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Syntax_Complete.sps
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Syntax_Complete.sps
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### ##discriptive statistics - Gender age and total subjects.
DESCRIPTIVES VARIABLES=iid age
/STATISTICS=MEAN STDDEV RANGE MIN MAX.
##discriptive statistics - Gender
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
DESCRIPTIVES VARIABLES=age
/STATISTICS=MIN MAX MEAN STDDEV.
SPLIT FILE OFF.
CROSSTABS
/TABLES=iid BY gender
/FORMAT=AVALUE TABLES
/CELLS=COUNT
/COUNT ROUND CELL.
##discriptive statistics - Race
CROSSTABS
/TABLES=iid BY race
/FORMAT=AVALUE TABLES
/CELLS=COUNT
/COUNT ROUND CELL.
### General preferences of the subjects they look for in the opposite sex
DESCRIPTIVES VARIABLES=attr1_1 sinc1_1 intel1_1 fun1_1 amb1_1 shar1_1
/STATISTICS=MEAN STDDEV.
------------------------------------------------------------------------------------------------------------
### Gender preferences of the subjects they look for in the opposite sex
DATASET ACTIVATE DataSet1.
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
DESCRIPTIVES VARIABLES=attr1_1 sinc1_1 intel1_1 fun1_1 amb1_1 shar1_1
/STATISTICS=MEAN STDDEV VARIANCE RANGE MIN MAX.
SPLIT FILE OFF.
### t test for statistical significant analysis
T-TEST GROUPS=gender(1 0)
/MISSING=ANALYSIS
/VARIABLES=attr1_1 amb1_1 shar1_1 sinc1_1 intel1_1 fun1_1
/CRITERIA=CI(.95).
### Distirbution by each one of the 6 parameters - Gender preferences of the subjects they look for in the opposite sex
---Attraction -------------------------------------------------------------------------------------------------
DATASET ACTIVATE DataSet1.
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
FREQUENCIES VARIABLES=attr1_1
/STATISTICS=STDDEV MINIMUM MAXIMUM MEAN
/ORDER=ANALYSIS.
SPLIT FILE OFF.
---since -------------------------------------------------------------------------------------------------
DATASET ACTIVATE DataSet1.
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
DATASET ACTIVATE DataSet1.
FREQUENCIES VARIABLES=sinc1_1
/ORDER=ANALYSIS.
SPLIT FILE OFF.
---intel -------------------------------------------------------------------------------------------------
DATASET ACTIVATE DataSet1.
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
DATASET ACTIVATE DataSet1.
FREQUENCIES VARIABLES=intel1_1
/STATISTICS=STDDEV MINIMUM MAXIMUM MEAN
/ORDER=ANALYSIS.
SPLIT FILE OFF.
---fun -------------------------------------------------------------------------------------------------
DATASET ACTIVATE DataSet1.
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
DATASET ACTIVATE DataSet1.
FREQUENCIES VARIABLES=fun1_1
/STATISTICS=STDDEV MINIMUM MAXIMUM MEAN
/ORDER=ANALYSIS.
SPLIT FILE OFF.
---amb -------------------------------------------------------------------------------------------------
DATASET ACTIVATE DataSet1.
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
DATASET ACTIVATE DataSet1.
FREQUENCIES VARIABLES=amb1_1
/STATISTICS=STDDEV MINIMUM MAXIMUM MEAN
/ORDER=ANALYSIS.
SPLIT FILE OFF.
---shar -------------------------------------------------------------------------------------------------
DATASET ACTIVATE DataSet1.
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
DATASET ACTIVATE DataSet1.
FREQUENCIES VARIABLES=shar1_1
/STATISTICS=STDDEV MINIMUM MAXIMUM MEAN
/ORDER=ANALYSIS.
SPLIT FILE OFF.
------------------------------------------------------------------------------------------------------------
###Ather sex Preference analysis - What do you think the opposite sex looks for in a date?
DATASET ACTIVATE DataSet1.
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
DESCRIPTIVES VARIABLES=attr2_1 sinc2_1 intel2_1 fun2_1 amb2_1 shar2_1
/STATISTICS=MEAN STDDEV VARIANCE RANGE MIN MAX.
SPLIT FILE OFF.
------------------------------------------------------------------------------------------------------------
### What men/women really want VS what the other sex think they want?
DATASET ACTIVATE DataSet1.
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
DESCRIPTIVES VARIABLES=attr1_1 sinc1_1 intel1_1 fun1_1 amb1_1 shar1_1 attr2_1 sinc2_1 intel2_1
fun2_1 amb2_1 shar2_1
/STATISTICS=MEAN STDDEV MIN MAX.
SPLIT FILE OFF.
------------------------------------------------------------------------------------------------------------
### what is the difference between individual yes decision and no decision
T-TEST GROUPS=dec(1 0)
/MISSING=ANALYSIS
/VARIABLES=attr fun shar intel sinc amb
/CRITERIA=CI(.95).
