Relevant bibliographies by topics / Hierarchical Linear Modeling

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Hierarchical Linear Modeling'

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

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Journal articles on the topic "Hierarchical Linear Modeling"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Hierarchical Linear Modeling"

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Timberlake, Allison M. "Sample Size in Ordinal Logistic Hierarchical Linear Modeling." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/eps_diss/72.

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Most quantitative research is conducted by randomly selecting members of a population on which to conduct a study. When statistics are run on a sample, and not the entire population of interest, they are subject to a certain amount of error. Many factors can impact the amount of error, or bias, in statistical estimates. One important factor is sample size; larger samples are more likely to minimize bias than smaller samples. Therefore, determining the necessary sample size to obtain accurate statistical estimates is a critical component of designing a quantitative study. Much research has been conducted on the impact of sample size on simple statistical techniques such as group mean comparisons and ordinary least squares regression. Less sample size research, however, has been conducted on complex techniques such as hierarchical linear modeling (HLM). HLM, also known as multilevel modeling, is used to explain and predict an outcome based on knowledge of other variables in nested populations. Ordinal logistic HLM (OLHLM) is used when the outcome variable has three or more ordered categories. While there is a growing body of research on sample size for two-level HLM utilizing a continuous outcome, there is no existing research exploring sample size for OLHLM. The purpose of this study was to determine the impact of sample size on statistical estimates for ordinal logistic hierarchical linear modeling. A Monte Carlo simulation study was used to investigate this research query. Four variables were manipulated: level-one sample size, level-two sample size, sample outcome category allocation, and predictor-criterion correlation. Statistical estimates explored include bias in level-one and level-two parameters, power, and prediction accuracy. Results indicate that, in general, holding other conditions constant, bias decreases as level-one sample size increases. However, bias increases or remains unchanged as level-two sample size increases, holding other conditions constant. Power to detect the independent variable coefficients increased as both level-one and level-two sample size increased, holding other conditions constant. Overall, prediction accuracy is extremely poor. The overall prediction accuracy rate across conditions was 47.7%, with little variance across conditions. Furthermore, there is a strong tendency to over-predict the middle outcome category.
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Kolnik, Shira. "Responding to Joint Attention: Growth and Prediction to Subsequent Social Competence in Children Prenatally Exposed to Cocaine." Scholarly Repository, 2008. http://scholarlyrepository.miami.edu/oa_theses/158.

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Responding to Joint Attention (RJA) involves an infant's ability to follow a gaze or point by a partner. Prenatal cocaine exposure (PCE), which places a child in danger of numerous risks, has been accepted as having subtle effects on developmental outcomes such as social competence and associated socio-emotional outcomes. The current study looked at a sample of 166 children prenatally exposed to cocaine who were attending an early intervention program. The study established group and individual trajectories of responding to joint attention from 12, 15, and 18 months of age. Hierarchical modeling identified two groups, a delay group and an average group, while individual trajectories identified a linear pattern of growth of RJA. Both individual and group trajectories indicated that children with higher RJA from 12 to 18 months demonstrated better social competence at three years of age and first grade. The delay and average group showed significant differences on later social competence measures, but not problem behaviors, such that RJA, a positive behavior, may connect more closely with later positive behaviors than with behavior problems. RJA may therefore be useful in a preventative intervention targeted at enhancing positive social behaviors and as an important and simple screening tool for possible delay early in a child's life, helping to deliver early intervention services in a targeted and effective manner.
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Doyle, Heather Sue. "A Longitudinal Study Of Relational Aggression Among Females Using Hierarchical Linear Modeling." Kent State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=kent1278606288.

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Haardoerfer, Regine. "Power and Bias in Hierarchical Linear Growth Models: More Measurements for Fewer People." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/eps_diss/57.

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Hierarchical Linear Modeling (HLM) sample size recommendations are mostly made with traditional group-design research in mind, as HLM as been used almost exclusively in group-design studies. Single-case research can benefit from utilizing hierarchical linear growth modeling, but sample size recommendations for growth modeling with HLM are scarce and generally do not consider the sample size combinations typical in single-case research. The purpose of this Monte Carlo simulation study was to expand sample size research in hierarchical linear growth modeling to suit single-case designs by testing larger level-1 sample sizes (N1), ranging from 10 to 80, and smaller level-2 sample sizes (N2), from 5 to 35, under the presence of autocorrelation to investigate bias and power. Estimates for the fixed effects were good for all tested sample-size combinations, irrespective of the strengths of the predictor-outcome correlations or the level of autocorrelation. Such low sample sizes, however, especially in the presence of autocorrelation, produced neither good estimates of the variances nor adequate power rates. Power rates were at least adequate for conditions in which N2 = 20 and N1 = 80 or N2 = 25 and N1 = 50 when the squared autocorrelation was .25.Conditions with lower autocorrelation provided adequate or high power for conditions with N2 = 15 and N1 = 50. In addition, conditions with high autocorrelation produced less than perfect power rates to detect the level-1 variance.
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Xiang, Yun. "Ethnic differences in achievement growth: Longitudinal data analysis of math achievement in a hierarchical linear modeling framework." Thesis, Boston College, 2009. http://hdl.handle.net/2345/676.

