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62 Cards in this Set
- Front
- Back
statistics
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the science of collecting, analyzing, and interpreting data
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statistic
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a measure of some attribute of a sample
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case study
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an analysis of statistics of one element or a small sample of elements
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Non-experimental (observational) studies
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analyses that compare or measure similarities/differences within a group that we do not manipulate
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experimental studies
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analyses that allow us to compare or measure similarities/differences between groups that we manipulate
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true experimental studies
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studies comparing properties in groups that were randomly assigned
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quasi-experimental studies
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studies that compare properties of groups where assignment wasn’t possible (ex. Earthquake)
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random assignment
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every element in a group has an equal chance of being assigned to the different groups in the study
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Single blind studies
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experiments where the elements are unaware of the groups they are in
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double blind studies
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experiments where the elements and experimenters are unaware of the groups that they are in
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descriptive statistics
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numerical
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inferential statistics
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interpretations. Look at sample, make assumption about population.
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variables
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general characteristics, usually quantified, that VARY and can be used to compare or describe
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variability
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the fact that variables obtained often differ from one another
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good variability
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individual differences (due to the participants themselves)
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bad variability
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measurement error (due to the inability to measure something accurately) or unreliability (due to differences in responses to the same situation)
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construct
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a hypothetical or theoretical entity that is being explored in research (eg. emotions, learning)
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operational definition
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the systematic process of obtaining or measuring a construct
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levels
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the values that a construct can take on
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validity
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accuracy of measurement with respect to intent
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construct validity
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is it measuring what we’re interested in?
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predictive validity
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does the measure predict related behaviors/measures?
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concurrent validity
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does it relate to other measures that are supposed to be measuring the same thing?
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reliability
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consistency in measurement
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Internal reliability
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parallel forms and similar items
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continuous variables
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variables that can assume an infinite number of values
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discrete variables
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variables that have a finite set of values that they can be
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categorical (nominal) variables
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variables that have no numerical meaning
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quantitative variables
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numerical
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ordinal
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variables that have a natural order, but the precise distance between values is not defined
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interval
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variables that have values where the distance btwn them is meaningful and consistent
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ratio
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variables where there is a true zero and where ratios of values make sense
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independent variables
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All variables are independent in observational studies. Typically discrete.
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dependent variables
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caused by or changed by the independent variable
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advantages of mode
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-easy to calculate
-can be used with nominal, ordinal, interval, and ratio data |
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disadvantages of mode
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-gives little info about the entire distribution
-sampling variability |
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advantages of median
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-good for distributions that are skewed/have extreme outliers
-less sampling variability |
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disadvantages of median
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-does not represent all of the scores in the distribution
-doesn't always tell much about discrete data |
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advantages of mean
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-lowest sampling variability
-takes all scores in distribution into account -used in higher-order calculations |
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disadvantages of mean
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-the only measure of central tendency that is sensitive to outliers
-does not work with ordinal or nominal data |
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population
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a complete set of people, events, or scores that we’re interested in
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parameter
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the measurable characteristic of the population that is of interest
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sample
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a subset or portion of that population
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statistic
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the measurable characteristic of the sample of the population that we’re interested in
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convenience sampling
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taking a select sample from the population that is easily accessible
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volunteer sampling
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sampling that is obtained through the willing participation of particular individuals
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true random sampling
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taking the entire population and selecting a sample randomly from that population
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cluster sampling
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random sampling of organized groups of individuals from the population
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stratified (representative) sampling
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identifying some major characteristics of interest in the population and generating a sample that is proportionally equivalent to the population
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pseudo-random sampling
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taking everyone that is accessible from a population and selecting a sample randomly from that group
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random assignment
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creating groups by giving each participant an equal chance of being in the experimental conditions/levels
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randomized block design (stratified assignment)
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creating equivalent groups based on important characteristics
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convenient assignment
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assignment of individuals based on experimenter discretion
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sample statistics
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quantities that characterize samples of raw scores
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sampling statistics
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quantities that characterize sampling distributions of statistics
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Subjective probability
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probability based on an individual’s opinion of the likelihood that an event will occur, or that an event or relationship is due to more than chance
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expected probability
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a measure of the actual probability of an outcome if the outcomes were random and repeated many times
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hypothesis testing
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statistically verifying that the probability of an outcome is significantly different from chance
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null hypothesis
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a statement that implies no effect, differences, or similarities on or between variables w/in a population of interest (results obtained were merely due to chance)
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alternative hypothesis
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a statement that implies that the null hypothesis is false
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type 1 error
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null hypothesis is TRUE, but you reject it
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type 2 error
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null hypothesis is FALSE, but you retain it
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