Evan Picton, Research Analyst

Wenatchee Valley College

*Late Registration’s Influence on

Academic Outcomes

*Research Question

*Do students who register early for their courses do better than students who register later?

*What variables should be controlled for in order to isolate the unique variance associated with registration time?

*What analysis should be used?

*What students should be included in this analysis?

*What outcome should be used?

*How should early versus late registration be determined?

*Research Design

*Population: Credit Students (No Basic Skills, ESL or Continuing Education Students)

*Primary Statistical Analysis: Multiple Regression

*Outcome: Decimal Grade earned (excludes pass/fail classes) & Quarter GPA*Goal: Develop two separate models to predict these

outcomes using time of registration

*Nature of Research: Exploratory, not confirmation of an existing model

*Control Variables identified using simple correlational analysis and iterative regression modeling

*Primary Predictor: Time of Registration

*Modeled as a Continuous Variable (days) not a Dichotomous “On Time/Late” variable.

*Collapsing a continuous variable, like time, into categories is problematic.

*Loss of information about individual differences, loss of effect size and power, the occurrence of spurious main effects or interactions, risk of overlooking non-linear effects (attached PDF - MacCallum et al, 2002)

Adobe Acrobat Document

*Determining Date of Registration-Utilized daily enrollment snapshots pulled from a database that was

constantly being updated with the latest enrollment information. These snapshots were pulled during the interval of time between the first possible date of registration to the registration deadline of spring quarter.

SID ITEMDate of

Registration

Absolute Deadline to

Register

Date of Registration Less Absolute

Deadline SID.ITEM

123456789 8134 4/13/2012 4/9/2012 -4 123456789.8134

123456789 8134 4/12/2012 4/9/2012 -3 123456789.8134

123456789 8134 4/11/2012 4/9/2012 -2 123456789.8134

*Outcome Variable: Decimal Grade Earned

*Population: Credit Students

SID ITEMDecimal Grade

Max of (Date of Registration - Absolute Deadline) SID.ITEM

123456789 8134 2.00 -2123456789.8134

123456789 3456 3.00 11123456789.3456

123456789 1389 1.00 9123456789.1389

*Outcome Variable: Quarter GPA

*Population: Credit Students

SID Quarter GPA

Average (Max of (Date of Registration -

Absolute Deadline))

123456789 3.35 6

*Control VariablesStudent Variable Control ListAgeGreater Than 45 Accumulated Credits Prior to Spring QuarterLevel of Course Enrolled in for Spring (Developmental, 100 or 200)GenderMinorityRunning Start Status of Student for Spring QuarterFormally Enrolled in a Vocational ProgramFull-time Status for Spring Quarter (Enrolled in at least 12 credits)New Student Status For Spring Quarter 2012Enrollment in Math ClassesKind of Student (transfer, workforce, etc)Number of Developmental Courses Enrolled in for SpringNumber of Vocational Courses Enrolled in for SpringNumber of Transfer Courses Enrolled in for Spring

*Descriptive Statistics for Sample

Race Student Focus Area

Status Count % Status Count %Non-Minority 1934 58.95% Academic 2184 66.57%

Minority 1347 41.05% Vocational 783 23.86%Grand Total 3281 100.00% Multiple 314 9.57%

Grand Total 3281 100.00%

Gender

Status Count %Not Reported 12 0.37%

Female 1919 58.49%Male 1350 41.15%

Grand Total 3281 100.00%

*Results: Significant Predictors(for decimal grade model)

Student Variable Control ListGreater Than 45 Accumulated Credits Prior to Spring QuarterGenderRunning Start Status of Student for Spring QuarterEnrollment in Math ClassesNumber of Developmental Courses Enrolled in for SpringNumber of Vocational Courses Enrolled in for Spring

-The process for identifying significant control variables involved first separately looking at the zero order level correlations that all the controls had with the outcome variable of decimal grade earned. The variables with no significant relationship at that level were removed.

The next step involved putting the remaining predictors into a preliminary regression model predicting decimal grade earned. The registration time variable was included in this early model. The controls that accounted for significant outcome variance at this step were retained and are listed in the table below.

*Results: Decimal Grade Prediction Model

-The first order effects model accounted for a very modest amount of variance in Decimal Grade Earned.

Model Summary

Model R R SquareAdjusted R

SquareStd. Error of the Estimate

1 .315 .099 .098 1.2292

*Results: Decimal Grade Model Coefficients

-Decimal grade earned had a gradual relationship with registration time

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.

Collinearity Statistics

B Std. Error Beta Tolerance VIF1 (Constant) 2.320 .041   56.168 .000    

Days registered before deadline

.008 .001 .098 9.343 .000 .960 1.042Gender (male ref group) -.144 .027 -.055 -5.331 .000 .995 1.005

# of Vocational Classes Enrolled .106 .012 .097 9.077 .000 .927 1.078

# of Developmental Classes Enrolled -.298 .030 -.111 -9.886 .000 .830 1.204

Running Start Status for Spring .319 .040 .084 7.915 .000 .927 1.079

Student Accumulated 45 credits .312 .028 .120 10.996 .000 .880 1.137

Enrollment in Math Classes -.421 .044 -.102 -9.572 .000 .923 1.084

*Primary Predictor of Interest: Days Registered Before Deadline(decimal grade model)-The relationship between Days Registered and the outcome of

Decimal Grade earned was, once control variables were accounted for, gradual. The relationship was also linear. Adding a quadratic term to the model (Days*Days) did not improve the predictive power of the model in a meaningful way. The same result was found for the cubic term. Furthermore, the Days variable did not interact with any of the other predictors used in this model.

Days Registered Before Deadline

Predicted Impact on Decimal Grade Earned

1 +0.00810 +0.0820 +0.1630 +0.2440 +0.32

*Results: Quarter GPA Prediction Model

-The first order effects model accounted for a very modest amount of variance in Quarter GPA.

Model Summary

Model R R SquareAdjusted R

SquareStd. Error of the Estimate

1 .334 .111 .109 1.0807250

         

* Results: Quarter GPA Model Coefficients

-Quarter GPA had a gradual relationship with registration time

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.

Collinearity Statistics

BStd. Error Beta

Tolerance VIF

1 (Constant) 2.220 .070 

31.559 .000   

Average Days Registered Before Deadline

.012 .002 .133 7.796.000 .933 1.072

Running Start Status for Spring .412 .058 .121 7.124 .000 .940 1.064

Formally Enrolled in a Vocational Program

.192 .060 .068 3.192 .001 .600 1.668

Gender -.181 .039 -.078 -4.693 .000 .985 1.015

Greater Than 45 Accumulated Credits Prior to Spring Quarter

.372 .040 .162 9.280 .000 .886 1.128

Enrollment in Math Courses -.213 .065 -.055 -3.293 .001 .976 1.024

Kind of Student (T) -.240 .048 -.103 -5.026 .000 .644 1.552

*Discussion

-Different results than what requestors expected. Relatively low amount of variance accounted for.

-First Time Spring Students vs. First Time Fall Students

-Students seem to need to register very early for it to have an impact - within the first week of registration. Cause and effect unclear

-Did not extensively explore interactions between the other predictors. (Math*RS)

-Consistent with your college?

-This research will be repeated for Fall quarter 2012. Students have a long interval of time to register for Fall Quarter. Difficult to predict how this might impact the relationship between days registered before deadline and academic outcomes.