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Thesis advisor: Henry Braun<br>Given the call for greater understanding of racial inequality in student achievement in K-12 education, this study contributes a comprehensive, quantitative, longitudinal examination of the achievement gap phenomenon, with particular attention to the organization characteristics of schools and school districts. Employing data from a large number of districts in a single state, it examines the trends in achievement and the growth in achievement after the passage of NCLB. It focuses on mathematics performance from grade 6 to grade 8. Both a traditional descriptive approach and one employing Hierarchical Linear Models were applied and compared. The purpose was not to determine which methodology is superior but to provide complementary perspectives. The comparison between the two approaches revealed similar trends in achievement gaps, but the HLM approach offered a more nuanced description. Nonetheless the results suggest that it is useful to employ both approaches. As to the main question regarding ethnicity, it appears that even if student ethnicity is confounded with other indicators, such as initial score and socio-economic status, it is still an important predictor of both achievement gaps and achievement growth gaps. Moreover, demographic profiles at the school and district levels were also associated with these gaps<br>Thesis (PhD) — Boston College, 2009<br>Submitted to: Boston College. Lynch School of Education<br>Discipline: Educational Research, Measurement, and Evaluation
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Chapman, Sara Bernice. "Student Growth Trajectories with Summer Achievement Loss Using Hierarchical and Growth Modeling." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/5970.

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Using measures of student growth has become more popular in recent years—especially in the context of high stakes testing and accountability. While these methods have advantages over historical status measures, there is still much evidence to be gathered on patterns of growth generally and in student subgroups. To date, most research studies dealing with student growth focus on the effectiveness of specific interventions or examine growth in a few urban areas. This project explored math, reading, and English language arts (ELA) growth in the students of two rural school districts in Utah. The study incorporated hierarchical and latent growth methods to describe and compare these students’ growth in third, fourth and fifth grades. Additionally, student characteristics were tested as predictors of growth. Results showed student growth as complex and patterns varied across grade levels, subjects and student subgroups. Growth generally declined after third grade and students experienced summer loss in the second summer more than the first. Females began third grade ahead of their male peers in ELA and reading and began at a similar level in math. Male students narrowed the gap in reading and ELA in fourth and fifth grade and pulled ahead of their female peers in math in third grade. Low SES students were the most similar to their peers in math and ELA growth but were ahead of their peers in reading. Hispanic and Native American students started consistently behind white students in all subjects. Hispanic students tended to grow faster during the school year but lost more over the summer months. Native American students had more shallow growth than white students with a gradual decline in growth in fourth and fifth grades. ELA and reading growth were more closely related to each other than with math growth. Initial achievement estimates were more highly correlated with subsequent growth than previous years’ growth. A cross-classified model for teacher-level effects was attempted to account for students changing class groupings each school year but computational limits were reached. After estimating subjects and grade levels separately, results showed variance in test scores was primarily due to student differences. In ELA and reading, school differences accounted for a larger portion of the overall variance than teacher differences.
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Wu, Wenhao. "High-performance matrix multiplication hierarchical data structures, optimized kernel routines, and qualitative performance modeling /." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-07092003-003633.

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Oliveira, Izabela Regina Cardoso de. "Modeling strategies for complex hierarchical and overdispersed data in the life sciences." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-12082014-105135/.

