Interpretation of Statistics in 2002 Student Network Computing Survey

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Computer Task Average Hrs/wk Std Dev.

Overall Campus Results

The following table indicates the average computing hours per individual for each type of activity as well as the percent of total computing time in that activity. Also included are the total average hours per week spent on computing. Statistics are collected for all students, students living on campus, and students living off campus.

Results of Student Network Computing Survey

Surveys Course MU Personal Total

Returned Specific General Activities Ave. Hrs.

On and Off Campus

Ave Hours/wk 1259 11.99 9.0 22.95 43.9

Percentages 27.30% 20.41% 52.28%
On Campus

Ave Hours/wk 834 11.49 8.6 25.82 45.9

Percentages 25.05% 18.67% 56.28%
Off Campus

Ave Hours/wk 425 12.96 9.7 17.33 40.0

Percentages 32.37% 24.34% 43.29%
Upon inspecting the hours for each individual, it was found that there were outliers that tended to skew the data to the right. For example one student reported spending a total of 135 hours per week on computing. The overall median total number of hours spent on computing was 37; so, when compared with the mean of 43.9, there is definitely skewness to the right. The large values reported by some of the students may be due to double counting across categories.
The greatest percentage of computing time is being spent on personal activities followed by course specific activities, and finally MU General activities. For Course Specific activities, off campus students spent more hours per week (12.96 hours) than on campus students (11.49 hours). Using a one way analysis of variance, this difference was found to be very significant with an observed significance level of p=.01. The same was true with MU-general tasks with off campus students spending an average of 9.7 hours per week where on-campus students spending 8.6 hours per week. This difference was found to be significant at p=.03. On the other hand, on campus students spent an average of 25.82 hours per week on the computer for personal tasks, whereas off-campus students averaged only 17.33 hours. The significance of this difference was extremely high with an observed significant level less than .001.

Undergraduate Students

The following table presents the average hours per week that 1^st year through 4^th year spend in the three types of computer activities:

Course Personal

Specific MU General Activities Overall

Year of Average Average Average Average Number of

Student Hrs/wk Hrs/wk Hrs/wk Hrs/wk Respondents
1st Year 10.51 7.67 28.74 46.93 330

2nd Year 10.67 8.20 23.26 42.13 394

3rd Year 12.93 10.57 19.80 43.30 276

4th Year 14.43 9.57 17.92 41.92 216

To determine if time spent on personal activities significantly declines from 1^st year students through 4^th year students, a linear regression was performed using average hours per week as the dependent variable and the year of the student as the independent variable. The resultant regression prediction equation was

Average hrs/wk on Personal Activities 30.95 – 3.38* year.

The slope of –3.38 was tested to be very significant at a significance level less than .001. The interpretation is that the average computer hours spent on personal activities approximately declines by 3.38 hours for each year that a student stays in school.
Another regression that used average computer hours spent on course specific tasks as the dependent variable was performed with year of student again as the independent variable. The regression prediction equation was:
Average hrs/wk on course specific tasks = 8.66 + 1.36* year.

For each year that a student stays in school, the number of hours spent on course related tasks approximately increases on the average by 1.36. Again the slope of 1.36 was tested to be highly significant at a level less than .001.

Since the above table shows that the average hours per week spent on MU General tasks increase each year from the first to the third year and then appears to decrease from the third to the fourth year, a linear regression was conducted using MU-general tasks as the dependent variable and year of student up through year three as the independent variable. The prediction equation was:

Average hrs/wk on MU-general tasks = 5.925 + 1.417*year

Again the linear trend coefficient (1.417) was extremely significant at a level less than .001. To determine if a significant drop in usage of MU-general tasks occurs from years 3 to 4, A one way analysis of variance was conducted with year as the classification variable. The test resulted in no significant drop in MU-general task usage between years 3 and 4. The high variation in the amount of usage among 3^rd and 4^th year students was largely responsible for this nonsignificance.
3. Comparison between the group of SEAS and IDS students with the group of A&S, EAP, SBA, and SFA Students on

Hours Spent on Personal Tasks

The group consisting of students enrolled in either the SEAS and IDS was compared with the group of A&S,EAP,SBA, and SFA students on the average hours per week of personal usage on computers. The results were amazing. The average hours of the former group was 22.847 hrs/wk and 22.967 hrs/wk for the latter. A one way analysis of variance indicated no significant difference.

3a. Comparison between Student Hours Spent on Course Related Tasks, MU Tasks, and Personal Tasks Among All

Divisions.

