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62 Cards in this Set
- Front
- Back
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Internal validity
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questions cause-effect relationships
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External validity
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not assuming results can be generalized to other contexts
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Construct validity
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shows if it measures an underlying psychological construct
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Content validity
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shows if it measures everything about a specific topic
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Criterion validity
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shows if it measures a set of abilities needed for something
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reliability
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consistency
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test-retest reliability
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shows if a test is reliable over time
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parallel forms reliability
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shows if different forms of a test are consistent
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internal consistency reliability
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shows if the items on a test/survey all measure one thing
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interrater reliability
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shows if raters are consistent
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Test for test-retest reliability
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pearson r (pretest and posttest)
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test for parallel forms reliability
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pearson r (form A and form B)
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test for internal consistency reliability
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chronbach's alpha on all item responses
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test for interrater reliability
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divide number of agreements by total number of observations
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independent variable
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the cause (what is manipulated, example: SSRI, CBT)
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dependent variable
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the outcome or effect (example: mood and depression)
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nominal data
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categorical, mutually exclusive categories (ex: gender, democrat/republican/indepdendent, happy/sad)
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ordinal data
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rank ordered, ranks show which is greater or less than another but not by how much (ex: 1st is better than 2nd place but we don't know by how much they won based on place) (ORDinal for ORDer)
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interval data
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regular number scale (ex: test scores)
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reporting nominal data
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using n and %
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reporting ordinal data
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using frequencies, mean rank, interval data, M and SD and may also want N
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descriptive statistics
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used to describe or summarize data (M and SD)
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inferential statistics
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statistical comparisons that determine if a significant difference or reltaionship exists between populations or samples
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range
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highest number minus lowest number
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variance
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square of standard deviation
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standard deviation
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the average deviation from the mean
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normal distribution
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mode, mean, and median are equal, bell shaped, symmetrical
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confidence interval
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estimates the true population mean (in a range) based on a sample mean
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95% confidence interval
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you can be 95% confident that he true population mean is in the interval
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coefficient of determination
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how much of the variance in one variable can be accounted for by the variabnce in another variable (square the r-value to find this)
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research hypothesis
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your educated guess about what hte data will show, states there is a statistical significance between groups
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type 1 error
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rejecting null hypothesis when it is actually true, 5% chance of this when alpha is .05
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type 2 error
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accepting null hypothesis when it is not true, decreases when sample size increases
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t test
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compares two means, results in a t value
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one sample t test
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compares sample mean to another value to determine if they are the same or different. The other value can be a population mean, probability of an event occuring, or other known value
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independent samples t test
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compares the means of 2 groups on one variable to determine if they are the same or different. The 2 groups must be independent of each other
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paired samples t test
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compares 2 means to determine if same or different. The 2 means can come from one group measured twice or 2 groups matched by at least one variable.
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t value positive or negative?
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absolute value!
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chronbach's alpha
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measures internal consistency
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chronbach's alpha varies between what numbers
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0 and 1 (but can get higher)
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reliability scores that are acceptable
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.7 or higher
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effect size
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d, a simple way to quantify difference between two groups, measure the magnitude of treatment
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correlation coeffecient very strong relationship
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.8 to 1
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correlation coeffecient strong relationship
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.6 to .8
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correlation coeffecient moderate relationship
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.4 to .6
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correlation coeffecient weak relationship
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.2 to .4
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correlation coeffecient no relationship
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.0 to .2
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validity
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the property of an assessment tool that indicates that the tool does what it says it does
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critical value
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the value you would expect the test statistic to yield if the null hypothesis is true
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obtained value
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the result of a specific statistical test
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why ANOVA over t tests
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ANOVA can compare more than 2 means, t tests only 2, less type 1 error
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one-way ANOVA
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1 IV, 1 DV
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between-subjects ANOVA
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2 or more IV, 1 DV
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within-subjects ANOVA
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no IV, 2 or more DV
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mixed-model ANOVA
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1 or more IV, 2 or more DV
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Tukey
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a post-hoc (after the fact) test
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HSD
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honestly...significant difference.
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ANCOVA
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an ANOVA with a covariate
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linear regression
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measures relationships (like pearson r but WITH prediction), uses one variable to explain or predict another
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multiple regression
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Shows how the combination of predictor variables is related to the dependent variable
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chi square
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used to test nominal (categorical) data
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nonparametric test
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doesn’t make assumptions about the population parameters
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