Transcript Quantitative analysis

Quantitative analysis
Alessandra Fermani
[email protected]
Variables
variable type: numeric or string
Dependent: satisfation
Independent: age, gender
Ordinal: children, adolescents, adult etc…
Likert scale eg. 1= never (disagree) 2 3 4 5 6 7 =
always (agree) (odd - better)
Dummy: dicotomic variables eg. Yes/no or gender
Unidirectional / bidirectional
relationship between variables
• bidirectional (correlation, regression)
• unidirectional (cause and effect)
Formula: Trust index
significance
• p<.05
good level p<.01, p<.001
• Rule of transcription:
eg: (F (1, 2114) = 7.11, p < .01)
Descriptive statistics
• To take statistics: Frequencies, mean, median,
mode
to operate dispersion, use standard
deviation (SD)
Mean or average
• In statistics, mean and expected value are
used synonymously to refer to one measure of
the central tendency either of a probability
distribution or of the random variable
characterized by that distribution.
Eg. 10 students, grades in a test:
5,7,4,8,5,6,5,7,6,4
mean equal 5,7 because
(5+7+4+8+5+6+5+7+6+4/10 = 5,7)
Standard deviation
Deviazione standard o varianza = dispersione dei dati attorno alla
media
In statistics and probability theory, the standard deviation (SD)
(represented by the Greek letter sigma, σ) measures the amount of
variation or dispersion from the average
• Classroom A – student’s grades:
2,7,4,4,3,4,5,4,4,1,6,4,4,5,4,3
• Classroom B - student’s grades: 6,4,3,4,5,5,2,3,4,2,1,3,5,7,4,6
mean is 4 (GPA), the same in both, but classes are different. the
classroom B is more different compare to classrom A and the SD is
the index that measures.
Median =
In statistics, the median is the numerical value
separating the higher half of a data sample, a population, or
a probability
distribution, from the lower half
Legenda: 1 very good, 2 good, 3 not bad, 4 sufficient, 5 not
sufficient
9 students
scores: 1,4,1,2,3,2,5,2,4
Put in order 1,1,2,2,2,3,4,4,5
Median= (9+1)/2 = 5 position therefore is 2 (good)
Formula
i= n+1/2
Mode
• The mode is the value that appears most often in a
set of data.
• Eg. 100 subjects are divided into three categories: 33
prefer action movies; 54 romantic ; 13 horror
• The mode is «category of romantic movies» because
this category is most represented
Inferential Statistics
• Correlation = In statistics, dependence is any
statistical relationship between two random
variables or two sets
of data. Correlation refers to any of a broad
class of statistical relationships involving
bidirectional dependence. (2 variables are
associated: perfect positive +1, perfect
negative -1);
• Regression = measure as independent
variables (predictors) associated with the
dependent variable are better
Eg. Correlation
more/more; more/less
*** = P<.001 **= minor.01 *= minor.05
Variable
Self Concept
Clarity
Extraversion
Emotional
stability
Openness to
experience
.12**
-.09*
.21**
-.06
-.09*
.11***
-.11**
.16**
Educational identity
Commitment
Exploration in
Depth
Integration with linear regression
Table: Standardized Betas and Proportion Explained Variance for the Regression
Analyses of SCC, emot. stab. and personality on Identity (italian 1976)
Variable
Self Concept
Clarity
Extraversion
Emotional
Openness to
stability
experience
.11**
.16**
.22**
.16**
(.02)
(.13**)
(.16*)
(.22**)
-.21**
-.08*
-.25**
.14**
(-.18**)
(-.01)
(-.14**)
(.23**)
.11**
.03**
.06**
.08**
Commitment
Exploration in
Depth
Total R2
• Chi square, T- test
• ANOVA and MANOVA compare means
(variables independent or fix factor as age or
e.g. Motivation with variables dependent as
satisfation). More 3 groups «v» on post hoc
test-Takey
• Factor analysis = (data reduction) is
a statistical method used to
describe variability among observed,
correlated variables in terms of a potentially
lower number of unobserved variables
called factors. (PCA and EFA are 2 type of
exploratory factor analysis). Cronbach’s alpha
>.60
Cluster analysis = (data reduction) or clustering is the
task of grouping a set of objects in such a way that
objects in the same group (called a cluster) are more
similar (in some sense or another) to each other than to
those in other groups (clusters).
GORE (2000) 2 steps (only Likert scale no dummy and
standard. ):
1) Hierarchic for number of cluster
2) No Hierarchic (K mean) for the best classification
,5
0,0
Zscore: trust F
Zscore: comm F
-,5
Zscore: clos F
-1,0
Zscore: trust M
-1,5
Zscore: clos M
e
M
r
fo
te
P
Cluster Number of Case
l
bo
de
/M
P
rte
fo
/M
P
le le P
bo bo
de de
M
-2,0
Zscore: comm M
rte
fo
Mean
1,0
Statistical software:
Why ?
• To predict
• To understand
SPSS
• 1 version 1968 IBM
• Last: 22.0 (13 agosto 2013)
• Language: java
• Java System: Microsoft Windows, Mac OS,
Linux ect…
Manual and video
• http://www.ateneonline.it/chiorri/studenti/isbn6
556-1_guidaSPSS.pdf
• ftp://public.dhe.ibm.com/software/analytics/spss
/documentation/statistics/20.0/en/client/Manual
s/IBM_SPSS_Statistics_Core_System_Users_Guid
e.pdf
• Video (it):
https://www.youtube.com/watch?v=ftU4TauCshg
2 windows
• Data view
• variable view (name, Type, with, decimals,
label, values, missing, columns, align,
measure)
• Application