Evaluating Computer Selfâ Efficacy (CSE) Effects

Evaluating Computer Self‐ Efficacy (CSE) Effects For Older Adult Participants in a Public Library Computer Training Program James H. Brown – LIS 591 | May 7, 2013 (rev.) Instructor : Dr. Peekhaus ‐‐ CSE RQ, LR, Meth

CSE Effects for Older Adults in Library Computer Training Program

Evaluating Computer Self-Efficacy (CSE) Effects for Older Adult Participants in a Metropolitan Public Library Computer Training Program Two major trends of the late twentieth century have intersected in important ways to define the 21st century and transform society in America: the aging of the American population and the rapid development and deployment of information and computer-based technologies (Czaja & Lee, 2007; DeOllos & Morris, 1999; Hawkins, 2005). There were 40.2 million people over the age of 65 in the U.S. in 2010 (U.S. Census Bureau, 2010). Access to digital information is now recognized as a fundamental right of the U.S. citizen and America is rapidly moving toward a digitally-accessed government (Atkinson, 2000; Hawkins, 2005; Warschauer, 2002). The computer technology revolution has not gone unnoticed by older adults over the decades. In 2012, for example, for the first time, more than half (53%) of all older adults over age 65 have reported accessing the Internet or email on their computers (Zickuhr & Madden, 2012). But in spite of this, statistics have consistently shown that this group of adults trails all others in computer technology use. Connectivity is 97% for Americans of ages 18-29, 91% for those aged 30-49 years old, and 77% for other older adults who are aged 50-64 years old (Zickuhr & Madden, 2012). There is, and always has been, a disparity (the “digital divide”) in access to and use of computer technology in the United States that is related to age (over the age of 65), income (less than $2000 per month), ethnicity (minorities—African Americans and Hispanics), education (less than high school), and location (rural and some crowded and diverse urban areas; U.S. Census Bureau, 2007). Age includes all other categories. Researchers have repeatedly suggested that lacking computer technology skills may restrict the ability of some older adults to live independently and to participate fully in American society by using digital information and engaging in electronic commerce (Chaffin & Harlow,


2005; DeOllos & Morris, 1999; Dickinson & Hill, 2007; Furlong, 1989; Morrell, Park, Mayhorn, & Kelley, 2000; Saunders, 2004). Although some older adults simply do not want to use computers (Dickinson & Gregor, 2006), most recent research studies indicate that most older adults do want to learn about computer technology; that they seek computer training to do so; and that they can be successful at it, if the program adjusts to accommodate aspects of the aging process and adopts an age-appropriate training methodology (e.g., Baldi, 1997; Czaja et al., 2001; Czaja et al., 2006; Hollis-Sawyer & Sterns, 1999; Jones & Bayen, 1998; Laguna & Babcock, 2000; Mayhorn et al., 2004; Morrell, 2002). Need for Computer Training and the Public Library There is a need to train older adults to learn about computer technology basics so that they can become comfortable with the new technology and then use it to their advantage in later life, whether they seek later careers or simply use computer technology to improve their daily personal lives. In this regard, Xie and Bugg (2009) have noted the particularly appropriate role that the American public library can play in meeting computer training needs for older adults, as the library has a mission to provide free or low-cost access to computer information services; library branches are generally accessible to their surrounding communities; and they have the necessary computers and Internet infrastructure in place to support computer technology for their patrons. Segrist (2004) suggests that training programs need to incorporate strategies to improve the comfort level of older adults and to provide continual support as older adults try to learn and use new knowledge with computers. For most training programs, this would be impossible. Training ends at the close of the session or program. However, within the setting for training


and support that exists within the public library, such a goal may be achievable (Lawton, 2001; Ng, 2008). For example, Xie and Bugg (2009), in their development of a public library computer training program on the Internet for older adults, reported that “computer interest and efficacy significantly increased” during their library Internet computer training classes (p. 155). The Role of Computer Self-Efficacy in Computer Skills Training In a previous qualitative study of nine participants (ages 64-80) in an ongoing public library computer training program, I found a range of reported comfort levels that these older adults had with their knowledge of computer technology, their perceived level of computer skills, and their technology usage. An important finding of the study was that although some of the older adults did not appear to have learned many concrete computer skills, they nevertheless were highly satisfied with what they felt that they had learned, and they were not afraid to learn more about computers if they saw the need to do so in the future (Brown, 2011). Did these older adult training participants gain this comfort level with computer technology as a result of their experiences within the computer training classes they took at the library? In this proposed study, I want to examine this particular aspect of participation in a large public library computer training program which is often attended by older adults in the surrounding library communities. Before proceeding, I will define a few important terms for the study. I begin with the concept of self-efficacy, which is central to this study.


