The Effect of Offering Distance Education on 

Enrollment in Onsite Training at the National 

Conservation Training Center

By Joan M. Ratz, Rudy M. Schuster, and Ann H. Marcy

Open-File Report 2011–1138

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia McNutt, Director

U.S. Geological Survey, Reston, Virginia 2011

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Suggested citation:

Ratz, J.M., Schuster, R.M., and Marcy, A.H., 2011, The effect of offering distance education on 

enrollment in onsite training at the National Conservation Training Center: U.S. Geological Survey 

Open-File Report 2011–1138, 35 p.

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copyright owners to reproduce any copyrighted material contained within this report.
iii

Contents

Executive Summary....................................................................................................................................................... 1

Background ................................................................................................................................................................... 2

Part I: Analysis of Training Data .................................................................................................................................... 3

Method....................................................................................................................................................................... 4

Results....................................................................................................................................................................... 5

Descriptive Statistics for Variables ......................................................................................................................... 5

Relationships among Variables.............................................................................................................................. 8

Discussion.................................................................................................................................................................18

Part II: Survey...............................................................................................................................................................20

Method......................................................................................................................................................................20

Survey Development.............................................................................................................................................20

Past Training Experience...................................................................................................................................20

Preferences for Mode of Training ......................................................................................................................20

Reasons for Enrolling ........................................................................................................................................21

Behavioral Intention...........................................................................................................................................21

Demographics ...................................................................................................................................................21

Sampling Strategy.................................................................................................................................................22

Data-Collection Process........................................................................................................................................22

Results......................................................................................................................................................................23

Survey Quality.......................................................................................................................................................23

Response Rate..................................................................................................................................................23

Other Quality Indicators .....................................................................................................................................23

Onsite versus Distance Education: Experience, Preferences, and Intentions .......................................................25

Experience.........................................................................................................................................................25

Preferences .......................................................................................................................................................25

Behavioral Intention...........................................................................................................................................26

Reasons for Enrolling: Checklist Results...............................................................................................................27

Discussion.................................................................................................................................................................27

Conclusion....................................................................................................................................................................28

A Preliminary Characterization..................................................................................................................................28

Future Study .............................................................................................................................................................29

Acknowledgments ........................................................................................................................................................29

References Cited..........................................................................................................................................................29

Appendix 1—Training Data...........................................................................................................................................31

Supplemental Statistical Reporting............................................................................................................................31

Appendix 2—Survey.....................................................................................................................................................32

Survey Questions ......................................................................................................................................................32
iv

Figures

1. Average enrollments in distance education and NCTC courses by region........................................................... 9

2. Course location by region .................................................................................................................................. 10

3. Average number of courses by type for each region.......................................................................................... 11

4. Percent of supervisors in each region compared to the expected percent......................................................... 13

5. Average enrollment for supervisors and nonsupervisors in four types of training .............................................. 14

6. Average enrollments for training types............................................................................................................... 15

7. Average enrollment in instructor-led courses in each region.............................................................................. 16

8. Course format by region .................................................................................................................................... 17

9. Frequency of enrollment in course formats by supervisory status ..................................................................... 18

10. Experience with distance education and onsite training..................................................................................... 26

Tables

1. Variables and coding descriptions...................................................................................................................... 4

2. Frequency and percents of regional affiliation.................................................................................................... 5

3. Enrollments in courses onsite at NCTC.............................................................................................................. 6

4. Enrollments in instructor-led courses offsite....................................................................................................... 6

5. Enrollments in all types of distance courses....................................................................................................... 7

6. Enrollments in instructor-led courses ................................................................................................................. 7

7. Number and proportion of supervisors in each region...................................................................................... 12

8. Ten most frequent reasons for selecting type of training.................................................................................. 27

1-1. Paired t-tests comparing average enrollment in distance and NCTC courses within region............................ 31

1-2. Comparison of types of training within region.................................................................................................. 31

2-1. Survey content, questions, and question numbers .......................................................................................... 32

2-2. Correlations among questions......................................................................................................................... 34
1

The Effect of Offering Distance Education on Enrollment 

in Onsite Training at the National Conservation 

Training Center

By Joan M. Ratz, Rudy M. Schuster, and Ann H. Marcy

Executive Summary

This study was requested by the Division of Education Outreach (DEO) at the National 

Conservation Training Center (NCTC) of the U.S. Fish and Wildlife Service (FWS). To complete this 

study, the DEO partnered with the Policy Analysis and Science Assistance Branch (PASA) of the U.S. 

Geological Survey. The objective of the study was to explore the effect that providing training courses 

through use of distance education technology would have on the enrollment in courses offered on the 

campus at NCTC. The study includes two components: analysis of existing training enrollment data for 

the time period October 1, 2007 to June 24, 2009, and a survey of FWS employees regarding their 

preferences for onsite training and distance education.

We were provided a dataset which included data for a sample of FWS employees that included 

number of training courses in which each individual had enrolled, both onsite at NCTC and through 

several distance education options, and excluded enrollment data for the Department of the Interior 

(DOI) mandatory training courses. Using this dataset, we created a database that included for each 

individual in the sample their region, supervisory status, number of enrollments in online training 

(OLT), instructor-led web-based classes (WEB), correspondence courses (CORR), courses onsite at 

NCTC (NCTC), and instructor-led courses offsite at a location other than NCTC (OFF). We created 

composite variables to account for all distance education options (DISTANCE), and for training type: 

instructor led or independent. The training records for 2,823 FWS employees were included in the 

database.

Our analyses focused on differences between enrollment in distance education and onsite 

courses with a focus on the effects of regional affiliation, supervisory status, and course format. While 

this study was restricted by the limited time frame and available information, we were able to describe 

patterns in the dataset but were unable to provide much explanation for the effects we detected. There 

were regional differences in enrollment in distance education as opposed to onsite training. Generally, 

the regions closest to the NCTC campus had higher enrollment in onsite training and regions farther 

away had higher enrollment in distance education. Of the distance education options, the offsite training 

demonstrated higher enrollment rates than the other options. Nonsupervisors were more likely to enroll 

in training at NCTC and supervisors were more likely to enroll in instructor-led training offsite. 

Enrollment in instructor-led courses was higher than in independent courses, although this may result

from fewer offerings of independent courses. Overall, the results seem to depict a supplemental

relationship between distance education and onsite training rather than a competitive relationship.

The second component of the study involved a survey of FWS employees regarding their 

preferences for distance education and onsite training. The survey was conducted in conjunction with 

another survey of FWS employees on training topics that FWS employees would be interested in 
2

receiving via distance education technologies. The questions included on the survey were developed 

based on existing literature on training and distance education. The survey was administered online and 

911 FWS employees responded. The focal issues addressed on the survey included experience with 

distance education and onsite courses at NCTC, preference for distance education and onsite courses, 

and intention to enroll in distance education or onsite courses. The survey also included a checklist of 

reasons that could influence the decision to take distance education instead of an onsite course and the 

decision to take an onsite course instead of distance education. 

The results indicate that the survey respondents were more experienced with onsite courses at 

NCTC than they were with distance education. Survey respondents from regions 5 and 9 were more 

experienced with onsite courses than respondents from most other regions. Overall, survey respondents 

preferred onsite training and were more likely to enroll in onsite courses than in distance education. 

However, these differences were not large. When asked to indicate reasons that would lead them to 

choose one type of training over the other, practical reasons were more frequently indicated as 

influential in the decision to take distance education and interacting with others was more frequently 

cited as a reason to take onsite training.

This is an exploratory study and the results should be interpreted as preliminary rather than 

conclusive. We conclude that the information we assembled and analyzed indicates that distance 

education currently functions as a supplemental approach to training when participation in training 

onsite at the NCTC campus is impractical. 

Background

The U.S. Fish and Wildlife Service’s (FWS) National Conservation Training Center (NCTC)

provides training through onsite and distance education classes. The provision of both avenues of 

training is essential to the success of the NCTC, although the onsite training contributes more to the 

financial health of the center. The NCTC must balance the demands of meeting the needs of their 

customers—primarily but not exclusively FWS employees—with generating sufficient income to cover 

operating expenses. As with many organizations providing both onsite and distance education options,

the NCTC questions the effect that offering distance education has on onsite course enrollment.

This study was requested and funded by the Division of Education Outreach (DEO) at NCTC. 

As part of planning future distance education programming, the DEO believed it would be prudent to 

anticipate the effect that increasing distance education courses would have on onsite training. The DEO 

partnered with the Policy Analysis and Science Assistance Branch (PASA) of the U.S. Geological 

Survey to complete this study. We conducted exploratory research to characterize the potential 

relationship between distance education and onsite enrollment at NCTC.

There are several forms that a relationship between enrollment in distance education and onsite 

courses could take. The two training processes could be independent of each other, such that enrolling in 

one type of training does not affect the likelihood of enrolling in the other type of training. Distance 

education and onsite training could be positively related—this would mean that enrolling in one type of 

training would increase the likelihood of enrolling in the second type of training. The two training 

processes could be negatively or inversely related—this would mean that enrolling in one type of 

training would decrease the likelihood of enrolling in the second type of training. The nature of the 

relationship between onsite and distance education may depend on which type of training an individual 

took first. For example, taking a distance education course first may make an individual more likely to 

enroll in onsite training at NCTC. If this is the case, then the distance education program is in some 

manner functioning as a marketing tool for the onsite training. This may be an undetected benefit of 

distance education. 
3

In higher education, over the last several years, online enrollments have grown at a faster rate 

than enrollments overall (Allen and Seaman, 2007). This could indicate an increasing preference for 

distance education. Some individuals value the convenience with which distance education can be 

obtained more than other considerations in choosing to take a course via distance education or in a 

traditional classroom setting (Roblyer, 1999). This convenience arises from the flexibility provided by 

many technologies used in distance education. A lecture viewed online can be started and stopped at the 

employee’s convenience. Alternatively, the increasing rate in online enrollments could indicate the 

increased practicality of providing and acquiring training through distance education. Improvements in 

accessibility and functionality of technology have in turn improved the quality of training that can be 

provided via technology. In the past, the state of technology limited the degree of interaction between 

trainer and trainee. With the advent of web-based training, trainers and trainees can interact in real time. 

Use of technology in the workplace is increasingly widespread and results in more individuals having 

access to training through distance education technologies. Individuals may have reasons that make it 

less likely that they can participate in training that requires travel to a specific location. When deciding 

what type of training in which to enroll, individuals may weigh their preferences against what may be 

most practical given their circumstances.

