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51 Cards in this Set
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Statistics

refer to numbers that describe a sample


Descriptive Statistics

organize data from a sample by showing it in a meaningful way. You cannot draw conclusions from data beyond the sample. Examples are percentiles, frequency distributions, graphs, measures of central tendency, an variability.


Parameters

refer to numbers that describe populations


Nominal Variables

A type of frequency distribution. No order or relationship among the variables other than to separate them into groups. ex: male, female, repub, democ.


rejecting the null hypothesis

null hypothesis says no relationship exists


Ordinal Variables

type of frequency distribution. Variables are arranged b order.


Alpha level of significance

<.05 or <.01


Interval Variables

type of frequency distribution. Can show order AND spacing because equal spacing lie between the values. There is no real zero. Ex: temperature


Type 1 errors

incorrectly rejecting the null hypothesis


Ratio Variables

type of frequency distribution. Have order, equal intervals, and a real zero. Ex: Age


Type 2 errors

wrongly accept null hypothesis


Mean

Average of a set. Standard error of the mean calculates how "off" the mean might be in either direction


ttests

compare means of 2 groups


Median

Value that lies in the center of a number set organized in ascending order. If there's an even number of values, take the average of the two middle values.


chisquare tests

ncases in a sample. Will tell us whether groups are significantly different in size


Mode

the most frequently occurring value in a set


ANOVA (Analysis of variance)

flexible. like a ttest in that it analyzes differences among means. can do more than 2 groups


Variance or Standard Deviation

tells us how much variation there is among n number of scores in a distribution.
Variance = (((Score 1Mean)^2+...(Score nMean)^2)/n)^1/2 

Oneway ANOVA

tests whether the means on one outcome or dependent variable are sig different across groups


The Normal Distribution

bell curve. It's unimodal (one hump) and majority of scores fall in the middle range.


Twoway ANOVA

test the effects of two independent variables or treatment conditions at once


Zscores

on a normal distribution refer to haow many standard deviations a score is from the mean (range from 3 to 3)


factorial analysis of variance

used when an experiment involves more than one independent variable. This analysis can separate the effects of diffeent levels of different variables. Can isolate main effects and can identify interaction effects. You can combine the independent variables


Standard normal distributions

normal distributions can be standardized so that one can compare tests with different standard deviations. Standardizing makes the mean 0 and the standard deviation 1.


Analysis of Covariance (ANCOVA)

tests wheteher at least two groups covary. Can adjust for pre existing differences between groups.


zscores and percentile ranks on the normal distribution

68% scores live within one standard deviation of the mean.
from 3 to 2 = 2% 2 to 1 = 14% 1 to 0 = 34% 0 to 1 = 34% 1 to 2 = 14% from 2 to 3 = 2% 

Criterion referenced tests

measure mastery in a particular area


positively skewed distribution

hump skewed to the left


Domain referenced tests

measure less defined properties (like intelligence) and need to be checked for reliability and validity.


Negatively skewed distribution

hump skewed to the right


Bimodal distribution

two humps


Reliability

How stable a measure is


Platykuric distribution

Flat equal frequency across all values.


Testretest reliability

measured by the same individual taking the same test more than once. on a test with high testretest reliability that person would get approx the same score each time.


Correlations

show relationships NOT CAUSALITY between variables.


Splithalf reliability

measured by comparing an individuals performance on two halves of ht esame test. This reveals the internal consistency of a test. Can also increase internal consistency by item analysis (analyzing how a large group responded to each measure item)


Positive Correlation

As one variable increases so does the other one


Validity

how well the test measures a construct


Negative Correlation

As one variable increases the other variable decreases


Internal validity

measures the extent to which the different items within a measure "hang together" and test the same thing


Curvilinear correlation

variable relationship looks like a curved line. Example: Arousal and performance. Low arousal and high arousal lead to poor performance, but a medium amt of arousal leads to successful performance.


External Validity

the extenet to which a test measures what it intends to measure. 4 aspects are concurrent validity, constuct validity, content validity, face validity.


Zero correlation

no relationship


Concurrent validity

whether scores on a new measure positively correlate with other measures known to test the same construct. Process is cross validation.


Pearson r correlation coefficient

expresses correlation. r ranges from 1 to 1. 1 indicates perfect negative correlation, 1 indicates perfect positive correlation, 0 indicates no relationship. Strength of relationship measured by how far away it is from zero.


Construct Validity

whether the test really taps the abstract concept being measured


Spearman r correlation coefficient

correlation used when the data is in the form of ranks. Detemines the line that describes a linear relationship


content validity

whether the content of the test covers a good sample of the construct being measured


Regression

the step beyond correlations. A statistical regression allows you to identify a relationship between two variqables and make predictions about one variable based on another variable.


face validity

whether the test items simply look like they measure the construct


Campbell and Fiske

created the multitraitmultimethod technique to determine th validity of tests