Definitions and Descriptions Self-efficacy arises from Bandura (1986) and involves “beliefs in one’s abilities to mobilize the motivation, cognitive resources and courses of action needed to meet situational demands” (Bandura & Wood, p. 260). Computer self-efficacy (CSE) may be defined as “a judgment of one’s capability to use a computer” (Compeau & Higgins, 1995). In this study, the situational demands are those related to attending the classes in computer instruction and persisting in the activities that lead to learning about computer technology. It has been shown that CSE is a factor that has been associated with greater levels of computer technology use for older adults (e.g., Czaja et al., 2006). In this research study, I will attempt to follow-up my earlier qualitative findings of the public library’s older adult participants, in order to see if they may have gained in CSE while completing the basic computer skills classes in the library’s computer literacy program. I will use the Computer Self-Efficacy Scale for Adults (CSESA; Brown, 2007), which hypothesizes that the construct of total computer self-efficacy (T_CSE) is composed of three equal components regarding confidence in one’s ability to acquire the necessary knowledge, skills, and abilities that are related to the use of computer hardware (CSE_H), computer software (CSE_S), and computer Internet-related skills (CSE_I). The CSESA was designed to evaluate introductory-level computer training courses which stress information and practice on computer hardware, software, and skills related to using the Internet. The CSESA is a questionnaire composed of 36 items in total, with 12 items each assigned to the three subscales representing the domains of hardware, software, and Internet computer skills. A six-point Likert scale, with response options ranging from completely disagree (the


least confident response, code level=1) to completely agree (the most confident response, code level= 6), is employed for the instrument. As the data that will be collected are ordinal in nature (responses to a six-point Likert scale), a non-parametric quantitative treatment of the CSESA survey instrument is anticipated as I gather responses to this survey; this approach will be explained in detail in later sections. There is no age requirement for participation in the adult training programs of the library branches of this study, other than the assumption that attendees of the program will not be attending traditional high-school classes. Although this study employs the term older adults, attendees of the program typically are those of age 40 and above and may include adults who are much younger, especially if they are having difficulties gaining access to other training programs. The CSESA will differentiate among age groups in order to determine whether CSE effects are age-related. In the pilot study for the CSESA (Brown, 2007), for example, older adults aged 60-80 years old had significantly lower CSE levels compared to all other adults (ages ranging from 20 to 59 years old). There is a need to determine whether older adults benefit from a program of basic computer instruction in ways that not only enhance their knowledge and competencies of the subjects being taught, but also in ways that enhance their comfort level with computers in general so that they are more likely to actually use the technologies they have just learned about. A key predictor for future computer technology use, within the training scenario, is computer self-efficacy (Baack & Brown, 1991; Czaja, Sharit, Charness, Fisk, & Rogers, 2001; Chaffin & Harlow, 2004; Morris, 1994). The CSESA instrument employed for this study seeks to measure this particular phenomenon.


Research Questions With regard to the older adult participants of a library computer training program, this study seeks to determine whether a relationship exists between the participants’ CSE level and their attendance in the classes of the library’s computer training program. In particular, participants may attend any or all classes in a nine-week sequence of two-hour computer training classes (18 hours of instruction), progressing from such topics as: basic computing skills, such as how to use a mouse and turn a computer on; to using an email; Internet use for searches and information; word processing; spreadsheet use; and creating presentations. In this study, I will use the CSESA instrument in order to help me answer the following research questions: (1) Do the older adult participants of this computer training program demonstrate significantly different levels of CSE when their pre-training CSESA scores (total CSE, CSE_T; hardware CSE, CSE_H; software CSE, CSE_S; and/or Internet CSE, CSE_I), are compared to their post-training CSE scores, at the end of their computer training? (Or, is there a post- vs. pre-training CSE effect?) (2) Does a relationship exist between the participants’ CSE (CSE_T) level, as shown in their CSESA scores, as the participants complete their computer training experiences, beginning with their learning basic computer terminology and skills (lesson 1; CSE_H type training), to Internet training (lessons 2-4, CSE_I type training), and finally, to specific types of software training (lessons 5-9, CSE_S type training)? (3) Are there any significant differences in participants’ CSE level that may be related to their reported gender, age groups, the location of computer instruction (library class


location), their reported prior computer experience, and/or their self-reported level of perceived computer competence (low, intermediate, high)? Literature Review This study, which seeks to examine computer self-efficacy effects for computer training of older adults in an established library basic computer skills training program, focuses on the training of older adults to use computer technology and key ways in which self-efficacy impacts their successful learning of computer technology. This literature review begins by considering the central concept of self-efficacy, as proposed by Albert Bandura. It then focuses on computer self-efficacy and other variables which have been identified by researchers regarding how adults learn from computer training. The development and use of instruments designed to measure computer self-efficacy under a variety of research and training conditions are discussed in some detail. The physical and psychological status of older adults presents a special concern in the delivery of computer training programs for this population. The review looks briefly at some of the ways in which training could accommodate aging variables in order to facilitate learning and mastery of the skills being taught. In conducting this literature review, I used primarily the EBSCO database interface and included the additional databases as follows: Academic Search Complete; Education Research Complete; ERIC; Library, Information Science Technology & Abstracts with Full Text; and PsycINFO. The literature search was conducted using combinations of one or more of the following keywords: social cognitive theory (SCT), self-efficacy, computer self-efficacy (CSE), CSE instrument, adult, older adult, senior, computer, technology, computer training, aging variables. Selection criteria for inclusion involved the use of only peer-reviewed journal articles.


