Background Motivation is a significant psychological
Motivation is a significant psychological concept and plays a crucial role in education.
Psychologists illustrate motivation through various perspectives—humanistic (Maslow, 1943),
behaviorist (Skinner, 1953), and social-cognitive (Bandura, 1991). Generally, motivation implies
that an individual’s drive, desire, and willingness play a significant role in functions. Social
involvement and personal responsibility are promoted by motivation (Tabernero & Hernandez,
2011). A high level of motivation increases the likelihood of an individual behaving and
responding to fulfill particular standards (Bandura, 1991). Motivation is one of the significant
influences on educational outcomes. Motivated students are more likely to value learning
activities and produce positive performance (Zimmerman, 2008; 2000b). Motivation leads
individuals to choose a systematic and deep approach to learning (Prat-Sala & Redford, 2010).
Self-efficacy is at the core of motivation. It refers to people’s belief that they can achieve
and master tasks (Bandura, 1991; Schunk & Pajares, 2002). It affects their drive to set goals,
develop plans, and control environmental factors to accomplish tasks. Self-efficacy enhances
students’ academic performance (Komarraju & Nadler, 2013) and increases the likelihood of
engagement in the self-regulation process (Zimmerman, 2000a).
There are significant correlations between self-efficacy and self-regulation (Ghonsooly &
Ghanizadeh, 2011; Uzuntiryaki-Kondakci & Capa-Aydin, 2013). Self-regulation is defined as an
individuals’ ability to control emotional, behavioral, and cognitive functions (Zimmerman,
1998). Those who can self-regulate are more capable of controlling behaviors, inhibiting
impulsivity, being flexible to change, and regulating emotional responses (1998). Self-regulation
is an essential cognitive ability that enhances social interactions, psychological health, and
academic performance. Klapp (2016) emphasizes the crucial role of self-regulation in reducing
negative emotions. Self-regulation has a strong impact on enhancing intrinsic and extrinsic
motivation. Pintrich and Schunk (1996) indicated that goal orientation, as a process of
selfregulation, improves intrinsic motivation more than outcome rewards.
Self-regulation, self-efficacy, and academic motivation have reciprocal correlations in
which the constructs influence each other. For example, utilizing self-regulatory strategies
enhance students’ academic motivation and self-efficacy. Kormos and Csizer (2014) developed a
model that suggests motivational factors—the purpose of learning, orienting effort to achieve a
goal, and personal belief—are effective in promoting self-regulation. Similarly, Yusuf (2011)
explained the mediational role of self-efficacy on achievement motivation, learning strategies
and academic achievement. However, relatively little research has been done to analyze the
complex relationships between the three variables—self-regulation, self-efficacy, and academic
motivation. This study investigated a hypothesized model that describes the complex
relationships between these variables within the framework of SCT. The hypothesized model
suggested that self-efficacy and self-regulation predict academic motivation.
Rationale for the Study
Enrollment in higher education is viewed as a transition point when students experience
difficulty in adapting to a new system of education in addition to dealing with other occupational
and social responsibilities (Busse & Walter, 2017; Wang & Pomerantz, 2009). Students
experience massive maladaptive changes in their motivation to learn which in turn affect their
academic success, retention, effective engagement in learning, and occupational training
activities (Dresel & Grassinger, 2013).
Several factors impact academic motivation among university students. They are related
to faculty assessments and feedback, campus activities, and educational environment (Rowell &
Hong, 2013); as well as to self-esteem, self-confidence, expectancy, emotional regulation, and
goal commitment (Zimmerman, 1998). Self-efficacy and self-regulation contribute to academic
motivation (Bandura,1991; Deci & Ryan 2008). Few studies have been conducted to determine
the impacts of self-efficacy and self-regulation on academic motivation among university
students, particularly in the United States. The majority of studies reviewed were conducted in
cultures such as Iran, Africa, and Hong Kong (Alafgani & Purwandari, 2019; Lavasani et al.,
2011; Ning & Downing, 2010).
Statement of the Problem
There is evidence that students’ motivation to learn and level of self-efficacy decreases
over their academic years (Busse & Walter, 2017; Dresel & Grassinger, 2013; Rizkallah & Seitz,
2017). Lack of motivation negatively impacts students’ academic performance and tend to lead
students to disengage from learning activities, underachieve, or drop out of school (Wang &
Pomerantz, 2009). During the first year of university students show a significant decrease in
academic motivation, self-concept, mastery-approach goals, and the subjective value of their
course of studies. This decline in academic motivation is associated with a negative impact on
self-regulatory strategies (Dresel & Grassinger, 2013; Wang & Pomerantz, 2009).
Ben-Eliyahu (2011) argues that the absence of motivation inhibits the construction of
self-regulatory strategies such as setting goals, planning, and monitoring behaviors. Lack of
motivation also affects students’ performance and enthusiasm, and students lose their
productivity and creativity. Thus, motivation and self-regulation cooperate in improving learning
operations. Impairment of self-regulation has a negative effect on academic achievement,
motivation, and mental health. Thirteen percent of graduate students suffer from depression and
2% of them engage in suicide attempts or have mental health problems (Eisenberg et al., 2007).
Failure in self-regulation leads an individual to commit crimes or to alcohol addiction and drug
use (Baron, 2003). It contributes to problems such as financial issues, obesity, performance
impairment, crime, and drug and alcohol addiction (Kruglanski & Higgins, 2007). Low
selfefficacy also impacts motivation because it correlates strongly with high levels of worry,
anxiety, and depression (Tahmassian & Moghadam, 2011). When students are depressed and
anxious, they lack the ability to regulate negative emotions.
Purpose of the Study
The purpose of the study was to test a theoretical model of the influence of selfregulation
and self-efficacy on academic motivation. In particular, a hypothesized model of the relationship
between these variables was created and data measuring the self-regulation, selfefficacy, and
academic motivation of undergraduate students was collected and analyzed through
Structural Equation Modeling (SEM).
Conceptual Framework
The conceptual framework for this study is based on Bandura’s SCT (1986) and the
SelfDetermination Theory (SDT) proposed by Deci and Ryan (1985).
Social Cognitive Theory
According to SCT, humans learn within a social context. Social interactions influence the
initiating and attainment of behaviors. The triadic reciprocal determinism of SCT assumes that
behavior, internal factors, and the environment interact during the process of learning. Therefore,
self-efficacy and self-regulatory abilities affect academic motivation. Individuals observe a
model that scaffolds a particular behavior, then form a belief to perform this behavior
successfully. Hence, they tend to set goals and plan and they become motivated to engage in task
performance. However, observation alone is not enough to perform effectively. Bandura
emphasizes the role of experience which involves monitoring one’s performance and cognitive
functions. Mastering a wide range of experiences increases individuals’ belief in their abilities,
which in turn improves their self-regulation and motivation (Bandura, 1991).
SCT and Self-Efficacy
Self-efficacy influences individuals’ thoughts, affects, motivation, and actions, which
impact directed and organized purposeful behaviors. The system of beliefs, including selfefficacy
of competence and beliefs of the changeability or controllability of the environment, improves
people’s motivation to achieve goals. Hence, people with high levels of self-efficacy and beliefs
in their abilities to control environmental factors are more likely to use their personal
competencies and abilities to adapt to environments to produce successful performance
(Zimmerman, 2000b). Therefore, they enhance their self-efficacy and motivation to set
challenging goals. Individuals’ engagement in self-reflective processes leads to perceived
capabilities to perform a particular task; and such beliefs enhance the processes of internal
motivation (Bandura, 1994). According to SCT, humans build self-efficacy beliefs through four
major resources: mastery experience, vicarious experience, social persuasion, and emotional and
physical reaction (1994). The integral impact of personal factors and environmental influences
was clear among students who believe themselves competent in mathematics (Schunk & Usher,
2019). Those students tend to engage in class activities, make an effort to learn, and persevere.
When teachers recognize their performance and environmental influence, the students’
selfefficacy improves and encourages motivation (2019). Environmental influences and personal
factors are incorporated in the formation of self-efficacy beliefs. Self-efficacy can be developed
by observing a successful model (Bandura, 1994). Also, productive feedback and persuasive
comments from significant models increases the sense of efficacy (Bandura, 1997; Schunk &
Usher, 2019). Social and cognitive influences are significant predictors of self-efficacy. They
include model observation, self-monitoring, goal settings, self-evaluation, and comparison with
social standards (Schunk & Usher, 2019).
One of the most important personal influences for developing self-efficacy is achieving
goals. Success then develops beliefs in one’s capabilities. Emotional arousal that individuals
experience while engaged in behavior also affects self-efficacy. Low-level anxiety increases
selfefficacy whereas high-level anxiety decreases self-efficacy (Bandura, 1994; Schunk & Usher,
2019). In terms of behavioral influences, individuals who believe that they are efficacious in
performing a task, usually get involved in activities, persist in difficulties, and perform well
(Schunk & Usher, 2019; Zimmerman & Moylan, 2009).
SCT and Self-Regulation
Bandura (1994) defines self-regulation as the human tendency to achieve a sense of
agency in which individuals believe in their capacity to control their actions and environment.
The sense of agency can be achieved by directing thoughts and actions (Usher & Schunk, 2018).
Human actions are not only a consequence of environmental factors; indeed, individuals
intentionally choose their environment in a way that contributes to achieving their learning
objectives. This demonstrates the reciprocal aspects of this theory (Bandura, 1997).
Self-regulation processes are highly dependent on self-monitoring, self-evaluation, and
effective self-reaction (Zimmerman & Moylan, 2009). The cyclical model of self-regulation
(Figure 1) comprises three main phases—forethought, performance, and self-reflection. The
forethought phase assists individuals in motivating themselves and organizing their performance.
Figure 1
A Cyclical Phase Model of Self-regulation that Integrates Metacognitive Processes and Key Measures
of Motivation
People select strategies, plan, and build motivation. In the performance phase individuals
implement the selected strategies and monitor the progress of their actions. The self-reflective
phase consists of evaluating outcomes and making attribution of such outcomes. When desired
outcomes are achieved satisfaction occurs; however, if outcomes did not meet specified
standards, modification is made (Zimmerman & Moylan, 2009).
This cyclical model of self-regulation represents the reciprocal interactions between
personal, behavioral, and environmental influence (Usher & Schunk, 2018). After the
selfreflective phase, if learners discover that the applied strategies were effective, they go back to
the performance phase. However, if their strategies need modifications, they return to the
forethought phase to adopt new strategies. During these processes, personal influence (cognition)
interacts with behavioral and environmental influence and vice versa (Schunk & DiBenedetto,
2020; Zimmerman & Moylan, 2009).
Deliberate thinking guides the self-regulatory process by considering emotional,
motivational, and actual performance. Attention is important to the success of self-regulation
(Zimmerman & Moylan, 2009). Bandura (1991) emphasizes the role of knowledge about one’s
performance because cognitive regulation of motivation is based on an anticipatory, proactive
system that includes effective self-monitoring, self-evaluation, self-incentive, and self-reaction.
SCT and Academic Motivation
According to SCT, the ability to regulate motivation, affect, and action is significant in
developing motivation. Therefore, setting goals and planning is not enough to perform
effectively (Bandura, 1991). However, the engagement in self-evaluative processes where one
compares outcomes of actions to personal standards will produce self-reactive influences
(Bandura & Cervone, 1986). Self-reactive influences consist of self-satisfaction, perceived
selfefficacy, self-set goals. The effective use of self-reactive influence motivates a person
positively, whereas using self-incentive because of self-reactive influence enhances one’s
motivation to accomplish the desired behavior. Zimmerman (1998) demonstrated that people
who tend to reward themselves after attainment differ in their ability to regulate their motivation
and action from those who did not use self-incentive. Self-evaluation and self-incentive lead to
selfsatisfaction which in enhances motivation to pursue performance. For instance, when
individuals evaluate their performance based on specific standards and reward themselves when
they are satisfied with the outcome, their motivation to accomplish more increases. Bandura
(1991) indicated that self-evaluation, whether based on personal standards or social comparison,
improves self-satisfaction when goals are met, which enhances academic motivation. SCT posits
behavioral and environmental influences impact motivation and they in turn are affected by
motivation (1991). For instance, observing a successful model who has relatively similar
characteristics and abilities improves motivation (Bandura, 1986).
Academic motivation is affected by factors such as internal beliefs, cognitions, and social
interactions. Outcome expectancies and value affect motivation to act; and expecting positive
results develop the desire to engage in productive behaviors (Schunk & DiBenedetto, 2020)
Students who acknowledge the significance of learning tasks and value learning outcomes, are
more likely to be motivated and to engage in learning activities. Individuals’ beliefs in their
abilities significantly affect motivation (Schunk & DiBenedetto, 2020). Social interactions,
where positive comments and feedback from significant others imply the effective abilities to
perform well, improve a sense of efficacy and increases one’s motivation for further functions. In
addition, social comparison as a personal influence has a significant effect on motivation in
which comparing oneself with an observed model facilitates building motivation to perform a
task (Bandura, 1986; Schunk & Usher, 2019). Motivation is affected by behavioral influences
such as choosing to engage in activities, making an effort, persisting when difficulties occur, and
regulating the environment (Schunk & DiBenedetto, 2020).
Self-Determination Theory
SDT, developed by Deci and Ryan (1985), demonstrates human motivation. Their theory
suggests that humans develop and change by satisfying three main psychological needs—
competency, relatedness, and autonomy. Competency is knowing how to obtain external and
internal outcomes and the ability to perform effectively. Relatedness is connecting thoughts and
behaviors with social norms and acting accordingly. Autonomy refers to the ability to initiate and
regulate one’s performance (Deci et al., 1991; Ryan & Deci, 2020).
SDT suggests three types of motivation that energize and direct human behaviors and
activities. (1) Intrinsic motivation—which leads to volitionally engaging in a behavior because of
a sense of satisfaction and pleasure without any interest in external contingencies. (2) Extrinsic
motivation—which refers to integrating the behavior’s value into the sense of self. (3) Controlled
motivation—which comprises external regulation (explains the external reinforcements such as
rewards or punishments that direct people to engage in a behavior or activity) and introjected
regulation (individuals behave to avoid the feeling of shame, to develop self-esteem, or for the
sake of ego-involvement) (Deci & Ryan, 2008; Ryan & Deci, 2020). The theory also
distinguishes between autonomous motivation (individuals become self-determined; it consists of
both intrinsic motivation and extrinsic motivation specifically the identified regulation) and
controlled motivation (Ryan & Deci, 2020).
An autonomy continuum explains the processes of internalization where humans integrate
the external contingencies into internal processes (Deci et al., 1991). To achieve positive
outcomes, it is imperative to enhance autonomous regulation through internalized and integrated
extrinsically motivated behaviors. The internalization processes emphasize the role of fulfilling
the needs of relatedness, competence, and autonomy. Even though personal experiences and
outcomes are important in the process of internalization, social factors have significant impacts
in which the engagement of extrinsically motivated behavior can be attributed to fulfilling the
sense of belonging because such behavior is valued by significant others. Promoting competence
assists internalization; hence enhancing self-efficacy is a key to people tending to engage in a
valuable performance through relevant social groups only when they believe it is efficacious.
Also, the experience of autonomy is essential to facilitate internalization (Deci et al.,
1991; Ryan & Deci, 2000).
SDT and Self-regulation
SDT posits an autonomy continuum that distinguishes between self-regulation
(autonomy) and external regulation (heteronomy) (Deci et al., 1991, Ryan & Deci, 2000). The
autonomy continuum explains the degree of self-determined behavior where individuals develop
autonomous motivation rather than controlled motivation. Autonomously oriented people engage
in performance because they have an interest in and value the outcomes of the activities. In
contrast, people who are control-oriented act for the sake of external forces such as rewards or
punishment avoidance.
Autonomy can be developed through considering students’ feelings and allowing them to
have choices and to make decisions (Ryan & Deci, 2002). Identified regulation, when behavior is
relatively internal, correlates with students’ tendency to adopt regulatory strategies such as
coping mechanisms and planning for effort. In contrast, students with external regulation of
motivation were less interested in learning processes, avoided effort, and blamed others when a
failure occurred (Ryan & Deci, 2000).
Perceiving learning activities as personally important indicates advanced levels of
selfdetermination among students. Self-determined students perform learning activities out of
pleasure, interest, and value; they persist and produce a high level of academic achievement
(Ryan & Deci, 2006). In contrast, students who perceive learning as pressured or engage in
learning processes because of external demands are more likely to quit when facing obstacles and
to produce low levels of achievement (Niemiec & Ryan, 2009). An autonomous-supportive
environment is significant in fostering self-regulation. Students who perceive autonomy support
show high levels of autonomous self-regulation (Deci & Ryan, 2008; Ryan & Deci, 2020).
Creating an environment that satisfies autonomy, competence, and relatedness needs with autonomy
support from parents, teachers, or instructors can promote competence and autonomous self-
regulation (Deci & Ryan, 2008). In contrast, students who experience thwarting of their psychological
needs (autonomy, competence, and relatedness) report controlled selfregulation. Autonomous
motivation such as intrinsic motivation and identified regulation are associated with autonomous self-
regulation, perceived competence, and perceived high academic performance. In contrast, introjected
regulation and external regulation leads to controlled selfregulation and incompetence (Jeno & Diseth,
2014).
SDT and Self-efficacy
SDT emphasizes the satisfaction of psychological needs (autonomy, relatedness, and
competence) to enhance human behaviors. Competence as a psychological need is related to
selfefficacy. Competence is a broader concept that illustrates how much people believe they have
an effective role in their society. Self-efficacy within SDT is called perceived competence which
is a significant factor for motivation (Ryan & Deci, 2006). SDT is about the level of beliefs and
the quantity of one’s motivation and why one holds such a belief. SDT also explains how such a
distinction of motivation affects the consequences of behavior. This concept facilitates the
differentiation between autonomous and controlled actions.
Students who have an internal locus of causality (or control) believe that they have
control over their learning processes and thus engage in self-determined behavior. Students who
have an external locus of control believe they have little control over their learning outcomes and
are more likely to perform controlled behavior (Deci et al., 1991). Perceived competence
mediates the relationship between positive feedback and intrinsic motivation. The integration of
feeling competent and autonomy, particularly the locus of control, significantly affects intrinsic
motivation (Ryan & Deci, 2000).
There is evidence that fulfilling the needs of competence will foster a sense of
selfefficacy (Ryan & Deci, 2020). There are four sources of self-efficacy: mastery experience,
vicarious experience, social persuasion, and physiological and emotional states (Bandura, 1997).
Therefore, feedback from teachers and parents plays a significant role in constructing students’
beliefs in their capabilities and control over their actions. Negative feedback undermines
students’ sense of competence while positive feedback promotes perceived competence which in
turn influences intrinsic motivation (Deci et al., 1991).
Perceived autonomy is associated with self-efficacy (Ryan & Deci, 2020). An educational
environment that supports autonomy and treats students as active learners is imperative to
encourage competency. When students have opportunities to be responsible for their learning
processes and the freedom to make decisions and have unique perspectives, they then will be
motivated to regulate their learning, utilize effective strategies, and evaluate their progress. As a
result, successful outcomes will increase belief in one’s capabilities to perform well. Satisfying
the needs for autonomy promotes self-determined behavior which then constructs self-efficacy
(Ryan & Deci, 2020).
