Identifying Significant Personal and Program Factors that Predict OnlineDoctoral Students’ Integration

Doctoral attrition rates are consistently documented throughout the literature to be between 40% and 60%. They can be as high as 70% for Doctor of Education (EdD) programs (Bowen & Rudenstine, 1992; Nettles & Millet, 2006), and the attrition rates for programs offered in the online (i.e., via the internet) can be 10% to 20% higher than programs offered in a residential format (Holder, 2007; Rovai, 2002; Terrell, Snyder, & Dringus 2009). Tinto (2006-2007) acknowledged, “the process of student retention differs in different institutional settings, residential and non-residential” (p. 4). However, no matter the program type or institution, integration is a key factor associated with the decision to leave or stay. 

The concepts of social and academic integration are central to Tinto’s (1975) seminal persistence work that originally focused on residential, undergraduate students and was expanded to doctoral students (Tinto, 1993). Tinto (1975) posited that in order to persist, undergraduate students need to integrate into academic (e.g., evidenced by GPA) and social systems (e.g., extracurricular activities) within the university. Further, for undergraduate students, high satisfaction levels of integration into one system may compensate for low satisfaction levels of integration in the other system, and the student may still persist (Tinto, 1975, 1993). Tinto (1993) also suggested that doctoral student persistence is “shaped by the personal and intellectual interactions that occur within and between students and faculty and the various communities that make up the academic and social systems of the institution” (p. 231). These interactions become stronger as the student moves through the stages of the doctoral program (Tinto, 1993).

Tinto (1993) and others (see Lovitts, 2001) describe the importance of academic and social integration, and that the lines separating the two become blurred, or even intertwined at the doctoral level.  Unlike undergraduate students, for doctoral students high levels of integration in one cannot compensate for low levels of integration ion the other (Lovitts, 2001; Tinto, 1993).  However, recent research has revealed another conceptualization of integration of doctoral students in DE programs.  Holmes and Rockinson-Szapkiw (2018) found the term program integration to be much more comprehensive than academic integration and social integration, They also found evidence that the type of interaction (academic or social) is not what is important; rather the importance rests in who or what the interaction is with (Holmes & Rockinson-Szapkiw, 2018).  In other words, program integration is comprised of three factors—faculty integration, student integration, and curriculum integration.  Holmes and Rockinson-Szapkiw (2018) offer the following definitions:

Faculty integration is the satisfaction level with the nature and quality of academic and non-academic student-faculty interactions that take place during the distance doctoral program. Student integration is the satisfaction level with the nature and quality of academic and non-academic student-student interactions that take place during the distance doctoral program. Curriculum integration is the satisfaction level with the quality and relevancy of the curriculum in the distance doctoral program. (p. ) .

Grounded in persistence (Tinto, 1975, 1987, 1993) and in consideration of the distance education and doctoral literature , a model was created to examine the predictive association of personal and program factors with distance EdD students’ program A multiple regression (LR), a common method of predicting and modeling in educational literature, was used.

Via a convenience and snowball sampling, data were collected from 232 students enrolled in an online Doctor of Education (EdD) program with second generation characteristics at both public and private institutions. Researchers sent out emails and posted via professional organization listservs invitation to participate in an anonymous survey.  In the invitation, the criteria for participation was outlined and students verified eligibility through a series of survey questions: 1) participation in a CPED or second generation EdD program and 2) participation in a program in which 80% of course work is taken online. Students reported that their online Doctor of Education (EdD) program required between 54 and 63 credit hours.

The demographics of this sample are consistent with the National Science Foundation Report (2015) that demonstrates that women and Caucasians are the primary recipients of education doctorates in the U.S.  The majority of participants were Caucasian (n=179, 77.2%) and women (n = 174, 75%). There were also 33 (14.2%) African American, 12 (5.2%) Hispanic, 3 (1.3%) Asian, 1 (.4%) American Indian, and 4 (1.7%) other classified participant(s). The majority reported their age range as 30-39 (n = 63, 27.2%) or 40-49 (n = 85, 36.6%). Most of the students were married (n = 187, 80.6%) with a little over half having children under 18 living in the home (n = 125, 53.9%). Almost all of the participants worked full time (n = 133, 89.9%) and worked as K–12 teachers, K–12 administrators, counselors, university staff, higher education faculty, and higher education administrators.

