AS Psychology - Holah.co.uk - Correlation Here are some exam style questions. Here is a tick off what you need to know sheet for correlations. Don't let yourself fall into the trap of believing that when there is a strong correlation between two variables that one of the variables causes the other. A matching coefficient quiz. When conducting correlational analysis it is important to operationalise the variables. A cloze hypothesis quiz. Home > Investigations > Correlation Correlation for Psychological Investigations Correlation refers to a measure of how strongly two or more variables are related to each other. A positive correlation means that high values of one variable are associated with high values of the other. A negative correlation means that high values of one variable are associated with low values of the other. If there is no correlation between two variables they are said to be uncorrelated. A correlation coefficient refers to a number between -1 and +1 and states how strong a correlation is.
Writing a research article: advice to beginners Once the research question is clearly defined, writing the paper becomes considerably easier. The paper will ask the question, then answer it. The key to successful scientific writing is getting the structure of the paper right. The basic structure of a typical research paper is the sequence of Introduction, Methods, Results, and Discussion (sometimes abbreviated as IMRAD). Each section addresses a different objective. The authors state: (i) the problem they intend to address—in other terms, the research question—in the Introduction; (ii) what they did to answer the question in the Methods section; (iii) what they observed in the Results section; and (iv) what they think the results mean in the Discussion. In turn, each basic section addresses several topics, and may be divided into subsections (Table 1). The Methods section should provide the readers with sufficient detail about the study methods to be able to reproduce the study if so desired. References should be used wisely.
"Graphs": Connect Fours Revision Quiz You will see a wall of 16 clues. You need to group them into 4 rows of 4 connected items. Simply click four cards to identify a group. You score 1 point for each group found within 2.5 minutes. After arranging all 4 groups (or when time runs out) the correct groups are shown. This quiz is based on, but is not affiliated with, the 'connect wall' element in the BBC quiz show 'Only Connect' gap between bars line of best fit sometimes drawn frequency on y axis (bars) continuous frequency data (not bars) +1 Point? dots not joined dots are joined frequency on y axis (not bars) simplified by plotting mean or total scores +1 Point? good for comparing two conditions/groups on one graph continuous frequency data (bars) discrete categories co-variables on axes frequency polygon sometimes drawn correlation no gap between bars data not continuous
Retraction Of Scientific Papers For Fraud Or Bias Is Just The Tip Of The Iceberg Publishing clinical trials in medical journals can help doctors and scientists rise through the ranks of the research hierarchy. While most play the publication game fairly, some cheat. Whereas all misconduct undermines the public’s trust in science – such as the recent retracted paper about gay canvassers – health research scandals put the health of millions of patients around the world in jeopardy. Professionals and patients depend on results from systematic reviews of clinical trials, which evaluate all the evidence on a particular issue, to know whether or not treatments are safe and effective. Falsified Reports Most medical journal editors and systematic reviewers take clinical trial reports at face value with little or no effort to confirm whether a particular trial even took place. As part of the investigation, the London School of Hygiene & Tropical Medicine editors contacted the editor of the journal that published one of the doubtful trials. Bias In Reviews
How Quickly Do Systematic Reviews Go Out of Date? A Survival Analysis From the Ottawa Health Research Institute, University of Ottawa, Chalmers Research Group, and Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada. Disclaimer: The authors of this report are responsible for its content. Statements in the report should not be construed as endorsement by the Agency for Healthcare Research and Quality or the U.S. Acknowledgments: The authors thank Keith O'Rourke for statistical advice, Jessie McGowan and Tamara Rader for assistance with searches, and Alison Jennings for assistance with development of the meta-analytic worksheet. Grant Support: By the Agency for Healthcare Research and Quality, U.S. Potential Financial Conflicts of Interest: None disclosed. Reproducible Research Statement: The data set is available to interested readers by contacting Dr. Requests for Single Reprints: Kaveh G. Current Author Addresses: Drs. Ms. Mr. Author Contributions: Conception and design: K.G. Analysis and interpretation of the data: K.G.
Systematic review Systematic reviews are a type of literature review that collects and critically analyzes multiple research studies or papers, using methods that are selected before one or more research questions are formulated, and then finding and analyzing studies that relate to and answer those questions in a structured methodology.[1] They are designed to provide a complete, exhaustive summary of current literature relevant to a research question. Systematic reviews of randomized controlled trials are key in the practice of evidence-based medicine,[2] and a review of existing studies is often quicker and cheaper than embarking on a new study. An understanding of systematic reviews, and how to implement them in practice, is highly recommended for professionals involved in the delivery of health care. Characteristics[edit] A systematic review aims to provide a complete, exhaustive summary of current literature relevant to a research question. Stages[edit] The main stages of a systematic review are:
PLOS Medicine: Financial Conflicts of Interest and Reporting Bias Regarding the Association between Sugar-Sweetened Beverages and Weight Gain: A Systematic Review of Systematic Reviews Abstract Background Industry sponsors' financial interests might bias the conclusions of scientific research. We examined whether financial industry funding or the disclosure of potential conflicts of interest influenced the results of published systematic reviews (SRs) conducted in the field of sugar-sweetened beverages (SSBs) and weight gain or obesity. Methods and Findings We conducted a search of the PubMed, Cochrane Library, and Scopus databases to identify published SRs from the inception of the databases to August 31, 2013, on the association between SSB consumption and weight gain or obesity. We identified 17 SRs (with 18 conclusions). An important limitation of this study is the impossibility of ruling out the existence of publication bias among those studies not declaring any conflict of interest. Conclusions Financial conflicts of interest may bias conclusions from SRs on SSB consumption and weight gain or obesity. Please see later in the article for the Editors' Summary Methods
Monitoring 101: Investigating performance issues - Datadog This post is part of a series on effective monitoring. Be sure to check out the rest of the series: Collecting the right data and Alerting on what matters. The responsibilities of a monitoring system do not end with symptom detection. Once your monitoring system has notified you of a real symptom that requires attention, its next job is to help you diagnose the root cause. Often this is the least structured aspect of monitoring, driven largely by hunches and guess-and-check. This post describes a more directed approach that can help you to find and correct root causes more efficiently. This series of articles comes out of our experience monitoring large-scale infrastructure for our customers. A word about data There are three main types of monitoring data that can help you investigate the root causes of problems in your infrastructure. By and large, work metrics will surface the most serious symptoms and should therefore generate the most serious alerts. It’s resources all the way down 1.