Girelli et al. (2018) constructed a model that predicts undergraduate students’ intention to
drop out by examining their perceived autonomy support from teachers and parents; and how this
autonomy support influences their motivation and self-efficacy. Students who perceive autonomy
support from teachers and parents develop greater levels of autonomous motivation and self-
efficacy. In addition, students who attend university because of intrinsic motivation and beliefs in
their capabilities were less likely to want to drop out of school and more likely to experience
academic adjustment (Girelli et al., 2018)
SDT and Academic Motivation
The theory identifies several types of motivation: intrinsic motivation, identified
regulation, introjected regulation, and external regulation. The basic motivation is intrinsic
motivation which promotes self-determined functions. Self-determined students tend to engage
in learning activities and produce positive academic performance compared to students who are
less self-determined (Vallerand et al., 1992). Students who report high levels of intrinsic
motivation show advanced academic progress. Those who learn to attain knowledge and
implement information were compared to those who learn materials to do well on a test. The
findings demonstrated that students with intrinsic motivation and autonomous regulation show
greater conceptual learning than extrinsically motivated students (Deci et al., 1991). Students
with intrinsic motivation demonstrated high levels of enjoyment in academic settings, positive
emotions, and satisfaction with academic activities (Deci et al., 1991; Vallerand et al., 1992)
An autonomous-supportive approach enhances academic motivation. This approach helps
students in the process of internalization which in turn facilitates the integration of external
regulation to become part of intrinsic motivation (Deci & Ryan, 2008; Ryan & Deci, 2020).
Graduate students involved in practical learning activities show greater levels of intrinsic
motivation compared to undergraduate students where the focus was on attaining theoretical
knowledge (Koludrović & Ercegovac, 2015). A study was conducted to investigate the role of
psychological needs fulfillment—autonomy, relatedness, and competence. The researchers
suggested a motivational model for examining what factors may predict academic motivation.
The path analysis results indicated significant correlations between autonomy and academic
motivation as well as competence and academic motivation. Competence was a better predictor
of intrinsic academic motivation than autonomy which was mediated by identity development.
Relatedness was not a significant predictor of academic motivation (Faye & Sharpe, 2008).
In terms of improving academic motivation through satisfying competence and
relatedness needs, positive feedback and interpersonal involvement of teachers and parents were
effective in enhancing intrinsic motivation. An autonomy-supportive environment facilitates the
internalization process of external regulation (Deci et al., 1991). Autonomy-supportive teachers
consider students’ perspectives and provide them with a rationale to implement activities, as well
as the opportunity to choose learning activities and to take initiative for their academic work.
Supporting autonomy leads to supporting relatedness needs and competence, specifically when
teachers provide constructive feedback (Ryan & Deci, 2020).
Research Questions
This exploratory study examined a hypothesized model of the influence of self-
regulation and self-efficacy on academic motivation, among undergraduate students in the
United States. The primary research question was, “Are the theoretical covariance matrix and the
empirical or observable covariance matrix equal?” This main question addressed the following
research question, was the hypothesized theoretical model a good fit to the sample? The
subresearch questions were:
1. Was there a significant correlation between self-regulation and self-efficacy?
2. Did self-regulation affect academic motivation?
3. Did self-efficacy affect academic motivation?
Research Hypotheses
The main hypothesis of this study was that the reproduced covariance matrix proposed in
the theoretical model and the observed sample covariance matrices were equal. In simple terms,
this meant that the structural model would be a good fit with the observed data. Using the
conceptualized model depicted in Figure 2, this study hypothesized (1) There was a significant
correlation between the two exogenous variables, self-regulation and self-efficacy, (2)
Selfregulation had a significant, direct effect on the endogenous variable academic motivation,
(3) Self-efficacy had a significant, direct effect on the endogenous variable academic motivation.
Figure 2
Self-efficacy and Self-regulation Predict Academic Motivation
Significance of the Study
The significance of this study is girded by the fact that the demand for higher education
has grown in different societies. Higher education aims not only to provide knowledge but also to
offer vocational training to prepare qualified members of society. However, current statistics
indicate that the number of enrolled students in higher education has declined. Researchers found
that university students tend to underachieve or drop out of school as a result of an inability to
adapt easily during the transition period from secondary education to higher education (Wang &
Pomerantz, 2009). One reason underlying this phenomenon is students’ lack of motivation to
learn, self-efficacy, and a self-regulatory mechanism (Busse & Walter, 2017; Dresel &
Grassinger, 2013; Rizkallah & Seitz, 2017). Hopefully, my findings can benefit society and
governments by offering information regarding critical variables that influence the motivation of
students in higher education. This information may enhance knowledge of academic motivation,
Self
-
efficacy
Control of
learning
Self
-
efficacy
of learning
Academic
motivation
Metacognitive
self
-
regulation
Intrinsic
motivation to
know
Intrinsic
motivation to
accomplish
self
-
regulation
Intrinsic
motivation to
stimulate
Time &
environment
Effort
regulation
external
regulation
Amotivation
Extrinsic
motivation
-
identified
Extrinsic
motivation
-
introjected
which will lead to a decrease in the number of students who drop out of school and an increase in
the number of graduate students who will serve in different fields to improve society.
The outcome of the current study can help policymakers and personnel of higher
education to improve students’ academic motivation by emphasizing the role of enhancing
students’ beliefs in their capabilities and integrating effective self-regulatory processes in higher
education learning and curriculum. The findings of the study can contribute to increasing the
understanding of critical factors that impact students’ motivation to learn. Such significant
knowledge is going to provide faculty and students with important strategies and techniques
related to developing motivation to learn. For instance, instructors can focus on planning lectures
to incorporate self-efficacy and self-regulatory strategies. Students who enroll in higher
education can also concentrate on developing their beliefs in self and practicing self-regulatory
strategies whenever their motivation to learn abates. Even though many studies have investigated
academic motivation, very few were conducted with the higher education population.
Although previous studies have investigated the correlation between self-efficacy and
academic motivation (Bandura, 1991; Cerino 2014) and self-regulation and academic motivation
(Cetin, 2015; Ning & Downing 2010), there is a lack of studies that focus on predicting the role
of self-efficacy and self-regulation on academic motivation. This justified the existence of this
study. This study can serve as a guide for researchers to investigate the combination of the
study’s variables among different populations and to detect other factors that may predict
academic motivation among university students.
Definition of Terms
Academic motivation refers to the intrinsic or extrinsic orientation that drives one to set
goals and prepare plans to perform in a particular way. Thus, motivation is the interest or the will
that drive students to accomplish academic goals (Ryan & Deci, 2000; Vallerand et al., 1992).
Amotivation refers to the concept of describing individuals’ tendency to disengage in
activities or actions as a result of the absence of desire or to the lack of valuing an outcome
(Vallerand et al., 1992).
Control of learning beliefs refers to students’ beliefs in their ability to control their effort
and a successful outcome will be attributed to the extent of effort rather than external factors
such as luck or instructors (Pintrich et al., 1993).
Effort regulation refers to students’ abilities to manage themselves during the process of
learning despite the obstacles and difficulties that they may encounter to achieve desired goals
(Pintrich et al., 1993).
External regulation refers to factors that drive behavior to obtain rewards or avoid
punishment (Vallerand et al., 1992).
Extrinsic motivation refers to factors that enhance students’ desire to perform effectively
to achieve academic success such as esteem or reward (Ryan & Deci, 2000; Vallerand et al.,
1992).
Identified regulation indicates that the reason for the engagement is not fully external but
the regulating behavior is relative to its value and personal reasons (Vallerand et al., 1992).
Intrinsic motivation refers to the internal desire students have to engage in academic
activities such as satisfaction (Ryan & Deci, 2000; Vallerand et al., 1992).
Intrinsic motivation to accomplishment refers to the pleasure and satisfaction individuals
experience when accomplishing something (Vallerand et al., 1992).
Intrinsic motivation to know refers to the pleasure and satisfaction individuals
experienced when they learn, understand, and explore new things (Vallerand et al., 1992).
Intrinsic motivation to experience stimulation is defined as engaging in activities due to
the experience of excitement, enthusiasm, or aesthetics (Vallerand et al., 1992).
Introjected regulation refers to the tendency to engage in behavior to improve self-esteem
or avoid anxiety and a sense of guilt (Vallerand et al., 1992).
Metacognitive self-regulation refers to individuals’ ability to conduct effective strategies
that assist in controlling and regulating performance such as setting goals, planning, monitoring,
and modifying behaviors (Pintrich et al., 1993).
Self-efficacy refers to the belief in one’s capabilities to conduct the well-organized
behavior needed to accomplish a task (Schunk & Pajares, 2002; Zimmerman, 2000a). It includes
judgments about one’s ability to accomplish a task as well as one’s confidence in the skills to
perform that task (Pintrich et al., 1993).
Self-efficacy for learning refers to both expectancy for success and confidence in one's
ability to accomplish a task where expectancy for success is more related to the performance and
expectations than the judgment of one’s abilities and skills and how much confidence the
students have in their capabilities (Pintrich et al., 1993).
Self-regulation refers to the individuals’ ability to control their emotional, cognitive, and
behavioral responses and make changes and adjustments to adapt successfully (Bandura, 1991;
Zimmerman, 2000a).
Time and study environment management: time management refers to the effective use of
study time including daily, weekly, and monthly plans and schedules. Whereas study
management refers to students’ tendency to avoid distraction and prepare a quiet and organized
study environment (Pintrich et al., 1993).
Limitations of the Study
The limitations of this study were as follows:
1. The self-report questionnaires used imply a response bias because participants may
have faked their responses to look good or to respond according to their socially desirable norm.
2. The Likert scales may have been subject to participants misinterpreting the meaning of
the scale points. Thus, some may have responded around the midpoint areas of the scale, whereas
others may have responded on the extreme edge points of the scale.
3. The convenience sampling method used in this study may have limited the
generalization of the findings to similar populations.
Delimitations of the Study
This study was limited to undergraduate students 18–22 years old. Although academic
motivation is influenced by a variety of psychological and social factors, the primary focus of
this study was on the effect of self-regulation and self-efficacy on academic motivation. A
structural model was used to analyze and interpret the data, instead of a measurement model,
because the researcher focused on the predictive roles of self-regulation and self-efficacy in
academic motivation.
CHAPTER 2
LITERATURE REVIEW
Organization of the Literature Review
This chapter is divided into five main sections: (1) literature search strategies; (2)
historical and theoretical overviews of motivation generally as well as academic motivation,
selfregulation, and self-efficacy; (3) the relationship between the variables self-regulation and
academic motivation; (4) the relationship between self-efficacy and academic motivation; (4) the
relationships between self-efficacy, self-regulation, and academic motivation; (5) an analysis and
synthesis of the literature review.
Literature Search Strategies
The purpose of this literature review was to demonstrate how the primary resources
contributed to understanding the research problem. It prevented unnecessary duplication of
research while revealing any gaps which might require additional research. Synthesizing prior
research helped determine my research.
I used two databases: James White Library and Google Scholar. In James White Library,
I used Articles/Databases, Education, ERIC, PsycINFO, and Academic Search Complete-
EBSCO. I used the following search terms—self-regulation and academic motivation, self-
regulation and self-efficacy, self-efficacy and academic motivation, and self-regulation and
selfefficacy with their correlation to academic motivation. I selected peer-reviewed literature
published within the last ten years (2009–2020), focused primarily on studies conducted in
academic settings with adult subjects. I used the same process to search Google Scholar.
Motivation: A Brief Historical Overview
The concept of motivation is rooted in Ancient Greek philosophers, primarily Plato and
Aristotle (Gollwitzer & Oettingen, 2001). Plato contemplated the idea of a hierarchy organized
around emotional, rational, and dietary components. Aristotle believed that the components of
the hierarchy could be used as motivators of human behaviors. He viewed the dietary and
emotional components (pain or pleasure) as irrational motivators. The Ancient Greeks based
motivational activities on three primary components—the body’s desire, feeling pain or pleasure,
and spiritual effort of will (Gollwitzer & Oettingen, 2001).
Later, Descartes declared the will to be a more effective motivator than the physical body,
therefore, articulating the first theory of motivation (Gollwitzer & Oettingen, 2001). Descartes
believed that the power of will is a strong motivator because the human mind has mental, moral,
and intellectual mechanisms that induce will (Gollwitzer & Oettingen, 2001), whereas the body’s
needs are just physical and biopsychological forces that interact naturally
with environmental factors to fulfill satisfaction (Gollwitzer & Oettingen, 2001).
In the early twentieth century, human behaviors were attributed to physiological needs.
Sigmund Freud (1924) addressed the life instinct idea which suggests that human behavior is
driven by instinct. He believed humans react to satisfy physiological needs which then reduces
the levels of stress or anxiety because of deprivation. Some researchers (Lewin, 1936; Skinner,
1935) denied the idea of restricting motivational factors to instincts while ignoring other
potential elements. Therefore, researchers such as Pavlov (1927) and Skinner (1935) conducted
several studies and assessments to analyze human motivation from a variety of perspectives
including behaviorism, humanism, and cognitive approaches.
Behaviorism
Behaviorists explained motivation based on the stimulus-response model and classical
conditioning perspective (Rensh et al., 2020). Theorists such as Pavlov (1927), Thorndike
(1989), Watson (1913), and Skinner (1935) believed that environmental factors guide human
behavior, thus reinforcements are the main drivers of actions. Gestalt psychology (Lewin, 1936)
contributed to the theoretical concept of motivation, hypothesizing goal formation as promoting
achievement motivation.
Humanism
Humanism emphasizes the role of psychological needs regarding motivation and
direction of behaviors. Maslow (1943) suggested that human needs motivate individual behavior
and response. He postulated a hierarchy of needs through which humans progressed. He
identified the needs, in order from lowest to highest as physiological, safety, love, self-esteem,
and self-actualization. Hence, being motivated to satisfy deficiency needs is essential for
reaching the level of growth and self-actualization.
McClelland (1987) attributed human behaviors to the acquired need for power,
achievement, and affiliation. Herzberg (1959) based his motivation theory model of employee
performance on two factors—motivator factors that have a positive impact on workers’ function
and the hygiene factor that negatively affects their performance. Alderfer (1969) developed the
ERG theory which categorizes Maslow’s hierarchy of needs into three phases: Existence,
Relatedness, and Growth. Rogers (1951) attributed human behavior to the tendency to satisfy
self-actualization. Allport (1961) emphasized the important role of conscious motivation in
human behavior. The concept of autonomy of motives indicates that the recent motive is
independent of its original condition (Rensh et al., 2020).
Cognitive Psychology
Cognitive psychology has contributed to the literature on motivation. Heckhausen and
Heckhausen (2008) conceptualized motivation as a cognitive process. SCT by Bandura (1991)
plays a crucial role in understanding the motivation of behaviors as a construct. To illustrate the
characteristics of the motivation behind the social interaction processes, Rotter (1966) initiated
the locus of control concept which is defined as the belief in one’s control. Locus of control is
internal—individuals attribute the outcome of performance to internal resources or external—
related to external environmental factors. Nuttin (1964) theorized motivation as goals and the
process of achieving them.
Several theories investigated motivation in terms of significant factors such as outcome
expectancy and perceived equity. Vroom’s (1964) expectancy theory suggested that motivation
can be affected by expectation. Therefore, individuals perform a specific action because they
believe it will lead to a desirable outcome which in turn enhances satisfaction. The equity theory
of motivation by Adams (1965) assumes that fairness and social equity influence individuals’
motivation. Lawler and Porter (1967) developed a model based on the expectancy theory and the
equity theory. This model suggests that needs, expectancy, and rewards affect the levels of
motivation (Rensh et al., 2020).
SDT focuses on the quality rather than the quantity aspects (Deci & Ryan, 2008). The
theory categorizes motivation into intrinsic motivation, extrinsic motivation, and amotivation.
Academic Motivation: A Conceptual Overview
It was clear from the historical overview that motivation is an interesting psychological
phenomenon that has been studied for many years. Researchers tried to understand motivation in
education to gain insight into why students who willingly engage in learning activities perform
better in academic subjects (Deci & Ryan, 2008). Accordingly, the academic motivation concept
has developed through a variety of motivational dimensions including beliefs or perceptions,
values, and goals (Rowell & Hong, 2013). The concept also advanced as a result of
psychological components in SCT (Bandura, 1991) and SDT (Deci & Ryan, 2008; Ryan & Deci,
2000).
The components of individuals’ beliefs or perceptions of motivation are self-efficacy,
autonomy, and attributional beliefs. Self-efficacy refers to individuals’ beliefs in their ability to
accomplish a task (Bandura, 1991). Students who possess high levels of self-efficacy are more
likely to be motivated when they engage in learning activities, make the effort to succeed, and
persevere when difficulties occur (Schunk & Pajares, 2002). Students with low efficacy beliefs
perform poorly, disengage in learning activities, and give up whenever they encounter difficulties
(Wang & Pomerantz, 2009). The sense of autonomy, students’ belief that they have control over
their goals and behavior formation, is imperative. Autonomous learners tend to be active during
learning procedures, engage in classroom and task performance, regulate time and effort toward
learning, and become self-determined learners (Ryan & Deci, 2000). Attributional beliefs
identify the way students attribute their learning outcomes which in turn affect their subsequent
performance. There are three main components of attributional beliefs: locus of control, stability,
and controllability (Rowell & Hong, 2013). Students who attribute their academic achievement
to effort tend to be academically motivated because such attribution is based on internal locus of
control, unstable cause, and controllable factors.
Goals are fundamental components of academic motivation. They assist students in
forming plans and procedures that affect their cognitive, emotional, behavioral responses. Goal
orientation consists of mastery goal orientation and performance goal orientation. Mastery
goaloriented students perform better than performance goal-oriented students because they
believe abilities can be developed, and successful performance results from their effort. Hence,
they utilize effective strategies and hold a positive attitude to their learning. Performance
goaloriented students tend to avoid challenging tasks and attribute their failure to the lack of
abilities.
Mastery goal orientation enhances students’ sense of competency and their intrinsic motivation
(Ames & Archer, 1988). Bandura (1991) believes that setting goals and planning motivate
individuals to achieve their goal by regulating required actions and effective strategies.
Value is an essential component of academic motivation. Students who value the task
tend to engage in learning activities and perform well. However, students who perceive the
course/task as valueless, become unmotivated to participate effectively in learning. The value of
learning a task is derived from three elements of the course—intrinsic value (interesting),
attainment value (important), and utility value (useful) (Eccles, 2005).
SDT differentiates between intrinsic motivation and extrinsic motivation. Intrinsically
motivated students engage in learning activities because of experiencing pleasure and enjoyment.
Conversely, extrinsically motivated students perform to obtain external rewards or grades and to
avoid feelings of shame (Deci & Ryan, 2008; Vallerand et al., 1992).
Self-Regulation: A Historical/Theoretical Overview
An interval analysis of self-regulation studies conducted by Post et al. (2006) analyzed
studies that defined self-regulation and its developmental processes, defined factors that
influence self-regulation, and studied with the general overview of self-regulation and its
implication. As a result of the analysis, the researchers identified the theoretical perspective of
self-regulation in chronological order, grouped in four categories: precursory, emergent,
contemporary, and expansionism.
Precursory 1891–1950
During the precursory period, self-regulation was discussed based on the behaviorist’s
overview in which the external factors influence self-control. The emphasis was on the role of
drives to fulfill the sense of joy and pleasure or avoid pain. In 1891, the definition of will or
volition was attributed to disobedience response, while the child behavior was expressed within
the unconscious response and automatic reactions (Post et al., 2006).
In the twentieth century, the self-regulation concept was discussed in many terms.
Selfrealization implied the rejection and refusal of pain. Behavioral consequences are important
in the formation of regulation (Thorndike, 1898). Evolution of consciousness theorized behavior
as a result of cognition and systematic thought. Freud (1924) determined the self-regulatory
processes according to the control of internal drives that may affect the adaptation of the
behaviors. Pavlov (1927) demonstrated self-regulation based on external factors and correlated
learning to an automatic response to a conditioned stimulus.
