JITE v37n1 - An Investigation of Computer Anxiety Among Vocational-Technical Teachers
An Investigation of Computer Anxiety Among Vocational-Technical Teachers
Harrison Hao Yang
State University of New York at OswegoDominic Mohamed
Florida International UniversityBarbara Beyerbach
State University of New York at OswegoComputer technology has the capacity to affect the efficiency and productivity of education. To capture computer-related improvements in efficiency and productivity, educators must learn, through pre-service and in-service training activities, what the computer is and what it can do. Computer anxiety among educators has been considered a stumbling block to integrating computers into education programs ( Gunter, Gunter, & Wiens, 1998 ; Reznich, 1996 ; Yang, 1996 ).
Many studies have examined the relationship of computer anxiety to various demographic variables, such as, gender, age, and academic major or teaching field. There are also a number of studies on the relationship of computer experience with computer anxiety ( Anderson, 1996 ; Ayersman, 1996 ; Cooper & Stone, 1996 ; D'Amico, Baron, & Sissons, 1995 ; Fitzgerald, Hardin, & Hollingsead, 1997 ; Harris & Grandgenett, 1996 ; McInerney, McInerney, & March, 1997 ; Whitley, 1996 ). Even though a significant body of research exists, the results of most studies are inconsistent ( Maurer, 1994 ).
Studies that focus only on the relationship between demographic variables and computer anxiety may be misleading because demographic variables and computer anxiety both have a relationship with computer-related experience. The direct relationship between computer-related experience and computer anxiety seems clear ( Bohlin & Hunt, 1995 ; Chen, 1986 ; Hadfield, Maddux, & Love, 1997 ; Heinssen, Glass, & Knight, 1987 ; Maurer, 1994 ; Reed, Ervin, & Oughton, 1995 ). Several studies have suggested that prior computer-related experience also should be taken into account as a covariate when examining the relationship between computer anxiety and demographic variables ( Chen, 1986 ; Maurer, 1994 ; McInerney, McInerney & Sinclair, 1994 ; Yang, 1996 ). Both the demographic characteristics and computer-related experience of vocational-technical educators vary. It seems, therefore, that in examining the relationship between computer anxiety in vocational-technical educators and demographic variables, a more comprehensive study is needed, which controls for the effect of computer-related experience .
The purpose of this study was to investigate how computer-related experience affects the relationship of computer anxiety in vocational-technical educators to selected demographic variables: learning style, age, gender, ethnic/cultural background, teaching/professional area, educational level, and school type. Based on the purposes of this study, the following question guided the investigation: To what extent does computer anxiety in vocational-technical educators relate to demographic variables, before and after controlling for computer experience?
The results of this study could assist administrators and planners of training programs to assist vocational-technical teachers more effectively, by identifying educators who need to be exposed to computer training strategies that minimize anxiety, counter negative attitudes, and reduce resistance to computer usage.
Method
Participants and Design
The participants of this study were selected from a population of employed vocational-technical educators in Dade County, Florida. Located in an urban area in south Florida, Dade County has the largest population in the state. A list of vocational-technical educators sorted by teaching/professional fields was obtained from the Dade County School Board. The list was then categorized according to school type. Educators identified as "To Be Announced" and "Part-time" were excluded.
Survey research was used to obtain specific information from a representative sample of these educators about computer anxiety levels, learning styles, and selected demographic variables. The major design option chosen was a simple random selection, using a table of random numbers, modified by stratification across teaching/professional fields within the population sample. The final population consisted of all vocational-technical educators in Dade County who were teaching or working at middle schools, senior high schools, and vocational-technical/adult education centers. Teachers in the sample represented the following teaching or professional fields: Business, Agribusiness, Industrial, Health Occupations, Technology, Family and Consumer Science, and other. A stratified random sample of 245 educators (25%) was selected from the total population of nearly 980 educators.
Instruments
Each participant in the study was administered a Learning Style Inventory (LSI) ( Kolb, 1985 ), a short-form Computer Anxiety Scale (COMPAS) ( Oetting, 1983 ), and a closed form of the participant inventory constructed by the researchers of this study.
Learning-Style Inventory.
