In Part 1 of this unit, students will learn about data collection, graphing skills (both by hand and computer aided [Desmos]), and the fundamental mathematical patterns of the course: horizontal line, proportional, linear, quadratic, and inverse. Students perform several experiments, each targeting a different pattern and build the mathematical models of physical phenomena. During each experiment, students start with an uninformed wild guess, then through inquiry and making sense through group consensus, can make an accurate data informed prediction.
Keynote presentations, videos, workshop materials, and other resources from the 2020 CQI Statewide Conference for Child Welfare and Probation at UC Davis
The Open Science movement is rapidly changing the scientific landscape. Because exact definitions are often lacking and reforms are constantly evolving, accessible guides to open science are needed. This paper provides an introduction to open science and related reforms in the form of an annotated reading list of seven peer-reviewed articles, following the format of Etz et al. (2018). Written for researchers and students - particularly in psychological science - it highlights and introduces seven topics: understanding open science; open access; open data, materials, and code; reproducible analyses; preregistration and registered reports; replication research; and teaching open science. For each topic, we provide a detailed summary of one particularly informative and actionable article and suggest several further resources. Supporting a broader understanding of open science issues, this overview should enable researchers to engage with, improve, and implement current open, transparent, reproducible, replicable, and cumulative scientific practices.
By using the hook of Halley’s comet, dark matter, and dark energy students data mine Newton’s Law of Universal Gravity and build an and evaluate other arguments for the Big Bang.
The students will use ACC basketball statistics to practice the process of converting fractions to decimals then to percents and will learn how to create and edit a spreadsheet. They will then use this spreadsheet to analyze their data. This unit is done during the basketball season which takes approximately 15 weeks from the middle of November to the middle of March. Teachers must have Clarisworks to open the sample spreadsheet in the lesson, but may recreate it in another spreadsheet program.
- Statistics and Probability
- Material Type:
- Lesson Plan
- University of North Carolina at Chapel Hill School of Education
- Provider Set:
- LEARN NC Lesson Plans
- Susan Dougherty
- Date Added:
Students work as physicists to understand centripetal acceleration concepts. They also learn about a good robot design and the accelerometer sensor. They also learn about the relationship between centripetal acceleration and centripetal force governed by the radius between the motor and accelerometer and the amount of mass at the end of the robot's arm. Students graph and analyze data collected from an accelerometer, and learn to design robots with proper weight distribution across the robot for their robotic arms. Upon using a data logging program, they view their own data collected during the activity. By activity end , students understand how a change in radius or mass can affect the data obtained from the accelerometer through the plots generated from the data logging program. More specifically, students learn about the accuracy and precision of the accelerometer measurements from numerous trials.
OSF Guides are self-help introductions to using the Open Science Framework (OSF). OSF is a free and open source project management tool that supports researchers throughout their entire project lifecycle. This OSF Guides covers the topic of accessing your OSF account: Create an OSF Account Sign in to OSF Claim an Unregistered Account Reset Your Password
OSF Guides are self-help introductions to using the Open Science Framework (OSF). OSF is a free and open source project management tool that supports researchers throughout their entire project lifecycle. This OSF Guides covers the topics using add-on storage services in the OSF, including: Connect Amazon S3 to a Project Connect Bitbucket to a Project Connect Box to a Project Connect Dataverse to a Project Connect Dropbox to a Project Connect figshare to a Project Connect GitHub to a Project Connect GitLab to a Project Connect Google Drive to a Project Connect OneDrive to a Project Connect ownCloud to a Project
The veracity of substantive research claims hinges on the way experimental data are collected and analyzed. In this article, we discuss an uncomfortable fact that threatens the core of psychology’s academic enterprise: almost without exception, psychologists do not commit themselves to a method of data analysis before they see the actual data. It then becomes tempting to fine tune the analysis to the data in order to obtain a desired result—a procedure that invalidates the interpretation of the common statistical tests. The extent of the fine tuning varies widely across experiments and experimenters but is almost impossible for reviewers and readers to gauge. To remedy the situation, we propose that researchers preregister their studies and indicate in advance the analyses they intend to conduct. Only these analyses deserve the label “confirmatory,” and only for these analyses are the common statistical tests valid. Other analyses can be carried out but these should be labeled “exploratory.” We illustrate our proposal with a confirmatory replication attempt of a study on extrasensory perception.
This textbook is an introductory coverage of algorithms and data structures with application to graphics and geometry.
This resource provides access to analyses related to core training for child welfare social workers in the Northern California region.
Ongoing technological developments have made it easier than ever before for scientists to share their data, materials, and analysis code. Sharing data and analysis code makes it easier for other researchers to re-use or check published research. These benefits will only emerge if researchers can reproduce the analysis reported in published articles, and if data is annotated well enough so that it is clear what all variables mean. Because most researchers have not been trained in computational reproducibility, it is important to evaluate current practices to identify practices that can be improved. We examined data and code sharing, as well as computational reproducibility of the main results, without contacting the original authors, for Registered Reports published in the psychological literature between 2014 and 2018. Of the 62 articles that met our inclusion criteria, data was available for 40 articles, and analysis scripts for 37 articles. For the 35 articles that shared both data and code and performed analyses in SPSS, R, Python, MATLAB, or JASP, we could run the scripts for 31 articles, and reproduce the main results for 20 articles. Although the articles that shared both data and code (35 out of 62, or 56%) and articles that could be computationally reproduced (20 out of 35, or 57%) was relatively high compared to other studies, there is clear room for improvement. We provide practical recommendations based on our observations, and link to examples of good research practices in the papers we reproduced.
