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CHAPTER 4
RESULTS
Introduction
This study investigated the computer literacy and computer application skills of
undergraduate college students. The study consisted of two phases: a faculty survey and a
student assessment.
One phase of the study consisted of a Web-based faculty survey administered to a
stratified random sample of faculty members from four-year public institutions in the
state of Missouri. The purpose of this faculty survey was to identify basic computer skills
needed by undergraduate students to be academically successful in post-secondary
education. The study also examined the data collected for trends and differences between
independent variables of subject/content area, institution, gender, and years of faculty
experience.
The other phase of the study consisted of a series of student assessments
administered to a group of undergraduate students enrolled in a computer literacy course.
The assessments were administered prior to any instruction of the specific topic being
tested. The purpose of these assessments was to evaluate the computer competencies of
students entering post-secondary education. The study also examined the data collected
for trends and differences between independent variables such as home state, number of
high school computer courses taken, gender, and major field of study.
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Population and Sample
Faculty Survey
The population eligible for inclusion in this portion of the study consisted of
4,223 faculty members from 13 four-year public institutions in the state of Missouri as
listed in the Official Manual State of Missouri 2003-2004 (Missouri Secretary of State,
2004). A table of recommended sample sizes (n) for population (N) with finite sizes,
developed by Krejcie and Morgan and adapted by Patten (2004), was used to determine
sample size. According to the table, and for purposes of this study, a finite population
size N = 4223 revealed a sample size n = 357 as the goal for this study.
The population was divided into strata (subgroups) according to institution. This
stratified random sample ensured that subgroups were represented in the correct
proportions. According to Patten (2004), the same percentage of participants, not the
same number of participants, were drawn from each stratum. Faculty members from each
stratum (institution) were randomly selected through the use of the randomize function in
Microsoft Excel.
A total of 1,416 emails, with the survey link and password, were sent to the
stratified, randomly selected faculty members and 426 survey responses were received.
This represented a 30% response rate. One response was rejected because the participant
selected a ‘no’ response to the question agreeing to participate in the study and 20 were
rejected because of incomplete answers to survey items. This provided 405 usable
surveys for the study resulting in a 29% usable survey return rate. Table 7 illustrates the
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proportionate stratified random sampling results. Strata have been numerically coded to
protect the identity of the institutions.
Table 7 Proportionate Stratified Random Sample Results from a Population Divided into 13
Institution Strata Stratum # of faculty
(population) Proportion
of population
Stratum sample size
# of faculty contacted
Survey responses
Usable survey
responses
1 380 .09 32 127 47 44 2 42 .01 4 14 0 0 3 122 .03 10 41 14 14 4 198 .05 17 66 20 19 5 153 .04 13 51 17 15 6 200 .05 17 67 36 36 7 335 .08 28 112 41 40 8 564 .13 47 189 50 47 9 314 .07 26 105 29 27
10 1055 .25 88 354 84 80 11 378 .09 32 127 40 38 12 237 .06 20 79 23 21 13 245 .06 21 82 25 24
Total 4223 1.00 354 1416 426 405
Student Assessment
The population eligible for inclusion in the student assessment portion of the
study consisted of college freshmen from a small mid-western university enrolled in a
computer literacy course. Permission was requested, through student consent forms, to
use their assessment scores as part of the study. Signed consent forms were collected
from 259 students. Ten participants were rejected because of missing demographic data,
and 85 were rejected because of missing assessment scores. This resulted in a total of 164
students with demographic data and all assessment components complete. According to a
table of recommended sample sizes (n) for population (N), developed by Krejcie and
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Morgan and adapted by Patten (2004), a population size N = 259 and thus a sample size n
= 155 was the goal for this study. Thus, 164 participants met the sample size goal.
Statistical Analysis – Faculty Survey
Demographic Descriptive Statistics
The results of the faculty survey’s initial question on the importance of computer
literacy/skills in relation to student academic success at the post-secondary level
indicated a strong importance of computer literacy/skills. As indicated in Table 8, two
hundred and fifty-nine (64%) of the respondents indicated computer literacy/skills were
very important and 128 (31.6%) respondents indicated computer literacy/skills were
important. Seventeen respondents (4.2%) indicated somewhat important and one
respondent (0.2%) indicated not important.
Table 8 Importance of Computer Literacy/Skills in Relation to Student Academic Success
Scale f %
Not important 1 .2
Somewhat important 17 4.2
Important 128 31.6
Very important 259 64.0
Total 405 100.0
Of the 405 faculty survey respondents, 26 (6.4%) were Instructors, 111 (27.4%)
were Assistant Professors, 124 (30.6%) were Associate Professors, 138 (34.1%) were
Full Professors, and six (1.5%) were identified as other including Department Chairs,
Emeritus faculty, Co-directors of departments, etc.
Table 9 indicates the frequency and percent of faculty survey respondents in
various departments or content areas. The “Other” category included areas such as
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Library Science, Law, various medical areas, Engineering, Plant Microbiology and
Pathology, Industrial Technology, Criminology, Kinesiology, Ecology, Safety Sciences,
Social Work, Veterinary Medicine, Communication Science and Disorders,
Forestry/Atmospheric Science, Biomedical Sciences, Nursing, Electrical and Computer
Engineering, Anthropology, Optometry, Environmental Engineering, etc.
Table 9 Demographic Breakdown of Department or Content Area
f %
Accounting/Econ/Finance 25 6.2
Agriculture 15 3.7
Art 11 2.7
Biological Sciences 14 3.5
Business Administration 14 3.5
Chemistry/Physics/Science Education 26 6.4
Communication/Theatre/Languages 11 2.7
Computer Science/Information Systems 11 2.7
Education 37 9.1
English 18 4.4
Family & Consumer Sciences 7 1.7
Geology/Geography 7 1.7
Health/PE/Recreation/Dance 14 3.5
History/Humanities/Philosophy/Political Science 26 6.4
Marketing/Management 10 2.5
Mass Communications/Broadcasting/Digital Media/Journalism 4 1.0
Math/Statistics 17 4.2
Modern Languages 2 .5
Music 15 3.7
Psychology/Sociology/Counseling 25 6.2
Other 96 23.7
Total 405 100.0
Of the 405 faculty respondents, 158 (39%) were female and 247 (61%) were
male. Seventy-nine (19.5%) have been employed for five years or less at their current
institution, 129 (31.9%) have been employed from 5-10 years, 109 (26.9%) have been
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employed from 11-20 years, 67 (16.5%) have been employed 21 – 30 years, and 21
(5.2%) have been employed for over 30 years at their current institution.
In regards to the number of years in education, of the 405 respondents, 25 (6.2%)
have been in education less than five years, 90 (22.2%) have been in education from 5-10
years, 115 (28.4%) have been in education from 11-20 years, 96 (23.7%) have been in
education from 21-30 years, and 79 (19.5%) have been in education for over 30 years.
Reliability
The survey consisted of five major sections; computer concepts, word processing,
spreadsheet, presentation, and database items. Each of these sections (subscales)
consisted of eight individual survey items.
Cronk (2004) indicates one way to test reliability is to assess internal consistency
of the data by conducting an item-total analysis. The Spearman rho correlation was used
to conduct this analysis because the data were nominal. According to Cronk (2004), item-
total correlations should be positive and greater than 0.3 to indicate internal consistency.
Tables 10 -14 illustrate that computer concept items, word processing items, spreadsheet
items, presentation items, and database items were found to be internally consistent
within each subscale as none of the Spearman rho correlation values fell below 0.5. Table
15 indicates that all subscales within the survey were also found to be internally
consistent with rho values in the 0.7 to 0.8 range.
