CHAPTER 4 RESULTS. This study investigated the computer literacy and computer application skills of

Transcription

1 54 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.

2 55 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 (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 s, 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

3 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 Total 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

4 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 Important Very important Total 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 57

5 58 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 Agriculture Art Biological Sciences Business Administration Chemistry/Physics/Science Education Communication/Theatre/Languages Computer Science/Information Systems Education English Family & Consumer Sciences Geology/Geography Health/PE/Recreation/Dance History/Humanities/Philosophy/Political Science Marketing/Management Mass Communications/Broadcasting/Digital Media/Journalism Math/Statistics Modern Languages 2.5 Music Psychology/Sociology/Counseling Other Total 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

6 59 employed from years, 67 (16.5%) have been employed 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 years, 96 (23.7%) have been in education from 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), itemtotal correlations should be positive and greater than 0.3 to indicate internal consistency. Tables 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 Internet, , system software, and exploring the Web

7 Computer Concept Variables n rho p Current issues, emerging technologies, spreadsheet concepts, and data storage Presentation packages, special purpose programs, multimedia/virtual reality, and input/output Database concepts, telecommunications, and networks Ethics and security Web page creation Locating and evaluating information on the Internet, effectively using search engines, determining the credibility of information Table 11 Spearman rho Correlations for Word Processing Items Word Processing Variables n rho p-value Getting Started Insert and Modify Text Create and Modify Paragraphs Format Documents Manage Documents Working with Graphics Workgroup Collaboration Creating and Modifying Graphics Table 12 Spearman rho Correlations for Spreadsheet Items Spreadsheet Variables n rho p-value Working with Cells and Cell Data Managing Workbooks Formatting and Printing Workbooks Modifying Workgroups Creating and Revising Formulas Creating and Modifying Graphics Workgroup Collaboration Integrating a spreadsheet with other software Table 13 Spearman rho Correlations for Presentation Items Presentation Variables n rho p-value Creating Presentations Inserting and Modifying Text Inserting and Modifying Visual Elements Modifying Presentation Formats Printing Presentations Working with Data from Other Sources Managing and Delivering Presentations Workgroup Collaboration

8 61 Table 14 Spearman rho Correlations for Database Items Database Variables n rho p-value Getting Started Creating and Using Databases Creating and Modifying Tables Creating and Modifying Queries Creating and Modifying Forms Viewing and Organizing Information Producing Reports Integrating with Other Applications Table 15 Spearman rho Correlations between Subscales Subscale Variables n rho p-value Computer Concepts Word Processing Spreadsheet Presentation Database 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 to a high of with all having p = These results demonstrated a high level of internal reliability. The survey as a whole had a reliability coefficient of which also

9 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 Word Processing Spreadsheet Presentation Database All subscales combined 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, , 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 62

10 63 software, word processing, and inside the computer; Item 2 topics (67.4%) including understanding the Internet, , 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% Understanding the Internet, , system software, and exploring the Web 3 0.7% Spreadsheets, current issues, emerging 41 technologies, and data storage 10.1% Presentations, special purpose programs, 39 multimedia/virtual reality, input/output 9.6% Databases, telecommunications, 55 and networks 13.6% Ethics and security % Web page creation % Locating and evaluating information 5 on the Internet 1.2% % % % % % % % % % % % % % % % % % % % % % % % % In addition to the computer concepts component of the faculty survey, it also included items related to four computer application areas; word processing, spreadsheet,

11 64 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 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% % % % Insert and modify text 6 1.5% % % % Create and modify paragraphs 8 2.0% % % % Format documents 9 2.2% % % % Manage documents 5 1.2% % % % Working with graphics % % % % Workgroup collaboration Creating and modifying graphics 9.9% % 37.8% % 36.0% % Although many of the items in the spreadsheet section were very close, the 16.3% % highest percentages indicated three of the eight items were important, while the other five