The 1930s saw the formation of the behaviorism perspective. In 1940, psychological
research studied latent learning, reinforcement, persistence, discriminative conditioning, and
repetition stimulating. Miller and Dollard (1941) integrated the behaviorism perspective with
Freud’s point of view. They theorized social learning which suggests a strategy of planning to
obtain rewards in which actions are regulated by internal desire and external environmental
factors. Thorne (1946) wrote the first article about the concept of self-regulation. He referred to
self-regulation as intelligent adaptation.
Emergent 1950–1970
Miller and Dollard’s (1941) social learning theory was the turning point of the emergent
period. This period discussed self-regulation from cognitive perspectives with the denial of the
behavioral approach in terms of controlling action by external factors. Research focused on
reflection, reaction, and reevaluation. The factors that affect the self-regulatory processes include
the limitation of fear and the levels of motivation toward reinforcements with emphasis on the
impact of compliance in self-regulation.
The scholarship of cognitive scientists has affected the perspective of self-regulation.
Piaget (1952) emphasized the role of mental structures on the processes of adaptation and
regulation of external environmental factors. Individuals’ schemes influence assimilation and
accommodation processes to perceive stimuli. SCT emerged when Heider (1958) investigated
how to predict future events. The cognitive structures, particularly social schemata, organized the
information regarding social construct and persons. Vygotsky (1962) demonstrated how
selfregulatory processes influence the social and cultural environment. He believed that social
interaction, including scaffolding and language (cooperative dialogue and private speech), is
essential for the development of self-regulation. Such interaction should occur within the child’s
Zone of Proximal Development, which represents the abilities of an individual and what one can
learn or achieve with support. Thus, self-regulation develops as a result of personal factors and
social interaction through the process of accommodation and adaptation.
Self-regulation as a construct was unknown during the 1970s and early 1980s. However,
scholars such as Dale Schunk, Ann Brown, Michael Pressley, Joel Levin, and Donald
Meichenbaum, conducted a variety of research studying aspects of the self-regulatory processes
such as imagery, self-instruction, goal setting, and effective use of strategies. The turning point of
the development of self-regulation was in 1986 during a symposium (Zimmerman, 2008) at the
American Educational Research Association Annual Meeting. In this symposium, Zimmerman
defined self-regulation as a metacognitive construct consisting of different processes. Since then,
researchers such as Barbara McCombs, Lyn Corno, Mary McCaslin, Richard Newman, Dale
Schunk, and Monique Boekaerts and others, have investigated selfregulation as an integrated
system that includes self-control, self-monitoring, self-concept, and learning strategies
(Zimmerman, 2008).
Contemporary (1970–1990)
Flavell, Friedrichs, and Hoyt (1970) distinguished between metacognitive strategies
including monitoring and self-regulation and cognitive abilities. The contribution of SCT
(Bandura, 1991) advanced the study of self-regulation where self-evaluation is imperative during
learning from social observation. Subsequently, the information processing model illustrated how
individuals organize information and can effectively engage in processing such information
through short-term memory, working memory, and long-term memory. Self-control, thus, assists
individuals in directing attention toward important information and shield or ignore distracting
stimuli (Post et al., 2006). Winne (1995) developed a model to conceptualize self-regulation
based on information processing theory. The model was updated in 1998 into The WinneHadwin
Model of Self-regulated Learning to differentiate self-regulation profiles according to
metacognitive aspects (Panadero, 2017).
Expansionism (1990–2006)
In education, the core aim was to develop students’ abilities to regulate their thoughts,
emotions, and behaviors. Therefore, most of the research incorporated the perspectives of
behaviorism of Vygotsky (1962) and Bandura to demonstrate self-regulation. Other studies
focused on examining self-regulation across varied cultures, different ages, and a variety of
teaching approaches and special needs (Post et al., 2006).
The researchers developed several models to demonstrate the self-regulation construct
within a framework of social and cognitive impact. To illustrate, Zimmerman (1989) developed
the model A Triadic Analysis of Self-regulated Functioning. This model is congruent with
Bandura’s perspective and includes three factors—environmental, personal, and behavioral.
Subsequently, Zimmerman developed two more models. (1) A Multilevel Model of Selfregulatory
Training (2000) posits four stages (observation, emulation, self-control, and selfregulation) that
enhance students’ competency to develop self-regulation. (2) The Cyclical Phase of Self-
regulation (2009) consisting of metacognitive and motivational processes (Zimmerman &
Moylan, 2009, 2013). Pintrich’s Self-Regulated Learning SRL Model (2000) illustrated four
phases of self-regulatory learning processes: forethought, monitoring, control, and reaction and
reflection.
Panadero (2017) reviewed two other models. (1) Boekaerts’s model (1996) explained six
components of self-regulation which were revised later into the Adaptable Learning Model. Her
latest version is the Dual Processing Self-regulation Model which was extended in 2011 to
include volitional strategies and emotion regulation strategies. (2) Efklides (2011) developed the
Metacognitive and Affective Model of Self-regulated Learning based on SCT. The model
determines the interaction between metacognitive, motivation, and affect. It differentiates
between two levels: the top-down level (person) which demonstrates the interaction of
individuals’ competencies in the task domain, and the bottom-up level (task x person) which
illustrates the function of self-regulation where activities are considered data-driven and
metacognitive abilities control actions and motivation (Panadero, 2017).
During the 1980s, researchers developed several assessment tools to measure the
construct of self-regulation (Zimmerman, 2008). They included the Learning and Study
Strategies Inventory (LASSI; Weinstein et al., 1987), the Self-Regulated Learning Interview
Scale (SRLIS; Zimmerman & Martinez-Pons, 1986, 1988), and the Motivated Strategies for
Learning Questionnaire (MSLQ; Pintrich et al., 1993).
Self-Efficacy: A Brief Historical Overview
The study of self is traced back to the Greek philosophers such as Plato, Aristotle, and
Socrates who defined self as a soul and spiritual entity (Remes & Sihvola, 2008). During the
Middle Ages, Aquinas (1975) introduced the idea of mind and body duality in which soul and
body are integrated to illustrate the concept of self. In 1659, Descartes (2008, trans.) established
the philosophy of thinking. He believed that doubt proved one’s existence because doubt is a
form of thinking. Cartesian rationalism emphasized the inner process of self-awareness which is
considered the foundation of metacognitive processes. However, belief during past eras was
mostly attributed to religion (Descartes, 1659; trans. 2008).
In the twentieth century, the study of self and self-beliefs developed based on William
James’ (1890) publication, The Consciousness of Self, in which he distinguished between the self,
I, and the self, me, as knower and known. This philosophy presented the concept of selfreflection
which Bandura (1997) later explained. James was also the pioneer of the self-esteem concept.
In the 1900s Cooley (1902) explained the self through The Looking-Glass Self Theory. In
1923 Sigmund Freud advanced his Psychoanalytic Theory, which theorized that self comprises
three components—id, ego, and superego. While behaviorist psychologists focused on external
stimuli, humanistic psychologists focused on the study of self. For instance, Maslow’s (1943)
hierarchy of needs described human motivation as fulfilling different needs to achieve selfesteem
and self-actualization. Although, initially, Bandura based his worldview on behaviorists’
perspective, he rejected the idea of limiting human functions only by biological and
environmental factors. He believed humans play an active role through their thoughts. Therefore,
Bandura was a pioneer in the concept of self-efficacy (Schunk & Pajares, 2002).
The self-efficacy foundational concept emerged before the development of SCT. In the
1970s, Bandura explained motivation in terms of outcome expectations. Later he conducted
therapeutic techniques for people who have phobias. Even though the participants were
motivated to apply the techniques regardless of their fear of outcome expectations, some could
not implement the techniques in real-life situations. Bandura attributed these individual
differences to self-efficacy. He believed that self-efficacy has a stronger effect on motivation than
outcome expectations do (Zimmerman, 2000).
In 1986, Bandura proposed the SCT which emphasizes the role of self-efficacy in
cognitions, behaviors, emotions, and motivations. In the period 1991–1997, he concluded that
people perceive beliefs in self through interaction with the environment in which they create
beliefs of their capabilities. He conducted several studies to determine the power of self-efficacy
on regulating and motivating human actions (Bandura, 1991; 1997).
The Relationship Between Self-Regulation and Academic Motivation
According to the Cyclical Model of Self-regulated Learning Processes (Zimmerman &
Moylan, 2009), the self-regulation process consists of three phases—forethought, self-control or
performance, and self-reflection. During the forethought phase, people engage in task analysis
and self-motivation through observing a model. It was hypothesized that involvement in these
phases is cyclical where self-regulation affects motivation and motivation also influences
selfregulatory processes in another task (Bandura, 1991, Zimmerman, 2000a). Such a hypothesis
explains the controversy among researchers regarding whether self-regulation affects motivation
or motivation influences self-regulation.
Ning and Downing (2010) found evidence for the assumption that motivation and
selfregulation have a reciprocal relationship. Their longitudinal study examined the reverse
relationship between motivation and self-regulation as well as how this relationship affects
students’ performance. The study found that undergraduate students demonstrate the reciprocal
effect between motivation and self-regulation because students who tend to regulate their
function become more motivated to accomplish more tasks. The opposite is also true. The
students’ academic performance was impacted by this relationship.
In contrast to these findings, Cetin (2015) investigated the impact of self-regulation and
academic motivation on university students’ academic achievement. The study identified a
relationship between academic motivation and self-regulated learning. However, there was no
significant evidence that these variables predict academic achievement, except goal setting which
is one of the self-regulatory factors that was found to be a good predictor of students’
achievement (Cetin, 2015).
Undoubtedly, test anxiety negatively impacts students’ academic performance. A study of
208 university students aimed to determine the relationship between self-regulated learning and
academic motivation (competence and autonomy) while excluding the effect of test anxiety. The
findings revealed a statistically significant correlation between self-regulated learning and
academic motivation. Test anxiety did not affect this relationship. The variation of motivational
components also did not affect the correlation between self-regulated learning and academic
motivation (Miller, 2010).
Valinasab and Zeinali (2018) sought to demonstrate the relationship between academic
emotions, self-regulated learning, academic motivation, and academic achievement. The study
indicated that self-regulated learning correlated with academic motivation, and positive academic
emotions are positively and significantly related to self-regulated learning and academic
motivation. However, negative emotions have a negative relationship with both self-regulated
learning and academic motivation. This means that positive emotions—hope, pride, and
pleasure—enhance students’ motivation to learn and their self-regulation skills. However,
negative emotions—sadness, anxiety, and anger—reduce the desire and motivation to engage in
learning activities and negatively affect self-regulation. Self-regulation had a significant,
positive, direct effect on academic achievement. Academic motivation, however, did not affect
academic achievement. The study demonstrated that self-regulated learning plays a moderating
role between academic emotions and academic achievement.
To understand the role of self-regulation and academic motivation on academic
performance, Ariani (2016) studied a group of undergraduate students (n = 326). They
hypothesized the implementation of a flexible assessment system would improve students’
motivation to learn because they become independent learners who can detect their strengths and
weaknesses. The results of the study indicated that a flexible assessment system had a positive
effect on academic motivation and self-regulation. Academic motivation had a significant
positive impact on self-regulation and academic performance. The study also found that
academic motivation had a moderating role on the influence of the flexible assessment system on
academic performance and self-regulation. The mediated role was found through self-regulation
on the effect of the flexible assessment system and academic motivation on performance. This
meant that the impact of the flexible assessment system on academic performance was mediated
by self-regulation and academic motivation.
Saki and Nadari (2018) investigated the variables that predict academic motivation. They
found that students with high levels of self-concept and self-regulated learning have high levels
of intrinsic and extrinsic motivation. In comparison, students with low levels of self-concept and
self-regulated learning lacked academic motivation. In addition, the study proposed that
academic motivation can be predicted by self-concept and self-regulated learning among high
school students.
Previous research provided evidence for the correlation between self-regulation,
selfefficacy, and academic motivation through well-developed and clear argumentative studies.
Yet, contradictory results have been found with the ability of self-regulation to predict academic
motivation among Iranian students (Saki & Nadari, 2018). However, academic motivation
predicted self-regulation among Indonesian students (Ariani, 2016). This supports the idea that
different cultures have a variant perspective. Therefore, it is imperative to examine the role of
self-regulation in predicting academic motivation among undergraduate students in the United
States where diversity may contribute to research on academic motivation.
The Relationship Between Self-Efficacy and Academic Motivation
Bandura (1991) defined self-efficacy as an individual’s belief in his/her ability to
complete a task. He suggested four resources that affect the formation of self-efficacy. They are
mastery experiences, vicarious experience (which refers to observing a model), social persuasion,
and physiological response awareness. Self-efficacy enhances an individual’s performance and
creativity as well as the ability to deal with difficulties and obstacles (Zimmerman, 2000b).
According to SCT, self-efficacy is a key to learning and gaining knowledge because people who
believe in their capabilities tend to have high levels of motivation and the ability to regulate
themselves (Bandura, 1991). Regarding the correlation between self-efficacy and academic
motivation, Ball and Edelman (2018) found that, for English students who believe that they had
poor English literacy skills, their motivation to learn and use self-efficacy were moderate or
below moderate even if they perceived English as very important.
By the same token, learning motivation significantly correlated with self-efficacy among a group
of medical science students (Hassankhani et al., 2015)
To examine a theoretical model that indicates a correlation between learning-oriented
motivation, lifelong learning tendencies, and students’ self-efficacy, Akyol (2016) studied a
sample of 382 university students who were education majors in five different departments. Of
the 382 students, 29.06% were studying information technology, 26.70% were studying the
English language, 13.61% were studying history, and 8.38% were studying music. Also, 22.25%
of the candidate teachers were involved in classroom teaching. Most of the participants (60.07%)
were females and the rest (39.53%) were males. The analysis indicated that (1) students have
high levels of learning-oriented motivation, a lifelong tendency to learning, and self-efficacy
perception; and (2) the three variables are significantly correlated. SEM demonstrated that the
relationship between learning-oriented motivation and self-efficacy perception was mediated by
lifelong learning tendencies.
Further investigation of self-efficacy and academic motivation and its effect on learning
activities have been conducted regarding students’ tendency to procrastinate. For instance, Cerino
(2014) examined self-efficacy and academic motivation as an explanation of procrastination and
found that self-efficacy, academic motivation, and procrastination were correlated among
university students. Academic motivation was a strong predictor of procrastination while self-
efficacy had no impact when controlling for academic motivation. The findings of this study
were consistent with Malko and Mutlu’s (2018) research which aimed to determine whether
academic self-efficacy or academic motivation predicts academic procrastination. The results
indicated a negative relationship between academic self-efficacy and academic procrastination
and between academic motivation and academic procrastination. The analysis demonstrated that
academic self-efficacy and academic motivation predict academic procrastination. The
researchers also conducted a partial correlation to identify whether the correlation between
academic self-efficacy and academic procrastination would change after controlling for academic
motivation. They found that motivation has a mediating role in the relationship between
academic self-efficacy and academic procrastination.
To improve university students’ self-efficacy and academic motivation, Mantasiah and
Yusri (2018) conducted an experimental study using the Pay It Forward Learning Model. The
model is based on the idea that each individual has an effective role in making changes in his or
her learning environment. Such an idea was assumed to increase students’ self-efficacy and their
academic motivation. The researchers utilized the experimental method, specifically the
preposttest, to investigate the effectiveness of the model. After applying the Pay It Forward
Model in four meetings, the researchers ran a paired sample t-test to detect any improvement in
selfefficacy and academic motivation compared to the pre-test results. They found a significant
increase in both self-efficacy and academic motivation among the students. Students who have
low self-efficacy beliefs and who lack academic motivation are more likely to procrastinate in
learning (Cerino, 2014; Malko & Mutlu, 2018).
SDT addresses the role of satisfying competence, relatedness, and autonomy needs to
enhance academic motivation and efficacious beliefs (Deci & Ryan, 2008). According to this
perspective, students who enrolled in the Pay it Forward program developed high levels of
selfefficacy and academic motivation because each student explained the materials to another
group of two or three students. Thus, playing an active role in the class increases the sense of
relatedness, competence, and autonomy (Mantasiah & Yusri, 2018).
The literature review revealed only one study of whether self-efficacy played a
significant role in predicting both academic motivation and self-control and self-management.
Other studies were well organized; they used a correlational method to investigate the
relationship between self-efficacy and academic motivation among university students.
Therefore, there is a lack of prediction methods for self-efficacy. The prediction method
contributes to identifying the magnitude and direction of the relationship and it is currently
recommended (Rensh et al., 2020) when investigating psychological phenomena.
The Relationships between Self-Efficacy, Self-Regulation, and Academic Motivation
Previous research suggested a dynamic correlation between self-efficacy, self-regulation,
and academic motivation. To demonstrate that correlation, Yusuf (2011) employed a model that
hypothesized a correlation between these variables. The model was tested on 300 undergraduate
students. The analyzed data confirmed that self-efficacy, academic motivation, and self-regulated
strategies were significantly correlated. Alafghani and Purwandari (2019) studied the relationship
between self-efficacy, academic motivation, self-regulated learning, and academic achievement.
The variables were significantly correlated. Students with high self-efficacy and academic
motivation were more likely to engage in regulating their learning. Self-regulated learning
moderated the relationship between academic motivation and academic achievement.
Consistent with the findings related to the impacts of students’ motivation and
selfefficacy on self-regulatory strategies, Prat-Sala and Redford (2010) examined the correlation
between intrinsic and extrinsic motivation, self-efficacy, and studying approaches. They found
that intrinsic and extrinsic motivation influenced the selectiveness of study approaches. A high
level of motivation drives systematic approaches to studying. In addition, students’ self-efficacy
influenced their approach to study—low self-efficacy leads students to avoid deep approaches to
studying. In contrast, Arik (2019) suggested that self-efficacy is a core predictor of university
students’ academic motivation, self-control, and self-management. However, academic
motivation was not a determinant of self-control and self-management among university
students.
Taking a different perspective, Saeid and Eslaminejad (2017) examined the role of
selfdirected learning in predicting self-efficacy and academic motivation. Self-directed learning
in the study comprised positive self-concept, independence in learning, informed acceptance,
responsibility for learning, love of learning, creativity, positive view of the future, accepting
learning, study and problem-solving skills (including some metacognitive, self-regulatory skills).
They found that (1) self-directed learning was significantly correlated with both self-efficacy and
academic motivation; (2) the independence in learning factor was the best predictor of a student’s
self-efficacy; and (3) skills such as studying and problem-solving were the best predictors of
academic motivation.
The first procedure of the forethought phase within the self-regulatory process is goal
settings. Goal setting plays a crucial role in academic motivation (Bandura, 1991; Zimmerman &
Moylan, 2009). Researchers examined a variety of goal orientations that influence academic
motivation. AL-Baddareen et al. (2014) examined the effect of self-efficacy, goal achievement
(mastery goals and performance goals), and metacognition on academic motivation among
university students. The researchers hypothesized that the relationship between achievement
goals and academic motivation is mediated by metacognition and self-efficacy. The analysis
indicated that all independent variables and the dependent variable were significantly correlated.
However, performance goals had no correlation with self-efficacy and a weak correlation with
the other variables, even though it was significant. The combination of metacognition, mastery
goals, performance goals, and self-efficacy significantly predicted academic motivation. Among
these variables, mastery goals and metacognition were significant predictors of students’
academic motivation, whereas self-efficacy and performance goals had no significant
contribution in predicting academic motivation.
In contrast, self-efficacy was a significant predictor of academic motivation in a study by
Ng (2012). The study investigated the role of self-efficacy, control beliefs, and four types of
achievement goals (mastery development goals, extrinsic work goals, performance-approach
goals, and social enhancement goals) on the learning performance of university students enrolled
in a distance course. The study hypothesized that self-efficacy and control beliefs mediate the
effect of achievement goals on learning strategies and students’ attitude toward learning.
