What is Data? Data is a collection of facts, such as numbers, words, measurements, observations or just descriptions of things. Qualitative vs Quantitative Data can be qualitative or quantitative. Qualitative data is descriptive information (it describes something) Quantitative data is numerical information (numbers) Quantitative data can be Discrete or Continuous: Discrete data can only take certain values (like whole numbers) Continuous data can take any value (within a range) Put simply: Discrete data is counted, Continuous data is measured Example: What do we know about Arrow the Dog? Qualitative: He is brown and black He has long hair He has lots of energy Quantitative: Discrete: He has 4 legs He has 2 brothers Continuous: He weighs 25.5 kg He is 565 mm tall To help you remember think "Quantitative is Quantity" More Examples Your friends' favorite holiday destination The most common given names in your town How people describe the smell of a new perfume Collecting Data can be collected in many ways.
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.
Levels of Measurement Levels of Measurement Author(s) Dan Osherson and David M. Lane Prerequisites Variables Learning Objectives Define and distinguish among nominal, ordinal, interval, and ratio scales Identify a scale type Discuss the type of scale used in psychological measurement Give examples of errors that can be made by failing to understand the proper use of measurement scales Types of Scales Although procedures for measurement differ in many ways, they can be classified using a few fundamental categories. Nominal scales When measuring using a nominal scale, one simply names or categorizes responses. Ordinal scales A researcher wishing to measure consumers' satisfaction with their microwave ovens might ask them to specify their feelings as either "very dissatisfied," "somewhat dissatisfied," "somewhat satisfied," or "very satisfied." On the other hand, ordinal scales fail to capture important information that will be present in the other scales we examine. Interval scales Ratio scales
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.
Data Collection Basics of Data Collection Author(s) Heidi Zeimer Prerequisites None Learning Objectives Describe how a variable such as height should be recorded Choose a good response scale for a questionnaire Most statistical analyses require that your data be in numerical rather than verbal form (you can’t punch letters into your calculator). Table 1. You may ask, “Why not simply ask subjects to write their height in inches in the first place?” Let’s take another example. Table 2. Measurement Examples Example #1: How much information should I record? Say you are volunteering at a track meet at your college, and your job is to record each runner’s time as they pass the finish line for each race. The point is that you should think very carefully about the scales and specificity of information needed in your research before you begin collecting data. Example #2 Pretend for a moment that you are teaching five children in middle school (yikes!)
"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
Variables Variables Author(s) Heidi Ziemer Prerequisites none Learning Objectives Define and distinguish between independent and dependent variables Define and distinguish between discrete and continuous variables Define and distinguish between qualitative and quantitative variables Independent and dependent variables Example #1: Can blueberries slow down aging? 1. Example #3: How bright is right? 1. Levels of an Independent Variable If an experiment compares an experimental treatment with a control treatment, then the independent variable (type of treatment) has two levels: experimental and control. Qualitative and Quantitative Variables In the study on the effect of diet discussed above, the independent variable was type of supplement: none, strawberry, blueberry, and spinach. Discrete and Continuous Variables
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