Organizations and educational institutions offering courses via distance education and traditional 

onsite classroom courses are concerned about the effect of the distance education option on onsite

enrollment. Offering distance education could potentially draw enrollment away from onsite courses. In 

this case, the total number of individuals receiving training would stay about the same but would be 

distributed differently between the two options. Alternatively, distance education could create 

opportunities for some people to participate in training who could not participate in onsite training. In 

this case, overall enrollment in training would increase. Olsgaard (1987) compared onsite class size with 

distance education enrollment over time. He concluded that onsite class size benefitted from the 

provision of distance education offerings, with the onsite class size increasing over time. Without 

longitudinal data collection, the relationship between distance education and onsite enrollment can be a 

difficult one to characterize. 

Ridley and others (1997) conducted a multimethod study to evaluate the effect that online course 

offerings had on enrollments in traditional on campus courses. The purpose of their study was to 

determine if the option of online classes drew students away from regular courses. They used three 

methods to address their concerns. First, they conducted a survey of students regarding the reasons why 

they would enroll in an online class rather than a traditional class. Second, they analyzed existing data 

on credit hours earned and online course enrollment. Finally, they compared archival data of credit 

hours and commuting distance before and after the online courses were available. Based upon their 

multi-method approach, Ridley and others (1997) concluded that online courses did not compete with 

on campus courses but had an additive effect on overall enrollment. 

Similar to the multimethod approach used by Ridley and others (1997), we used existing training 

enrollment data and a survey to describe the relationship between distance education course and onsite

course enrollments at the NCTC campus in Shepherdstown, West Virginia.

Part I: Analysis of Training Data

Staff at NCTC provided a set of data on training registration (course enrollment) that had been 

downloaded from DOI Learn. DOI Learn is an online platform used to manage training for employees 

of Department of the Interior (DOI) agencies. The data we received reported the training enrollment for 

FWS employees who had taken onsite and distance training through NCTC during the time period from 
4

October 1, 2007 to June 24, 2009. The training courses included in this dataset did not include the 

mandatory training required for all DOI employees.

Method

The original dataset we received included 5,616 records. Each record reported an enrollment in a 

course. In some cases, there were multiple records for a single individual. For example, Employee A

may have enrolled in a diversity seminar held at a location other than NCTC, a webinar on climate 

change, and an onsite course on habitat conservation. Employee A would have three records in the 

database. The original database included the following information for each record: employee email 

address, identification as a supervisor according to the Federal Personnel Payroll System (FPPS), class 

name, course name (each course may include multiple classes), and course type (“ILT” indicated 

instructor-led training, “OLT” indicated online training).

It was necessary to format and recode the information in the original dataset to make the data 

suitable for analysis. We used the employee email addresses to identify which records should be 

combined so that, in the final dataset, each record would represent all course enrollments for a single 

employee. Email addresses were also used to identify the regional affiliation of each employee. Once 

we had identified the region and finished combining the records, we deleted the email addresses from 

the database. Using class name, course name, and course type we created a more specific coding for 

course enrollment. We created separate variables for online training (OLT), instructor-led web-based 

courses (WEB), correspondence courses (CORR), instructor-led onsite training (NCTC), and instructorled courses at locations other than the NCTC campus (OFF). We created composite variables to 

represent all distance education courses (DISTANCE), instructor-led courses (INSTRUCT), self-paced 

independent courses (INDEPEND), and categorical variables to represent training location 

(LOCATION) and instructional format (FORMAT). Courses provided through distance education can 

be formatted either as instructor-led or as independent, self-paced courses. Onsite courses are assumed 

to be instructor-led. In table 1, we list each variable and a description of the coding.

Table 1. Variables and coding descriptions.

Variable Description

Region Number = Region of FWS

FPPS_Supervisor No = not a formal supervisor

Yes = a formal supervisor

OLT Number of Online classes

WEB Number of Instructor-led web-based classes

CORR Number of Correspondence classes

NCTC Number of Instructor-led classes at NCTC

OFF Number of Instructor-led classes offsite

DISTANCE Number of all types of classes NOT taken onsite at NCTC = OLT + WEB + CORR + 

OFF

INSTRUCT Number of classes taken that were instructor led (regardless of mode of distribution) = 

WEB + NCTC + OFF

INDEPEND Number of classes taken that were independent and self-paced = OLT + CORR

LOCATION NCTC = Onsite at NCTC only

DIST = Distance courses only

BOTH = Both onsite and offsite courses

FORMAT INST = Instructor led only

INDEP = Independent courses only

BOTH = Both types of courses
5

The variables OLT, WEB, CORR, NCTC, and OFF are mutually exclusive. The instructor-led 

web-based classes are not included in the number of online classes. The final dataset includes condensed 

training records for 2,823 FWS employees who took training between October 1, 2007 and June 24, 

2009.

We attempted to determine the dates of training in order to classify employees based upon 

whether they had taken distance education or onsite training first. The process was cumbersome and 

required an additional dataset. Because we did not have the entire training-enrollment history for all 

FWS employees, we would not have known the extent to which the training in this sample was 

representative of an employee’s training history. For example, based on the data for this limited time 

period, it may appear that Employee B had taken a distance education course in December 2007 and an 

onsite course in February 2008. We would have classified Employee B as “distance education first”.

However if Employee B had taken only onsite training prior to October 2007—the time period not 

included in our dataset—then our classification would have been in error. The possibility of 

misclassification was substantial and therefore we decided against including this information in our 

dataset and analyses. 

All statistical analyses reported in this report were conducted using PASW 18, a statistical 

software package from IBM SPSS Statistics.

Results

Descriptive Statistics for Variables

For each variable, we report the frequency of each possible code for the records available in the 

dataset. The dataset includes training records for employees in all of the FWS regions. In table 2, we 

report the number of records from each region, the percent of all records in the database represented by 

each region, the percent of all FWS employees in each region from the FWS’ Management Directive 

715 (an online, service-wide plans report for fiscal year [FY] 2008 for the Equal Employment 

Opportunity Commission [EEOC]; U.S. Fish and Wildlife Service, 2008), and the percent of the 

regional workforce who enrolled in training during the specified time period. Percents are rounded to 

the nearest whole number and may not sum to 100 percent.

The majority of employees who participated in training during the time period covered by the 

dataset were not supervisors. Formal FPPS supervisory status was held by 760 (27 percent) of 

employees in the dataset.

Most employees included in the dataset had not taken any online training (2,755; 98 percent). 

Sixty seven employees (2 percent) had enrolled in one online course. Only one employee had enrolled

Table 2. Frequency and percents of regional affiliation.

Region Frequency Percent in dataset Percent of FWS Percent of region 

enrolled in training

1 (Pacific) 276 10% 12% 28%

2 (Southwest) 431 15% 10% 53%

3 (Midwest) 288 10% 11% 32%

4 (Southeast) 405 14% 15% 33%

5 (Northeast) 297 11% 9% 41%

6 (Mountain-Prairie) 262 9% 11% 30%

7 (Alaska) 263 9% 6% 51%

8 (Pacific Southwest) 351 12% 9% 46%

9 (Headquarters) 250 9% 16% 19%
6

in two online courses. Most employees had not enrolled in any webinars (2,461; 87 percent). Three 

hundred twenty-nine employees (12 percent) had enrolled in one webinar, twenty-six (1 percent) had 

enrolled in two webinars, and 4 (less than 1 percent) employees had enrolled in three webinars. A single 

record of enrollment occurred for each of the levels of 4, 5, and 6 webinars. Most employees had not 

enrolled in any correspondence courses (2,817, 100 percent). One correspondence course enrollment 

was recorded for 5 employees (less than 1 percent) and enrollment in two correspondence courses was 

recorded for 1 employee. Because of the low enrollment in correspondence courses, we did not conduct 

any separate analyses on the variable CORR.

Nearly half of the records indicated no enrollment in onsite courses at NCTC. For those records 

that do indicate enrollment in onsite courses, the number of onsite courses ranges from one to seven. 

The frequency of the number of onsite courses and respective percents are provided in table 3.

Table 3. Enrollments in courses onsite.

Number of enrollments Frequency Percent

0 1,255 45%

1 1,174 42%

2 273 10%

3 94 3%

4 19 1%

5 6 <1%

6 2 <1%

7 1 <1%

Slightly over half of the records indicated no enrollment in instructor-led courses held offsite at a 

location other than the NCTC campus. The frequency of the number of offsite courses and respective 

percents are provided in table 4.

Table 4. Enrollments in instructor-led courses offsite.

Number of enrollments Frequency Percent

0 1,482 53%

1 1,096 39%

2 161 6%

3 44 2%

4 12 <1%

5 8 <1%

6 8 <1%

7 4 <1%

9 4 <1%

13 2 <1%

14 2 <1%

We created a combined variable, DISTANCE, that included all enrollments other than 

instructor-led courses at NCTC. To determine the value of DISTANCE for each record, we added OLT,

WEB, CORR, and OFF. The records of almost half of the employees included in the dataset indicate 

enrollment in at least one type of distance training. Table 5 includes the number of distance course 

enrollments, frequency of those values in the dataset, and percents.
7

Table 5. Enrollments in all types of distance courses.

Number of enrollments Frequency Percent

0 1,154 41%

1 1,315 47%

2 245 9%

3 58 2%

4 19 1%

5 10 <1%

6 7 <1%

7 6 <1%

8 1 <1%

9 4 <1%

13 2 <1%

14 1 <1%

15 1 <1%

We created a categorical variable, LOCATION, with three classes. The record for each 

employee was categorized as “distance only” if the record included enrollments only for distance 

education courses; 1,255 records (45 percent) were included in this category. The record was 

categorized as “NCTC only” if the record included enrollments only for onsite courses at NCTC; 1,154 

records (41 percent) were included in this category. The record was categorized as “both” if the record 

included enrollments for distance education and onsite courses; 414 records (15 percent) were included 

in this category. 

Onsite courses are assumed to be instructor led. We created variables based on the number of 

enrollments in courses that were instructor led and in courses that were independent. The majority of 

records (99 percent) indicate enrollment in at least one instructor-led course. The breakdown in number 

of enrollments in instructor-led courses is provided in table 6, with respective frequencies and percents.

