Self-Beliefs:
Predicting Persistence of
Families First Participants in Adult Basic Education
Dr. Mary Ziegler
Dr. Sherry Bain
Dr. Sherry Bell
Dr. Steve McCallum
Dr. Donna Brian
Center for Literacy Studies
University of Tennessee, Knoxville
600 Henley St., Suite 312
Knoxville, TN 37996-4135
Self-Beliefs: Predicting Persistence
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Executive Summary
Although Families First provides adult education classes for participants without a high
school credential, the program has yielded mixed results, in part because many welfare recipients
who choose to enroll do not persist long enough to increase their basic skills or earn a General
Educational Development (GED) certificate. The purpose of this study was to identify selfbeliefs of Families First participants that are capable of predicting persistence in adult basic
education. The self-beliefs or dispositional variables that welfare recipients develop and hold to
be true may be powerful forces in their success or failure in educational or employment
activities. Four dispositional variables are associated with persistence. Attitudes toward school
stem from past schooling experiences that helped shape adults’ perceptions including beliefs
about the efficacy of attending school. Self-efficacy is the belief about one’s abilities: an estimate
of one’s confidence for successfully accomplishing a particular task such as mathematics or
reading. Resilience is the ability to manage or cope with adversity or stress in effective ways;
resilient people bounce back from adversity. Attribution is the belief about the cause of one’s
success or failure. The study addressed the question: To what extent do dispositional factors
predict whether welfare recipients who enroll in adult basic education persist in the program?
Study participants were 254 Families First clients who enrolled in Adult Education from
ten different counties in Tennessee. Their attendance was tracked for 90 days from the time of
their enrollment. All participants were administered a survey that included self-report questions
developed to elicit their attitudes toward school, self-efficacy, resilience, and attributions for
failure experiences in adult education. The items ultimately comprised the Adult Education
Persistence Scale (AEPS). Persistence, the primary focus of the study, was operationalized in
two ways: (a) the percentage of attendance (the number of hours in attendance divided by the
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total number of class hours available) and (b) “high” vs. “low” attendees with those who
attended 25% (or less) of class time during a 12-week period referred to as “low attendees” and
those who attended 75% (or more) of the time during this period referred to as “high attendees.”
Using the appropriate analyses, 30 items were selected for the AEPS. The score means of the
high attendee scale were significantly higher than for low attendees. The AEPS Grand Score
mean correlated more highly with percentage of attendance than either TABE Math or Reading.
The AEPS Grand Score accounted for 11% of the variance between the high attendees
and the low attendees and age accounted for an additional 4%. Using AEPS Grand Scores and
age as predictors, 76% of the cases were accurately predicted into two groups: high attendees
(the 41 participants who attended 75% or more of the time) and low attendees (the 92
participants who attended 25% or less of class time). Other variables also show differences
between the high attendees and the low attendees. The AEPS is a useful tool for adult education
professionals who want to provide effective educational services for Families First participants.
Examination of participants’ responses on the AEPS can identify areas amenable to intervention.
Specific suggestions for adult education teachers include: (a) provide training to
recognize unhealthy attributions (e.g., “Things never go right for me.”) and work toward healthy
attributions (e.g., “Things will work out if I keep at it.”); (b) create an adult-oriented
environment; (c) make academic tasks meaningful, relevant, and “do-able”; (d) provide
opportunities for frequent success experiences; (e) openly address attitudes and attributions; and
(f) model and validate “I can” attitudes and statements. Self-beliefs account for only a part of the
persistence of a welfare recipient in adult basic education; however, the AEPS is a first step in
assessing self-beliefs and the impact they have on persistence for welfare recipients who enrolled
in adult basic education classes.
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Introduction and Background
More than 44% of adults who receive welfare do not have high school diplomas or
General Educational Development (GED) certificates (Fox, Cunningham, Thacker, & Vickers,
2001). Without this basic level of educational preparation, many will have difficulty acquiring
jobs and advancing in the workforce. Although Families First, Tennessee’s welfare reform
initiative, provides the opportunity for adults to improve their basic skills and earn a GED, the
results are mixed. Up to 25% of Families First participants who choose to enroll in adult basic
education drop out in less than 30 days, and many of those who remain have irregular attendance
and leave before achieving their goals (Ziegler, Ebert, & Henry, 2002).
Yet some welfare recipients persevere despite the obstacles they encounter. One reason
for this persistence may be the self-beliefs or dispositional variables that, according to Quiqley
(1987), have promising potential to improve retention rates. However, these variables are
underrepresented in the literature (Cross, 1981). Comings, Parrella, and Soricone (1999)
identified self-beliefs or dispositional variables in their study of “persisters” in adult basic
education programs in Massachusetts and concluded that “researchers must help to develop both
better measures of and tools for measuring persistence” (p. 73). The goal of this study was to
assess dispositional variables and determine the relative power that these variables have to
predict the persistence of Families First participants who enroll in adult basic education. For this
study, dispositional variables are defined as attitudes toward school, self-efficacy, resilience, and
attributions. Each dispositional variable will be discussed in the following sections.
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Attitudes Toward School
Attitudes toward school stem from past schooling experiences that influenced students’
perceptions about the efficacy of attending school, the likelihood that attending school will
increase one’s academic skills, the ability of the teacher to provide effective instruction, and the
potential return of the investment of time in school. Ziegahn (1992) and Quigley (1997) noted
that most adults who have low literacy skills value education and learning, but many resist
schooling because of the failure they experienced in the past. Numerous additional researchers
assert that past negative school experiences are the primary de-motivating influence for their lack
of participation in adult basic education (Beder, 1990; Malicky & Norman, 1994; Quigley, 1997,
1993; Van Tilberg & DuBois, 1989; Velazquez, 1996; Wikelund, Reder, & Hart-Landsberg,
1992). Beder (1990, 1991) and Quigley (1997, 2000) argue that prior school experience plays an
important role in an adult’s decision not to persist. Offering empirical support for this view,
Baldwin (1991) interviewed more than 7,000 GED graduates who identified the main reason
they left school from a list of 44 items. A factor analysis identified seven categories, three related
to past school experiences. Hayes (1988) used the Deterrents to Participation Scale to survey 160
adults currently enrolled in basic education classes about their past reasons for nonparticipation.
She found five factors, one of which was “negative attitude toward classes.” Finally, Beder
(1989) questioned 175 Iowa residents about their failure to attend adult basic education, finding
dislike of school as one of four main reasons they did not participate.
In contrast, other researchers have found that not all individuals who dropped out disliked
school. From interviews with 45 adult basic education students drawn from a larger sample,
Courtney, Jha, and Babchuk (1994) hypothesized that adults interpret their initial experience of
entering an adult basic education class as either “the chance to do it over…to redeem
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themselves,” or an experience that reminds them “of what they disliked about school” (p. 192).
Other studies found that prior school experiences may play only a minor role in an adult’s
decision to persist in adult basic education (Comings, Parrella, & Soricone, 1999; Long, 2001).
