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16 Cards in this Set
- Front
- Back
- 3rd side (hint)
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Dependent Variable (DV) |
The effect (you think) |
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Descriptive statistics |
Numbers or graphs that summarize (describe) a set of data |
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Extraneous variable |
Any third variable (A variable besides the independent variable that might be causing of the dependent variable) |
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Independent variable (IV) |
The cause (you think) Values of the independent variable are called levels or treatments. In a true experiment the IV is manipulated by the researcher. |
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Inferential statistics |
Techniques that allow us to address questions about what's going on in a population by collecting data from a sample. |
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Interval scale |
Equal spacing between values |
Example: Celsius temperature. Zero is arbitrary. Rating scales usually considered interval scale. |
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Mean |
Arithmetic average; sum of scores divided by the number of scores. Population mean Mu is a parameter Sample mean X is a statistic |
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Nominal scale |
Unordered categories |
Examples are depression, gender, eye color, religious affiliation. Assignment of numbers of categories is arbitrary. |
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Ordinal scale |
Ordered categories (Order is known , spacing is unknown) |
Examples are grade level, class level, birth order, places in a race. Values belong in a specified order; no equal spacing between values. |
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Parameter |
Characteristics of populations ( often unmeasurable, due to not having access to all population) |
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Population |
All individuals in a specified group (the population is defined by the researcher) |
Examples are all college students in the US, all college students in California, and all college students in community college in California. |
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Qualitative variable |
Variable whose levels are different kinds, not different amounts |
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Quantitative variable |
Variable whose levels indicate different amounts |
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Ratio scale |
Has a true zero point (0=0) ( can't be a negative) |
If a question starts with number of... it's usually a ratio. Examples are weight, number of siblings, Kelvin temperature. Ratio statements make sense, example: someone who weighs 200 lb weighs twice as much who weighs 100 lb. |
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Sample |
A subset of the population |
May or may not be representative of the population |
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Statistic |
Characteristics of samples (measurable) |
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