Table 10 Spearman rho Correlations for Computer Concepts Items
Computer Concept Variables n rho p
Computer and information literacy, introduction to application software, word processing concepts, and inside the computer
405 0.555 0.000
Internet, email, system software, and exploring the Web 405 0.632 0.000
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Computer Concept Variables n rho p
Current issues, emerging technologies, spreadsheet concepts, and data storage
405 0.754 0.000
Presentation packages, special purpose programs, multimedia/virtual reality, and input/output
405 0.755 0.000
Database concepts, telecommunications, and networks 405 0.726 0.000 Ethics and security 405 0.626 0.000 Web page creation 405 0.727 0.000 Locating and evaluating information on the Internet, effectively using search engines, determining the credibility of information
405 0.561 0.000
Table 11 Spearman rho Correlations for Word Processing Items
Word Processing Variables n rho p-value
Getting Started 405 0.586 0.000 Insert and Modify Text 405 0.716 0.000 Create and Modify Paragraphs 405 0.761 0.000 Format Documents 405 0.747 0.000 Manage Documents 405 0.723 0.000 Working with Graphics 405 0.804 0.000 Workgroup Collaboration 405 0.686 0.000 Creating and Modifying Graphics 405 0.764 0.000
Table 12 Spearman rho Correlations for Spreadsheet Items
Spreadsheet Variables n rho p-value
Working with Cells and Cell Data 405 0.885 0.000 Managing Workbooks 405 0.889 0.000 Formatting and Printing Workbooks 405 0.886 0.000 Modifying Workgroups 405 0.873 0.000 Creating and Revising Formulas 405 0.859 0.000 Creating and Modifying Graphics 405 0.821 0.000 Workgroup Collaboration 405 0.781 0.000 Integrating a spreadsheet with other software 405 0.819 0.000
Table 13 Spearman rho Correlations for Presentation Items
Presentation Variables n rho p-value
Creating Presentations 405 0.860 0.000 Inserting and Modifying Text 405 0.852 0.000 Inserting and Modifying Visual Elements 405 0.889 0.000 Modifying Presentation Formats 405 0.870 0.000 Printing Presentations 405 0.785 0.000 Working with Data from Other Sources 405 0.836 0.000 Managing and Delivering Presentations 405 0.828 0.000 Workgroup Collaboration 405 0.794 0.000
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Table 14 Spearman rho Correlations for Database Items
Database Variables n rho p-value
Getting Started 405 0.895 0.000 Creating and Using Databases 405 0.931 0.000 Creating and Modifying Tables 405 0.918 0.000 Creating and Modifying Queries 405 0.877 0.000 Creating and Modifying Forms 405 0.869 0.000 Viewing and Organizing Information 405 0.926 0.000 Producing Reports 405 0.871 0.000 Integrating with Other Applications 405 0.909 0.000
Table 15 Spearman rho Correlations between Subscales
Subscale Variables n rho p-value
Computer Concepts 405 0.808 0.000 Word Processing 405 0.732 0.000 Spreadsheet 405 0.819 0.000 Presentation 405 0.835 0.000 Database 405 0.803 0.000
Cronbach’s alpha was also performed as another reliability measure to test for
internal consistency. Cronk (2004) notes that Cronbach’s alpha uses a scale to measure a
single construct and then determines the extent to which all items are measuring the same
construct. As noted by Cronk (2004), a reliability coefficient close to 1.00 indicates good
internal consistency and a coefficient close to 0.00 indicates poor internal consistency.
Cronbach’s alpha was used to determine the internal reliability of the survey instrument.
The instrument was tested in its entirety, and the five individual sections of the survey
were tested independently. The Cronbach’s alpha reliability coefficients for the
individual sections (subscales) of the survey ranged from a low of 0.8338 to a high of
0.9675 with all having p = 0.000. These results demonstrated a high level of internal
reliability. The survey as a whole had a reliability coefficient of 0.8620 which also
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demonstrated a high degree of internal consistency. Table 16 summarizes the reliability
coefficients of the survey instrument.
Table 16 Cronbach’s Alpha Reliability Coefficients
Subscale Variables n alpha p
Computer Concepts 405 0.8338 .000 Word Processing 405 0.8864 .000 Spreadsheet 405 0.9484 .000 Presentation 405 0.9453 .000 Database 405 0.9675 .000 All subscales combined 405 0.8620 .000
Descriptive Statistics
Each of the sections (subscales) of the survey consisted of eight individual survey
items. In the computer concepts section, topics were grouped according to the computer
literacy course at the researcher’s institution. Item 1 included the topics of computer and
information literacy, introduction to application software, word processing, and inside the
computer. Item 2 included the topics of understanding the Internet, email, system
software, and exploring the Web. Item 3 included the topics of spreadsheets, current
issues, emerging technologies, and data storage. Item 4 included the topics of
presentation packages, special purpose programs, multimedia/virtual reality, and
input/output. Item 5 included the topics of databases, telecommunications, and networks.
Item 6 included the topics of ethics and security, Item 7 included Web page creation, and
Item 8 included locating and evaluating information on the Internet.
Table 17 displays the frequency and percentage results of the 405 faculty
respondents in regards to the importance of various computer topics. Of the eight survey
items in the computer concepts section, four were considered very important: Item 1
topics (60.2%) including computer and information literacy, introduction to application
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software, word processing, and inside the computer; Item 2 topics (67.4%) including
understanding the Internet, email, system software, and exploring the Web; Item 6 topics
(42.2%) including ethics and security; and Item 8 topics (68.4%) including locating and
evaluating information on the Internet. Computer concept topics considered important
were: Item 3 topics (36.8%) including spreadsheets, current issues, emerging
technologies, and data storage; and Item 4 topics (39%) including presentation packages,
special purpose programs, multimedia/virtual reality, and input/output. Item 5 topics
(38.5%) including databases, telecommunications, and networks and Item 7 topics
(39.8%) including Web page creation were considered somewhat important.
Table 17 Frequencies and Percentages for Computer Concepts (n = 405)
(NI – Not important, SI = Somewhat important, I = Important, VI = Very important)
Item NI SI I VI
Computer/information literacy, introduction to application software, word processing, and inside the computer
5 1.2%
24 5.9%
132 32.6%
244
60.2%
Understanding the Internet, email, system software, and exploring the Web
3 0.7%
35 8.6%
94 23.2%
273
67.4%
Spreadsheets, current issues, emerging technologies, and data storage
41 10.1%
108 26.7%
149
36.8%
107 26.4%
Presentations, special purpose programs, multimedia/virtual reality, input/output
39 9.6%
97 24.0%
158
39.0%
111 27.4%
Databases, telecommunications, and networks
55 13.6%
156
38.5%
120 29.6%
74 18.3%
Ethics and security 15 3.7%
67 16.5%
152 37.5%
171
42.2%
Web page creation 114 28.1%
161
39.8%
84 20.7%
46 11.4%
Locating and evaluating information on the Internet
5 1.2%
26 6.4%
97 24.0%
277
68.4%
In addition to the computer concepts component of the faculty survey, it also
included items related to four computer application areas; word processing, spreadsheet,
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presentation, and database skills. Each application consisted of skills grouped together
into eight survey items. The faculty survey items along with the skills associated with
each item can be found in Appendix I. Results of the computer application skills
component are reported in Tables 18-21.
In regards to the word processing section, five of the eight items resulting in very
important were opening/closing a document and using help (80.5%), inserting and
modifying text (72.6), creating and modifying paragraphs (64.2%), formatting documents
(58.0%), and managing documents (64.2%). Two of the eight items were considered
important including working with graphics (36.3%) and creating and modifying graphics
(35.1%). Workgroup collaboration resulted in somewhat important (37.8%).
Table 18 Frequencies and Percentages for Word Processing Skills (n = 405)
(NI – Not important, SI = Somewhat important, I = Important, VI = Very important)
Item NI SI I VI
Getting started 3 0.7%
15 3.7%
61 15.1%
326
80.5%
Insert and modify text 6 1.5%
22 5.4%
83 20.5%
294
72.6%
Create and modify paragraphs 8 2.0%
37 9.1%
100 24.7%
260
64.2%
Format documents 9 2.2%
43 10.6%
118 29.1%
235
58.0%
Manage documents 5 1.2%
33 8.1%
107 26.4%
260
64.2%
Working with graphics 31 7.7%
112 27.7%
147
36.3%
115 28.4%
Workgroup collaboration 40 9.9%
153
37.8%
146 36.0%
66 16.3%
Creating and modifying graphics
39 9.6%
138 34.1%
142
35.1%
86 21.2%
Although many of the items in the spreadsheet section were very close, the
highest percentages indicated three of the eight items were important, while the other five
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items were considered somewhat important. The three areas resulting in important were
working with cells and cell data (29.1%) managing workbooks (29.4%), and creating and
modifying graphics (33.3%). The other five areas of formatting and printing workbooks
(37.0%), modifying workgroups (42.0%), creating and revising formulas (30.1%),
workgroup collaboration (43.2%), and integrating with other software (40.7%) were
considered somewhat important.
Table 19 Frequencies and Percentages for Spreadsheet Skills (n = 405)
(NI – Not important, SI = Somewhat important, I = Important, VI = Very important)
Item NI SI I VI
Working with cells and cell data 60 14.8%
116 28.6%
118
29.1%
111 27.4%
Managing workbooks 69 17.0%
108 26.7%
119
29.4%
109 26.9%
Formatting and printing workbooks
79 19.5%
150
37.0%
98 24.2%
78 19.3%
Modifying workgroups 88 21.7%
170
42.0%
102 25.2%
45 11.1%
Creating and revising formulas 96 23.7%
122
30.1%
95 23.5%
92 22.7%
Creating and modifying graphics
65 16.0%
125 30.9%
135
33.3%
80 19.8%
Workgroup collaboration 106 26.2%
175
43.2%
95 23.5%
29 7.2%
Integrating with other software 98 24.2%
165
40.7%
104 25.7%
38 9.4%
Presentation skills results show two areas at very important including creating
presentations (44.0%) and inserting and modifying text (41.5%). Four areas were
considered important including inserting and modifying visual elements (37.0%),
modifying presentation formats (32.1%), printing presentations (33.8%), managing and
delivering presentations (34.8%). Spreadsheet skills involving working with data from
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other sources resulted in a tie between somewhat important and important (35.1%).
