The robustness of qualitative comparative analysis (QCA) results features high on the agenda of methodologists and practitioners. This article aims at advancing this debate on several fronts. First, in line with the extant literature, we take a comprehensive view on robustness arguing that decisions on calibration, consistency, and frequency…

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…

Statistical literacy has a large and important role in the teaching of statistics. Most mathematics and statistics courses are hierarchical, and the earlier material forms the foundation for later material. We construct a hierarchical structure for an introductory statistics course using Rasch analysis of the student scripts for the final…

Transcription is an integral part of much qualitative data analysis, yet rarely has it received close attention in debates over the use (or non-use) of "computer assisted qualitative data analysis software" (CAQDAS). This article draws upon a mixed-methods study that involved transcribing conversational interviews with carers, third…

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…

Visual data have dramatically increased in quantity in the digital age, presenting new opportunities for social science research. However, the extensive time and labor costs to process and analyze these data with existing approaches limit their use. Computer vision methods hold promise but often require large and nonexistent training data to…

From a new institutional theoretical perspective, this article explores school actors' sense-making linked to data-based decision making (DBDM) policy in general and processes of data analysis in particular. The study revealed how actors' interpretation of and response to DBDM pointed to strong and weak couplings between and within the local…

Students now have readily available and powerful tools to access, manipulate, combine, and visualize data. Acquiring data and visual literacy requires more than knowledge of how to use these tools. Students need to engage with assignments that challenge them to make relatively complex visualizations, interpret them, and explain why these…

The R programming language and computing environment is a powerful and common platform used by life science researchers and educators for the analysis of big data. One of the benefits of using R in this context is its ability to visualize the results. Using R to generate visualizations has gained in popularity due to the increased number of R…

The purpose of the study was to understand the experiences of data scientists regarding common skills and strategies of interpreting and creating data visualizations. In this Delphi study, the participants were researchers in Data Science using three rounds of surveys. Skills and strategies were identified after Delphi Panel 1 and then brought…

A version of the classic rotationally resolved infrared (IR) spectrum of a diatomic molecule experiment has been developed using the POGIL framework to more fully engage students in the collection, modeling, analysis, and interpretation of the data. An analysis of the experimental protocol reveals that the POGIL approach actively engages students…

The ability to interpret and compare data distributions is an important educational goal. Inherent in the statistical concept of distribution is the need to focus not only on individual data points or small groups of data points (so-called local view), but to perceive a distribution as a whole, allowing to recognize global features such as center,…

This article introduces the concept of the carrying capacity of data (CCD), defined as an integrated, evaluative judgment of the credibility of specific data-based inferences, informed by quantitative and qualitative analyses, leavened by experience. The sequential process of evaluating the CCD is represented schematically by a framework that can…

Multilevel modeling is commonly used with clustered data, and much emphasis has been placed specifically on the multilevel linear model (MLM). When modeling clustered ordinal data, a multilevel ordinal model with cumulative logit link assuming proportional odds (i.e., multilevel cumulative logit model) is typically used. Depending on the research…

Despite the rising popularity of big data, there is speculation that evaluators have been slow adopters of these new statistical approaches. Several possible reasons have been offered for why this is the case: ethical concerns, institutional capacity, and evaluator capacity and values. In this method note, we address one of these barriers and aim…