The qualifier adult or older adult was used in combination with other key words whenever possible to limit the scope of individual searches. This proposal updates and narrows the focus of my previous qualitative research (Brown, 2011); in particular, I conducted a new search for computer self-efficacy instruments used in conjunction with research studies that targeted computer skills acquisition for older adults within a training setting. Seven studies were found that were deemed relevant to this proposal and met these requirements. I attempted to keep the scope of the review as narrowly focused on training and CSE measurement issues as possible, using adults and older adults as the target populations, which was a primary criterion for inclusion in this study. Self-Efficacy and Social Cognitive Theory The concept of self-efficacy is derived from Albert Bandura’s theory of behavioral change, which has been named social cognitive theory (SCT), because it proposes that human functioning occurs in a triadic codetermination in which behavior, personal characteristics, and the environment interactively determine the course of events in one’s life (Bandura, 2012). This causal loop is called reciprocal determination (Bandura, 2012). Bandura’s original work was based on perceived self-efficacy, which he described as “the conviction that one can successfully execute the behavior required to produce the outcomes” (Bandura, 1977, p. 193). Self-efficacy is central to the theory, because it allows for “people to have a hand in shaping events and the course their lives take” (Bandura, 2012, p. 11). Although the theory always refers to perceived self-efficacy, the author recognizes that it has been linguistically shortened to self-efficacy in literature (Bandura, 2012). Bandura (1977) proposed that four factors are related to the development of self-efficacy and provide sources for efficacy expectations: performance accomplishments, vicarious


experiences, verbal persuasion, and emotional arousal. The performance aspect of efficacy expectations is particularly important, as successes raise the expectation for mastery, while repeated failures lower them (Bandura, 1977). Bandura anticipated that self-efficacy effects could generalize to other situations or activities, usually most effectively for those closely related to those which have already been mastered. By vicarious experiences, Bandura refers to social modeling or observing a desired behavior and then attempting to perform it; this brings about a weaker expectation for success than simply learning the activity itself. Verbal persuasion refers to encouraging a person that he or she has the capability to master a difficult situation, and it may also involve providing aids to perform the action. Stressful situations often cause an emotional response, for example, computer anxiety. In order for a person to attempt a task under stressful conditions, some means for alleviating the stress must be used. Such procedures as relaxation and biofeedback, or desensitization may be used to assist persons in this condition (Bandura, 1977). In later refinements of the theory, Bandura suggested that the magnitude (task difficulty) and strength (confidence in a particular ability) of the task to be performed should be considered. For example, one might ask the respondent if he can perform a given task at all (‘Yes/No’), and if ‘Yes’, then to rate the level of his confidence on a scale of 1 (low)-10 (high, see Downey & Zeltmann, 2009). Bandura (2012) has also suggested that instruments measuring self-efficacy should be unipolar, that is, start from 0 (no confidence) and range upward. There is no level below that of no confidence (zero confidence). Computer self-efficacy has been conceptualized as an individual’s perception of his or her capability related to specific computer skills and knowledge (Murphy et al., 1989). Research


has demonstrated that students with higher self-efficacy tend to persist more in the face of difficulty (Torkzadeh & Van Dyke, 2002), whereas those with lower self-efficacy tend to engage in fewer challenging activities (Bandura, 1977, 1982). Moos and Azevedo (2009) conclude that “students’ perceptions of their capabilities to meet situational demands are related to their performance, persistence, and choice” (p. 578). While computer self-efficacy has long been identified as an important factor in the mastery of computer technology, it has primarily been studied under restricted conditions and for small sample populations. The measurement of CSE effect for this study will be employed under actual training conditions across a large and diverse adult training population and within the context of an ongoing intervention across years at a public library. Thus, this study offers the opportunity to extend our knowledge about what role CSE can play in making computer technology more accessible and comfortable for a very large population of adults in an urban setting and in a program that is expected to have a long lifespan. Computer Self-Efficacy: Contexts, Instruments, and Variables Moos and Azevedo (2009) conducted a literature review of factors related to computer self-efficacy and “the relationship between computer self-efficacy, learning outcomes, and learning processes” related to computer-based learning environments (CBLE, p. 576). They report a finding by Dunlop (2005) that undergraduate students participating in a 16-week (semester) computer-based training program, originally having very low levels of computer selfefficacy, experienced a significantly higher level of computer self-efficacy by the end of the course instructional period. This proposal seeks to extend our understanding about whether computer self-efficacy effects might also accompany the gradual mastery of basic computer


