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54 Cards in this Set

  • Front
  • Back
nominal definition and examples
categories no rank no order examples, gender, eye color,
limited to frequency of occurence (aka frequency data)
categorical, ranked, more or less of something; likert scale, can only describe in terms of more or less cannot apply math prniciples
ordered and equal can add or subtract; zero is arbitrary but does not imply absence of (e.g. temp)
ordered equal intervals absolute zero ex. height weight reaction time
normal curve
tall and peaked
flat or plateau-like
central limit theorem three principles
1. sampling distribution of means increasingly approaches normal as sample size increases regardless of the shape of the population distribution
2. mean of sampling distribution equals population mean
3. SD of sample equals SD of population from which you drew your sample
why/when do a one or two tailed test
1. one tail when you expect the results will show a specific direction
2. two tail when predicted direction of results are uncertain
alpha- definition and alternatve namesq
rejection region
region of unlikely values, critical region
Type I error
rejecting a trye null hypothesis- probability is always equal to alpha
Type II error
probability of retaining a false null hypothesis;;probability is equal to beta
ability to reject null
how to increase condience
reduce alpha
to increase power
1.increase alpha
2.increase sample size
3.use a reliable DV measure tail test
chi square test
nominal variable
used when analyzing frequency of a category
use multiple chi square if more than one variable
when to use parametric tests
when to use nonparametric tests
t-test for single sample/group
aka student t test
used to compare treatment group to population mean
t-test for correlated samples
comparing subjects to other ss or even themselves.
pre-test, post-test
t-test for independent samples
have a tx group and control group and comparing them
ANOVA when to use and why
use for two or more group means use to manage experiment wise error rate manages possibility of Type I error
one way ANOVA
one IV
2+ Ind Grps
1 DV
2 way (factorial) ANOVA
2+ IV's
2+ Ind Grps
1 DV
1+ IVs
2+ Dvs
use instead of 2 ANOVAs to control for experiment-wise error rates
analysis of covariance
ANOVA plus regression analysis
reduces within group vriability
considered more powerful test
randomized block ANOVA
blocks extraneous variables and use as aonther IV
used to study unique effects on the DV
split plot/mixed ANOVA
-used with mixed design study
study mixed design of a within and between groups variable
e.g. two between groups (tx vs control) and one within gps
trend analysis
IV describe in terms of shape and form
four outcomes: linear, cubic, quadratic, quartic
why use post hoc
use after conducting planned comparisons
least vulnerable to Type I more vulnerable to Type II
least vulnerable to Type I most vulnerable to Type II
most vulnerable to type I error least vulnerable to type II
f score
in correlation ______ is to IV as _____ is to DV
predictor, criterion
Pearson r when and under what assumptions
select whenever u have two continuous variables
1. linearity
2.unrestricted range
3. homoscedasticity
what is assumption of linearity mean relative to pearson r
should be able to draw a straight line; if not linear would underestimate degree of association between the two variables
assumption of unrestricted range relative to pearson r
must be an unrestricted range of scores along x and y axes consequence would underestimate relationship
assumption of homoscedasticity
range of y scores should be ablout the same at all values of x range- if not homoscedastic, would not represent full range of scores between x and y
spearman rho
select with two rank-ordered variables (e.g. rank in high school and rank in SAT
one continuous one artifically dichotomous variable
point biserial
one continuous varialb ena done true dichotomy (e.g.scores and gender
two continuous nonlinear relationship variables (on test, level of anxiety and performance on a memory test
regression (aka prediction) analysis
used to predict a score on some criterion based uopn obtained score on a predictor e.g sat score, college GPA
multivariate techniques for prediction
assesses degree of association between 3 or more varialbes using to make predictions
multiple regression
two predictors one or more criterion
ex. SAT V, SAT M---college GPA
canonical correlations
2+ predictors
2+ continuous criterion
assertive+years experience---predict job rating yearly sales
discriminant analysis
1 discrete criterion

battery of tests---choose college major
stepwise multiple regression
manages for error, determining the fewest number of variables to determine.
observed in stepwise multiple regression, if two variables are highly correlated remove one as they are redundant and increase chance for error (jewish mother taking two temps)
Path analysis
one way, consider relationships in observed (measured) variables
one or two way paths, relatioship between observed and latent variables
looking at main effects
examine marginal means
effects of each IV without considering effects of other IVs
interaction effects
look for the cross, if no cross no interaction
interaction occurs when effect of one IV are different at different levels of the other IV