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24 Cards in this Set
- Front
- Back
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Forms a ratio - numerator measures the actual difference between the sample data and the population hypothesis. The estimated standard error in the denominator measures how much difference is reasonable to expect between a sample mean and a population mean |
t-statistic
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The t-test does not require...
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any prior knowledge about the population mean or the population variance
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Two basic assumptions of the one-sample t-test
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Independent Observations & Normality
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A larger value for estimated standard error produces...
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a smaller value (closer to zero) for t
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Larger the variance...
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larger the error
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Measures the magnitude of the treatment effect by finding the difference between the mean for the treated sample and the mean for the untreated population.
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Numerator of estimated d
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Sample standard deviation in the denominator standardizes the mean difference the mean difference into standard deviation units
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Denominator of estimated d
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Indicates that the size of the treatment effect is equivalent to one standard deviation
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An estimated d of 1.00
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Alternative method for measuring effect size. Determine how much variability in the scores is explained by the treatment effect. Accounts for percentage of variance.
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r-squared
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Only slightly affected by changes in the size of the sample.
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measures of r-squared
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A design in which the dependent variable is measured two or more times for each individual in a single sample. the same group of subjects is used in all of the treatment.
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Repeated Measures Design (Within-subject design)
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No risk that the participants in one treatment are substantially different from the participants in another
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Main advantage of repeated measures design
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Each individual in one sample is matched with an individual in the other sample. The matching is done so that the two individuals are equivalent (or nearly equivalent) with respect to a specific variable that the researcher would like to control
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Matched-subjects Design
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Assumptions of paired sample t-test
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Independent Variables & Normality
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Advantages of Repeated-Measures t-tests
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Number of Subjects, Study Changes Over Time, & Individual Differences
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Requires fewer subjects than an independent measure.
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Number of Subjects
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Well suited for studying learning, development, or other changes that take place over time
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Study changes over time
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reduces or eliminates problems caused by individual differences
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Individual differences
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Changes in scores that are caused by participation in an earlier treatment and can distort the mean differences found in repeated-measures research studies
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Order Effects
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way to deal with time-related factors and order effects. participants are randomly divided into two groups, with one group receiving treatment 1, followed by treatment 2; the other group receiving treatment 2 followed by treatment 1. Goal is to distribute any outside effects evenly over 2 treatments.
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Counterbalancing
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Uses separate group of participants for each treatment condition (or for each population). Also known as between subjects design.
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Independent Measures Research Design (Independent sample t-test)
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Assumptions of independent sample t-test
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1. Observations are independent. 2. Two populations are normal. 3. Two populations must have equal variance.
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The two populations being compared must have the same variance.
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Homogeneity of Variance |
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Test to see if the homogeneity of variance assumption is satisfied. Based on the principle that a sample variance provides an unbiased estimate of the population variance.
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Hartley's F-Max Test |