The majority of records (2,750, 98 percent) indicate no enrollment in independent self-paced 

courses such as correspondence courses. Seventy one records (3 percent) indicate enrollment in one 

independent course. One record indicates enrollment in two independent courses, and one record 

indicates enrollment in three independent courses. 

Table 6. Enrollments in instructor-led courses.

Number of enrollments Frequency Percent

0 31 1%

1 1,857 66%

2 596 21%

3 216 8%

4 65 2%

5 28 1%

6 12 <1%

7 8 <1%

8 2 <1%

9 3 <1%

12 1 <1%

13 2 <1%

14 1 <1%

15 1 <1%
8

We created a categorical variable, FORMAT, with three classes. The classes were “Instructor,” 

“Independent,” and “Both.” Employees who had only taken courses that were instructor led, either at 

NCTC or as an instructor-led webinar, were classified as “Instructor”; 2,750 employees (97 percent) fit 

this classification. In a similar manner, employees who had only taken independent courses were 

classified as “Independent”; 31 employees (1 percent) were included in this category. Those who had 

taken at least one instructor-led and at least one independent course were classified as “Both”; 42 

employees (2 percent) were included in this category.

Based upon the frequencies of the variables in the available records, 56 percent of the FWS 

employees who enrolled in training at NCTC within the time period from October 1, 2007 to June 24, 

2009 were enrolled in onsite training at the NCTC campus. Forty-seven percent enrolled in instructorled training at an offsite location and 59 percent enrolled in some type of distance education. Roughly 

equal proportions of employees have taken distance education only (45 percent) or onsite training only 

(41 percent) and a smaller proportion (15 percent) has enrolled in both types of training. A striking 

majority of employees (97 percent) have only taken training that was instructor led, either onsite or via 

distance education.

This is only a summary description of the variables. In order to fully understand how the options 

of onsite and distance education relate to one another, we analyzed relationships among the variables 

included in the dataset.

Relationships among Variables

If practical concerns such as travel time and cost are issues that influence enrollment in distance 

education or onsite courses, then we would expect the geographically defined regions of the FWS to 

relate to enrollment in distance courses, enrollment in onsite courses, and the LOCATION variable 

(NCTC only, distance only, both). 

We conducted an analysis of variance (ANOVA) to determine if enrollment in distance courses, 

as represented by the combined variable DISTANCE, was related to region. The results indicated a 

significant regional difference: F (8, 2,814) = 45.29, p = .00, R2 = .11. According to standard 

interpretation (Morgan and others, 2001), this is a medium effect size. However, upon examination of 

the averages and standard deviations of DISTANCE for each region, we discovered that the standard 

deviation for region 2 (1.96) was much larger than the next largest standard deviation—the standard 

deviation for region 9 was .82. The frequency data reported as part of the descriptive statistics for the

dataset indicate that there may be some extreme values (also known as outliers) in the data. A few 

records indicated that individuals had enrolled for up to 15 courses for some types of training. We 

defined outliers as any value 10 or above for any of the training type variables. All subsequent analyses 

were conducted with the outliers removed. We reanalyzed the relationship between enrollment in 

distance courses and region. The results were similar to the previous analysis and indicated a significant 

regional difference: F (8, 2,810) = 47.13, p = .00, R2

To determine if enrollment in onsite courses at NCTC was related to region, we conducted an 

ANOVA. The results indicated a significant regional difference: F (8, 2,814) = 33.07, p = .00, R

= .12. There were several statistically significant 

differences among regions. The average enrollment in distance courses for region 2 was higher than for 

all other regions. The average enrollment in distance courses for region 5 was lower than for all other 

regions. Although NCTC is organizationally located in region 9, it is physically located within the 

geographic boundaries of region 5 which may make onsite courses more accessible, and distance 

courses less necessary or attractive, to employees in region 5. The average distance course enrollment 

for region 7 was higher than enrollments for regions 1, 3, 6, and 9. The average enrollment in distance 

courses for region 6 was lower than for regions 4 and 8. 

2 = .09. 
9

This is considered an effect of medium size. The statistically significant differences among regions are 

as follows. The average enrollment in NCTC courses was higher for region 5 than for all other regions. 

The average enrollment in NCTC courses was higher for region 9 than for regions 1, 2, 4, 7, and 8. The 

average enrollment for region 3 was higher than for regions 2 and 7. The average enrollment in NCTC 

courses for region 6 was higher than for region 2. The average enrollment in distance education and 

NCTC courses for each region are plotted in figure 1. 

We conducted a series of within-region paired t-tests to compare the average enrollment in 

distance courses to the average enrollment in NCTC courses. Regions 2 and 7 had average enrollments 

in distance courses that were different at a statistically significant level (p <. 01) and higher than their 

average enrollments in NCTC courses. Regions 3, 5, 6, and 9 had average enrollments in NCTC courses 

that were different at a statistically significant level (p <. 01) and higher than their average enrollments 

in distance courses. The enrollments in distance and NCTC courses were not different in regions 1, 4 

and 8. Details of these analyses are provided in table 1-1 in appendix 1.

We conducted analyses to determine if employees enrolled in onsite NCTC courses only, 

distance education courses only, or both as indicated by the categorical variable of LOCATION differed 

by region. We used a nonparametric test, Cramer’s V, to determine if there was a relationship between 

LOCATION and region or if the two variables were independent of each other. The results were 

significant: V = .24, p < .01. This indicates the presence of a moderately strong relationship between 

region and the location of training courses. The frequencies for each level in the LOCATION variable 

are provided for each region in figure 2. 

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

REGION

AVERAGE ENROLLMENT

DISTANCE

NCTC

DISTANCE 0.74 1.4 0.6 0.81 0.25 0.52 1.06 0.8 0.63

NCTC 0.65 0.51 0.85 0.69 1.38 0.74 0.51 0.64 0.95

1 2 3 4 5 6 7 8 9

Figure 1. Average enrollments in distance education and NCTC courses by region. [NCTC, instructor-led onsite 

training at the National Conservation Training Center] 
10

0

50

100

150

200

250

300

REGION

FREQUENCY

Both 39 67 40 59 29 26 46 64 44

DISTANCE 128 266 111 199 36 101 156 173 85

NCTC 109 98 137 147 232 135 61 114 121

1 2 3 4 5 6 7 8 9

Figure 2. Course location by region. [NCTC, instructor-led onsite training at the National Conservation 

Training Center]

It is apparent from figure 2, that most employees have either taken training only through distance 

education or only at NCTC. Fewer employees have taken training via both options. We used the 

composite variable DISTANCE for these analyses. We used the individual type of training variables for 

online, web-based, onsite, and instructor-led offsite in additional analyses to determine if enrollment in 

specific types of training was related to region.

We used a repeated-measures ANOVA with a Greenhouse-Geisser adjustment to the degrees of 

freedom. The type of training (OLT, WEB, NCTC, OFF; these variables are defined in table 1.) was the 

repeated measure and region was a between-subjects factor. The results indicated a significant effect for 

type of training: F (1.86, 5,229.29) = 787.34, p < .01, η2 = .20 (eta squared [η2] is a measure of effect 

size used with ANOVA), and a significant interaction between type of training and region: F (14.89, 

5,229.29) = 47.91, p < .01, η2 = .10. The effect for type of training is considered a medium to large 

effect by conventional standards (Morgan and others, 2001), and the effect for the interaction is 

considered medium sized. The effect for region was significant but with a small effect size: F (8, 2,810) 

= 11.69, p < .01, η2

In figure 3, we provide a depiction of the average number of each type of course by region. 

Significant differences among training types within regions are indicated in figure 3. Detailed results of 

= .03. We followed up on the significant interaction by analyzing the types of 

training for each region separately. We used a Bonferroni adjustment to the significance level for the 

multiple comparisons.
11

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

REGION

AVERAGE NUMBER OF COURSES

OLT 0.02 0.02 0.03 0.03 0.02 0.02 0.03 0.01 0.05

WEB 0.2 0.08 0.22 0.28 0.08 0.06 0.1 0.11 0.14

NCTC 0.64 0.51 0.85 0.69 1.38 0.74 0.51 0.64 0.95

OFF 0.53 1.29 0.34 0.5 0.15 0.44 0.93 0.69 0.44

1 2 3 4 5 6 7 8 9

Figure 3. Average number of courses by type for each region. Averages in the same column that do not share 

subscripts are significantly different from each other at p < .01. [OLT, online training; WEB, instructor-led webbased courses; NCTC, instructor-led onsite training at the National Conservation Training Center; OFF, 

instructor-led courses offsite] 

the analyses are reported in table 1-2 in appendix 1. The effect sizes for all of these analyses were 

medium to large in size and are reported in table 1-2.

The results of these analyses suggest regional differences in enrollment in the types of training. 

Average enrollments are lower in OLT and WEB courses than in the other types of training courses in 

most regions. However, regions differ in whether courses at NCTC and offsite differ from each other 

and in which direction. There are no differences between NCTC and offsite average course enrollment 

for regions 1, 4, and 8. Average enrollments in NCTC and offsite courses are different from each other 

in the remaining regions. In regions 3, 5, 6, and 9, average enrollment in NCTC courses is higher than in 

offsite courses. Average enrollment is higher in offsite courses than in NCTC courses within regions 2 

and 7. 

Another characteristic of employees that may affect which type of training they enroll in is 

supervisory status. Differences between supervisors and nonsupervisors in enrollment rates could result

from several situations. Because supervisors approve training, they may approve certain types of 

training for themselves and other supervisors and approve different types of training for nonsupervisors. 

Another scenario may be that topics that are more relevant to supervisors are provided in particular 

training formats and not in others. Certain events, such as regional reorganization, could have placed 

additional burden on supervisors during the time period covered by the dataset that would have made 

a a a a a a a a a

b b b b ab a b b a

c c c c c b c c b

c d b c b c d c c
12

supervisors less likely to take training than nonsupervisors. Finally, there may be an organizational 

culture present in FWS or within regions that promotes training differentially for supervisors and 

nonsupervisors. We investigated regional differences in supervisory status in the training dataset and the 

effects of supervisory status on training type.

