Tara's Eppp Flashcards Statistics

Front 1

nominal definition and examples

Back 1

categories no rank no order examples, gender, eye color,
limited to frequency of occurence (aka frequency data)

Front 2

ordinal

Back 2

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

Front 3

interval

Back 3

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

Front 4

ratio

Back 4

ordered equal intervals absolute zero ex. height weight reaction time

Front 5

mesokurtic

Back 5

normal curve

Front 6

leptokurtic

Back 6

tall and peaked

Front 7

platokurtic

Back 7

flat or plateau-like

Front 8

central limit theorem three principles

Back 8

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

Front 9

why/when do a one or two tailed test

Back 9

1. one tail when you expect the results will show a specific direction
2. two tail when predicted direction of results are uncertain

Front 10

alpha- definition and alternatve namesq

Back 10

rejection region
region of unlikely values, critical region

Front 11

Type I error

Back 11

rejecting a trye null hypothesis- probability is always equal to alpha

Front 12

Type II error

Back 12

probability of retaining a false null hypothesis;;probability is equal to beta

Front 13

power

Back 13

ability to reject null

Front 14

how to increase condience

Back 14

reduce alpha

Front 15

to increase power

Back 15

1.increase alpha
2.increase sample size
3.use a reliable DV measure
4.one tail test

Front 16

chi square test

Back 16

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

Front 17

when to use parametric tests

Back 17

interval/ratio

Front 18

when to use nonparametric tests

Back 18

nominal/ordinal

Front 19

t-test for single sample/group

Back 19

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

Front 20

t-test for correlated samples

Back 20

comparing subjects to other ss or even themselves.
pre-test, post-test

Front 21

t-test for independent samples

Back 21

have a tx group and control group and comparing them

Front 22

ANOVA when to use and why

Back 22

use for two or more group means use to manage experiment wise error rate manages possibility of Type I error

Front 23

one way ANOVA

Back 23

one IV
2+ Ind Grps
1 DV

Front 24

2 way (factorial) ANOVA

Back 24

2+ IV's
2+ Ind Grps
1 DV

Front 25

MANOVA

Back 25

1+ IVs
2+ Dvs
use instead of 2 ANOVAs to control for experiment-wise error rates

Front 26

ANCOVA

Back 26

analysis of covariance
ANOVA plus regression analysis
reduces within group vriability
considered more powerful test

Front 27

randomized block ANOVA

Back 27

blocks extraneous variables and use as aonther IV
used to study unique effects on the DV

Front 28

split plot/mixed ANOVA

Back 28

-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

Front 29

trend analysis

Back 29

IV describe in terms of shape and form
four outcomes: linear, cubic, quadratic, quartic

Front 30

why use post hoc

Back 30

use after conducting planned comparisons

Front 31

Scheffe

Back 31

least vulnerable to Type I more vulnerable to Type II

Front 32

Tukey

Back 32

least vulnerable to Type I most vulnerable to Type II

Front 33

fisher

Back 33

most vulnerable to type I error least vulnerable to type II

Front 34

f score

Back 34

msb/msw

Front 35

in correlation ______ is to IV as _____ is to DV

Back 35

predictor, criterion

Front 36

Pearson r when and under what assumptions

Back 36

select whenever u have two continuous variables
Assumptions
1. linearity
2.unrestricted range
3. homoscedasticity

Front 37

what is assumption of linearity mean relative to pearson r

Back 37

should be able to draw a straight line; if not linear would underestimate degree of association between the two variables

Front 38

assumption of unrestricted range relative to pearson r

Back 38

must be an unrestricted range of scores along x and y axes consequence would underestimate relationship

Front 39

assumption of homoscedasticity

Back 39

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

Front 40

spearman rho

Back 40

select with two rank-ordered variables (e.g. rank in high school and rank in SAT

Front 41

biserial

Back 41

one continuous one artifically dichotomous variable

Front 42

point biserial

Back 42

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

Front 43

eta

Back 43

two continuous nonlinear relationship variables (on test, level of anxiety and performance on a memory test

Front 44

regression (aka prediction) analysis

Back 44

used to predict a score on some criterion based uopn obtained score on a predictor e.g sat score, college GPA

Front 45

multivariate techniques for prediction

Back 45

assesses degree of association between 3 or more varialbes using to make predictions

Front 46

multiple regression

Back 46

two predictors one or more criterion
ex. SAT V, SAT M---college GPA

Front 47

canonical correlations

Back 47

2+ predictors
2+ continuous criterion
assertive+years experience---predict job rating yearly sales

Front 48

discriminant analysis

Back 48

1 discrete criterion

battery of tests---choose college major

Front 49

stepwise multiple regression

Back 49

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

Front 50

multicolinearity

Back 50

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)

Front 51

Path analysis

Back 51

one way, consider relationships in observed (measured) variables

Front 52

LISREL

Back 52

one or two way paths, relatioship between observed and latent variables

Front 53

looking at main effects

Back 53

examine marginal means
effects of each IV without considering effects of other IVs

Front 54

interaction effects

Back 54

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