skills for adults participating in training programs in a public, non-formal (library) classroom setting. Lowe and Holton (2005) propose a system theory of computer-based learning and instruction (CBL, CBI) that consists of variables related to input, process, and output. These variables may be further categorized as being related to the support or design aspects of computer-based instruction for adults. Computer self-efficacy (CSE) is one of three variables Lowe and Holton believe impact CBI design; the other two are self-directness and learning goal level. With respect to CSE, Lowe and Holton believe that CSE “is essential in the learning and using of computers” (p. 169) and further, that individuals with higher CSE will exert more effort and persist in learning even difficult tasks, while those with lower CSE may attempt to avoid these tasks altogether. Saadé and Kira (2009) examine the mediating effect of computer self-efficacy (CSE) on computer anxiety (ANX) and perceived ease of use (PEU) of a computer interface (learning management system, LMS). They employ a survey methodology using an instrument containing 18 items related to the three constructs (PEU, ANX, and CSE). Survey data from 645 university students were analyzed. They first tested the hypothesis that ANX was significantly related to PEU (H1: ANX  PEU). They then tested the hypothesis that CSE mediated this effect (H2: ANX  (CSE)  PEU). H1 was confirmed with a significant effect of anxiety on perceived ease of use for (ANX  PEU, β= -0.276, p=.0001), while H2 was also confirmed, showing that computer self-efficacy reduced (mediated) anxiety effects on PEU (ANX  (CSE)  PEU, β= -0.094, p=.001). This finding helps to guide instruction and training in order to focus on


increasing levels of comfort, reducing anxieties, and providing support for students with the learning of new computer experiences. It might be useful, therefore, to know the anxiety levels at the beginning of the training session. Laganá, Oliver, Ainsworth, and Edwards (2011) used their own Older Adults’ Computer Technology Attitudes Scale (OACTAS) and the Computer User Self-Efficacy Scale (Cassidy & Eachus, 2002) to train 96 older adults on the topics of word processing, email, and information searching, using six weeks of individual training. They found significantly improved scores on both measures (OACTAS, F(1,91)= 27.82, p < .001; Computer self-efficacy, F(1,90)= 12.87, p= .001) for post-test compared to pre-test values. The OACTAS examined four variables related to technology use: CCVI, comfort communicating via the Internet; SACT, satisfaction with available computer technology; PhysCCT, physical comfort with computer technology; and PsyCCT, psychological comfort with computer technology. Results were obtained using factor analysis and analysis of variance for comparisons of the experimental group vs. control group; they report a randomized sample, with 48 participants assigned to each of the groups. The finding suggests that attention must be paid to both psychological and physical aspects of training, and that these variables tend to accompany other gains in computer self-efficacy (see also Laganá, 2008). Celik and Yesilyurt (2013) found that computer self-efficacy was correlated with twelve other factors related to computer anxiety (negatively), attitudes toward computer technology (positively), and computer education (positively). They concluded that it was important for both students and teachers to have a high level of computer self-efficacy to improve their performance when adopting computer supported education.


Arning and Ziefle (2008) developed and validated an instrument to test computer expertise of older adults. Their instrument was normed using populations of older adults (N=40, age M=58.2 years) and younger adults (N=40, age M=22.6 years), with an acceptable level of difficulty demonstrated for the older adult population. Arning and Ziefle attempted to differentiate between computer expertise (relevant procedural and declarative knowledge of computer concepts) and computer experience (frequency of actual use of a computer). It was found that computer expertise was the best indicator of actual computer performance when the participants were tested in their ability to use a personal digital assistant (PDA). However, Arning and Ziefle (2008) admit that the results are based on a small population, and that the results based on performance of a small hand-held device (PDA) may not be an appropriate test of computer performance. Computer Self-Efficacy and Competence Research My primary interest in the Downey-Zeltmann (2009) study to follow is that it employs the construct of competency as well as generalized computer self-efficacy. In my own instrument, I employ the CSESA to assess how confident older adults are about accomplishing specific skills (competencies) that are either directly taught during their training sessions or are competencies that could be learned in the future using self-directed study. I will explain in detail how the competency-efficacy components are handled by the CSESA employed in this study in the methodology section of this proposal. Downey and Zeltmann (2009) take note of two opposing conceptual positions presented in the literature regarding the relationship of computer self-efficacy (CSE) and competence. The first position is that CSE is more strongly associated with skills training early in the process of