To determine if any relationship existed between regional affiliation and supervisory status, we 

first calculated a measure of association for categorical variables, Cramer’s V. The results of the 

analysis were significant: V = .19, p < .01, and indicative of a weak association between the two 

variables. We conducted follow-up analyses in order to pinpoint the nature of the relationship between 

supervisory status and region. If supervisory status and region were independent, we would expect 

roughly the same proportion of supervisors in each region. Supervisors make up 27 percent of the 

dataset. We would expect that 27 percent of employees in each region would be supervisors. We used 

this percent to determine an expected number of supervisors in each region. For example, 276 

employees from region 1 were in the database. We would expect that 27 percent of them (75) would be 

supervisors. In table 7 we provide the expected number, the actual number and the actual percent of 

supervisors for each region. Figure 4 depicts the percent of supervisors in each region in the training 

database relative to the expected percent.

Table 7. Number and proportion of supervisors in each region.

Region Total number Expected number of 

supervisors

Actual number of 

supervisors Actual percent

1 276 75 79 29%

2 431 116 109 25%

3 288 78 52 18%

4 405 109 78 19%

5 297 80 71 23%

6 262 71 46 18%

7 263 71 120 45%

8 351 95 129 37%

9 250 68 76 30%

Regions 1, 7, 8, and 9 have actual percents of supervisors that exceed the expected values, 

although the values for regions 1 and 9 do not exceed the expected value of 27 percent by much. This 

comparison is based on the percent of supervisors in the training database. A better analysis would 

compare the actual percent of supervisors participating in training against the actual percent of 

supervisors in the FWS workforce. For example, if the actual percent of supervisors in the FWS 

workforce was 15 percent, then all regions would be demonstrating that supervisors participated in 

training more than would be expected. On the other hand, if the actual percent of supervisors in the 

FWS was 40 percent, then most regions would be demonstrating that supervisors participated in training 

less than would be expected. However, we lack that information and are therefore unable to make that 

comparison.

We conducted a repeated-measures ANOVA, with a Greenhouse-Geisser adjustment to degrees 

of freedom and a Bonferroni adjustment for multiple comparisons, to determine if supervisory status 

was related to enrollment in different types of training. The interaction between supervisory status and 

training type indicated a small but significant effect: F (1.80, 5,076.63) = 66.14, p < .01, η2 = .02. We 

conducted follow-up ANOVAs for each training type to isolate the interactive effect. There was no 

difference in enrollment in web-based courses for supervisors and nonsupervisors. There were 
13

29%

25%

18% 19%

23%

18%

45%

37%

30%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1 2 3 4 5 6 7 8 9

REGION

PERCENT OF EMPLOYEES 

CLASSIFIED AS SUPERVISORS

Expected

Actual

Figure 4. Percent of supervisors in each region compared to the expected percent.

significant differences in average number of enrollments for online training, NCTC courses, and offsite 

courses. The effect sizes were all small. The analysis for online training yielded significant results but

with a very small effect size: F (1, 2,821) = 9.85, p < .01, η2 = .003. Nonsupervisors had a higher 

average enrollment in online courses than supervisors. There was a significant difference in enrollment 

in courses at NCTC between supervisors and nonsupervisors: F (1, 2,821) = 67.89, p < .01, η2 = .02. 

Nonsupervisors had a higher average enrollment in courses at NCTC than did supervisors. The analysis 

for instructor-led courses offsite indicated a significant difference between average enrollments between 

supervisory groups: F (1, 2,821) = 59.11, p < .01, η2

Supervisory status does appear related to type of training in which employees enroll. As noted, 

we did not have available information to determine why these differences exist. For example, we 

noticed that some of the instructor-led offsite courses were diversity seminars. Diversity seminars may 

be viewed as more appropriate for supervisors and that may, in part, explain why supervisors have a 

higher average enrollment in offsite courses. Based on the information included in the training dataset, 

we cannot identify the underlying reasons for the differences with any degree of certainty. 

= .02. The average number of offsite course 

enrollments was higher for supervisors than for nonsupervisors. The average enrollments for supervisors 

and nonsupervisors are provided in figure 5.

One striking feature of figure 5 is that the average enrollments in NCTC and in offsite courses 

appear much higher than the online and web-based courses. In turn, average enrollment in web-based 

courses seems higher than enrollments in online training. The NCTC, offsite, and web-based courses are 

all instructor led, but only the NCTC and offsite courses are characterized by face-to-face interaction 
14

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

TYPE OF TRAINING

AVERAGE NUMBER OF COURSE 

ENROLLMENTS

Nonsupervisor 0.03 0.14 0.83 0.54

Supervisor 0.01 0.15 0.54 0.83

OLT WEB NCTC OFF

Figure 5. Average enrollment for supervisors and nonsupervisors in four types of training. [OLT, online training; 

WEB, instructor-led web-based courses; NCTC, instructor-led onsite training at the National Conservation 

Training Center; OFF, instructor-led courses offsite]

among students and teachers. We investigated the effect of course format on enrollment by analyzing 

differences in enrollment rates for the four training types, correlating enrollment rates between 

instructor-led and independent courses, analyzing regional and supervisory status differences for 

instructor-led and independent course enrollment, and determining if regional and supervisory 

differences exist among categories of the FORMAT variable—instructor-led only, independent only, or 

enrollment in both types.

The results of a prior analysis provide information about the differences in enrollment rates for 

the four training types. The repeated-measures ANOVA we conducted to determine if supervisory status 

was related to enrollment in different types of training also indicated a significant main effect for type of 

training: F (1.80, 5,076.63) = 569.23, p < .01, with a medium to large effect size: η2

The multiple comparisons indicated that average enrollment for NCTC and offsite courses do 

not differ from each other. All other comparisons are significantly different at p < .01 with a Bonferroni 

adjustment for multiple comparisons. 

= .16. The average 

enrollments for the four training types are provided in figure 6.

We had created two composite variables, INSTRUCT and INDEPEND, that represent different 

course formats regardless of delivery medium. The variable INSTRUCT is the sum of enrollments in 

instructor-led training delivered in web-based, offsite, and NCTC courses. The variable INDEPEND is 
15

0.02

0.15

0.69 0.68

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

OLT WEB NCTC OFF

TRAINING TYPE

AVERAGE NUMBER OF COURSES 

ENROLLED IN BY EACH INDIVIDUAL

Figure 6. Average enrollments for training types. [OLT, online training; WEB, instructor-led web-based courses; 

NCTC, instructor-led onsite training at the National Conservation Training Center; OFF, instructor-led courses 

offsite]

the sum of enrollments in self-paced courses: online training and correspondence courses. When we 

calculated the correlation between these two variables to determine the degree to which they were 

related, we found that the correlation was nonsignificant: r = .00, p = .99. However, this result may be 

due to the low variability of values for the variable INDEPEND. Only 3 percent of records in the dataset 

have a nonzero value for INDEPEND. This, in effect, renders the variable of INDEPEND a constant 

value that lacks the variability necessary to detect a correlational relationship.

We analyzed regional and supervisory status differences for instructor-led and independent 

course enrollment. We conducted four ANOVAs with the variables INSTRUCT and INDEPEND as 

dependent variables and regional affiliation and supervisory status as factors. The analyses using 

INSTRUCT as the dependent variable demonstrated a small regional effect: F (8, 2,809) = 11.79, p < 

.01, η2 = .03, but no effect for supervisory status; F (1, 2,816) = .03, n.s. (non-significant). The followup comparisons with p = .01 and a Bonferroni adjustment for multiple comparisons indicated that region 

2 had a higher average enrollment in instructor-led courses than all other regions except region 5. 

Region 5 differed only from region 6; it had a higher average enrollment than region 6. The average 

enrollment in instructor-led courses for each region are provided in figure 7. 
16

1.37

1.85

1.41

1.48

1.61

1.24

1.54

1.44

1.53

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

1 2 3 4 5 6 7 8 9

REGION

AVERAGE NUMBER OF INSTRUCTOR-LED 

COURSES ENROLLED IN BY EACH INDIVIDUAL

Figure 7. Average enrollment in instructor-led courses in each region.

The analyses using INDEPEND as the dependent variable demonstrated no regional effect: F (8, 

2,814) = 1.68, n.s., but a very small effect for supervisory status: F (1, 2,821) = 9.66, p < .01, η2

We classified whether employees had taken training that was all instructor led, all independent 

or a mix of both for the categorical variable FORMAT. We used a nonparametric measure, Cramer’s V,

= .003. 

The average enrollment in independent courses was higher for nonsupervisors (.03) than for supervisors 

(.01). 

to analyze the relationships between FORMAT and the variables of region and supervisory status. The 

analysis comparing FORMAT and region yielded a small but significant effect: V = .10, p < .01. 

However, the contingency table for these two variables was rather sparse and the results of this analysis 

may be less reliable than if the table were not sparse. The sparseness was due to the low number of 

employees who took either only independent courses or courses of both types. The frequency for each 

category of FORMAT by region is provided in figure 8.

The analysis comparing FORMAT and supervisory status also resulted in a small but significant 

effect: V = .07, p < .01. The frequency for each category of FORMAT by supervisory status is provided 

in figure 9. 

It is clear that there are more employees in the training database who have taken only instructorled courses. This pattern is apparent in the breakdown of the FORMAT variable by region and by 

supervisory status.
17

Figure 8. Course format by region. [INDEP, independent, self-paced classes; INST, instructor-led classes] 

0

50

100

150

200

250

300

350

400

450

REGION

FREQUENCY OF ENROLLMENT 

IN DIFFERENT COURSE FORMATS

BOTH 1 12 1 8 4 4 1 1 10

INDEP 4 1 10 4 2 1 7 1 1

INST 271 418 277 393 291 257 255 349 239

1 2 3 4 5 6 7 8 9
18

0

500

1000

1500

2000

2500

SUPERVISORY STATUS

FREQUENCY OF ENROLLMENT 

IN DIFFERENT COURSE FORMATS

BOTH 36 6

INDEP 30 1

INST 1997 753

Nonsupervisor Supervisor

Figure 9. Frequency of enrollment in course formats by supervisory status. [INDEP, independent, self-paced 

classes; INST, instructor-led classes]

Discussion

The results of the analyses of the training database can be summarized around three focal issues: 

regional differences, differences based on supervisory status, and implications of training format. There 

are significant regional differences in enrollment rates for courses provided through distance education 

or onsite at NCTC. The pattern of differences suggests that regions located closer to NCTC (specifically 

regions 3, 5 and 9) have higher enrollment rates in onsite courses and regions located farther away from 

NCTC (specifically regions 2, 7, and 8) have higher enrollments in distance education. Region 1, which 

is far from NCTC, and region 4, which is close to NCTC, do not demonstrate any differences in 

enrollment between onsite and distance education. Region 6 which is farther away from NCTC does 

exhibit higher enrollment in onsite courses than distance education courses. We do not know the reason 

for this. One possibility may be that the organizational culture of region 6 is more supportive of training 

at NCTC and/or training in general. The regional effects are so consistent that it may be worth 

considering the regions as different markets for training. Some regions may present a greater market for 

training via distance education options whereas others are clearly more likely to participate in training 

onsite at NCTC.

