weight by pspwght.
fre pbldmn.
RECODE pbldmn (1=1)(2=0) into pbldmn_2cat.
VARIABLE LABELS pbldmn_2cat ‘taking part or not in lawful public demonstration last 12 months?’.
VALUE LABELS pbldmn_2cat 1’yes’ 0’no’.
fre pbldmn pbldmn_2cat.

fre gndr.
RECODE gndr (1=1)(2=0) into gndr_2cat.
VARIABLE LABELS gndr_2cat ‘gender=male’.
VALUE LABELS gndr_2cat 1’male’ 0’female’.
fre gndr gndr_2cat.

fre domicile.
RECODE domicil (1 2=1)(3 thru 5=0) into domicil_2cat.
VARIABLE LABELS domicil_2cat ‘domicil=big city’.
VALUE LABELS domicil_2cat 1’big city or outskirts’ 0’not big city’.
fre domicil domicil_2cat.

fre edulvlb.
RECODE edulvlb (0 thru 213=1)(313 thru 800=0) INTO edulvlb_primary_dummy.
VARIABLE LABELS edulvlb_primary_dummy ‘Highest education level=primary’.
VALUE LABELS edulvlb_primary_dummy 1’Primary or below’ 0’Higher than primary’.
fre edulvlb edulvlb_primary_dummy.

fre edulvlb.
RECODE edulvlb (313 thru 520=1)(0 thru 213=0)(610 thru 800=0) INTO edulvlb_secondary_dummy.
VARIABLE LABELS edulvlb_secondary_dummy ‘Highest education level=secondary’.
VALUE LABELS edulvlb_secondary_dummy 1’Secondary’ 0’Not secondary’.
fre edulvlb edulvlb_secondary_dummy.

fre edulvlb.
RECODE edulvlb (0 thru 520=0)(610 thru 800=1) INTO edulvlb_tertiary_dummy.
VARIABLE LABELS edulvlb_tertiary_dummy ‘Highest education level=tertiary’.
VALUE LABELS edulvlb_tertiary_dummy 1’Tertiary’ 0’Not tertiary’.
fre edulvlb edulvlb_tertiary_dummy.

LOGISTIC REGRESSION pbldmn_2cat WITH gndr_2cat domicil_2cat edulvlb_secondary_dummy edulvlb_tertiary_dummy.

*===========================================================.

*Another method, without creating the dummy variables manually:.
fre edulvlb.
RECODE edulvlb (0 thru 213=1)(313 thru 520=2)(610 thru 800=3) INTO edulvlb_3cat.
VARIABLE LABELS edulvlb_3cat ‘Highest education level’.
VALUE LABELS edulvlb_3cat 1’Primary or below’ 2’Secondary’ 3’Tertiary’.
fre edulvlb edulvlb_3cat.

LOGISTIC REGRESSION pbldmn_2cat WITH gndr_2cat domicil_2cat edulvlb_3cat
/categorical edulvlb_3cat
/contrast (edulvlb_3cat)=indicator(1).

b1: The log odds for taking part in a lawful public demonstration in the last 12 months among men is higher by 0,603 than for women, everything else held constant. So, this is true for only those who are coded as 0 for the domicile and 0 for the education. So, only for those, who are small city residents and their highest level of education is primary education.

0 for the domicile = those who are living in small cities

0 in the education = those who have primary education (the reference category). So, those who are coded with 0 in secondary and in tertiary education are the ones that have primary education.

b3: The log odds of taking part in the demonstration is higher by 2,266 among secondary educated people than among primary educated people, everything else held constant. (So in this case, when your variable has more than 2 categories you compare it to the reference category.)

everything else held constant = this is true only for women who live in a small city

Exp(b1): The odds for taking part in a lawful public demonstration in the last 12 months for men are 1,828 times as high as for women, everything else held constant. So, this is true for only those who are coded as 0 for the domicile and 0 for the education. So, only for those, who are small city residents and their highest level of education is primary education.

Exp(b2): The odds for taking part in a lawful public demonstration in the last 12 months for big city residents are 3,167 times as high as for small city residents, everything else held constant. (So, this is true only for those who are women and their highest education level is primary education.)

Exp(b4): The odds for taking part in a lawful public demonstration in the last 12 months among people whose highest education level is secondary education are 10,115 times as high as for people whose highest education level is primary education, everything else held constant.

Exp(b0): The odds for taking part in a lawful public demonstration in the last 12 months among women living in small cities, whose highest education level is primary education. (So, this is the case when all the independent variables are 0.)

In the following table, called “Categorical Variables Codings” you can find out how the SPSS automatically created for you the dummy variable.

The category coded with only zeros is the reference category. -> primary or below

In the regression table “highest education level(1)” refers to secondary education. -> this is marked as 1 in the parameter coding (1) column

In the regression table “highest education level(2)” refers to tertiary education. -> this is marked as 1 in the parameter coding (2) column

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