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.
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
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!)
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
Descriptive Statistics Descriptive Statistics Author(s) Mikki Hebl Prerequisites none Learning Objectives Define "descriptive statistics" Distinguish between descriptive statistics and inferential statistics Table 1. Descriptive statistics like these offer insight into American society. For more descriptive statistics, consider Table 2 which shows the number of unmarried men per 100 unmarried women in U.S. Table 2. NOTE: Unmarried includes never-married, widowed, and divorced persons, 15 years or older. These descriptive statistics may make us ponder why the numbers are so disparate in these cities. You probably know that descriptive statistics are central to the world of sports. Table 3. There are many descriptive statistics that we can compute from the data in the table. Examining Table 3 leads to many other questions.
Inferential Statistics Inferential Statistics Author(s) Mikki Hebl and David Lane Prerequisites Descriptive Statistics Learning Objectives Distinguish between a sample and a population Define inferential statistics Identify biased samples Distinguish between simple random sampling and stratified sampling Distinguish between random sampling and random assignment Populations and samples Example #1: You have been hired by the National Election Commission to examine how the American people feel about the fairness of the voting procedures in the U.S. A sample is typically a small subset of the population. Example #2: We are interested in examining how many math classes have been taken on average by current graduating seniors at American colleges and universities during their four years in school. Example #3: A substitute teacher wants to know how students in the class did on their last test. In Example #3, the population consists of all students in the class. Sampling Bias is Discussed in More Detail Here
Sampling Bias Sampling Bias Author(s) David M. Lane Prerequisites Inferential Statistics (including sampling) Learning Objectives Recognize sampling bias Distinguish among self-selection bias, undercoverage bias, and survivorship bias It is important to keep in mind that sampling bias refers to the method of sampling, not the sample itself. Self-Selection Bias Imagine that a university newspaper ran an ad asking for students to volunteer for a study in which intimate details of their sex lives would be discussed. A self-selection bias can result when the non-random component occurs after the potential subject has enlisted in the experiment. Undercoverage Bias A common type of sampling bias is to sample too few observations from a segment of the population. A detailed analysis by Squire (1988) showed that it was not just an undercoverage bias that resulted in the faulty prediction of the election results. Survivorship Bias
Sampling error and non-sampling error The subject of statistics is rife with misleading terms. I have written about this before in such posts as Teaching Statistical Language and It is so random. But the terms sampling error and non-sampling error win the Dr Nic prize for counter-intuitivity and confusion generation. To start with, the word error implies that a mistake has been made, so the term sampling error makes it sound as if we made a mistake while sampling. Well this is wrong. And the term non-sampling error (why is this even a term?) Fortunately the Glossary has some excellent explanations: Sampling Error “Sampling error is the error that arises in a data collection process as a result of taking a sample from a population rather than using the whole population.Sampling error is one of two reasons for the difference between an estimate of a population parameter and the true, but unknown, value of the population parameter. Non-sampling error: And it proceeds to give some helpful examples. Table summarising types of error.