Statistical Analyses Flashcards
Pearson Correlation
Used to determine the extent of the linear relationship between 2 variables
How many IVs and DVs with a Pearson Correlation
0 IV
0 DV
Instead, 2+ variables (measured on the same people)
Independent Groups t-test
determines relationship between an IV with 2 levels and a DV with interval characteristics
Independent Groups t-test # of IVs and DVs
1 IV - between subjects, 2 levels
1 DV - interval characteristics
Correlational Groups t-test
determines relationship between an IV with 2 levels and a DV with interval characteristics
Correlational Groups t-test # of IVs and DVs
1 IV - within subjects, 2 levels
1 DV - interval characteristics
One-way ANOVA
determine a relationship between IV with more than 2 levels and a DV
One-way ANOVA # of IVs and DVs
1 IV - between subjects, 3 or less levels
1 DV - interval characteristics
One-way repeated-subjects ANOVA
determine relationship between IV with 3 or less levels and DV but single group receives all conditions of interest
one-way repeated-subjects ANOVA # of IVs and DVs
1 IV - within subjects, 3 or less levels
1 DV - interval characteristics
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)
Two-way (factorial) between-subjects ANOVA
involves 1 DV and 2 or more IVs with 2 or more levels
Two-way (factorial) between-subjects ANOVA # of IVs and DVs
IVs - 2 or less, between subjects
DV - 1, interval characteristics
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
Two-way (factorial) repeated measures ANOVA
IVs - 2 or less, within subjects with 2 or less levels
DVs - 1, interval characteristics, variables
One-way between subjects ANCOVA
assess the influence of 1 IV on 1 DV after effects of 1 or more covariates have been removed
One-way between subjects ANCOVA # of IVs and DVs
IVs - 1, 2 or less between subjects
DVs - 1, interval characteristics
Covariate - 1 or more
One-way repeated measures ANCOVA
assess the influence of 1 IV on 1 DV after the effects of covariates is removed
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
Two-way (factorial) between-subjects ANCOVA
assess influence of 1 IV on 1 DV after effects of covariates removed
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
Two-way (factorial) repeated-measures ANCOVA
assess influence of 1 IV (within groups) on 1 DV after effects of covariates have been removed
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
One-way between subjects MANOVA
relationship between 1 IV and 2+ DVs
One-way between subjects MANOVA # of IVs and DVs
IVs - 1, between subjects, 2+ levels
DVs - 2+, interval characteristics
One-way repeated measures MANOVA
relationship between 1 IV (within subjects) and 2+ DVs
One-way repeated measures MANOVA # of IVs and DVs
IVs - 1, within subjects, 2+ levels
DVs - 2+, interval characteristics
Two-way (factorial) between subjects MANOVA
relationship between 2+ IVs and 2+ DVs
Two-way (factorial) between subjects # of IVs and DVs
IVs - 2+, between subjects, 2+ levels
DVs - 2+, interval characteristics
Two-way (factorial) repeated measures MANOVA
relationship between 2+ IVs (within subjects) and 2+ DVs
Two-way (factorial) repeated-measures MANOVA
IVs - 2+, within subjects, 2+ levels
DVs - 2+, interval characteristics
One-way between subjects MANCOVA
relationship between 1 IV and 2 DVs after adjusting for covariates
One-way between subjects MANCOVA # of IVs and DVs
IVs - 1, between subjects, 2 or more levels
DVs - 2, interval characteristics
Covariates - 1+
One-way repeated measures MANCOVA
relationship between 1 IV (within subjects) and 2 DVs after adjusting for covariates
One-way repeated measures MANCOVA # of IVs and DVs
IVs - 1, within subjects, 2 or more levels
DVs - 2, interval characteristics
Covariates - 1+
Two-way (factorial) between subjects MANCOVA
relationship between IVs and DVs after adjusting for covariates
Two-way (factorial) between subjects MANCOVA # of IVs and DVs
IVs - 2+, between subjects, 2+ levels
DVs - 2, interval characteristics
Covariates - 1+
Two-way (factorial) repeated measures MANCOVA
assessing influence of IVs (within subjects) on DVs after adjusting for covariates
Two-way (factorial) repeated measures MANCOVA # of IVs and DVs
IVs - 2+, within subjects, 2+ levels
DVs - 2, interval characteristics
Covariates - 1+
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
Standard Multiple Regression # of IVs and DVs
IVs - 2+, continuous or discrete
DVs - 1, continuous
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
Sequential (hierarchical) Multiple Regression # of IVs and DVs
IVs - 2+, continuous or discrete
DVs - 1, continuous
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
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)
Endogenous Variables
In path analysis
those variables modeled as dependent on other variables
all arrows point away
Exogenous Variables
In path analysis
those not dependent on other variables
arrows point to it
Structural Equation Modeling
per Ullman, 2001:
described as a combination of exploratory factor analysis and multiple regression
Manifest Variables
In SEM - serve as indicators of the underlying construct represented by the observable variables
Latent Variables
usually theoretical constructs that cannot be observed directly
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
Factor Analysis
used to identify underlying factors responsible for covariation among independent varables
Steps in factor analysis
choice of variables
selection of the proper number of factors to retain
method of rotation
What is the citation for research designs?
Mertler and Vaneeta, 2013
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
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.