Workgroup collaboration skills resulted in somewhat important (38.0%).
Table 20 Frequencies and Percentages for Presentation Skills (n = 405)
(NI – Not important, SI = Somewhat important, I = Important, VI = Very important)
Item NI SI I VI
Creating presentations 26 6.4%
67 16.5%
134 33.1%
178
44.0%
Inserting and modifying text
22 5.4%
68 16.8%
147 36.3%
168
41.5%
Inserting and modifying visual elements
30 7.4%
92 22.7%
150
37.0%
133 32.8%
Modifying formats 49 12.1%
122 30.1%
130
32.1%
104 25.7%
Printing presentations 33 8.1%
104 25.7%
137
33.8%
131 32.3%
Working with data from other sources
48 11.9%
142
35.1%
142
35.1%
73 18.0%
Managing and delivering presentations
32 7.9%
94 23.2%
141
34.8%
138 34.1%
Workgroup collaboration 77 19.0%
154
38.0%
111 27.4%
63 15.6%
In regards to the database results, all items resulted in somewhat important.
Table 21 Frequencies and Percentages for Database Skills (n = 405)
(NI – Not important, SI = Somewhat important, I = important, VI = Very important)
Item NI SI I VI
Getting Started 76 18.8%
122
30.1%
121 29.9%
86 21.2%
Creating and Using Databases 84 20.7%
132
32.6%
110 27.2%
79 19.5%
Creating and Modifying Tables 85 21.0%
150
37.0%
112 27.7%
58 14.3%
Creating and Modifying Queries 113 27.9%
154
38.0%
95 23.5%
43 10.6%
Creating and Modifying Forms 122 30.1%
154
38.0%
95 23.5%
34 8.4%
Viewing and Organizing Information
91 22.5%
153
37.8%
96 23.7%
65 16.0%
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Item NI SI I VI
Producing Reports 80 19.8%
123
30.4%
114 28.1%
88 21.7%
Integrating with other Applications 103 25.4%
157
38.8%
96 23.7%
49 12.1%
Of the faculty members surveyed, 185 (45.7%) respondents indicated a computer
literacy/skills course was required of all majors within their department, while 220
(54.3%) indicated a computer literacy/skills course was not required of majors within
their department.
As summarized in Table 22, the survey results indicated that 345 (85.1%)
respondents strongly agree (48.1%) or agree (37.0%) that a computer literacy/skills
course or equivalent test out should be required of all undergraduate students.
Table 22 Frequencies and Percentages for Computer Course or Equivalent Testout Required for
All Undergraduate Students
Strongly Disagree
Disagree
Agree
Strongly Agree
Require course or equivalent test out 8 2.0%
52 12.8%
150 37.0%
195 48.1%
It is customary to report measures of central tendency and measures of dispersion
when describing data. The mean is the most powerful measure of central tendency, while
the standard deviation is the most powerful measure of dispersion (Cronk, 2004). Since
the individual items, within each section of the survey instrument, have been proven to be
internally consistent, a composite score for each section (subscale) will be used
throughout the rest of this report rather than individual items. Each individual item within
a section had a minimum score of one (not important) to a maximum score of four (very
important). The composite score was calculated by adding the scores for all eight
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individual items within each subscale. The composite score had a minimum score of eight
and a maximum score of 32. Table 23 summarizes the descriptive data for each subscale
(dependent variable).
Table 23 Descriptive Statistics for Dependent Variables (n = 405)
Dependent Variable Mean sd
Computer concepts 24.1852 4.56610
Word processing skills 25.9926 4.63387
Spreadsheet skills 19.3802 6.76542
Presentation skills 22.7926 6.36446
Database skills 18.6889 7.14018
Trends and Relationship Comparisons
Analysis of variance (ANOVA) is a procedure for evaluating the mean
differences between two or more groups of subjects that vary on a single independent
variable (Gravetter & Wallnau, 2004; Cronk, 2004). A one-way ANOVA was used to
compare the mean of computer concepts (dependent variable) with the department or
content area of the faculty member (independent variable). As illustrated in Table 24, a
significant difference was found among departments (F (20, 384) = 3.39, p = .000).
Tukey’s HSD was used to determine the nature of the differences between the
departments as shown in Table 25. For the sake of brevity, only significant differences
are shown in the table. This analysis revealed that a department of History/Humanities/
Philosophy/Political Science had a significantly lower rating (m = 21.12, sd = 3.76) on
computer concepts than the Computer Science/Information Systems Department (m =
27.45, sd = 3.59). The Education Department (m = 27.59, sd = 3.24) had significantly
higher ratings on computer concepts than several other departments including
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Chemistry/Physics/Science Education (m = 21.96, sd = 5.11), English (m = 22.72, sd =
4.73), History/Humanities/Philosophy/ Political Science (m = 21.11, sd = 3.76),
Mathematics/Statistics (m = 22.41, sd = 4.84), Psychology/Sociology/Counseling (m =
22.76, sd = 4.18), and the ‘Other’ category (m = 24.34, sd = 4.50). All other departments
showed no significant difference in the importance of computer concepts.
Table 24 One-way ANOVA comparing Computer Concepts by Department
SS df MS F p
Between Groups 1265.170 20 63.259 3.394 .000
Within Groups 7157.941 384 18.640
Total 8423.111 404
Table 25 Tukey’s HSD comparing Computer Concepts by Department
Department Department Mean
Difference Std. Error Sig.
CS/IS History/Humanities/ Philosophy/ Political Science
6.3392(*) 1.55291 .009
Education Chemistry/Physics/ Science Education
5.6331(*) 1.10487 .000
English 4.8724(*) 1.24072 .016
History/Humanities/ Philosophy/Political Science
6.4792(*) 1.10487 .000
Math/Statistics 5.1828(*) 1.26503 .009
Psychology/Sociology/ Counseling
4.8346(*) 1.11777 .003
Other 3.2508(*) 0.83544 .018 * The mean difference is significant at the .05 level.
A one-way ANOVA was used to compare the mean of word processing skills
(dependent variable) with the department or content area (independent variable). As
illustrated in Table 26, a significant difference was found among departments (F (20,
384) = 2.87, p = .000). Tukey’s HSD was used to determine the nature of the differences
between the departments as shown in Table 27. This analysis revealed the Education
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Department (m = 28.97, sd = 3.01) had higher ratings for word processing skills than
other departments including History/Humanities/Philosophy/Political Science (m = 23.38,
sd = 3.67), Mathematics/Statistics (m = 22.47, sd = 6.28), Psychology/Sociology/
Counseling (m = 24.28, sd = 4.40), and the ‘Other’ category (m = 25.64, sd = 5.11). The
other departments showed no significant difference in the importance of word processing
skills.
Table 26 One-way ANOVA comparing Word Processing Skills by Department
SS df MS F p
Between Groups 1128.478 20 56.424 2.871 .000
Within Groups 7546.500 384 19.652
Total 8674.978 404
Table 27 Tukey’s HSD comparing Word Processing Skills by Department
Department Department Mean
Difference Std. Error Sig.
Education History/Humanities/ Philosophy/Political Sci
5.5884(*) 1.13446 .000
Math/Statistics 6.5024(*) 1.29891 .000
Psychology/Sociology/ Counseling
4.6930(*) 1.14771 .009
other 3.3376(*) 0.85782 .018 * The mean difference is significant at the .05 level.
