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Daniel McNeish; Patrick D. Manapat – Structural Equation Modeling: A Multidisciplinary Journal, 2024
A recent review found that 11% of published factor models are hierarchical models with second-order factors. However, dedicated recommendations for evaluating hierarchical model fit have yet to emerge. Traditional benchmarks like RMSEA <0.06 or CFI >0.95 are often consulted, but they were never intended to generalize to hierarchical models.…
Descriptors: Factor Analysis, Goodness of Fit, Hierarchical Linear Modeling, Benchmarking
Qinxin Shi; Jonathan E. Butner; Robyn Kilshaw; Ascher Munion; Pascal Deboeck; Yoonkyung Oh; Cynthia A. Berg – Grantee Submission, 2023
Developmental researchers commonly utilize longitudinal data to decompose reciprocal and dynamic associations between repeatedly measured constructs to better understand the temporal precedence between constructs. Although the cross-lagged panel model (CLPM) is commonly used in developmental research, it has been criticized for its potential to…
Descriptors: Models, Longitudinal Studies, Developmental Psychology, Behavior Problems
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Cao, Chunhua; Kim, Eun Sook; Chen, Yi-Hsin; Ferron, John – Educational and Psychological Measurement, 2021
This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates…
Descriptors: Goodness of Fit, Hierarchical Linear Modeling, Computation, Models
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Hendrix, Peter; Sun, Ching Chu – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2021
For the most part, the effects of lexical-distributional properties of words on visual word recognition are well-established. More uncertainty remains, however, about the influence of these properties on lexical processing for nonwords. The work presented here investigates the mechanisms that guide nonword processing through an analysis of lexical…
Descriptors: Incidence, Semantics, Reliability, Language Processing
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Baek, Eunkyeng; Luo, Wen; Henri, Maria – Journal of Experimental Education, 2022
It is common to include multiple dependent variables (DVs) in single-case experimental design (SCED) meta-analyses. However, statistical issues associated with multiple DVs in the multilevel modeling approach (i.e., possible dependency of error, heterogeneous treatment effects, and heterogeneous error structures) have not been fully investigated.…
Descriptors: Meta Analysis, Hierarchical Linear Modeling, Comparative Analysis, Statistical Inference
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Park, Sunyoung; Natasha Beretvas, S. – Journal of Experimental Education, 2021
When selecting a multilevel model to fit to a dataset, it is important to choose both a model that best matches characteristics of the data's structure, but also to include the appropriate fixed and random effects parameters. For example, when researchers analyze clustered data (e.g., students nested within schools), the multilevel model can be…
Descriptors: Hierarchical Linear Modeling, Statistical Significance, Multivariate Analysis, Monte Carlo Methods
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Hsu, Hsien-Yuan; Lin, Jr-Hung; Kwok, Oi-Man; Acosta, Sandra; Willson, Victor – Educational and Psychological Measurement, 2017
Several researchers have recommended that level-specific fit indices should be applied to detect the lack of model fit at any level in multilevel structural equation models. Although we concur with their view, we note that these studies did not sufficiently consider the impact of intraclass correlation (ICC) on the performance of level-specific…
Descriptors: Correlation, Goodness of Fit, Hierarchical Linear Modeling, Structural Equation Models
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Xiao, Yang; Han, Jing; Koenig, Kathleen; Xiong, Jianwen; Bao, Lei – Physical Review Physics Education Research, 2018
Assessment instruments composed of two-tier multiple choice (TTMC) items are widely used in science education as an effective method to evaluate students' sophisticated understanding. In practice, however, there are often concerns regarding the common scoring methods of TTMC items, which include pair scoring and individual scoring schemes. The…
Descriptors: Hierarchical Linear Modeling, Item Response Theory, Multiple Choice Tests, Case Studies
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Boedeker, Peter – Practical Assessment, Research & Evaluation, 2017
Hierarchical linear modeling (HLM) is a useful tool when analyzing data collected from groups. There are many decisions to be made when constructing and estimating a model in HLM including which estimation technique to use. Three of the estimation techniques available when analyzing data with HLM are maximum likelihood, restricted maximum…
Descriptors: Hierarchical Linear Modeling, Maximum Likelihood Statistics, Bayesian Statistics, Computation
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Harel, Daphna; McAllister, Tara – Journal of Speech, Language, and Hearing Research, 2019
Purpose: Research in communication sciences and disorders frequently involves the collection of clusters of observations, such as a series of scores for each individual receiving treatment over the course of an intervention study. However, little discipline-specific guidance is currently available on the subject of building and interpreting…
Descriptors: Communication Disorders, Intervention, Scores, Guidance
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Kamienkowski, Juan E.; Carbajal, M. Julia; Bianchi, Bruno; Sigman, Mariano; Shalom, Diego E. – Discourse Processes: A multidisciplinary journal, 2018
When a word is read more than once, reading time generally decreases in the successive occurrences. This Repetition Effect has been used to study word encoding and memory processes in a variety of experimental measures. We studied naturally occurring repetitions of words within normal texts (stories of around 3,000 words). Using linear mixed…
Descriptors: Repetition, Eye Movements, Reading, Cognitive Processes
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Black, Ryan A.; Yang, Yanyun; Beitra, Danette; McCaffrey, Stacey – Journal of Psychoeducational Assessment, 2015
Estimation of composite reliability within a hierarchical modeling framework has recently become of particular interest given the growing recognition that the underlying assumptions of coefficient alpha are often untenable. Unfortunately, coefficient alpha remains the prominent estimate of reliability when estimating total scores from a scale with…
Descriptors: Psychological Testing, Test Reliability, Goodness of Fit, Factor Analysis
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Walker, A. Adrienne; Engelhard, George, Jr. – Applied Measurement in Education, 2015
The idea that test scores may not be valid representations of what students know, can do, and should learn next is well known. Person fit provides an important aspect of validity evidence. Person fit analyses at the individual student level are not typically conducted and person fit information is not communicated to educational stakeholders. In…
Descriptors: Test Validity, Goodness of Fit, Educational Assessment, Hierarchical Linear Modeling
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Heemsoth, Tim; Retelsdorf, Jan – Measurement in Physical Education and Exercise Science, 2018
Educational research emphasizes the advantages of multimethod designs. However, if the design comprises different perspectives, the question of construct validity emerges. We related this question to student and teacher ratings of student-student relations, which are of high interest in research on physical education. In our study, 2,160 students…
Descriptors: Educational Research, Factor Analysis, Peer Relationship, Physical Education Teachers
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Cheung, Chris Hin Wah; Kennedy, Kerry J.; Leung, Chi Hung; Hue, Ming Tak – British Journal of Religious Education, 2018
This paper explored the impact of religious engagement (religious background, religious service attendance and religious activities participation) on adolescents' civic and social values. Attitudes towards the influence of religion on society were investigated as a possible mediator/moderator of religious engagement. A model based on Western…
Descriptors: Role of Religion, Religious Factors, Hierarchical Linear Modeling, Social Values
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