Well that depends on several factors, one of which is the course in question. In addition, we need to examine other variables… In other words… THE COMPLEX ANSWER Student Success: More than meets the eye!
To encourage more complex and complete interpretations of student success Issues of self-selection Comparison group To include student-level variables Student characteristics that could influence success Two applications Specific target course vs. comparison course Student support services PURPOSE
Target course Course of interest TC in syntax Comparison course How do you select comparison course? CC in syntax Time frame Proximal effect of target course Distal effect of target course Uses first-time students Uses first attempt at target course and comparison course Success = A, B, C, P, CR, IP No success = D, F, W, NP, NC, IF BEFORE YOU START
Create a long MIS data file Create a first-time student (FTS) file SB15 = 1 See Creates first-time student file syntax Merge long MIS and FTS file See Creates data file for analysis syntax CREATE DATA FILE
STEP I: Cleans data file Data earlier than 2000 will need a different term variable Change target course (line 61) Change comparison course (line 63) STEP 2: Creates 3 cohorts Target course first Comparison course first Both target and comparison course in same semester Change target course (lines 19, 21, 24, 26) Change comparison course (lines 20, 22, 25, 27) NOW FOR THE FUN PART!
STEP 3: Identifies terms Concurrent Following Subsequent STEP 4: Creates variables to calculate success rates Concurrent Following Subsequent STEP 5: Generates success rate output Change destination for output as Excel file (line 208) NOW FOR THE FUN PART!
Move from success rates to regression Use high school data to include prior success rates What else can we examine? Gender Ethnicity Age Education Plan Concurrent GPA (excluding target and comparison courses) Other student-level variables? GET CRAZY WITH THE CHEEZ WHIZ!
Possible independent variables Participation vs. no participation Fulfill required hours vs. failure to fulfill required hours Total number of hours of support service Type of support service Prior exposure to support service Prior success rate Prior number of units Dependent variable Success in a specific course (tied to the student support service) Regression THE COMPLEX ANSWER
EFFECT OF STUDENT SUPPORT SERVICE ON COURSE SUCCESS VariableBWald Stat.Sig.Exp(B) Standardized Student Success Rate1.69818221.96.0005.46 Course with student support services-.922542.66.000.398 Number of Hours spent in support services.6361489.53.0001.889 (Number of Hours spent in support services ) 2 (to capture non-linear effects)-.035333.05.000.965
Carefully and thoughtfully explain why simple answers are inadequate Highlight need to consider variables other than the course in and of itself Present data in a user-friendly way IMPROVING DIGESTION OF DATA
INSTEAD OF THIS… VariableBWald Stat.Sig.Exp(B) Standardized Student Success Rate1.69818221.96.0005.46 Course with student support services-.922542.66.000.398 Number of Hours spent in support services.6361489.53.0001.889 (Number of Hours spent in support services ) 2 (to capture non-linear effects)-.035333.05.000.965
Why arent you doing it the same way as last time? We need to look at the right subset of students. Theres more than just numbers. So what you are saying is that what I do doesnt matter. BE PREPARED FOR QUESTIONS LIKE…
The reason these interventions and courses exist is to help students succeed If the research suggests this is not the case, how can we make the intervention more effective? Where has it worked? Why? WHEN IT COMES DOWN TO IT…
Please feel free to contact me to discuss. Suggestions for improving syntax always welcome! Karen Rothstein: email@example.com Andrew Fuenmayor: firstname.lastname@example.org THANKS FOR LISTENING!