A one-way ANOVA was used to compare the mean of spreadsheet skills
(dependent variable) with the department or content area (independent variable). As
illustrated in Table 28, a significant difference was found among departments (F (20,
384) = 6.24, p = .000). Tukey’s HSD was used to determine the nature of the differences
between the departments as shown in Table 29. This analysis revealed that Accounting/
Economics/Finance (m = 25.04, sd = 6.10) had higher ratings for spreadsheet skills than
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other departments including Art (m = 13.64, sd = 5.68), Communications/Theatre/
Languages (m = 15.73, sd = 3.20), English (m = 14.89, sd = 4.63), History/Humanities/
Philosophy/ Political Science (m = 13.62, sd = 5.19), Music (m = 15.33, sd = 5.16),
Psychology/ Sociology/Counseling (m = 17.00, sd = 4.88), and the ‘Other’ category (m =
19.06, sd = 7.30). Agriculture (m = 24.13, sd = 3.85) had higher spreadsheet ratings than
Art, English, History/Humanities/Philosophy/ Political Science, Music, and
Psychology/Sociology/ Counseling. Business Administration (m = 24.07, sd = 4.50) had
higher spreadsheet ratings than Art, English, History/Humanities/Philosophy/Political
Science, and Music. Chemistry/ Physics/Science Education (m = 22.54, sd = 6.22) had
higher spreadsheet ratings than Art, English, History/ Humanities/Philosophy/Political
Science, and Music. Computer Science/Information Systems Department (m = 25.64, sd
= 5.85) had higher spreadsheet ratings than Art, Communication/Theatre/Languages,
English, History/Humanities/ Philosophy/Political Science, Music, and
Psychology/Sociology/Counseling. The Education Department (m = 20.95, sd = 5.86)
and the ‘Other’ category had a higher spreadsheet rating than History/Humanities/
Philosophy/Political Science departments.
Table 28 One-way ANOVA comparing Spreadsheet Skills by Department
SS df MS F p
Between Groups 4536.719 20 226.836 6.242 .000
Within Groups 13954.723 384 36.340
Total 18491.442 404
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Table 29 Tukey’s HSD comparing Spreadsheet Skills by Department
Department Department Mean
Difference Std. Error Sig.
Accounting Art 11.4036(*) 2.18112 .000
Comm/Theatre/Languages 9.3127(*) 2.18112 .004
English 10.1511(*) 1.86347 .000
History/Humanities/ Philosophy/Political Sci
11.4246(*) 1.68859 .000
Music 9.7067(*) 1.96884 .000
Psyc/Soc/Counseling 8.0400(*) 1.70506 .001
Other 5.9775(*) 1.35357 .002
Agriculture Art 10.4970(*) 2.39298 .003
English 9.2444(*) 2.10751 .003
History/Humanities/ Philosophy/Political Sci
10.519(*) 1.95459 .000
Music 8.8000(*) 2.20122 .012
Psyc/Soc/Counseling 7.1333(*) 1.96884 .046
Business Admin Art 10.4351(*) 2.42887 .004
English 9.1825(*) 2.14818 .004
History/Humanities/ Philosophy/Political Sci
10.4560(*) 1.99836 .000
Music 8.7381(*) 2.24019 .018
Chem/Physics/Sci Ed Art 8.9021(*) 2.16827 .008
English 7.6496(*) 1.84841 .007
History/Humanities/ Philosophy/Political Sci
8.9231(*) 1.67195 .000
Music 7.2051(*) 1.95459 .037
CS/IS Art 12.0000(*) 2.57048 .001
Comm/Theatre/Lang 9.9091(*) 2.57048 .021
English 10.7475(*) 2.30707 .001
History/Humanities/ Philosophy/Political Sci
12.0210(*) 2.16827 .000
Music 10.3030(*) 2.39298 .004
Psyc/Soc/Counseling 8.6364(*) 2.18112 .014
Education History/Humanities/ Philosophy/Political Sci
7.3306(*) 1.54269 .001
Other History/Humanities/ Philosophy/Political Sci
5.4471(*) 1.33276 .009
* The mean difference is significant at the .05 level.
73
A one-way ANOVA was used to compare the mean of presentation software
skills (dependent variable) with the department or content area (independent variable). As
illustrated in Table 30, a significant difference was found among departments (F (20,
384) = 5.16, p = .000). Tukey’s HSD was used to determine the nature of the differences
between the departments as shown in Table 31. This analysis resulted in the Agriculture
Department (m = 25.67, sd = 3.50) having higher presentation skill ratings than History/
Humanities/Philosophy/Political Science (m = 18.27, sd = 5.77) and Music (m = 16.93, sd
= 6.11). Business Administration (m = 28.00, sd = 2.80) had higher ratings than English
(m = 18.67, sd = 5.36), History/Humanities/Philosophy/Political Science, Math (m =
19.24, sd = 7.81), Music, and Psychology/Sociology/Counseling (m = 20.80, sd = 6.07).
The Computer Science/Information Systems Department (m = 25.55, sd = 5.65) had
higher presentation ratings than Music. The Education Department (m = 27.54, sd = 3.59)
had higher presentation ratings than Chemistry/Physics/Science Education (m = 21.69, sd
= 6.10), History/Humanities/Philosophy/Political Science, English, Math/Statistics,
Music, Psychology/Sociology/Counseling, and the ‘Other’ category (m = 23.35, sd =
6.17). The ‘Other’ category had higher presentation skills ratings than
History/Humanities/ Philosophy/Political Science and Music.
Table 30 One-way ANOVA comparing Presentation Skills by Department
SS df MS F p
Between Groups 3463.948 20 173.197 5.155 .000
Within Groups 12900.630 384 33.595
Total 16364.578 404
74
Table 31 Tukey’s HSD comparing Presentation Skills by Department
Department Department Mean
Difference Std. Error Sig.
Agriculture History/Humanities/ Philosophy/Political Sci
7.3974(*) 1.87931 .016
Music 8.7333(*) 2.11646 .008
Business Admin English 9.33333(*) 2.06545 .002
History/Humanities/ Philosophy/Political Sci
9.7308(*) 1.92141 .000
Math/Statistics 8.7647(*) 2.09186 .006
Music 11.0667(*) 2.15392 .000
Psyc/Soc/Counseling 7.2000(*) 1.93481 .033
CS/IS Music 8.6121(*) 2.30083 .031
Education Chemistry/Physics/Sci Ed 5.8482(*) 1.48328 .015
English 8.8739(*) 1.66565 .000
History/Humanities/ Philosophy/Political Sci
9.2713(*) 1.48328 .000
Math/Statistics 8.3052(*) 1.69829 .000
Music 10.6072(*) 1.77417 .000
Psyc/Soc/Counseling 6.7405(*) 1.50060 .002
Other 4.1864(*) 1.12158 .032
Other History/Humanities/ Philosophy/Political Sci
530849(*) 1.28144 .014
Music 6.4208(*) 1.60924 .013 * The mean difference is significant at the .05 level.
A one-way ANOVA was used to compare the mean of database software skills
(dependent variable) with the department or content area (independent variable). As
illustrated in Table 32, a significant difference was found among departments (F (20,
384) = 3.68, p = .000). Tukey’s HSD was used to determine the nature of the differences
between the departments as shown in Table 33. This analysis revealed that
Accounting/Economics/Finance (m = 23.24, sd = 7.15) had higher database skills ratings
than Art (m = 13.10, sd = 5.15), Chemistry/Physics/Science Education (m = 15.31, sd =
6.43), English (m = 14.17, sd = 6.32), and History/Humanities/Philosophy/Political
75
Science (m = 15.65, sd = 6.29). The Computer Science/Information Systems Department
had higher database ratings than Art, Chemistry/Physics/Science Education, English,
History, and Music (m = 15.40, sd = 6.77). The department of Education (m = 22.19, sd =
6.28) had higher database ratings than Art, Chemistry/Physics/Science Education,
English, and History/Humanities/Philosophy/Political Science.
Table 32 One-way ANOVA comparing Database Skills by Department
SS df MS F p
Between Groups 3309.254 20 165.463 3.675 .000
Within Groups 17287.546 384 45.020
Total 20596.800 404
Table 33 Tukey’s HSD comparing Database Skills by Department
Department Department Mean
Difference Std. Error Sig.
Accounting/ Econ/Finance
Art 10.1491(*) 2.42765 .006
Chemistry/Physics/ Science Education
7.9323(*) 1.87944 .005
English 9.0733(*) 2.07410 .003
History/Humanities/ Philosophy/Political Sci
7.5862(*) 1.87944 .011
CS/IS Art 12.1818(*) 2.86101 .005
Chemistry/Physics/Sci Ed 9.9650(*) 2.41334 .008
English 11.1061(*) 2.56784 .003
History/Humanities/ Philosophy/Political Sci
9.6189(*) 2.41334 .013
Music 9.8727(*) 2.66346 .035
Education Art 9.0983(*) 2.30422 .015
Chemistry/Physics/Sci Ed 6.8815(*) 1.71705 .012
English 8.0225(*) 1.92817 .007
History/Humanities/ Philosophy/Political Sci
6.5353(*) 1.71705 .025
* The mean difference is significant at the .05 level.