The literature provides no clear definition of what comprises past negative school
experiences because each study describes this phenomenon in a different way. Although past
school experiences may have significant consequences for participation and engagement in adult
basic education, the fragmentations and contradictions found in the literature suggest the need for
further research.
Self-Efficacy
As a psychological construct, self-efficacy was first described by Bandura (1977, 1978,
1986), who proposed that a person’s expectations of mastery or success influence both his or her
decision to perform a difficult task as well as the amount of effort and persistence assigned to the
task. Simply put, self-efficacy is a person’s belief about how effective he or she will be in
performing a given task. In general, high self-efficacy has been found to increase engagement in
a given task, increase the likelihood of performing the task successfully, and, in turn, further
increase self-efficacy. Low self-efficacy is associated with task-avoidance, poor performance,
and steadily decreasing self-efficacy over time. Applied to the domain of adult literacy, two
individuals at the same reading level will be differentially motivated to improve their reading
skills, based on their respective levels of self-efficacy. Bandura proposed that four types of
experiences can influence self-efficacy: prior experiences with similar tasks, vicarious
experience, verbal persuasion, and level of anxiety. Prior experience may be difficult to change
but the remaining three variables may be amenable to training (see Gist & Mitchell, 1992).
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Self-Beliefs: Predicting Persistence
The relationship between self-efficacy and academic success or persistence has been
reviewed by Gorrell (1990) and McMillan, Simonetta, and Singh (1994). Others have explored
the relationship between weight reduction and self-efficacy (Chambliss & Murray, 1979),
children’s literacy and self-efficacy (Schunk, 1994), and diving performance and self-efficacy
(Feltz, 1982).
Generic instruments have been developed to measure self-efficacy across many types of
tasks. For example, the Self-Efficacy Scale developed by Sherer, Maddux, Mercandante,
Prentice-Dunn, Jacobs, and Rogers (1982) was designed to investigate the relationship between
self-efficacy and success in vocational, educational, and military careers. However, to develop
the instrument, the authors limited their participants to military veterans attending an inpatient
alcoholism treatment unit, suggesting problems of generalization to unlike groups.
To improve applicability to specific groups, Huang, Lloyd, and Mikulecky (1999)
suggested developing self-efficacy instruments tied to a target group of individuals approaching
a specific task, e.g., in their case, adults learning English as a second language (ESL). Scales
designed for generic purposes, across groups of people and across tasks, may be too broad to
measure self-efficacy for a specific task. Huang and colleagues, using a custom-designed
instrument, were able to show a strong relationship between self-efficacy scores on several
factors and ESL students’ general class placement. Applied to welfare recipients in adult basic
education programs, self efficacy as a predictor of persistence or success has not been explored.
Based upon current literature, an instrument pursuing this relationship should be customdesigned to suit the particular tasks that the population of interest will face.
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Resilience
Resilience is the ability to manage or cope with adversity or stress in effective ways;
resilient people bounce back from adversity. The level of resiliency can be viewed as a function
of risk factors intersecting with protective factors. Risk factors result from stressful life events
(such as abuse, losing a job, or being the victim of a crime). Protective factors might include
skills, personality factors, and environmental supports. Resilience has only recently been
discussed in the psychology of coping literature (O’Neal, 1999). In the developmental literature,
resilience has been described as “the ability to use internal and external resources successfully to
resolve stage-salient developmental issues” (Egeland, Carlson & Sroufe, 1993, p. 518).
More frequently, investigations on the subject of hardiness have appeared in
psychological literature. Hardiness, in essence the same as resilience (see O’Neal, 1999), was
first investigated by Kobasa and Maddi (e.g., Kobasa, 1979; Maddi & Kobasa, 1981). Described
by Maddi and Khoshaba (1994), hardiness is a “sense of self-in-world that emphasizes
commitment, control, and challenge” (p. 272). The three elements, commitment, control, and
challenge, were later modified in name and definition by Nowack (1989), who defined (a)
involvement [renaming commitment] as a “commitment…to one’s work, family, self, hobbies;”
(b) challenge, as “attitudes around viewing life changes as challenges, as opposed to threats;”
and (c) control, as “beliefs that one has a sense of control over significant outcomes in life”
(p. 150).
The relationship between hardiness and mental health has been explored in several
studies (e.g., Kobasa, 1979; Kobasa, Maddi & Courington, 1981). The relationship between
hardiness and physical health has been similarly explored (e.g., Nowack 1989). In addition, a
study of the construct validity of an instrument, The Revised Hardiness Scale (Brookings &
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Bolton, 1997) found significant relationships between hardiness and happiness, and between
hardiness and low anxiety.
Measures of resilience or hardiness have been used to evaluate those at high risk for
failure (e.g., educational failure), those who are chronically exposed to high levels of stress (e.g.,
those living below the poverty level), and those vulnerable to various stress-related disorders
(e.g., cardiovascular disease). For example, Cappella and Weinstein (2001) investigated
predictors of resilience in high school students at risk for academic failure. The authors identified
characteristics of low-achieving students who were able to overcome significant barriers in order
to meet the academic demands of high school.
Like self-efficacy, the construct of resilience has not been studied among welfare
recipients in adult education programs. Dispositional factors that protect an individual from
continued stress may conceivably add to persistence in the face of difficulties in learning basic
literacy skills.
Attributions
Attributions are beliefs about the causes of one’s success or failure; motivation is
influenced by the reasons people give (attributions) for success and failure. People may attribute
their success or failure to their ability, effort, the context of the situation (including task
difficulty), or luck. Applied to adult basic education, participants who believe they achieve
success because of effort will be more likely to persist during difficult times.
The attributions or explanations individuals make for events in their lives are related to
their adjustment and achievement. A strong body of research indicates that higher achieving and
better adjusted persons attribute their successes to internal causes and failures to external causes
while lower achieving and more poorly adjusted students attribute their failures to internal causes
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and successes to external causes (e.g., Bell, 1990; Bell & McCallum, 1995; Rotter, 1975;
Seligman, 1991; Weiner, 1979). The tendency to attribute one’s success to internal causes and
one’s failures to external causes (“self-serving” bias) is well-documented (see Kinderman &
Bentall, 2000; Marsh, 1984; Weiner, 1979) and is considered “healthy” in that it contributes to
maintenance of high self-esteem. However, an extreme tendency to attribute successes to internal
causes and failure to external causes is related to paranoid thinking and behavior (Kinderman &
Bentall, 2000). The opposite pattern of attributing success to external causes and failure to
internal causes is related to depression (Bell, McCallum & Doucette, 2002; Kinderman &
Bentall; 2000; Seligman, 1991).
Persons who view success and failure as beyond their control tend to develop a mind set
referred to as “learned helplessness” (Abramson, Garber, & Seligman, 1980; Abramson,
Seligman & Teasdale, 1978). Individuals with learned helplessness tend to exhibit lowered selfesteem and lowered self-efficacy, or a lowered sense of competence. Individuals who are
frequently exposed to failure or negative consequences despite effortful behavior are considered
at risk for developing learned helplessness. Once a person learns a “helpless” mindset, effortful