The percent of employees who were identified as supervisors also differed by region. It is 

difficult to interpret this effect without additional information. We used the percent of supervisors in the 

dataset overall as a benchmark. However, the percent of supervisors in the workforce for each region 

would give a better assessment of whether supervisors are participating in more training than would be 
19

expected. Based on the available data, there are differences among the regions in the percent of 

supervisors participating in training, but we do not know if this effect is due to some regions having 

higher proportions of supervisors in their workforce or if this difference could indicate a difference 

across regions in the value placed on training for supervisors.

Another effect of supervisory status occurred in comparisons with enrollment in onsite or 

distance education. The results of our analyses indicate that nonsupervisors were more likely to enroll in 

training at NCTC and supervisors were more likely to take training that was offered offsite. As with 

other effects found in this exploratory study, we do not know the reason underlying this effect. One 

potential explanation is that the types of training offered at NCTC and offsite differ in content such that 

the offsite courses are more relevant to supervisors. An alternate explanation may be that supervisors 

believe that onsite training at NCTC is necessary in order to develop their employees.

When the training formats are compared against each other, the enrollments in NCTC courses 

and offsite courses are not significantly different from each other. They both differ from enrollment in 

instructor-led web-based courses and independent online training—which also differ from each other. 

Enrollment in web-based courses is lower than in either NCTC or offsite courses and enrollment in 

online training courses is even lower. The offsite training seems to be the most popular distance 

education format. These differences cannot be interpreted as based on preferences. The different 

enrollment rates may differ based on the number of courses offered through each option. 

We grouped courses into instructor led (NCTC, offsite, and web based) and independent (online 

training and correspondence courses). Enrollment levels in instructor-led versus independent courses do 

not seem to be related. Overall, enrollment in instructor-led courses is higher than in independent 

courses; perhaps not as many independent courses are offered. We did observe a regional difference in 

enrollment in instructor-led courses: region 2 had a higher enrollment rate in these courses than most 

other regions. Region 2 also had a higher enrollment rate in distance education than onsite at NCTC. 

More specifically, region 2 had a higher rate of enrollment in offsite courses than in other training 

options. Enrollment in independent courses does not seem to be affected by region. There is an effect of 

supervisory status on enrollment in independent courses such that nonsupervisors are more likely to 

enroll in independent courses than are supervisors. 

The primary limitation of this study was the limitations of the data set. Specifically, the limited 

time covered and limited information included in the dataset prohibited more in-depth analyses. Due to 

the limited time period covered in the data set, we could not fully assess the relationship between 

distance education and onsite training. It would have been useful in characterizing the relationship if we 

knew which type of training employees had experienced first. The training dataset used for these 

analyses did not include any measure of organizational tenure. Employees who are close to retirement 

may be less likely to invest time into pursuing training and may take training that is easiest to access. 

Additionally, it is difficult to draw firm conclusions regarding the differences between onsite and 

distance education without including the content of the courses offered through these different modes. 

The results indicated that supervisory status was related to type of training and we noticed that some of 

the instructor-led offsite courses were diversity seminars. These seminars may be more appropriate for 

supervisors. Differences in the specific courses offered through onsite and distance education training 

could explain some of the differences in enrollment between these modes of training.

This study was exploratory and provides some preliminary answers to the question of how 

providing distance education courses will affect enrollment in onsite courses. Early in discussions 

regarding the development of this project, the question was posed as to whether or not distance 

education could function as a gateway to onsite education. At this time, with the currently available 

data, we cannot offer a definitive answer to that question. This dataset of training enrollments suggests 
20

that distance education and onsite education operate independently. When the records of employees 

were categorized as having taking classes at NCTC only, through distance education only, or through 

both options, most employees had taken courses through only one option. If distance education were 

currently operating as a gateway, we would expect to see more individuals who had taken training 

through both options. Distance education and onsite training appear at this time to be complementary 

means to provide training.

Part II: Survey

The analysis of the existing training data provides information regarding enrollment behavior. A

survey was included in the study to provide information regarding employee preference for distance 

education or onsite training. We conducted another survey of FWS employees on a similar topic. The 

other survey addressed specific topics for potential training that would be of interest to FWS employees 

who engage in education and outreach activities and specific modes of distance education such as video 

conferencing, computer-mediated training, and satellite television. The intent of that survey was to 

provide information to the DEO regarding training on which topics could be effectively provided 

through which means of distance education. Because of the similarities in the objectives of that study

and this study, and because some questions worked for both, we combined the questions into a single 

survey questionnaire. This approach was more efficient in terms of cost and time and it reduced the 

burden on FWS employees. In this report, we summarize the survey development and administration 

processes. More detail is provided in the completion report for the study of topics for distance education 

(Ratz and others, 2011a). 

Method

Survey Development

We reviewed the published literature on distance education technology and training to determine 

what questions should be asked in this survey. We determined that we needed to include questions on 

past training experience, preferences for training type, reasons for enrolling in distance education or in 

onsite training, and behavioral intentions. We developed questions relating to distance education and 

then developed parallel questions relating to onsite training. Survey questions were reviewed by DEO 

personnel prior to the finalization of the survey. The questions are provided in table 2-1 in appendix 2.

The survey questions, the corresponding response scales, and frequency data are provided in the report 

to respondents for the survey (Ratz and others, 2011b).

Past Training Experience

Experience with distance education technologies can be measured by the number of prior 

courses taken with each type of distance education technology (Arbaugh, 2005; DeBourgh, 2003). 

Individuals who have taken distance education in the past are more likely to be more comfortable with

distance education (Jedlicka and others, 2002) and report a preference for distance education (Harris and 

Gibson, 2006). We asked respondents about their previous experience with distance education and with 

onsite training.

Preferences for Mode of Training

Several of the learning models that have been proposed to explain attitudes and behavior toward 

online learning note that the learners’ beliefs and attitudes relate to their intent to use and actual use of 
21

online technology for learning (Saade and others, 2007). Research studies have found support for the 

link between attitudes toward technology and learning outcomes when using technology (Daley and 

others, 2001; Klein and others, 2006). Preference is an attitude related to intent. Preference for a type of 

distance education technology is predictive of the intent to use the same distance education technology. 

Thompson and Lynch (2003) and Harris and Gibson (2006) provided example questions to measure

preferences from their studies of distance education and we adapted these questions as our measures of 

preferences.

Reasons for Enrolling

Roblyer (1999) developed a four-factor description of characteristics for choosing either distance 

education or traditional classroom education. Logistical factors include practical issues in participating 

in a course such as the distance to course site; control factors include when and how long to complete 

course activities; personal interaction factors include preferences for interacting with the instructor and 

other students; and technology perspectives include attitudes regarding and prior experiences with 

technology and distance education. Employees can choose distance education or onsite training for 

different reasons. 

Wallace (1996) conducted a study to identify the reasons why university students chose to enroll 

in distance education courses rather than traditional courses on campus. Wallace developed a 

questionnaire with a listing of reasons that were grouped into the following five categories: situational

(work and family issues), institutional (availability of on-campus courses), psychosocial (lack of 

academic confidence), learning preference (preference for independence), and goals (personal and 

academic goals). The students in the study were asked to rate the importance of each reason. The 

categories that most related to choice to enroll in distance education were goals, psychosocial, and 

learning preference. Situational and institutional reasons related to choice to enroll in distance education 

but not to the same extent as the other reasons. 

We created a list of reasons based upon literature and upon characteristics of the FWS that could 

influence the type of training chosen by employees. We asked respondents to indicate all of the reasons 

on the list that would influence them to take training via distance education instead of onsite and to 

indicate all reasons that would influence them to take onsite training rather than distance education. 

Data derived from checklists such as these can be difficult to analyze statistically (Bilder and Loughin, 

2004) so we treated these questions as qualitative rather than quantitative measures.

Behavioral Intention

Attitudes toward distance education are predictive of the intention to enroll in online courses 

(Robinson and Doverspike, 2006). As part of this survey, we incorporated a measure of behavioral 

intention to enroll in distance education from NCTC and a measure of behavioral intention to enroll in 

onsite training at NCTC. Our questions were based on the behavioral intent measure used in research by 

Robinson and Doverspike (2006).

Demographics

We included two questions that appeared early in the survey and functioned as screening 

questions. One question asked about the percent of time on the job that involved conservation and/or 

environmental education or outreach programming. The second question asked if the respondent’s job 

involved supervising anyone whose job involved those tasks.

We included questions regarding age and organizational tenure. There were three organizational

tenure questions: how long the respondents had been with the FWS, how long they had been at their 
22

current duty station, and how long they had been in their particular position. To help us determine the 

representativeness of our sample, we asked respondents about their employment status with FWS 

(permanent or term/temporary), the region in which their duty station is located, their WG/GS/GM 

level, and the numerical code for their Job Series.

Sampling Strategy

We used a convenience sample that was stratified by region, not a random sample. We designed 

our stratified-sampling strategy to include individuals from each region proportional to the number of 

employees in each region. We believed it was important to ensure input from all regions because issues 

can vary across regions. We used the information about employment from the FWS EEOC FY2008 plan 

(U.S. Fish and Wildlife Service, 2008) to calculate percentages for the sampling protocol. To determine 

the total number of employees to sample and number within each region, we started by identifying the 

smallest region (region 7) and determining the minimum number of responses from that region 

necessary for a sufficient regional sample size. Based upon that number, we then extrapolated sample 

sizes for the remaining regions. According to the FWS EEOC FY2008 plan (U. S. Fish and Wildlife 

Service, 2008), the employees in region 7 represent six percent of the FWS workforce. We aimed for a 

minimum of 45 respondents within region 7 and we assumed that our survey would have a minimum 50

percent response rate; therefore, in order to have 45 respondents from region 7, we needed to send the 

survey to 90 employees in region 7. The 90 employees in the sample from region 7 should compose six

percent of the total survey sample. We determined that our overall sample size should be 1,488. Given 

the data in the FY08 report (U. S. Fish and Wildlife Service, 2008); a sample of 1,488 is approximately 

18 percent of the employees of the FWS. 

