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Taking a general theory and applying it to a specific case |
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Making broad generalizations based on a specific case. [Good for generating ideas but error prone] |
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REJECTING the null hypothesis (concluding that there is a significant difference when there is not). |
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ACCEPTING the null hypothesis (concluding that there aren't significant differences when in fact there are). |
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The prediction that there are no significant differences in the data. |
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An estimated range which is likely to include the true number. Indicates the reliability of an estimate. |
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Degree to which a measure differs from other measures that it theoretically shouldn't be similar to. |
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Literature review that uses statistics to sum up the results of several studies. |
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Attempts to collect all information on a topic in a systematic way. Important component of evidence-based health care. |
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Involves group of people who have something in common and who remain part of study group for extended period of time. Used in health research to describe and predict disease risk factors, causes, incidence, natural history, and prognosis.
Can be prospective or retrospective. |
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Studies why a characteristic exists by comparing histories of groups with and without the characteristic. |
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Looks at 1 or more groups at 1 period of time. |
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Test is internally valid if it is free from nonrandom error and bias. |
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Study has external validity if it produces results that can be applied to the target population. |
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Loss of data due to participants not completing full study. |
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A study's ability to detect an effect. |
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Correlation between scores from one time to another. |
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Refers to the extent to which 2 or more individuals agree on their measurement of an item. |
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Refers to the extent to which a measure assesses the skills or characteristics it is intended to measure. |
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Has 1)randomization, 2)manipulation (of the independent variable), and 3)control |
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quasi-experimental design |
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When you have at least 2 already existing groups and make one the experimental group.
Lacks random assignment. |
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Categorical data. Uses Chi-square.
Ex: male/female |
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Indicates the order of the data according to some criterion. |
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Equal distance between data points.
No zero value, negative values can be used.
Ex: temperature (Celsius) |
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Has an absolute zero value and equal distances between data points.
Ex: A is twice the size of B. |
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More powerful and accurate than non-parametric. Data must be of interval or ratio level of measurement.
Ex: ANOVA, t-tests, correlation |
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Less powerful than parametric. Used with nominal and ordinal data.
Ex: chi-square |
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