Students’ attitude referred to the sense of interest, enjoyment, and perceived value of doing a
course. Findings indicated that self-efficacy and control beliefs significantly predict learning
strategies, regulatory strategies, and attitudes toward learning. Therefore, self-efficacious
students who believe they controlled the learning outcomes tended to utilize deep strategies,
regulate their skills, manage their effort, and seek help when needed. Those students show a
positive attitude toward learning through expressing their interests and enjoyment and valuing
what they are learning.
To enhance self-efficacy and academic motivation, Yuka (2017) conducted an
experimental study to identify the effect of goal setting (goal commitment, google difficulty, and
goal specificity), intrinsic motivation, and self-efficacy in extensive reading among
undergraduate students enrolled in the Business Administration and Economics departments. The
study involved students in the extensive reading program (ER), which includes 170 books of
both graded and leveled readers. The ER program consisted of 12 sessions each lasting twenty
minutes during which students chose books independently. At the beginning of each session
students filled in two sheets: ER record and self-evaluation. They wrote their goals, the number
of words they expected to read, and commented on the content. On the self-evaluation sheet they
wrote what they had accomplished compared to their goals and evaluated their progress, as well
as what challenges or obstacles they encountered. The ER program included metacognitive
selfregulation strategies such as goal setting, self-monitoring, and self-evaluation. The results
revealed that goal difficulty and goal commitment have a direct effect on intrinsic motivation
whereas goal specificity did not. The modified model demonstrated that goal specificity has no
direct effect on both intrinsic motivation and self-efficacy. In addition, goal commitment was the
only variable among goal setting variables that had a direct influence on self-efficacy. Thus, goal
commitment can be considered an important factor or the best predictive factor of intrinsic
motivation and self-efficacy.
The self-reflective phase is the process of self-evaluation and causal attribution that affect
the adoption of new behavior (Zimmerman, 2009). Wang, Chen, et al. (2017) implemented
selfreflection intervention to improve college students’ positive thinking (self-confidence,
selfsatisfaction, optimism, and appreciation), self-regulation, and academic motivation. The
researchers measured self-confidence in terms of students’ beliefs in their capabilities to master a
task. The analysis of the study demonstrated that self-reflection intervention was effective in
improving positive thinking, learning motivation, and self-regulation. Most importantly, these
three variables were directly and significantly related to each other (Wang, Chen, et al., 2017). In
addition, Lavasani et al. (2011) conducted an experimental study to predict self-efficacy,
academic motivation, and academic achievement via self-regulation strategies. The selfregulation
strategies program included instructing students how to set goals, monitor progress, assess
behaviors, create a well-established environment, and make information meaningful. After the
implementation, the researchers examined the effectiveness of the program on self-efficacy,
academic motivation, and academic achievement. The results of the study indicated that students
who received self-regulatory strategies training showed high levels of self-efficacy, academic
motivation, and academic performance compared to a control group that did not receive training
on the program (Lavasani et al., 2011).
In the realm of education, it is recommended that educators adopt an autonomous–
supportive environment that facilitates the transition of extrinsic motivation into internalized
motivational forces (Deci & Ryan, 2008). Duchatelet and Donche (2019) conducted a study that
suggests that the type of academic motivation, whether autonomous motivation or controlled
motivation, should be accounted for in higher education when developing self-efficacy and
selfregulation. Therefore, the study examined the correlation between academic motivation,
selfefficacy, and self-regulation. It also investigated how students’ perceived autonomy support
influenced the relationship between self-efficacy, self-regulation, and academic motivation. The
data were collected from 230 bachelor’s degree students at a Dutch university. The SEM
indicated that autonomous motivation was significantly correlated with self-efficacy and
selfregulation. However, controlled motivation was not significantly correlated with self-efficacy
and self-regulation. Amotivation was negatively correlated with self-efficacy. In addition, the
assessment of the contribution of students’ perceived autonomy support demonstrated that the
behavior of autonomy-supportive teachers was positively correlated with autonomous
motivation, but negatively correlated with amotivation. A perceived autonomy-supportive teacher
was significantly related to self-efficacy, but not to self-regulation. The results, after eliminating
non-significant baths, demonstrated that autonomous motivation has a direct relationship with
self-efficacy and self-regulation. Most importantly perceived autonomysupportive instruction
mediated the relationship between academic motivation and self-efficacy, but it has no mediated
role in the correlation between academic motivation and self-regulation. An active learning
environment where students have freedom of choice, get quizzes, and participate in group
discussions were recommended by previous research, but these findings determine the role of
academic motivation in the way students perceived autonomy-supportive instruction. Amotivated
students seem not applicable to such learning environments where only autonomous motivated
students can benefit from autonomy-supportive environments to enhance their self-efficacy and
self-regulation.
According to a study by Vallerand et al. (1992), amotivated students believe that they
have no control over their actions, and thus attribute their performance outcome to something
beyond their control. In addition, motivation has been found to be negatively associated with
persistence. Hence, amotivated students have poor ability of effort regulation so they easily quit
whenever difficulties and obstacles occur. These findings affirm the hypothesis that control
beliefs/locus of control is imperative in forming motivational systems and self-regulatory
mechanisms. Researchers found that internal locus of control predicts self-regulation among
college students (Sidola et al., 2020). One study investigated the relationships between
selfregulation and locus of control (individuals’ belief that they have control over their actions
and the consequences). The study also sought to identify the predicting role of self-regulation
and the locus of control in willingness to communicate among 222 undergraduate English
foreign language learners. The findings revealed a significant correlation between self-regulation,
locus of control, and willingness to communicate. Students who use regulatory strategies take
responsibility and believe that their internal factors control their performance. The locus of
control played a significant role in predicting students’ willingness to communicate rather than in
self-regulation (Arkavazi & Nosratinia, 2018).
Current thinking calls for the prediction method regarding the psychological processes of
students’ activities to better understand effective practices that will help in developing academic
motivation and self-regulation (Rensh et al., 2020). However, not much research has been done
in the predictive method (AL-Baddareen et al., 2014; Arik, 2019). Most research used correlation
and experimental design (Lavasani et al., 2011; Wang, Chen, et al., 2017; Yuka, 2017). This study
should fill in the research gap regarding the prediction of academic motivation, selfregulation,
and self-efficacy in the United States because most research was conducted in other countries.
Conclusion
Academic motivation plays a crucial role in students’ academic success. SDT researchers
differentiate between intrinsic motivation and extrinsic motivation. Because intrinsic motivation
enhances students’ involvement in educational activities, researchers recommend an
autonomous-supportive education system that helps students to internalize their extrinsic
motivational factors (Ryan & Deci, 2020). Promoting a sense of autonomy demands enhancing
the abilities of students to regulate cognitive, emotional, and behavioral responses as well as their
beliefs in their ability and controllability.
SCT suggests a reciprocal correlation between self-regulation, motivation, and
selfefficacy (Bandura, 1991). Research supports the cyclical model of self-regulation. Ning and
Dawning (2010) found such a correlation where both self-regulation and achievement affect are
influenced by the other variable. There is evidence that academic motivation affects
selfregulation (Alafghani & Purwandari, 2019; Ariani, 2016); whereas other studies
demonstrated that self-regulation predicts students’ motivation (Mirhossini et al., 2018; Saki &
Nadari, 2018).
Self-efficacy also predicts self-regulation and academic motivation (Alafghani & Purwandari,
2019; AL-Baddareen et al., 2014, Arik, 2019).
The purpose of this research review was to help the reader understand the relationships
between self-regulation, self-efficacy, and academic motivation. The connections between these
variables supported the conceptual model hypothesized in the current study. Students’ beliefs in
their capabilities and their abilities to regulate learning processes influence academic motivation.
This is significant because undergraduate students who lack motivation experience academic
difficulties and may drop out of school. More research and testing are required to gain a better
understanding of why undergraduate students’ motivation declined and which psychological
factors affect their academic motivation. Helping students to form efficacious beliefs and to
regulate their emotions, behaviors, and cognitions is extremely important in Western society
where the lack of studies in this field was noticeable.
CHAPTER 3
METHODOLOGY
Introduction
This study utilized a model based on SCT. The model hypothesized that self-efficacy
(control for learning beliefs, self-efficacy of learning) and self-regulation (metacognitive
selfregulation, time and study environment management, and effort regulation) predict academic
motivation (intrinsic motivation, extrinsic motivation, and amotivation).
Type of Study
I used a non-experimental quantitative methodology and deductively developed a
theoretical model based on SCT and SDT and previous studies to determine the relationship
between self-regulation, self-efficacy, and academic motivation (Figure 2, p. 28). The correlation
design was adopted because the study aimed to look at the relationship between the variables
through predictive correlation design to examine the variance of one variable based on the
variance of other variables. Specifically, model-testing design was adopted because the study
examined a theoretical model which proposed that self-regulation and self-efficacy predict
students’ academic motivation. To collect an adequate number of participants in a relatively short
time, the survey method was chosen.
Population and Sample
For fall 2018 16.6 million students—56% female, and 44% male—enrolled in institutions
of higher education in the United States (Hussar et al., 2020). The students were 8.7 million
White (not of Hispanic origin), 3.4 million Hispanic, 2.1 million Black, 1.1 million Asian, 0.6
million non-residents, and .6 million two or more other races.
A non-probability sampling method was used because samples were selected according
to the researcher’s subjective judgment. The study is based on convenience or accidental
sampling. Participants were selected based on availability. The surveys were hosted online
through QuestionPro, hence the sample was limited to those who have access to and were willing
to use the internet. The scales of the study are as follows: (1) self-reported demographic
information questionnaire; (2) 24 items measuring self-regulation; (3) 14 items measuring
selfefficacy; and (4) 28 items measuring academic motivation. I chose the sample size by adding
the number of the items on the three surveys and multiplying that total by five (number of
participants for each item). Research suggested a sample size between 5-10 for each item (Hair et
al., 2010). Accordingly, the suitable size for this study was 330 participants—349 students
participated which was adequate for conducting SEM.
Research Hypotheses
The main hypothesis of this study was that the reproduced covariance matrix proposed in
the theoretical model and the observed sample covariance matrices were equal. In simple terms,
this means that the structural model would be a good fit with the observed data. Using the
conceptualized model depicted in Figure 2 (p. 28), this study hypothesized (1) there is a
significant correlation between the two exogenous variables, self-regulation, and self-efficacy;
(2) self-regulation has a significant direct effect on the endogenous variable academic
motivation; (3) self-efficacy has a significant direct effect on the endogenous variable academic
motivation.
Definition of Variables
Academic Motivation
Academic motivation (AM) was conceptually defined as the intrinsic or extrinsic
orientation (reasons) that drives one to engage in a behavior. It is the interest or the will that
drives students to accomplish academic goals. This was a latent variable measured by external
regulation, introjected regulation, identified regulation, intrinsic motivation-knowledge, intrinsic
motivation-accomplishment, intrinsic motivation-stimulation subscale, and amotivation
(Vallerand et al., 1992). The latent variable was measured by scores on 28 items taken from the
AMS.
External regulation (ExME) was conceptually defined as factors that drive behavior to
obtain rewards or avoid punishment (Vallerand et al., 1992). It was instrumentally defined by
four items (Q18, Q31, Q33, Q45). The scale included items such as “In order to obtain a more
prestigious job later on.” For the operational definition, items 18, 31, 33, and 45 which measured
external regulation were scored using a 7-point Likert scale. The values for scoring ranged from
one (does not correspond at all) to seven (corresponds exactly). The score was obtained by
summing up the responses to each item. The minimum score for the Scale was four and the
maximum was 28.
Introjected regulation (ExMN) was conceptually defined as the tendency to engage in a
behavior to improve self-esteem or avoid anxiety and sense of guilt (Vallerand et al., 1992). It
was instrumentally defined by four items (Q24, Q30, Q37, Q43). The scale included items such
as “To prove to myself that I am capable of completing my college degree.” For the operational
definition, items 24, 30, 37, and 43 were scored using a 7-point Likert scale. The values for
scoring ranged from one (does not correspond at all) to seven (corresponds exactly). The score
for the scale was obtained by summing up the responses to each item. The minimum score for the
Scale was four and the maximum 28.
Identified regulation (ExMD) was conceptually defined as the reason for the engagement
was not fully external but regulating behavior becomes relatively due to its value and personal
reasons (Vallerand et al., 1992). It was instrumentally defined by four items (Q20, Q44, Q26,
Q39). The scale included items such as “Because I believe that a few additional years of
education will improve my competence as a worker.” For the operational definition, items 20, 44,
26, and 39 are scored using a 7-point Likert scale. The values for scoring range from one (does
not correspond at all) to seven (corresponds exactly). The score for the scale is obtained by
summing up the responses to each item.
Intrinsic motivation-knowledge (InMK) is conceptually defined as the pleasure and
satisfaction individuals experience when they learn, understand, and explore new things
(Vallerand et al., 1992). It was instrumentally defined by four items (Q19, Q25, Q32, Q38). The
scale included items such as “For the pleasure I experience when I discover new things never
seen before.” For the operational definition, items 19, 25, 32, and 38 were scored using a 7-point
Likert scale. The values for scoring ranged from one (does not correspond at all) to seven
(corresponds exactly). The score for the scale was obtained by summing up the responses to each
item.
Intrinsic motivation-accomplishment (InMC) was conceptually defined as the pleasure
and satisfaction individuals experience when accomplishing something (Vallerand et al., 1992). It
was instrumentally defined as four items (Q23, Q29, Q36, Q42). The scale included items such
as “For the satisfaction I feel when I am in the process of accomplishing difficult academic
activities.” For the operational definition, items 23, 29, 36, and 42 were scored using a 7-point
Likert scale. The values for scoring ranged from one (does not correspond at all) to seven
(corresponds exactly). The score for the scale was obtained by summing up the responses to each
item.
Intrinsic motivation-stimulation (InMS) was conceptually defined as engaging in
activities because of the experience of excitement, enthusiasm, or aesthetic experience (Vallerand
et al., 1992). It was instrumentally defined as four items (Q21, Q27, Q34, Q40). The scale
included items such as “For the intense feelings I experience when I am communicating my own
ideas to others.” For the operational definition, items 21, 27, 34, and 40 were scored using a
7-point Likert scale. The values for scoring ranged from one (does not correspond at all) to seven
(corresponds exactly). The score for the scale was obtained by summing up the responses to each
item.
Amotivation (AMOT) was conceptually defined as individuals’ tendency to disengage in
activities or actions as a result of the absence of desire or the lack of valuing an outcome (Ryan
& Deci, 2000). It was instrumentally defined as four items (Q22, Q28, Q35, Q41). The scale
included items like “I don't know; I can't understand what I am doing in school.” For the
operational definition, items 22, 28, 35, and 41 were scored using a 7-point Likert scale. The
values for scoring ranged from one (does not correspond at all) to seven (corresponds exactly).
The score for the scale was obtained by summing up the responses to each item.
Self-regulation (SR)
SR was defined as the metacognitive strategies by which students control and regulate
their cognition, effort, time, and environment resources (Garcia & McKeachie, 2005). This was a
latent variable measured by metacognitive self-regulation, time and study environment
management, and effort regulation (Pintrich et al., 1993). The latent variable SR was measured
by scores on 24 items from MSLQ scales (Pintrich et al.,1993). Self-regulation included three
subscales: metacognitive self-regulation, time and study environment management, and effort
regulation. Responses to all items were summed to obtain the total score for the SR Scale.
Metacognitive self-regulation (SMR) was conceptually defined as students’ abilities to
regulate and control their cognitive strategies including planning, monitoring, and regulating
abilities (Pintrich et al., 1993). Metacognitive self-regulation was instrumentally defined as 12
items (MQ1–MQ12) that measured metacognitive self-regulation, where MQ1 and MQ8 were
reversed items. The metacognitive self-regulation scale included items such as “If course
materials are difficult to understand, I change the way I read the material” in a positive direction
and others like “During class time I often miss important points because I’m thinking of other
things” in a negative direction. For the operational definition, items MQ1–MQ12 measured
metacognitive self-regulation were scored using a 7-point Likert scale. The values for scoring
ranged from one (not at all true of me) to seven (very true of me). The score for the scale was
obtained by summing up the responses to each item.
Time and study environment (SRTE) conceptually represented the effective use of study
time and environment including daily, weekly, and monthly plans and schedules as well as the
tendency to avoid distraction and prepare a quiet and organized study environment (Pintrich, et
al.,1993). It was instrumentally defined as eight items TQ1–TQ8, where three items were
reversed (TQ3, TQ7, TQ8). This scale included items such as “I usually study in a place where I
can concentrate on my coursework” in a positive direction and others like “I find it hard to stick
to a study schedule” in a negative direction. For the operational definition, items TQ1 through
TQ8 were scored using a 7-point Likert scale. The score ranged from one (not at all true of me)
to seven (very true of me). The score for the scale was obtained by summing up the responses to
each item.
Effort regulation (SREF) was conceptually defined as students’ abilities to manage
themselves during learning despite the obstacles and difficulties that they may encounter to
achieve desired goals (Pintrich, et al.,1993). It was instrumentally defined as four items (FQ1–
FQ4), where FQ1 and FQ3 were reversed items. This scale included items such as “I work hard
to do well in the class even if I don’t like what we are doing” in a positive direction and others
like “When coursework is difficult, I give up or only study the easy parts” in a negative direction.
For the operational definition, items QF1–QF4 were scored using a 7-point Likert scale. The
values for scoring ranged from one (not at all true of me) to seven (very true of me). The score
for the scale was obtained by summing up the responses to each item. The score ranged from
four to 28.
Self-efficacy (SE)
Self-efficacy (SE) referred to students’ beliefs that their efforts to learn would result in
positive outcomes. Self-efficacy included judgments about one’s ability to accomplish a task and
one’s confidence in one’s skills to perform that task (Pintrich et al., 1993). This was a latent
variable measured by control of learning beliefs and self-efficacy for learning and performance
(Pintrich et al., 1993). The latent variable SE was measured by scores on 12 items from the
MSLQ scales. The scale included two subscales: control of learning beliefs and self-efficacy for
learning and performance. Responses to all items were summed to obtain the total score for the
SR Scale.
Control of learning beliefs (SEC) was conceptually defined as students’ beliefs in the role
of effort in which the reason for successful outcome will be attributed to the extent of effort
rather than external factors such as luck or teachers (Pintrich et al.,1993). It was instrumentally
defined as four items CQ1–CQ4 from the MSLQ (Pintrich et al., 1993). This scale included items
such as “If I try hard enough, then I will understand the course material.” For the operational
definition, items CQ1–CQ4 were scored using a 7-point Likert scale. The values for scoring
ranged from one (not at all true of me) to seven (very true of me). The score for the scale was
obtained by summing up the responses to each item. The score ranged from four to 28.
Self-efficacy for learning and performance (SELP) was conceptually defined as
individuals’ expectancy for success and confidence about personal capabilities to accomplish a
task (Pintrich et al., 1993). It was instrumentally defined as 8 items (LQ1–LQ8). This scale
included items like “I'm confident I can do an excellent job on the assignments and tests in the
course.” For the operational definition, items LQ1–LQ8 were scored using a 7-point Likert scale.
The values for scoring ranged from one (not at all true of me) to seven (very true of me). The
score for the scale was obtained by summing up the responses to each item. The minimum score
for the scale was 8 and the maximum 56.
Instrumentation
The instruments utilized by this study comprised four sections. Section one elicited
selfreported demographic information including age, gender, ethnicity, and employment. The
other three sections assessed academic motivation, self-efficacy, and self-regulation respectively.
