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Chunhua Cao; Yan Wang; Eunsook Kim – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Multilevel factor mixture modeling (FMM) is a hybrid of multilevel confirmatory factor analysis (CFA) and multilevel latent class analysis (LCA). It allows researchers to examine population heterogeneity at the within level, between level, or both levels. This tutorial focuses on explicating the model specification of multilevel FMM that considers…
Descriptors: Hierarchical Linear Modeling, Factor Analysis, Nonparametric Statistics, Statistical Analysis
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
Son, Sookyoung; Hong, Sehee – Educational and Psychological Measurement, 2021
The purpose of this two-part study is to evaluate methods for multiple group analysis when the comparison group is at the within level with multilevel data, using a multilevel factor mixture model (ML FMM) and a multilevel multiple-indicators multiple-causes (ML MIMIC) model. The performance of these methods was evaluated integrally by a series of…
Descriptors: Hierarchical Linear Modeling, Factor Analysis, Structural Equation Models, Groups
Ekpenyong, John A.; Owan, Valentine J.; Ogar, Joseph O.; Undie, John A. – Cogent Education, 2022
Research has assessed the diverse characteristics of principals and teachers in analysing students' educational outcomes at various levels. However, these studies often focus on the cognitive domain of learning, ignoring the affective and psychomotor aspects. Bridging this gap, we used hierarchical linear regression to link two inputs of teachers…
Descriptors: Hierarchical Linear Modeling, Outcomes of Education, Secondary School Students, Foreign Countries
Huang, Francis L. – School Psychology Quarterly, 2018
The use of multilevel modeling (MLM) to analyze nested data has grown in popularity over the years in the study of school psychology. However, with the increase in use, several statistical misconceptions about the technique have also proliferated. We discuss some commonly cited myths and golden rules related to the use of MLM, explain their…
Descriptors: Hierarchical Linear Modeling, School Psychology, Misconceptions, Correlation
Dombrowski, Stefan C.; McGill, Ryan J.; Canivez, Gary L. – School Psychology Quarterly, 2018
The Woodcock-Johnson (fourth edition; WJ IV; Schrank, McGrew, & Mather, 2014a) was recently redeveloped and retains its linkage to Cattell-Horn-Carroll theory (CHC). Independent reviews (e.g., Canivez, 2017) and investigations (Dombrowski, McGill, & Canivez, 2017) of the structure of the WJ IV full test battery and WJ IV Cognitive have…
Descriptors: Factor Analysis, Achievement Tests, Cognitive Tests, Cognitive Ability
Villares, Elizabeth; Mariani, Melissa; Sink, Christopher A.; Colvin, Kimberly – Measurement and Evaluation in Counseling and Development, 2016
Researchers analyzed data from elementary teachers (N = 233) to further establish the psychometric soundness of the Teacher My Class Inventory-Short Form. Supporting previous psychometric research, confirmatory factor analyses findings supported the factorial validity of the hypothesized five-factor solution. Internal reliability estimates were…
Descriptors: Factor Analysis, Elementary School Teachers, Psychometrics, Validity
Stapleton, Laura M.; Yang, Ji Seung; Hancock, Gregory R. – Journal of Educational and Behavioral Statistics, 2016
We present types of constructs, individual- and cluster-level, and their confirmatory factor analytic validation models when data are from individuals nested within clusters. When a construct is theoretically individual level, spurious construct-irrelevant dependency in the data may appear to signal cluster-level dependency; in such cases,…
Descriptors: Multivariate Analysis, Factor Analysis, Validity, Models
Salloum, Serena J.; Goddard, Roger D.; Larsen, Ross – Teachers College Record, 2017
Background: Schools face pressure to promote equitable student outcomes as the achievement gap continues to persist. The authors examine different ways in which social capital has been conceptualized as well as prior theory and research on its formation and consequences. While some theoretical and empirical work conceptualizes social capital as a…
Descriptors: Social Capital, Academic Achievement, Correlation, High School Students
McGill, Ryan J.; Canivez, Gary L. – Journal of Psychoeducational Assessment, 2016
As recommended by Carroll, the present study examined the factor structure of the Wechsler Intelligence Scale for Children-Fourth Edition Spanish (WISC-IV Spanish) normative sample using higher order exploratory factor analytic techniques not included in the WISC-IV Spanish Technical Manual. Results indicated that the WISC-IV Spanish subtests were…
Descriptors: Children, Intelligence Tests, Spanish, Factor Analysis
Chen, Jing; Lin, Tzu-Jung; Ku, Yu-Min; Zhang, Jie; O'Connell, Ann – Scientific Studies of Reading, 2018
Concept of word--the awareness of how words differ from nonwords or other linguistic properties--is important to learning to read Chinese because words in Chinese texts are not separated by space, and most characters can be productively compounded with other characters to form new words. The current study examined the effects of reader, word, and…
Descriptors: Vocabulary Development, Reading Comprehension, Chinese, Grade 5
Oliveri, Maria; McCaffrey, Daniel; Ezzo, Chelsea; Holtzman, Steven – Applied Measurement in Education, 2017
The assessment of noncognitive traits is challenging due to possible response biases, "subjectivity" and "faking." Standardized third-party evaluations where an external evaluator rates an applicant on their strengths and weaknesses on various noncognitive traits are a promising alternative. However, accurate score-based…
Descriptors: Factor Analysis, Decision Making, College Admission, Likert Scales
Early, Diane M.; Sideris, John; Neitzel, Jennifer; LaForett, Doré R.; Nehler, Chelsea G. – Grantee Submission, 2018
The Early Childhood Environment Rating Scale-Third Edition (ECERS-3) is the latest version of one of the most widely used observational tools for assessing the quality of classrooms serving preschool-aged children. This study was the first assessment of its factor structure and validity, an important step given its widespread use. An ECERS-3…
Descriptors: Rating Scales, Early Childhood Education, Educational Quality, Factor Structure
Bear, George G.; Yang, Chunyan; Chen, Dandan; He, Xianyou; Xie, Jia-Shu; Huang, Xishan – School Psychology Quarterly, 2018
Objective: The purpose of this study was to examine differences between American and Chinese students in their perceptions of school climate and engagement in school, and in the relation between school climate and engagement. Method: Confirmatory factor analyses were used to support the factor structure and measurement invariance of the two…
Descriptors: Foreign Countries, Comparative Education, Educational Environment, Learner Engagement
Seo, Eunjin; Lee, You-kyung – Educational Psychology, 2018
We examine the intrinsic value students placed on schoolwork (i.e. academic intrinsic value) and social relationships (i.e. social intrinsic value). We then look at how these values predict middle and high school achievement. To do this, we came up with four profiles based on cluster analyses of 6,562 South Korean middle school students. The four…
Descriptors: Friendship, Academic Achievement, Educational Benefits, Barriers