I am working the data of binary variables on 30 districts and 8 questions (q01 to q08). Here is the data I have used:

[aafaque ali]

district	q01	q02	q03	q04	q05	q06	q07	q08	population
District 1	0	1	0	0	0	1	0	0	892
District 2	0	0	0	1	0	1	0	0	138
District 3	0	1	0	1	0	0	0	1	923
District 4	1	1	0	1	1	0	0	0	887
District 5	0	1	1	0	1	1	0	0	514
District 6	1	0	1	1	1	0	1	1	578
District 7	0	0	0	1	1	0	1	0	393
District 8	1	0	0	1	0	1	0	0	566
District 9	0	0	0	0	1	0	0	1	514
District 10	1	0	0	0	0	0	0	1	770
District 11	0	0	0	0	1	1	1	1	207
District 12	1	1	0	1	0	0	0	1	625
District 13	0	1	0	0	0	1	1	0	550
District 14	1	0	1	1	0	0	0	0	596
District 15	1	1	0	0	1	0	0	1	250
District 16	1	1	1	0	1	1	0	0	481
District 17	1	1	1	0	1	0	1	0	553
District 18	1	0	1	1	0	0	1	1	652
District 19	1	0	0	0	0	1	0	0	503
District 20	0	1	1	1	0	0	0	1	234
District 21	1	1	0	1	0	1	0	1	883
District 22	1	1	0	0	0	1	0	0	344
District 23	1	1	0	0	1	0	0	0	238
District 24	1	1	1	1	0	1	0	1	944
District 25	1	0	1	1	1	0	1	0	940
District 26	0	0	0	1	0	1	1	0	730
District 27	0	0	0	0	1	1	0	0	890
District 28	1	1	1	1	1	0	0	0	916
District 29	0	0	0	1	0	0	1	1	959
District 30	0	1	0	1	0	1	0	1	404

```stata
recode c01 (1=1 "1") (2=0 "0"), generate(binary_c01)
recode c02 (1=1 "1") (2=0 "0"), generate(binary_c02)
recode c03 (1=1 "1") (2=0 "0"), generate(binary_c03)
recode c04 (1=1 "1") (2=0 "0"), generate(binary_c04)
recode c05 (1=1 "1") (2=0 "0"), generate(binary_c05)
recode c06 (1=1 "1") (2=0 "0"), generate(binary_c06)
recode c07 (1=1 "1") (2=0 "0"), generate(binary_c07)
recode c08 (1=1 "1") (2=0 "0"), generate(binary_c08)
egen total_sum = rowtotal ( binary_c01 binary_c02 binary_c03 binary_c04 binary_c05 binary_c06 binary_c07 binary_c08)
gen R_Sum = total_sum / 8
collapse (mean) R_Sum , by(district)
gen aggregate_population = R_Sum * Population
**Normalized R_Sum**
egen R_Sum_min = min(R_Sum)
egen R_Sum_max = max(R_Sum)
gen R_Sum_normalized = (R_Sum - R_Sum_min) / (R_Sum_max - R_Sum_min)
**Normalized aggregate_population**
egen aggregate_population_min = min(aggregate_population)
egen aggregate_population_max = max(aggregate_population)
gen aggregate_population_normalized = (aggregate_population - aggregate_population_min) / (aggregate_population_max - aggregate_population_min)
**Listing**
list district R_Sum aggregate_population R_Sum_normalized aggregate_population_normalized
End
The code runs without errors, and I observe the following:

• One district has a normalized aggregate population value of 1.
• One district has a normalized aggregate population value of 0.
• The remaining districts have values between 0 and 1 after normalization.

I understand that normalization should result in values between 0 and 1, but here one district is exactly "1" and the other is exactly "0", also I want to confirm if this is the correct approach for normalizing aggregate population values.

Any advice or improvements to my approach would be greatly appreciated.

Thank you for your assistance.

[Carlo Lazzaro]

Aafaque:
the best (yet inconclusive) advice I can give you is reposting your query on the General forum, as this one (as per FAQ) is for practising purposes only. Thanks.