To identify specific employees to include in the sample, we selected employees based upon 

information provided in two lists: FWS employees subscribed to the Visitor Outreach, Interpretation, 

Communications, and Education Services (VOICES) electronic distribution list (for employees engaged 

in environmental education) and FWS employees who had taken training through NCTC during the 

time period from October 1, 2007 to June 24, 2009. The second list was created from the data provided 

in the database used in the first part of this study on the relationship between enrollment in distance 

education and in onsite training. In selecting the sample for this survey, we gave preference to those on 

the VOICES distribution list because this strategy was more suitable for the primary survey on distance 

education.

Data-Collection Process

A letter written by Janet Carrier Ady, the Chief of the DEO, was emailed to the individuals in 

the survey sample to introduce the survey. Even though the message was from the DEO Chief, it was 

sent by PASA personnel to maintain the privacy and identity of those in the survey sample. No 

personnel at NCTC know the names of those included in the survey sample. A few days after the 

introductory email was sent, we emailed a message including a link to the survey to the FWS employees 

in the survey sample. The survey was administered online by using KeySurvey© software. We emailed

a reminder to those who had not yet completed the survey about one week after the initial survey 

distribution. A final reminder was sent to those who had not submitted a complete survey on the last day 

survey data were being collected.
23

Results

Frequencies of responses to all questions are provided in a report to respondents for the survey 

(Ratz and others, 2011b) so we do not reiterate them here. We provide some summary information 

regarding the response rate and other quality indicators which are discussed in detail in Ratz and others

(2011a). We describe the analyses for the questions from the survey that are most pertinent to the 

relationship between enrollment in distance education and in onsite training.

Survey Quality

Response Rate

Of the 1,488 surveys sent, 48 were undeliverable because either the individual was no longer 

with the FWS or the recipient’s mail box was filled over its quota. Four individuals requested to be 

removed from the survey sample. There were 98 individuals in the survey sample who were out of the 

office at some point during the data collection. Eight of those individuals were out of the office for the 

duration of the data collection period. This left us with a potential sample size of 1,428. Eight hundred 

sixty-four individuals submitted a completed survey in the survey software. Partial responses were 

received from 47 individuals who started but did not finish the survey online. We reviewed their 

responses and determined that most of them had answered more than half of the survey questions when 

they exited the survey. We included these partial responses for a total of 911 respondents. Our overall 

adjusted response rate was 64 percent. Every region had a regional response rate of at least 50 percent.

Other Quality Indicators

When using a survey to collect information, five characteristics of the survey research project 

must be considered to judge the quality of the survey and determine to what extent the information from 

the survey can be used. The five characteristics are survey reliability, survey validity, statistical power, 

sample representativeness, and nonresponse bias. These characteristics are discussed in detail in Ratz 

and others (2011a) and we summarize the results here.

Reliability is an indication of the consistency of measurement (for more detail, see Murphy and 

Davidshofer, 1998). For any measurement instrument—such as a survey—to be useful, it must be 

reliable. There is no method to determine the reliability of single item measures and the questions 

included in the enrollment survey were all single item measures. This means that we used single 

questions to measure each characteristic. The questions included in the larger survey comprised several 

subscales that demonstrated adequate levels of reliability as determined by a measure of internal 

consistency (Cronbach’s alpha). 

When evaluating the validity of a survey, we are interested in evidence that the survey is 

measuring the characteristics that we intended it to measure. Construct validity addresses whether a 

survey measures a specific characteristic of interest (Murphy and Davidshofer, 1998). In order to 

demonstrate the evidence for construct validity, there must be known relationships among the 

characteristics being measured. Based upon the published literature, we expected that the questions 

regarding experience with, preference for, and intention to take distance education would correlate 

positively with each other and that the questions regarding experience with, preference for, and intention 

to take onsite training would correlate positively with each other. Questions referencing different 

training types, such as the question about experience with onsite training and the question about 

intention to enroll in distance education, would either exhibit no correlational relationship or a negative 

(inverse) correlation. We found this pattern in our data. The correlations are provided in table 2-2 in 

appendix 2. Another approach to establishing evidence for construct validity, is to correlate a subscale 
24

score with some external measure (Murphy and Davidshofer, 1998). In this case we used the screening 

question that asked what percentage of the job was involved in conservation and environmental 

education as an external measure. Because there is no underlying reason why the “percentage of the 

job” should relate to experience, preference, or behavioral intention to enroll in either distance education 

or onsite training, we expect that none of these questions will correlate with this screening question. As 

reported in table 2-2, none of these correlations were significantly different from zero. We believe the 

available correlational evidence supports the conclusion that this survey measures experience with, 

preferences for, and behavioral intentions toward distance education and onsite training.

Statistical power is a characteristic of individual statistical tests and is highly influenced by how 

a survey research project is conducted. Statistical power is essentially the probability that a statistical 

test will lead to a correct conclusion (Murphy and Myors, 1998). The power of a statistical test is 

affected by the size of the effect anticipated in the population of interest. The size of the effect in the 

population cannot be altered to increase the power of statistical tests in the study. One of the primary 

methods to influence statistical power is through the size of the sample. More data mean more powerful 

analyses. The dataset for this study is based on the responses of 911 respondents. A dataset of this size 

ensures high power in analyses. We clearly have sufficient power for the statistical analyses to yield 

results that can be used for decisionmaking.

In addition to needing a sufficient number of respondents to provide adequate statistical power, 

the respondents need to be representative of the population of interest. Representativeness means that 

the sample is similar in type and distribution of characteristics to the population of interest. The primary 

approach to achieving data from a representative sample is a careful sampling strategy. We designed our 

stratified sampling strategy to include individuals from each region proportional to the number of 

employees in each region. As described in Ratz and others, 2011a, we compared our sample with the 

population distribution of the FWS workforce and determined that our sample was proportionally 

representative. However, the sample for the overall survey was done with the intent to include 

employees who were involved in conservation and environmental education. Our interest in this smaller 

survey is to compare preferences and intentions with respect to distance education versus onsite 

education. This issue is not limited to employees whose work involves conservation and environmental 

education. Our sample and therefore our results may not be as representative of employees in the FWS 

whose work does not directly involve education and outreach.

Nonresponse occurs when individuals to whom the survey is sent do not respond to the survey 

(Burkell, 2003; Dillman and others, 2002). Nonresponse bias refers to bias in survey results from 

differences in demographics or attitudes between those who do and do not respond to a survey (Burkell, 

2003; Hudson and others, 2004; Sax and others, 2003). The critical issue to address is whether the 

nonresponse bias influences the outcome and interpretation of survey results. While a high response rate 

can minimize the likelihood of nonresponse bias, it does not guarantee the absence of bias (Groves and 

Peytcheva, 2008). However, nonresponse is not necessarily an indicator of bias (Burkell, 2003; 

Rogelberg and Luong, 1998; Sax and others 2003). According to Moore and Tarnai (2002, p. 198), 

“...if there are no differences between respondents and nonrespondents, then there is no nonresponse 

error regardless of the response rate.” We used the method of comparing the survey responses of early 

and later responders to assess nonresponse bias and found no evidence to indicate the presence of bias. 

Our approach to evaluating nonresponse bias is described in detail in the survey completion report (Ratz 

and others, 2011a).
25

Onsite versus Distance Education: Experience, Preferences, and Intentions

We examined the characteristics of past training experience, preference for mode of training, and 

behavioral intentions to enroll in training to see if they differed for onsite versus distance education 

options. Because region appears to have an effect on training options based on the results of our analysis 

of the training dataset, we also examined regional effects. To accomplish this, we conducted a series of 

repeated-measures ANOVAs, with a significance cutoff equal to .01 and with a Bonferroni adjustment 

for multiple comparisons when necessary. We included region as a between-subjects factor. 

Experience

The repeated-measures ANOVA comparing experience with distance education and experience 

with onsite training yielded several significant effects. There was a main effect for training type, a main 

effect for region, and an interaction between training type and region. The analysis comparing the 

experience values between training type was significant with a large effect size: n = 911, F (1, 902) = 

575.95, p < .01, η2

The interaction between experience and regional affiliation was significant but with a small 

effect size: F (8, 902) = 8.56, p < .01, η

= .37. This indicates an overall difference in the sample between experience with 

distance education and experience with onsite training. The average score for experience with distance 

education was 2.25; this corresponds to a survey-scale value between one distance education course 

(score = 2) and two distance education courses (score = 3). The average score for experience with onsite 

education was 3.91 which corresponds to a survey scale value just under three courses taken onsite 

(score = 4). On average, survey respondents have taken slightly more than one distance education

course and almost four onsite courses.

2 = .04. We conducted follow-up univariate ANOVAs separately 

for each type of training, with region as a factor in the analysis. The analysis for experience with onsite 

education indicated significant differences among regions; the size of the difference was moderate: F (8, 

902) = 10.68, p < .01, η2= .09. Comparisons between regions indicated that regions 5 and 9 reported 

more onsite course experience than regions 1, 2, 6, 7, and 8. Regions 3 and 4 were not different from 

any other regions. The analysis for experience with distance education indicated significant differences 

in values for experience between regions, but the size of the difference was very small: F (8, 902) = 

3.10, p < .01, η2

There are distinct differences between experience with having taken distance education courses 

and onsite courses at NCTC. In all regions, the survey respondents reported having taken more onsite 

courses than distance courses on average. We cannot conclude that this reflects a preference for onsite 

courses; it may indicate that more courses are offered onsite and therefore are available to more 

employees. We did ask survey respondents to indicate their preference for distance education and onsite 

training.

= .03. The follow-up multiple comparisons did not indicate any significant differences 

between regions on experience with distance education. This may be due to the conservative 

significance cutoff we used (p = .01) and the adjustment to significance for the multiple comparisons,

which makes the significance level even more conservative. The average scores for experience with 

distance education and with onsite training for each region are provided in figure 10.

Preferences

The repeated-measures ANOVA comparing the values of preference for distance education with 

the preference for onsite training indicated a significant main effect for training type. The analysis 

comparing preferences between training types was significant with a medium effect size: n = 856, F (1, 

847) = 91.30, p < .01, η2 = .10. The average scores for preferences for both onsite and distance 

education fall between the survey scale values of 3 (Neither agree nor disagree) and 4 (Slightly agree). 
26

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

REGION

EXPERIENCE (NUMBER OF 

COURSES TAKEN)

Distance 2.7 1.97 2.53 2.2 1.99 2.47 1.91 2.34 2.15

Onsite 3.57 3.65 4.07 4.04 4.64 3.8 3.43 3.46 4.5

1 2 3 4 5 6 7 8 9

Figure 10. Experience with distance education and onsite training.