Academic motivation was measured by conducting the AMS college version (Vallerand et al.,
1992). The scale was translated from a French measure of motivation which was developed
based on SDT. It consisted of seven subscales (External regulation, Introjected regulation,
Identified regulation, Intrinsic motivation-knowledge, Intrinsic motivation-accomplishment,
Intrinsic motivation-stimulation, and Amotivation) each contained four items, totaling 28. It is a
self-report questionnaire on a 7-point Likert scale from one (Does not correspond at all) to seven
(Corresponds exactly). Researchers conducted confirmatory factor analysis (CFA), internal
consistency, and test-retest of the seven subscales to investigate the psychometric analyses
(Vallerand et al., 1992). They conducted one study on 745 university students. The internal
consistency of the subscales was high, ranging from .83 to .86 except for the identification
subscale (α = .62). The researchers also conducted another study on 75 university students to
assess temporal stability where test-retest results showed acceptable reliability in a period of one
month. The score ranged from .72 to .78. with a mean test-retest correlation of .79. These results
were identical to the original French-Canadian version.
Data were collected by utilizing the MSLQ (Pintrich et al., 1993). This self-report
questionnaire was developed based on a cognitive-social perspective with a consideration of the
dynamic correlation between motivation and the use of learning strategies. The questionnaire was
designed to assess college students’ motivation and learning strategies including cognitive and
metacognitive strategies. During the process of developing the scales, between 1982–1986, the
researchers developed 50–140 items that have been administered to more than 1,000
undergraduate students. The last version of the questionnaire consisted of a motivation section
and a learning strategy section with a total of 15 subscales. The questionnaire consisted of 81
items ranging from one (not at all true of me) to seven (very true of me) and can be scored on a
7-point Likert scale. The motivation section was developed on three main constructs—
expectancy, value, and affect. It contained six subscales. The learning strategy section is based on
three main constructs—cognitive, metacognitive, and resource management. The MSLQ includes
15 scales, each of them measuring different aspects. According to Garcia and McKeachie (2005)
the questionnaire is modular and can be conducted according to the instructors’ or researchers’
purpose:
The MSLQ is not a fixed entity being sold by a publisher; it is in the public domain, and
we have always intended that the MSLQ be used in whatever ways will meet the needs of
potential users. Accordingly, we encourage users to use the MSLQ in its entirety or to
select whatever subscales are relevant for their purposes, in whatever format is most
practical. (p.120)
The psychometric analyses showed the questionnaire has good reliability and validity.
The researchers conducted CFA and they checked the predictive validity (Pintrich et al., 1993).
The internal consistency for self-efficacy scales were robust, having scales of .93 on the
coefficient alpha. Learning strategies scales demonstrated acceptable reliability where most of
the coefficient alpha were above .70. The researchers, through predictive analyses, found that the
motivational scales, including self-efficacy scales, were correlated to students’ performance and
final grade—those who had high self-efficacy performed better on the final grade. The learning
strategies construct scales were also found to have a significant correlation with academic
performance and final grade. Students who tended to utilize deep cognitive processes were more
likely to achieve higher than those who scored low on the learning strategies scales. The
correlation between the self-efficacy subscales were good (r = .44); so also, the correlation
between the learning strategies scales which ranged between (r = .58 to r = .70).
Data Collection
Before collecting the data, the researcher obtained approval from Andrews University’s
Institutional Review Board (Appendix A). QuestionPro hosted the surveys online. Participants
were provided with an informed consent form (Appendix B) which (1) explained the purpose of
the study and the significant role of their cooperation; (2) assured the participants of their right to
withdraw without penalty; (3) demonstrated that their data and information would be secure—
only the researcher and her committee members would have access to the data. The data were
collected, they were transferred into Excel and SPSS.
Analysis of the Data
SPSS and IBM SPSS Amos were used for statistical analysis. The research aimed to
investigate if the hypothesized model, which suggested the role of self-regulation and
selfefficacy in predicting academic motivation, fitted the data. Therefore, the null hypothesis
stated that the structural covariance matrix was equivalent to the empirical covariance matrix.
SEM was conducted, particularly the maximum likelihood estimation (MLE). SEM is a series of
statistical methods that explains the relationship between multiple independent variables with
multiple dependent variables. SEM is a confirmatory technique that contains a combination of
factor analysis and multiple regression that will assess both measurement and structural
relationships.
The Advantages of Using SEM
SEM was suitable because of its ability to determine complex theoretical structures with
multiple dependent variables. The technique allows for identifying the correlation and also
explaining if the variance is possible. A significant feature of SEM is that it accounts for
measurement errors.
Creating a Data File
I created a data file in Excel and SPSS using the data from QuestionPro software.
Screening the Data
Before conducting SEM, SPSS was used to screen the data to check for and deal with
outliers, missing data, missing values. Any case of missing value was deleted because the sample
was large enough and the deletion did not affect the statistical power.
Developing the Model Specification
The data were cleaned, and the hypothesized model developed by IBM SPSS Amos (path
diagram). Ovals or circles represent latent variables, while rectangles or squares represent
measured variables. Residuals are always unobserved, so they are represented by ovals or circles.
The correlations and covariances are represented by bidirectional arrows, which represent
relationships without an explicitly defined causal direction.
Assessing Model Fit
First, the measurement model was tested using CFA which tests the relationships between
factors and latent variables or between latent variables and other latent variables, but not does not
identify direction. Once the measurement model indicates a good fit, the structural model can be
tested. Second, path analysis was conducted to run the structural model; and Observed Variable
Path Analysis (OVPA) tested the relationships among constructs represented by direct measures
(observed variables), which were the items or subscale. Next, Latent Variable Path Analysis
(LVPA) which simultaneously tested measurement and structural parameters CFA and OVPA
was done. This analysis incorporated the relationship between observed and latent variables
(measures and factors), relationships between latent variables, and errors and residuals that were
left over from the prediction.
The null hypothesis was analyzed using the absolute fit indices and relative fit indices.
The common absolute fit indices, Model x2, should be non-significant when p > .05 indicating a
good fit. For Root Mean Squared Error of Approximation (RMSEA), an acceptable fit would be
< .10; and a good fit < .05. For the Standardized Root Mean Squared Residual (SRMR) values
below .08 suggest a good fit (Keith, 2019). A Goodness of Fit Index (GFI) > .90 is considered a
good fit. Common relative fit indices including Normed Fit Index (NFI), Incremental Fit Index
(IFI), and Comparative Fit Index (CFI) all range from 0–1; generally, values > .90 are considered
good (Meyers et al., 2016).
Model Modification
If the model does not indicate a good fit with the data, it can be improved to fit the data.
The modification would be through checking the modification indices and connecting the
suggested errors if they are logically correlated.
CHAPTER 4
RESULTS
Introduction
The study hypothesized that self-regulation and self-efficacy predict academic motivation
among university students. The hypothesized model suggested self-regulation and self-efficacy
predict academic motivation. Self-regulation was measured by (a) metacognitive regulation
(SRM), (b) time and study environment management (SRTE), and (c) effort regulation (SREF).
Self-efficacy was indicated by (a) control of learning beliefs (SEC), and (b) selfefficacy of
learning and performance (SELP). The outcome variable, academic motivation, was indicated by
(a) Intrinsic motivation to know (InMK), (b) Intrinsic motivation to accomplishment (InMC), (c)
Intrinsic motivation to experience stimulation (InMS), (d) External regulation (InME), (e)
Introjected regulation (InMN), (f) Identified regulation (InMD), and (g) Amotivation (AMOT).
This chapter discusses the sample, demographic characteristics, descriptive statistics of
the measurement variables, procedure of the analysis to test the hypothesis, and results of the
original SEM as well as the adjusted model. The last section summarizes the chapter.
Data Screening
A total of 1,582 persons viewed the link to the survey. Viewers who were not
undergraduate students aged 18–22 years old were excluded and 352 participants completed the
survey. After screening the data, three cases were eliminated because of some missing data. The
remaining 349 participants were included in the analysis.
Demographic Characteristics
The 349 participants were undergraduate students 18–22 years old. The participants were
80.2% female (N = 280) and 19.8% male (N = 69) (Table 1). The majority of the students were
Caucasian or White (62.2%), 10.6% Black or African American, 10.6% Asian, 8.9% Hispanic or
Latino, 3.4% Multiracial, and 1.4 % American Indian or Alaska Native (Table 1). Among the
participants 73.3% were unemployed, 18.9% were employed part-time, and 7.7% were employed
full-time (see Table 1).
Table 1
Demographic Characteristics of Participants in the Data
Variable
Gender
Male
69
19.8
Female
280
80.2
Total
349
100
Employment
Full-time employment
27
7.7
Part-time employment
66
18.9
Unemployed
7
2
Student
249
71.3
Total
349
100
Ethnicity
Hispanic or Latino
31
8.9
American Indian or Alaska Native
5
1.4
Asian
37
10.6
Black or African American
37
10.6
Variable
N
%
Native Hawaiian or Other Pacific
Islander
2
0.6
Caucasian or White
217
62.2
Multiracial
12
3.4
Other
1
0.3
N
%
Prefer not to say
7
2
Total
349
100
Observed Variables Description
Table 2 presents the descriptive statistics of the observed variables including means and
standards deviations. Metacognitive self-regulation (M = 4.25, SD = 0.86), time and study
environment management (M = 4.48, SD = 0.74), effort regulation (M = 4.15, SD = 0.79),
control of learning beliefs (M = 4.24, SD = 1.04), self-efficacy of learning and performance (M =
5.03, SD = 1.07), extrinsic motivation external regulation (M = 5.31, SD = 1.19), extrinsic
motivation identified (M = 5.53, SD = 1.12), extrinsic motivation integrated (M = 5.17, SD =
1.33), intrinsic motivation to know (M = 5.01, SD = 1.24), intrinsic motivation to experience
stimulation (M = 3.95, SD = 1.39), intrinsic motivation to accomplish (M = 4.59, SD = 1.32), and
amotivation (M = 2.77, SD = 1.67).
Zero-Order Correlations
Table 2 indicates that some variables have statistically significant correlations where p
values were less than .05. The majority of the correlations between the observed variables were
weak or moderate. Other correlations were not statistically significant: (1) between extrinsic
motivation external regulation (ExME) (r = -.01, p = .85) and intrinsic motivation to experience
stimulation (InMS); (2) between control of learning beliefs (SEC) and effort regulation (SREF)
Table 2
Measured Variables Correlation and Descriptive Statistics
ExME ExMN ExMD InMK InMC InMS AMOT SMR SRTE SREF SEC SELP
ExME
.473**
.580**
.332**
.246**
-0.01
-.287**
.120*
.296**
.185**
.162**
.344**
ExMN
.523**
.546**
.635**
.335**
-.230**
.285**
.256**
.263**
.156**
.420**
ExMD
.579**
.486**
.201**
-.521**
.298**
.458**
.323**
.133*
.515**
InMK
.733**
.534**
-.354**
.462**
.389**
.341**
.135*
.542**
InMC
.617**
-.224**
.485**
.275**
.281**
.266**
.490**
InMS
.111*
.418**
0.096
0.092
.188**
.262**
AMOT
-.134*
-.472**
-.490**
.191**
-.346**
SMR
.491**
.424**
.253**
.547**
SRTE
.678**
0.023
.543**
SREF
-0.036
.537**
SEC
.359**
Mean
5.31
5.17
5.53
5.01
4.59
3.95
2.77
4.25
4.48
4.15
4.24
5.03
SD
1.18
1.32
1.11
1.24
1.32
1.38
1.67
0.86
0.73
0.79
1.03
1.07
Skewness
-0.58
-0.56
-0.69
-0.19
-0.24
-0.01
0.52
-0.16
-0.39
0.3
-0.25
-0.31
(r = -.03, p = .51); and between time and study environment management (SRTE) (r = .02, p =
.67). In addition, amotivation (AMOT) had no statistically significant correlation with effort
regulation (SREF) (r = .09, p = .08) and time and study environment management (SRTE) (r =
.09, p = .07). Even though the correlations were found between some variables, they were not
high which helped to avoid the problem of collinearity.
Hypotheses Testing
To examine the null hypotheses, which indicates that the structural covariance matrix is
equivalent to the empirical covariance matrix, SEM with Maximum Likelihood estimation
(MLE) method was conducted. The SEM that was configured for the present study, based on the
data from 349 undergraduate student participants, is shown in Figure 3. It was conducted to
investigate the hypothesis that self-regulation and self-efficacy predict academic motivation. All
these variables were latent variables in this model. The model specified two direct paths from
self-regulation to academic motivation and from self-efficacy to academic motivation. The
latent variable of academic motivation, used as the outcome variable in the model, was
indicated by seven of the subscales of AMS—intrinsic motivation to know, intrinsic motivation
to accomplish, intrinsic motivation to stimulate, extrinsic motivation integrated, extrinsic
motivation identified, extrinsic motivation, external regulation, and amotivation. The first
exogenous (predictor) latent variable represented self-regulation which was indicated by three
indicators—metacognitive self-regulation, time and study environment management, and effort-
regulation (Pintrich et al., 1993). The second exogenous, latent variable represented selfefficacy
which was indicated by two subscales—control of learning beliefs and self-efficacy of learning
and performance.
Fit indices demonstrated a statistically significant Chi-square with a value of 271.569, df
= 40, p = .000, indicating that this hypothesized model did not fit our data because the Chisquare
value is very large. In addition, GFI = .875, NFI = .874 and CFI = .889, indicated a poor fit
because all values were less than 0.9. Most importantly, RMSEA (.129) and SRMR (.090)
Figure 3
The Hypothesized Model
were greater than the optimal fit of .08 or less. Therefore, the data set did not confirm my
hypothesized model. I then adjusted the previous model after an examination of the modification
indexes, estimated parameters, regression weight, and standardized regression weight.
The Adjusted Model
I considered modification indexes and theory before developing an adjusted model. Error
term correlation was observed between same scale items, a significant factor loading of SEC and
AMOT on SR. Heywood case was observed in SELP and the variance error was fixed to 0.
Finally, a significant error term correlation between SEC and AMOT was included assuming that
shared variance between these items was not explained by the model. An adjusted SEM that fit
the data much better emerged (see Figure 4). A Chi-square with a value of 187.547, df = 37, p =
.000 was obtained. However, because of the sensitivity of Chi-square to the sample size and the
complexity of the model other fit indices were considered (Schermelleh-Engel et al., 2003,
Vandenberg, 2006).
Figure 4
The Modified Model
Other fit indices that were significantly better than those in the original model were
considered. The GFI improved to .918, the NFI improved to .913 and the CFI improved to .928.
The RMSEA and SRMR dropped to .108 and .072, respectively, both values were well within an
acceptable range. Therefore, this last model adequately fitted the data and was much better than
the original SEM. The model configuration accounted for approximately 41% (R2 = .407) of the
variance of academic motivation.
In terms of the measurement model, all the pattern coefficients linking the measured
variables to their latent variables were statistically significant. In the adjusted model, there were
two significant paths between self-regulation (SR) and amotivation (AMOT); and between
selfregulation (SR) and control of learning beliefs (SEC). This result was based on the
psychometric characteristics of the items used, so self-regulation (SR) was not only the
explanation for some proportion of the variance in metacognitive self-regulation (SMR), time
and study environment management (SRTE), and effort-regulation (SREF), but also in control of
learning beliefs (SEC) and amotivation (AMOT).
The construct model indicated that the exogenous variables were significantly correlated
(r = .69, p < .01) as expected. This indicated that self-regulation and self-efficacy have a
statistically significant correlation. In addition, the direct path from self-regulation to academic
motivation was statistically significant (standardized coefficient = .236 unstandardized
coefficient = .106 with a standard error of .036, p = .003), indicating that self-regulation predicts
(β = .24; p < .01) academic motivation. The direct path from self-efficacy to academic
motivation was statistically significant (standardized coefficient = .452 unstandardized
coefficient = .184 with a standard error of .038, p = .00). Therefore, self-efficacy (β = .45; p <
.01) was the best predictor of academic motivation. Self-regulation (β = .24; p < .01) was the
lowest predictor of academic motivation. There was a correlation between error five and error
12 indicating that there was some variance between control of learning beliefs and amotivation
that could be explained by this model.
Summary of Findings
The SEM techniques were conducted to determine if the theoretical covariance matrix and the
imperial covariance matrix were equal. The hypothesized model for this study did not
statistically fit the collected data. As a result, some modifications were made to improve the
model. The modified model statistically fitted the data (GFI = .918, NFI = .913, CFI = .928,
RMSEA = .108 SRMR = .072). Self-regulation and self-efficacy have a statistically significant
correlation (r = .69, p < .01). Self-efficacy (β = .45; p < .01) is the better predictor of academic
motivation compared to self-regulation (β = .24; p < .01).
The results of the study were presented in this chapter. First, the demographic characteristics of
the sample, in addition to data screening, were illustrated. Second, the observed variables,
including means and standard deviation, were described. Third, the analysis of SEM was
demonstrated for both the hypothesized model and the modified model.
CHAPTER 5
SUMMARY, FINDINGS, DISCUSSION, CONCLUSIONS, AND RECOMMENDATIONS
Introduction
This chapter summarizes the current study and presents an overview of the purpose of the
study, research problem, summary of literature, significance of the study, hypothesis, and
methodology. The chapter also provides the findings of the study and discusses the results with
reference to the literature review. The last section identifies the limitations which impacted the
results, discusses the results of the study, and provides suggestions and recommendations for
future research and practice.
Research Problem
Despite the significant role of academic motivation in students’ learning outcomes
(Zimmerman, 2008; 2000b) students’ motivation decreases over their school years. There is
evidence that undergraduate students show low levels of academic motivation and low value of
academic materials, self-concept, and formation of mastery-oriented behaviors (Dresel &
Grassinger, 2013; Wang & Pomerantz, 2009). Amotivated students tend to drop out of school,
perform poorly, and disengage from learning activities (Wang & Pomerantz, 2009). Also,
amotivated students cannot regulate their learning processes (Dresel & Grassinger, 2013).
Deficiency in self-regulation is associated with depression (Eisenberg et al., 2007) and addiction
(Kruglanski & Higgins, 2007). Lack of self-efficacy is correlated with anxiety and depression
(Tahmassion & Moghadam, 2011). It is noteworthy that students with low levels of academic
motivation, self-efficacy, and self-regulation produce low levels of academic achievement.
Therefore, it is imperative to understand students’ academic motivation and the psychological
factors that may contribute to developing academic motivation among students.
Purpose of the Study
The purpose of the study was to test a theoretical model of the influence of selfregulation
and self-efficacy on academic motivation. In particular, a hypothesized model of the relationship
between these variables was created and data measuring the self-regulation, selfefficacy, and
academic motivation of undergraduate students were collected and analyzed through SEM.
Significance of the Study
The current study investigated whether self-regulation and self-efficacy predict academic
motivation among undergraduate students. Previous studies reported a decline in students’
academic motivation over their school years, particularly the first year of university study (Busse
& Walter, 2017; Dresel & Grassinger, 2013; Rizkallah & Seitz, 2017). This decline in motivation
influenced students’ academic achievement or led to dropping out (Wang & Pomerantz, 2009).
The finding of this study will help to explain factors that influence academic motivation. The
examination of variables within various domains—cognition, motivation/affect, behavior, and
context—enriches the understanding of academic motivation. Variables under investigation in
this study contain a variety of components (metacognition, time and study environment
management, effort-regulation, self-efficacy beliefs, and motivational factors) that will provide
significant information regarding the predicting of academic motivation.
The findings will guide policy makers, curriculum committees, higher education personnel,
and faculty to apply strategies that promote students’ self-efficacy and self-regulation. The
literature suggested predictive correlational research contributes to understanding psychological
components such as academic motivation among students (Rensh et al., 2020).
The review of previous studies also indicated that most studies of motivation were conducted in
non-American cultures. Therefore, the current study filled in these gaps by conducting predictive
correlation methods to investigate academic motivation among students in the United States.