12 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 % % % % Managing workbooks % % % % Formatting and printing workbooks % % % % Modifying workgroups % % % % Creating and revising formulas % % % % Creating and modifying graphics % % % % Workgroup collaboration % Integrating with other software % 43.2% % 23.5% % 7.2% % 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 65

13 66 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 % % % % Inserting and modifying text % % % % Inserting and modifying visual elements % % % % Modifying formats % % % % Printing presentations % % % % Working with data from other sources % % % % Managing and delivering presentations 7.9% Workgroup collaboration % 23.2% % 34.8% % 34.1% % 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 % % % % Creating and Using Databases % % % % Creating and Modifying Tables % % % % Creating and Modifying Queries % % % % Creating and Modifying Forms Viewing and Organizing Information 30.1% % 38.0% % 23.5% % 8.4% %

14 Item NI SI I VI Producing Reports % % % % Integrating with other Applications % % % % 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 Agree Strongly Disagree Require course or equivalent test out 8 2.0% % % Agree % 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 67

15 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 Word processing skills Spreadsheet skills Presentation skills Database skills 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 68

16 69 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 Within Groups Total Table 25 Tukey s HSD comparing Computer Concepts by Department Mean Department Department Difference Std. Error Sig. CS/IS History/Humanities/ Philosophy/ Political Science (*) Education Chemistry/Physics/ Science Education (*) English (*) History/Humanities/ Philosophy/Political Science (*) Math/Statistics (*) Psychology/Sociology/ Counseling (*) Other (*) * 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

17 70 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 Within Groups Total Table 27 Tukey s HSD comparing Word Processing Skills by Department Mean Department Department Difference Std. Error Sig. Education History/Humanities/ Philosophy/Political Sci (*) Math/Statistics (*) Psychology/Sociology/ Counseling (*) other (*) * 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

18 71 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 Within Groups Total

19 Table 29 Tukey s HSD comparing Spreadsheet Skills by Department Department Department Mean Difference Std. Error Sig. Accounting Art (*) Comm/Theatre/Languages (*) English (*) History/Humanities/ Philosophy/Political Sci (*) Music (*) Psyc/Soc/Counseling (*) Other (*) Agriculture Art (*) English (*) History/Humanities/ Philosophy/Political Sci (*) Music (*) Psyc/Soc/Counseling (*) Business Admin Art (*) English (*) History/Humanities/ Philosophy/Political Sci (*) Music (*) Chem/Physics/Sci Ed Art (*) English (*) History/Humanities/ Philosophy/Political Sci (*) Music (*) CS/IS Art (*) Comm/Theatre/Lang (*) English (*) History/Humanities/ Philosophy/Political Sci (*) Music (*) Psyc/Soc/Counseling (*) Education History/Humanities/ Philosophy/Political Sci (*) Other History/Humanities/ Philosophy/Political Sci (*) * The mean difference is significant at the.05 level. 72

20 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 Within Groups Total

21 74 Table 31 Tukey s HSD comparing Presentation Skills by Department Mean Department Department Difference Std. Error Sig. Agriculture History/Humanities/ Philosophy/Political Sci (*) Music (*) Business Admin English (*) History/Humanities/ Philosophy/Political Sci (*) Math/Statistics (*) Music (*) Psyc/Soc/Counseling (*) CS/IS Music (*) Education Chemistry/Physics/Sci Ed (*) English (*) History/Humanities/ Philosophy/Political Sci (*) Math/Statistics (*) Music (*) Psyc/Soc/Counseling (*) Other (*) Other History/Humanities/ Philosophy/Political Sci (*) Music (*) * 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

22 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 Within Groups Total Table 33 Tukey s HSD comparing Database Skills by Department Department Accounting/ Econ/Finance Department Art Mean Difference Std. Error Sig (*) Chemistry/Physics/ Science Education (*) English (*) History/Humanities/ Philosophy/Political Sci (*) CS/IS Art (*) Chemistry/Physics/Sci Ed (*) English (*) History/Humanities/ (*) Philosophy/Political Sci Music (*) Education Art (*) Chemistry/Physics/Sci Ed (*) English (*) History/Humanities/ Philosophy/Political Sci * The mean difference is significant at the.05 level (*)

