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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) 

ordinal

categorical, ranked, more or less of something; likert scale, can only describe in terms of more or less cannot apply math prniciples


interval

ordered and equal can add or subtract; zero is arbitrary but does not imply absence of (e.g. temp)


ratio

ordered equal intervals absolute zero ex. height weight reaction time


mesokurtic

normal curve


leptokurtic

tall and peaked


platokurtic

flat or plateaulike


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


power

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 4.one tail test 

chi square test

nonparametric
nominal variable used when analyzing frequency of a category use multiple chi square if more than one variable 

when to use parametric tests

interval/ratio


when to use nonparametric tests

nominal/ordinal


ttest for single sample/group

aka student t test
used to compare treatment group to population mean 

ttest for correlated samples

comparing subjects to other ss or even themselves.
pretest, posttest 

ttest 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 

MANOVA

1+ IVs
2+ Dvs use instead of 2 ANOVAs to control for experimentwise error rates 

ANCOVA

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


Scheffe

least vulnerable to Type I more vulnerable to Type II


Tukey

least vulnerable to Type I most vulnerable to Type II


fisher

most vulnerable to type I error least vulnerable to type II


f score

msb/msw


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
Assumptions 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 rankordered variables (e.g. rank in high school and rank in SAT


biserial

one continuous one artifically dichotomous variable


point biserial

one continuous varialb ena done true dichotomy (e.g.scores and gender


eta

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 Mcollege GPA 

canonical correlations

2+ predictors
2+ continuous criterion assertive+years experiencepredict job rating yearly sales 

discriminant analysis

1 discrete criterion
battery of testschoose college major 

stepwise multiple regression

manages for error, determining the fewest number of variables to determine.


multicolinearity

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


LISREL

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 