The 1985 version of Kolb's LSI was selected for use in this study. The LSI consists of 12 simple sentence completion items, which require the respondent to rank order 4 sentence endings that correspond to the 4 learning modes: concrete experience (CE), reflective observation (RO), abstract conceptualization (AC), and active experimentation (AE). The highest number of choices relevant to a learning mode yields a raw score varying from 12 to 48. This score can be used to classify an individual into one of four learning style types: converger, diverger, assimilator, or accommodator. The LSI also yields two combination scores that indicate the extent to which the individual emphasizes abstractness over concreteness (AC-CE) and action over reflection (AE-RO). The combination raw scores vary from +36 to -36. The entire LSI comes in a self-scoring booklet containing the inventory, the Learning Style profile, and the Learning Style type grid.
The reliability data of the LSI for the four basic scores and two combination scores indicate good internal consistency as measured by Cronbach's Standardized Scale alpha (n=268). The combination scores indicate almost perfect additivity (1.0) as measured by Tukey's Additivity Test ( Kolb, 1984 ).
Computer Anxiety Scale.
The short form of Oetting's COMPAS was used for this study. The reasons for choosing the COMPAS were as follows: (a) The objective of the investigation was to measure computer anxiety; (b) The testing time was limited; and (c) The COMPAS is reportedly valid for measuring vocational-technical teachers' computer anxiety levels ( Gordon, 1993 ).
The short form of the COMPAS consists of Likert-type items for which respondents report their subjective feelings of anxiety. The overall computer anxiety scale range is 10 to 50. The subscale ranges and their respective classifications are 10-19 (very relaxed/confident), 20-26 (generally relaxed/comfortable), 27-32 (some mild anxiety present), 33-36 (anxious/tense), and 37-50 (very anxious).
The COMPAS has been reviewed by psychologists Kleinmuntz ( 1985 ) and Wise ( 1985 ). Even though Kleinmuntz questioned the importance of measuring computer anxiety, both reviews indicated that if one wishes to measure computer anxiety, the COMPAS is the test to use. Using Cronbach's alpha, Oetting ( 1983 ) calculated the overall internal consistency reliability for the short form as r = .88. According to Oetting, the total score on the short form correlates very highly (r = .96) with the total score on the long form, but no subscale scores can be obtained.
Participant Inventory.
The participant inventory form was designed to collect demographic and background data about the participants. It consists of questions related to age, ethnic/cultural background, gender, educational level, teaching/professional area, school type, the number of computer-related courses or training workshops completed, self-ranked computer skills, and self-perception toward computer usage.
Procedure
All 245 educators were sent the survey, along with a letter of explanation of this study. In order to preserve anonymity, the survey package was not marked or numbered in any way. The return rate was 84%. Of the returned packages, 80.8% provided usable data. Data were considered unusable if one or more of the forms (LSI, COMPAS, and the participant inventory) were incomplete or completed incorrectly.
Results
Demographic Variables
Table 1 shows the educators' demographic variables.
Table 1 Characteristics of Respondents a
Variable N Percent
Age Under 29 years 12 5.9 30-39 years 46 22.8 40-49 years 74 36.6 50 or more years 70 34.7 Ethnic/Cultural Background White, Non-Hispanic 116 57.4 Black, Non-Hispanic 20 9.9 Hispanic and Other 66 32.7 Gender Male 92 45.5 Female 110 54.5 Highest Education Level Less than 4 years of college 32 15.8 4 years of college 64 31.7 Graduate level 106 52.5 Teaching/Professional Area Business and Marketing 48 23.8 Health 20 9.9 Family & Consumer Science 36 17.8 Technology 32 15.8 Trade and Industrial 34 16.8 Other 32 15.8 School Type Vocational/Adult Education Center 62 30.7 Senior High School 90 44.6 Middle School 46 22.8 Other 4 2.0 Learning Style Accommodator 32 15.8 Diverger 26 12.9 Converger 92 45.5 Assimilator 52 25.7
a Total participants were 202. Fewer than 3 in 10 of the educators were younger than 40 years old (28.7%). The majority were over 40 (71.3%). This result might be explained by the work experience of vocational-technical educators. Previously collected data showed that vocational teachers had an average of 17 years of teaching experience. Nationally, 66 percent of public-secondary vocational teachers had paid nonacademic work experience that related directly to their subject specialization. On the other hand, only 19 percent of all academic teachers had such work experience ( Data File, 1995 ).