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In this work, we study the so-called combined models, generalized linear mixed models with extension to allow for overdispersion, in the context of genetics and breeding. Such flexible models accommodates cluster-induced correlation and overdispersion through two separate sets of random effects and contain as special cases the generalized linear mixed models (GLMM) on the one hand, and commonly known overdispersion models on the other. We use such models while obtaining heritability coefficients for non-Gaussian characters. Heritability is one of the many important concepts that are often quantified upon fitting a model to hierarchical data. It is often of importance in plant and animal breeding. Knowledge of this attribute is useful to quantify the magnitude of improvement in the population. For data where linear models can be used, this attribute is conveniently defined as a ratio of variance components. Matters are less simple for non-Gaussian outcomes. The focus is on time-to-event and count traits, where the Weibull-Gamma-Normal and Poisson-Gamma-Normal models are used. The resulting expressions are sufficiently simple and appealing, in particular in special cases, to be of practical value. The proposed methodologies are illustrated using data from animal and plant breeding. Furthermore, attention is given to the occurrence of negative estimates of variance components in the Poisson-Gamma-Normal model. The occurrence of negative variance components in linear mixed models (LMM) has received a certain amount of attention in the literature whereas almost no work has been done for GLMM. This phenomenon can be confusing at first sight because, by definition, variances themselves are non-negative quantities. However, this is a well understood phenomenon in the context of linear mixed modeling, where one will have to make a choice between a hierarchical and a marginal view. The variance components of the combined model for count outcomes are studied theoretically and the plant breeding study used as illustration underscores that this phenomenon can be common in applied research. We also call attention to the performance of different estimation methods, because not all available methods are capable of extending the parameter space of the variance components. Then, when there is a need for inference on such components and they are expected to be negative, the accuracy of the method is not the only characteristic to be considered.<br>Neste trabalho foram estudados os chamados modelos combinados, modelos lineares generalizados mistos com extensão para acomodar superdispersão, no contexto de genética e melhoramento. Esses modelos flexíveis acomodam correlação induzida por agrupamento e superdispersão por meio de dois conjuntos separados de efeitos aleatórios e contem como casos especiais os modelos lineares generalizados mistos (MLGM) e os modelos de superdispersão comumente conhecidos. Tais modelos são usados na obtenção do coeficiente de herdabilidade para caracteres não Gaussianos. Herdabilidade é um dos vários importantes conceitos que são frequentemente quantificados com o ajuste de um modelo a dados hierárquicos. Ela é usualmente importante no melhoramento vegetal e animal. Conhecer esse atributo é útil para quantificar a magnitude do ganho na população. Para dados em que modelos lineares podem ser usados, esse atributo é convenientemente definido como uma razão de componentes de variância. Os problemas são menos simples para respostas não Gaussianas. O foco aqui é em características do tipo tempo-até-evento e contagem, em que os modelosWeibull-Gama-Normal e Poisson-Gama-Normal são usados. As expressões resultantes são suficientemente simples e atrativas, em particular nos casos especiais, pelo valor prático. As metodologias propostas são ilustradas usando dados de melhoramento animal e vegetal. Além disso, a atenção é voltada à ocorrência de estimativas negativas de componentes de variância no modelo Poisson-Gama- Normal. A ocorrência de componentes de variância negativos em modelos lineares mistos (MLM) tem recebido certa atenção na literatura enquanto quase nenhum trabalho tem sido feito para MLGM. Esse fenômeno pode ser confuso a princípio porque, por definição, variâncias são quantidades não-negativas. Entretanto, este é um fenômeno bem compreendido no contexto de modelagem linear mista, em que a escolha deverá ser feita entre uma interpretação hierárquica ou marginal. Os componentes de variância do modelo combinado para respostas de contagem são estudados teoricamente e o estudo de melhoramento vegetal usado como ilustração confirma que esse fenômeno pode ser comum em pesquisas aplicadas. A atenção também é voltada ao desempenho de diferentes métodos de estimação, porque nem todos aqueles disponíveis são capazes de estender o espaço paramétrico dos componentes de variância. Então, quando há a necessidade de inferência de tais componentes e é esperado que eles sejam negativos, a acurácia do método de estimação não é a única característica a ser considerada.
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Hudek, Natasha. "Risk and Resilience in the Internalizing Outcomes of Children in Out-of-Home Care." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37969.