Research Hypotheses
The main hypothesis of this study was that the reproduced covariance matrix proposed in
the theoretical model and the observed sample covariance matrices were equal. In simple terms,
this means that the structural model would be a good fit with the observed data. Using the
conceptualized model depicted in Figure 2 (p. 28), this study hypothesized (1) There is a
significant correlation between the two exogenous variables, self-regulation and self-efficacy; (2)
Self-regulation has a significant direct effect on the endogenous variable academic motivation;
(3) Self-efficacy has a significant direct effect on the endogenous variable academic motivation.
Summary of the Literature
This section provides a brief historical synopsis of the primary variables of this study
and concludes with the research outcomes which address the interrelationships among them.
The Relationship Between Self-Regulation and Academic Motivation
Academic motivation refers to the interest or the will that drive students to accomplish
academic goals. Intrinsic motivation refers to the internal desire students have to engage in
academic activities, e.g., satisfaction; whereas extrinsic motivation refers to factors such as
esteem or reward that enhance students’ desires to perform effectively to achieve academic
success (Ryan & Deci, 2000; Vallerand et al., 1992). According to the SDT, motivation is
influenced by three main psychological needs—competence, relatedness, and autonomy. An
autonomy continuum was established to illustrate the types of motivation and how individuals
engage in self-determined behavior instead of controlled behaviors (Deci & Ryan, 2008). The
theorists suggest that academic motivation can be enhanced through the process of
internalization in which students integrate extrinsically- and intrinsically-motivated behaviors
(Deci & Ryan, 2008; Ryan & Deci, 2020).
Self-determined students can initiate proper actions and pursue desired outcomes (Deci &
Ryan, 2008; Ryan & Deci, 2020). Therefore, the ability to regulate one’s behaviors, emotions,
and cognitive functions are essential to satisfying the sense of autonomy. There was evidence
that self-regulated students present high levels of academic motivation (Ariani, 2016; Ning &
Downing, 2010; Valinasab & Zeinali, 2018). Moreover, self-regulation strategies and academic
motivation were positively related to academic achievement (Ariani, 2016; Ning & Downing,
2010). Students who are highly motivated in academic settings and capable of regulating their
learning processes show positive emotions (Valinasab & Zeinali, 2018), and prefer flexible
assessment systems (Ariani, 2016).
The Relationship Between Self-Efficacy and Academic Motivation
The belief system significantly impacts one’s sense of competence (Ryan & Deci, 2000;
Zimmerman, 2000b). SCT emphasizes the dynamic interaction between personal, behavioral,
and environmental factors where self-efficacy is a fundamental motive to behave (Bandura,
1991).
Previous studies investigated the relationship between self-efficacy and academic
motivation. Results findings found that students who believe in their competence and academic
abilities show high levels of academic motivation (Hassankhani et al., 2015). However, students
who reported low self-efficacy beliefs are less likely to engage in learning activities and their
academic motivation was low (Ball & Edelman, 2018).
Other studies focus on the impact of self-efficacy and academic motivation on
procrastination (Cerino, 2014; Malko & Mutlu, 2018). According to these studies, students who
procrastinate have low motivation to engage in learning activities and low beliefs in their
academic potential.
Engaging in an active learning environment influences students’ self-efficacy and
academic motivation (Mantasiah & Yusri, 2018). After implementing Pay It Forward Learning,
students developed self-efficacy and academic motivation because they had an active role in the
learning processes and connecting with peers during the lessons.
The Relationship Between Self-Efficacy, Self-Regulation, and Academic Motivation
According to the literature review academic motivation, self-regulation, and self-efficacy
are correlated among undergraduate students (Alafghani & Purwandari, 2019; Yusuf, 2011).
Academically motivated students who believe in their abilities tend to regulate their learning
process (Alafghani & Purwandari, 2019); intrinsically motivated students have a deeper
approach to learning than extrinsically motivated students (Prat-Sala & Redford, 2010). Students
with mastery-oriented goals and the ability to conduct metacognitive strategies have increased
levels of academic motivation (AL-Baddareen et al., 2014). Two experimental studies that aimed
to develop self-efficacy and academic motivation through self-regulatory strategies (Lavasani et
al., 2011) and goal commitment (Yuka, 2017), provided evidence of the effectiveness of these
interventions. Students’ positive thinking, self-regulation, and academic motivation were
effectively improved via self-reflection intervention (Wang, Chen et al., 2017).
Amotivated students report a lack of control beliefs and persistence (Vallerand et al.,
1992). However, students with an internal locus of control perceive themselves as active
learners: able to regulate themselves (Arkavazi & Nosratinia, 2018; Sidola et al., 2020), seek
help, manage their effort, show interest and enjoyment, and value the learning tasks (Ng, 2012).
Methodology
This study adopted a quantitative, non-experimental, model-testing design. The sampling
method was convenient because participants were recruited online through QuestionPro. All
participants were undergraduate students ages 18 to 22 enrolled in a university in the United
States. They responded to self-report surveys including a demographic questionnaire, MSLQ
(Pintrich et al., 1993) to measure self-regulation and self-efficacy, and AMS (Vallerand et al.,
1992) to assess academic motivation.
After screening and cleaning the data, 349 undergraduate students participated. The data
were analyzed by SPSS and AMOS. To examine the prediction role of self-regulation and
selfefficacy in academic motivation SEM, maximum likelihood of estimation (MLE) was
conducted.
Summary of Demographics
A total of 349 undergraduate students completed the surveys. Most of the participants
were female (80.2%, n = 280), Caucasian or white (62.2%, n = 217), and unemployed (73.3%, n
= 256). The number of Asian students (10.6%, n = 37) and Black or African American (10.6%, n
= 37) students were equal. They were followed by the White Hispanic or Latino (8.9%, n = 31)
and Multiracial (3.4%, n = 2). The lowest ethnic groups were American Indian or Alaska Native
(1.4%, n = 5) and Native Hawaiian or other Pacific Islander (0.6%, n = 2). The remaining
participants did not specify their ethnic identity (2.3%, n = 8).
Summary of Findings
The current study hypothesized that self-regulation and self-efficacy predict students’
academic motivation. SEM technique was conducted to examine whether the theoretical
covariance matrix is equivalent to the empirical covariance matrix. Analysis of the data indicated
that the initial model (Figure 3, 76) did not fit the data, where Chi-square value was 271.569, df
= 40, p = .000, and poor fit indices were found (GFI = .875, NFI = .874, CFI = .889, RMSEA =
.129. SRMR = .090). Therefore, an exploratory analysis was conducted, and some modifications
were made based on modification indices and theory to improve the fit indices.
Adjusted Model
The modifications made included correlating error terms between same scale items,
identifying the significant factor loading of SEC and AMOT on SR. Also, in Heywood case that
was observed in SELP, the variance error was fixed to 0. Last, correlating error term between
SEC and AMOT was made. As a result, the adjusted model (Figure 4, p. 77) showed an
acceptable fit between the theoretical covariance matrix and the empirical covariance matrix
(GFI = .918, NFI = .913, CFI = .928, RMSEA = .108, and SRMR = .072), indicating that the
data fitted the hypothesized model. Overall, the adjusted model explained 41% of the variance of
academic motivation, in which self-efficacy (β = .45; p < .01) was the better predictor of
academic motivation compared to self-regulation (β = .24; p < .01). In addition, there was a
significant correlation between self-regulation and self-efficacy (r = .69, p < .01).
Correlational Path from Self-regulation and Self-efficacy
According to the conceptual framework of the current study, self-regulation and
selfefficacy were assumed to be correlated. The adjusted model provides evidence that these two
variables are correlated (r = .69, p < .01). Students who tended to utilize metacognitive
strategies, regulate their effort, and manage their time and study environment were more likely to
believe in their capabilities and that they have control over their actions.
Discussion
This finding is congruent with previous studies. For instance, the model suggested by
Yusuf (2011) provided evidence that self-regulation and self-efficacy are correlated among
undergraduate students. In addition, studies that used a prediction design supported this finding
because prediction indicates relationships between the predictors and the outcome variables.
However, the literature contradicted results regarding the prediction role of these variables. Some
studies suggested that self-efficacy predicts self-regulatory strategies, but others indicated that
self-regulation affected students’ beliefs in their learning capacity. To illustrate, Alafghani and
Purwandari (2019) demonstrated that self-efficacy and self-regulation are associated—students
who believed in their capabilities and were highly motivated opted to regulate their learning
processes. Arik (2019) argued that efficacious students tend to engage in controlling their
behavior and manage learning performance. Ng (2012) concluded that self-efficacy and control
beliefs predict students’ abilities to conduct regulatory strategies.
On the other hand, several studies suggested that self-regulation is a significant predictor
of self-efficacy. According to Saeid and Eslaminejad (2017) self-directed learners who take
responsibility, utilize metacognitive strategies, accept learning, have positive self-concepts, and
are independent were more likely to have high levels of self-efficacy. Among these factors,
Independency was the best predictor of self-efficacy. Self-efficacy and control of beliefs were
significantly associated with a tendency to conduct deep strategies, regulate learning
performance and effort, and present interest and enjoyment.
Furthermore, an experimental study (Lavasani et al., 2011) supported the findings of the
current study regarding the correlation between self-regulation and self-efficacy. The researchers
conducted a self-regulation strategies program to promote self-efficacy and academic motivation.
The program included instructions that taught students how to set goals, monitor progress, assess
behaviors, create a well-established environment, and make information meaningful. The
findings of the study indicated that self-regulatory strategies training for the experimental group
improved self-efficacy, academic motivation, and academic performance when compared with
the control group. Yuka (2017) indicated that implementing self-regulation strategies can
enhance students’ self-efficacy. Wang (2017) conducted a self-reflective program to improve
self-efficacy, self-regulation, and academic motivation. The results indicated students developed
beliefs in their capabilities, tended to utilize self-regulatory skills, and were academically
motivated. These findings suggest that students who already believe in their learning competence
are more likely to regulate their thoughts and behavior. While students trained to utilize
regulatory strategies can develop a belief in their ability to perform well and control their
behaviors and environment. Interestingly, a correlational study found that self-efficacy predicted
self-regulation; while experimental studies determined that learning self-regulatory strategies
improved self-efficacy. Perhaps, the contradicting results between these studies may be attributed
to the differences in methodology. To better understand this, future research may conduct
experimental studies that implement self-efficacy programs to improve self-regulation skills,
while other studies may adopt a correlational predictive design to examine whether selfregulation
predicts self-efficacy.
Regardless of these contradictory results, the findings of previous studies explain the
SCT perspective of the cyclical relationships between personal, behavioral, and environmental
factors. Previous achievement leads to satisfaction and confidence in personal competence.
Students build self-efficacy through monitoring and evaluating their performance; at the same
time when students hold efficacy beliefs, they tend to engage in regulatory strategies.
Predictive Direct Effect from Self-regulation to Students’ Academic Motivation
The third hypothesis of the current study suggests that self-regulation predicts academic
motivation. Academic motivation in this study was indicated by seven factors—intrinsic
motivation to know, intrinsic motivation to accomplish, intrinsic motivation to experience
stimulation, external regulation, identified regulation, integrated regulation, amotivation.
According to the SEM, self-regulation (metacognitive self-regulation, time and study
environment management, and effort-regulation) was a statistically significant predictor (β = .24;
p < .01) of academic motivation. Hence, university students who utilized metacognitive and
selfregulatory strategies, managed time and study environments, and persevered when they
encountered difficulties showed high levels of academic motivation.
Discussion
This finding is consistent with previous research indicating self-regulation predicted
university students’ academic motivation (Saki & Nadari, 2018). Previous studies conducted to
enhance students’ academic motivation via promoting self-regulatory strategies support the role
of self-regulation in predicting academic motivation. For instance, an intervention based on goal
setting revealed that goal commitment was a significant predictor of intrinsic motivation (Yuka,
2017). A self-regulation strategy program effectively enhanced students’ academic motivation
(Lavasani et al., 2011). The experimental group—which was taught how to set goals, monitor
and evaluate learning behaviors, and establish an effective environment—had high scores in
academic motivation compared to the control group. Similarly, self-directed learners were highly
motivated toward academic activities through their independent learning skills, adoption of
problem-solving techniques, and effective study skills (Saeid & Eslaminejad, 2017).
This finding contradicts assumptions that academic motivation predicts self-regulation
abilities where academically motivated students with advanced beliefs in their potential tended to
regulate their learning and performance (Alafghani & Purwandari, 2019). Other research
indicated that academic motivation impacts self-regulation (Ariani, 2016; Dresel & Grassinger,
2013). However, Arik (2019) refuted that claim.
These results may differ from the findings of this study due to cultural differences. The
first study examined Indonesian students (Alafghani & Purwandari, 2019) and the second
research was done in Germany (Dresel & Grassinger, 2013). These contradictory results may
also be attributed to differences in statistical techniques used or the way these studies
conceptualize self-regulation and academic motivation. For instance, the Dresel and Grassinger
(2013) study conducted multiple linear regression and considered students’ self-efficacy,
subjective value, and achievement goals as indicators of academic motivation. The literature
review revealed that self-regulation, self-efficacy, and academic motivation intertwined/
overlapped in terms of defining the concept or identifying its indicators. Therefore, future
research could be more specific in defining each variable as a construct instead of considering
self-efficacy as one component of academic motivation, or self-regulation constructs. It is vital to
differentiate between these variables in future research to better understand how these constructs
affect each other.
The reciprocal correlation between these variables suggested by SCT (Bandura, 1991)
and the cyclical model of self-regulated learning (Zimmerman, 2008) demonstrates why some
studies found self-regulation as the predictor while others found academic motivation as the
predictor. This can be explained by understanding that when students observe a model and
analyze the performed task, they believe they can also perform the task. Then, they set goals,
plan, and select suitable strategies to increase their academic motivation. Once motivated,
students engage in higher levels of self-regulatory processes such as self-monitoring,
selfevaluation, persistence, and adjustment of maladaptive behavior. This is clearly illustrated in
a study that stated goal setting, specifically goal commitment, predicted academic, intrinsic
motivation (Yuka, 2017); while another study that examined metacognition and effort regulation
indicated that self-regulation predicted academic motivation. To understand these cyclical
relationships, future research could investigate both direction paths from self-regulation to
academic motivation, and from academic motivation to self-regulation.
Predictive Direct Effect from Self-efficacy to Academic Motivation
The current study hypothesized that self-efficacy, indicated by control of learning beliefs and
self-efficacy for learning and performance, would predict academic motivation. There was a
statistically significant predicting role of self-efficacy in academic motivation (β = .45; p < .01).
Discussion
This result is congruent with Arik’s (2019) finding that self-efficacy was a predictor of
both academic motivation and self-management. In addition, self-concept which implies
selfefficacy was a statistically significant predictor of academic motivation (Saki & Nadari,
2019). The predicting correlation implies correlated relationships between predictor variables
and outcome variables. Hence, this finding is consistent with studies that determined
relationships between self-efficacy and academic motivation (Alafghani et al., 2019; Ball &
Edelman, 2018; Hassankhani et al., 2015; Yusuf, 2011).
This finding is also consistent with Ng’s (2012) study that investigated the effect of
selfefficacy and control of beliefs on students’ attitude toward learning. There was evidence that
efficacious students who believed in their ability to control valued their learning and showed
interest and enjoyment in academic settings.
However, this conclusion is contrary to findings that self-efficacy did not predict
students’ academic motivation (AL-Baddareen et al., 2014). Rather, self-efficacy was a
suppressor variable. This result was attributed to the multicollinearity of self-efficacy with
mastery goal, performance goal, and metacognition.
Most research investigating self-efficacy and academic motivation among university
students adopted a correlational design; very few studies used a prediction design. The current
findings contributed to identifying the effect of self-efficacy on academic motivation. It is
imperative to note that self-efficacy has different concepts and psychometric properties from
academic motivation (Zimmerman, 2000b). Thus, future research could investigate the impacts
of self-efficacy on academic motivation and distinguish between these constructs to better
understand them.
Direct Path from Self-regulation to Amotivation
The adjusted model demonstrated a significant path from self-regulation (β = -.32; p <
.01) to amotivation (academic motivation indicator). This indicated that self-regulation explained
some variance in amotivation.
Discussion
It is a logical conclusion that university students who reported high levels of
selfregulation abilities showed decreased levels of or lack of motivation or interest. Such
findings were consistent with SDT’s theory that lack of autonomy and agency to control one’s
emotions, cognitions, and behaviors reduced motivation to initiate function (Deci & Ryan, 2020).
This finding aligned with Saki and Nadari’s (2018) assertions that students who exhibited low
levels of self-regulation were amotivated in academic performance. Disability to regulate
negative emotions, such as anxiety or anger, produced amotivation or absence of internal desire
to participate in learning processes (Valinasab & Zeinali, 2018).
This finding of the current study was also congruent with the argument that students who
quit or give up when they encountered difficulties showed a lack of motivation (Vallerand et al.,
1992). Autonomy-supportive environments have a negative correlation with amotivation
(Duchatelet & Donche, 2019).
This unexpected path from self-regulation to amotivation is related to the psychometric
characteristics of the items used. This indicates that self-regulation explains some proportion of
the variance in amotivation, metacognitive self-regulation, time and study environment
management, effort regulation, and control of learning beliefs. Therefore, future studies could
examine the psychometric analysis of self-regulation scales in the MSLQ questionnaire to
explain this finding.
Conclusion
Students’ academic motivation is an essential component for achievement and knowledge
attainment in higher education. Intrinsically motivated students will be interested not only in
obtaining theoretical knowledge from study materials but also in engaging in occupational
practices related to the field of study. The current study sought to examine a hypothesized model,
based on SCT and SDT, to determine the influence of self-regulation and self-efficacy in
academic motivation. According to SEM analysis, the initial model did not fit the observed data,
therefore, an adjusted model was developed based on exploratory analysis and modification
indices. The adjusted model with a Chi-square value of 187.547 (df = 37, p = .000) adequately
fitted the data as acceptable criterion fit indices were met (GFI = .918, NFI = .913, CFI = .928,
RMSEA = .108, and SRMR = .072). A significant correlation between self-regulation and
selfefficacy (r = .69, p < .01) was found. The adjusted model explained 41% of the variance in
academic motivation. Although both exogenous variables were statistically significant predictors
of academic motivation, self-efficacy was the better predictor (β = .45; p < .01) compared to
selfregulation (β = .24; p < .01).
Limitations
1. The findings of the current study were limited due to the utilization of a convenience
sampling method which affects generalization.
2. There was a gender imbalance because 80.2% of the participants were female. Other
psychological factors that may affect academic motivation that were not included in this study,
where self-regulation and self-efficacy explained 43% of the variance in academic motivation.
3. When interpreting the findings of the current study it is important to consider the
impacts of self-report questionnaires and the use of a Likert scale.
4. The examination of the hypothesized model and the obtained results were attributed to
the sample of this study; thus, it was possible to get different results in different regions and
different years of university study.
Recommendations
Recommendations for Future Research
1. Researchers should investigate the impacts of different psychological variables, e.g.,
students’ attitude toward higher education, attribution, competencies in academic motivation.
2. Researchers should examine social factors—e.g., the learning environment, teaching
methods, curriculum structures, and students’ interrelationships with teachers and peers—that
may affect the levels of academic motivation.
3. Previous studies showed a lack of prediction methods when studying academic
motivation. Although the current study fills in this gap, further studies are needed particularly for
examining the effect of self-efficacy on academic motivation.
4. Researchers should replicate the current study while conducting the randomized sample
method to better validate the hypothesized model.
5. The current study adopted three subscales of MSLQ to measure self-regulation; further
study should include the other subscales of help seeking and peer learning.
6. A mixed-methods research design is recommended to better understand academic
motivation and factors influencing this variable. Obtaining results from quantitative and
qualitative methods will enrich our understanding of academic motivation.
7. Researchers should examine the hypothesized model among male undergraduate
students to support generalizing the findings of the current study.