### what is the difference between female or male individual yes decision and no decision
SORT CASES BY dec.
SPLIT FILE LAYERED BY dec.
T-TEST GROUPS=gender(1 0)
/MISSING=ANALYSIS
/VARIABLES=attr fun shar intel sinc amb
/CRITERIA=CI(.95).
SPLIT FILE OFF.
------------------------------------------------------------------------------------------------------------
#### create positive rate
##step one: SUM all the positive selection and put it in a new column - dec_numb
AGGREGATE
/OUTFILE=* MODE=ADDVARIABLES
/BREAK=iid
/dec_o_sum=SUM(dec_o).
##step two: create a column with max partners for each subject.
selecting the round column as max partners for each subject.
##step three: create the precentage positive rate. if there is max 10 partners and 5 dec yes then the positive rate will be at 50%.
COMPUTE positive_rate=dec_o_sum * 100 / max_partner_round.
EXECUTE.
------------------------------------------------------------------------------------------------------------
### create a regression between the mean of each variable from the max partners and the positive rate percentage.
--find the mean for each variable.
AGGREGATE
/OUTFILE=* MODE=ADDVARIABLES
/BREAK=iid
/attr_o_mean=MEAN(attr_o)
/sinc_o_mean=MEAN(sinc_o)
/intel_o_mean=MEAN(intel_o)
/fun_o_mean=MEAN(fun_o)
/amb_o_mean=MEAN(amb_o)
/shar_o_mean=MEAN(shar_o)
/like_o_mean=MEAN(like_o).
### find the correlation between the positive rate and the mean score of each the six variables.
CORRELATIONS
/VARIABLES=Positive_Rate attr_o_mean sinc_o_mean intel_o_mean fun_o_mean amb_o_mean shar_o_mean
/PRINT=TWOTAIL NOSIG
/MISSING=PAIRWISE.
### find the correlation between the positive rate and the mean score of each the six variables on each gender.
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
CORRELATIONS
/VARIABLES=Positive_Rate attr_o_mean sinc_o_mean intel_o_mean fun_o_mean amb_o_mean shar_o_mean
/PRINT=TWOTAIL NOSIG
/MISSING=PAIRWISE.
SPLIT FILE OFF gender.
### create a multiple regression to see each variable and his contribute to the model using SEPWISE method.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT positive_rate
/METHOD=STEPWISE attr_o_mean sinc_o_mean intel_o_mean fun_o_mean amb_o_mean shar_o_mean.
### create a graph to the reggression based on the statistical significant values of the model.
--step 1 - create a multiple regression and save the nustandardize values in a new column.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT positive_rate
/METHOD=ENTER attr_o_mean fun_o_mean
/SAVE PRED.
--step 2 - create a graph for the standardized values.
GRAPH
/SCATTERPLOT(BIVAR)= Unstandardized_values_for_mulitple_regression_scaterdot WITH positive_rate
/MISSING=LISTWISE.
------------------------------------------------------------------------------------------------------------
### what is the difference between match and non-match couples?
T-TEST GROUPS=match(1 0)
/MISSING=ANALYSIS
/VARIABLES=fun attr shar intel sinc amb
/CRITERIA=CI(.95).
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
## find difference between age group.
--first create an age group for partners and subjects.
RECODE age_o (18 thru 24=1) (25 thru 31=2) (32 thru 38=3) (39 thru 45=4) (46 thru 55=5) INTO
Age_Group_Partner.
VARIABLE LABELS Age_Group_Partner 'Age_Group_Partner'.
EXECUTE.
RECODE age (18 thru 24=1) (25 thru 31=2) (32 thru 38=3) (39 thru 45=4) (46 thru 55=5) INTO
Age_Group_Subject.
VARIABLE LABELS Age_Group_Subject 'Age_Group_Subject'.
EXECUTE.
--- frequencies to age groups
SORT CASES BY gender.
SPLIT FILE LAYERED BY gender.
DATASET ACTIVATE DataSet1.
FREQUENCIES VARIABLES=Age_Group_Subject
/STATISTICS=STDDEV MINIMUM MAXIMUM MEAN
/ORDER=ANALYSIS.
SPLIT FILE OFF gender.
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 20.2.19 -------------------------------
-- create a filter before three way anova test - filter on subjects positive decision and age groups - 1,2,3, race group = black, asian, latino and european
USE ALL.
COMPUTE filter_$=(dec=1 & (Age_Group_Subject=1 | Age_Group_Subject=2 | Age_Group_Subject=3) &
(race=1 | race=2 | race=3 | race=4)).
VARIABLE LABELS filter_$ 'dec=1 & (Age_Group_Subject=1 | Age_Group_Subject=2 | '+
'Age_Group_Subject=3) & (race=1 | race=2 | race=3 | race=4) (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
--- Three way anova on positive decision of the subjects - gender, age group and race on attractiveness.
