#frequencytables

[michaelflorezrico]

Generalized anxiety disorder frequency tables (GAD) - running my first program

Now, in order to refine my research question, I will focus the study on a particular sample from the data set, letting the research question as it will be next presented:

Is drinking/drug use associated with generalized anxiety disorder (GAD) in young adults who have consumed alcohol/drugs in the last 12 months and have presented generalized anxiety episodes during this time?

The variables selected in order to start the data analysis regarding my research question are: 

S9Q14DR= ONLY/ALL EPISODE(S) IN THE LAST 12 MONTHS BEGAN AFTER DRINKING/USE DRUGS.

S9Q14FR= STOPPED DRINKING/USE DRUGS 1+ MONTHS AFTER ONLY/ALL EPISODE(S) IN THE LAST 12 MONTHS.

S9Q14HR= CONTINUE TENSE/NERVOUS AFTER ONLY/ALL EPISODE(S) WHEN STOPPED DRINKING/USE DRUGS IN THE LAST 12 MONTHS.

S9Q14JR= ONLY/ALL EPISODE(S) PRIOR THE LAST 12 MONTHS BEGAN AFTER DRINKING/USE DRUGS.

S9Q14LR= STOPPED DRINKING/USE DRUGS 1+ MONTHS AFTER ONLY/ALL EPISODE(S) PRIOR TO THE LAST 12 MONTHS.

S9Q14NR= CONTINUE TENSE/NERVOUS AFTER ONLY/ALL EPISODE(S) WHEN STOPPED DRINKING/USE DRUGS PRIOR TO THE LAST 12 MONTHS.

Now, using SAS Studio to get the frequency tables for the selected variables, and taking as data sample young adults between 18 and 25 years old, the results are:

NOTE: The code use to get these results is presented at the end of this publication.

AGE (DATA SAMPLE)

S9Q14DR

S9Q14FR

S9Q14HR

S9Q14JR

S9Q14LR

S9Q14NR

CODE

LIBNAME mydata "/courses/d1406ae5ba27fe300 " access=readonly; DATA new; set mydata.nesarc_pds;

LABEL S9Q14DR="ONLY/ALL EPISODE(S) IN THE LAST 12 MONTHS BEGAN AFTER DRINKING/USE DRUGS";

LABEL S9Q14FR="STOPPED DRINKING/USE DRUGS 1+ MONTHS AFTER ONLY/ALL EPISODE(S) IN THE LAST 12 MONTHS";

LABEL S9Q14HR="CONTINUE TENSE/NERVOUS AFTER ONLY/ALL EPISODE(S) WHEN STOPPED DRINKING/USE DRUGS IN THE LAST 12 MONTHS";

LABEL S9Q14JR="ONLY/ALL EPISODE(S) PRIOR THE LAST 12 MONTHS BEGAN AFTER DRINKING/USE DRUGS";

LABEL S9Q14LR="STOPPED DRINKING/USE DRUGS 1+ MONTHS AFTER ONLY/ALL EPISODE(S) PRIOR TO THE LAST 12 MONTHS";

LABEL S9Q14NR="CONTINUE TENSE/NERVOUS AFTER ONLY/ALL EPISODE(S) WHEN STOPPED DRINKING/USE DRUGS PRIOR TO THE LAST 12 MONTHS";

IF AGE LE 25;

PROC SORT; by IDNUM; PROC FREQ; TABLES S9Q14DR S9Q14FR S9Q14HR S9Q14JR S9Q14LR S9Q14NR AGE; RUN;

[readmydata-blog]

Here are all eight frequency tables of my data

table1-code1; breast cancer percentage

codes from 1 to 6 are assigned to show the percentage areas.

1: 1% - 10%; 2: 10%-20%; 3: 20%-30%; 4: 30%-50%; 5: 50%-75%; 6: 75-100%

table2-code2; GDP per Capita in USD

codes from 1 to 7 are assigned to show the GDP per capita areas.

1: GDP<2000; 2: 2000<GDP<5000; 3: 5000<GDP<10000; 4: 10000<GDP<20000 ; 5: 20000<GDP<35000 ; 6: 35000<GDP<50000; 7: GDP>50000

table3-code3; CO2 Emission per Capita

codes from 1 to 5 are assigned.

1: CO2<1; 2: 1<CO2<5; 3: 5< CO2 <10; 4: 10< CO2 <20; 5: CO2>20

table4-code4; Female Employment Percentage

codes from 1 to 5 are assigned.

1: 1% - 20%; 2: 20%-40%; 3: 40%-60%; 4: 60%-80%; 5: >80%

table5-code5; Internet Use Rate

codes from 1 to 5 are assigned.

1: <1%; 2: 1%-10%; 3: 10%-30%; 4: 30%-50%; 5: >50%

table6-code6; Life Expectancy in Years

codes from 1 to 5 are assigned.

1: 40<Life Exp<50; 2: 50< Life Exp <60; 3: 60< Life Exp <70; 4: 70< Life Exp <80; 5: Life Exp >80

table7-code7; Urban Rate

codes from 1 to 6 are assigned.

1: Urban<30%; 2: 30%-50%; 3: 50%-60%; 4: 60%-70%; 5: 70%-80%; 6: Urban 80%-100%

table8-code8; Urban Rate

codes from 1 to 6 are assigned.

1: use<1%; 2: 1%-5%; 3: 5%-15%; 4: 615%-30%; 5: 30%-50%; 6: use>50%

table9-code9; Cholesterol

codes from 1 to 4 are assigned.

1: 4-4.5; 2: 4.5-5; 3: 5-5.5; 4: >5.5

[dataandstuff]

Frequency Tables and Groupings

Started grouping the data using quarterly percentile bands just to see what it looks like. Used the income and internet users data for this with 1 lowest and 4 highest. Results are kinda predictable, I think. More work to be done to correlate the two datasets together somehow

Income (GDP per capita):

= GDP per capita <= 750

= GDP per capita between 751 and 2,500

= GDP per capita between 2,501 and 10,000

= Greater than 10,000 (the rich ones)

Internet (per 100 users):

= less than 10 people

= between 11 and 30 people

= between 31 and 50 people

= more than 50 people (over half the population are surfing)