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| Variables given descriptive names |
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| Variables need to be arranged by order |
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| Shows order and spacing, equal space lie between the values, do not include a real zero |
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| Have order, equal intervals, and a real zero (Ex: age) |
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| Three measures of central tendency |
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| Square root of the variance, tells you the average extent to which scores were different from the mean (large SD= scores were highly dispersed, SD small= scores were very close together) |
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| How much variation there is among n number of scores in a distribution, how much each score differs from the mean |
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| Distribution only has one hump |
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| How many standard deviations a score is from the mean, normal distributions range from -3 to +3 |
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| Transformation of a z-score T= 10(Z)+50 |
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| 68% of scores lie within one standard deviation (in either direction) of the mean |
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| As one variable increases so does the other |
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| As one variable goes up, the other goes down |
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| Not simple and linear, looks like a curved line, ex: arousal and performance= low arousal and high arousal lead to poor performance, but a medium amount of arousal leads to successful performance |
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| Pearson r correlation coefficient |
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| ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation) |
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| Spearman r correlation coefficient |
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| Used when the data is in the form of ranks |
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| Allows you to not only identify a relationship between two variables but also to make predictions about one variable based on another variable |
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| Statistics vs. Parameters |
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| Statistics refer to numbers that describe a sample, while parameters refer to numbers that describe populations |
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| When you incorrectly reject the null hypothesis= you thought your findings were significant but they were really only caused by chance |
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| When you wrongly accept the null hypothesis, tests showed your findings to be insignificant when in fact they were significant |
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| The hypothesis that no real differences or patterns exist (Researchers hope to reject the null hypothesis NOT accept) |
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| Significance level used by most researchers |
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| Alpha level of <.05 or <.01 |
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| How well the test measures a construct |
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| Measures the extent to whcih the different items within a measure "hang together" and test the same thing |
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| The extent to which a test measures what it intends to measure (4 aspects of external validity) |
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| External validity- whether the test items simply look like they measure the construct |
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| External validity- whether the content of the test covers a good sample of the construct being measured (not just part of it). |
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| External validity- Whether the scores on a new measure positively correlate with other measures known to test the same construct. This process is cross validation. |
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| External validity- Whether the test really taps the abstract concept being measured |
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| Test that are tried out on huge groups of people in order to create norms |
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| Criterion-reference tests |
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| Measure mastery in a particular area or subject |
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| Attempt to measure less-defined properties (like intelligence) and need to be checked for reliability and validity |
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| How stable the measure is |
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| Measured by the same individual taking the same test more than once. On a test with high test-retest reliability, that person would get approximately the same score each time |
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| Measured by comparing an individual's performance on two halves of the same test (odd vs. even questions) - reveals internal consistency. Can also increase internal consistency of a measure by performing an item analysis |
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| Analyzing how a large group responded to each item on the measure. Increases internal consistency. Weeds out the dud or problematic questions so they can be replaced with better questions |
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