The means and standard deviations of hours spent on each of the three classes of tasks for each division is given

below:

----------course--------- -----------muse---------- ---------personal--------

COLLEGE N Mean Std Dev Mean Std Dev Mean Std Dev
Arts and Science 494 10.8922065 8.8170845 9.19979757 9.25817494 22.8227733 22.3397680

SBA 383 12.1956919 8.3881711 8.71527415 8.19782856 22.9932115 19.1081355

EAP 174 11.1321839 7.9104306 9.31350575 8.36576106 23.3477011 23.8883136

SEAS 97 16.6061856 15.7934112 9.42422680 9.23541393 24.5489691 21.8203675

Fine Arts 76 13.7794868 13.1757112 7.35789605 6.67840418 22.8986842 19.6421689

IDS 35 12.7357143 7.0697530 8.80000000 6.42427611 18.1285714 11.2304358

A multiple analysis of variance was conducted to determine if the group of three classes of tasks show significant differences for hours spent among the 6 colleges. A Wilk’s lambda of .962 yielded an observed significance level of less than .0001 indicating that there are very strong differences. One way analysis of variances conducted for each category of tasks indicated that there were significant differences in hours spent on course related tasks among the colleges but insignificant differences for MU related and personal tasks (for personal tasks this is due to the high standard deviations).
A further analysis, using a Tukey multiple comparison procedure that controls for Type I experimentwise error rate checked for significant differences among divisions for hours spent on course related tasks. Results indicated that students in the SEAS division spent significantly more hours per week than students in SBA,EAP, and Arts and Sciences on these tasks. Similar analysis conducted on the other two categories (muse and personal) indicated no significant differences among the colleges. However, an independent one way of analysis of variance did indicate a significant difference between IDS and SEAS on hours spent on personal tasks (observed significant level = .02). This result should be treated with skepticism since this test did not employ the Tukey correction.
4. Individual Computer Tasks

The average hours week spent on each computer task are tabulated below.

Computer Task Average Hrs/wk Std Dev.

Word processing,database,spreadsheets 6.02 5.80

Library usage 2.38 2.73

Publishing web sites, accessing graphics 0.99 2.06

Using Blackboard 1.37 1.85

Accessing a course web site 1.23 2.23

Emailing faculty,fellow students 2.54 3.07

Accessing myMiami 1.78 2.75

Applying for admission,financial assistance 0.17 0.49

Checking registration,loan status,grades,DARS reports 0.87 0.97

Taking CBT courses 0.13 0.66

Performing tasks for student clubs,organizations 0.81 2.16

Registering for training 0.01 0.12

Performing part-time,university-related work 2.19 4.72

Searching for work through financial assistance,CPPO 0.46 1.32

Publishing personal web sites 0.52 1.95

Emailing friends and family 8.75 11.45

Checking news,weather,and sports 2.66 3.17

Online shopping,travel 1.14 1.75

Computer games 1.73 3.64

Downloading music,movies 3.17 6.63

Managing finances 0.94 2.19

Surfing the web 4.04 4.98

Total hrs/wk spent on computer 43.91 28.40

Average hrs/wk over all 22 tasks 2.00 1.29
Note that the high standard deviation for each task indicates large variations between students in the hours per week spent on each task. The two tasks that students spend the most time are e-mail to family and friends (8.75 hrs/wk) and the use of discipline specific software such as word-processing and spreadsheets (6.02 hrs/wk). The total of the two average times is 14.77 hrs/wk which is more than one-third of the total average hours per week usage of 43.91.
The average hours/wk spent per task is 2.00 with 1.29 hrs/wk as the standard deviation. The above two tasks were the only ones that were at least 2 standard deviations above this average hours/wk per task.

5. Comparisons of Time Spent on Each Computer Task for Each Year Student is in School

The average hours per week spent for each computer task is tabulated below for each year of student.

Average Hours/Wk

Question Label 1^st Yr 2^nd Yr 3^rd Yr 4^th Yr P-Value

CS1 Word processing,database,spreadsheets 5.46 5.21 6.42 7.29 <.0001

CS2 Library usage 1.99 2.03 2.49 3.01 <.0001

CS3 Publishing web sites, accessing graphics .626 .878 1.25 1.39 <.0001

CS4 Using Blackboard 1.44 1.37 1.38 1.27 .736

CS5 Accessing a course web site .989 1.18 1.38 1.46 .051

MU1 Emailing faculty,fellow students 2.06 2.49 2.87 2.63 .009

MU2 Accessing myMiami 2.64 1.48 1.71 1.19 <.0001

MU3 Applying for admission,financial assistance .219 .146 .196 .078 .004

MU4 Checking registration,loan status,grades,DARS reports .841 .834 .929 .868 .607

MU5 Taking CBT courses .100 .223 .025 .167 .001

MU6 Performing tasks for student clubs,organizations .464 .796 1.34 .690 <.0001

MU7 Registering for training .015 .013 .008 .005 .730

MU8 Performing part-time,university-related work 1.19 2.02 2.88 2.76 <.0001

MU9 Searching for work through financial assistance,CPPO .133 .197 .614 1.19 <.0001