behavior decreases.
A relationship between attributions and persistence has been demonstrated under
experimental conditions and in real-life situations (Seligman, 1991; Weiner, 1979). In several
experimental studies, Weiner (1979) demonstrated that subjects who attributed failure on a task
to events deemed uncontrollable tended to make less effort in subsequent situations. Those that
fail to persist tend to blame their failure on events beyond their control. There is some evidence
that failure attributions may more powerfully predict other psychological constructs than do
success attributions (Bell, McCallum & Doucette, 2002). A recent literature review yielded no
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studies focused specifically on the relationship between attributions and persistence in adult
basic education programs. However, there is evidence that persons who fail to persist in
obtaining an education tend to make external attributions, “blaming the system” (Flanagan &
Tucker, 1999) and that individuals who drop out of college tend to make external attributions for
their leaving (Shields, 1995).
The self-beliefs that Families First participants develop and hold to be true may be
powerful forces in their success or failure in educational, training, or employment activities.
Many recipients may have difficulty persisting in adult basic education not because they are
incapable of performing successfully but because they have come to believe that they cannot
perform successfully.
Dispositional Variables and Families First Participants
A primary purpose of this study is to help adult education professionals identify those
Families First participants who are most likely to persist in attending adult basic education, and,
conversely, identify those who are least likely to persist. By identifying those who will be most
and least likely to persist, adult educators can choose how to most effectively allot their
resources. That is, some adult educators may choose to spend time and energy helping those
students most likely to persist, assuming that those students will be most likely to complete their
GED and go on to obtain successful employment. Others may choose to expend resources on
those least likely to persist, assuming that those are most at-risk of dropping out thus reducing
their chances for self-sufficiency. A secondary purpose is to provide specific suggestions to adult
education professionals that will increase the persistence of Families First participants in their
programs.
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No formal method exists to assess dispositional variables and the extent to which these
variables predict persistence for welfare recipients in adult basic education. Consequently, an
instrument was developed to assess the dispositional variables of attitudes toward school, selfefficacy, resilience, and attributions for failure in the context of adult education activities; and
the relative power of these variables (among other demographic and environmental variables) to
predict persistence for Families First participants who enroll in adult basic education classes was
determined.
Method
Participants
Participants in this study were Families First welfare recipients from 10 different counties
in Tennessee who enrolled in adult education classes during a 90-day enrollment period. Of the
254 participants, 245 reported their gender as female, nine as male; 175 reported their ethnicity
as African-American, 74 as Euro-American, and 3 as Hispanic-American. Most were from urban
areas (198). Their mean age was 27.09 (SD = 6.87).
Information reported by participants showed that all but 3 had at least one dependent in
the home, and most had one or two dependents (136); a sizable minority had 3 or 4 dependents
(83), and a few (21) had 5 or more dependents. One-hundred and sixty-four of these participants
had been enrolled in adult education classes before, 111 had repeated a grade, 91 admitted to
“having difficulty learning,” 228 reported having access to transportation, and 239 reported
presence of “family support.”
The participants included in this study are a reasonable reflection of the population they
are intended to represent on the variables of age, gender, and race. However, for setting (urban
vs. rural), the sample varies from the norm because of its predominantly urban make-up. Table 1
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includes demographic variables and a comparison between the demographics of the sample
population and the characteristics of the Families First (FF) population (Fox, Cunningham,
Thacker, & Vickers, 2001).
Procedure
As a part of their intake into an adult basic education program, all participants completed
a general survey that was designed to obtain demographic data. In addition, program staff
administered, the Adult Education Persistence Scale (AEPS), a self-report scale developed for
this study. The items were read aloud to participants to mitigate the potentially low reading
levels. Upon formally enrolling in the Adult Education program, all participants were
administered the Test of Adult Basic Education (TABE). Their TABE Reading Scale score
average was 548.59 (SD = 87.02). Their TABE Math Scale score average was 509.06
(SD = 75.12).
Table 1
Demographic Match of Sample Percentages and Means to the Families First Population (FF)
______________________________________________________________________________
FF sample
Study sample
______________________________________________________________________________
Age (M)
33.7
27.1
Gender
Female
95.7%
96.5%
Male
04.3%
03.5%
Race
African American
60.5%
68.9%
European American
38.0%
29.0%
Hispanic American
1.0%
1.2%
Setting
Urban
45.9%
77.9%
Rural
54.1%
22.1%
Highest grade completed (M)
11.0
10.1
______________________________________________________________________________
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Instruments
Operationalization of persistence. “Persistence” is the primary focus of this study;
persistence has been operationally defined in two ways: (a) percentage attendance, i.e., the
number of hours in attendance divided by the total number of class hours available (M = 39.25,
SD = 29.92), or (b) high attendees (those who attended 75% or more of the class time) vs. low
attendees (those who attended 25 % or less of the class time during a 12-week period). Forty-six
participants were identified as high attendees; 100 were identified as low attendees.
Adult Education Persistence Scale (AEPS): Attitude Component. The AEPS includes two
components. The first includes items that assess self-report of educational school experience,
self-efficacy, and resilience and is referred to as the Attitude Component; the second assesses
attributions (e.g., luck, context, effort, and ability) regarding adult education failure situations
and is referred to as the Attribution Component (see Appendix). The Attitude Component was
developed using expert opinion and results of item and factor analyses; participants responded to
questions using a 4-point Likert-like scale with descriptors of Strongly Disagree, Disagree,
Agree, or Strongly Agree. Items that were stated in the negative were recoded for analyses;
therefore higher scale scores indicate stronger agreement with positive responses. In the initial
pilot testing, completed several weeks before the primary data were gathered, 55 adult education
students responded to 45 attitude items. Using factor and item analyses (e.g., item factor
loadings, item-scale correlation coefficients), 30 attitude items were selected to be administered
to all 254 participants. Again, using appropriate item analyses the fourteen best items (attitude
toward school, 7 items; self-efficacy, 4 items; and resilience, 3 items) were selected for the final
version of the scale. These items demonstrated means between 2.6 and 3.6 and corrected item-
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total correlations between .35 and .60. A measure of internal consistency for the scale was
computed and a reliability estimate (Cronbach alpha) of .82 was obtained. This scale produced a
mean of 46 and a standard deviation of 5 for the participants in the present study, with a standard
error of the measure of .33. Of note, the AEPS Attitude Component rounded score mean for the
high attendee group (M = 48, SD = 6) is significantly higher than low attendee group mean (M =
45, SD = 5; t [138] = 2.73, p = .01). (Rounded scores facilitate practitioner comparison.)
To further investigate the predictive potential of the Attitude Component of the AEPS
Scale, Pearson correlation coefficients were calculated between the Attitude Component and
percent attendance (r = .20, p = .002). Of interest, the correlation coefficient between the
