Defines what "Out of Range Values" are and how to account for them in data collection and statistical analysis.

This course is designed to lay the foundations of good empirical research in the social sciences. It does not deal with specific techniques per se, but rather with the assumptions and the logic underlying social research. Students become acquainted with a variety of approaches to research design, and are helped to develop their own research projects and to evaluate the products of empirical research.

This curriculum, which can be used in whole or in part, provides background legislative initiatives, evaluations of Family Preservation/Support Programs in different areas of the country, and techniques in evaluating community-based programs. Chapters include: a description of the development of Family Preservation/Family Support programs including key federal legislation and California's implementation process; a review of current literature on both family support and family preservation evaluations; a state-wide matrix of County Five-Year Plans for the Family Preservation/Support Program Initiative, summaries of 10 county Five-Year Plans, and case studies of three counties; information on single-subject designs including the nature and scope of single-subject research and its relationship to time-series design; information on collecting and analyzing administrative level data to determine whether change has occurred in a target community; and analysis of administrative level data within a single-system design framework. This module addresses Child Welfare Policy, Planning and Administration competencies. (343 pages)Rogers, K., Ferguson, C., Barth, R. P., & Embry, R. (1998).

This webinar provides an overview of TOP Factor: its rationale, how it is being used, and how each of the TOP standards relate to individual scores. We also cover how to get involved with TOP Factor by inviting interested community members to suggest journals be added to the database and/or evaluate journal policies for submission.

Abstract
For knowledge to benefit research and society, it must be trustworthy. Trustworthy research is robust, rigorous, and transparent at all stages of design, execution, and reporting. Assessment of researchers still rarely includes considerations related to trustworthiness, rigor, and transparency. We have developed the Hong Kong Principles (HKPs) as part of the 6th World Conference on Research Integrity with a specific focus on the need to drive research improvement through ensuring that researchers are explicitly recognized and rewarded for behaviors that strengthen research integrity. We present five principles: responsible research practices; transparent reporting; open science (open research); valuing a diversity of types of research; and recognizing all contributions to research and scholarly activity. For each principle, we provide a rationale for its inclusion and provide examples where these principles are already being adopted.

Despite the potential benefits of sequential designs, studies evaluating treatments or experimental manipulations in preclinical experimental biomedicine almost exclusively use classical block designs. Our aim with this article is to bring the existing methodology of group sequential designs to the attention of researchers in the preclinical field and to clearly illustrate its potential utility. Group sequential designs can offer higher efficiency than traditional methods and are increasingly used in clinical trials. Using simulation of data, we demonstrate that group sequential designs have the potential to improve the efficiency of experimental studies, even when sample sizes are very small, as is currently prevalent in preclinical experimental biomedicine. When simulating data with a large effect size of d = 1 and a sample size of n = 18 per group, sequential frequentist analysis consumes in the long run only around 80% of the planned number of experimental units. In larger trials (n = 36 per group), additional stopping rules for futility lead to the saving of resources of up to 30% compared to block designs. We argue that these savings should be invested to increase sample sizes and hence power, since the currently underpowered experiments in preclinical biomedicine are a major threat to the value and predictiveness in this research domain.

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.

Differentiate between four kinds of research methods: surveys, field research, experiments, and secondary data analysisUnderstand why different topics are better suited to different research approaches

Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.

This presentation provides a short and simplified overview of the different stages of the qualitative research process. In continuation of this we will talk about planning, consultation, consideration of many practical as well as scientific and ethics matters. For further information regarding the planning stages of qualitative research, please look at the video: The Work Process and Procedures of a Qualitative Research Project - part 1

This course develops logical, empirically based arguments using statistical techniques and analytic methods. Elementary statistics, probability, and other types of quantitative reasoning useful for description, estimation, comparison, and explanation are covered. Emphasis is on the use and limitations of analytical techniques in planning practice.

This course develops skills in research design for policy analysis and planning. The emphasis is on the logic of the research process and its constituent elements. The course relies on a seminar format so students are expected to read all of the assigned materials and come to class prepared to discuss key themes, ideas, and controversies. Since the materials draw broadly on the social sciences, and since students have diverse interests and methodological preferences, ongoing themes in our discussions will be linking concepts to planning scholarship in general and considering how different epistemological orientations and methodological techniques map on to planning specializations.

Psychologists test research questions using a variety of methods. Most research relies on either correlations or experiments. With correlations, researchers measure variables as they naturally occur in people and compute the degree to which two variables go together. With experiments, researchers actively make changes in one variable and watch for changes in another variable. Experiments allow researchers to make causal inferences. Other types of methods include longitudinal and quasi-experimental designs. Many factors, including practical constraints, determine the type of methods researchers use. Often researchers survey people even though it would be better, but more expensive and time consuming, to track them longitudinally.

It is expected that Students who take part in this course have completed almost all courses of their MSc and are about to start on their Master Orientation project, their Literature Study or MSc thesis depending on their chosen MSC track.

It is of little value to take this course early, so please plan accordingly!
Course Contents The aim of the course is to be a research-driven preparation for the aerospace engineering MSc thesis in the final year of the MSc. It will help you prepare for the challenges of your thesis work.

The course will consist of 7 lectures and will be taught online using video lectures in periods 1, 2 and 3 and face-to-face using traditional lectures in period 4.

The lecture set up is as follows:
1. Research Design in MSc - Introduction to research, research framework
2. Research Methods - Stages of a project, Research objective, research questions, research strategy, research methods
3. Data Analysis - Quantitative & Qualitative methods
4. Validation & Verification - How to validate & verify your work?
5. Project Management & Peer review of draft Project plan - How to manage your project and your thesis progress. Project plan peer review
6. Planning - How to plan, expectations, Gannt Charts
7. Literature Review - How to carry out a scientific literature review? Differences between review and research

Please be advised that all lectures are also available via Blackboard for those following the online version. It is possible to do this course by distant learning, attendance in the 4th period, though highly appreciated, is not mandatory!
Study Goals At the end of the course the student will:
- be aware of the expectations of an MSc student
- be able to formulate a research question and research aim
- be able to set up a research plan for their MOP/Literature Study/MSc thesis
- be able to write a literature review based on the research plan with a view to select appropriate methodologies for their MOP/MSc thesis

Education Method (Online) Lectures, Assignments and voluntary Peer review of each others research plans and literature studies

This advanced course in anthropology engages closely with discussions and debates about ethnographic research, ethics, and representation.

There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.