A growing body of literature suggests manipulating some graphing conventions can affect visual analysis and adherence to graphing conventions varies widely in single case design publications. Yet use of graphing conventions by the wider population of behavior analysts is largely unexplored. The current study examined relations among types of…

We introduce and evaluate three tests for publication selection bias based on excess statistical significance (ESS). The proposed tests incorporate heterogeneity explicitly in the formulas for expected and ESS. We calculate the expected proportion of statistically significant findings in the absence of selective reporting or publication bias based…

The purpose of the current study was to conduct a thorough review of the literature on the interview-informed synthesized contingency analysis (IISCA) developed by (Hanley et al., Journal of Applied Behavior Analysis 47:16-36, 2014)) and its subsequent treatment outcomes. A total of 39 articles were identified with 235 participants participating…

This dissertation examined classification outcome differences among four popular individual supervised machine learning (ISML) models (logistic regression, decision tree, support vector machine, and multilayer perceptron) when predicting minor class membership within imbalanced datasets. The study context and the theoretical population sampled…

Some Cochrane reviews were declared stable or closed, that is, not in need of updating. For some of them, it has been declared that conclusions will not (or it is unlikely they will) change with further studies. We explored whether there is a discernable decision-making pattern for decisions about the conclusiveness and stabilization of these…

The 0.05 boundary within Null Hypothesis Statistical Testing (NHST) "has made a lot of people very angry and been widely regarded as a bad move" (to quote Douglas Adams). Here, we move past meta-scientific arguments and ask an empirical question: What is the psychological standing of the 0.05 boundary for statistical significance? We…

School absences can negatively impact a child's schooling, including the loss of teacher-led lessons, peer interactions, and, ultimately, academic achievement. However, little is known about the long-term consequences of school absences for overall educational attainment and labour market outcomes. In this paper, we used data from the 1970 British…

Randomized controlled trials have proliferated in education, in part because they provide an unbiased estimator for the causal impact of interventions. It is increasingly recognized that many such trials in education have low power to detect an effect if indeed there is one. However, it is less well known that low powered trials tend to…

Using the Hartung-Knapp method and 95% prediction intervals (PIs) in random-effects meta-analyses is recommended by experts but rarely applied. Therefore, we aimed to reevaluate statistically significant meta-analyses using the Hartung-Knapp method and 95% PIs. In this methodological study, three databases were searched from January 2010 to July…

Background: Research reproducibility and effect replication has become a topic of major concern throughout the social sciences. During the last decade, low replication rates of published research findings became a major issue across disciplines in the social and behavioral sciences, leading to the public proclamation of a "replication…

Using data from multiple informants has long been considered best practice in education. However, multiple informants often disagree on similar constructs, complicating decision-making. Polynomial regression and response-surface analysis (PRA) is often used to test the congruence effect between multiple informants on an outcome. However, PRA…

The residual differential item functioning (RDIF) detection framework was developed recently under a linear testing context. To explore the potential application of this framework to computerized adaptive testing (CAT), the present study investigated the utility of the RDIF[subscript R] statistic both as an index for detecting uniform DIF of…

We investigate two non-iterative estimation procedures for Rasch models, the pair-wise estimation procedure (PAIR) and the Eigenvector method (EVM), and identify theoretical issues with EVM for rating scale model (RSM) threshold estimation. We develop a new procedure to resolve these issues--the conditional pairwise adjacent thresholds procedure…

Addressing cognitive disparities has become a paramount concern in computational thinking (CT) education. The intricate and nuanced relationships between CT and cognitive variations emphasize the needs to accommodate diverse cognitive profiles when fostering CT skills, recognizing that these cognitive functions can manifest as either strengths or…

To detect differential item functioning (DIF), Rasch trees search for optimal split-points in covariates and identify subgroups of respondents in a data-driven way. To determine whether and in which covariate a split should be performed, Rasch trees use statistical significance tests. Consequently, Rasch trees are more likely to label small DIF…