Social scientists seeking to inform policy or public action must carefully consider how to identify effects and express inferences because actions based on invalid inferences will not yield the intended results. Recognizing the complexities and uncertainties of social science, we seek to inform inevitable debates about causal inferences by…

This article demonstrates how a technique called Agent-Based Modelling can address a significant challenge for effective interdisciplinarity. Different disciplines and research methods make divergent assertions about what a satisfactory explanation requires. However, without a unified framework analysing the implications of these differences…

Theories of causation in philosophy ask what makes causal claims true and establish the so-called truth conditions allowing one to separate causal from noncausal relationships. We argue that social scientists should be aware of truth conditions of causal claims because they imply which method of causal inference can establish whether a specific…

Library and information science (LIS) research is becoming more quantitative. However, statistics is not extensively taught within LIS research methods courses, and statistics courses are uncommon within LIS programs. Previous research on statistics in LIS revealed that researchers have mainly relied on descriptive statistics in publications. This…

Students often believe that statistical significance is the only determinant of whether a quantitative result is "important." In this paper, I review traditional null hypothesis statistical testing to identify what questions inferential statistics can and cannot answer, including statistical significance, effect size and direction,…

Research questions regarding how, for whom, and where a treatment achieves its effect on an outcome have become increasingly valued in substantive research. Such questions can be answered by causal moderated mediation analysis, which assesses the heterogeneity of the mediation mechanism underlying the treatment effect across individual and…

This comment discusses some issues about the use and reporting of experimental studies in education, illustrated by a recently published study that claimed (i) that an educational innovation was effective despite outcomes not reaching statistical significance, and (ii) that this refuted the findings of an earlier study. The two key issues raised…

Purpose: Speech-evoked neurophysiological responses are often collected to answer clinically and theoretically driven questions concerning speech and language processing. Here, we highlight the practical application of machine learning (ML)-based approaches to analyzing speech-evoked neurophysiological responses. Method: Two categories of ML-based…

Purpose: Nonexperimental research, defined as any kind of quantitative or qualitative research that is not an experiment, is the predominate kind of research design used in the social sciences. How to unambiguously and correctly present the results of nonexperimental research, however, remains decidedly unclear and possibly detrimental to applied…

Graduate students in political science come to the discipline interested in exploring important political questions, such as "What causes war?" or "What policies promote economic growth?" However, they typically do not arrive prepared to address those questions using quantitative methods. Graduate methods instructors must…

Amid recent progress in cognitive development research, high-quality data resources are accumulating, and data sharing and secondary data analysis are becoming increasingly valuable tools. Integrative data analysis (IDA) is an exciting analytical framework that can enhance secondary data analysis in powerful ways. IDA pools item-level data across…

The purpose of this paper is to provide an easy template for the inclusion of the Bayes factor in reporting experimental results, particularly as a recommendation for articles in the "Journal of Problem Solving." The Bayes factor provides information with a similar purpose to the "p"-value--to allow the researcher to make…

It is more common for educational and psychological data to be nonnormal than to be approximately normal. This tendency may lead to bias and error in point estimates of the Pearson correlation coefficient. In a series of Monte Carlo simulations, the Pearson correlation was examined under conditions of normal and nonnormal data, and it was compared…

Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding…

This research synthesis was conducted to understand the effectiveness of interventions designed to improve learning from informational text for students with learning disabilities in elementary school (K-5). The authors identified 18 studies through a comprehensive search. The interventions were evaluated to determine treatment effects and to…