23 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 = , 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 = , sd = 18.03) had higher overall ratings than History/Humanities/Philosophy/ Political Science. Business Administration (m = , 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 = , sd = 17.46) had higher overall ratings than English, History/Humanities/Philosophy/Political Science, Math/Statistics, Music, Psychology/Sociology/Counseling (m = , sd = 20.50), and Other category (m = , 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 Within Groups Total

24 77 Table 35 Tukey s HSD comparing Total Composite Score by Department Mean Difference Std. Error Sig. Department Department Accounting/ English Econ/Finance (*) History/Humanities/ Philosophy/Political Sci (*) Agriculture History/Humanities/ Philosophy/Political Sci (*) Business Admin English (*) History/Humanities/ Philosophy/Political Sci (*) CS/IS English (*) History/Humanities/ Philosophy/Political Sci (*) Math/Statistics (*) Music (*) Education English (*) History/Humanities/ Other (*) Philosophy/Political Sci Math/Statistics (*) Music (*) Psyc/Soc/Counseling (*) Other (*) History/Humanities/ Philosophy/Political Sci * 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 Within Groups Total

25 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 = , sd = 24.70) was not significantly different from the mean of the male subjects (m = , sd = 25.59). Table 37 Independent Samples t-test comparing Total Composite Score by Gender t df p Equal variances assumed Equal variances not assumed Table 38 Total Composite Score Group Statistics by Gender Std. Error Mean Gender n Mean sd female male 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 = 78

26 , 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 Between Groups Concepts Within Groups Total Word Processing Between Groups Within Groups Total Spreadsheet Between Groups Within Groups Total Presentation Between Groups Within Groups Total Database Between Groups Within Groups Total 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.

27 80 Table 40 One-way ANOVA comparing Overall Rating by Years of Experience SS df MS F p Between Groups Within Groups Total 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 Hypothesis Effect Value F df Error df p Wilks Department Lambda 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.

28 81 Table 42 Follow-up Univariate ANOVAs comparing Five Dependent Variables by Department/Content Area Type III Sum of Squares df MS F p Source Dependent Variable Depart Computer Concepts Word Processing Spreadsheet Presentation Database Error Computer Concepts Word Processing Spreadsheet Presentation Database Total Computer Concepts Word Processing Spreadsheet Presentation Database 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 Hypothesis Effect Value F df Error df p Institution Wilks Lambda 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

29 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 Hypothesis Effect Value F df Error df p Institution Wilks Lambda 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 Hypothesis Effect Value F df Error df p Gender Wilks Lambda 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.

30 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 Word Processing Spreadsheet Presentation Database Error Computer Concepts Word Processing Spreadsheet Presentation Database Total Computer Concepts Word Processing Spreadsheet Presentation Database 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. 83

31 Table 47 Summary of 2(Gender) by 22 (Department) Analysis of Variance for Overall Score Type III Source Sum of Squares df MS F p Department Gender Department * Gender Error Total 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 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 yrs yrs over 25 yrs Total 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

32 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 students, 24 (14.6%) had a graduating class of students, 33 (20.1%) had a graduating class of 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 students students students over 600 students Total 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

33 86 Computer Courses f % Valid Percent Cumulative Percent Total 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 Agriculture Art Biological Sciences 1.6 Business Administration Computer Science / Information Systems Education English Family & Consumer Science Geology / Geography 1.6 Health / Physical Education / Recreation / Dance History / Humanities / Philosophy / Political Science Marketing / Management Mass Communications / Broadcasting / Digital Media / Journalism Mathematics / Statistics 1.6 Music Psychology / Sociology / Counseling Other Undecided Total

34 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, , 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),