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Ricca, Bernard P.; Blaine, Bruce E. – Journal of Experimental Education, 2022
Researchers are encouraged to report effect size statistics to quantify treatment effects or effects due to group differences. However, estimates of effect sizes, most commonly Cohen's "d," make assumptions about the distribution of data that are not always true. An alternative nonparametric estimate of effect size, relying on the median…
Descriptors: Nonparametric Statistics, Computation, Effect Size
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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
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Stefanie A. Wind; Benjamin Lugu – Applied Measurement in Education, 2024
Researchers who use measurement models for evaluation purposes often select models with stringent requirements, such as Rasch models, which are parametric. Mokken Scale Analysis (MSA) offers a theory-driven nonparametric modeling approach that may be more appropriate for some measurement applications. Researchers have discussed using MSA as a…
Descriptors: Item Response Theory, Data Analysis, Simulation, Nonparametric Statistics
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Rashelle J. Musci; Joseph Kush; Elise T. Pas; Catherine P. Bradshaw – Grantee Submission, 2024
Given the increased focus of educational research on what works for whom and under what circumstances over the last decade, educational researchers are increasingly turning toward mixture models to identify heterogeneous subgroups among students. Such data are inherently nested, as students are nested within classrooms and schools. Yet there has…
Descriptors: Hierarchical Linear Modeling, Data Analysis, Nonparametric Statistics, Educational Research
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Parian Haghighat; Denisa Gandara; Lulu Kang; Hadis Anahideh – Grantee Submission, 2024
Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque…
Descriptors: Prediction, Learning Analytics, Multivariate Analysis, Regression (Statistics)
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Elif Tekin-Iftar; Melinda Jones Ault; Belva C. Collins; Seray Olcay; H. Deniz Degirmenci; Orhan Aydin – Journal of Special Education, 2024
We conducted a descriptive analysis and meta-analysis of single-case research design (SCRD) studies investigating the effectiveness of the graduated guidance procedure. Once we identified studies through electronic databases and reference lists, we used What Works Clearinghouse (WWC) Standards to evaluate each study. Then, we described studies in…
Descriptors: Meta Analysis, Effect Size, Nonparametric Statistics, Evidence Based Practice
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Kane Meissel; Esther S. Yao – Practical Assessment, Research & Evaluation, 2024
Effect sizes are important because they are an accessible way to indicate the practical importance of observed associations or differences. Standardized mean difference (SMD) effect sizes, such as Cohen's d, are widely used in education and the social sciences -- in part because they are relatively easy to calculate. However, SMD effect sizes…
Descriptors: Computer Software, Programming Languages, Effect Size, Correlation
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Corinne Huggins-Manley; Anthony W. Raborn; Peggy K. Jones; Ted Myers – Journal of Educational Measurement, 2024
The purpose of this study is to develop a nonparametric DIF method that (a) compares focal groups directly to the composite group that will be used to develop the reported test score scale, and (b) allows practitioners to explore for DIF related to focal groups stemming from multicategorical variables that constitute a small proportion of the…
Descriptors: Nonparametric Statistics, Test Bias, Scores, Statistical Significance
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Hongxi Li; Shuwei Li; Liuquan Sun; Xinyuan Song – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Structural equation models offer a valuable tool for delineating the complicated interrelationships among multiple variables, including observed and latent variables. Over the last few decades, structural equation models have successfully analyzed complete and right-censored survival data, exemplified by wide applications in psychological, social,…
Descriptors: Statistical Analysis, Statistical Studies, Structural Equation Models, Intervals
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Olsson, Ulf – Practical Assessment, Research & Evaluation, 2022
We discuss analysis of 5-grade Likert type data in the two-sample case. Analysis using two-sample "t" tests, nonparametric Wilcoxon tests, and ordinal regression methods, are compared using simulated data based on an ordinal regression paradigm. One thousand pairs of samples of size "n"=10 and "n"=30 were generated,…
Descriptors: Regression (Statistics), Likert Scales, Sampling, Nonparametric Statistics
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Roya Shoahosseini; Purya Baghaei; Hossein Khodabakhshzadeh; Hamid Ashraf – Language Testing in Asia, 2024
C-Test is a gap-filling test designed to measure first and second language proficiency. Over the past four decades, researchers have shown the fit of C-Test data to parametric item response theory (IRT) models, but no study so far has shown the fit of C-Tests to nonparametric IRT models. The purpose of this study is to contribute to the ongoing…
Descriptors: Item Response Theory, Nonparametric Statistics, Language Proficiency, Second Language Learning
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Ting Ye; Ted Westling; Lindsay Page; Luke Keele – Grantee Submission, 2024
The clustered observational study (COS) design is the observational study counterpart to the clustered randomized trial. In a COS, a treatment is assigned to intact groups, and all units within the group are exposed to the treatment. However, the treatment is non-randomly assigned. COSs are common in both education and health services research. In…
Descriptors: Nonparametric Statistics, Identification, Causal Models, Multivariate Analysis
Ahmed, Inzamamul; Jena, Ananta Kumar – Online Submission, 2023
A workshop in environmental sustainability was organized to develop the skills and competencies in mitigating climate change, adapting climate risk management, managing waste, adapting sustainable human settlements and using sustainable transport, and developing skills of reducing CO2 emissions, and conserving energy and ecosystem, & educating…
Descriptors: Nonparametric Statistics, Climate, Sustainability, Workshops
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Wind, Stefanie A. – Educational and Psychological Measurement, 2022
Researchers frequently use Mokken scale analysis (MSA), which is a nonparametric approach to item response theory, when they have relatively small samples of examinees. Researchers have provided some guidance regarding the minimum sample size for applications of MSA under various conditions. However, these studies have not focused on item-level…
Descriptors: Nonparametric Statistics, Item Response Theory, Sample Size, Test Items
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Falk, Carl F.; Feuerstahler, Leah M. – Educational and Psychological Measurement, 2022
Large-scale assessments often use a computer adaptive test (CAT) for selection of items and for scoring respondents. Such tests often assume a parametric form for the relationship between item responses and the underlying construct. Although semi- and nonparametric response functions could be used, there is scant research on their performance in a…
Descriptors: Item Response Theory, Adaptive Testing, Computer Assisted Testing, Nonparametric Statistics
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