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Flashcards in Statistical Analyses Deck (56):
1

Pearson Correlation

Used to determine the extent of the linear relationship between 2 variables

2

How many IVs and DVs with a Pearson Correlation

0 IV
0 DV
Instead, 2+ variables (measured on the same people)

3

Independent Groups t-test

determines relationship between an IV with 2 levels and a DV with interval characteristics

4

Independent Groups t-test # of IVs and DVs

1 IV - between subjects, 2 levels
1 DV - interval characteristics

5

Correlational Groups t-test

determines relationship between an IV with 2 levels and a DV with interval characteristics

6

Correlational Groups t-test # of IVs and DVs

1 IV - within subjects, 2 levels
1 DV - interval characteristics

7

One-way ANOVA

determine a relationship between IV with more than 2 levels and a DV

8

One-way ANOVA # of IVs and DVs

1 IV - between subjects, 3 or less levels
1 DV - interval characteristics

9

One-way repeated-subjects ANOVA

determine relationship between IV with 3 or less levels and DV but single group receives all conditions of interest

10

one-way repeated-subjects ANOVA # of IVs and DVs

1 IV - within subjects, 3 or less levels
1 DV - interval characteristics

11

Tukey HSD test

post-hoc test used after null hypothesis has been rejected to determine the nature of the relationship between variables
(planned contrast is considered more appropriate)

12

Two-way (factorial) between-subjects ANOVA

involves 1 DV and 2 or more IVs with 2 or more levels

13

Two-way (factorial) between-subjects ANOVA # of IVs and DVs

IVs - 2 or less, between subjects
DV - 1, interval characteristics

14

Two-way (factorial) repeated measures ANOVA

involves 1 DV and 2 or more IVs with 2 or more levels but single group receives all conditions of interest

15

Two-way (factorial) repeated measures ANOVA

IVs - 2 or less, within subjects with 2 or less levels
DVs - 1, interval characteristics, variables

16

One-way between subjects ANCOVA

assess the influence of 1 IV on 1 DV after effects of 1 or more covariates have been removed

17

One-way between subjects ANCOVA # of IVs and DVs

IVs - 1, 2 or less between subjects
DVs - 1, interval characteristics
Covariate - 1 or more

18

One-way repeated measures ANCOVA

assess the influence of 1 IV on 1 DV after the effects of covariates is removed

19

One-way repeated measures ANCOVA # of IVs and DVs

IVs - 1, within subjects, 2 or less levels
DVs - 1, interval characteristics
Covariates - 1 or more

20

Two-way (factorial) between-subjects ANCOVA

assess influence of 1 IV on 1 DV after effects of covariates removed

21

Two-way (factorial) between subjects ANCOVA # of IVs and DVs

IVs - 1, between subjects, 2 or more levels
DVs - 1, interval characteristics
Covariates - 1 or more

22

Two-way (factorial) repeated-measures ANCOVA

assess influence of 1 IV (within groups) on 1 DV after effects of covariates have been removed

23

Two-way (factorial) repeated-measures ANCOVA # of IVs and DVs

IVs - 1, within subjects, 2 or more levels
DVs - 1, interval characteristics
Covariates - 1 or more

24

One-way between subjects MANOVA

relationship between 1 IV and 2+ DVs

25

One-way between subjects MANOVA # of IVs and DVs

IVs - 1, between subjects, 2+ levels
DVs - 2+, interval characteristics

26

One-way repeated measures MANOVA

relationship between 1 IV (within subjects) and 2+ DVs

27

One-way repeated measures MANOVA # of IVs and DVs

IVs - 1, within subjects, 2+ levels
DVs - 2+, interval characteristics

28

Two-way (factorial) between subjects MANOVA

relationship between 2+ IVs and 2+ DVs

29

Two-way (factorial) between subjects # of IVs and DVs

IVs - 2+, between subjects, 2+ levels
DVs - 2+, interval characteristics

30

Two-way (factorial) repeated measures MANOVA

relationship between 2+ IVs (within subjects) and 2+ DVs

31

Two-way (factorial) repeated-measures MANOVA

IVs - 2+, within subjects, 2+ levels
DVs - 2+, interval characteristics

32

One-way between subjects MANCOVA

relationship between 1 IV and 2 DVs after adjusting for covariates

33

One-way between subjects MANCOVA # of IVs and DVs

IVs - 1, between subjects, 2 or more levels
DVs - 2, interval characteristics
Covariates - 1+

34

One-way repeated measures MANCOVA

relationship between 1 IV (within subjects) and 2 DVs after adjusting for covariates

35

One-way repeated measures MANCOVA # of IVs and DVs

IVs - 1, within subjects, 2 or more levels
DVs - 2, interval characteristics
Covariates - 1+

36

Two-way (factorial) between subjects MANCOVA

relationship between IVs and DVs after adjusting for covariates

37

Two-way (factorial) between subjects MANCOVA # of IVs and DVs

IVs - 2+, between subjects, 2+ levels
DVs - 2, interval characteristics
Covariates - 1+

38

Two-way (factorial) repeated measures MANCOVA

assessing influence of IVs (within subjects) on DVs after adjusting for covariates

39

Two-way (factorial) repeated measures MANCOVA # of IVs and DVs

IVs - 2+, within subjects, 2+ levels
DVs - 2, interval characteristics
Covariates - 1+

40

Standard Multiple Regression

assesses relationship between IVs and DVs though IVs can be continuous
all IVs entered at once but each assessed as it if had been entered last
each IV evaluated in terms of what it adds to the prediction of the DV

41

Standard Multiple Regression # of IVs and DVs

IVs - 2+, continuous or discrete
DVs - 1, continuous

42

Sequential (hierarchical) Multiple Regression

Assesses relationship between IVs and DVs though IVs can be continuous
IVs entered in an order specified by the researcher
order assigned according to logical or theoretical considerations (based on hypothesis or past research)
each IV assessed in terms of what it adds to the equation at its own point of entry

43

Sequential (hierarchical) Multiple Regression # of IVs and DVs

IVs - 2+, continuous or discrete
DVs - 1, continuous

44

Path Analysis

extension of Multiple Regression
test models of causal relationships among variables, specify and test models of causal relationships among variables
estimate direct and indirect effects

45

Goals of Path Analysis

understand patterns of correlations among the regions and explain as much of the regional variable as possible with the model specified
focus is on the entire model (reject, modify, or accept)

46

Endogenous Variables

In path analysis
those variables modeled as dependent on other variables
all arrows point away

47

Exogenous Variables

In path analysis
those not dependent on other variables
arrows point to it

48

Structural Equation Modeling

per Ullman, 2001:
described as a combination of exploratory factor analysis and multiple regression

49

Manifest Variables

In SEM - serve as indicators of the underlying construct represented by the observable variables

50

Latent Variables

usually theoretical constructs that cannot be observed directly

51

Discriminant Function Analysis

describes major differences among groups following a MANOVA analysis
Predict/classify subjects into groups based on a combination of measures
Examines the capacity of multiple variables to distinguish groups

52

Factor Analysis

used to identify underlying factors responsible for covariation among independent varables

53

Steps in factor analysis

choice of variables
selection of the proper number of factors to retain
method of rotation

54

What is the citation for research designs?

Mertler and Vaneeta, 2013

55

What is involved in a two-way mixed model ANOVA?

At least 1 within and 1 between subjects with an IV
2 or more IV's
1 DV

56

What is the purpose of the mixed model ANOVA?

Used to test for differences between two or more independent groups whilst subjecting participants to repeated measures.