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Internalizing problems are prevalent in childhood and adolescence in both community and out-of-home populations. Internalizing symptoms are frequently associated with problems in other areas of functioning as well. For children in out-of-home care, who face additional adversities such as maltreatment and witnessing traumatic events, internalizing problems have shown increased prevalence while less frequently addressed in research. The current studies used longitudinal data collected across 7 years from a sample of 1,765 children, 5 to 14 years old, in out-of-home care in Maryland, USA. Data consisted mainly of Child and Adolescent Needs and Strengths (CANS) assessments, as well as demographic information (age, sex, and race/ethnicity) and out-of-home placement type. In Study 1 we examined the trajectories of anxiety and depression across age and time in care separately and evaluated a comprehensive model of resilience for each outcome using hierarchical linear modeling. This exploratory model included both indicators of internal resilience (i.e. cognitive, emotional, spiritual, physical, behavioural) and environmental risk and resilience factors (i.e. family, acculturation, community, placement, school functioning, social functioning) related to internalizing problems in children and adolescents. Results showed anxiety was fairly stable over time in care and age, with few significant predictors aside from already well-known risk factors. Depression results showed a slight increase across age and decrease across time in care with several more significant predictors than the anxiety model. While both models showed overlap in predictors, they also included predictors unique to each outcome. In Study 2 we examined the reciprocal relationships across time between anxiety, depression, and significant risk and protective factors from Study 1. Using time lagged hierarchical linear models we found few significant relationships related to anxiety, and largely unidirectional relationships between depression and relevant factors over time. Two factors, traumatic stress and placement in residential treatment care, displayed reciprocal relationships with depression over time. However, our results largely did not support the direct resilience feedback mechanisms proposed between variables for either outcome, but revealed other possible mechanisms at work (i.e. dual cascades developmental model) to explain maladaptation towards depression in particular, but also anxiety. Findings are discussed in terms of theoretical implications, future research directions, and applied implications for prevention/intervention programs for internalizing problems for children in out-of-home care.
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Osterhout, Rebecca Ellen. "An examination of the association between behavior and attributions in an engaged sample using hierarchical linear modeling." Diss., Online access via UMI:, 2005.

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Books on the topic "Hierarchical Linear Modeling"

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Garson, G. David. Hierarchical linear modeling: Guide and applications. Sage Publications, 2013.

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Garson, G. Hierarchical Linear Modeling: Guide and Applications. SAGE Publications, Inc., 2013. http://dx.doi.org/10.4135/9781483384450.

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ERIC Clearinghouse on Assessment and Evaluation., ed. The advantages of hierarchical linear modeling. ERIC Clearinghouse on Assessment and Evaluation, University of Maryland, 2000.

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Garson, G. David. Hierarchical linear modeling: Guide and applications. Sage Publications, 2013.

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Gupta, Amit. Effect of service climate on service quality: Test of a model using hierarchical linear modeling. Indian Institute of Management, 2002.

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Garson, G. David. Hierarchical Linear Modeling: Guide and Applications. SAGE Publications, Incorporated, 2013.

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Brown, Carol E. HELM, hierarchical environment for linear modeling. 1989.

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Hierarchical Linear Modeling: Guide and Applications. 2013.

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Bryk, Congdon, Cheong, and Raudenbush. HLM 5: Hierarchical Linear and Nonlinear Modeling. Scientific Software International, 2001.

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HLM 5 : Hierarchical Linear and Nonlinear Modeling. Scientific Software Int. Inc, 2000.

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Book chapters on the topic "Hierarchical Linear Modeling"

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Leyland, Alastair H., and Peter P. Groenewegen. "Hierarchical Linear Modeling." In Encyclopedia of Quality of Life and Well-Being Research. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_1286.

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Graves, Scott. "Hierarchical Linear Modeling." In Encyclopedia of Child Behavior and Development. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_1359.

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Leyland, Alastair H., and Peter P. Groenewegen. "Hierarchical Linear Modeling." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_1286.

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Matsuyama, Yutaka. "Hierarchical Linear Modeling (HLM)." In Encyclopedia of Behavioral Medicine. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_407.

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Orbell, Sheina, Havah Schneider, Sabrina Esbitt, et al. "Hierarchical Linear Modeling (HLM)." In Encyclopedia of Behavioral Medicine. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_407.

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Matsuyama, Yutaka. "Hierarchical Linear Modeling (HLM)." In Encyclopedia of Behavioral Medicine. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-6439-6_407-2.

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Leckie, George. "Cross-Classified Hierarchical Linear Modeling." In Encyclopedia of Quality of Life and Well-Being Research. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_627.

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Leckie, George. "Cross-classified Hierarchical Linear Modeling." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_627.

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Leckie, George. "Cross-Classified Hierarchical Linear Modeling." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-69909-7_627-2.

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Khine, Myint Swe. "Hierarchical Linear Modeling and Multilevel Modeling in Educational Research." In Methodology for Multilevel Modeling in Educational Research. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9142-3_1.

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Conference papers on the topic "Hierarchical Linear Modeling"

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Ouyang, Guanyu, Yang Xiao, Cong Wang, and Wei Wei. "Linear and nonlinear hierarchical modeling strategy for dynamic soft sensor." In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2021. http://dx.doi.org/10.1109/icaica52286.2021.9498178.

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Stylianou-Georgiou, Agni, Elena Papanastasiou, and Sadhana Puntambekar. "Analyzing collaborative processes and learning from hypertext through hierarchical linear modeling." In the 8th iternational conference. Association for Computational Linguistics, 2007. http://dx.doi.org/10.3115/1599600.1599727.