learning computer skills (e.g.,Yi & Im, 2004; Venkatesh, 2000); the second is that the relationship is strongest when skills mastery is complete (e.g., Gist & Mitchell, 1992; Mitchell et al., 1994). General Computer Self-Efficacy Downey and Zeltmann (2009) propose that general computer self-efficacy (GCSE) influences (is related to) competence in two dimensions (low level, high level), for six computer applications. They further assume that performance will be impacted, but this is not tested in their study. They ask, how will GCSE be impacted by competence level (low vs. high) in the six application domains rated? Downey and Zeltmann (2009) employed the instrument of Compeau and Higgins (1995) to measure the computer self-efficacy perceptions of 630 Midshipmen from the Navy Reserve Officers Training Program (NROTC) from thirteen universities (chosen randomly from 57 candidate institutions). The respondents for this study were young (M= 21.1 years old), primarily male (83.6%), and college educated (M= 2.4 years of college). The GCSE instrument inquired whether the respondents felt that they could complete an unspecified job using an unfamiliar piece of software (‘Yes/No’ response), and if so (‘Yes’ response), they rated what level of assistance they required to do so (on a scale of 1-10). Computer competence was measured using an instrument from Munro et al. (1997), which ranked respondents on a scale of 0 (no knowledge) to 7 (complete knowledge) regarding six computer application domains (email, word processing, graphics, spreadsheet, web design, and database applications). Results of the GCSE survey were then median split using the upper


half (high competence) vs. lower half (low competence) scores of the Munro et al. competence survey. This was done for each of the six application domains. Self-Efficacy at High/Low Levels of Competence Downey and Zeltmann (2009) employed structural equation modeling to determine if a significant relationship existed between GCSE and each of the 12 experimental conditions (six computer applications at low and high levels of competence). First, they found that competence was highest for email (M=9.36 high, M= 5.98 low) and word processing (M= 8.93 high, M= 5.72 low) applications; competence was intermediate for graphics processing (M= 6.95 high, M= 3.76 low) and spreadsheets (M=6.87 high, M= 3.68 low); and competence was lowest for web design (M= 5.21 high, M= 0.13 low) and database applications (M= 4.23 high, M= 0.12 low). Downey and Zeltmann (2009) found that GCSE was significantly and positively correlated with both competence levels of email, word processing, and graphics applications. In addition, there was also a positive correlation between GCSE and high competence levels for database, spreadsheets, and web design. For these applications, however, GCSE did not correlate with low levels of competence. The possibility exists here that at low levels of competence for these applications, the difficulty of using the software is perceived as too great to persist in learning them unless they were provided a considerable amount of support in doing so. Finally, Downey and Zeltmann (2009) map the predicted values for GSCE under the low/high competence conditions for the six applications and conclude that “as an individual moves from novice to expert, in terms of the predictability of GCSE. . . the relationship [between competence and GCSE] resembles a bell curve” (p. 106). The GCSE increases as mastery progresses, reaches a maximum somewhere in the middle of mastery, and then declines as total


mastery approaches” (p. 106). The authors conclude that “Trainers… should focus…on the students’ self-efficacy as a catalyst to learning the skills” (p. 106). We have learned that it is important to consider the level of expertise when designing a course for instruction of adults and that some novice learners may require considerable levels of support in order to become successful. Aging and Program Training Design Issues for Older Adults The training of older adults in computer technology is abundantly documented in research studies (Baldi, 1999; Kim, 2008). While research studies published prior to 2000 tended to emphasize how the aging variables influenced the abilities of older adults to learn about new technologies (Baack & Brown, 1991; Baldi, 1997; Echt, Morrell, & Park, 1998; Ellis & Alaire,1999; Hollis-Sawyer & Sterns, 1999; Jones & Bayen, 1998), more recent studies have explored the experiences of older adults in their investigations of both the process and the psychosocial environment of older adult learning of computer technologies (e.g., Chaffin & Harlow, 2005). Githens (2007) proposes that older adults will not only live longer, but they also desire to be active and learn throughout their lives. Older adulthood encompasses many decades of life, often starting as early as age 50 (AARP, for example). Older adulthood may involve periods of life which continue middle age; involve physical and mental losses; or require physical dependence on others for support. These periods may recur and need not be linear. Githens is one of few researchers to advocate e-learning programs for older adults and to recognize the value of older adults in the workforce. E-learning programs would include those for personal growth and change, those to train or re-train for work-related experiences, and workplace learning provided by employers and various volunteer work agencies (Githens, 2007).


