Exam 3 Flashcards
1
Q
when to use an F distribution
A
when working with more than 2 samples
2
Q
when is ANOVA used?
A
with 2+ nominal independent variables, and an interval dependent variable. Analyzes if 2+ groups differ from each other in one or more characteristics
3
Q
why not use multiple t-tests instead of an ANOVA?
A
p increases with each test (weak evidence against the null. Higher chance of making a type 1 error - rejecting null when null is true)
4
Q
F statistic
A
a value you get when you run an ANOVA test or a regression analysis to find out if the means between two populations are significantly different
5
Q
F distribution
A
distribution of all the possible F stastics
6
Q
F =
A
variance between-groups / variance within-groups
s^2between / s^2within
7
Q
variance between-groups
A
estimate of the population variance based on differences among group means
8
Q
variance within-groups
A
estimate of the population variance based on differences within sample distributions
9
Q
another way to think about F ratios
A
each score in a sample is a combination of treatment effects and individual variability or error
10
Q
if between-groups variance is 8, and within-groups variance is 2, what would F be?
A
4
11
Q
between-groups variance equation
A
s^2 = SSbetween / dfbetween
12
Q
SSbetween / dfbetween
A
between-groups variance
13
Q
dfbetween=
A
2(groups) - 1
14
Q
within-groups variance equation
A
SSwithin / dfwithin
15
Q
SSwithin / dfwithin
A
within-groups variance
16
Q
dfwithin=
A
dfgroup1 + dfgroup2
17
Q
one-way ANOVA
A
1 nominal variable with 2+ levels and a scale DV
18
Q
within-groups ANOVA
A
more than 2 samples with same participants. Also called repeated-measures
19
Q
between-groups ANOVA
A
more than 2 samples with different participants in each sample
20
Q
homoscedasticity
A
assumption of ANOVA. Samples come from populations with the same variance
21
Q
effect size for ANOVA
A
r^2
22
Q
formula for calculating effect size for ANOVA
A
r^2 = SSbetween / SStotal
23
Q
small effect size for ANOVA
A
r^2 = .01
24
Q
medium effect size for ANOVA
A
r^2 = .09
25
large effect size for ANOVA
r^2 = .25
26
post-hoc tests determine...
which groups are different
27
when you have three groups, and F is significant, how do you now where the difference(s) are?
post-hoc tests
28
type of post-hoc tests
Tukey HSD, Bonferonni
29
Tukey HSD test
widely used post hoc test that uses means and standard error
30
bonferroni test
post-hoc test that provides a more strict critical value for every comparison of means. We use a smaller critical region to make it more difficult to reject the null. Determine the number of comparisons we plan to make, divide the p level by the number of comparisons
31
one-way within-groups ANOVA
same participants do something multiple times. Used when we have one IV with at least 3 levels, a scale DV, and the same participants in each group
32
benefits of within-groups ANOVA
we reduce error due to differences between the groups. We know that the groups are identical for all of the same participants. We are able to reduce within-groups variability due to differences for the people in our study across groups
33
matched groups
use different people who are similar on all of the variables that we want to control. We can analyze our data as if the same people are in each group, giving us additional
34
two-way ANOVAs
used to evaluate effects of more than one IV on a DV. Used to determine individual and combined effects of the IVs
35
interaction
occurs when 2 IVs have an effect in combination that we do not see when looking at each IV individually
36
when to use Two-Way ANOVAs
to evaluate effects of 2 IVs, it is more efficient to do a single study than two studies with 1 IV each. Can explore interactions between variables
37
cell
box depicting a unique combination of levels of IVs in a factorial design
38
main effect
when one IV influences the DV
39
interaction effect
when the effect of one IV on the DV changes as a result of the level of a second IV
40
two types of interactions in ANOVA
quantitative, qualitative
41
correlation
co-variation or co-relation between two variables. These variables change together. usually scale (interval or ratio) variables
42
correlation coefficient
a statistic that quantifies a relation between two variables. Can be either + or -. Falls between -1.00 and 1.00. The value of the number (not the sign) indicates the strength of the relation
43
positive correlation
association between variables such that high scores on one variable tend to have high scores on the other variable. A direct relation between the variables
44
negative correlation
association between variables such that high scores on one variable tend to have high scores on the other variable. An inverse relation between the variables
45
Pearson Correlation Coefficient
a statistic that quantifies a linear relation between two scale variables. Symbolized by the italic r when based on sample data, italic p ("rho") when it is a population parameter
46
psychometrics
used in the development of tests and measures
47
psychometricians
use correlation to examine reliability and validity
48
reliability
consistent measure. Particular type: test-retest reliability. Ex: how fast a pitcher can throw a baseball
49
validity
measures what it was designed or intended to measure. Correlation is used to calculate validity, and can be used to establish validity (much more difficult than establishing reliability)
50
partial correlation
