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Summary MCRS SPSS - Y $11.94   Add to cart

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Summary MCRS SPSS - Y

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This a summary of all the SPSS statistics that is needed for the exam, provided by a honours student. This will give you a clear overview of the SPSS. I got a 8.9 for this exam.

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  • December 10, 2023
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  • 2023/2024
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SPSS MCRS


Make notes in syntax!
*title  enter

Reverse code variable (als je variable negatief staat en de rest positief)
Transform  recode into different variables  same name for label klick change  old en
new values  value: 1 = value: 5 and so on 3=3 (has to be included)  continue and paste
You don’t necessarily have to reverse code for the factor analysis. But you do have to
reverse code for the reliability analysis.

Recode variable (in groepen maken)
Transform  recode into different variable  select variable  change name/label click
change  old and new values  range: ex 17-19 vink add and so on



Cronbach’s alpha
Measures the internal consistency between items in a scale
Make sure not to mix positively and negatively worded questions – alpha will be negative
 reverse code variables if needed
Analyze  scale  reliability analysis  select items (same scale needed)  statistics: vink
scale if item deleted

Factor analysis
Analyze  dimension reduction  factor  select items 
Descriptive: univariable descriptive, initial solution, coefficients, significance levels, KMO and
Barlett’s test of sphericity
Extraction: method  principal axis factoring, scree plot
Rotation: direction oblimin
Options: sorted by size and suppress small coefficients (0.30)

Compute
Transfrom  compute  target variable; give it a name  use + to combine 2 varibles 
use * for x  MEAN(variable, variable,)

Correlation (pearsons r)
Analyze  correlation  bivariate
+ = positive direction and - = negative direction
-1 = perfect negative strength and +1 = perfect positive strong
0 = no correlation
Magnitude of correlation indicated strength of association:
Under 0.3 = weak / between 0.3 and 0.5 = moderate / bigger dan 0.5 = strong

Missing values
Data  define variable properties  select all  continue 

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