Ng’s (2008) grounded-theory study of ten older adults enrolling in a computer literacy program at a social center in Hong Kong revealed how this group progressed from “anxious novices to motivated experts” (p. 1). Ng’s (2008) study illustrates the influence that the urban environment may have on older adults learning of computer technologies. His study includes consideration of the context for learning at three levels: societal (providing norms and values), peer and family (providing encouragement and recognition), and the classroom (providing curriculum). The environment can be either enabling, thus leading to success in learning; or discouraging, leading to resistance and possible failure in learning computer tasks. Would a program that is customized to the needs of older adult learning of computer technologies result in improved attitudes toward computer usage? Segrist (2004) used results of a training program offered by SeniorNet to 30 seniors (24 women and 6 men, ages 47 to 86) to measure participant attitudes toward computers using the Attitudes Toward Computer Questionnaire (ATCQ, Jay & Willis, 1992). This questionnaire measures computer-related attitudes on the seven scales of comfort, efficacy, gender, locus of control, dehumanization, interest, and utility. This is one of the most commonly used scales and has been validated with an older adult population. Using a paired t-test (before and after training comparisons) revealed no statistically significant differences in any of the dimensions, although Segrist notes that there was a trend toward more comfort with computer technology following the training. The study of aging variables on older adult performance of computer-related tasks is one of the most researched aspects of computer usage by older adults. The two final examples of this section highlight the general findings of a host of researchers interested in this particular topic.


Chaffin-Harlow Model for Computer Instruction Chaffin and Harlow (2005) were among the most adamant of researchers in favor of older adult learning of computer technologies. They posited a model of older adult learning of computer technology based on the premise that older adults can use computer technology in order to solve problems, to seek information on the Internet, and to maintain a level of independence and autonomy which can enhance their lives significantly. Given the proper motivation, older adults can gain the necessary computer skills, provided certain physiological and psychological barriers are addressed within an encouraging and supportive environment. The model stresses a biopsychosocial approach which recognizes and uses the many resources that older adults bring to their study of computer technology. At the same time, the model also acknowledges the importance of dealing with feelings of computer anxiety; of assisting older adults to build their self-confidence and thereby increase self-efficacy as they learn new tasks; and of accommodating the physical, cognitive, and sensory infirmities which are prevalent age-related limitations for learning in older adults. According to Chaffin and Harlow (2005), older adults can find expression through the use of word processing; can relieve social isolation by using the computer to communicate via the Internet; and can enhance their learning through the satisfaction of discovering new knowledge through the computer. The Chaffin-Harlow (2005) model for older adult learning of computer technology employs a teaching strategy to test new ideas, remove barriers to learning, and to “initiate cognitive learning” through an enlarged sense of self-worth for older adults (Chaffin & Harlow, 2005).


Predictions for Technology Use by Older Adults Czaja et al. (2006) tested 1204 adults that were ages ranging from 18 to 91, and concluded that older adults (age M= 70.49 years old) were less likely than middle-aged adults (age M=49.93 years old) or younger adults (age M=22.02 years old) to use computer technology or the Internet. Factors which predicted the use of technology included computer anxiety, fluid intelligence, and crystallized intelligence. Demographics of this sample included age, gender, education, occupational status, ethnicity, and general health compared with others of their age, satisfaction with health, and problems reported with health (Czaja et al., 2006). The researchers found that cognitive abilities and attitudinal variables were simultaneously important predictors of computer use. The importance of crystallized intelligence (the accumulated knowledge that usually occurs as a result of experience) was paired with general domain knowledge (knowledge about computers in general and work tasks that could be performed) in predicting technology use. Two other factors important to predicting technology use were level of education and wealth, with those of higher education and higher incomes much more likely to adopt technologies (Czaja et al., 2006). Czaja and Lee (2007) extend the discussion on usability problems caused by age-related changes (decline) in cognition by discussing ways in which computer interface designs can accommodate the needs of older adult users. Design issues include screen design, input device design, commands and operating procedures, inadequate training and instructional support (Czaja & Lee, 2007). In essence, Czaja and Lee (2007) confirmed that older adults (no matter whether 55 or 85 years old) were slower, less efficient, and at times less able to use new technologies, regardless of the type of device. However, at the same time, it was evident that, with training, older adults were able to use these technologies successfully and those older adults


were not “technophobic” (Czaja & Lee, 2007, p. 343). The researchers proposed designing computer and information interfaces that are useful for older adults by making the focus a usercentered one; determining user needs and performance experimentally; and using iterative and participative design (Czaja & Lee, 2007). Summary This brief review of the literature has examined computer self-efficacy (CSE) as a variable which can be employed to assist in the instruction of older adults who are attempting to learn about computer technology. The process of learning is complicated for adults, and especially for older adults, as they have to overcome aging variables and are often outside a formal learning environment (such as school) and must resort to self-directed methods in order to learn computer technology on their own. Recent research suggests that developing increased levels of computer self-efficacy can assist adults in learning new technology and that there is a relationship between computer self-efficacy and skill mastery (competence). Unlike the finding of Downey and Zeltmann, the CSESA instrument for this study would predict that the highest level of CSE would occur at a point of total mastery (or at least, a feeling of mastery) of the skills being taught. Training programs which employ the CSESA to monitor CSE during an active sequence of training classes could benefit by using the results to gauge the effectiveness of the instruction in maintaining the engagement of the participants during the training process.