Attitude Component and percentage attendance was considerably higher in one urban setting,
Memphis (r = .33, p < .001)
Adult Education Persistence Scale (AEPS): Attribution Component. The initial
Attribution Component included 15 attribution statements, each describing a hypothetical failure
scenario that might occur in adult education settings. Participants assigned a rating of 1 (strongly
disagree) to 4 (strongly agree) to each of four potential causes for the failure outcome, ability,
effort, luck, and context. However, because strongly agree (scale rating = 4) indicated strong
endorsement for a failure attribution (as an explanation for the failed outcome) the items were
recoded for analyses. For example, a rating of 4 on this item, “If I cannot do a reading
assignment, it is probably because I am unlucky” indicates that being unable to do the reading
assignment is due to luck.
Initial pilot data were obtained from 15 items administered to 55 Families First
participants several weeks before the final data were obtained. Using factor and item analyses, 10
items were selected and administered to all 254 participants. For the External Attribution portion,
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eight items assessing luck and context were chosen, and demonstrated means between 1.8 to 2.5
and corrected item-total correlations between .27 and 59, and were chosen. A measure of internal
consistency for the scale was computed and a reliability estimate (Cronbach alpha) of .72 was
obtained. The Internal Attribution portion was comprised of eight items assessing ability and
effort, with means ranging from 1.9 to 2.30 and corrected item-total correlations between .40 and
.71. A reliability estimate of .77 was obtained for this scale. The 254 participants’ rounded mean
and standard deviation for the External portion is 16 and 3, respectively; the rounded mean and
standard deviation for the Internal portion is 17 and 4, respectively.
Criterion-related validity is evaluated by comparing the scale scores with external
variables considered to provide a direct measure of the behavior in question, and is sometimes
obtained in the process of test development. Previous examination of the data suggests age and
ethnicity might be considered such valid criteria, and Internal and External Attribution score
totals might also reflect this association. Such a positive relationship is noted between age
(r [242] = .161, p = .01) for the Internal Attributional Scale totals; however, this relationship is
not significant for the External Scale totals. In order to evaluate the power of these two scales to
discriminate between high or low attendance, the score means for the high attendee group (M =
15.26, SD = 3.40) were compared to the low attendee score means (M = 16.79, SD = 2.96;
t [141] = 2.75, p = .007) and found to be significantly different. This difference is not noted for
Internal Scale mean scores.
To investigate the predictive potential of these scales, Pearson correlation coefficents
were calculated between percent attendance and Internal and External Attributional portion score
totals. The significant negative correlation noted between percent attendance and the External
Attributional Scale score totals suggests that increasing attendance is associated with decreasing
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level of endorsement for external sources of failure within context or luck (r = -.20, p = .002).
The relationship between the Internal portion and percentage attendance is not significant. There
is a significant relationship between Internal Attribution Scale scores and TABE Reading
achievement test scores (r = .27, p < .001) and TABE Math achievement test scores (again, r =
.27, p < .001), defining the relationship between TABE Reading and Math and failure attributed
ability/effort. The coefficients between TABE Reading and Math scores and External
Attributions were not statistically significant.
Development of the AEPS Grand Score. The three scale score totals (AEPS Attitude
Component and the AEPS Internal and External portions of the Attribution Component) were
summed to produce a AEPS Grand Score in the following manner: Attitude Component score
plus Internal Attributional Scale score plus External Attributional Scale score. The AEPS Grand
Score mean is 87 (rounded), and the standard deviation is 6 (rounded). Skewness and kurtosis
indicators are all within acceptable limits. The AEPS Grand Scores correlate with age
significantly (r [232] =.14, p = .04). An independent t-test examining the relationship between
the grand scale mean totals and ethnicity yielded a nonsignficant difference. Discriminant
validity of the scale was investigated by examining scale score differences between high and low
attendees; the high attendee scale score rounded mean (M = 90, SD = 7.00) is significantly higher
than low attendee score mean (M = 85, SD = 5; t [131] = 4.80, p < .001). In addition, Pearson
correlation coefficients were also calculated for AEPS Grand Scores and percentage attendance,
and a significant value obtained, r (323) =.32, p <.001. Again this significant relationship is
slightly more pronounced in urban settings (r [178] = .35, p < .001), and is even stronger in one
particular urban setting, Memphis, r (109) = .43, p < .001. Importantly, the AEPS Grand Score
mean correlates more highly with percentage attendance than with either TABE Math or
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Reading. For example the coefficient between the AEPS Grand Score mean and TABE reading
is .08 (p = .18); the correlations between the AEPS Grand Score mean and TABE Math is even
lower.
Results
Relationship Between Persistence and Critical Demographic Variables
Several significant relationships are noted between persistence (defined as percent
attendance) and some of the demographic characteristics, such as ethnicity, age, and number of
dependents under 18. African-American students persisted to a significantly lesser degree than
did Euro-Americans (t = 2.38, p = .02). The attendance of older students was better than for
younger students (r = .22, p < .001). Those students with one or two dependents attended
significantly more regularly than did students with no dependents or students with three or more
dependents (F = 3.84, p = .01).
When persistence was operationalized as high attendees (n = 100, M = 86.5, SD = 7.42)
vs. low attendees, as compared to an attendance rate equal to or less than 25% (n = 46, M = 9.33,
SD = 7.83), average age was found to be significantly different (between the two groups). The
high attendee mean age of 29.27 (SD = 7.85) was significantly greater than the average age of
the low attendee mean age of 25.61 (SD = 5.80).
Best Predictors of Persistence
In order to determine the relative power of dispostional variables to predict persistence,
two multivariate analyses were used to help select those variables most likely to predict
persistence: a stepwise multiple regression and a discriminant function analysis. Percentage
attendance became the criterion variable for the regression analysis and both dispositional and
situational variables were entered as predictors, as described below. For the discriminant
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function analysis two groups were formed based on high attendance (75%) and low attendance
(25%).
Variables entered into the Stepwise Multiple Regression equation included the AEPS
Grand Scores (representing self-efficacy, resilience, and attitude toward school, plus attributions
for educational), achievement variables (TABE Reading scores, TABE Math scores, last grade
completed, self-report of learning difficulty), demographic variables (age, ethnicity, and gender),
and situational variables (urban vs. rural setting, number of dependents, accessibility to
transportation, and level of family support). Using a criterion of .05, only two variables
successfully entered the regression equation; the AEPS Grand Score totals entered the regression
formula first, contributing 11% of the variance to the prediction of percentage attendance; age
then entered the equation and accounted for an additional 4% of the variance. None of the
remaining variables accounted for enough of the variance in the criterion to contribute
significantly. Of interest, when the same variables were entered for participants in urban areas
only (n=198), the AEPS Grand Score entered the regression formula first, as before, and
contributed 12% of the variance to the prediction of percent attendance; age then entered the
equation and accounted for an additional 6 % of the variance. When the sample was further
limited to the scores from Memphis (n=125), the AEPS Grand Score accounted for 17% of the
variance in the prediction of percent attendance. None of the other dispositional or situational
