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21 Cards in this Set
- Front
- Back
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Population |
Total number of person/s from which data can be drawn
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Sample |
Subset of population selected for study |
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Sampling frame/target population |
Group from which sample is collected |
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Simple random sample |
Where everyone has equal chance/probability to be in the sample |
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Population parameter |
Unknown population value |
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Descriptive statistics |
Statistics that describe the sample representing the population |
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Inferential statistics |
Statistics that infer/make conclusions about the impact of variables |
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Variable |
Something that can be changed or varied |
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Independent variable |
Factor that is manipulated in an experiment |
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Dependent variable |
Factor that is measured in an experiment Q. Use of patient controlled analgesia to reduce intensity of pain (IV/DV?) |
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Extraneous variable |
Variable that can affect the reliability of the study but can be controlled |
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Confounding variable |
Variable that can affect the reliability of the study but difficult/cannot be controlled |
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Demographic variable |
Characteristics of of participants (age, gender, education, employment ect.) |
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Exposure |
Determinant or influencing factor (harmful/beneficial) |
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Outcome (dependent variable) |
. |
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Sampling variation |
Varying results from sample to sample |
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Sampling error |
Difference between estimated value (of parameter) and it’s real/true value |
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Nominal data |
Def: Something that exists in name only • Names/categories only (Binary = two categories) No meaningful relationship between thecategories No info regarding magnitude/size Examples: Colours, religion, nationality (Binary nominal variables): Pass/Fail, Dead/Alive, Present/Absent, Smoker/Non-smoker. |
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Ordinal data |
No mathematical scale but has an order (preserves more information than the Nominal scale) Categories based on ranking (or order). Can arrange in order/magnitude Relationship between categories. Gaps/Intervals between the categories are not numerically equal or equidistance. Examples: Winners (1st, 2nd, 3rd) in a race. Disease Severity measured as 'mild, moderate & severe' Smoking status: Non-smoker, light smoker, moderate smoker & heavy smoker. |
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Interval data/variables/scale |
Data that have "intervals' between each measurement. Intervals are numerically EQUAL or equidistance (difference between 10 & 15 is same as between 25 & 30). No true zero point (Zero does not represent the absence of the attribute/variable) Examples: Temperature, depression ratings, risk assessment scores, IQ test scores. Some are debatable.
Zero degree Celsius * No temperature or no heat?? Zero score on an IQ test * No intelligence?? |
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Ratio variables/data/scale |
Meets the same criteria as Interval (Intervals are numerically equal/equidistance (difference between 10 & 15 is same as between 25 & 30). There is an absolute/real/true Zero (zero represents the complete absence of that variable or characteristic) Examples: Money, Heart beat/min, income, annual sales, enzyme activity, unemployment rate, crime rate. How about assessment marks? Interval or Ratio?? |