The anonymous survey participants completed consisted of validated instruments and questions developed by the researchers. Program integration served as the criterion variable and was measured using the Distance Doctoral Program Integration Scale (DDPIS; Holmes & Rockinson-Szapkiw, 2018).

Each program and personal variable was assessed using a single survey items. These predictor variables are summarized in Table 1.

 

Variable

Survey Question

Survey answer (dummy code or likert-type scale)

Personal

 

 

Sex

Please indicate your sex.

Male (1)

Female (0)

Race

Please indicate your race.

Caucasian (1)

Black (0)

Asian (0)

Hispanic (0)

American Indian (0)

Other (0)

Age

Please indicate your age range.

Under 19 (1)

20-29

30-39

40-49

50-59

60-69

70-79

80-89

Marital Status

What is your

Married (1)

Single (0)

Widowed (0)

Divorced (0)

Other (0)

Presence of children in the home

Do you have children in your home under the age of 18?

Yes (1)

No(0)

Program Stage

What stage of the program are you in?

Course work, year 1 (1)

Course work (year 2 or 3) up to comprehensive exam (2)

Dissertation (proposal) (3)

Dissertation (research, passed proposal defense) (4)

Cohort

Are you part of a doctoral cohort?

Yes (1)

No(0)

Synchronous Sessions

How often per semester do you participate in real time (synchronous) program-related activities using web-based or mobile technology  (e.g., live lectures, live discussions, live study groups, etc.)?

Add

Orientation

Did you complete an orientation for your program?

Yes (1)

No(0)

 

A hierarchical multiple regression analysis was conducted to examine how online doctoral persistence can be explained by institutional and integration variables. This analysis was chosen as it is commonly used when researchers want to understand the relationship between predictor variables and a categorical outcome or criterion variable (Warner, 2013). Assumption testing was completed prior to conducting the analysis.We conducted assumption tests for the six assumptions associated with a hierarchical multiple regression in order to ensure the robustness of the analysis with the data set, including (1) independence of observations, (2) linearity, (3) homoscedasticity, (4) multicollinearity, (5) no significant outliers, and (6) normality. No gross assumption violations were found, so the chosen parametric analyses were deemed robust and we continued by conducting the  hierarchical linear regression analysis.

We found that the personal factors played a significant role in explaining program integration (F(5, 226) = 8.56, p <.001). Examination of the coefficient demonstrated that 15.9% (R2 =.159) of the variance in program integration was explained by demographic and familial factors. When the program factors were added to the regression model, the model improved significantly (Fchange(4,222) = 8.21, p <.001, R2change =.108). The entire model containing the linear combination of the personal and program variables was significant (F (9,222) = 9.01, p <.001), explaining 26.8% of the variance in online EdD students’ program integration. Several variables made individual significant contributions to explaining program integration, including sex, race, (see Table 4). Examination of the mean scores demonstrated that men and Caucasians on average had higher program integration scores than their women and minority counterparts (see Table 1). Online EdD students who participated in cohorts also perceived higher program integration than those that did not. Finally, as the frequency of synchronous sessions increased so did students program integration.

Table 4. Hierarchical Regression Analysis Results for Both Blocks

 

R²

F Ratio

B

SE

β

t

p

Block 1*

      .159

8.56

 

 

 

 

<.001

Block 2*

.268

9.01

 

 

 

 

 <.001

Sex*

 

 

.388

.099

.232

3.901

<.001

Race*

 

 

.374

.101

.219

3.715

<.001

Age

 

 

-.030

.043

-.045

-.690

.491

Marital Status

 

 

-.053

.117

-.029

-.455

.649

Children (under 18)*

 

 

.036

.094

.025

.384

.701

Stage in Program

 

 

-.007

.043

-.010

-.170

.865

Synchronous*

 

 

.072

.019

.220

3.748

<.001

Cohort*

 

 

.483

.106

.278

4.539

<.001

Orientation

 

 

-.013

.091

-.009

-.140

.889

Note. * p < .05

Predictors examined included the personal variables and the program variables ….. The findings cohere with previous research (e.g., Earl-Novell, 2006; Hoskins & Goldberg, 2005; Wao & Onwuegbuzie, 2011) and theory (Tinto, 1997) as the entire model was found to significantly predict online EdD  students’ program integration .