UNIANOVA attr BY gender Age_Group_Subject race
/METHOD=SSTYPE(3)
/INTERCEPT=INCLUDE
/POSTHOC=Age_Group_Subject race(SCHEFFE)
/PLOT=PROFILE(gender*Age_Group_Subject gender*race Age_Group_Subject*race
gender*Age_Group_Subject*race)
/PRINT=ETASQ DESCRIPTIVE
/CRITERIA=ALPHA(.05)
/DESIGN=gender Age_Group_Subject race gender*Age_Group_Subject gender*race Age_Group_Subject*race
gender*Age_Group_Subject*race.
---triple interaction source check - gender, age group and race.
DATASET ACTIVATE DataSet1.
SORT CASES BY Age_Group_Subject race.
SPLIT FILE LAYERED BY Age_Group_Subject race.
T-TEST GROUPS=gender(1 0)
/MISSING=ANALYSIS
/VARIABLES=attr
/CRITERIA=CI(.95).
SPLIT FILE OFF.
--- Three way anova on positive decision of the subjects - gender, age group and race on fun.
UNIANOVA fun BY gender Age_Group_Subject race
/METHOD=SSTYPE(3)
/INTERCEPT=INCLUDE
/POSTHOC=Age_Group_Subject race(SCHEFFE)
/PLOT=PROFILE(gender*Age_Group_Subject gender*race Age_Group_Subject*race
gender*Age_Group_Subject*race)
/PRINT=ETASQ DESCRIPTIVE
/CRITERIA=ALPHA(.05)
/DESIGN=gender Age_Group_Subject race gender*Age_Group_Subject gender*race Age_Group_Subject*race
gender*Age_Group_Subject*race.
--double interaction source check - gender and race.
DATASET ACTIVATE DataSet1.
SORT CASES BY race.
SPLIT FILE LAYERED BY race.
T-TEST GROUPS=gender(1 0)
/MISSING=ANALYSIS
/VARIABLES=fun
/CRITERIA=CI(.95).
SPLIT FILE OFF.
FILTER OFF.
USE ALL.
EXECUTE.
--------------------------------------------------------------------------------------------------------------------------------------------------------------------
--- Total race dates by other races - to check the positive decision of each race on other race.
-- European total dates by race
USE ALL.
COMPUTE filter_$=(race=2).
VARIABLE LABELS filter_$ 'race=2 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
FREQUENCIES VARIABLES=race_o
/PIECHART FREQ
/ORDER=ANALYSIS.
FILTER OFF.
USE ALL.
EXECUTE.
-- European total positive desicion by race
USE ALL.
COMPUTE filter_$=(race=2 AND dec=1).
VARIABLE LABELS filter_$ 'race=2 AND dec=1 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
FREQUENCIES VARIABLES=race_o
/PIECHART FREQ
/ORDER=ANALYSIS.
FILTER OFF.
USE ALL.
EXECUTE.
-- African American total dates by race
USE ALL.
COMPUTE filter_$=(race=1).
VARIABLE LABELS filter_$ 'race=2 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
FREQUENCIES VARIABLES=race_o
/PIECHART FREQ
/ORDER=ANALYSIS.
FILTER OFF.
USE ALL.
EXECUTE.
-- African American total positive desicion by race
USE ALL.
COMPUTE filter_$=(race=1 AND dec=1).
VARIABLE LABELS filter_$ 'race=1 AND dec=1 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
FREQUENCIES VARIABLES=race_o
/PIECHART FREQ
/ORDER=ANALYSIS.
FILTER OFF.
USE ALL.
EXECUTE.
-- Latino total dates by race
USE ALL.
COMPUTE filter_$=(race=3).
VARIABLE LABELS filter_$ 'race=3 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
FREQUENCIES VARIABLES=race_o
/PIECHART FREQ
/ORDER=ANALYSIS.
FILTER OFF.
USE ALL.
EXECUTE.
-- Latino total positive desicion by race
USE ALL.
COMPUTE filter_$=(race=3 AND dec=1).
VARIABLE LABELS filter_$ 'race=3 AND dec=1 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
FREQUENCIES VARIABLES=race_o
/PIECHART FREQ
/ORDER=ANALYSIS.
FILTER OFF.
USE ALL.
EXECUTE.
-- Asian total dates by race
USE ALL.
COMPUTE filter_$=(race=4).
VARIABLE LABELS filter_$ 'race=4 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
FREQUENCIES VARIABLES=race_o
/PIECHART FREQ
/ORDER=ANALYSIS.
FILTER OFF.
USE ALL.
EXECUTE.
-- Asian total positive desicion by race
USE ALL.
COMPUTE filter_$=(race=4 AND dec=1).
VARIABLE LABELS filter_$ 'race=4 AND dec=1 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
FREQUENCIES VARIABLES=race_o
/PIECHART FREQ
/ORDER=ANALYSIS.
FILTER OFF.
USE ALL.
EXECUTE.