By the end of this section, you will be able to:Explain what a correlation coefficient tells us about the relationship between variablesRecognize that correlation does not indicate a cause-and-effect relationship between variablesDiscuss our tendency to look for relationships between variables that do not really existExplain random sampling and assignment of participants into experimental and control groupsDiscuss how experimenter or participant bias could affect the results of an experimentIdentify independent and dependent variables

This workbook forms the basis of a Professional Graduate Certificate Module in Assessment.It offers a comprehensive collection of materials and activities in the realm of assessment.Please note: supplemental worksheets to accompany this are also available.

Students conduct Internet research to investigate the purpose and current functioning status of some of the largest dams throughout the world. They investigate the success or failure of eight dams and complete a worksheet. While researching the dams, they also gain an understanding of the scale of these structures by recording and comparing their reservoir capacities. Students come to understand that dams, like all engineered structures, have a finite lifespan and require ongoing maintenance and evaluation for their usefulness.

The main topics of study considered in light of the above learning outcomes are:Research philosophy (e.g. positivism, empiricism, naturalism)Formulating empirical research questions and conceptual research modelsCausality effects and relationshipsValidity and ReliabilityScales of measurement (e.g. nominal, ordinal, interval, ratio)Sampling methods (e.g. experiment, survey, observations) and measure instruments (e.g. Likert scales, semantic differential, event versus time sampling)Experimental design (e.g. within and between-subjects, factorial design, counter-balancing, Latin square)Biases in empirical research approaches (e.g. confounding variables, statistical power)Data preparation (e.g. standardization of data, reliability analysis, Inter-rater reliability)Hypothesis testing, t-test, (M)ANOVA, correlation, regression analysisNon-parametric approaches to data analysis

Human Supervisory Control of Automated Systems discusses elements of the interactions between humans and machines.  These elements include: assignment of roles and authority; tradeoffs between human control and human monitoring; and human intervention in automatic processes.  Further topics comprise: performance, optimization and social implications of the system; enhanced human interfaces; decision aiding; and automated alterting systems.  Topics refer to applications in aerospace, industrial and transportation systems.

Introduction to Sociology 2e adheres to the scope and sequence of a typical, one-semester introductory sociology course. It offers comprehensive coverage of core concepts, foundational scholars, and emerging theories, which are supported by a wealth of engaging learning materials. The textbook presents detailed section reviews with rich questions, discussions that help students apply their knowledge, and features that draw learners into the discipline in meaningful ways. The second edition retains the book’s conceptual organization, aligning to most courses, and has been significantly updated to reflect the latest research and provide examples most relevant to today’s students. In order to help instructors transition to the revised version, the 2e changes are described within the preface.

Define and describe the scientific methodExplain how the scientific method is used in sociological researchUnderstand the function and importance of an interpretive frameworkDefine what reliability and validity mean in a research study

This course introduces the theory and technology of micro/nano fabrication. Lectures and laboratory sessions focus on basic processing techniques such as diffusion, oxidation, photolithography, chemical vapor deposition, and more. Through team lab assignments, students are expected to gain an understanding of these processing techniques, and how they are applied in concert to device fabrication. Students enrolled in this course have a unique opportunity to fashion and test micro/nano-devices, using modern techniques and technology.

This paper investigates the conceptual relationship between openness and reproducibility using a model-centric approach, heavily informed by probability theory and statistics. We first clarify the concepts of reliability, auditability, replicability, and reproducibility–each of which denotes a potential scientific objective. Then we advance a conceptual analysis to delineate the relationship between open scientific practices and these objectives. Using the notion of an idealized experiment, we identify which components of an experiment need to be reported and which need to be repeated to achieve the relevant objective. The model-centric framework we propose aims to contribute precision and clarity to the discussions surrounding the so-called reproducibility crisis.

The output gap is one (of many) economic indicators used by economists to measure the strength of the economy. What exactly is the output gap, and how accurately does it predict the state of the economy? Read the November 2012 issue for an explanation of the output gap and answers to these questions.

