Distances derived from word embeddings can measure a range of gradational relations--similarity, hierarchy, entailment, and stereotype--and can be used at the document- and author-level in ways that overcome some of the limitations of weighted dictionary methods. We provide a comprehensive introduction to using word embeddings for relation…

Large language models (LLMs), through their exposure to massive collections of online text, learn to reproduce the perspectives and linguistic styles of diverse social and cultural groups. This capability suggests a powerful social scientific application--the simulation of empirically realistic, culturally situated human subjects. Synthesizing…

The integration of artificial intelligence (AI), and particularly generative AI, into research methods is rapidly transforming both academic and industry marketing research, including both methods practices and education regarding these practices. AI application within methods offers new opportunities for enhancing efficiency, automating…

In sparse data meta-analyses (with few trials or zero events), conventional methods may distort results. Although better-performing one-stage methods have become available in recent years, their implementation remains limited in practice. This study examines the impact of using conventional methods compared to one-stage models by re-analysing…

This paper is a critical exposition on three major issues related to 'interpretive research conducted by researchers who claim they engaged in mixed methods' research. First, to provide context, we demonstrate that the term 'mixed' is inappropriate for the research practices usually adopted by its exponents. Secondly, we argue, expositions in…

Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the…

Research of issues related to disability is consistently evolving in several social science related fields such as social work, psychology, sociology, and education. Disability research often employs large public datasets for researchers to conduct secondary analysis. However, these datasets come with many limitations that can impact the overall…

Small sample sizes contribute to several problems in research and knowledge advancement. This conceptual replication study confirmed and extended the inflation of type II errors and confidence intervals in correlation analyses of small sample sizes common in kinesiology/exercise science. Current population data (N = 18, 230, & 464) on four…

Although parallel analysis has been found to be an accurate method for determining the number of factors in many conditions with complete data, its application under missing data is limited. The existing literature recommends that, after using an appropriate multiple imputation method, researchers either apply parallel analysis to every imputed…

Background: Randomized controlled trials (RCTs) give unbiased estimates of average effects. However, positive effects for the majority of students may mask harmful effects for smaller subgroups, and RCTs often have too small a sample to estimate these subgroup effects. In many RCTs, covariate and outcome data are drawn from a larger database. For…

Teachers need instructionally useful data to make timely and appropriate decisions to meet their students with intensive needs (Filderman et al., 2019). Teachers have still experienced difficulty in instructional decision making in response to students' CBM data (Gesel et al., 2021). This is because data itself that was used for simply determining…

Determining the number of factors in exploratory factor analysis is arguably the most crucial decision a researcher faces when conducting the analysis. While several simulation studies exist that compare various so-called factor retention criteria under different data conditions, little is known about the impact of missing data on this process.…

N-of-1 trials, a special case of Single Case Experimental Designs (SCEDs), are prominent in clinical medical research and specifically psychiatry due to the growing significance of precision/personalized medicine. It is imperative that these clinical trials be conducted, and their data analyzed, using the highest standards to guard against threats…

Research on input processing and processing instruction has often employed a scoring method known as trials to criterion to observe the effects of instruction that emerge during training. Despite its common use in this research (see Fernández, 2021) this metric has never been evaluated critically. The present study first discusses several…

Composite scores are an exceptionally important psychometric tool for behavioral science research applications. A prototypical example occurs with self-report data, where researchers routinely use questionnaires with multiple items that tap into different features of a target construct. Item-level missing data are endemic to composite score…