A statistics lesson on describing and making claims from data representations, specifically linearly increasing data. Applies ideas of rate-of-change to develop writing a linear equation to fit the data, using the equation to interpolate and extrapolate additional information, and integrating the mathematical interpretation appropriately into a social sciences argument.
Students prepare for the associated activity in which they investigate acceleration by collecting acceleration vs. time data using the accelerometer of a sliding Android device. Based on the experimental set-up for the activity, students form hypotheses about the acceleration of the device. Students will investigate how the force on the device changes according to Newton's Second Law. Different types of acceleration, including average, instantaneous and constant acceleration, are introduced. Acceleration and force is described mathematically and in terms of processes and applications.
Students investigate the motion of a simple pendulum through direct observation and data collection using Android® devices. First, student groups create pendulums that hang from the classroom ceiling, using Android smartphones or tablets as the bobs, taking advantage of their built-in accelerometers. With the Android devices loaded with the (provided) AccelDataCapture app, groups explore the periodic motion of the pendulums, changing variables (amplitude, mass, length) to see what happens, by visual observation and via the app-generated graphs. Then teams conduct formal experiments to alter one variable while keeping all other parameters constant, performing numerous trials, identifying independent/dependent variables, collecting data and using the simple pendulum equation. Through these experiments, students investigate how pendulums move and the changing forces they experience, better understanding the relationship between a pendulum's motion and its amplitude, length and mass. They analyze the data, either on paper or by importing into a spreadsheet application. As an extension, students may also develop their own algorithms in a provided App Inventor framework in order to automatically note the time of each period.
This is a free textbook teaching introductory statistics for undergraduates in Psychology. This textbook is part of a larger OER course package for teaching undergraduate statistics in Psychology, including this textbook, a lab manual, and a course website. All of the materials are free and copiable, with source code maintained in Github repositories.
Python es un lenguaje de programación general que es útil para escribir scripts para trabajar con datos de manera efectiva y reproducible. Esta es una introducción a Python diseñada para participantes sin experiencia en programación. Estas lecciones pueden enseñarse en un día (~ 6 horas). Las lecciones empiezan con información básica sobre la sintaxis de Python, la interface de Jupyter Notebook, y continúan con cómo importar archivos CSV, usando el paquete Pandas para trabajar con DataFrames, cómo calcular la información resumen de un DataFrame, y una breve introducción en cómo crear visualizaciones. La última lección demuestra cómo trabajar con bases de datos directamente desde Python. Nota: los datos no han sido traducidos de la versión original en inglés, por lo que los nombres de variables se mantienen en inglés y los números de cada observación usan la sintaxis de habla inglesa (coma separador de miles y punto separador de decimales).
- Computer Science
- Information Science
- Measurement and Data
- Material Type:
- The Carpentries
- Alejandra Gonzalez-Beltran
- April Wright
- Christopher Erdmann
- Enric Escorsa O'Callaghan
- Erin Becker
- Fernando Garcia
- Hely Salgado
- Juan Martín Barrios
- Juan M. Barrios
- Katrin Leinweber
- Laura Angelone
- Leonardo Ulises Spairani
- Maxim Belkin
- Miguel González
- Nicolás Palopoli
- Nohemi Huanca Nunez
- Paula Andrea Martinez
- Raniere Silva
- Rayna Harris
- Sarah Brown
- Silvana Pereyra
- Spencer Harris
- Stephan Druskat
- Trevor Keller
- Wilson Lozano
- Date Added:
To study the availability of psychological research data, we requested data from 394 papers, published in all issues of four APA journals in 2012. We found that 38% of the researchers sent their data immediately or after reminders. These findings are in line with estimates of the willingness to share data in psychology from the recent or remote past. Although the recent crisis of confidence that shook psychology has highlighted the importance of open research practices, and technical developments have greatly facilitated data sharing, our findings make clear that psychology is nowhere close to being an open science.
In this study, we examined participants' choice behavior in a sequential risk-taking task. We were especially interested in the extent to which participants focus on the immediate next choice or consider the entire choice sequence. To do so, we inspected whether decisions were either based on conditional probabilities (e.g., being successful on the immediate next trial) or on conjunctive probabilities (of being successful several times in a row). The results of five experiments with a simplified nine-card Columbia Card Task and a CPT-model analysis show that participants' choice behavior can be described best by a mixture of the two probability types. Specifically, for their first choice, the participants relied on conditional probabilities, whereas subsequent choices were based on conjunctive probabilities. This strategy occurred across different start conditions in which more or less cards were already presented face up. Consequently, the proportion of risky choices was substantially higher when participants started from a state with some cards facing up, compared with when they arrived at that state starting from the very beginning. The results, alternative accounts, and implications are discussed.
There is broad interest to improve the reproducibility of published research. We developed a survey tool to assess the availability of digital research artifacts published alongside peer-reviewed journal articles (e.g. data, models, code, directions for use) and reproducibility of article results. We used the tool to assess 360 of the 1,989 articles published by six hydrology and water resources journals in 2017. Like studies from other fields, we reproduced results for only a small fraction of articles (1.6% of tested articles) using their available artifacts. We estimated, with 95% confidence, that results might be reproduced for only 0.6% to 6.8% of all 1,989 articles. Unlike prior studies, the survey tool identified key bottlenecks to making work more reproducible. Bottlenecks include: only some digital artifacts available (44% of articles), no directions (89%), or all artifacts available but results not reproducible (5%). The tool (or extensions) can help authors, journals, funders, and institutions to self-assess manuscripts, provide feedback to improve reproducibility, and recognize and reward reproducible articles as examples for others.