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| Standardized scores that help compare to normal curve = ... |
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| "value near the center of all values" |
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| Central location identified by... (4) |
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| percentile, mean, median, mode |
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| "way in which values cluster around center" |
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| Variation identified by... (5) |
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| frequencies, percentiles, range, variance, SD |
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| "average score; sum of individual scores divided by 'n'" |
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| "most frequently occurring score; useful when mean has been distorted by extreme scores" |
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| "difference between highest and lowest scores" |
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| "dispersion of scores that looks at deviation of individual scores from the mean" |
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| "square root of variance" |
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| Reliability test for nominal data |
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| % agreement & Kappa statistic |
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| Reliability test for ordinal data |
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| Reliability test for continuous data |
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| Association test for nominal data |
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| Prediction test for ordinal data |
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| Prediction test for continuous data |
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| Post-hoc tests needed for which tests? |
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| ANOVAs (to show where diff is) |
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| Criterion variable; aka... |
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| Univariate ANOVA = ____IV |
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| Multivariate ANOVA = _____IV |
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| Factorial ANOVA = effect of ____ IV on ___ DV |
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2 factors (IVs);
factor1: 2 levels,
factor2: 3 levels |
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| Linear regression equation |
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| Linear regression equation: y=... |
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| Linear regression equation: a=... |
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| Linear regression equation: b=... |
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| slope of line = regression coefficient |
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| Linear regression equation: x=... |
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| Linear regression: Residuals |
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| distances of Y from regression line |
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| "the method of obtaining the regression line with the best fit" |
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| Assumptions for linear regression analysis (5) |
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1. straight line in regression is only approximation of true regression
2. for a value of x, assume random distribution of y scores
3. mean of scores will fall on regression line
4. normal distribution
5. SDs are equal |
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| Shape of normal plot of residuals in linear regression |
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| Shape of plot of residuals in linear regression when assumptions not met |
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| What test to use when linear regression results in curvilinear line of fit? |
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| The value found by dividing residuals by SD of residual distribution |
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| r-squared (linear regression) = |
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| coefficient of determination |
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| In linear regression: "represents total variance (in percentage) in the y scores that can ge explained by x" |
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| coefficient of determination |
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| In linear regression: "SD of the variance of errors on either side of regression line" |
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| standard error of the estimates (SEE) |
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| 1-sentence summary for coefficient of determination (variance) |
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| "____% of the variance can be explained by [IVs] & [DVs]." |
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| In linear regression: purpose of ANOVA of regression |
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| provide p value for regression model |
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| Linear regression: ANCOVA assumptions (4) |
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- linearity of covariates
- homogeneity of slopes
- independence of covariate
- reliability of covariate |
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| "Ability to obtain a positive test when target position is really present" |
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| "Ability of a test to obtain a negative result when condition is really absent" |
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| value cut-off for "high" sensitivity |
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| formula for false negative rate |
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| formula for false positive rate |
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| Likelihood ratios: values for significant and non-significant |
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0-0.2, >5 = significant
0.5-2 = non-significant
1 = useless ("not important")
(importance of 0.2-0.5 and 2-5 depends on data) |
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| (true positive)÷(true pos+false neg) |
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| (true negative)÷(false pos+true neg) |
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| Likelihood ratio: formula for positive |
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| sensitivity÷(1-specificity) |
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| Likelihood ratio: formula for negative |
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| (1-sensitivity)÷specificity |
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