The average score for preference for onsite training was 3.88 and the average score for preference for 

distance education was 3.24. The main effect for region and the interaction effect between training type 

and region on preferences were not significant.

Behavioral Intention

The repeated-measures ANOVA comparing the values of intention to enroll in distance 

education and intention to enroll in onsite training resulted in a significant main effect for training type 

but no effect for the interaction between training type and region. The main effect for region was not 

significant. The analysis comparing the intention values between training type was significant with a 

very small effect size: n = 858, F (1, 849) = 11.37, p < .01, η2

Overall, the survey respondents reported more experience with onsite than distance education, 

greater preference for onsite than distance education, and a slightly greater intention to enroll in onsite 

than in distance education. On the survey, we asked respondents to indicate using a checklist what 

factors would influence them to take training via distance education rather than onsite, and to indicate 

= .003. The average scores for intention to 

enroll in onsite and distance education fall between the survey scale values of 4 (Neither likely nor 

unlikely) and 5 (Slightly likely). The average score for intention to enroll for onsite training was 4.96 

and the average score for intention to enroll for distance education was 4.64.
27

using the same checklist what factors would influence them to take training onsite rather than through 

distance education. 

Reasons for Enrolling: Checklist Results

We treated the responses to the two checklists—reasons why respondents would select distance 

education instead of onsite training and why they would select onsite training instead of distance 

education—as qualitative data. These checklists fall into the category of multiple-response variables 

which are difficult to analyze (Bilder and Loughin, 2004). One of the concerns with checklists is that it 

cannot be determined if the lack of a check mark means that data is missing (a respondent skipped the 

question) or if it is a negative response indicating the respondent is not influenced by this factor. In table 

8 we provide the top ten reasons from each checklist with the number of respondents selecting that 

reason.

Table 8. Ten most frequent reasons for selecting type of training.

Distance education instead of onsite Onsite instead of distance education

Scheduling with commitments in my work life n=696 Interaction with instructor n=765

Scheduling with commitments in my personal life n=648 Interaction with other students n=745

Cost issues n=500 Meeting others n=670

Freedom to complete training when I can fit it in n=459 It’s easier for me to learn this way n=492

Travel time n=457 Level of interest in the topic n=465

Transportation issues n=355 It’s efficient to learn this way n=265

Level of interest in the topic n=342 Scheduling with commitments in my work life n=141

Independent pace of learning n=247 Scheduling with commitments in my personal life n=113

It’s efficient to learn this way n=172 Travel time n=63

Needing permission from supervisor n=164 Transportation issues*

Cost issues*

n=59

* These two reasons received equal numbers of marks on the checklist.

Although no analyses can be done on the checklist data, we can make some general observations 

based on the frequency with which each reason was selected under each training type. It appears that the 

practical issues of scheduling, cost, and travel time were more frequently perceived as being influential 

in the decision to take distance education instead of onsite. Personal interaction issues seem to be more 

frequently cited as influential in the decision to take onsite courses instead of distance education. 

Discussion

We conducted a survey of FWS employees to obtain information regarding preferences with 

respect to participating in training through distance education and onsite at the NCTC campus. The 

survey was designed based on the existing literature on distance education and traditional classroom 

training preferences. The primary characteristics measured were: experience with distance education 

provided by NCTC and onsite training at NCTC, level of preference for distance education and onsite 

training, and intention to enroll in either distance education or onsite training. The data were collected 

using online survey software. The survey demonstrated adequate quality and the sample size was 

sufficient to detect even small effects in the analyses.

In terms of experience as measured by numbers of courses taken in the past, FWS employees in 

the survey sample indicated that they were more experienced with onsite training at NCTC and less 

experienced with distance education provided by NCTC. Onsite courses have been offered for a longer 

period of time than the relatively new program of distance education. A larger number of courses are 

offered through onsite training than are offered through distance education. If a FWS employee has 
28

taken a course from NCTC, it is more likely to have been an onsite course rather than a distance 

education course. The number of onsite courses taken in the past differed based upon region. Region 5, 

the region in which NCTC is geographically located, and region 9, the region in which NCTC is 

organizationally located, had higher reported rates of enrollment in onsite courses than most of the other 

regions. This result seems to indicate that practical issues such as distance, time, and travel affect the 

propensity to enroll in onsite courses. There were no regional differences in enrollment in distance 

education courses, perhaps these courses are more equally accessible, or inaccessible, to all regions.

The measures of preferences and behavioral intentions favored onsite training. Preferences and 

intentions appeared to be more consistent across regions. When asked to indicate why they would 

choose to take distance education courses over onsite courses, the respondents tended to endorse 

practical issues as influential in that decision. Scheduling with work commitments and home 

commitments were the most frequently identified reasons for taking distance education. Cost, travel 

time, and transportation issues were included in the 10 most frequent reasons to take distance education. 

When asked to indicate why they would take onsite training over distance education courses, the 

influences that reflected interpersonal interaction were the most frequently endorsed. Interactions with 

the instructor, interactions with other students, and meeting others were the three most frequently cited 

reasons for taking onsite training at NCTC instead of through distance education.

Conclusion

The published literature on the effect that distance education options have on traditional course 

enrollments is limited. We conducted an exploratory study to characterize the relationship between 

enrollment in distance education and in onsite courses at NCTC. We used two methods in order to 

obtain the most complete understanding of this relationship possible using the available information. 

We analyzed a time-limited dataset which included enrollment in both onsite and distance education 

courses. One of the primary pieces of information missing from the training dataset was a measure of 

individual preference. We conducted a survey of FWS employees to acquire information on preferences 

with respect to training via onsite or distance education options.

Because this is an exploratory study it is difficult to offer firm conclusions, but we believe the 

evidence we assembled and analyzed is sufficient to permit a preliminary characterization of the 

relationship between onsite and distance education enrollment.

A Preliminary Characterization

Perhaps once distance education becomes more established, there may be more competition with 

onsite training. Right now, onsite training seems to be preferred and distance education may provide a 

supplemental source for training that is utilized when practical issues make it necessary. Based on the 

results of these analyses, it appears unlikely that increasing distance education courses will have a 

substantial adverse impact on onsite course enrollments. This is the way the relationship appears now; 

this is not necessarily how it will always be.

Distance education is relatively new in comparison to the onsite training. There are two factors 

that may have an influence on increasing FWS employee preference for distance education. As more 

options for distance education are provided—either more courses or through more varied media—

employees may enroll more frequently in distance education. Second, improvements in distance 

education methods, technology, and interface may result in increased willingness to participate. As they 

gain experience with distance education, employees’ preference for distance education as a training 

option may increase.
29

Over time, distance education may be more competitive with onsite training. At the time the 

survey data was collected (during the year 2010) most employees in the survey cited interpersonal 

interactions as reasons they would rather take training onsite at NCTC than distance education.

However, these interactions can occur in some distance education media. Employees may be most 

familiar with interaction face-to-face and prefer to enroll in courses at NCTC to accomplish that face-toface interaction. In the future, available technology may make interaction easier and as employees 

acquire more experience with using these distance education media they may feel more comfortable 

interacting with others using media. This may also occur as the result of a demographic change, as a 

generation that has never known a world without social media becomes the primary workforce. 

Nevertheless, we cannot assume that the desire for face-to-face interaction will diminish. Onsite training 

may always be preferred. 

Future Study

Monitoring and periodic analysis of enrollment levels in both onsite courses and distance 

education courses will provide a more complete depiction of the complementary versus competitive 

nature of offering training through these two options. The specific type of distance education media 

would be an important characteristic to consider. For example, in the survey on topics for distance 

education (Ratz and others, 2011a) that was completed in conjunction with this study, the most 

preferred distance education option was instructor-led training offsite—at a location other than the 

NCTC campus. Ongoing monitoring of the preferences and training needs of FWS employees will 

enable NCTC to strategically offer onsite and distance education courses.

Acknowledgments

This study was requested and funded by the Division of Education Outreach at the U.S. Fish and 

Wildlife Service National Conservation Training Center. We are grateful for the opportunity to conduct 

this research. We are indebted to the Pat Houghton who helped us obtain the training database that was 

so essential to this project and who patiently answered our many questions. We would like to thank Don 

Tollefson (NCTC) and Dr. Clifford Blizard (Atlanta Nature Examiner, Southeast Institute For PlaceBased Education) for their thoughtful reviews of this report.

References Cited

Allen, I.E., and Seaman, Jeff, 2007, Online nation—Five years of growth in online learning: Needham, 

Mass., The Sloan Consortium, accessed November 9, 2009, at 

Arbaugh, J.B., 2005, Is there an optimal design for on-line MBA courses?: Academy of Management 

Learning and Education, v. 4, no. 2, p. 135–149.

http://php.auburn.edu/outreach/dl/pdfs/online_nation.pdf

Bilder, C.R., and Loughin, T.M., 2004, Testing for marginal independence between two categorical 

variables with multiple responses: Biometrics, v. 60, no. 1, p. 241–248.

Burkell, Jacquelyn, 2003, The dilemma of survey nonresponse: Library & Information Science 

Research, v. 25, no. 3, p. 239–263.

Daley, B.J.; Watkins, Karen; Williams, S.W.; Courtenay, Bradley; Davis, Mike; and Dymock, Darryl, 

2001, Exploring learning in a technology-enhanced environment: Educational Technology and 

Society, v. 4, no. 3, p. 126–138.

DeBourgh, G.A., 2003, Predictors of student satisfaction in distance-delivered graduate nursing 

courses—What matters most?: Journal of Professional Nursing, v. 19, no. 3, p. 149–163.
30

Dillman, D.A., Eltinge, J.L., Groves, R.M., and Little, R.J.A., 2002, Survey nonresponse in design, data 

collection, and analysis, in Groves, R.M., Dillman, D.A., Eltinge, J.L., and Little, R.J.A., eds., Survey 

nonresponse: New York, John Wiley & Sons, Inc., p. 3–26.

Groves, R.M., and Peytcheva, Emilia, 2008, The impact of nonresponse rates on nonresponse bias—A

meta-analysis: Public Opinion Quarterly, v. 72, no. 2, p. 167–189.