Methodology The purpose of this research proposal is to explore the extent to which any relationships may exist between the computer self-efficacy (CSE) of urban public library patrons and their participation in the library’s established computer literacy classes. To explore this possibility, I will use an instrument specifically developed for this purpose, the Computer Self-Efficacy Scale for Adults (CSESA; Brown, 2007) to gather the data for this proposal. The CSESA measures the single latent construct (variable) of computer self-efficacy (total CSE, CSE_T), which is hypothesized to be the sum of three component competencies in computer-related skills related to: computer hardware use (CSE_H), computer software use (CSE_S), and the ability to understand and use key aspects of the Internet (CSE_I). In other words, according to the CSESA score, CSE_T = CSE_H + CSE_S + CSE_I. Latent variables represent attributes that are not directly measurable, such as level of confidence, so other variables are used to estimate or approximate the intended construct. In the CSESA, level of confidence in performing specific computer-related tasks is an indicator for computer selfefficacy. A more detailed discussion of the CSESA will follow in subsequent sections. In particular, this study seeks to answer the following research questions: (RQ #1) Do the adult participants of the library’s computer training program demonstrate significantly different levels of CSE when their pre-training CSESA scores (total CSE, CSE_T; hardware CSE, CSE_H; software CSE, CSE_S; and/or Internet CSE, CSE_I), are compared to their post-training CSE scores, at the end of their computer training? (Or, is there a post- vs. pre-training CSE effect?)


(RQ #2) Are there any significant changes to patrons’ CSE (CSE_T) level, as shown in their CSESA scores, as the participants of the library computer training program progress through the various stages of their training, beginning with their learning basic computer terminology and skills? Or, is the CSESA related to the number of classes taken? (RQ #3) Are there any significant differences in CSE that are related to the particular demographics of the study, including those which may be related to: o The gender of the participants? o The reported level of education? o The library branch providing the computer training? o The ZIP codes of the library branches providing the computer training (related to income and population attributes of particular areas)? o The number of classes (amount of experience) that the participants report having taken on the CSESA survey? o The level of perceived competency (low, intermediate, high) as reported by the participants on the CSESA survey? [The age group is expected to be primarily that of older adults and is not expected to be a variable for this study; however this information will still be collected. Note—The demographic section of the CESEA will be modified to allow the addition of # of classes taken, identification of branch location, and current class to be added to the demographic information collected.]


Philosophy and Approach The first question usually posed for a research study design is whether the approach will be that of a quantitative or qualitative nature. The answer is driven both by the research questions themselves and the philosophical tenets of the investigator, as embodied in the investigation. In this study, for example, I seek to understand whether a relationship might exist between older adult patrons’ perceived level of confidence in handling basic computer literacy tasks, and the extent to which they have participated (number of classes taken) in basic computer class instruction at their local public library branches. The confidence level of each participant is measured using a scale developed for that purpose, the Computer Self-Efficacy Scale for Adults (CSESA), which attempts to score and quantify the latent variable (an underlying attribute or characteristic of something which changes in value; DeVellis, 2003) or construct, computer self-efficacy (CSE), which can then be used to assess whether CSE differs after patrons’ participation in the library’s computer training program. In addition, it can also be used to compare differences in CSE among other variables of the study, such as the participants’ age, gender, their prior computer training experience, their perceived level of computer skill competence, and the location of their classes. In general, this research study, then, will employ a number of statistical techniques in order to determine the outcome of the research questions which have been posed. In doing so, I will employ associational (correlation) research (Fraenkel & Wallen, 2009) in order to determine the relationship CSE and participation in the computer training classes. I will also employ comparative statistical methods (analysis of variance) to see if I can detect any differences among the other variables of the study, in order to conclude whether they had any


significant impact on the CSE scores of the participants. These methods will be explained in some detail in the analysis sections to follow. Creswell (2008) describes quantitative research as “a type of educational research in which the researcher decides what to study; asks specific, narrow questions; collects quantifiable data from participants; analyzes these numbers using statistics; and conducts the inquiry in an unbiased, objective manner” (p. 46). As has been described, that is the intent of this study. This contrasts with qualitative research, which Creswell (2008) defines as one that “relies on the views of the participants; asks broad, general questions; collects data consisting largely of words (or text) from participants; describes and analyzes these words for themes; and conducts the inquiry in a subjective, biased manner” (p. 46). In my previous study of older adult participants in urban library computer training classes (Brown, 2011), I posed research questions related to how the patrons made meaning of their learning experiences as they participated in these classes. I used detailed personal interviews to arrive at themes that shed light on their experiences. Based on their words and my observations of them as well as my interpretations of the meaning of what they shared with me, I proposed what I thought was the meaning of the classes to these participants. This was the stuff of qualitative research. Concomitant with each of these two research approaches and their related methodologies are a cluster of philosophical assumptions which lead to the acceptance and use of them. For example, in quantitative work, there is a reliance on finding an independent and knowable truth, based on a specific procedure that may lead to the ability to discover relationships and then generalize them into laws. For the qualitative researcher, however, there is no one reality, and the truth is always ambiguous. We seek to understand phenomena, but they are never