The ethnic/cultural background of the vocational-technical educators of Dade County reflects South Florida's culturally diverse environment. Nearly 1 in 10 (9.9%) respondents were black, non-Hispanic; more than 30 percent (32.7%) of the respondents were Hispanic and other; and almost 6 in 10 (57.4%) were white, non-Hispanic. Approximately 55% of the respondents were women and 46% were men. Most (84.2%) had completed at least four years of college. This finding seemed to match the pattern of the entire nation. The majority of the vocational-technical educators (88%) had bachelor's degrees. Half had advanced degrees.
The school type of vocational-technical educators in Dade County followed the national pattern, with some variation: three in ten Dade County educators taught in vocational education schools, compared to two in ten nationwide. Nearly seven in ten taught in comprehensive high schools, compared to eight in ten nationwide.
The respondents' learning styles were identified by using Kolb's LSI. Interestingly, most educators in vocational-technical education had learning styles that were less focused on people and more concerned with ideas and abstract concepts. Over 71% of the educators tended to be convergers and assimilators, that is, they preferred to learn by thinking, they analyzed ideas logically, and they planned systematically. Their actions resulted from an intellectual understanding of situations. In contrast, only about 29 percent of the responders preferred to learn from feeling, they tended to be accommodators and divergers. They learned from specific experiences, they related to people, and they were sensitive to people's feelings. Additionally, a majority (61%) preferred to learn by doing, they intended to be convergers and accommodators. They had the ability to get things done, they were risk-takers, and they influenced people and events through action. The rest preferred to learn by watching and listening, they intended to be divergers and assimilators. They carefully observed before making judgements, viewed issues from different perspectives, and looked for meaning in situations. Learning styles were classified as accommodator (15.8%), diverger (12.9%), converger (45.5%), and assimilator (25.7%).
Respondents' Perceptions of Computer Usage
Table 2 shows that a majority of the responding vocational-technical educators had a positive attitude toward participation in computer-based training and the use of computers in the classroom. However, there were some differences between their opinions about computer-based training and computer implementation in classrooms. None of the respondents believed that computer-based training was unnecessary for vocational-technical educators. Most of respondents (97%) thought there was a need to train vocational-technical educators in the use of computer technology in the classroom or laboratory. However, fewer respondents (44.6%) rated highly the extent to which computer technology was an essential component of their classroom; three percent (3%) of the respondents indicated that applying computer technology to their classroom was not important at all. This finding may be explained by the fact that respondents were in a variety of professional/teaching fields. Some respondents indicated that computer-based training for computer knowledge and skills was essential; however, computer technology did not necessarily play a critical role in their professional domain.
Table 2
Respondents Perception of Computer Usage Variable n Percent
Rating scale: the need for computer-based training Low 6 3.0 Moderate 44 21.8 High 152 75.2 Rating scale: computer technology applied in the classroom None 6 3.0 Low 38 18.8 Moderate 68 33.7 High 90 44.6
The survey shows that most of the educators were involved in computer-based training. Eighty-five percent of the respondents indicated they participated at least once in a computer-related training program or class. However, only 28 percent of the respondents indicated that they had a high level of computer skill and knowledge; 30 percent indicated they had little or no skill or knowledge.
Computer Anxiety Levels of Vocational-Technical Educators
Table 3 presents the descriptive data on the anxiety level scores of vocational-technical educators as measured by the COMPAS. The scores ranged from a maximum of 50 to a minimum of 10. The mean COMPAS score for the sample was 20.92 (SD = 7.62). Less than one-quarter of the respondents (24.8%) were experiencing some computer anxiety at the levels COMPAS depicts as "some mild anxiety present," "anxious/tense," or "very anxious."
Table 3 COMPAS Scores of Respondents for Overall Computer Anxiety
Computer Anxiety Levels Range Frequency Percent
Very anxious 37-50 6 3.0 Anxious/tense 33.36 4 2.0 Some mild anxiety present 27-36 40 19.8 Generally relaxed/comfortable 20-26 54 26.7 Very relaxed/confident 10-19 98 48.5
Demographic Variables and Computer Anxiety Table 4 contains data on computer anxiety and demographic variables, before taking into account and making adjustments for initial differences in computer related experiences.
One-way analyses of variance (ANOVA) indicated that there were no significant differences for computer anxiety among learning style (p=.95), age (p=.34), gender (p=.08), and ethnicity (p=.50). There were significant differences (a < .05) for computer anxiety among educational attainment level (p=.01), teaching/professional area (p <. 001), and school type (p < .001).
