Classical evaluation methods, assessments, exams, and so forth accentuate the perception of one against all, professor versus learners. Including students in the assessment process, allows transforming the professor from an opponent to a critical friend, with the role of helping students to recognize both their strengths and weaknesses. However,…

Although Cattaneo et al. (2019) provided a data-driven framework for power computations for Regression Discontinuity Designs in line with rdrobust Stata and R commands, which allows higher-order functional forms for the score variable when using the non-parametric local polynomial estimation, analogous advancements in their parametric estimation…

Multiple baseline (MB) designs are becoming more prevalent in educational and behavioral research, and as they do, there is growing interest in combining effect size estimates across studies. To further refine the meta-analytic methods of estimating the effect, this study developed and compared eight alternative methods of estimating intervention…

Objectively scoring constructed-response items on educational assessments has long been a challenge due to the use of human raters. Even well-trained raters using a rubric can inaccurately assess essays. Unfolding models measure rater's scoring accuracy by capturing the discrepancy between criterion and operational ratings by placing essays on an…

The presence of clustered data is common in the sociobehavioral sciences. One approach that specifically deals with clustered data but has seen little use in education is the generalized estimating equations (GEEs) approach. We provide a background on GEEs, discuss why it is appropriate for the analysis of clustered data, and provide worked…

Recent, high-profile, large-scale, preregistered failures to replicate uncover that many highly-regarded experiments are "false positives"; that is, statistically significant results of underlying null effects. Large surveys of research reveal that statistical power is often low and inadequate. When the research record includes selective…

In this article, the author will discuss processes used by quantitative researchers to render judgments and decisions about the results of their statistical analyses, highlighting what "'p'-values" represent and how "p"-values became ubiquitous in quantitative social science research. Suggestions for alternative ways to measure…

Coefficient alpha reliability persists as the most common reliability coefficient reported in research. The assumptions for its use are, however, not well-understood. The current paper challenges the commonly used expressions of coefficient alpha and argues that while these expressions are correct when estimating reliability for summed scores,…

Many studies demonstrate that detecting statistical regularities in linguistic input plays a key role in language acquisition. Yet, it is unclear to what extent statistical learning is involved in more naturalistic settings, when young children have to acquire meaningful grammatical elements. In the present study, we address these points, by…

Background: Studying causal effects is an important aim in education. Causal relationships indicate how well implements (e.g., interventions) work for the target subjects. A good strategy to get the inference in such relationships is to conduct randomized experiments. However, random assignment is limited in education research, even is discouraged…

Considered normative from the second half of the 20th century (Danziger, 1990), null hypothesis statistical testing (NHST) has received consistent, largely unheeded criticism. Critiques have received more attention in recent years with the recognition of the replication crisis in the social sciences and the American Statistical Society's statement…

Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models widely used in educational, psychological and social sciences. In many applications of CDMs, certain hierarchical structures among the latent attributes are assumed by researchers to characterize their dependence structure. Specifically, a directed acyclic…

Purpose: Many studies using machine learning (ML) in speech, language, and hearing sciences rely upon cross-validations with single data splitting. This study's first purpose is to provide quantitative evidence that would incentivize researchers to instead use the more robust data splitting method of nested k-fold cross-validation. The second…

Critical quantitative methods provide opportunities for Queer Theory to challenge, re-define, and re-claim the historically privileged research tradition. In this paper, I begin by summarizing the various binaries that oppress research and individuality. I then engage with Queer Theory and my own intersectional positionality to propose a nonbinary…

Using national data from the US Department of Education, Integrated Postsecondary Education Data System (IPEDS) and the US Census Bureau, trends in graduate school enrollment percentages for Hispanic and White students from 2002 to 2018 were analyzed and compared. Major findings from three regression analyses included: (1) a strong, statistically…