SPSS tests Flashcards

1
Q

Compute independent t-test

A

Analyse > compare means > independent samples t-test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Compute paired samples t-tes

A

Analyse > compare means > paired samples t-test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Compute mann whitney test

A

Create two variables and define grouping variable
Analyse> non parametric tests > legacy dialogs> 2 independent samples
Compute median
Calculate effect size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Compute wilcoxon

A

Define two variables and enter the data
Analyse> non parametric tests > legacy dialogs > 2 related samples
Compute median
Calculate effect size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Compute one way ANOVA

A

Analyse> general linear model > univariate
Options> descriptive statistics and homogneity tests
Post hoc> tick bonferroni or LSD
Calculate effect size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Compute repeated measures ANOVA

A

Data one row per participant
Analyse> general linear model> repeated measures
Move each level of IV to within subjects variables
Options> descriptive statistics
Post hoc> paired samples t-test (move each variable until all comparisons are made)
Calculate effect size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Compute independent factorial ANOVA

A

Data entered in 3 coloumns: One for each IV, one for DV
Analyse> general linear model > univariate
Options> descriptive statistics and homogenity tests
Calculate effect size
Conduct simple effects analyses: independent t-test and bonferroni
Data> select cases> select if condition is satisfied

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Compute repeated measures factorial ANOVA

A

Analyse> general linear model> repeated measures
Options> descriptive statistics and estimates of effect size
EM means> display means for IVs (not interaction)
Plots> add 2 plots vice versa> line chart
Simple effects: paired samples t-test and bonferroni> add every possible pair

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Compute mixed model factorial ANOVA

A

Analyse> general linear model> repeated measures
Options> move IVs under display means (not interaction)
Tick descriptive statistics, homogeneity tests and estimates of effect size
Simple effects: select cases> select if condition is satisfied
Compute paired samples t-test
Data> select cases> select all cases
Compute independent t-test
Bonferroni correction

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Compute Friedmans ANOVA

A

Analyse> non parametric > legacy dialogs> k related samples
Tick friedmans
Post hoc: wilcoxon and bomferroni correction
Compute medians
Calculate effect size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Compute kruskal wallis test

A

Analyse> non parametric tests> legacy dialogs> k independent samples
Post hoc: compute mann whitney with bonferroni correction
Compute effect size
Compute medians: data> split file> organise output by groups> move IV under groups based on> tick sort file by grouping variable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Compute Pearsons

A

Analyse> correlate> bivariate
Tick pearson, two tailed and flag significant correlations
Square r
Calculate df by hand

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Compute partial correlation

A

Analyse> correlate> Partial
Tick two tailed and display actual significance level
Square partial correlation to get effect size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Compute spearmans rho

A

Analyse> correlate> bivariate

Tick spearman> tick two tailed> tick flag significant correlations

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Compute kendalls tau

A

Analyse> correlate> bivariate

Tick kendall t> tick two tailed> tick flag significant correlations

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Compute simple regression

A

Analyse> regression> linear
Move criterion variable under dependent
Move predictor variable under independent

17
Q

Compute multiple regression

A

Analyse> regression> linear
Move outcome variable under dependent
Move predictor variable under independent
Select enter under method
Statistics> estimates, model fit, descriptives, part and partial correlations and collinearity diagnostics