a technique that quantifies the degree of association between two variables after statistically removing the association of a third variable. Allows us to quantify the relation between two variables, controlling for the correlation of each of these variables with a third related variable
51
regression _____, correlation _____
predicts, describes
52
simple linear regression
statistical tool that lets us predict an individual's score on the DV based on the score on one IV
53
linear regression
intercept: predicted value of Y when X = 0
slope: the amount that Y is predicted to increase for an increase of 1 in X
54
regression with z scores
calculate the z score, multiply he z score by the correlation coefficient. Convert the z score to a raw score
55
determining the regression equation
1) find the z score for X
2) use the z score to calculate the predicted Y value
3) convert the z score to its raw score
56
determining the regression equation: calculating the slope
1) find the z score of X of 1
2) use the z score to calculate the predicted score on Y
3) convert the z score to its raw score
4) find a predicted score
57
standard error of the estimate
indicates the typical distance between the regression line and the actual data points
58
multiple regression
statistical technique that includes 2+ predictor variables in a prediction equation
59
stepwise regression
a type of multiple regression in which computer software determines the order in which IVs are included in the equation. The default in many computer software programs
60
strength of using stepwise regression
relies on data, rather than theory. Especially good when a researcher is not certain of what to expect in a study
61
structural equation modeling (SEM)
a statistical technique that quantifies how well sample data "fit" a theoretical model that hypothesizes a set of relations among multiple variables. Encourages researchers to think of variables as a series of connections
62
chi square test is a ____ test
nonparametric
63
when to use nonparametric tests
- DV is nominal
- either the DV or IV is ordinal
- when sample size is small
- when underlying pop isn't normal
64
limitations of nonparametric tests
- can't easily use confident intervals or effect sizes
- have less statistical power than parametric tests
- nominal and ordinal data provide less info
- more likely to commit Type II error
65
chi-square test for goodness-of-fit
nonparametric test when we have 1 nominal variable. Determines whether or not the observed categories are similar or different from the hypothesized relative frequencies within those same categories
66
chi-square test for independence
nonparametric test when we have 2 nominal variables. Determines whether the first variable is related to the second variable or not
67
Cramer's V (phi)
the effect size for chi-square test for independence
68
when looking at an ANOVA source table, what value is of interest to researchers?
between-groups F
69
if lines are separate but parallel when you draw lines to connect bars in a bar graph of a factorial ANOVA
there is a main effect
70
if lines intersect when you draw lines to connect bars in a bar graph of a factorial ANOVA
there is an interaction
71
MANOVA (multivariate analysis of variance)
ANOVA in which there is more than one dependent variable
72
ANCOVA (analysis of covariance)
ANOVA that statistically subtracts the effect of a possible confounding variable
73
MANCOVA (multivariate analysis of covariance)
an ANOVA with multiple dependent variables and the inclusion of a covariate
74
quantitative interaction
treatment effect varies in magnitude, but is always the same direction
75
qualitative interaction
treatment effect changes direction
76
marginal mean
the mean of a row or a column in a table that shows the cells of a study with a two-way ANOVA design
77
standardized regression coefficient
a standardized version of the slope in a regression equation. The predicted change in the dependent variable in terms of standard deviations for an increase of 1 standard deviation in the independent variable
78
orthogonal variable
is an independent variable that makes a separate and distinct contribution in the prediction of a dependent variable, as compared with the contributions of another variable
79
multiple regression
statistical technique that includes 2+ predictor variables in a prediction equation
80
hierarchical multiple regression
a type of multiple regression in which the researcher adds independent variables into the equation in an order determined by theory
81
factorial ANOVA
catch-all phrase for two-way, three-way, and higher-order ANOVAs
82
factor
a term used to describe an independent variable in a study with more than one independent variable
83
when the p value is small (< .05)
strong evidence against the null
84
when the p value is large (> .05)
weak evidence against the null
85
z score
measure of how many standard deviations below or above the population mean a raw score is. Used in calculating regression lines
86
standardized regression equation
zY hat = (rxy)(zx)
87
regression to the mean
the tendency of scores that are particularly high or low to drift toward the mean over time
88
The predicted z-score on the dependent variable will always be ____ to its mean than the z-score for the independent variable
closer
why?: regression to the mean)
89
proportionate reduction in error
also called the coefficient of determination. quantifies how much more accurate our predictors are when we use a regression line vs. mean
90
adjusted standardized residuals
the difference between the observed frequency and he expected frequency for a cell in a chi-square research design, divided by the standard error, also called adjusted residual