variables successfully entered the regression equation (see Table 2).
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
20
Table 2
Multiple Regression Analysis of a Set of Predictor Variables Explaining Variance in Persistence
Independent
Variable
AEPS
R
R2
.33
.11
R2
Change
.11
Age
.37
.14
.03
Note: AEPS is Adult Education Persistence Scale.
Standardized
Beta
.30
F
Change
23.41
p
<.001
.18
6.86
<.01
Results from the Discriminant Function analysis revealed the power of the AEPS Grand
Score to predict high and low attendee groups. Based on the best predictors as revealed in the
multiple regression, both AEPS Grand Score and age were entered in the discriminant function.
Standardized canonical discriminant function coefficients for AEPS Grand Scores and age are
.81 and .62, respectively. A Wilks’s Lambda statistic of .78 produced a Chi-Square of 32.85
(p < .001). Using AEPS Grand Scores and age as predictors, 76% of the cases were accurately
predicted back into two groups: high attendees (the 41 participants who attended 75% or more of
class time) and low attendees (the 92 participants who attended 25% or less of class time). See
Table 3 for the prediction table.
Table 3
High Attendees vs. Low Attendees as Predicted by Adult Education Persistence Scale and Age
Using a Discriminant Function Analysis
Actual group
n
Low attendees
92
High attendees
41
Predicted group membership
Low attendees
High attendees
83
9
(90%)
(10%)
The University of Tennessee Center for Literacy Studies, 2002
23
(56%)
18
(44%)
Self-Beliefs: Predicting Persistence
21
Characteristics of Low Persisting and High Persisting FF Participants in Adult Basic Education
Characteristics associated with the high vs. low attendees differ. Perhaps the two most
important variables to examine are the AEPS Grand Score and age, because these two variables
predicted persistence significantly. The AEPS Grand Score mean for the high attendee group is
five points higher (90, rounded) than the mean for the low attendee group (85, rounded). The
average age of the high attendee group is 29 (rounded); the age of the low attendee group is 26
(rounded). Other variables also show differences. For example, the TABE Reading mean is
551(rounded) for the high attendee group vs. 531 (rounded) for the low attendee group; similarly,
the (rounded) TABE Math score is higher for the high attendee group vs. the lower attendee
group (507 vs. 498).
Within the high attendee group, 59% were African-American and 41% were EuroAmerican; of those in the low attendee group, 75% were African-American and 25% were EuroAmerican. The high attendee group had an average of one less dependent than those in the low
attendee group. Of the high attendees, 78% came from urban areas, 22% from rural areas; of the
low attendee group, 79% came from urban areas and 21% from rural areas. Within the high
attendee group, 39% had repeated a grade; 46% of the low attendee group had repeated a grade.
There is no difference in the last grade completed between the high attendees and low
attendees; both report completing the 10th grade, on average. Of the high attendee group, 47%
reported difficulty learning, as opposed to 53% who did not; of the low attendee group, 32%
reported learning problems, as opposed to 68% who did not. Of the high attendee group, 89%
reported access to transportation, while 11% reported no access. Of the low attendee group, 81%
reported access to transportation and 19% reported no access. Within the high attendees, 94 %
reported adequate family support and 6% reported lack of adequate family support; the low
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
22
attendees 96% reported adequate family support and 4% reported lack of adequate family
support.
Discussion and Conclusion
The purpose of this study was to identify salient dispositional variables and to determine
the relative power of these variables to predict persistence among welfare recipients enrolled in
adult basic education programs. The AEPS was originally developed to measure variables that
might impede or enhance persistence, including scales measuring attitude toward school, selfefficacy, resilience, and attributions. The final instrument, based on items with the strongest
predictive power for the population under study, represents a relatively brief questionnaire with
significant predictive qualities (see Appendix for final instrument). When AEPS Grand Scores
were included along with demographic, situational, and achievement variables in an analysis
evaluating predictive value for persistence, AEPS scores were the strongest predictor, followed
by the single demographic variable of age, with older students showing greater persistence.
AEPS scores and age correctly classified 76% of the participants into low and high persistence
groups.
The TABE reading and math scores were among the demographic, situational, and
achievement variables entered into the analysis to predict persistence. Intuitively, one or both of
the TABE scores and other demographic variables (highest grade completed, perceived family
support, etc.) might be expected to contribute to persistence in adult basic education, based upon
the relationship these skills have with success. However, only the AEPS and age contributed
significantly when persistence was the operationalization of success. Because this study was
carried out in a limited time frame, only a small portion of participants actually completed the
ultimate goal of the instructional program, obtaining a GED. There are some salient differences
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
23
between those adult education students who persist in attending adult education classes as
opposed to those who are less persistent, as noted previously. For example, those in the high
attendee group were more likely to have higher AEPS Grand Scores, higher TABE Reading and
Math scores, to have one less dependent, to report learning difficulties, and are slightly more
likely to report lack of transportation. A long-term follow-up of participants will provide more
information about the contributions made by other demographic, situational, and achievement
variables, as well as the AEPS, toward increasing basic skills or obtaining a GED.
These findings suggest that the AEPS can be a useful instrument in predicting persistence
of welfare recipients in similar adult basic education programs. Depending upon goals and
resources of agencies that implement adult education programs, results of the AEPS can help to
identify groups of welfare recipients likely to stay long enough and attend frequently enough to
meet their goals (e.g., earning a GED), or to identify those at risk for dropping out and to provide
the extra services needed to help participants successfully complete the program.
In considering generalization of these results, potential users of the AEPS are advised to
keep these demographic features of the sample in mind, and if possible, to collect local
normative information. The sample includes significantly more urban participants; consequently,
results will generalize better to that portion of the population. Potential users of the AEPS should
keep these demographic features of the sample in mind, and if possible, collect local normative
information.
Examination of participant’s responses on elements of the AEPS can identify areas
amenable to intervention. For instance, Families First participants with low levels of self-efficacy
toward reading and math skills should be offered chances at incremental successes on academic
tasks and given appropriate feedback (Deci & Ryan, 1985). Some enrollees may have developed
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
24
a mindset of “learned helplessness” (Abramson, Garber, & Seligman, 1980) because of
inconsistent or negative school and life experiences and as a result may have a lowered sense of
competence. These individuals might be supported in taking more positive views toward the
effects of their efforts. Research supports the effectiveness of attribution retraining with other
populations (e.g., Forsterling, 1985; Gillham, Reivich, Jaycox, & Seligman, 1995; Jaycox,
Reivich, Gillham & Seligman, 1994). Families First participants may be expected to benefit from
attribution retraining, focusing directly on addressing their self-talk by teaching them to
recognize unhealthy attributions (e.g., “Things never go right for me.”; “This work is too hard.”)
and to practice healthy attributions (e.g., “I can do this if I try.”; “Things will work out if I keep
at it.”). Those who have negative attitudes toward school would benefit from settings that are
oriented to the adult interests of employment, family, and community (Alamprese, 1999).
Specific suggestions for adult education teachers include:

