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A software is needed that can be used for beginners in learning instrumentation and automation. One of the main diagrams and learning of these two concepts is the Ladder Diagram. For beginners it is easier to use software that is ready to use without having to install the Personal Computer. One software that can be used is PLC Fiddle. Through this paper, we provide an overview of what features are in the PLC fiddle. And explained how to use these features for learning instrumentation and automation, for beginners. We provide several Online Video Tutorials for learning PLC Fiddle.

Background Failure to appropriately account for unmeasured confounding may lead to erroneous conclusions. Quantitative bias analysis (QBA) can be used to quantify the potential impact of unmeasured confounding or how much unmeasured confounding would be needed to change a study’s conclusions. Currently, QBA methods are not routinely implemented, partly due to a lack of knowledge about accessible software. Also, comparisons of QBA methods have focused on analyses with a binary outcome. Methods We conducted a systematic review of the latest developments in QBA software published between 2011 and 2021. Our inclusion criteria were software that did not require adaption (i.e., code changes) before application, was still available in 2022, and accompanied by documentation. Key properties of each software tool were identified. We provide a detailed description of programs applicable for a linear regression analysis, illustrate their application using two data examples and provide code to assist researchers in future use of these programs. Results Our review identified 21 programs with 62 % created post 2016. All are implementations of a deterministic QBA with 81 % available in the free software R. There are programs applicable when the analysis of interest is a regression of binary, continuous or survival outcomes, and for matched and mediation analyses. We identified five programs implementing differing QBAs for a continuous outcome: treatSens, causalsens, sensemakr, EValue, and konfound. When applied to one of our illustrative examples, causalsens incorrectly indicated sensitivity to unmeasured confounding whereas the other four programs indicated robustness. sensemakr performs the most detailed QBA and includes a benchmarking feature for multiple unmeasured confounders. Conclusions Software is now available to implement a QBA for a range of different analyses. However, the diversity of methods, even for the same analysis of interest, presents challenges to their widespread uptake. Provision of detailed QBA guidelines would be highly beneficial.

Psychometric network analysis (PNA) has been gaining great popularity over the past decade. As a promising dimensionality assessment method, existing research has shown that PNA is able to outperform traditional methods such as exploratory factor analysis in examining the internal structure of a latent construct, and various R packages have been developed to carry out PNA. Yet, PNA has not been widely used in various fields due to researchers’ lack of familiarization with this method and the available R packages. Therefore, this study aims to briefly review the PNA method, compare different R packages, and provide step-by-step guidance on how to use these R packages to conduct PNA using a personality dataset.

In education research, social network analysis is being widely used to study different interactions and their overall implications. Recently, there has also been a surge in the development of software tools to implement social network analysis. In this article, we review two popular R packages, igraph and statnet suite, in the context of network summarization and modeling. We discuss different aspects of these packages and demonstrate some of their functionalities by analyzing a friendship network of lawyers. Finally, we end with recommendations for using these packages along with pointers to additional resources for network analysis in R.

Given the natural hierarchical structure in school-setting data, multilevel modeling (MLM) has been widely employed in education research using a number of different statistical software packages. The purpose of this article is to review a recent feature of Stat-JR, the statistical analysis assistants (SAAs) embedded in Stat-JR (Version 1.0.5), with regard to their use for MLM. In this article, we review the features of Stat-JR's SAAs and illustrate how to implement SAAs, using one of the Stat-JR interfaces to analyze multilevel models for the 1982 High School and Beyond data set. Results from Stat-JR SAA are compared with the results using HLM7.01 software. We also discuss recommendations and implications for future users of SAAs.

Whilst a broad range of project portfolio management (PPM) tools is developed to enable and automate the PPM processes, there is a limited understanding of the factors affecting their adoption and deployment in organizations. This research presents a systematic approach that integrates the topic modeling with technology-organization-environment (TOE) framework to identify the salient factors affecting the adoption of PPM software from online software reviews. The proposed approach consists of four main steps: reviews collection and preprocessing, factors prediction, factors ranking, and factors integration into TOE framework. The online software reviews used in this study were gathered from Gartner and included 877 reviews for 13 widely used PPM software tools. The results of this research revealed that several factors could affect the adoption decision of PPM software in organizations. These results provide several theoretical and practical implications, and thus should help both researchers and practitioners in the deployment of more user-accepted PPM software and practices.

Architecture reviews have evolved over the past decade to become a critical part of our continuing efforts to improve the state of affairs. We use them to identify project problems before they become costly to fix and to provide timely information to upper management so that they can make better-informed decisions. It provides the foundation for reuse, using commercially available software, and getting to the marketplace fast. The reviews also help identify best practices to projects and socialize such practices across the organization, thereby improving the organization's quality and operations.

There is an increasing interest in reporting test subscores for diagnostic purposes. In this article, we review nine popular R packages (subscore, mirt, TAM, sirt, CDM, NPCD, lavaan, sem, and OpenMX) that are capable of implementing subscore-reporting methods within one or more frameworks including classical test theory, multidimensional item response theory, cognitive diagnostic models, and factor analysis. A real data example is used to illustrate how to examine whether subscores should be reported and how to obtain subscores. We also briefly compare the features of selected packages for reporting subscores.

In this article, we review four software packages for implementing propensity score analysis in R: Matching, MatchIt, PSAgraphics, and twang. After briefly discussing essential elements for propensity score analysis, we apply each package to a data set from the Early Childhood Longitudinal Study in order to estimate the average effect of elementary school special education services on math achievement in fifth grade. In the context of this real data example, we evaluate documentation and support resources, built-in quantitative and graphical diagnostic features, and methods available for estimating a causal effect. We conclude by making some recommendations aimed at helping researchers decide which package to turn to based upon their familiarity with propensity score methods, programming in R, and the type of analysis being conducted.