generalizable into laws (Fraenkel & Wallen, 2009). See Table 1 for a summary of the differences in quantitative and qualitative assumptions and research procedures. Table 1. Comparison of Quantitative and Qualitative Research Process and Assumptions Quantitative Research Discover nature of reality and how it works. Facts and values are distinct from one another. Predict and explain, leading to generalizable truth (laws).

Assumptions Reality Facts/values Purpose of research

Researcher stands apart from study. Quantitative Research Explanatory in nature Major role in justification

Researcher position

Specific, narrow, observable, measurable Predetermined instrument; numeric data; large samples Statistical analysis; trends, relationships, comparisons

Purpose

Standard, fixed; objective

Evaluation of data

Research Process Identifying research problem Literature review

Collecting data Analysis and interpretation

Qualitative Research Reality exists in multiple forms as mental construction. Facts and values are intertwined. Understand and explain the meaning of things; not generalizable as laws. Research is an instrument and part of the study. Qualitative Research Exploratory in nature Supplemental role in justification General, broad, participant experiences Emerging text or data; themes; small numbers of individuals Descriptive, thematic development; meaning of findings Flexible; reflexive, biased

Note: Adapted from Creswell (2008) and Fraenkel & Wallen, (2009). In this research study, I rely on the philosophical assumptions of the quantitative methodology, seeking to find factual differences that rely on testing hypotheses about the truth: for example, do patrons experience a significant change in CSE when they attend the computer training classes at their public library? The answer will be yes or no. In qualitative work, of course, this type of answer is never obtained.


Research Design In the sections to follow, I will use the outline proposed by Holton III and Burnett (2003) in describing the research process for this study. I will first describe the variables of the study and then the measurement tool, the Computer Self-Efficacy Scale for Adults (CSESA), which was designed for this study. I conclude with a discussion of two analytical methods which will be employed in this study—association (correlation, using the Pearson coefficient) and comparison (analysis of variance, using ANOVA methods). Variables The primary variables of this study are the computer classes being taught at the public library (the independent, treatment, or manipulated variable) and the CSESA scores for the participants (dependent, or outcome variable, related to the independent variable). The CSESA scores (using a six-point Likert scale for each item) and the number of classes attended are both examples of continuous (interval) data and may be treated statistically as such (DeVellis, 2005; Holton III & Burnett, 2005). Other variables of this study are primarily derived from demographic categories (e.g., age group, gender, location of class, perceived level of competency, and educational level) which may be used for comparison grouping for later statistical analysis. Variables of these grouping types may be either nominal (categories, such as gender) or ordinal (e.g., perceived level of computer knowledge self-rated as low, intermediate, or high by the respondent).


Measurement Tool—CSESA Instrument The instrument used for this study is the Computer Self-Efficacy Scale for Adults (CSESA), a survey containing 36 items divided into three subscales of 12 items each, representing the domains of computer hardware, computer software, and Internet-related skills (Brown, 2007). The instrument proposes that overall or total computer self-efficacy (T_CSE) is a construct which is composed equally from the three subscale components (see Figure 1).

The survey itself contains two parts. In the first part, demographic data is collected which is relevant to the study. In the second part, three groupings of 12 items each are presented to make the survey items easier to answer. Items are randomized so that the domains are not presented in any particular order. Each of the survey items begins with the prompt, I feel confident … followed by a domain-related skill which the respondent then rates the degree to which he or she agrees with the statement. A 6-point Likert scale, with response items ranging from completely disagree (lowest level of confidence, code= 1) to completely agree (highest level of confidence, code= 2), is used to assess the respondent’s rating for each item. Items for the CSESA represent a range of skills which vary from the very basic (I feel confident using a computer keyboard) to more advanced computer-related skill (I feel confident setting up a computer network in my home) for each of


the three domains of the construct. Care was taken to make the survey understandable and relevant for those adults who may have mastered few actual computer-related skills, as might be the case for older adults taking computer classes for the first time (Brown, 2007). The validity and reliability of the CSESA was demonstrated in the technical report accompanying the instrument. Results in which the Cronbach’s alpha coefficients are above α = .90 rate as very reliable; α = .89 to α = .80 represent good reliability; results between α = .79 to α =.70 are of fair reliability; results below α = .70 are marginal (α = .69-.60) to unacceptable (

Evaluating Computer Selfâ Efficacy (CSE) Effects

Evaluating Computer Selfâ Efficacy (CSE) Effects

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