TAPING IN DATA

Each line represents a participant.

In transversal studies, each column is a different variable.

In longitudinal studies, each column is a condition of the independent variable.

In “Data View” (tab in down left corner), only numbers can be included.

In “Variable View” (tab in down left corner), each line is a variable. It is important to determine the measure of each variable (in last column, nominal, ordinal o scale).
CONVERSION FROM QUANTITATIVE TO QUALITATIVE OR ORDINAL VARIABLE
Transformar – recodificar en diferentes variables – introducir la variable a transformar – poner otro nombre – antiguos y nuevos valores – (you have the possibility of choosing ranges – ELEGIR)
STANDARD SCORES
Analyze – Descriptive statistics – descriptive – click “save standardized values as variable”
In order to sum or subtract values of two variables in each participant:
Transformar – calcular variable
ASSUMPTIONS
1. NORMALITY (in the quantitative variable/s, test normal distribution):
Analyze – nonparametric tests – 1 sample KS
When Kolmogorov Smirnov Z presents sig ≥ 0.05, the distribution is normal
2. HOMOSCEDASTICITY (in the relationship between qualitative/ordinal – quantitative variables)
Analyze – Compare Means – OneWay ANOVA – Options – Homogeneity of variance test
When Levene statistic presents sig ≥ 0.05, there is homoscedasticity
3. LINEARITY (in the relationship between ordinal/quantitative (with more than two groups – quantitative variables)
Analyze – Compare Means – Means – Options – Test for linearity
sig < 0.05 – There is linearity
4. INDEPENDENCE OF ERRORS (in the relationship between variables)
Analyze – Regression – Linear – Statistics – DurbinWatson
1.5 < d < 2.5 – Errors are independent
Note in paired samples: you only need to take into account the sphericity (assumptions 14 have not to be checked).
GRAPHS
Relationship between two quantitative variables – Graphs  Legacy Dialogs Scatter/Dot
Relationship between a qualitative and a quantitative variable – Graphs  Legacy Dialogs  Bar
STATISTICS
0. Compare values in the sample and the population of reference.
Comparar medias – prueba T para una media.
In “valor para una media”, include the value of the population.
Sig < 0.05 – The sample comes from a different population.
1. CHI SQUARE TEST
Analyze – Descriptive statistics –crosstabs
Include one variable in ROWS and the other in COLUMNS
Statistics – click in Chisquare – OK
Sig < 0.05 – relationship between variables / differences across groups.
2. FISHER EXACT TEST
Analyze – Descriptive statistics –crosstabs
Include one variable in ROWS and the other in COLUMNS
Statistics – click in Chisquare – OK
Fisher’s Exact Test Sig < 0.05 – relationship between variables / differences across groups.
3. MCNEMAR TEST
Analyze – Descriptive statistics –crosstabs
Include one variable in ROWS and the other in COLUMNS
Statistics – click in McNemar – OK
Sig < 0.05 – relationship between variables / differences across groups.
4. MANN WHITNEY U
Analyze – Nonparametric Tests – Two Independent Samples – MannWhitney U
Sig < 0.05 – relationship between variables / differences across groups.
5. KRUSKAL WALLIS H
Analyze – Nonparametric Tests – k Independent Samples – KruskalWallis H
Sig < 0.05 – relationship between variables / differences across groups.
6. WILCOXON T TEST
Analyze – Nonparametric Tests – Two Related Samples  Wilcoxon
Sig < 0.05 – relationship between variables / differences across groups.
7. T (INDEPENDENT SAMPLES):
Analyze – Compare Means – IndependentSamples T test
Sig < 0.05 – relationship between variables / differences across groups.
8. T (PAIRED SAMPLES):
Analyze – Compare Means – PairedSamples T test
Sig < 0.05 – relationship between variables / differences across groups.
9. F (TRANSVERSAL):
Analyze – compare means – oneway ANOVA –
Sig < 0.05 – relationship between variables / differences across groups.
9.1. POST_HOC CONTRASTS: posthoc – Scheffé, Tukey
9.2. A PRIORI CONTRASTS: contrasts – coefficients – add
9.2.1. In tendency contrasts: + polynomial
10. F (LONGITUDINAL):
Analyze – general linear model – repeated measures
GreenhouseGeisser Sig < 0.05 – relationship between variables / differences across groups.
11. F (MIXED DESIGNS):
Analizar – modelo lineal general – medidas repetidas.
The factor within groups (intrasujetos) is the longitudinal variable. Its number of levels is its number of measures.
The factor between groups (intersujetos) is the transversal variable.
Sig < 0.05: relationship between variables / differences across groups:

sig of the between groups test (intersujetos) (the last table that appears) to see if there are differences in the conditions of the transversal variable

In the within groups test (intrasujetos):

Sig of factor 1 (esfericity) to see if there are differences in the longitudinal variable.

The most interesting: sig of factor 1 * the transversal variable to see if there are differences in the interaction.
12. PEARSON CORRELATION:
Analyze  correlate  bivariate  Pearson
Sig < 0.05: relationship between variables
13. SPEARMAN CORRELATION:
Analyze  correlate  bivariate  Spearman
Sig < 0.05: relationship between variables
14. SIMPLE LINEAR REGRESSION:
Analize – regression – linear
Sig < 0.05: relationship between variables 