Evan Picton, Research Analyst Wenatchee Valley College

* 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?

* 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

* 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)

-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. SIDITEM Date of Registration Absolute Deadline to Register Date of Registration Less Absolute DeadlineSID.ITEM /13/20124/9/ /12/20124/9/ /11/20124/9/

* Population: Credit Students SIDITEM Decimal Grade Max of (Date of Registration - Absolute Deadline)SID.ITEM

* Population: Credit Students SIDQuarter GPA Average (Max of (Date of Registration - Absolute Deadline))

Student Variable Control List Age Greater Than 45 Accumulated Credits Prior to Spring Quarter Level of Course Enrolled in for Spring (Developmental, 100 or 200) Gender Minority Running Start Status of Student for Spring Quarter Formally Enrolled in a Vocational Program Full-time Status for Spring Quarter (Enrolled in at least 12 credits) New Student Status For Spring Quarter 2012 Enrollment in Math Classes Kind of Student (transfer, workforce, etc) Number of Developmental Courses Enrolled in for Spring Number of Vocational Courses Enrolled in for Spring Number of Transfer Courses Enrolled in for Spring

RaceStudent Focus Area StatusCount%StatusCount% Non-Minority %Academic % Minority %Vocational % Grand Total %Multiple % Grand Total % Gender StatusCount% Not Reported120.37% Female % Male % Grand Total %

Student Variable Control List Greater Than 45 Accumulated Credits Prior to Spring Quarter Gender Running Start Status of Student for Spring Quarter Enrollment in Math Classes Number of Developmental Courses Enrolled in for Spring Number 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.

-The first order effects model accounted for a very modest amount of variance in Decimal Grade Earned. Model Summary ModelRR Square Adjusted R Square Std. Error of the Estimate

-Decimal grade earned had a gradual relationship with registration time Coefficientsa Model Unstandardized Coefficients Standardize d Coefficients tSig. Collinearity Statistics BStd. ErrorBetaToleranceVIF 1 (Constant) Days registered before deadline Gender (male ref group) # of Vocational Classes Enrolled # of Developmental Classes Enrolled Running Start Status for Spring Student Accumulated 45 credits Enrollment in Math Classes

-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

-The first order effects model accounted for a very modest amount of variance in Quarter GPA. Model Summary ModelRR Square Adjusted R Square Std. Error of the Estimate

-Quarter GPA had a gradual relationship with registration time Coefficientsa Model Unstandardized Coefficients Standar dized Coeffici ents tSig. Collinearity Statistics B Std. ErrorBeta Toleran ceVIF 1 (Constant) Average Days Registered Before Deadline Running Start Status for Spring Formally Enrolled in a Vocational Program Gender Greater Than 45 Accumulated Credits Prior to Spring Quarter Enrollment in Math Courses Kind of Student (T)

-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 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.