76
A one-way ANOVA was used to compare the overall composite scores with the
department or content area. As illustrated in Table 34, a significant difference was found
among departments (F (20, 384) = 4.76, p = .000). Tukey’s HSD was used to determine
the nature of the differences between the departments as shown in Table 35. This analysis
revealed Accounting/Economics/Finance (m = 122.80, sd = 23.68) rated higher on all
computer concepts and application skills than English (m = 96.67, sd = 20.04) and
History/Humanities/Philosophy/Political Science (m = 92.04, sd = 20.28). Agriculture (m
= 122.87, sd = 18.03) had higher overall ratings than History/Humanities/Philosophy/
Political Science. Business Administration (m = 125.14, sd = 16.08) had higher overall
ratings than English and History/Humanities/Philosophy/Political Science. Computer
Science/Information Systems had higher overall ratings than English,
History/Humanities/Philosophy/Political Science, Math/Statistics (m = 99.06, sd =
28.87), and Music (m = 97.53, sd = 20.45). Education (m = 127.24, sd = 17.46) had
higher overall ratings than English, History/Humanities/Philosophy/Political Science,
Math/Statistics, Music, Psychology/Sociology/Counseling (m = 103.76, sd = 20.50), and
Other category (m = 111.17, sd = 24.27). The ‘Other’ category had higher overall ratings
than History/Humanities/Philosophy/Political Science.
Table 34 One-way ANOVA comparing Total Composite Score by Department
SS df MS F p
Between Groups 46393.933 20 2319.697 4.755 .000
Within Groups 187345.435 384 487.879
Total 233739.368 404
77
Table 35 Tukey’s HSD comparing Total Composite Score by Department
Department Department Mean
Difference Std. Error Sig.
Accounting/ Econ/Finance
English 26.1333(*) 6.82785 .023
History/Humanities/ Philosophy/Political Sci
30.7615(*) 6.18706 .000
Agriculture History/Humanities/ Philosophy/Political Sci
30.8282(*) 7.16169 .004
Business Admin English 28.4762(*) 7.87101 .046
History/Humanities/ Philosophy/Political Sci
33.1044(*) 7.32209 .002
CS/IS English 33.6061(*) 8.45322 .013
History/Humanities/ Philosophy/Political Sci
38.2343(*) 7.94463 .000
Math/Statistics 31.2139(*) 8.54701 .042
Music 32.7394(*) 8.76800 .032
Education English 30.5766(*) 6.34746 .000
History/Humanities/ Philosophy/Political Sci
35.2048(*) 5.65248 .000
Math/Statistics 28.1844(*) 6.47184 .003
Music 29.7099(*) 6.76100 .003
Psyc/Soc/Counseling 23.4832(*) 5.71848 .008
Other 16.0766(*) 4.27411 .029
Other History/Humanities/ Philosophy/Political Sci
19.1282(*) 4.88330 .017
* The mean difference is significant at the .05 level.
A one-way ANOVA was used to compare the overall composite scores with the
institution. As illustrated in Table 36, no significant difference was found (F (11,393) =
0.89, p = .546). The importance of computer concepts and computer application skills did
not differ significantly between institutions.
Table 36 One-way ANOVA comparing Total Composite Score by Institution
SS df MS F p
Between Groups 5706.446 11 518.768 .894 .546
Within Groups 228032.922 393 580.236
Total 233739.368 404
78
Tables 37 and 38 illustrate the results of an independent-samples t test comparing
the overall mean score of male subjects to the overall mean score of female subjects. No
significant difference was found (t (403) = 1.33, p = .183). The mean of the female
subjects (m = 113.03, sd = 24.70) was not significantly different from the mean of the
male subjects (m = 109.77, sd = 25.59).
Table 37 Independent Samples t-test comparing Total Composite Score by Gender
t df p
Equal variances assumed 1.334 403 .183
Equal variances not assumed 1.321 323.378 .187
Table 38 Total Composite Score Group Statistics by Gender
Gender n Mean sd
Std. Error Mean
female 158 113.0316 24.70267 1.96524
male 247 109.7652 23.59089 1.50105
However, when using a one-way ANOVA to compare each dependent variable
(computer concepts, word processing, spreadsheet, presentation, and database skills) with
gender there were significant differences as illustrated in Tables 39. Post-hoc tests were
not performed because there were fewer than three groups. A significant difference was
found between gender and computer concepts (F (1, 403) = 14.47, p = .000). Analysis
revealed that female faculty (m = 25.25, sd = 4.37) rated the importance of computer
concepts higher than male (m = 23.51, sd = 4.57). A significant difference was found
between gender and word processing (F (1, 403) = 8.08, p = .005). Analysis revealed that
female faculty (m = 26.80, sd = 4.25) rated the importance of word processing higher
than male (m = 25.47, sd = 4.80). A significant difference was found between gender and
spreadsheet skills (F (1, 403) = 4.68, p = .031). Analysis revealed that male faculty (m =
79
19.96, sd = 6.48) rated the importance of spreadsheet skills higher than female (m =
18.47, sd = 7.11). A significant difference was found between gender and presentation
skills (F (1, 403) = 4.56, p = .033). Analysis revealed that female (m = 23.63, sd = 6.55)
rated the importance of presentation skills higher than male (m = 22.26, sd = 6.19). No
significant difference was found between database and gender (F (1, 403) = .173, p =
.678).
Table 39 One-way ANOVA comparing Dependent Variables by Gender
SS df MS F p
Computer Concepts
Between Groups 291.997 1 291.997 14.472 .000
Within Groups 8131.114 403 20.176
Total 8423.111 404
Word Processing Between Groups 170.481 1 170.481 8.079 .005
Within Groups 8504.497 403 21.103
Total 8674.978 404
Spreadsheet Between Groups 212.448 1 212.448 4.684 .031
Within Groups 18278.994 403 45.357
Total 18491.442 404
Presentation Between Groups 182.938 1 182.938 4.556 .033
Within Groups 16181.640 403 40.153
Total 16364.578 404
Database Between Groups 8.822 1 8.822 .173 .678
Within Groups 20587.978 403 51.087
Total 20596.800 404
A one-way ANOVA was used to compare the overall composite scores with the
years of faculty experience in education. As illustrated in Table 40, no significant
difference was found (F (4, 400) = 1.98, p = .097). The importance of computer concepts
and computer application skills did not differ significantly when compared to the
respondent’s years of educational experience.
80
Table 40 One-way ANOVA comparing Overall Rating by Years of Experience
SS df MS F p
Between Groups 4528.703 4 1132.176 1.976 .097
Within Groups 229210.665 400 573.027
Total 233739.368 404
Cronk (2004) describes Multivariate Analysis of Variance (MANOVA) as a test
that involves more than one dependent variable and is used to reduce Type I error
inflation. A one-way MANOVA was calculated examining the effect of content area on
all five dependent variables; computer concepts, word processing, spreadsheet,
presentation, and database scores. A significant effect was found (Lambda(100, 1858) =
.425, p = .000).
Table 41 One-way MANOVA comparing Five Dependent Variables by Department/Content Area
Effect Value F
Hypothesis df Error df p
Department
Wilks’ Lambda
.425 3.562 100 1858.503 .000
Follow-up univariate ANOVAs (Table 42) indicated that the importance of
computer concepts (F(20,384) = 3.39, p = .000), word processing (F(20,384) = 2.87, p =
.000), spreadsheet (F(20,384) = 6.24, p = .000), presentation (F(20,384) = 5.16, p =
.000), and database skills (F(20,384) = 3.68, p = .000) were all significantly different by
department/content area.
81
Table 42 Follow-up Univariate ANOVAs comparing Five Dependent Variables by
Department/Content Area
Source Dependent Variable Type III Sum of Squares df MS F p
Depart Computer Concepts 1265.170 20 63.259 3.394 .000
Word Processing 1128.478 20 56.424 2.871 .000
Spreadsheet 4536.719 20 226.836 6.242 .000
Presentation 3463.948 20 173.197 5.155 .000
Database 3309.254 20 165.463 3.675 .000
Error Computer Concepts 7157.941 384 18.640
Word Processing 7546.500 384 19.652
Spreadsheet 13954.723 384 36.340
Presentation 12900.630 384 33.595
Database 17287.546 384 45.020
Total Computer Concepts 245317.000 405
Word Processing 282299.000 405
Spreadsheet 170607.000 405
Presentation 226763.000 405
Database 162053.000 405
A one-way MANOVA was calculated examining the effect of institution on all
five dependent variables; computer concepts, word processing, spreadsheet, presentation,
and database scores. Table 43 indicates no significant effect was found (Lambda(55,
1804) = .880, p = .637). None of the five dependent variables were significantly
influenced by institution.