Table 4 One-Way ANOVA on Computer Anxiety
Variable M SD n F
Learning Style 0.13 Accomodator 20.63 5.03 32 Diverger 20.31 8.89 26 Converger 21.24 7.90 92 Assimilator 20.85 7.92 52 Age 1.12 20-29 years old 17.67 7.50 12 30-39 years old 20.30 7.11 46 40-49 years old 21.73 8.24 74 50 or more years 21.03 7.25 70 Educational
Attainment Level3.68* Less than 4 years college 25.86 7.75 14 Non-degree Certification 17.22 4.57 18 4 years college 21.44 7.88 64 More than 4 years college 20.59 7.56 106 Gender 1.74 Males 21.93 7.18 92 Females 20.07 7.90 110 Ethnic/Cultural
Background0.70 White, non-Hispanic 21.55 6.96 116 Black, non-Hispanic 20.40 8.97 20 Hispanic 20.19 8.46 62 Teaching/
Professional Area5.81* Business/Marketing 17.13 6.51 48 Health 24.00 9.79 20 Family & Consumer Science 21.61 7.58 36 Technology 19.82 6.29 32 Trade/Industrial 23.94 7.32 34 School Type 8.97* Vocational/Adult Education 22.90 8.68 62 Senior High School 19.49 6.45 90 Middle School 22.83 6.92 46
* p < .05 Table 5 contains data on demographic variables, self-ranked computer competence (1 = none, 2 = low, 3 = moderate, 4 = high) and computer-based training (determined by number of training classes or sections completed).
ANOVA indicated that there were no significant differences for self-ranked computer competence and computer-based training among learning style (p=.22, p=.67), age (p=.48, p=.70), and ethnic/cultural background (p=.61, p=.97). ANOVA indicated that there were significant differences for self-ranked computer competence and computer-related training among educational attainment level (p=.01, p<.001), gender (p=.03, p< .001),
Table 5 One-Way ANOVA on Self-Ranked Computer Competence and Computer-Based Training
Competence Training M SD F M SD F
Learning Style 1.49 .51 Accomodator 2.69 .78 4.56 5.00 Diverger 2.86 .78 6.54 6.81 Converger 3.02 .85 5.91 6.88 Assimilator 3.00 .84 5.92 6.47 Age 2.68 .47 20-29 years old 3.50 .52 7.17 7.07 30-39 years old 2.91 .84 6.30 6.26 40-49 years old 3.00 .88 5.20 5.89 Educational Attainment Level 3.72* 5.98* Less than 4 years college 2.57 .94 2.29 3.54 Non-degree Certification 2.89 .90 3.00 3.11 4 years college 2.75 .76 4.56 5.05 More than 4 years college 3.11 .82 7.45 7.44 Gender -2.15* -3.77* Males 2.80 .80 4.04 4.10 Females 3.05 .84 7.24 7.67 Ethnic/Cultural Background .50 .03 White, non-Hispanic 2.98 .76 5.83 6.85 Black, non-Hispanic 2.80 1.11 5.90 6.75 Hispanic 2.90 .86 5.58 5.82 Teaching/Professional Area 10.05* 25.67* Buisness/Marketing 3.50 .65 12.54 7.65 Health 2.50 .95 2.30 2.83 Family & Consumer Science 2.83 .77 3.56 5.03 Technology 3.13 .49 6.19 5.25 Trade/Industrial 2.65 .92 2.47 1.85 School Type 5.06* 9.54* Vocational/Adult Education 2.71 .89 3.58 4.56 Senior High School 3.13 .86 7.93 7.60 Middle School 2.87 .62 5.00 5.10
* p < .05 teaching/professional area (p < .001, p <.001), and school type (p=.01, p < .001).
Table 6 contains data on computer anxiety and demographic variables, after taking into account and making adjustments for initial differences in computer-related experiences (self-ranked computer competence and computer-based training).
Analyses of covariance (ANCOVA) indicated that there were no significant differences for computer anxiety among learning style (p=.13), age (p=.20), gender (p=.63), ethnic/cultural background (p=.67), and teaching/professional area (p=.53). There were significant differences (a <.05) for computer anxiety on educational attainment level (p=.003), and school type (p=.01).
