Science Research." Journal of Motor Learning and Development 8, no. 1 (2020): 166–73. http://dx.doi.org/10.1123/jmld.2019-0003.

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Analyzing data in the exercise sciences can be challenging when trying to account for physical changes brought about by maturation (e.g., growth in height, weight, heart/lung capacity, muscle-to-fat ratio). In this paper, we present an argument for using hierarchical linear modeling (HLM) as an approach to analyzing physical performance data. Using an applied example from Butterfield, Lehnhard, Lee, and Coladarci, we will show why HLM is an appropriate analysis technique and provide other examples of where HLM will be beneficial.
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43

Davis, Dawn H., Phill Gagné, Laura D. Fredrick, Paul A. Alberto, Rebecca E. Waugh, and Regine Haardörfer. "Augmenting Visual Analysis in Single-Case Research With Hierarchical Linear Modeling." Behavior Modification 37, no. 1 (2012): 62–89. http://dx.doi.org/10.1177/0145445512453734.

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44

Ette, Ene I. "Comparing non-hierarchical models: Application to non-linear mixed effects modeling." Computers in Biology and Medicine 26, no. 6 (1996): 505–12. http://dx.doi.org/10.1016/s0010-4825(96)00031-5.

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45

Vecchio, R. "Comments: From the editor Special issue: Focus on hierarchical linear modeling." Journal of Management 23, no. 6 (1997): 721. http://dx.doi.org/10.1016/s0149-2063(97)90025-8.

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46

Mowen, Thomas J., and Scott E. Culhane. "Modeling Recidivism within the Study of Offender Reentry." Criminal Justice and Behavior 44, no. 1 (2016): 85–102. http://dx.doi.org/10.1177/0093854816678647.

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Although there are multiple statistical approaches used in understanding reentry, there is little consensus on the benefits and limitations of some of the more popular techniques as they relate to each other. Here, two common methods, lagged dependent variable modeling and hierarchical generalized linear modeling, are contrasted. To examine how particular modeling strategies may lead to different understandings of recidivism within reentry, we use data from the Serious and Violent Offender Reentry Initiative (SVORI; N = 1,697) to provide an example of the two statistical approaches and discuss the benefits and limitations of each strategy. While researchers will need to make important decisions about which strategy best addresses their research question, results of our analyses show that in dealing with reentry data across more than two waves, a hierarchical generalized linear model is often the preferred approach.
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47

Ji, Jingbo, Ruiyi Wang, and Youxu Liu. "Neural Network Modeling and Multiple Linear Regression Modeling in Data Trend Prediction." Mathematical Modeling and Algorithm Application 4, no. 3 (2025): 38–42. https://doi.org/10.54097/6wcshf80.

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This paper is based on neural network model and multiple linear regression model around data trend prediction. Firstly, feature engineering is carried out to provide rich data dimensions for the follow-up by extracting various features such as base features, contribution rate features, athlete features, etc. Second, the feed-forward neural network-based regression model was selected for medal data prediction after testing various models, and entropy weighting and hierarchical analysis were also used to construct models for specific data prediction; at the same time, multivariate linear regression models were used to evaluate the performance of the models in conjunction with K-fold cross validation. Finally, these models not only have high goodness-of-fit in data prediction, but also have high accuracy and credibility in prediction results, which is a significant advantage in data prediction.
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48

Gagné, Phill, and Carolyn F. Furlow. "Automating Multiple Software Packages in Simulation Research for Structural Equation Modeling and Hierarchical Linear Modeling." Structural Equation Modeling: A Multidisciplinary Journal 16, no. 1 (2009): 179–85. http://dx.doi.org/10.1080/10705510802561543.

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49

Timiryanova, Venera, Kasim Yusupov, and Ruzel Salimyanov. "Relationship Between Consumption and Personal Income Within a Hierarchically Structured Spatial System." Spatial Economics 16, no. 4 (2020): 91–112. http://dx.doi.org/10.14530/se.2020.4.091-112.

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Differentiation in the level of socio-economic development of territories is largely manifested in both inter-regional and intra-regional differences in personal income and consumption of goods. In this regard the methods of hierarchical analysis (HLM, Hierarchical Linear Modeling) that make it possible to study variation at several levels taking into account both municipal and regional factors are acquiring special relevance. Along with hierarchical effects, neighborhood effects can be distinguished. This is possible due to the imposition of a spatial adjacency matrix on the data observing spatial interactions within the spatial-hierarchical models (HSAM, Hierarchical Spatial Autoregressive Modeling). The aim of the study is to better understand the relationship between consumption and personal income within a hierarchically structured and spatially oriented economic system. The analysis uses the data from 2319 municipalities (i.e. municipal districts or rayons) and urban districts (okrugs) in 84 constituent entities of the Russian Federation for 2018. It showed that 38.4% of the variation among municipalities in terms of sold foods volume is explained by regional factors. The developed hierarchical (two-level) model revealed the positive impact of the volume of social benefits and taxable personal income in the municipality, and the volume of per capita retail trade at the level of the Russian Federation constituent entities, on the volume of foods sold within municipalities, and substantiate the negative impact of the Gini index increase, as well as highlight the positive spatial effect
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50

Sawyer, Robert, Jonathan Rowe, Roger Azevedo, and James Lester. "Modeling Player Engagement with Bayesian Hierarchical Models." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 14, no. 1 (2018): 257–63. http://dx.doi.org/10.1609/aiide.v14i1.13048.

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Modeling player engagement is a key challenge in games. However, the gameplay signatures of engaged players can be highly context-sensitive, varying based on where the game is used or what population of players is using it. Traditionally, models of player engagement are investigated in a particular context, and it is unclear how effectively these models generalize to other settings and populations. In this work, we investigate a Bayesian hierarchical linear model for multi-task learning to devise a model of player engagement from a pair of datasets that were gathered in two complementary contexts: a Classroom Study with middle school students and a Laboratory Study with undergraduate students. Both groups of players used similar versions of Crystal Island, an educational interactive narrative game for science learning. Results indicate that the Bayesian hierarchical model outperforms both pooled and context-specific models in cross-validation measures of predicting player motivation from in-game behaviors, particularly for the smaller Classroom Study group. Further, we find that the posterior distributions of model parameters indicate that the coefficient for a measure of gameplay performance significantly differs between groups. Drawing upon their capacity to share information across groups, hierarchical Bayesian methods provide an effective approach for modeling player engagement with data from similar, but different, contexts.
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