Make academic tasks meaningful, relevant, and “do-able.”

Provide opportunities for frequent success experiences.

Openly address attitudes and attributions.

Model and validate “I can” attitudes and statements.
This study confirms the study of Comings, et al., (1999) who found that “positive self” or
dispositional variables influence persistence. The AEPS addresses the need cited by these
authors to develop “both better measures of and tools for measuring persistence” (p. 73) and
corresponds to the recommendation of Huang, et al., (1999) that an instrument be tied to a target
group of individuals approaching a specific task, in this case, persistence of welfare recipients in
adult basic education. In considering generalization of these results, potential users of the AEPS
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
25
are advised to collect local normative information. The study sample includes significantly more
urban participants; consequently, results will generalize better to that portion of the population.
The AEPS is a first step in assessing dispositional variables and the impact they have on
persistence for welfare recipients who enrolled in adult basic education classes. Follow up on the
participants in this study can help determine whether the AEPS predicts not only persistence but
also receipt of the GED. Future research might explore the relationship of other dispositional
constructs with persistence and success in adult education. For instance, is there a basic level of
self-esteem that participants must have upon entry into adult basic education classes to predict
persistence and success?
Although persistence of Families First participants in adult basic education is
multifaceted and complex, dispositional variables play a role. Because the ultimate goal of
welfare reform is to assist adults who are reliant on welfare to become employed and achieve
self-sufficiency, educational attainment holds the promise of increasing earnings over time.
Since those who persist are likely to increase their basic skills or receive a GED (Ziegler &
Ebert, 1999), the AEPS is a valuable start in predicting persistence and identifying those selfbeliefs that are amenable to intervention. As a tool for practitioners, the AEPS provides
preventive information that can be used to increase the likelihood of persistence for welfare
recipients and, ultimately, achievement of their goals.
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
26
References
Abramson, L. Y., Garber, J., & Seligman, M. E. P. (1980). Learned helplessness in humans: An
attributional analysis. In J. Garber & M. E. P. Seligman (Eds.), Human helplessness:
Theory and applications (pp. 3-34). New York, NY: Academic Press.
Abramson, L. Y., Seligman, M. E. P., & Teasdale, J. D. (1978). Learned helplessness in humans:
Critique and reformulation. Journal of Abnormal Psychology, 37, 49-74.
Alamprese, J. A. (1999). Adult basic education: Strategies for supporting learning. ABT
Associates, Inc. Retrieved June 4, 2002 from
http://www.ed.gov/pubs/HowAdultsLearn/Alamprese.pdf
Baldwin, J. (1991). Why did they drop out? Reasons GED candidates give for leaving school.
GED profiles: Adults in transition, dedicated issue no. 4, 1-8.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change.
Psychological Review, 84, 191-215.
Bandura, A. (1978). Reflections on self-efficacy. Advances in Behaviour Research and Therapy,
1, 237-269.
Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice
Hall.
Beder, H. (1989). Reasons for nonparticipation among Iowa adults who are eligible for ABE.
Des Moines: Iowa Department of Education.
Beder, H. (1990). Reasons for nonparticipation in adult basic education. Adult Education
Quarterly, 40(4), 207-218.
Beder, H. (1991). Adult literacy: Issues for policy and practice. Malabar, FL: Krieger.
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
27
Bell, S. M. (1990). Student attributions for social and academic success and failure and their
relationships with self-concept, social functioning, and achievement. Unpublished
doctoral dissertation, University of Tennessee, Knoxville.
Bell, S. M., & McCallum, R. S. (1995). Development of a scale measuring student attributions
and its relationship to social functioning. School Psychology Review, 24, 271-286.
Bell, S. M., McCallum, R. S., & Doucette, J. (2002). Relationship of school-based attributions to
depression. Manuscript submitted for publication.
Brookings, J. B., & Bolton, B. (1997). A longitudinal structural equation analysis of stress,
hardiness, social support, depression, and illness. Multivariate Experimental Clinical
Research, 11, 109-120.
Cappella, E., & Weinstein, R. S. (2001). Turning around reading achievement: Predictors of high
school students’ academic resilience. Journal of Educational Psychology, 93, 758-771.
Chambliss, C. A., & Murray, E. J. (1979). Efficacy attribution, locus of control, and weight loss.
Cognitive Therapy and Research, 3(4), 349-353.
Comings, J., Parrella, A., & Soricone, L. (1999). Persistence among adult basic education
students in pre-GED classes (NCSALL Reports #12). Cambridge, MA: The National
Center for the Study of Adult Learning and Literacy, Harvard Graduate School of
Education.
Courtney, S., Jha, L. R., & Babchuk, W. (1994). Like school? A grounded theory of life in an
ABE/GED classroom. Adult Basic Education, 4(3), 172-195.
Cross, K. P. (1981). Adults as learners. San Francisco: Jossey-Bass.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human
behavior. New York: Plenum Press.
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
28
Egeland, B., Carlson, E., & Sroufe, L. A. (1993). Resilience as process. Development &
Psychopathology, 5, 517-128.
Feltz, D. L. (1982). Path analysis of the causal elements in Bandura’s theory of self-efficacy and
an anxiety-based model of avoidance behavior. Journal of Personality and Social
Psychology, 42(4), 764-781.
Flanagan, C. A., & Tucker, C. J. (1999). Adolescents’ explanations for political issues:
Concordance with their views of self and society. Developmental Psychology, 35(5),
1198-1209.
Forsterling, F. (1985). Attributional retraining: A review. Psychological Bulletin, 98, 495-512.
Fox, W. F., Cunningham, M. A., Thacker, A. R., & Vickers, B. B. (December, 2001). Families
First case characteristics study. Knoxville, TN: Center for Business and Economic
Research, The University of Tennessee.
Gillham, J. E., Reivich, K. J, Jaycox, L. H., & Seligman, M. E. P. (1995). Prevention of