Table 43 One-way MANOVA comparing Five Dependent Variables by Institution
Effect Value F
Hypothesis df Error df p
Institution Wilks’ Lambda .880 .922 55.000 1804.179 .637
A one-way MANOVA was calculated examining the effect of years of experience
in education on all five dependent variables; computer concepts, word processing,
spreadsheet, presentation, and database scores. Table 44 indicates no significant effect
82
was found (Lambda(20, 1314) = .926, p = .061). None of the five dependent variables
were significantly influenced by years of experience in education.
Table 44 One-way MANOVA comparing Five Dependent Variables by Yrs of Experience
Effect Value F
Hypothesis df Error df p
Institution Wilks’ Lambda .926 1.535 20.000 1314.333 .061
A one-way MANOVA was calculated examining the effect of gender on all five
dependent variables; computer concepts, word processing, spreadsheet, presentation, and
database scores. Table 45 indicates a significant effect was found (Lambda(5, 399) =
.884, p = .000).
Table 45 One-way MANOVA comparing Five Dependent Variables by Gender
Effect Value F
Hypothesis df Error df p
Gender
Wilks’ Lambda .884 10.448 5.000 399.000 .000
Follow-up univariate ANOVAs, as illustrated in Table 46, indicated that the
importance of database skills was not significantly influenced by gender (F(1,403) =
.173, p = .678). Computer concepts (F(1,403) = 14.47, p = .000), word processing (F(1,
403) = 8.08, p = .005), spreadsheet (F(1,403) = 4.68, p = .031), and presentation
(F(1,403) = 4.56, p = .033) were all significantly different by gender. This confirmed
earlier findings.
83
Table 46 Follow-up Univariate ANOVAs comparing Five Dependent Variables by Gender
Source Dependent Variable
Type III Sum of Squares df MS F p
Gender Computer Concepts
291.997 1 291.997 14.472 .000
Word Processing
170.481 1 170.481 8.079 .005
Spreadsheet 212.448 1 212.448 4.684 .031
Presentation 182.938 1 182.938 4.556 .033
Database 8.822 1 8.822 .173 .678
Error Computer Concepts
8131.114 403 20.176
Word Processing
8504.497 403 21.103
Spreadsheet 18278.994 403 45.357
Presentation 16181.640 403 40.153
Database 20587.978 403 51.087
Total Computer Concepts
245317.000 405
Word Processing
282299.000 405
Spreadsheet 170607.000 405
Presentation 226763.000 405
Database 162053.000 405
A 2 (gender) x 22 (department) between-subjects factorial ANOVA was
calculated comparing the overall score for subjects who were male or female and who
were one of 22 department categories. Table 47 shows a significant main effect for
department (F(20, 363 ) = 3.68, p = .000). These findings were reported earlier. The main
effect for gender was not significant (F(1, 363) = .52, p = .471 ). The interaction was also
not significant (F(20, 363) = 1.18, p = .265). The effect of the department was not
influenced by whether or not the faculty member was male or female.
84
Table 47 Summary of 2(Gender) by 22 (Department) Analysis of Variance for Overall Score
Source Type III
Sum of Squares df MS F p
Department 35685.634 20 1784.282 3.684 .000
Gender 252.798 1 252.798 .522 .471
Department * Gender 11472.736 20 573.637 1.184 .265
Error 175835.090 363 484.394
Total 5227297.000 405
Statistical Analysis – Student Assessment
Demographic Descriptive Statistics
Demographic data were collected on student participants including gender, age,
home state, size of high school graduating class, number of computer courses taken in
high school, and college major. Of the 164 student participants, 106 (64.6%) were female
and 58 (35.4%) were male. The majority of student participants (75.6%) were ages 18
and 19 with other ages ranging from 20 – 49. For purposes of further analysis, ages were
divided into three categories, those less than 20 years of age, those between 20 and 25
years of age, and those over 25 years of age as illustrated in Table 48.
Table 48 Frequencies and Percentages for Age (n = 164)
Age f %
Valid Percent
Cumulative Percent
18 – 19 yrs 124 75.6 75.6 75.6
20 – 25 yrs 38 23.2 23.2 98.8
over 25 yrs 2 1.2 1.2 100.0
Total 164 100.0 100.0
One hundred eight (65.9%) of the student participants indicated Missouri as their
home state, 29 (17.7%) students were from Iowa, 19 (11.6%) students were from
85
Nebraska, one student (0.6%) was from Kansas, and 7 (4.3%) students indicated other as
home state.
Students were asked the size of their high school graduating class. Five categories
were developed according to the results: 53 (32.3%) students had a high school
graduating class less than 100 students, 48 (29.3%) had a high school graduating class of
100 – 199 students, 24 (14.6%) had a graduating class of 200 – 399 students, 33 (20.1%)
had a graduating class of 400 – 599 students, and six students (3.7%) had 600 or more in
their high school graduating class. Results are summarized in Table 49.
Table 49 Frequencies and Percentages for Size of High School Graduating Class (n = 164)
Size f %
Valid Percent
Cumulative Percent
less than 100 53 32.3 32.3 32.3
100 – 199 students 48 29.3 29.3 61.6
200 – 399 students 24 14.6 14.6 76.2
400 – 599 students 33 20.1 20.1 96.3
over 600 students 6 3.7 3.7 100.0
Total 164 100.0 100.0
Student participants were also asked how many computer courses they had taken
in high school. As Table 50 summarizes, 44 (26.8%) students had no computer courses in
high school, 57 (34.8%) students had one computer course, 48 (29.3%) students had two
courses, 11 (6.7%) students had three courses, and four students (2.4%) had four or more
courses.
Table 50 Frequencies and Percentages for # of Computer Courses Taken in High School (n = 164)
Computer Courses f %
Valid Percent
Cumulative Percent
0 44 26.8 26.8 26.8
1 57 34.8 34.8 61.6
86
Computer Courses f %
Valid Percent
Cumulative Percent
2 48 29.3 29.3 90.9
3 11 6.7 6.7 97.6
4 2 1.2 1.2 98.8
6 2 1.2 1.2 100.0
Total 164 100.0 100.0
The top three areas in regards to major field of study resulted in 42 students
(25.6%) majoring in Education, 27 students (16.5%) majoring in Marketing/
Management, and 20 students (12.2%) were undecided. Table 51 summarizes other major
fields of study.
Table 51 Frequencies and Percentages for Major Field of Study (n = 164)
f %
Accounting / Economics / Finance 10 6.1
Agriculture 6 3.7
Art 12 7.3
Biological Sciences 1 .6
Business Administration 4 2.4
Computer Science / Information Systems 3 1.8
Education 42 25.6
English 2 1.2
Family & Consumer Science 9 5.5
Geology / Geography 1 .6
Health / Physical Education / Recreation / Dance 3 1.8
History / Humanities / Philosophy / Political Science 5 3.0
Marketing / Management 27 16.5
Mass Communications / Broadcasting / Digital Media / Journalism
3 1.8
Mathematics / Statistics 1 .6
Music 3 1.8
Psychology / Sociology / Counseling 3 1.8
Other 9 5.5
Undecided 20 12.2
Total 164 100.0
87
Reliability
The student assessment consisted of five major sections; computer concepts, word
processing, spreadsheet, presentation, and database skills. The computer concepts section
consisted of 150 multiple choice questions (25 questions from six different module areas)
covering various computer topics. Module one questions covered computer and
information literacy, introduction to application software, word processing concepts, and
inside the system. Module two questions covered understanding the Internet, email,
system software, and exploring the Web. Module three questions covered spreadsheets
concepts, current issues, emerging technologies, and data storage. Module four covered
presentations packages, special purpose programs, multimedia/virtual reality, and
input/output. Module five questions covered database concepts, telecommunications, and
networks. Module six questions covered creating a Web page, ethics, and security.
As an incentive to encourage student participation, any student who passed the
assessments at 80% mastery could test out of the course. This 80% mastery had been
used in the past for course test out purposes. Thus, the researcher agreed to use the same
procedures and tests as would be given for a course test out.
Item-total analysis was used to assess the internal consistency of the data. The
Pearson correlation coefficient was used to conduct this analysis because the data were
interval in nature. Correlations for the data revealed that Module one items (r = +.64, n =
164, p = .000, two tails), Module two items r = +.58, n = 164, p = .000, two tails),
Module three items (r = +.64, n = 164, p = .000, two tails), Module four items (r = +.73,
n = 164, p = .000, two tails), Module five items (r = +.67, n = 164, p = .000, two tails),
88
and Module six items (r = +.64, n = 164, p = .000, two tails) were all significantly related
to the computer concepts total. According to Cronk (2004), Pearson correlation
coefficients of 0.3 or higher are considered internally consistent. Table 52 summarizes
that the six modules, within the computer concept section, were found to be internally
consistent with Pearson correlation coefficients of 0.58 or higher.
