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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)


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)


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%


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)


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%


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



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




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



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


10.519(*) 1.95459 .000

Music 8.8000(*) 2.20122 .012


Business Admin Art 10.4351(*) 2.42887 .004

English 9.1825(*) 2.14818 .004


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


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


12.0210(*) 2.16827 .000

Music 10.3030(*) 2.39298 .004


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



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



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


9.7308(*) 1.92141 .000

Math/Statistics 8.7647(*) 2.09186 .006

Music 11.0667(*) 2.15392 .000


CS/IS Music 8.6121(*) 2.30083 .031

Education Chemistry/Physics/Sci Ed 5.8482(*) 1.48328 .015

English 8.8739(*) 1.66565 .000


9.2713(*) 1.48328 .000

Math/Statistics 8.3052(*) 1.69829 .000

Music 10.6072(*) 1.77417 .000


Other 4.1864(*) 1.12158 .032


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




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

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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



Accounting/ Econ/Finance

Art 10.1491(*) 2.42765 .006

Chemistry/Physics/ Science Education

7.9323(*) 1.87944 .005

English 9.0733(*) 2.07410 .003


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


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


6.5353(*) 1.71705 .025


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



Accounting/ Econ/Finance

English 26.1333(*) 6.82785 .023


30.7615(*) 6.18706 .000

Agriculture History/Humanities/ Philosophy/Political Sci

30.8282(*) 7.16169 .004

Business Admin English 28.4762(*) 7.87101 .046


33.1044(*) 7.32209 .002

CS/IS English 33.6061(*) 8.45322 .013


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


35.2048(*) 5.65248 .000

Math/Statistics 28.1844(*) 6.47184 .003

Music 29.7099(*) 6.76100 .003


Other 16.0766(*) 4.27411 .029


19.1282(*) 4.88330 .017



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 =

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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


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.

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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.

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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


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


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


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.

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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


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


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.

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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,

91

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


(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



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

93

A one-way ANOVA was used to compare the mean of presentation skills


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.

94

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


(dependent variable) with number of high school computer courses taken (independent


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.

95

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




.990, p = .425). Students who had taken computer courses in high school did not differ


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



1.47, p = .202). Students who had taken computer courses in high school did not differ


regards to presentation skills.

96

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

97

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.

98

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.

99

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


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.

100

Table 75 Frequencies and Percentages of Spreadsheet Skills Scores


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


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


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

101

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


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