* Encoding: UTF-8. *We are going to start with removing noncredit to keep our awards simple but there is no reason this analysis can not be done on noncredit students. Frequencies showed non credit has both 0 and missing values. SELECT IF (SYSMIS(NonCreditFlag) | NonCreditFlag = 0). EXECUTE. *Year will show us the cohort size for each year. All cohort members are in the data set, both respondents and non respondents. FREQUENCIES VARIABLES=Year /ORDER=ANALYSIS. *We will start with both respondents and nonrespondents and look at COMIS variables. *Create a new variable to see the outcome of student and awards (You can also use the completer flag) EXECUTE. FREQUENCIES VARIABLES=SP02 /ORDER=ANALYSIS. RECODE SP02 ('A'=1) ('S'=1) ('M'=2) ('B'=2) ('N'=2) ('L'=2) ('T'=2) ('F'=2) ('E'=3) (' '=99) INTO Outcome. VARIABLE LABELS Outcome 'The Outcome Of Respondent. '. EXECUTE. Value Labels Outcome 1 'AA or AS' 2 'Chancellor Office Approved Certificate' 3 'All Other Awards' 99 'No Award'. Execute. *We can now see who earned an award and who didn't". FREQUENCIES VARIABLES=Outcome /ORDER=ANALYSIS. *Let's explore the academic level of students in the cohort. Students with a 7 already hold an AA, Students with an 8 have a BA or higher. FREQUENCIES VARIABLES=STD4 /ORDER=ANALYSIS. IF ((STD4 = "7" | STD4 = "8") & (Outcome = 99)) Outcome=4. EXECUTE. Value Labels Outcome 1 'AA or AS' 2 'Chancellor Office Approved Certificate' 3 'All Other Awards' 99 'No Award' 4 'Previous Award'. Execute. *We can now look to see of the 'No Award' students how many held a previous award. FREQUENCIES VARIABLES=Outcome /ORDER=ANALYSIS. *In 2021 we added in variables for students who earned an award that was not CTE related met the cohort condition "within the prior 3 years completed 9 units that are SAM coded A-D, with at least one course SAM coded A-C". *These students are currently flagged as non-cte completers. CROSSTABS /TABLES=Year BY CompleterFlag /FORMAT=AVALUE TABLES /CELLS=COUNT /COUNT ROUND CELL. *There isn't a lot of Non-CTE Completers so lets add them into All other awards. IF (CompleterFlag = 2) & (Outcome = 99) Outcome=3. EXECUTE. *We can now look to see the data with non-cte award earners added. FREQUENCIES VARIABLES=Outcome /ORDER=ANALYSIS. *We can see that all other awards has increased. We also can note the limitation of not having this data prior to 2021. *That is as far as we can go using COMIS data. *If you are working in 2024 data Transfer is part of COMIS data and can be completed before looking at respondents. *The codebook is broken apart to show which data is from the Chancellor's Office and what is from the survey. *Lets us the variable modality to look at response rates from each year. FREQUENCIES VARIABLES=Modality /ORDER=ANALYSIS. RECODE Modality (MISSING=0) (1 thru Highest=1) INTO Response_Rate. VARIABLE LABELS Response_Rate 'Binary of Who Responded. '. EXECUTE. Value Labels Response_Rate 0 'Non Respondent' 1 'Respondent'. Execute. CROSSTABS /TABLES=Year BY Response_Rate /FORMAT=AVALUE TABLES /CELLS=COUNT ROW /COUNT ROUND CELL. *I can see that the response rate was highe in early years and declined over time so I will make sure to put that in my limitations. *Reminder, before looking at only respondents, no awards were about 39% FREQUENCIES VARIABLES=Outcome /ORDER=ANALYSIS. *Lets look at just respondents. FILTER OFF. USE ALL. SELECT IF (Response_Rate = 1). EXECUTE. FREQUENCIES VARIABLES=Outcome /ORDER=ANALYSIS. *We can see when we look at just respondents, the number of 'No Award' students decrease. *Award earners are more likely to complete the survey, so this is not a suprise. I again will mark in my limitations. *On the 2024 reports this is also shown in the demographics section of the PDF report. *Remember that a non-award earner had to complete "within the prior 3 years completed 9 units that are SAM coded A-D, with at least one course SAM coded A-C" to be included in the cohort. *Students who did not complete that threshold never made it to the cohort. *Lets look at transfer next IF ((TransferFlag = 1) & (Outcome = 99)) Outcome=5. EXECUTE. Value Labels Outcome 1 'AA or AS' 2 'Chancellor Office Approved Certificate' 3 'All Other Awards' 99 'No Award' 4 'Previous Award' 5 'Transfer'. Execute. FREQUENCIES VARIABLES=Outcome /ORDER=ANALYSIS. *Now lets add in industry certifications or journey level status. *The <=2023 survey instument have check boxes for these items respondents clicked. IF ((Certification = 1 | Journey= 1) & (Outcome = 99)) Outcome=6. EXECUTE. Value Labels Outcome 1 'AA or AS' 2 'Chancellor Office Approved Certificate' 3 'All Other Awards' 99 'No Award' 4 'Previous Award' 5 'Transfer' 6 'Industry Certification'. Execute. FREQUENCIES VARIABLES=Outcome /ORDER=ANALYSIS. *lastly lets consider students that met thier goal and if students are workin thier field of study. *We are going to consider that someone met thier goal if they stated they are currently taking fewer classes because their goals were met, they completed the program, or they transffered to another school. *>=2024 survey askes explicitly if they met thier goal. IF ((Fewer_Goals = 1) | (Fewer_Completed = 1) | (Fewer_Transfer = 1)) Met_Goal=1. EXECUTE. IF (Job_Similarity =1 | Job_Similarity =2) WFS=1. EXECUTE. IF ((WFS=1) & (Outcome = 99)) Outcome=7. IF ((Met_Goal=1) & (Outcome = 99)) Outcome=8. IF ((Met_Goal = 1 & WFS =1 ) & (Outcome = 99 | Outcome=8 | Outcome=7)) Outcome=9. EXECUTE. Value Labels Outcome 1 'AA or AS' 2 'Chancellor Office Approved Certificate' 3 'All Other Awards' 99 'No Award' 4 'Previous Award' 5 'Transfer' 6 'Industry Certification' 7 'Met Goals' 8 'Working in Field of Study' 9 'Met Goals and WFS'. Execute. FREQUENCIES VARIABLES=Outcome /ORDER=ANALYSIS. * Chart Builder. GGRAPH /GRAPHDATASET NAME="graphdataset" VARIABLES=Outcome COUNT()[name="COUNT"] MISSING=LISTWISE REPORTMISSING=NO /GRAPHSPEC SOURCE=INLINE. BEGIN GPL SOURCE: s = userSource(id("graphdataset")) DATA: Outcome = col(source(s), name("Outcome"), unit.category()) DATA: COUNT = col(source(s), name("COUNT")) COORD: polar.theta(startAngle(0)) GUIDE: axis(dim(1), null()) GUIDE: legend(aesthetic(aesthetic.color.interior), label("The Outcome Of Respondent. ")) GUIDE: text.title(label("Pie Chart Count of The Outcome Of Respondent.")) SCALE: linear(dim(1), dataMinimum(), dataMaximum()) SCALE: cat(aesthetic(aesthetic.color.interior), include("1.00", "2.00", "3.00", "4.00", "5.00", "6.00", "7.00", "8.00", "9.00", "99.00")) ELEMENT: interval.stack( position(summary.percent(COUNT)), color.interior(Outcome), label(summary.percent(COUNT)) ) END GPL.