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Hanson, Janet. "Hierarchical Linear Modeling of Relationships Between Teachers' Epistemological Beliefs and Students' Self-Efficacy." In 2019 AERA Annual Meeting. AERA, 2019. http://dx.doi.org/10.3102/1441168.

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Li Jing-hua and Xu Lei. "Service Performance Influence Factors of Zhejiang Administration Service Center: A Hierarchical Linear Modeling." In 2007 International Conference on Service Systems and Service Management. IEEE, 2007. http://dx.doi.org/10.1109/icsssm.2007.4280106.

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Kou, X. Y., S. T. Tan, and W. S. Sze. "Relation Oriented Modeling for Heterogeneous Object Design." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85589.

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Relation oriented modeling approaches are proposed to design heterogeneous objects. The heterogeneous object modeling process is viewed as representing and manipulating complex geometrical, topological and material variation relations with proper data structures. Linear list structure, hierarchical tree structures and more general graph structures are used to represent complex heterogeneous objects. The powerful non-manifold cellular representation and the hierarchical heterogeneous feature tree representation are combined to model complex objects with simultaneous geometry intricacies and compound material variations. We demonstrate that relations play critical roles in heterogeneous object design and under the relation oriented framework, heterogeneous objects can be modeled with generic, uniform representations. The proposed relation oriented modeling approaches are tested with a prototype heterogeneous CAD modeler and presented with different types of heterogeneous object examples.
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Palma, Jose. "Teacher/School Support, Academic Goals, and Proportion of Latino Students: A Hierarchical Linear Modeling Study." In 2019 AERA Annual Meeting. AERA, 2019. http://dx.doi.org/10.3102/1446795.

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Li, Da. "Three-Level Hierarchical Linear Modeling Analyses of the Relationship Between Political Culture and Teacher Autonomy." In 2020 AERA Annual Meeting. AERA, 2020. http://dx.doi.org/10.3102/1584227.

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Yang, Panpan. "Power to Estimate Moderator Effects in Two-Level Hierarchical Linear Modeling of Single-Case Data." In 2021 AERA Annual Meeting. AERA, 2021. http://dx.doi.org/10.3102/1682760.

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Yoon, Hyejin. "Investigating the Relationship Between Multilevel Factors and Math Achievement: Analysis Based on Hierarchical Linear Modeling." In 2023 AERA Annual Meeting. AERA, 2023. http://dx.doi.org/10.3102/2011401.

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De León, Nadia. "Neighboring Insights: HLM (Hierarchical Linear Modeling) Study of ERCE in Panama, Costa Rica, and Colombia." In 2024 AERA Annual Meeting. AERA, 2024. http://dx.doi.org/10.3102/2113870.

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Reports on the topic "Hierarchical Linear Modeling"

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Raudenbush, Stephen. Hierarchical Linear Models (HLM) and Multilevel Causal Inference. Instats Inc., 2023. http://dx.doi.org/10.61700/6mi8hginiy8rh469.

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Abstract:
This seminar introduces you to the theory and practice of multilevel modeling, and the logic of causal inference at multiple levels of analysis. The first day introduces you to two-level and three-level analysis, with an emphasis on how to build and interpret HLMs with a theoretically-rigorous foundation to guide estimation and inference. The second day considers how modern methods of causal inference can be applied to multilevel experimental and quasi-experimental designs. The new updated version of the HLM software will be used to illustrate model building and inference in a hands-on way. An official Instats certificate of completion with Professor Raudenbush is provided at the conclusion of the seminar. The seminar offers 2 ECTS Equivalent points for European PhD students.
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Raudenbush, Stephen. Hierarchical Linear Models (HLM) and Multilevel Causal Inference. Instats Inc., 2022. http://dx.doi.org/10.61700/47nvc1nfj731n469.

Full text
Abstract:
This seminar, taught by Professor Raudenbush, will introduce you to the theory and practice of multilevel modeling, and the logic of causal inference at multiple levels of analysis. The first day introduces you to two-level and three-level analysis, with an emphasis on how to build and interpret HLMs with a theoretically-rigorous foundation that will guide estimation and inference. The second day considers how modern methods of causal inference can be applied to multilevel experimental and quasi-experimental designs. The new and substantially updated version of the HLM software will be used to illustrate model building and inference in a hands-on way. All participants will receive a free 60-day trial license to this exciting update to the classic HLM software from Scientific Software International, and a 20% discount on a future purchase of HLM. An official Instats certificate of completion with Professor Raudenbush is provided at the conclusion of the seminar. The seminar offers 2 ECTS Equivalent points for European PhD students.
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