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Xiao Liu; Zhiyong Zhang; Lijuan Wang – Grantee Submission, 2024
In psychology, researchers are often interested in testing hypotheses about mediation, such as testing the presence of a mediation effect of a treatment (e.g., intervention assignment) on an outcome via a mediator. An increasingly popular approach to testing hypotheses is the Bayesian testing approach with Bayes factors (BFs). Despite the growing…
Descriptors: Sample Size, Bayesian Statistics, Programming Languages, Simulation
Caspar J. Van Lissa; Eli-Boaz Clapper; Rebecca Kuiper – Research Synthesis Methods, 2024
The product Bayes factor (PBF) synthesizes evidence for an informative hypothesis across heterogeneous replication studies. It can be used when fixed- or random effects meta-analysis fall short. For example, when effect sizes are incomparable and cannot be pooled, or when studies diverge significantly in the populations, study designs, and…
Descriptors: Hypothesis Testing, Evaluation Methods, Replication (Evaluation), Sample Size
Johnson, Roger W. – Journal of Statistics and Data Science Education, 2022
For ease of instruction in the classroom, the one-way analysis of variance F statistic is rewritten in terms of pairwise differences in individual sample means instead of differences of individual sample means from the overall sample mean. Likewise, the Kruskal-Wallis statistic may be rewritten in terms of pairwise differences in individual…
Descriptors: Statistics Education, Statistical Analysis, Hypothesis Testing, Sampling
Clintin P. Davis-Stober; Jason Dana; David Kellen; Sara D. McMullin; Wes Bonifay – Grantee Submission, 2023
Conducting research with human subjects can be difficult because of limited sample sizes and small empirical effects. We demonstrate that this problem can yield patterns of results that are practically indistinguishable from flipping a coin to determine the direction of treatment effects. We use this idea of random conclusions to establish a…
Descriptors: Research Methodology, Sample Size, Effect Size, Hypothesis Testing
Mani, Nivedita; Schreiner, Melanie S.; Brase, Julia; Köhler, Katrin; Strassen, Katrin; Postin, Danilo; Schultze, Thomas – Developmental Science, 2021
Developmental research, like many fields, is plagued by low sample sizes and inconclusive findings. The problem is amplified by the difficulties associated with recruiting infant participants for research as well as the increased variability in infant responses. With sequential testing designs providing a viable alternative to paradigms facing…
Descriptors: Bayesian Statistics, Infants, Language Acquisition, Vocabulary
Kelcey, Ben; Spybrook, Jessaca; Dong, Nianbo; Bai, Fangxing – Journal of Research on Educational Effectiveness, 2020
Professional development for teachers is regarded as one of the principal pathways through which we can understand and cultivate effective teaching and improve student outcomes. A critical component of studies that seek to improve teaching through professional development is the detailed assessment of the intermediate teacher development processes…
Descriptors: Faculty Development, Educational Research, Randomized Controlled Trials, Research Design
Nordstokke, David W.; Colp, S. Mitchell – Practical Assessment, Research & Evaluation, 2018
Often, when testing for shift in location, researchers will utilize nonparametric statistical tests in place of their parametric counterparts when there is evidence or belief that the assumptions of the parametric test are not met (i.e., normally distributed dependent variables). An underlying and often unattended to assumption of nonparametric…
Descriptors: Nonparametric Statistics, Statistical Analysis, Monte Carlo Methods, Sample Size
Ayodele, Alicia Nicole – ProQuest LLC, 2017
Within polytomous items, differential item functioning (DIF) can take on various forms due to the number of response categories. The lack of invariance at this level is referred to as differential step functioning (DSF). The most common DSF methods in the literature are the adjacent category log odds ratio (AC-LOR) estimator and cumulative…
Descriptors: Statistical Analysis, Test Bias, Test Items, Scores
Pustejovsky, James Eric; Furman, Gleb – AERA Online Paper Repository, 2017
In linear regression models estimated by ordinary least squares, it is often desirable to use hypothesis tests and confidence intervals that remain valid in the presence of heteroskedastic errors. Wald tests based on heteroskedasticity-consistent covariance matrix estimators (HCCMEs, also known as sandwich estimators or simply "robust"…
Descriptors: Hypothesis Testing, Sample Size, Regression (Statistics), Computation
Choi, In-Hee; Paek, Insu; Cho, Sun-Joo – Journal of Experimental Education, 2017
The purpose of the current study is to examine the performance of four information criteria (Akaike's information criterion [AIC], corrected AIC [AICC] Bayesian information criterion [BIC], sample-size adjusted BIC [SABIC]) for detecting the correct number of latent classes in the mixture Rasch model through simulations. The simulation study…
Descriptors: Item Response Theory, Models, Bayesian Statistics, Simulation
Porter, Kristin E. – Journal of Research on Educational Effectiveness, 2018
Researchers are often interested in testing the effectiveness of an intervention on multiple outcomes, for multiple subgroups, at multiple points in time, or across multiple treatment groups. The resulting multiplicity of statistical hypothesis tests can lead to spurious findings of effects. Multiple testing procedures (MTPs) are statistical…
Descriptors: Statistical Analysis, Program Effectiveness, Intervention, Hypothesis Testing
Spencer, Neil H.; Lay, Margaret; Kevan de Lopez, Lindsey – International Journal of Social Research Methodology, 2017
When undertaking quantitative hypothesis testing, social researchers need to decide whether the data with which they are working is suitable for parametric analyses to be used. When considering the relevant assumptions they can examine graphs and summary statistics but the decision making process is subjective and must also take into account the…
Descriptors: Evaluation Methods, Decision Making, Hypothesis Testing, Social Science Research
Gnambs, Timo; Staufenbiel, Thomas – Research Synthesis Methods, 2016
Two new methods for the meta-analysis of factor loadings are introduced and evaluated by Monte Carlo simulations. The direct method pools each factor loading individually, whereas the indirect method synthesizes correlation matrices reproduced from factor loadings. The results of the two simulations demonstrated that the accuracy of…
Descriptors: Accuracy, Meta Analysis, Factor Structure, Monte Carlo Methods
McGrath, April – Teaching & Learning Inquiry, 2016
Quantitative results from empirical studies are common in the field of Scholarship of Teaching and Learning (SoTL), but it is important to remain aware of what the results from our studies can, and cannot, tell us. Oftentimes studies conducted to examine teaching and learning are constrained by class size. Small sample sizes negatively influence…
Descriptors: Scholarship, Instruction, Learning, Class Size
García-Pérez, Miguel A. – Educational and Psychological Measurement, 2017
Null hypothesis significance testing (NHST) has been the subject of debate for decades and alternative approaches to data analysis have been proposed. This article addresses this debate from the perspective of scientific inquiry and inference. Inference is an inverse problem and application of statistical methods cannot reveal whether effects…
Descriptors: Hypothesis Testing, Statistical Inference, Effect Size, Bayesian Statistics

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