depressive symptoms in school children: Two-year follow-up. Psychological Science, 6,
343-351.
Gist, M. E., & Mitchell, T. R. (1992). Self-efficacy: A theoretical analysis of its determinants
and malleability. Academy of Management Review, 17(2), 183-211.
Gorrell, J. (1990). Some contributions of self-efficacy research to self-concept theory. Journal of
Research and Development in Education, 23(2), 73-81.
Hayes, E. (1988). A typology of low-literate adults based on perceptions of deterrents to
participation in adult basic education. Adult Education Quarterly, 39(1), 1-10.
Huang, S. C., Lloyd, P., & Mikulecky, L. (1999). ESL literacy self-efficacy: Developing a new
scale. (ERIC Document Reproduction Service No. ED427541)
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
29
Jaycox, L. H., Reivich, K. J., Gillham, J., & Seligman, M. E. P. (1994). Prevention of depressive
symptoms in school children. Behavioral Research and Therapy, 32, 301-316.
Kinderman, P., & Bentall, R. P. (2000). Self-discrepancies and causal attributions: Studies of
hypothesized relationships. British Journal of Clinical Psychology, 39, 255-273.
Kobasa, S. C. (1979). Stressful life events, personality, and health: An inquiry into hardiness.
Journal of Personality and Social Psychology, 37, 1-11.
Kobasa, S. C., Maddi, S. R., & Courington, S. (1981). Personality and constitution as mediators
in the stress-illness relationship. Journal of Health and Social Behavior, 22, 368-378.
Long, E. (2001). Enrollment and retention in adult basic education programs some theoretical
implications of a national study follow-up study. 2001 AERC Proceedings. Lansing, MI:
Michigan State University.
Maddi, S. R., & Khoshaba, D. M. (1994). Hardiness and mental health. Journal of Personality
Assessment, 63, 265-274.
Maddi, S. R., & Kobasa, S. C. (1981). Intrinsic motivation and health. In H. I. Day (Ed.),
Advances in intrinsic motivation and aesthetics (pp. 120-133). New York: Plenum.
Malicky, G. V., & Norman, C. A. (1994). Participation patterns in adult literacy programs. Adult
Basic Education, 4(3), 144-156.
Marsh, H. W. (1984). Relations among dimensions of self-attribution, dimensions of selfconcept, and academic achievement. Journal of Educational Psychology, 76, 1291-1308.
McMillan, J. H., Simonetta, L. G., & Singh, L. (1994). Student Opinion Survey: Development of
measures of student motivation. Educational and Psychological Measurement, 54(2),
496-505.
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
30
Nowack, K. M. (1989). Coping style, cognitive hardiness, and health status. Journal of
Behavioral Medicine, 12, 145-158.
O’Neal, M. R. (1999, November). Measuring resilience. Paper presented in symposium at the
annual meeting of the Mid-South Educational Research Association, Point Clear,
Alabama.
Quigley, B. A. (1987). Rethinking literacy education: The critical need for practice-based
change. San Francisco, CA: Jossey Bass.
Quigley, B. A. (1990). Hidden logic: Reproduction and resistance in adult literacy and adult
basic education. Adult Education Quarterly, 40(2), 103-115.
Quigley, B. A. (1993). Seeking a voice: Resistance to schooling and literacy. Adult Basic
Education, 3(2), 77-90.
Quigley, B. A. (1997). Rethinking literacy education: The critical need for practice-based
change. San Francisco: Jossey Bass.
Quigley, B. A. (2000). Beyond participation and stereotypes: Towards the study of engagement
in adult literacy education. AERC Proceedings. Retrieved January 16, 2002, from
http://www.edst.educ.ubc.ca/aerc/2000/quigleya1-web.htm
Rotter, J. B. (1975). Some problems and misconceptions related to the construct of internal
versus external control of reinforcement. Journal of Counseling and Clinical Psychology,
43(1), 56-67.
Schunk, D. H. (1994). Self-regulation of self-efficacy and attributions in academic settings. In D.
H. Schunk, & B. J. Zimmerman (Eds.), Self-regulation of learning and performance:
Issues and educational applications. (pp. 75-99). Hillsdale, NJ: Lawrence Erlbaum
Associates, Inc.
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
31
Seligman, M. E. P. (1991). The optimistic child. Boston: Houghton Mifflin.
Sherer, M., Maddux, J. E., Mercandante, B., Prentice-Dunn, S., Jacobs, B., & Rogers, R.W.
(1982). The Self-Efficacy Scale: Construction and validation. Psychological Reports, 51,
663-671.
Shields, N. (1995). The link between student identity, attributions, and self-esteem among adult,
returning students. Sociological Perspectives, 38(2), 261-272.
Valazquez, L. C. (1996, Summer). Voices from the fields: Community-based migrant education.
New Directions for Adult and Continuing Education, 70, 27-35.
Van Tilberg, E., & DuBois, J. (1989). Literacy students’ perceptions of successful participation
in adult education: A cross-cultural approach through expectancy valence. Proceedings of
the 30th Annual Adult Education Research Conference, pp. 308–313. Madison, WI:
University of Wisconsin.
Weiner, B. (1979). A theory of motivation for some classroom experiences. Journal of
Educational Psychology, 71(1), 3-25.
Wikelund, K., Reder, S., & Hart-Landsberg, S. (1992). Expanding theories of adult literacy
participation: A literature review. (Technical Report TR92-1). Philadelphia, PA: NCAL.
Ziegahn, L. (1992). Learning, literacy and participation: Sorting out priorities. Adult Education
Quarterly, 43(1), 30-49.
Ziegler, M., & Ebert, O. (1999). Making progress in adult basic education: How long does it
take? Knoxville, TN: Center for Literacy Studies, The University of Tennessee.
Ziegler, M., Ebert, O., & Henry, J. (2002). Characteristics of Families First participants
enrolled in adult education in 1996. Knoxville, TN: Center for Literacy Studies, The
University of Tennessee.
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Self-Beliefs: Predicting Persistence
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APPENDIX
ADULT EDUCATION PERSISTENCE SCALE
We will be using the
opinions and answers
you provide to
determine how we
can improve adult
education programs.
There are no right or
wrong answers.
Fill in each circle completely to choose your answer. If you
want to change your answer, mark an X through it or erase
it completely, then fill in your new choice.
Example:
Correct
●
Incorrect 
Incorrect 
Please fill in a circle under yes or no for each question.
Please do not skip
any part of the
Have you ever
been in adult education or GED classes before?