8. The current study investigated the hypothesized model among different ethnic groups;
however, most of the participants were Caucasian or white. Further research is needed to
examine the model among a variety of ethnic groups to understand how these variables correlate
in different cultural backgrounds.
Recommendation for Educational Practice
1. The university curriculum committees should consider the role of self-regulation and
self-efficacy in students’ academic motivation. Curriculum should be designed in a way that
allows students to practice self-reflection and that has instructions for explicit metacognitive
strategies. For instance, lessons’ activities may build to teach students planning, selecting
effective strategies, and assessing their performance to enhance their metacognitive abilities. The
curriculum committee may include real stories about successful people with inspired language to
enhance students’ self-efficacy. The objective and content of the curriculum should be wellstated
and organized. The activities should vary to cover self-regulation skills such as group discussion,
thoughtful reflection, and application of one’s worldview.
2. It would be better if university and college faculty articulate the objectives and
activities of the course in a way that enhances students’ academic motivation. For instance,
developing a course syllabus that is clear and timely organized with a calendar to identify dates
for required reading, papers, tests, and projects will help students to improve planning, and to
monitor their progress during the course. Teaching strategies, such as delivery methods and
learning activities, should include scaffolding of metacognitive skills and self-control which in
turn will help students to imitate their instructors’ behavior. Instructors may require students to
participate in teaching and presenting some aspects of the materials. This should improve their
autonomy and self-regulation through taking responsibilities, leading discussions, and
controlling learning tools and times during class. When students gain successful experience of
teaching, their self-efficacy and control beliefs will improve.
3. University and college faculty should create an autonomy-supportive environment
through offering constructive feedback, acknowledging students’ perspectives and feelings which
give students insight into their strengths and promote their confidence in their capabilities.
Providing choices and allowing students to get involved in decision-making will enhance
students’ sense of autonomy and self-regulation. Instructors should encourage using
metacognitive strategies and regulating resources (time and environment) and effort that will
help students to develop such skills and behavior.
4. Universities and colleges should construct the campus environment including events,
workshops, and activities in a way that improves the students’ sense of self-efficacy and ability
to regulate their learning performance. Students should be encouraged to engage in various clubs
where they have a sense of relatedness; this will increase their motivation and efficacy.
Providing students with opportunities to be involved in voluntary service and enroll in
servicelearning experiences will address campus responsibility to the community and improve
career development among students. Through such practical activities, students’ self-efficacy
will improve as well as their abilities to regulate themselves which in turn will enhance academic
motivation.
APPENDIX A
IRB APPROVAL
APPENDIX B
INFORMED CONSENT INFORMED CONSENT
You are being invited to participate in a research study titled self-regulation and
selfefficacy as predictors of academic motivation among university students. This study is being
done by Fatimah Aljuaid from the Andrews University. You were selected to participate in this
study because of your current enrollment at university education. The purpose of this research
study is investigating whether self-efficacy and self-regulation will predict academic motivation.
If you agree to take part in this study, you will be asked to complete an online survey. This
survey will ask about your self-regulatory strategies, self-efficacy, and academic motivation; and
it will take you approximately 20-25 minutes to complete. You may not directly benefit from this
research; however, we hope that your participation in the study may lead to better understanding
of variables that predict academic motivation. We believe there are no known risks associated
with this research study; however, as with any online related activity the risk of a breach of
confidentiality is always possible. To the best of our ability your answers in this study will
remain confidential. We will minimize any risks by storing the data file on a password protected
computer. None of the information gathered will identify you by name. Your participation in this
study is completely voluntary and you can withdraw at any time. You are free to skip any
question that you choose. If you have questions about this project or if you have a
researchrelated problem, you may contact the researcher’s advisor Elvin Gabriel (269-471-6223).
Or the researcher Fatimah Aljuaid, (313 290 7262). If you have any questions concerning your
rights as a research subject, you may contact the Andrews University IRB Office at (269) 471-
6361 or [email protected] By clicking “I agree” below you are indicating that you are at least 18
years old, have read and understood this consent form and agree to participate in this research
study.
Please print a copy of this page for your records.
APPENDIX C
DEMOGRAPHIC QUESTIONNAIRE & MSLQ & AMC
DEMOGRAPHIC
INFORMATION
Please respond to each of the following demographic items listed below.
Age:
Gender:
o Female o Male
Race/Ethnicity:
o Hispanic or Latino o American Indian or
Alaska Native o Asian o Black or African
American o Native Hawaiian or Other
Pacific Islander o Caucasian or White o
Multiracial o Other
o Prefer not to say
Employment Status: o Full-
time employment o Part-
time employment o
Unemployed Self-
regulation Scale
The following questions ask about your learning strategies and study skills in your academic
study. Remember there are no right or wrong answers, just answer as accurately as possible. Use
the scale below to answer the questions. If you think the statement is very true of you, circle 7; if
a statement is not at all true of you, circle 1. If the statement is more or less true of you, find the
number between 1 and 7 that best describes you.
Not at all Very true true of me of me
MQ1. During class time I often miss important points
because I'm thinking of other things.
1 2 3 4 5 6 7
MQ2. When reading for this course, I make up questions to
help focus my reading.
1 2 3 4 5 6 7
MQ3. When I become confused about something I'm reading
for this class, I go back and try to figure it out.
1 2 3 4 5 6 7
MQ4. If course materials are difficult to understand, I change
the way I read the material.
1 2 3 4 5 6 7
MQ5. Before I study new course material thoroughly, I often
skim it to see how it is organized.
1 2 3 4 5 6 7
MQ6. I ask myself questions to make sure I understand the
material I have been studying in this class.
1 2 3 4 5 6 7
MQ7. I try to change the way I study in order to fit the
course requirements and instructor's teaching style
1 2 3 4 5 6 7
MQ8. I often find that I have been reading for class but don't
know what it was all about.
1 2 3 4 5 6 7
MQ9. I try to think through a topic and decide what I am
supposed to learn from it rather than just reading it over
when studying.
1 2 3 4 5 6 7
MQ10. When studying for this course I try to determine
which concepts I don't understand well.
1 2 3 4 5 6 7
MQ 11. When I study for this class, I set goals for myself in
order to direct my activities in each study period.
1 2 3 4 5 6 7
MQ 12. If I get confused taking notes in class, I make sure I
sort it out afterwards.
1 2 3 4 5 6 7
TQ1. I usually study in place where I can concentrate on my
course work.
1 2 3 4 5 6 7
TQ2. I make good use of my study time for this course.
1 2 3 4 5 6 7
TQ3. I found it hard to stick to study schedule.
1 2 3 4 5 6 7
TQ4. I have a regular place set side for studying.
1 2 3 4 5 6 7
TQ5. I make sure I keep up with the weekly reading and
assignments for this course.
1 2 3 4 5 6 7
TQ6. I attend class regularly.
1 2 3 4 5 6 7
TQ7. I often find that I don't spend very much time on this
course because of other activities
1 2 3 4 5 6 7
TQ8. I rarely find time to review my notes or readings before
an exam.
1 2 3 4 5 6 7
FQ1. I often feel so lazy or bored when I study for this class
that I quit before I finish what I planned to do.
1 2 3 4 5 6 7
FQ2. I work hard to do well in this class even if I don't like
what we are doing.
1 2 3 4 5 6 7
FQ3. When course work is difficult, I give up or only study
the easy parts.
1 2 3 4 5 6 7
FQ4. Even when course materials are dull and uninteresting,
I manage to keep working until I finish.
1 2 3 4 5 6 7
self-efficacy scale
The following questions ask about your learning strategies and study skills in your academic
study. Remember there are no right or wrong answers, just answer as accurately as possible. Use
the scale below to answer the questions. If you think the statement is very true of you, circle 7; if
a statement is not at all true of you, circle 1. If the statement is more or less true of you, find the
number between 1 and 7 that best describes you.
Not at all Very true true of me of me
CQ1. If I study in appropriate ways, then I will be able to
learn the material in this course.
1 2 3 4 5 6 7
CQ2. It is my own fault if I don't learn the material in this
course.
1 2 3 4 5 6 7
CQ3. If I try hard enough, then I will understand the course
material.
CQ4. If I don't understand the course material, it is because I
didn't try hard enough.
1 2 3 4 5 6 7
LQ1. I believe I will receive an excellent grade in this class.
1 2 3 4 5 6 7
LQ2. I'm certain I can understand the most difficult material
presented in the readings for this course.
1 2 3 4 5 6 7
LQ3. I'm confident I can understand the basic concepts
taught in this course.
1 2 3 4 5 6 7
LQ4. I'm confident I can understand the most complex
material presented by the instructor in this course.
1 2 3 4 5 6 7
LQ5. I'm confident I can do an excellent job on the
assignments and tests in this course.
1 2 3 4 5 6 7
LQ6. I expect to do well in this class.
1 2 3 4 5 6 7
LQ7. I'm certain I can master the skills being taught in this
class.
1 2 3 4 5 6 7
LQ8. Considering the difficulty of this course, the teacher,
and my skills, I think I will do well in this class.
1 2 3 4 5 6 7
Academic motivation
The following questions ask about your learning strategies and study skills in your academic
study. Remember there are no right or wrong answers, just answer as accurately as possible. Use
the scale below to answer the questions. If you think the statement is Corresponds exactly, circle
7; if a statement Does not correspond at all, circle 1. If the statement is more or less
Corresponds, find the number between 1 and 7 that best describes you.
Does not
Corresponds at a Corresponds correspond at Corresponds a lot
Corresponds exactly
little moderately all
1 2 3 4 5 6 7
WHY DO YOU GO TO COLLEGE?
18. Because with only a high-school degree I would
not find a high-paying job later on.
1 2 3 4 5 6
7
19. Because I experience pleasure and satisfaction while
learning new things.
1 2 3 4 5 6
7
20. Because I think that a college education will help
me better prepare for the career I have chosen.
1 2 3 4 5 6
7
21. For the intense feelings I experience when I am
communicating my own ideas to others.
1 2 3 4 5 6 7
22. Honestly, I don't know; I really feel that I am
wasting my time in school.
1 2 3 4 5 6
7
23. For the pleasure I experience while surpassing
myself in my studies.
1 2 3 4 5 6
7
24. To prove to myself that I am capable of completing
my college degree.
1 2 3 4 5 6
7
25. In order to obtain a more prestigious job later on.
1 2 3 4 5 6 7
26. For the pleasure I experience when I discover new
things never seen before.
1 2 3 4 5 6
7
27. Because eventually it will enable me to enter the job
market in a field that I like.
1 2 3 4 5 6
7
28. For the pleasure that I experience when I read
interesting authors.
1 2 3 4 5 6
7
29. I once had good reasons for going to college;
however, now I wonder whether I should continue.
1 2 3 4 5 6
7
30. For the pleasure that I experience while I am
surpassing myself in one of my personal
accomplishments.
1 2 3 4 5 6
7
31. Because of the fact that when I succeed in college I
feel important.
1 2 3 4 5 6
7
32. Because I want to have "the good life" later on.
1 2 3 4 5 6
7
33. For the pleasure that I experience in broadening my
knowledge about subjects which appeal to me.
1 2 3 4 5 6 7
34. For the pleasure that I experience when I feel
completely absorbed by what certain authors have
written.
1 2 3 4 5 6 7
35. I can't see why I go to college and frankly, I couldn't
care less.
1 2 3 4 5 6 7
36. For the satisfaction I feel when I am in the process
of accomplishing difficult academic activities.
1 2 3 4 5 6 7
37. To show myself that I am an intelligent person.
1 2 3 4 5 6 7
38. Because my studies allow me to continue to learn
about many things that interest me.
1 2 3 4 5 6 7
39. Because I believe that a few additional years of
education will improve my competence as a worker.
1 2 3 4 5 6 7
40. For the "high" feeling that I experience while
reading about various interesting subjects.
1 2 3 4 5 6 7
41. I don't know; I can't understand what I am doing in
school.
1 2 3 4 5 6 7
42. Because college allows me to experience a personal
satisfaction in my quest for excellence in my
studies.
1 2 3 4 5 6 7
43. Because I want to show myself that I can succeed in
my studies.
1 2 3 4 5 6 7
44. Because this will help me make a better choice
regarding my career orientation.
1 2 3 4 5 6 7
45. In order to have a better salary later on.
1 2 3 4 5 6 7
APPENDIX D
STRUCTURAL EQUATION MODELING ANALYSIS TABLES
The Initial Model
Variable counts (Group number 1)
Number of variables in your model: 28 Number
of observed variables: 12 Number of unobserved
variables: 16 Number of exogenous
variables: 15
Number of endogenous variables: 13
Parameter Summary (Group number 1)
Weights
Covariances
Variance
s
Means
Intercepts
Tota
l
Fixed
16
0
1
0
0
17
Labeled
0
0
0
0
0
0
Unlabeled
11
13
14
0
0
38
Total
27
13
15
0
0
55
Sample Moments (Group number 1)
Sample Covariances (Group number 1)
AM In
OT MC
In
MS
InM
K
ExM
N
ExM
D
Ex
ME
SE
C
SE
LP
SM
R
SR
TE
SR
EF
AM
OT
2.78
7
InM
C
-.494
1.7
44
InM
S
.258
1.1
29
1.9
18
InM
K
-.734
1.2
02
.91
8
1.54
2
ExM
N
-.509
1.1
14
.61
7
.900
1.76
2
ExM
D
-.972
.71
6
.31
1
.803
.776
1.24
8
ExM
E
-.567
.38 5
-
.01
6
.488
.744
.768
1.40
6
SEC
.330
.36 3
.26 9
.174
.214
.153
.199
1.0 68
SEL
P
-.618
.69 3
.38 8
.721
.597
.616
.437
.39 7
1.1
45
SMR
-.198
.56 7
.51 3
.507
.335
.295
.126
.23 1
.51 .78 8 4
SRT
E
-.795
.36 6
.13 4
.488
.342
.516
.355
.02 4
.58 .43 7
9
1.0
19
SRE
F
-.940
.42 7
.14 6
.487
.401
.414
.252
-
.04
2
.66 .43 1
1
.78 6
1.3
21
C number = 26.705
Eigenvalues
7.311 3.359 1.697 1.407 .899 .760 .518 .427 .399 .366 .329 .274
Determinant of sample covariance matrix = .116
Sample Correlations (Group number 1)
AM In
OT MC
In
MS
InM
K
ExM
N
ExM
D
Ex
ME
SE
C
SE
LP
SM
R
SR
TE
SR
EF
AM
OT
1.00
0
InM
C
-.224
1.0
00
InM
S
.111
.61
7
1.0
00
InM
K
-.354
.73
3
.53
4
1.00
0
ExM
N
-.230
.63
5
.33
5
.546
1.00
0
ExM
D
-.521
.48
6
.20
1
.579
.523
1.00
0
ExM
E
-.287
.24
6
-
.01
0
.332
.473
.580
1.00
0
SEC
.191
.26
6
.18
8
.135
.156
.133
.162
1.0
00
SEL
P
-.346
.49
0
.26
2
.542
.420
.515
.344
.35
9
1.0
00
SMR
-.134
.48
5
.41
8
.462
.285
.298
.120
.25
3
.54
7
1.0
00
SRT
E
-.472
.27
5
.09
6
.389
.256
.458
.296
.02
3
.54
3
.49
1
1.0
00
SRE
F
-.490
.28
1
.09
2
.341
.263
.323
.185
-
.03
6
.53
7
.42
4
.67
8
1.0
00
C number = 24.887
Eigenvalues
4.973 1.821 1.292 1.071 .571 .507 .393 .341 .309 .288 .235 .200
Models
Default model (Default model)
Notes for Model (Default model)
Computation of degrees of freedom (Default model)
Number of distinct sample moments:
78
Number of distinct parameters to be estimated:
38
Degrees of freedom (78 - 38):
40
Result (Default model)
Minimum was achieved
Chi-square = 271.569
Degrees of freedom = 40
Probability level = .000
Group number 1 (Group number 1 - Default model) Estimates
(Group number 1 - Default model)
Scalar Estimates (Group number 1 - Default model)
Maximum Likelihood Estimates
Regression Weights: (Group number 1 - Default model)
Estimate
S.E.
C.R.
P Label
AcadMotiv <--
-
SE
.173
.037
4.646
***
AcadMotiv <--
-
SR
.129
.039
3.298
***
SREF
<--
-
SR
1.000
SRTE
<--
-
SR
.833
.064
13.062
***
SMR
<--
-
SR
.679
.059
11.590
***
SELP
<--
-
SE
1.000
SEC
<--
-
SE
.337
.048
7.009
***
ExME
<--
-
AcadMotiv
1.000
ExMD
<--
-
AcadMotiv
1.665
.211
7.894
***
ExMN
<--
-
AcadMotiv
1.829
.252
7.261
***
InMK
<--
-
AcadMotiv
2.578
.383
6.727
***
InMS
<--
-
AcadMotiv
1.826
.334
5.472
***
InMC
<--
-
AcadMotiv
2.404
.364
6.613
***
AMOT
<--
-
AcadMotiv
-1.501
.273
-5.502
***
Standardized Regression Weights: (Group number 1 - Default model)
Estimate
AcadMotiv <--
-
SE
.425
AcadMotiv <--
-
SR
.280
SREF
<--
-
SR
.823
SRTE
<--
-
SR
.781
SMR
<--
-
SR
.717
SELP
<--
-
SE
1.000
SEC
<--
-
SE
.350
ExME
<--
-
AcadMotiv
.370
ExMD
<--
-
AcadMotiv
.653
ExMN
<--
-
AcadMotiv
.599
InMK
<--
-
AcadMotiv
.906
InMS
<--
-
AcadMotiv
.584
InMC
<--
-
AcadMotiv
.794
AMOT
<--
-
AcadMotiv
-.393
Covariances: (Group number 1 - Default model)
Estimate
S.E.
C.R.
P
Label
SR <--> SE
.700
.074
9.461
***
e6 <-->
e7
.441
.058
7.583
***
e10 <-->
e12
.703
.097
7.236
***
e6 <-->
e8
.416
.063
6.597
***
e7 <-->
e12
-.480
.078
-6.195
***
e10 <-->
e11
.230
.056
4.139
***
e8
<-->
e11
.298
.051
5.886
***
e7
<-->
e8
.199
.046
4.312
***
e6
<-->
e10
-.320
.062
-5.164
***
e1
<-->
e3
-.141
.040
-3.554
***
e7
<-->
e10
-.242
.051
-4.724
***
e3
<-->
e5
.126
.037
3.371
***
e6
<-->
e12
-.250
.087
-2.882
.004
Correlations: (Group number 1 - Default model)
Estimate
SR <-->
SE
.692
e6 <-->
e7
.476
e10 <-->
e12
.413
e6 <-->
e8
.355
e7 <-->
e12
-.370
e10 <-->
e11
.258
e8
<-->
e11
.347
e7
<-->
e8
.221
e6
<-->
e10
-.263
e1
<-->
e3
-.345
e7
<-->
e10
-.258
e3
<-->
e5
.209
e6
<-->
e12
-.148
Variances: (Group number 1 - Default model)
Estimate
S.E.
C.R.