Table 6 ANCOVA on Computer Anxiety Controlling by Computer Related Experience
Computer Anxiety Variable Observed Mean Adjusted Mean
Learning Style Accomodator 20.63 19.42 Diverger 20.31 20.05 Converger 21.24 22.03 Assimilator 20.85 21.51 Age 20-29 years old 17.67 20.30 30-39 years old 20.30 19.48 40-49 years old 21.73 21.42 50 or more years 21.30 19.54 Educational
Attainment LevelLess than 4 years college 25.86 24.30 Non-degree certification 17.22 17.49 4 years college 21.44 20.99 More than 4 years college 20.59 22.33 Gender Male 21.94 21.21 Female 20.07 20.79 Ethnic/Cultural
BackgroundWhite, non-Hispanic 21.55 22.09 Black, non-Hispanic 20.40 19.82 Hispanic 20.19 20.24 Teaching/
Professional AreaBusiness/Marketing 17.13 19.78 Health 24.00 21.89 Family & Consumer Science 21.61 21.28 Technology 19.81 20.90 Trade/Industrial 23.94 22.65 School Type Vocational/Adult Education 22.90 21.88 Senior High School 18.49 19.69 Middle School 22.83 22.65
Conclusions and Discussion
The results of this study lead to a number of conclusions about computer anxiety. Computer-related experience does influence computer anxiety. Notice that after taking into account and making adjustments for initial differences in computer-related experience, the mean differences on computer anxiety among each demographic variable decreased. Teaching or professional area was found to be significantly related to computer anxiety before computer-related experience was taken into account. After taking differences in computer-related experience into account and making appropriate statistical adjustments, the relationship between computer anxiety and teaching/professional area was not significant.
The results of this study indicated there were no relationships between computer anxiety in vocational-technical educators and these demographic variables: age, ethnic/cultural background, and teaching/professional area. Learning style did not relate significantly to computer anxiety before differences in computer-related experience were taken into account. However, there was notable variation in computer anxiety when the results obtained prior to adjustments for computer-related experience (p=.95) are compared with the results obtained after such adjustments (p=.13). Although the COMPAS may be able to identify the influence of learning style on computer anxiety, the relationship between two variables needs more in depth investigation.
Two variables were found significantly related to computer anxiety even after taking into account, and making appropriate statistical adjustments for initial difference in computer-related experiences. These were educational level and school type. Perhaps it is easier for educators with more educational experience to gain confidence than it is for those with less educational experience. Also, the type of school environment may influence the development of teachers' self-perceptions about computers and computer use.
The results and conclusions of this study indicate that the following methods may be effective in reducing computer anxiety:
- Reduce computer anxiety by increasing computer-based training. Vocational-technical education administrators should provide educators with more opportunities to get hand-on experience with computers. Administrators should encourage educators spend more time in computer-based training and provide educators easy access to computers. More exposure could help reduce computer anxiety among vocational-technical educators.
- Reduce computer anxiety by enhancing computer competence. Easier and more efficient software should be adopted in vocational-technical education. Computer-based training programs should focus on concrete computer skills, rather than teaching abstract concepts and jargon. Initial training should introduce educators to application or productivity software (word processing, graphics, page layout or desktop publishing, slide show or presentation, database, spreadsheet and charting, hypermedia, and telecommunication programs), rather than to computer programming (BASIC, Pascal, C, C++, etc.).
- Reduce computer anxiety by increasing computer confidence. Computer-based training programs should be planned and developed to prevent the escalation of initial anxiety ( Yang, 1996 ). This could be accomplished by focusing on building confidence and a sense of personal control in an individualized, non-threatening learning environment and also by eliciting the efforts of family, trainers, peers, and colleagues to help dispel stereotypes.
- Reduce computer anxiety by improving computer perception. Computer-based training programs should be relevant to educators' interests and learning style. The training programs should provide hands-on learning, opportunities for feedback, supportive and caring instruction, and active learning experiences in which educators work on their own projects and see the application of computer skills to their area of study ( Comer & Geissler, 1998 ).
Authors
Yang is an Assistant Professor in the Department of Curriculum and Instruction at the State University of New York at Oswego.
Mohamed is an Associate Professor in the Department of Subject Specialization at Florida International Unviersity, Miami.
Beyerbach is a Professor in the Department of Curriculum and Instruction at the State University of New York at Oswego.
