$\mu$Cap is a software package (citeware) for economic experiments enabling experimenters to analyze emotional states of subjects using z-Tree and FaceReader™. $\mu$Cap is able to create videos of subjects on client computers based on stimuli shown on screen and restrict recording material to relevant time frames. Another feature of $\mu$Cap is the creation of time stamps in csv format at prespecified screens (or at prespecified points in time) during the experiment, measured on the client computer. The software makes it possible to import these markers into FaceReader™ easily. $\mu$Cap is the first program that significantly simplifies the process of connecting z-Tree and FaceReader™ with the additional benefit of extremely high precision. This paper describes the usage, underlying principles as well as advantages and limitations of $\mu$Cap. Furthermore, we give a brief outlook of how $\mu$Cap can be beneficial in other contexts.

This paper discusses aspects of recruiting subjects for economic laboratory experiments, and shows how the Online Recruitment System for Economic Experiments can help. The software package provides experimenters with a free, convenient, and very powerful tool to organize their experiments and sessions.

stratEst is a software package for strategy frequency estimation in the freely available statistical computing environment R (R Development Core Team in R Foundation for Statistical Computing, Vienna, 2022). The package aims at minimizing the start-up costs of running the modern strategy frequency estimation techniques used in experimental economics. Strategy frequency estimation (Stahl and Wilson in J Econ Behav Organ 25: 309–327, 1994; Stahl and Wilson in Games Econ Behav, 10: 218–254, 1995) models the choices of participants in an economic experiment as a finite-mixture of individual decision strategies. The parameters of the model describe the associated behavior of each strategy and its frequency in the data. stratEst provides a convenient and flexible framework for strategy frequency estimation, allowing the user to customize, store and reuse sets of candidate strategies. The package includes useful functions for data processing and simulation, strategy programming, model estimation, parameter testing, model checking, and model selection.

We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between test items arises from the influence of one or more common latent variables. Here, we present two generalizations of the network model that encompass latent variable structures, establishing network modeling as parts of the more general framework of structural equation modeling (SEM). In the first generalization, we model the covariance structure of latent variables as a network. We term this framework latent network modeling (LNM) and show that, with LNM, a unique structure of conditional independence relationships between latent variables can be obtained in an explorative manner. In the second generalization, the residual variance–covariance structure of indicators is modeled as a network. We term this generalization residual network modeling (RNM) and show that, within this framework, identifiable models can be obtained in which local independence is structurally violated. These generalizations allow for a general modeling framework that can be used to fit, and compare, SEM models, network models, and the RNM and LNM generalizations. This methodology has been implemented in the free-to-use software package lvnet, which contains confirmatory model testing as well as two exploratory search algorithms: stepwise search algorithms for low-dimensional datasets and penalized maximum likelihood estimation for larger datasets. We show in simulation studies that these search algorithms perform adequately in identifying the structure of the relevant residual or latent networks. We further demonstrate the utility of these generalizations in an empirical example on a personality inventory dataset.

MTVE is an open-source software tool (citeware) that can be applied in laboratory and online experiments to implement video communication. The tool enables researchers to gather video data from these experiments in a way that these videos can be later used for automatic analysis through machine learning techniques. The browser-based tool comes with an easy user interface and can be easily integrated into z-Tree, oTree (and other experimental or survey tools). It provides the experimenters control over several communication parameters (e.g., number of participants, resolution), produces high-quality video data, and circumvents the Cocktail Party Problem (i.e., the problem of separating speakers solely based on audio input) by producing separate files. Using some of the recommended Voice-to-Text AI, the experimenters can transcribe individual files. MTVE can merge these individual transcriptions into one conversation.

OpenMx is free, full-featured, open source, structural equation modeling (SEM) software. OpenMx runs within the R statistical programming environment on Windows, Mac OS–X, and Linux computers. The rationale for developing OpenMx is discussed along with the philosophy behind the user interface. The OpenMx data structures are introduced—these novel structures define the user interface framework and provide new opportunities for model specification. Two short example scripts for the specification and fitting of a confirmatory factor model are next presented. We end with an abbreviated list of modeling applications available in OpenMx 1.0 and a discussion of directions for future development.

Text is a major medium of contemporary interpersonal communication but is difficult for social scientists to study unless they have significant resources or the skills to build their own research platform. In this paper, we introduce a cloud-based software solution to this problem: ReChat, an online research platform for conducting experimental and observational studies of live text conversations. We demonstrate ReChat by applying it to a specific phenomenon of interest to political scientists: conversations among co-partisans. We present results from two studies, focusing on (1) self-selection factors that make chat participants systematically unrepresentative and (2) a pre-registered analysis of loquaciousness that finds a significant association between speakers’ ideological extremity and the amount they write in the chat. We conclude by discussing practical implications and advice for future practitioners of chat studies.

One pedagogical finding that has gained recent attention is the utility of active, effortful retrieval practice in effective learning. Essentially, humans learn best when they are asked to actively generate/recall knowledge for themselves, rather than receiving knowledge passively. In this paper, we (a) provide a framework for both practice and assessment within which students can organically develop active study habits, (b) share resources we have built to help implement such a framework in the linguistics classroom, and (c) provide some examples and evaluation of their success in the context of an introductory phonetics/phonology course.

A Microsoft® Visual Basic software, WinClbclas, has been developed to calculate the chemical formulae of columbite-supergroup minerals based on data obtained from wet-chemical and electron-microprobe analyses and using the current nomenclature scheme adopted by the Commission on New Minerals, Nomenclature and Classification (CNMNC) of the International Mineralogical Association (IMA) for columbite-supergroup minerals. The program evaluates 36 IMA-approved species, three questionable in terms of their unit-cell parameters, four insufficiently studied questionable species and one ungrouped species, all according to the dominant valance and constituent status in five mineral groups including ixiolite (MO2), wolframite (M1M2O4), samarskite (ABM2O8), columbite (M1M2O6) and wodginite (M1M2M32O8). Mineral compositions of the columbite supergroup are calculated on the basis of 24 oxygen atoms per formula unit. However, the formulae of the five ixiolite to wodginite groups can be estimated by the program on the basis of their cation and anion values in their typical mineral formulae (e.g. 4 cations and 8 oxygens for the wodginite group) with normalisation procedures. The Fe3+ and Fe2+ contents from microprobe-derived total FeO (wt.%) amounts are estimated by stoichiometric constraints. WinClbclas allows users to: (1) enter up to 47 input variables for mineral compositions; (2) type and load multiple columbite-supergroup mineral compositions in the data entry section; (3) edit and load the Microsoft® Excel files used in calculating, classifying, and naming the columbite-supergroup minerals, together with the total monovalent to hexavalent ion; and (4) store all the calculated parameters in the output of a Microsoft® Excel file for further data evaluation. The program is distributed as a self-extracting setup file, including the necessary support files used by the program, a help file and representative sample data files.

REAP-2 is an interactive dose-response curve estimation tool for Robust and Efficient Assessment of drug Potency. It provides user-friendly dose-response curve estimation for in vitro studies and conducts statistical testing for model comparisons with a redesigned user interface. We also make a major update of the underlying estimation method with penalized beta regression, which demonstrates great reliability and accuracy in dose estimation and uncertainty quantification. In this note, we describe the method and implementation of REAP-2 with a highlight on potency estimation and drug comparison.