P
Label
SR
.895
.110
8.126
***
SE
1.145
.087
13.19
1
***
e13
.110
.033
3.325
***
e4
.000
e1
.426
.065
6.573
***
e2
.397
.044
8.961
***
e3
.390
.044
8.957
***
e5
.931
.071
13.19
1
***
e6
1.205
.093
12.98
9
***
e7
.712
.060
11.92
7
***
e8
1.140
.092
12.45
5
***
e9
.276
.052
5.261
***
e10
1.230
.101
12.11
7
***
e11
.645
.065
9.983
***
e12
2.358
.183
12.85
2
***
Squared Multiple Correlations: (Group number 1 - Default model)
Estimate
AcadMotiv
.424
AMOT
.154
InMC
.631
InMS
.341
InMK
.821
ExMN
.359
ExMD
.426
ExME
.137
SEC
.123
SELP
1.000
SMR
.514
SRTE
.610
SREF
.678
Matrices (Group number 1 - Default model)
Implied Covariances (Group number 1 - Default model)
AM In
OT MC
In
MS
InM
K
ExM
N
ExM
D
Ex
ME
SE
C
SE
LP
SM
R
SR
TE
SR
EF
AM
OT
2.78
7
InM
C
-.688
1.7
47
InM
S
.181
1.0
67
1.8
65
InM
K
-.738
1.1
82
.89
7
1.54
2
ExM
N
-.523
1.1
36
.63
6
.899
1.77
8
ExM
D
-.957
.76
3
.33
8
.818
.780
1.24
1
ExM
E
-.536
.45
8
.02
9
.491
.765
.758
1.39
5
SEC
-.146
.23
4
.17
8
.251
.178
.162
.098
1.0
61
SEL
P
-.434
.69
5
.52
8
.745
.529
.481
.289
.38
6
1.1
45
SMR
-.242
.38
7
.29
4
.415
.294
.268
.161
.28
6
.47
5
.80
2
SRT
E
-.297
.47
5
.36
1
.509
.361
.329
.198
.19
7
.58
4
.50
6
1.0
19
SRE
F
-.356
.57
0
.43
3
.611
.434
.395
.237
.23
6
.70
0
.46
7
.74
6
1.3
21
I Correlations (Group number 1 - Default model)
AM In
OT MC
In
MS
InM
K
ExM
N
ExM
D
Ex
ME
SE
C
SE
LP
SM
R
SR
TE
SR
EF
AM
OT
1.00
0
InM
C
-.312
1.0
00
InM
S
.079
.59
1
1.0
00
InM
K
-.356
.72
0
.52
9
1.00
0
ExM
N
-.235
.64
5
.35
0
.543
1.00
0
ExM
D
-.514
.51
8
.22
2
.591
.525
1.00
0
ExM
E
-.272
.29
4
.01
8
.335
.485
.576
1.00
0
SEC
-.085
.17
2
.12
7
.196
.130
.141
.080
1.0
00
SEL
P
-.243
.49
1
.36
1
.561
.370
.404
.229
.35
0
1.0
00
SMR
-.162
.32
7
.24
0
.373
.246
.269
.152
.31
0
.49
6
1.0
00
SRT
E
-.176
.35
6
.26
2
.406
.268
.293
.166
.18
9
.54
0
.56
0
1.0
00
SRE
F
-.185
.37
5
.27
6
.428
.283
.308
.175
.20
0
.56
9
.45
4
.64
3
1.0
00
M Indices (Group number 1 - Default )
Covariances: (Group number 1 - Default model)
M.I.
Par Change
e12 <-->
SE
8.394
.168
e12 <-->
SR
28.917
-.297
e12 <-->
e13
4.314
.054
e11 <-->
e12
6.559
.134
e5
<-->
SE
9.991
.131
e5
<-->
SR
23.987
-.193
e5
<-->
e12
36.449
.407
e5
<-->
e11
14.696
.146
e5
<-->
e6
4.288
.095
e4
<-->
e5
8.223
.104
e3
<-->
e13
9.807
.043
e3
<-->
e12
4.858
.109
e3
<-->
e11
8.418
.081
e3
<-->
e10
7.237
.098
e2
<-->
e12
5.765
-.119
e2
<-->
e11
7.392
-.076
e2
<-->
e7
6.624
.068
e2
<-->
e3
4.158
-.050
e1
<-->
e13
4.678
-.035
e1
<-->
e12
32.330
-.331
e1
<-->
e7
9.640
-.097
e1
<-->
e5
16.233
-.168
Variances: (Group number 1 - Default model)
M.I.
Par
Change
Regression Weights: (Group number 1 - Default model)
M.I.
Par Change
AMOT <-- SR 17.515 -.335
-
AMOT <-- SEC 34.545 .405
-
AMOT <-- SRTE 19.576 -.311
-
AMOT <-- SREF 38.313 -.383
-
InMC <-- AMOT 7.890 .068
-
InMC <-- SEC 17.713 .164
-
InMC <-- SMR 4.345 .093
-
InMC <-- SRTE 4.240 -.082
-
InMK <-- SEC 4.219 -.079
-
ExME <-- SEC 4.004 .094
-
SEC <-- SR 11.005 -.190
-
SEC <-- AMOT 31.003 .170
-
SEC <-- SRTE 9.336 -.154
-
SEC <-- SREF 19.245 -.194
-
SELP <-- ExMD 5.710 .085
-
SELP <-- ExME 5.928 .081
-
SELP <-- SEC 8.140 .109
-
SMR <-- AcadMotiv 7.474 .248
-
SMR <-- AMOT 9.514 .069
-
SMR
<--
-
InMC
17.262
.118
SMR
<--
-
InMS
37.330
.167
SMR
<--
-
InMK
6.507
.077
SMR
<--
-
ExME
7.428
-.086
SRTE
<--
-
AMOT
16.798
-.092
SRTE
<--
-
InMC
5.052
-.064
SRTE
<--
-
InMS
11.433
-.093
SRTE
<--
-
ExMD
13.482
.123
SRTE
<--
-
ExME
11.242
.106
SREF
<--
-
AMOT
15.142
-.102
SREF
<--
-
InMS
6.120
-.080
SREF
<--
-
InMK
4.273
-.073
SREF
<--
-
ExMD
5.212
-.090
SREF
<--
-
SEC
17.314
-.178
Minimization History (Default model)
Iteratio n
Negative
eigenvalu
es
Conditio
n #
Smallest
eigenvalu
e
Diamete
r
F
NTrie
s
Ratio
0
e
11
-1.356
9999.00
0
2159.78
6
0
9999.00
0
1
e
9
-.235
1.398
1254.72
4
20
.576
2
e
4
-.144
.466
983.459
6
.822
3
e
2
-.046
.676
670.963
5
.861
4
e
*
0
137.905
.939
437.045
5
.679
5
e
0
148.976
.924
385.952
1
.483
6
e
0
172.539
.520
293.088
1
1.165
7
e
0
450.906
.432
277.893
1
1.213
8
e
0
1289.44
0
.501
273.535
1
1.146
9
e
0
3201.17
7
.305
271.933
1
1.194
10
e
0
5302.71
1
.256
271.606
1
1.114
11
e
0
7150.58
8
.076
271.570
1
1.068
12
e
0
7356.15
3
.015
271.569
1
1.012
13
e
0
7273.42
9
.000
271.569
1
1.000
Model Fit Summary
CMIN
Model
NPA
R
CMIN
DF
P
CMIN/DF
Default model
38
271.569
40
.000
6.789
Saturated model
78
.000
0
Independence model
12
2157.802
66
.000
32.694
RMR, GFI
Model
RMR
GFI
AGF
I
PGFI
Default model
.138
.875
.757
.449
Saturated model
.000
1.000
Independence model
.522
.375
.261
.317
Baseline Comparisons
Model
NFI
Delta1
RFI
rho1
IFI
Delta2
TLI
rho2
CFI
Default model
.874
.792
.891
.817
.889
Saturated model
1.000
1.000
1.000
Independence model
.000
.000
.000
.000
.000
Parsimony-Adjusted Measures
Model
PRATI
O
PNFI
PCFI
Default model
.606
.530
.539
Saturated model
.000
.000
.000
Independence model
1.000
.000
.000
NCP
Model
NCP
LO 90
HI 90
Default model
231.569
183.086
287.546
Saturated model
.000
.000
.000
Independence model
2091.802
1943.787
2247.170
FMIN
Model
FMIN
F0
LO 90
HI 90
Default model
.780
.665
.526
.826
Saturated model
.000
.000
.000
.000
Independence model
6.201
6.011
5.586
6.457
RMSEA
Model
RMSEA
LO 90
HI 90
PCLOSE
Default model
.129
.115
.144
.000
Independence model
.302
.291
.313
.000
AIC
Model
AIC
BCC
BIC
CAIC
Default model
347.569
350.518
494.062
532.062
Saturated model
156.000
162.054
456.696
534.696
Independence model
2181.802
2182.734
2228.063
2240.063
ECVI
Model
ECVI
LO 90
HI 90
MECVI
Default model
.999
.859
1.160
1.007
Saturated model
.448
.448
.448
.466
Independence model
6.270
5.844
6.716
6.272
HOELTER
Model
HOELTER
.05
HOELTE
R
.01
Default model
72
82
Independence model
14
16
Execution time summary
Minimization: .003
Miscellaneous: .329
Bootstrap: .000
Total: .332
Execution time summary
Adjusted Model
Parameter Summary (Group number 1)
Weights
Covariances
Variance
s
Means
Intercepts
Tota
l
Fixed
16
0
1
0
0
17
Labeled
0
0
0
0
0
0
Unlabeled
13
14
14
0
0
41
Total
29
14
15
0
0
58
Sample Moments (Group number 1)
Sample Covariances (Group number 1)
AM
OT
In
MC
In
MS
InM
K
ExM
N
ExM
D
Ex
ME
SE
C
SE
LP
SM
R
SR
TE
SR
EF
AM
OT
2.78
7
InM
C
-.494
1.7
44
InM
S
.258
1.1
29
1.9
18
InM
K
-.734
1.2
02
.91
8
1.54
2
ExM
N
-.509
1.1
14
.61
7
.900
1.76
2
ExM
D
-.972
.71
6
.31
1
.803
.776
1.24
8
ExM
E
-.567
.38
5
-
.01
6
.488
.744
.768
1.40
6
SEC
.330
.36
3
.26
9
.174
.214
.153
.199
1.0
68
SEL
P
-.618
.69
3
.38
8
.721
.597
.616
.437
.39
7
1.1
45
SMR
-.198
.56
7
.51
3
.507
.335
.295
.126
.23
1
.51
8
.78
4
SRT
E
-.795
.36
6
.13
4
.488
.342
.516
.355
.02
4
.58
7
.43
9
1.0
19
SRE
F
-.940
.42
7
.14
6
.487
.401
.414
.252
-
.04
2
.66
1
.43
1
.78
6
1.3
21
C number = 26.705
Eigenvalues
7.311 3.359 1.697 1.407 .899 .760 .518 .427 .399 .366 .329 .274
Determinant of sample covariance matrix = .116
Sample Correlations (Group number 1)
AM
OT
In
MC
In
MS
InM
K
ExM
N
ExM
D
Ex
ME
SE
C
SE
LP
SM
R
SR
TE
SR
EF
AM
OT
1.00
0
InM
C
-.224
1.0
00
InM
S
.111
.61
7
1.0
00
InM
K
-.354
.73
3
.53
4
1.00
0
ExM
N
-.230
.63
5
.33
5
.546
1.00
0
ExM
D
-.521
.48
6
.20
1
.579
.523
1.00
0
ExM
E
-.287
.24
6
-
.01
0
.332
.473
.580
1.00
0
SEC
.191
.26
6
.18
8
.135
.156
.133
.162
1.0
00
SEL
P
-.346
.49
0
.26
2
.542
.420
.515
.344
.35
9
1.0
00
SMR
-.134
.48
5
.41
8
.462
.285
.298
.120
.25
3
.54
7
1.0
00
SRT
E
-.472
.27
5
.09
6
.389
.256
.458
.296
.02
3
.54
3
.49
1
1.0
00
SRE
F
-.490
.28
1
.09
2
.341
.263
.323
.185
-
.03
6
.53
7
.42
4
.67
8
1.0
00
C number = 24.887
Eigenvalues
4.973 1.821 1.292 1.071 .571 .507 .393 .341 .309 .288 .235 .200
Models
Default model (Default model)
Notes for Model (Default model)
Computation of degrees of freedom (Default model)
Number of distinct sample moments: 78
Number of distinct parameters to be estimated: 41
Degrees of freedom (78 - 41): 37
Result (Default model)
Minimum was achieved
Chi-square = 187.547
Degrees of freedom = 37
Probability level = .000
Group number 1 (Group number 1 - Default model)
Estimates (Group number 1 - Default model)
Scalar Estimates (Group number 1 - Default model)
Maximum Likelihood Estimates
Regression Weights: (Group number 1 - Default model)
Estimate
S.E.
C.R.
P Label
AcadMotiv <--
-
SE
.184
.038
4.827
***
AcadMotiv <--
-
SR
.106
.036
2.950
.003
SREF
<--
-
SR
1.000
SRTE
<--
-
SR
.811
.057
14.151
***
SMR
<--
-
SR
.608
.055
11.012
***
SELP
<--
-
SE
1.000
SEC
<--
-
SE
.553
.068
8.129
***
ExME
<--
-
AcadMotiv
1.000
ExMD
<--
-
AcadMotiv
1.665
.211
7.877
***
ExMN
<--
-
AcadMotiv
1.835
.253
7.252
***
InMK
<--
-
AcadMotiv
2.594
.387
6.700
***
InMS
<--
-
AcadMotiv
1.821
.334
5.447
***
InMC
<--
-
AcadMotiv
2.420
.366
6.603
***
AMOT
<--
-
AcadMotiv
-.760
.237
-3.206
.001
AMOT
<--
-
SR
-.547
.098
-5.576
***
SEC
<--
-
SR
-.387
.085
-4.546
***
Standardized Regression Weights: (Group number 1 - Default model)
Estimate
AcadMotiv <--
-
SE
.452
AcadMotiv <--
-
SR
.236
SREF
<--
-
SR
.847
SRTE
<--
-
SR
.783
SMR
<--
-
SR
.670
SELP
<--
-
SE
1.000
SEC
<--
-
SE
.590
ExME
<--
-
AcadMotiv
.368
ExMD
<--
-
AcadMotiv
.651
ExMN
<--
-
AcadMotiv
.598
InMK
<--
-
AcadMotiv
.909
InMS
<--
-
AcadMotiv
.578
InMC
<--
-
AcadMotiv
.797
AMOT
<--
-
AcadMotiv
-.202
AMOT
<--
-
SR
-.325
SEC
<--
-
SR
-.376
Covariances: (Group number 1 - Default model)
Estimate
S.E.
C.R.
P
Label
SR
<-->
SE
.714
.075
9.577
***
e6
<-->
e7
.444
.058
7.617
***
e10 <-->
e12
.609
.088
6.920
***
e6 <-->
e8
.420
.063
6.655
***
e7 <-->
e12
-.459
.071
-6.501
***
e10 <-->
e11
.253
.056
4.538
***
e8
<-->
e11
.296
.050
5.876
***
e7
<-->
e8
.203
.047
4.347
***
e6
<-->
e10
-.319
.062
-5.165
***
e1
<-->
e3
-.144
.038
-3.799
***
e7
<-->
e10
-.238
.051
-4.649
***
e6
<-->
e12
-.272
.079
-3.431
***
e5
<-->
e12
.280
.066
4.260
***
e3
<-->
e5
.126
.038
3.353
***
Correlations: (Group number 1 - Default model)
Estimate
SR <-->
SE
.685
e6 <-->
e7
.477
e10 <-->
e12
.376
e6 <-->
e8
.357
e7 <-->
e12
-.375
e10 <-->
e11
.283
e8
<-->
e11
.347
e7
<-->
e8
.224
e6
<-->
e10
-.260
e1
<-->
e3
-.358
e7
<-->
e10
-.251
e6
<-->
e12
-.171
e5
<-->
e12
.214
e3
<-->
e5
.212
Variances: (Group number 1 - Default model)
Estimate
S.E.
C.R.
P Label
SR
.948
.109
8.688
***
SE
1.145
.087
13.19
1
***
e13
.112
.034
3.319
***
e4
.000
e1
.373
.059
6.315
***
e2
.395
.042
9.397
***
e3
.431
.043
9.905
***
e5
.819
.065
12.66
7
***
e6
1.207
.093
13.00
9
***
e7
.716
.060
11.95
1
***
e8
1.144
.092
12.43
6
***
e9
.267
.054
4.967
***
e10
1.251
.102
12.20
5
***
e11
.636
.065
9.860
***
e12
2.093
.160
13.05
1
***
Squared Multiple Correlations: (Group number 1 - Default model)
Estimate
AcadMotiv
.407
AMOT
.218
InMC
.636
InMS
.334
InMK
.827
ExMN
.358
ExMD
.423
ExME
.136
SEC
.185
SELP
1.000
SMR
.449
SRTE
.613
SREF
.718
Matrices (Group number 1 - Default model)
Implied Covariances (Group number 1 - Default model)
AM
OT
In
MC
In
MS
InM
K
ExM
N
ExM
D
Ex
ME
SE
C
SE
LP
SM
R
SR
TE
SR
EF
AM
OT
2.67
8
InM
C
-.654
1.7
45
InM
S
.117
1.0
87
1.8
79
InM
K
-.702
1.1
89
.89
5
1.54
2
ExM
N
-.496
1.1
37
.63
3
.902
1.78
2
ExM
D
-.910
.76
3
.33
7
.819
.782
1.24
2
ExM
E
-.543
.45
8
.02
6
.492
.768
.759
1.39
7
SEC
.213
.16
6
.12
5
.178
.126
.114
.069
1.0
06
SEL
P
-.608
.69
2
.52
1
.742
.525
.476
.286
.35
7
1.1
45
SMR
-.422
.34
1
.25
6
.365
.258
.235
.141
.14
3
.43 .78 5
2
SRT
E
-.563
.45
5
.34
2
.487
.345
.313
.188
.02
3
.58 .46 0
8
1.0
19
SRE
F
-.694
.56
0
.42
2
.601
.425
.385
.231
.02
8
.71 .43 4
3
.76
9
1.3
21
I Correlations (Gr number 1 - D )
AM
OT
In
MC
In
MS
InM
K
ExM
N
ExM
D
Ex
ME
SE
C
SE
LP
SM
R
SR
TE
SR
EF
AM
OT
1.00
0
InM
C
-.303
1.0
00
InM
S
.052
.60
0
1.0
00
InM
K
-.345
.72
5
.52
6
1.00
0
ExM
N
-.227
.64
5
.34
6
.544
1.00
0
ExM
D
-.499
.51
9
.22
1
.591
.525
1.00
0
ExM
E
-.281
.29
4
.01
6
.335
.486
.577
1.00
0
SEC
.130
.12
5
.09
1
.143
.094
.102
.058
1.0
00
SEL
P
-.347
.48
9
.35
5
.558
.367
.399
.226
.33
2
1.0
00
SMR
-.292
.29
2
.21
2
.333
.219
.238
.135
.16
1
.45
9
1.0
00
SRT
E
-.341
.34
1
.24
7
.389
.256
.278
.157
.02
2
.53
6
.52
4
1.0
00
SRE
F
-.369
.36
9
.26
8
.421
.277
.301
.170
.02
4
.58
1
.42
6
.66
3
1.0
00
M Indices (Group number 1 - D model)
Covariances: (Group number 1 - Default model)
M.I.
Par Change
e12 <-->
SE
4.519
.114
e5
<-->
e11
5.639
.083
e5
<-->
e9
4.373
-.073
e5
<-->
e6
4.886
.094
e4
<-->
e12
4.464
.095
e4
<-->
e10
4.031
-.073
e4
<-->
e7
5.433
.062
e3
<-->
SE
8.760
.091
e3
<-->
SR
4.388
-.061
e3
<-->
e13
10.742
.047
e3
<-->
e12
17.105
.193
e3
<-->
e11
11.847
.098
e2
<-->
e11
4.561
-.059
e2
<-->
e7
8.169
.076
e1
<-->
e13
5.292
-.036
e1
<-->
e12
7.227
-.138
e1
<-->
e7
5.055
-.068
Variances: (Group number 1 - Default model)
M.I.
Par
Change