Harris, M.L., and Gibson, S.G., 2006, Distance education vs face-to-face classes—Individual 

differences, course preferences and enrollment: Psychological Reports, v. 98, no. 3, p. 756–764.

Hudson, Darren; Seah, L.-H.; Hite, Diane; and Haab, Tim, 2004, Telephone presurveys, self-selection, 

and non-response bias to mail and internet surveys in economic research: Applied Economics Letters, 

v. 11, no. 4, p. 237–240.

Jedlicka, J.S., Brown, S.W., Bunch, A.E., and Jaffe, L.E., 2002, A comparison of distance education 

instructional methods in occupational therapy: Journal of Allied Health, v. 31, no. 4, p. 247–251.

Klein, H.J.; Noe, R.A.; and Wang, Chongwei, 2006, Motivation to learn and course outcomes—The 

impact of delivery mode, learning goal orientation, and perceived barriers and enablers: Personnel 

Psychology, v. 59, p. 665–702.

Moore, D.L., and Tarnai, John, 2002, Evaluating nonresponse error in mail surveys, in Groves, R.M., 

Dillman, D.A., Eltinge, J.L., and Little, R.J.A., eds., Survey nonresponse: New York, John Wiley and 

Sons, Inc., p. 197–211.

Morgan, G.A., Griego, O.V., and Gloeckner, G.W., 2001, SPSS for Windows—An introduction to use 

and interpretation in research: Mahwah, N.J., Lawrence Erlbaum Associates, 214 p.

Murphy, K.R., and Davidshofer, C.O., 1998, Psychological testing—Principles and applications (4th 

ed.): Upper Saddle River, N.J., Prentice Hall, 602 p.

Murphy, K.R., and Myors, Brett, 1998, Statistical power analysis—A simple and general model for 

traditional and modern hypothesis tests: Mahwah, N.J., Lawrence Erlbaum Associates, 120 p.

Olsgaard, J.N., 1987, The impact of a distance education program on enrollment patterns: Journal of 

Education for Library and Information Science, v. 27, no. 4, p. 272–279.

Ratz, J.M., Schuster, R.M., and Marcy, A.H., 2011a, A study of topics for distance education—A

survey of U.S. Fish and Wildlife Service employees: U.S. Geological Survey Open-File Report 2011–

1075, 66 p.

Ratz, J.M., Schuster, R.M., and Marcy, A.H., 2011b, A survey of U.S. Fish and Wildlife Service 

employees regarding topics for distance education—Summary report to respondents: U.S. Geological 

Survey Open-File Report 2011–1021, 38 p.

Ridley, D.R., Bailey, B.L., Davies, E.S., Hash, S.G., and Varner, D.A., 1997, Evaluating the impact of 

on-line course enrollments on FTEs at an urban university, in Annual Forum of the Association for 

Institutional Research, Orlando, Fla., 1997, Proceedings: Association for Institutional Research, 

Tallahassee, Fla., 26 p.

Robinson, R.P., and Doverspike, Dennis, 2006, Factors predicting the choice of an online versus a 

traditional course: Teaching of Psychology, v. 33, no. 1, p. 64–68.

Roblyer, M.D., 1999, Is choice important in distance learning? A study of student motives for taking 

internet-based courses at the high school and community college levels: Journal of Research on 

Computing in Education, v. 32, no. 1, p. 157–171.

Rogelberg, S.G., and Luong, Alexandra, 1998, Nonresponse to mailed surveys—A review and guide: 

Current Directions in Psychological Science, v. 7, no. 2, p. 60–65.

Saade, R.G.; He, Xin; and Kira, Dennis, 2007, Exploring dimensions to online learning: Computers in 

Human Behavior, v. 23, no. 4, p. 1721–1739.
31

Sax, L.J., Gilmartin, S.K., and Bryant, A.N., 2003, Assessing response rates and nonresponse bias in 

web and paper surveys: Research in Higher Education, v. 44, no. 4, p. 409–432.

Thompson, L.F., and Lynch, B.J., 2003, Web-based instruction—Who is inclined to resist it and why?: 

Journal of Educational Computing Research, v. 29, no. 3, p. 375–385.

U.S. Fish and Wildlife Service, 2008, U.S. Fish and Wildlife Service servicewide EEOC MD 715 plans, 

FY 2008: U.S. Fish and Wildlife Service, accessed August 18, 2009, at 

http://www.fws.gov/jobs/pdf/MD_715_FY08_o.pdf.

Wallace, Lori, 1996, Changes in the demographics and motivations of distance education students: 

Journal of Distance Education, v. 11, no. 1, p. 1–31.

Appendix 1—Training Data

Supplemental Statistical Reporting

Detailed results of analyses included in the body of the report are included here. In table 1-1, we 

report the results of the paired t-tests comparing within-region enrollment in distance and NCTC 

courses. The information reported includes the sample size, degrees of freedom, the value of the t-test 

statistic, and the value of test significance as indicated by the p-value.

Table 1-1. Paired t-tests comparing average enrollment in distance and NCTC courses within region.

Region N (Number of cases) Degrees of freedom Value of the t-test 

statistic Significance level (p)

1 276 275 -1.28 .20

2 427 426 -9.78 .00

3 288 287 3.23 .00

4 405 404 -1.70 .09

5 297 296 15.51 .00

6 262 261 3.09 .00

7 263 262 -6.99 .00

8 351 350 -2.41 .02

9 250 249 3.59 .00

Table 2-2 shows the results of a series of repeated-measure ANOVAs to compare the types of 

training (OLT, WEB, NCTC, OFF). A separate analysis was conducted for each region.

Table 1-2. Comparison of types of training within region. [All analyses reported in this table had a p-value of .00.]

Region N (Number of cases) Degrees of freedom F-value Eta-squared

1 276 1.93, 531.21 69.51 .20

2 427 1.38, 586.23 175.03 .29

3 288 1.82, 521.03 90.31 .24

4 405 1.99, 806.25 76.58 .16

5 297 1.41, 417.96 357.06 .55

6 262 1.45, 378.62 117.22 .31

7 263 1.74, 456.24 122.44 .32

8 351 1.69, 592.90 126.30 .27

9 250 1.84, 457.52 100.48 .29
32

Appendix 2—Survey

Survey Questions

Table 2-1. Survey content, questions, and question numbers.

Subscale name

Question 

number Question text

Experience onsite NCTC

Q1

Experience NCTC distance education

How many courses have you taken onsite at NCTC in Shepherdstown, WV?

Q2

Screening

How many courses have you taken from NCTC that were offered via distance education?

Q3

Q4

Considering all of your responsibilities, what percentage of your job involves conservation 

and/or environmental education or outreach programming?

Preference for onsite

In your job, do you supervise anyone (including employees, contractors, and volunteers) 

whose job involves work in conservation and/or environmental education, outreach 

programming, visitor services, or partnerships?

Q31a I prefer onsite classroom training over training by distance education technology.

Preference for distance education

Q31b I would take a training course administered by distance education technology instead of a 

classroom based course.

Checklist for distance education

Please indicate which of the following reasons would influence you to take training via any type of distance 

education instead of onsite at the NCTC:

Q32a Scheduling with commitments in my personal life.

Q32b Scheduling with commitments in my work life.

Q32c Transportation issues.

Q32d Physical disabilities.

Q32e Independent pace of learning.

Q32f Meeting others.

Q32g Interaction with other students.

Q32h Interaction with instructor.

Q32i It’s easier for me to learn this way.

Q32j Cost issues.

Q32k Needing permission from supervisor.

Q32l It’s efficient to learn this way.

Q32m Freedom to complete training when I can fit it in.
33

Subscale name

Question 

number Question text

Q32n Travel time.

Q32o Level of interest in the topic.

Checklist for NCTC

Please indicate which of the following reasons would influence you to take training onsite at NCTC instead 

of through distance education:

Q33a Scheduling with commitments in my personal life.

Q33b Scheduling with commitments in my work life.

Q33c Transportation issues.

Q33d Physical disabilities.

Q33e Independent pace of learning.

Q33f Meeting others.

Q33g Interaction with other students.

Q33h Interaction with instructor.

Q33i It’s easier for me to learn this way.

Q33j Cost issues.

Q33k Needing permission from supervisor.

Q33l It’s efficient to learn this way.

Q33m Freedom to complete training when I can fit it in.

Q33n Travel time.

Q33o Level of interest in the topic.

Behavioral Intention 

Q34 How likely is it that you would choose to take a course from NCTC via distance education 

instead of onsite at the NCTC campus?

Q35 How likely is it that you would choose to take an onsite course at the NCTC campus instead 

of an NCTC course through distance education?

Demographics

Q37 What is your employment status with the Fish and Wildlife Service?

Q38 What is your age (in years)?

Q39 How long have you worked for the U.S. Fish and Wildlife Service?

Q40 How long have you worked at your current duty station?

Q41 How long have you worked in your current position?

Q42 In which region is your duty station?

Q43 What is your WG/GS/GM level?

Q44 Do you subscribe to the VOICES listserv?

Q45 What is the numerical code for your Job Series?
34

Table 2-2. Correlations among questions. [“-“ indicates a negative correlation. Note: Number of cases used in calculating the correlation are in parentheses.]

Onsite 

experience (Q1)

Onsite 

preference 

(Q31a)

Onsite intention 

(Q35)

Distance 

experience (Q2)

Distance 

preference 

(Q31b)

Distance 

intention (Q34)

Onsite 

preference 

(Q31a)

0.10*

(858)

Onsite intention 

(Q35)

0.23*

(859)

0.45*

(853)

Distance 

experience (Q2)

0.01

(911)

-0.05

(858)

-0.08

(859)

Distance 

preference 

(Q31b)

0.00

(856)

-0.30*

(856)

-0.31*

(851)

0.07

(856)

Distance 

intention (Q34)

-0.14*

(860)

-0.39*

(854)

-0.41*

(858)

0.16*

(860)

0.49*

(852)

Screening -

percent of job 

(Q3)

0.04

(911)

-0.01

(858)

0.06

(859)

0.07

(911)

-0.05

(856)

0.01

(860)

* Correlation is significant at the 0.01 level (2-tailed).
35

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2150 Centre Ave., Bldg. C

Fort Collins, CO 80526-8118

(970) 226-9398

Or visit the Fort Collins Science Center Web site at:

http://www.fort.usgs.gov/