survey.
When
THANK
you were YOU
in school,
FOR
did you repeat any grades?


PARTICIPATING IN
THIS
When you
wereSURVEY.
in school, did you have any difficulty learning?


Do you have transportation to adult education or GED classes?


Are there family or friends who support your choice to get more education?


Please listen to each statement carefully before answering. Fill in only the one circle that you
think best shows how much you agree or disagree with that statement. There are no wrong or
right answers.
Strongly
Disagree
Disagree
Agree
Strongly
Agree
I am able to sit and work on math problems for a
long time.




2.
A GED can help you get a better job.




3.
I don’t like to read things that are hard.




1.
The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
33
4.
It’s good to get a chance to get back to school.




5.
Most teachers don’t know how to teach reading.




6.
Teachers can help you learn math.




7.
I am not good at trying something new.




8.
Being good at reading can help you pass the GED.




9.
When math problems get hard, I get a bad attitude.




10. Good reading skills are important.




11. I don’t like to work on math problems that are hard.




12. My faith in myself helps me to improve my math skills.




13. Teachers can help you learn reading.




14. Getting better math skills is a waste of time.




Please listen to each statement carefully before answering. Fill in only the one circle that you
think best shows how much you agree or disagree with that statement. There are no wrong or
right answers.
1.
2.
3.
Strongly
Disagree
Disagree
Agree
Strongly
Agree
If I have to repeat a math assignment, it is probably
because I am unlucky.




If I get a low score on a reading test, it is probably
because I am having a bad day.




If no one wants to work with me on a reading project, it
is probably because the people in the class are unfair.




The University of Tennessee Center for Literacy Studies, 2002
Self-Beliefs: Predicting Persistence
4.
34
If I am late to daycare, it is probably because my ride did
not show up.




If I cannot do a reading assignment, it is probably
because I am unlucky.




If I get a low score on a math problem, it is probably
because it just isn’t my day.




If I need extra help in reading, it is probably because
reading is hard for most people.




If I have to repeat a math assignment, it is probably
because the teacher is unfair.




If I get a low score on a reading test, it is probably
because I am not good at reading.




10. If I get a low score on a math problem, it is
probably because I am not good at math problems.




11. If I cannot do a reading assignment, it is probably
because I do not work hard on reading.




12. If I cannot do a math assignment, it is probably
because I am not good at math.




13. If no one wants to work with me on a reading project,
it is probably because I do not work hard on reading
projects.




14. If I cannot do a reading assignment, it is probably
because I am not good at reading.




15. If I am late to daycare, it is probably because I am
not good at being on time.




16. If no one wants to work with me on a reading project,
it is probably because I am not a good reader.




5.
6.
7.
8.
9.
The University of Tennessee Center for Literacy Studies, 2002