Assessment is the gathering of information to improve decision-making. There are many approaches to psychological assessment that can be used to assess people’s thoughts, emotions, and behaviors, including self-report questionnaires, questionnaires reported by others, interviews, observations, biopsychological assessments, performance-based assessments, archival approaches, and combinations of these. However, the field has paid insufficient attention to measurement. There has been a proliferation of pseudoscience in assessment, including inaccurate assessments that can potentially cause harm. It is crucial to use assessments that are supported by science—with strong psychometric properties, including reliability and validity, and to develop better assessments.

This book discusses the principles of psychological assessment to help researchers and clinicians better develop, evaluate, administer, score, integrate, and interpret psychological assessments. It is intended for use by graduate students, faculty, researchers, and practicing psychologists. The book discusses psychometrics (reliability and validity), the assessment of various psychological domains (behavior, personality, intellectual functioning), various measurement methods (e.g., questionnaires, observations, interviews, biopsychological assessments, performance-based assessments), and emerging analytical frameworks to evaluate and improve assessment, including generalizability theory, structural equation modeling, item response theory, and signal detection theory. The book also discusses considerations of ethics, test bias, and cultural and individual diversity.

The book provides analysis examples using the free software, R, to help readers apply the principles to research and practice. The text, analysis code/syntax, R output, figures, and interpretation are all integrated to help guide readers. The book uses the freely available petersenlab package for R, which includes many functions relevant for assessment. The dissemination of these principles and tools will promote the development of more efficient and accurate assessment strategies that will lead to important scientific advancements and improved intervention.

Students learn how biomedical engineers work with engineers and other professionals to develop dependable medical devices. Specifically, they learn about suction pumps, which are important devices to keep in good repair, especially when they are used in remote locations. Student teams brainstorm, sketch, design and create prototypes of suction pump protection devices to keep fluid from backing up and ruining the pump motors. Using a real suction pump, they conduct repeated trials to test their devices for reliability, making improvements as necessary.

Psychology is designed to meet scope and sequence requirements for the single-semester introduction to psychology course. The book offers a comprehensive treatment of core concepts, grounded in both classic studies and current and emerging research. The text also includes coverage of the DSM-5 in examinations of psychological disorders. Psychology incorporates discussions that reflect the diversity within the discipline, as well as the diversity of cultures and communities across the globe.Senior Contributing AuthorsRose M. Spielman, Formerly of Quinnipiac UniversityContributing AuthorsKathryn Dumper, Bainbridge State CollegeWilliam Jenkins, Mercer UniversityArlene Lacombe, Saint Joseph's UniversityMarilyn Lovett, Livingstone CollegeMarion Perlmutter, University of Michigan

By the end of this section, you will be able to:Explain what a correlation coefficient tells us about the relationship between variablesRecognize that correlation does not indicate a cause-and-effect relationship between variablesDiscuss our tendency to look for relationships between variables that do not really existExplain random sampling and assignment of participants into experimental and control groupsDiscuss how experimenter or participant bias could affect the results of an experimentIdentify independent and dependent variables

By the end of this section, you will be able to:Explain what a correlation coefficient tells us about the relationship between variablesRecognize that correlation does not indicate a cause-and-effect relationship between variablesDiscuss our tendency to look for relationships between variables that do not really existRecognize how correlations coefficients tell us about the relationships between variables specific to selection methods.

This book covers a wide range of topics that involve the use of probability to solve problems in engineering design and research. Although it is relevant for a wide range of disciplines, it draws heavily on the fields of civil engineering, environmental engineering and the geosciences. Specific topics include risk analysis, probabilistic design, reliability-based design (component and system reliability). Future versions of this book will include additional applications of probability theory related to continuous distributions, extreme value analysis and expanded chapters on component and system reliability. A number of practical examples and exercises are included, both analytic and numerical, some of which use an interactive feature that allows Python code to be used in the web browser without any additional software installation.