PA1 Publishing personal web sites .376 .513 .670 .515 .315

PA2 Emailing friends and family 12.2 9.35 6.77 5.26 <.0001

PA3 Checking news,weather,and sports 2.43 2.64 2.72 2.88 .399

PA4 Online shopping,travel 1.11 1.12 1.17 1.23 .858

PA5 Computer games 2.50 1.70 1.35 1.20 <.0001

PA6 Downloading music,movies 5.05 3.07 2.34 1.72 <.0001

PA7 Managing finances .917 .846 .838 1.28 .095

PA8 Surfing the web 4.15 4.03 3.94 3.84 .898
A multiple analysis of variance (MANOVA) indicated very significant differences on hours spent per week on this group of 22 tasks for each class of student. A Wilks’ lambda of .701 yielded an observed significance level less than .0001. Individual ANOVAs were done for each of the 22 questions with the classification variable being the year in school. The P-Value (also referred as observed significance level) for these ANOVAs are presented in the last column of the above table. Individual computer tasks that showed significance differences in hours per week for each class of students will have P-Values less than .05. These are the tasks represented by CS1, CS2, CS3, MU1, MU2, MU3, MU5, MU6, MU8, MU9, PA2, PA5, and PA6.
Looking at each task in detail, the significant differences (at a significance level of .05 or less) in hours per week spent in task CS1 were between 1^st and 4^th year students and 2^nd and 4^th year students. The same was true with the task described in CS2. The major significant differences in the task described by CS3 were between 1^st year and 3^rd year, 1^st year and 4^th year, and 2^nd and 4^th year.
Concentrating on MU related tasks, the major difference in hours/wk for MU1 were between 1^st and 3^rd year students.

MU2 task had the largest hours/wk for 1^st year students and this was significantly than the time for 2^nd , 3^rd , and 4^th year students. For the task described by MU3 , the major difference was between 1^st and 4^th year students. For MU5 more time was spent by 2^nd year students on CBT courses, and this was significant than the time spent by 4^th years. 3^rd year students

spent more time with MU6 task and this was significantly higher than any of the other classes. Both 3^rd and 4^th year students spent more time using computers for part-time work (MU8) and these were significantly larger than the time spent by 1^st years. With the exception between 1^st and 2^nd year students, the MU9 task had significant differences in hours per week between each possible pairwise combination of classes.
For personal tasks, PA2 had the most significant comparisons between each pair of classes. The only pair for which the difference in hours/week failed to be significant was between the 3^rd and 4^th year students. 1^st year spent more hours per week on computer games (PA5), and this was significantly higher than the other three classes. The same was true for the task in PA6 with 1^st year students spending significantly more time than each of the three other classes in downloading music and movies.
6. Comparisons of Time Spent on Each Computer Task for On Campus and Off Campus Students
The table below shows the average hours per week spent by on-campus and off-campus students for each individual computer task. A MANOVA indicated overall significant differences between on and off campus students on hours per week spent on the group of 22 tasks. A Wilks’ lambda of .875 yielded an observed significance level that was less than .0001. Tasks that showed significant differences between the two groups have p-values (observed significant levels) less

than .05.

Average Hours per Week

Question Label Off Campus On Campus P-value

CS1 Word processing,database,spreadsheets 6.51 5.76 .030

CS2 Library usage 2.75 2.20 .0007

CS3 Publishing web sites, accessing graphics 1.09 .943 .222

CS4 Using Blackboard 1.28 1.42 .220

CS5 Accessing a course web site 1.33 1.17 .252

MU1 Emailing faculty,fellow students 2.75 2.44 .085

MU2 Accessing myMiami 1.45 1.94 .003

MU3 Applying for admission,financial assistance .118 .192 .012

MU4 Checking registration,loan status,grades,DARS reports .916 .852 .265

MU5 Taking CBT courses .101 .146 .249

MU6 Performing tasks for student clubs,organizations .980 .716 .041

MU7 Registering for training .015 .008 .370

MU8 Performing part-time,university-related work 2.56 2.00 .049

MU9 Searching for work through financial assistance,CPPO .851 .268 <.0001

PA1 Publishing personal web sites .494 .541 .685

PA2 Emailing friends and family 5.67 10.32 <.0001

PA3 Checking news,weather,and sports 2.61 2.68 .708

PA4 Online shopping,travel 1.22 1.11 .260

PA5 Computer games 1.04 2.09 <.0001

PA6 Downloading music,movies 1.84 3.84 <.0001

PA7 Managing finances .969 .930 .765

PA8 Surfing the web 3.49 4.32 .005

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