Table 52 Pearson Correlation Coefficients for Computer Concepts Component
Computer Concept Variables n r p
Module 1 164 0.640 (*) 0.000 Module 2 164 0.581 (*) 0.000 Module 3 164 0.635 (*) 0.000 Module 4 164 0.727 (*) 0.000 Module 5 164 0.664 (*) 0.000 Module 6 164 0.643 (*) 0.000
* Correlation is significant at the 0.01 level (2-tailed).
Item-total analysis was conducted on the application assessments of word
processing, spreadsheet, presentation, and database skills. Correlations for the data
revealed that word processing items (r = +.85, n = 164, p = .000, two tails), spreadsheet
items (r = +.85, n = 164, p = .000, two tails), presentation items (r = +.85, n = 164, p =
.000, two tails), and database items (r = +.79, n = 164, p = .000, two tails) were all
significantly related to the combined application skills total. Table 53 shows that all four
application areas were found to be internally consistent with Pearson coefficients of 0.7
or higher.
Table 53 Pearson Correlation Coefficients for Application Skills Components
Application Variables n r p
Word processing 164 0.851 (*) 0.000 Spreadsheet 164 0.851 (*) 0.000 Presentation 164 0.851 (*) 0.000 Database 164 0.790 (*) 0.000
* Correlation is significant at the 0.01 level (2-tailed).
89
An item-total analysis was also conducted on the student assessment as a whole,
including both the computer concepts modules and the application skills assessments.
Correlations for the data revealed that computer concept items (r = +.85, n = 164, p =
.000, two tails), word processing items (r = +.73, n = 164, p = .000, two tails),
spreadsheet items (r = +.75, n = 164, p = .000, two tails), presentation items (r = +.74, n
= 164, p = .000, two tails), and database items (r = +.74, n = 164, p = .000, two tails)
were all significantly related to the total. Table 54 summarizes that all areas of the student
assessment were found to be internally consistent with Pearson correlation coefficients of
0.7 or higher.
Table 54 Pearson Correlation Coefficients for Student Assessment as a Whole
Subscale Variables n r p
Computer Concepts 164 0.845 (*) 0.000 Word processing 164 0.727 (*) 0.000 Spreadsheet 164 0.753 (*) 0.000 Presentation 164 0.742 (*) 0.000 Database 164 0.740 (*) 0.000
* Correlation is significant at the 0.01 level (2-tailed).
Cronbach’s alpha was also performed as another reliability measure to test for
internal consistency. As noted earlier, a reliability coefficient close to 1.00 indicates
strong reliability while numbers closer to 0.00 indicate weak reliability (Cronk, 2004).
The Cronbach’s alpha reliability coefficients for computer concepts (0.72) and for
application skills (0.86) demonstrated an acceptable level of internal reliability. The alpha
reliability coefficient of the student assessment as a whole (0.72) also demonstrated an
acceptable degree of internal consistency. Table 55 summarizes the reliability
coefficients of the student assessment instrument.
90
Table 55 Cronbach’s Alpha Reliability Coefficients for Student Assessment
Variables n alpha p
Computer Concepts Module 1 Module 2 Module 3 Module 4 Module 5 Module 6
164 0.7243 .000
Application Skills Word processing Spreadsheet Presentation Database
164 0.8561 .000
Student Assessment Computer Concepts (six modules) Application Skills (four applications)
164 0.8330 .000
Descriptive Statistics
The student assessment consisted of two major components: a computer concepts
section including six module assessments, and an application section including four
application assessments. The computer concepts modules covered various computer
related topics: Module one topics included computer and information literacy,
introduction to application software, word processing, and inside the system; Module two
topics included understanding the Internet, email, system software, and exploring the
Web; Module three topics included spreadsheet concepts, current issues, emerging
technologies, and data storage; Module four topics included presentations packages,
special purpose programs, multimedia/virtual reality, and input/output; Module five
topics included database concepts, telecommunications, and networks; and Module six
topics included creating a Web page, ethics, and security. The applications section of the
assessment evaluated student skills in the four application areas of word processing,
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spreadsheet, presentation, and database. The descriptive statistics are shown in Tables 56
and 57 for each of these assessment areas.
Table 56 Descriptive Statistics for Computer Concepts Section (n = 164)
Variable Mean sd
Module 1 55.44 10.798 Module 2 52.73 12.128 Module 3 52.22 11.858 Module 4 57.88 13.072 Module 5 50.56 12.476 Module 6 51.22 12.437 Overall 53.34 7.877
Table 57 Descriptive Statistics for Applications Section (n = 164)
Variable Mean sd
Word processing skills 64.68 16.047 Spreadsheet skills 47.54 16.149 Presentation skills 68.15 17.005 Database skills 42.88 15.868 Overall 55.81 13.601
Trends and Relationship Comparisons
One-way ANOVA tests were used to determine if any significant differences
existed between the proficiency level of students’ technology skills when grouped by
home state, number of high school computer courses taken, gender, and major field of
study.
Only one student indicated Kansas as a home state, so that student was grouped
with the ‘Other’ category for purposes of analysis. A one-way ANOVA was used to
compare the mean of computer concepts (dependent variable) with home state
(independent variable). As illustrated in Table 58, a significant difference was found
between home states (F(3, 160) = 3.00, p = .032). Tukey’s HSD was used to examine the
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nature of the differences between the student home states of Missouri, Iowa, and
Nebraska. No significant differences were found.
Table 58 One-way ANOVA comparing Computer Concepts by Home State
SS df MS F p
Between Groups 539.258 3 179.753 3.004 .032
Within Groups 9574.509 160 59.841
Total 10113.767 163
A one-way ANOVA was used to compare the mean of word processing skills
(dependent variable) with home state (independent variable). As illustrated in Table 59,
no significant difference was found between home states (F(4, 159) = .477, p = .752) in
regards to word processing skills.
Table 59 One-way ANOVA comparing Word Processing Skills by Home State
SS df MS F p
Between Groups 497.871 4 124.468 .477 .752
Within Groups 41473.641 159 260.841
Total 41971.512 163
A one-way ANOVA was used to compare the mean of spreadsheet skills
(dependent variable) with home state (independent variable). As illustrated in Table 60,
no significant difference was found between home states (F(4, 159) = 1.28, p = .280) in
regards to spreadsheet skills.
Table 60 One-way ANOVA comparing Spreadsheet Skills by Home State
SS df MS F p
Between Groups 1326.861 4 331.715 1.281 .280
Within Groups 41181.919 159 259.006
Total 42508.780 163
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A one-way ANOVA was used to compare the mean of presentation skills
(dependent variable) with home state (independent variable). As illustrated in Table 61,
no significant difference was found between home states (F(4, 159) = .942, p = .441) in
regards to presentation skills.
Table 61 One-way ANOVA comparing Presentation Skills by Home State
SS df MS F p
Between Groups 1091.301 4 272.825 .942 .441
Within Groups 46041.186 159 289.567
Total 47132.488 163
A one-way ANOVA was used to compare the mean of database skills (dependent
variable) with home state (independent variable). As illustrated in Table 62, no
significant difference was found between home states (F(4, 159) = .692, p = .599) in
regards to database skills.
Table 62 One-way ANOVA comparing Database Skills by Home State
SS df MS F p
Between Groups 702.179 4 175.545 .692 .599
Within Groups 40339.382 159 253.707
Total 41041.561 163
A one-way ANOVA was used to compare the mean of the overall assessment
with home state. As illustrated in Table 63, no significant difference was found between
home states (F(4, 159) = 1.663, p = .161) in regards to all assessment components
combined.
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Table 63 One-way ANOVA comparing Overall Assessment Score by Home State
SS df MS F p
Between Groups 579.812 4 144.953 1.663 .161
Within Groups 13858.874 159 87.163
Total 14438.686 163
A one-way ANOVA was used to compare the mean of computer concepts
(dependent variable) with number of computer courses taken in high school (independent
variable). As illustrated in Table 64, no significant difference were found (F( 5,158) =
.629, p = .678). The students who had taken computer courses in high school did not
differ significantly from the students who had not taken computer courses in high school
in regards to computer concepts.
Table 64 One-way ANOVA comparing Computer Concepts by # of High School Computer Courses
SS df MS F p
Between Groups 197.494 5 39.499 .629 .678
Within Groups 9916.273 158 62.761
Total 10113.767 163
A one-way ANOVA was used to compare the mean of word processing skills
(dependent variable) with number of high school computer courses taken (independent
variable). As illustrated in Table 65, no significant difference were found (F( 5,158) =
1.376, p = .236). Students who had taken computer courses in high school did not differ
significantly from the students who had not taken computer courses in high school in
regards to word processing skills.
