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The University of Texas-Pan American

The University of Texas-Pan American. Major Migration by College at UTPA. Presented by: S.J. Sethi, Ph.D. & Sam Shi Office of Institutional Research & Effectiveness. February 6, 2008. Outline. Introduction Data and Results Methodology - SPSS & Syntax Technical Reflections

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The University of Texas-Pan American

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  1. The University of Texas-Pan American Major Migration by College at UTPA Presented by: S.J. Sethi, Ph.D. & Sam Shi Office of Institutional Research & Effectiveness February 6, 2008

  2. Outline • Introduction • Data and Results • Methodology - SPSS & Syntax • Technical Reflections • Use of Results • Future Plans

  3. Introduction This report tracks the six-year graduation rates of the first-time, full-time & part-time 2000 cohort by the college of their intended major and by the college from which they graduated. Purpose: To get a better understanding of degree mobility of students and their graduation rates by college

  4. Data • 2000 cohort was 2,083 students; 633 (30%) graduated in 6 years. • 82% of the students declared a major; 18% were undecided. • 6 colleges at UTPA: • College of Arts & Humanities (8%) • College of Business (16%) • College of Education (9%) • College of Health Sciences and Human Services (14%) • College of Social Behavioral Sciences (12%) • College of Science and Engineering (24%)

  5. Results

  6. Results

  7. Results Table 3 Number of times students change majors UTPA 2000 First-time Full-time & Part-time Cohort

  8. Methodology - Files Used • Files Involved • Fall 2000 Cohort (Full-time + Part-time) • CBM001 certified files (Fall 2000 – Fall 2006 including Springs) • CBM009 certified files (AY2002 – AY2006) • Student files (Fall 2000 – Fall 2006 including Springs) – including variables or calculated variables that CBM001 doesn’t have • Variables Involved • ID – to merge between different files • Demographic variables • College, Department, Major, Classification, Full-time and Part-time status, Cumulative GPA (future study) in every enrollment semester and at the time of graduation

  9. Methodology - SPSS • Merge files • Merge the cbm001 certified files with the student files for each semester by student ID. • Merge the Fall 2000 Cohort file with the already merged cbm001 and student files to get every year’s enrollment status. • Merge the Fall 2000 Cohort file with the cbm009 graduation files to get students’ graduation status.

  10. Methodology - Syntax • Calculate Major Change /* Use this script with EXTREME CAUTION. Since it changes the existing values of the current variables. Apply this syntax only to the files in this folder.*/ compute maj_change = 0. /*take care of cases that a student miss a semester and come back with the same major*/ if(tb_major_20011 > '0' & tb_major_20012 <= '0') tb_major_20012 = tb_major_20011. If(tb_major_20012 > ‘0’ &tb_major_20021 <= ‘0’) tb_major_20021 = tb_major_20012. if(tb_major_20021 > '0' & tb_major_20022 <= '0') tb_major_20022 = tb_major_20021. if(tb_major_20022 > '0' & tb_major_20031 <= '0') tb_major_20031 = tb_major_20022. if(tb_major_20031 > '0' & tb_major_20032 <= '0') tb_major_20032 = tb_major_20031. if(tb_major_20032 > '0' & tb_major_20041 <= '0') tb_major_20041 = tb_major_20032. if(tb_major_20041 > '0' & tb_major_20042 <= '0') tb_major_20042 = tb_major_20041. if(tb_major_20042 > '0' & tb_major_20051 <= '0') tb_major_20051 = tb_major_20042. if(tb_major_20051 > '0' & tb_major_20052 <= '0') tb_major_20052 = tb_major_20051. if(tb_major_20052 > '0' & tb_major_20061 <= '0') tb_major_20061 = tb_major_20052. if(tb_major_20061 > '0' & tb_major_20062 <= '0') tb_major_20062 = tb_major_20061. if(tb_major_20062 > '0' & tb_major_20071 <= '0') tb_major_20071 = tb_major_20062. if (tb_major_20012 > '0' & tb_major_20011 > '0' & tb_major_20012 ~= tb_major_20011) maj_change = maj_change + 1. if (tb_major_20021 > '0' & tb_major_20012 > '0' & tb_major_20021 ~= tb_major_20012) maj_change = maj_change + 1. if (tb_major_20022 > '0' & tb_major_20021 > '0' & tb_major_20022 ~= tb_major_20021) maj_change = maj_change + 1. if (tb_major_20031 > '0' & tb_major_20022 > '0' & tb_major_20031 ~= tb_major_20022) maj_change = maj_change + 1. if (tb_major_20032 > '0' & tb_major_20031 > '0' & tb_major_20032 ~= tb_major_20031) maj_change = maj_change + 1. if (tb_major_20041 > '0' & tb_major_20032 > '0' & tb_major_20041 ~= tb_major_20032) maj_change = maj_change + 1. if (tb_major_20042 > '0' & tb_major_20041 > '0' & tb_major_20042 ~= tb_major_20041) maj_change = maj_change + 1. if (tb_major_20051 > '0' & tb_major_20042 > '0' & tb_major_20051 ~= tb_major_20042) maj_change = maj_change + 1. if (tb_major_20052 > '0' & tb_major_20051 > '0' & tb_major_20052 ~= tb_major_20051) maj_change = maj_change + 1. if (tb_major_20061 > '0' & tb_major_20052 > '0' & tb_major_20061 ~= tb_major_20052) maj_change = maj_change + 1. if (tb_major_20062 > '0' & tb_major_20061 > '0' & tb_major_20062 ~= tb_major_20061) maj_change = maj_change + 1. if (tb_major_20071 > '0' & tb_major_20062 > '0' & tb_major_20071 ~= tb_major_20062) maj_change = maj_change + 1. execute.

  11. Methodology - Syntax • Syntax for Tables 1 and 2 USE ALL. COMPUTE filter_$=(tb_major ~= "UNDE"). VARIABLE LABEL filter_$ 'tb_major ~= "UNDE" (FILTER)'. VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. FORMAT filter_$ (f1.0). FILTER BY filter_$. EXECUTE . CROSSTABS /TABLES=tb_coll BY tb_coll_graduation /FORMAT= AVALUE TABLES /CELLS= COUNT /COUNT ROUND CELL .

  12. FREQUENCIES VARIABLES=maj_change /ORDER= ANALYSIS . USE ALL. COMPUTE filter_$=(year_graduation > '0'). VARIABLE LABEL filter_$ "year_graduation > '0' (FILTER)". VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. FORMAT filter_$ (f1.0). FILTER BY filter_$. EXECUTE . FREQUENCIES VARIABLES=maj_change /ORDER= ANALYSIS . USE ALL. COMPUTE filter_$=(year_graduation <= '0'). VARIABLE LABEL filter_$ "year_graduation <= '0' (FILTER)". VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'. FORMAT filter_$ (f1.0). FILTER BY filter_$. EXECUTE . FREQUENCIES VARIABLES=maj_change /ORDER= ANALYSIS . Methodology - Syntax • Syntax forTable 3

  13. Technical Reflections • The importance of standardized variables • Automate and speed up the merging process • How to technically determine if a student’s major is changed or not especially when the student missed a semester?

  14. Use of Results • Understanding Student Behavior • Academic Student Advising • Evaluation of Academic Programs • Assessment at time of Entry

  15. Future Plans • Add more variables • How age and ethnicity affect major migration? • How GPA is affected after major migration? • The results as raw data for future study • Major migration pattern changes across years

  16. Questions Contact Information: S.J Sethi: sjsethi@utpa.edu Sam Shi: xshi@utpa.edu Office of Institutional Research & Effectiveness: 381-2383

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