This contribution deals with effect measures for covariates in ordinal data models to address the interpretation of the results on the extreme categories of the scales, evaluate possible response styles, and motivate collapsing of extreme categories. It provides a simpler interpretation of the influence of the covariates on the probability of the…

This article aims to clarify fundamental aspects of the process of assigning fuzzy scores to conditions based on family resemblance (FR) structures by considering prototype and set theories. Prototype theory and set theory consider FR structures from two different angles. Specifically, set theory links the conceptualization of FR to the idea of…

Participatory modeling (PM) is an engaged research methodology for creating analog or computer-based models of complex systems, such as socio-environmental systems. Used across a range of fields, PM centers stakeholder knowledge and participation to create more internally valid models that can inform policy and increase engagement and trust…

Spatial regression models provide the opportunity to analyze spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarizes the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In…

This article describes and justifies a reporting standard to improve data use documentation in Agent-Based Modelling. Following the development of reporting standards for models themselves, empirical modelling has now developed to the point where these standards need to take equally effective account of data use (which previously has tended to be…

We present a qualitative data analysis framework that integrates perspectives from theme analysis, cultural models analysis, grounded theory, and content analysis. We demonstrate how these research traditions are united in their aim to, first, uncover meaningful themes and, subsequently, to understand those themes' relationships to each other. To…

Big data analytics are prevalent in fields like business, engineering, public health, and the physical sciences, but social scientists are slower than their peers in other fields in adopting this new methodology. One major reason for this is that traditional statistical procedures are typically not suitable for the analysis of large and complex…

Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all…

This paper explores the potential of an online TimeBank for inclusive research to address some of the challenges related to the unequal distribution of power and money for researchers within and outside the academy working in collaboration. The problem, the concept of TimeBanking, and the relationship of TimeBanking to inclusive research…

It remains a dilemma for social work educators to teach research so that it can be embraced by students as an integral part of social work practice. This article reports on an initiative to design a research course focused on the integration of research with practice. Drawing on developments in data mining and techniques in secondary data…

The purpose of the current study was to help understand scholarly activity better among counseling psychology doctoral students. Two new variables were added to the previously created predictor model of scholarly activity: advisory working alliance and research competence. Three path analytic models were designed in the current study: (1) a…

This article explores how concept maps and mind maps can be used as data collection tools in mixed methods research to combine the clarity of quantitative counts with the nuance of qualitative reflections. Based on more traditional mixed methods approaches, this article details how the use of pre/post concept maps can be used to design qualitative…

Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture…

Purpose: This study aims to present Dramatic Problem Solving Facilitation Model (DPSFM) and Interactive Management (IM) as innovative alternative dispute resolution approaches that incorporate communication technologies in recording and analyzing data. DPSFM utilizes performance-based actions with facilitation methods to help participants design…

This study analyzed the reporting of multilevel modeling applications of a sample of 99 articles from 13 peer-reviewed journals in education and the social sciences. A checklist, derived from the methodological literature on multilevel modeling and focusing on the issues of model development and specification, data considerations, estimation, and…