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Table 65 One-way ANOVA comparing Word Processing by # of High School Computer Courses
SS df MS F p
Between Groups 1751.188 5 350.238 1.376 .236
Within Groups 40220.324 158 254.559
Total 41971.512 163
A one-way ANOVA was used to compare the mean of spreadsheet skills
(dependent variable) with number of high school computer courses taken (independent
variable). As illustrated in Table 66, no significant difference were found (F( 5,158) =
.990, p = .425). Students who had taken computer courses in high school did not differ
significantly from the students who had not taken computer courses in high school in
regards to spreadsheet skills.
Table 66 One-way ANOVA comparing Spreadsheet Skills by # of High School Computer Courses
SS df MS F p
Between Groups 1291.932 5 258.386 .990 .425
Within Groups 41216.848 158 260.866
Total 42508.780 163
A one-way ANOVA was used to compare the mean of presentation skills
(dependent variable) with number of high school computer courses taken (independent
variable). As illustrated in Table 67, no significant difference were found (F( 5,158) =
1.47, p = .202). Students who had taken computer courses in high school did not differ
significantly from the students who had not taken computer courses in high school in
regards to presentation skills.
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Table 67 One-way ANOVA comparing Presentation Skills by # of High School Computer Courses
SS df MS F p
Between Groups 2099.314 5 419.863 1.473 .202
Within Groups 45033.174 158 285.020
Total 47132.488 163
A one-way ANOVA was used to compare the mean of database skills (dependent
variable) with number of high school computer courses taken (independent variable). As
illustrated in Table 68, no significant difference were found (F( 5,158) = 1.094, p = .366).
Students who had taken computer courses in high school did not differ significantly from
the students who had not taken computer courses in high school in regards to database
skills.
Table 68 One-way ANOVA comparing Database Skills by # of High School Computer Courses
SS df MS F p
Between Groups 1373.752 5 274.750 1.094 .366
Within Groups 39667.809 158 251.062
Total 41041.561 163
A one-way ANOVA was used to compare the mean of the overall assessment
with number of high school computer courses taken. As illustrated in Table 69, no
significant difference was found between home states (F(5,158) = 1.478, p = .200) in
regards to all assessment components combined.
Table 69 One-way ANOVA comparing Overall Assessment Score by # of High School Computer
Courses Taken
SS df MS F p
Between Groups 645.171 5 129.034 1.478 .200
Within Groups 13793.515 158 87.301
Total 14438.686 163
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An independent-samples t test was conducted comparing the overall mean score
of male subjects to the overall mean score of female subjects. As illustrated in Tables 70
and 71, no significant difference was found (t (162) = -1.26, p = .210). The mean of the
female subjects (m = 53.89, sd = 8.07) was not significantly different from the mean of
the male subjects (m = 55.82, sd = 11.44) in regards to overall assessment scores.
Table 70 Independent Samples t-test comparing Overall Assessment Score by Gender
t df p
Equal variances assumed -1.259 162 .210
Equal variances not assumed -1.140 88.659 .257
Table 71 Total Composite Score Group Statistics by Gender
GENDER n Mean sd
Std. Error Mean
female 106 53.8931 8.06774 .78361
male 58 55.8247 11.44362 1.50262
However, when using one-way ANOVA tests to compare each dependent variable
(computer concepts, word processing, spreadsheet, presentation, and database skills) with
gender there was one significant difference. As illustrated in Table 72, a significant
difference was found between gender and computer concepts (F (1, 162) = 6.49, p =
.012). Analysis revealed that male students (m = 55.43, sd = 9.56) scored higher on
computer concepts than female (m = 52.20, sd = 6.55). In regards to word processing
skills (F (1, 162) = .884, p = .349), presentation skills (F (1, 162) = .001, p = .973),
spreadsheet skills (F (1, 162) = .036, p = .859), and database skills (F (1, 162) = .038, p =
.846), the mean of male students was not significantly different from the mean of female
students.
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Table 72 One-way ANOVA comparing Assessment Components by Gender
SS df MS F p
Between Groups 389.662 1 389.662 6.492 .012
Within Groups 9724.105 162 60.025
Computer Concepts
Total 10113.767 163
Between Groups 227.700 1 227.700 .884 .349
Within Groups 41743.813 162 257.678
Word Processing skills
Total 41971.512 163
Between Groups .329 1 .329 .001 .973
Within Groups 47132.159 162 290.939
Presentation skills
Total 47132.488 163
Between Groups 9.507 1 9.507 .036 .849
Within Groups 42499.274 162 262.341
Spreadsheet skills
Total 42508.780 163
Between Groups 9.556 1 9.556 .038 .846
Within Groups 41032.005 162 253.284
Database skills Total 41041.561 163
The computer literacy course at the researcher’s institution requires 80% mastery
for test out. It is believed that many students are obtaining adequate technology skills in
high school, and therefore they do not need a general computer literacy course in college.
As illustrated in Table 73, the study revealed that only three out of 164 students (1.8%)
mastered computer concepts with a score of 80% or higher and only 27 (16.5%) students
scored 60% or higher on the computer concepts portion of the assessment. One hundred
thirty-seven (84%) students scored less then 60% on the computer concepts assessment,
indicating the majority of students did not master, or even show proficiency on the
computer concepts portion of the assessment.
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Table 73 Frequencies and Percentages of Computer Concepts Scores
Frequency Percent Valid Percent
Cumulative Percent
>= 80% 3 1.8 1.8 1.8
60 - 79% 24 14.6 14.6 16.5
< 60% 137 83.5 83.5 100.0
Total 164 100.0 100.0
Thirty four out of 164 students (20.7%) mastered word processing skills with a
score of 80% or higher, 118 (72%) students scored 60% or higher, and 46 (28%) students
scored below 60%. Table 74 illustrates the majority of students scored 60% or higher, but
did not demonstrate mastery at 80% or higher on the word processing assessment.
Table 74 Frequencies and Percentages of Word Processing Skills Scores
Frequency Percent Valid Percent
Cumulative Percent
>= 80% 34 20.7 20.7 20.7
60 - 79% 84 51.2 51.2 72.0
< 60% 46 28.0 28.0 100.0
Total 164 100.0 100.0
Two out of 164 students (1.2%) mastered spreadsheet skills with a score of 80%
or higher, 50 (31%) students scored 60% or higher, and 114 (69.5%) students scored less
then 60% on the spreadsheet assessment. Table 75 indicates the majority of students did
not master at 80%, or even show proficiency at 60% on the spreadsheet assessment.
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Table 75 Frequencies and Percentages of Spreadsheet Skills Scores
Frequency Percent Valid Percent
Cumulative Percent
>= 80% 2 1.2 1.2 1.2
60 - 79% 48 29.3 29.3 30.5
< 60% 114 69.5 69.5 100.0
Total 164 100.0 100.0
Forty six out of 164 students (28%) mastered presentation skills with a score of
80% or higher, 126 (77%) students scored 60% or higher, and 38 (23%) students scored
below 60% on the presentation assessment. Table 76 indicates the majority of students
scored 60% or higher, but did not master presentation skills at 80% or higher.
Table 76 Frequencies and Percentages of Presentation Skills Scores
Frequency Percent Valid Percent
Cumulative Percent
>= 80% 46 28.0 28.0 28.0
60 - 79% 80 48.8 48.8 76.8
< 60% 38 23.2 23.2 100.0
Total 164 100.0 100.0
Two out of 164 students (1%) mastered database skills with a score of 80% or
higher, 31 (19%) students scored of 60% or higher, and 133 (81%) students scored below
60% on the database assessment. Table 77 illustrates the majority of students but did not
master at 80%, or even show proficiency at 60% on database skills.
Table 77 Frequencies and Percentages of Database Skills Scores
Frequency Percent Valid Percent
Cumulative Percent
>= 80% 2 1.2 1.2 1.2
60 - 79% 29 17.7 17.7 18.9
< 60% 133 81.1 81.1 100.0
Total 164 100.0 100.0
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Only two out of 164 students (1%) mastered the entire student assessment at 80%
or higher, 43 (26%) students scored 60% or higher, and 121 (74%) students scored less
then 60% on the overall student assessment. Table 78 indicates the majority of students
but did not master at 80% or higher, or even demonstrate proficiency at 60% or higher,
on the overall student assessment.
Table 78 Frequencies and Percentages of Student Assessment Scores
Frequency Percent Valid Percent
Cumulative Percent
>= 80% 2 1.2 1.2 1.2
60 - 79% 41 25.0 25.0 26.2
< 60% 121 73.8 73.8 100.0
Total 164 100.0 100.0