References
Anderson , A. A. (1996). Predictor of computer anxiety and performance in information systems. Computers in Human Behavior , 12 (1), 61-77 .
Ayersman , D. J. (1996). Effects of computer instruction, learning style, gender, and experience on computer anxiety. Computers in the Schools , 12 (4), 15-30.
Bohlin , R. M., & Hunt, N. P. (1995). Course structure effects on students' computer anxiety, confidence and attitudes. Journal of Educational Computing Research , 13 (3), 263-270.
Chen , M. (1986). Gender and computers: The beneficial effects of experience and attitudes. Journal of Educational Computing Research , 2 (3), 265-282.
Comer , F. G., & Geissler, C. (1998). A methodology for software evaluation . Proceedings of SITE 98, Washington , DC
Cooper , J., & Stone, J. (1996). Gender, computer-assisted learning, and anxiety: with a little help from a friend. Journal of Educational Computing Research , 15 (1), 67-91.
D'Amico , M., Baron, L. J., & Sissons, M. E. (1995). Gender differences in attributions about microcomputer learning in elementary school. Sex Roles: A Journal of Research , 33 (5-6), 353-385.
Data file. (1995, January). Vocational Education Journal.
Fitzgerald , G. E., Hardin, L., & Hollingsead, C. (1997). Engaging preservice teachers in hypermedia authoring: Process and outcomes. Journal of Educational Computer Research , 16 (2), 191-207.
Gunter , G. A., Gunter, R. E., & Wiens, G. A. (1998). Teaching pre-service teachers technology: An innovative approach . Proceedings of SITE 98, Washington, DC
Gordon , H. R. D. (1993). Analysis of the computer anxiety levels of secondary technical education teachers in West Virginia . (ERIC Document Reproduction Service No. ED 357 218)
Hadfield , O. D., Maddux, C. D., & Love, G. D. (1997). Critical thinking ability and prior experience as predictors of reduced computer aversion. Computers in the Schools , 13 (3-4), 13-29.
Harris , J. B., & Grandgenett, N. (1996). Correlates among teachers' anxiety, demographic, and telecomputing activity. Journal of Research on Computing in Education , 28 (3), 300-317.
Heinssen , R. K., Glass, C. R., & Knight, L. A. (1987). Assessing computer anxiety: Development and validation of the computer anxiety scale. Computers in Human Behavior , 3 (1), 49-59.
Kleinmuntz , B. (1985). Review of Oetting's computer anxiety scale . The Tenth Mental Measurements Yearbook. Lincoln: University of Nebraska Press.
Kolb , D. A. (1984). Experiential learning: Experience as the source of learning and development . Englewood Cliffs, NJ: Prentice-Hall.
Kolb , D. A. (1985). Learning-style inventory . Boston: McBer & Company.
Maurer , M. M. (1994). Computer anxiety correlates and what they tell us: A literature review. Computers in Human Behavior , 10 (3), 369-376.
McInerney , V., McInerney, D. M, & March, H. W. (1997). Effects of metacognitive strategy training within a cooperative group learning context on computer achievement and anxiety: An aptitude-treatment interaction study. Journal of Educational Psychology , 89 (4), 686-695.
McInerney , V., McInerney, D. M., & Sinclair, K. E. (1994). Student teachers, computer anxiety and computer experience. Journal of Educational Computing Research , 7 (1), 27-50.
Oetting , E. R. (1983). Manual: Oetting's computer anxiety scale (COMPAS). Ft. Collins, CO: Rocky Mountain Behavioral Science Institute.
Reed , W. M., Ervin, J. R., & Oughton, J. M. (1995). Computers and elementary education students: A ten-year analysis. Journal of Computing in Childhood Education , 6 (1), 5-24.
Reznich , C. (1996). Applying minimalist design principles to the problem of computer anxiety. Computers in Human Behavior , 12 (2), 245-261 .
Whitley , B. E. (1996). Gender differences in computer-related attitudes: It depends on what you ask. Computers in Human Behavior , 12 (2), 274-289 .
Wise , S. L. (1985). Review of Oetting's computer anxiety scale . The Tenth Mental Measurements Yearbook. Lincoln: The University of Nebraska Press.
Yang , H. (1996). An investigation of computer anxiety among vocational-technical teachers in Dade County, Florida . Doctoral dissertation, Florida International University, Miami.