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.

Systematic error, or 'bias' is of particular importance in any epidemiological investigation, and should be avoided wherever possible. Biases will reduce the validity of any results obtained, whether it be by overestimating or underestimating the frequency of disease in a population or the association between an exposure and disease. The forms of bias covered here can only be minimised through careful study design and execution - they cannot be accounted for in the analysis. Although confounding is considered by many authors as a form of bias, it can be accounted for during analysis, and so is covered separately.

In this module, we review the construct of emotional intelligence by examining its underlying theoretical model, measurement tools, validity, and applications in real-world settings. We use empirical research from the past few decades to support and discuss competing definitions of emotional intelligence and possible future directions for the field.

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

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

In this presentation, we will go through some of the very interesting results and our preliminary interpretation of the data from the SHOPUS project. Furthermore, we will talk about how using the shop model made it possible to achieve a very high compliance to the New Nordic Diet and the control diet (average Danish diet) and how to obtain the expected differences in food intake between those two study diets.

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.

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

This chapter covers:Why is Research Important?Approaches to ResearchAnalyzing FindingsEthicsFor more information, visit OpenStax College.

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.

In this review, the author discusses several of the weak spots in contemporary science, including scientific misconduct, the problems of post hoc hypothesizing (HARKing), outcome switching, theoretical bloopers in formulating research questions and hypotheses, selective reading of the literature, selective citing of previous results, improper blinding and other design failures, p-hacking or researchers’ tendency to analyze data in many different ways to find positive (typically significant) results, errors and biases in the reporting of results, and publication bias. The author presents some empirical results highlighting problems that lower the trustworthiness of reported results in scientific literatures, including that of animal welfare studies. Some of the underlying causes of these biases are discussed based on the notion that researchers are only human and hence are not immune to confirmation bias, hindsight bias, and minor ethical transgressions. The author discusses solutions in the form of enhanced transparency, sharing of data and materials, (post-publication) peer review, pre-registration, registered reports, improved training, reporting guidelines, replication, dealing with publication bias, alternative inferential techniques, power, and other statistical tools.