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Cite this: Chem. Educ. Res. Pract.,
2015, 16,237
Assessing motivation to learn chemistry: adaptation
and validation of Science Motivation Questionnaire
II with Greek secondary school students
Katerina Salta
ab
and Dionysios Koulougliotis*
c
In educational research, the availability of a validated version of an original instrument in a different
language offers the possibility for valid measurements obtained within the specific educational context
and in addition it provides the opportunity for valid cross-cultural comparisons. The present study aimed
to adapt the Science Motivation Questionnaire II (SMQ II) for application to a different cultural context
(Greece), a different age group (secondary school students) and with a focus on chemistry learning.
Subsequently, the Greek version of Chemistry Motivation Questionnaire II (Greek CMQ II) was used in
order to investigate Greek secondary school students’ motivation to learn chemistry for the first time.
The sample consisted of 330 secondary school students (163 boys–167 girls), of which 146 were in
lower secondary school (14–15 years old) and 184 were in upper secondary school (16–17 years old).
Confirmatory factor analyses provided evidence for the validity of Greek CMQ II, as well as for configural,
metric and scalar invariance, thus allowing meaningful comparisons between groups. The five motivation
components of the original instrument namely grade motivation, career motivation, intrinsic motivation,
self-efficacy, and self-determination were confirmed. Gender-based comparisons showed that girls had
higher self-determination relative to the boys irrespective of the age group. In addition, girls in lower
secondary school had higher career and intrinsic motivation relative to the boys of the same age group.
Age-based comparisons showed that lower secondary school students had higher grade motivation
relative to upper secondary school students.
Introduction
The study of the role of motivation in learning science is
constantly gaining the attention of science education researchers
(Koballa and Glynn, 2007). In recent science education literature,
motivation is considered as ‘‘a complex multidimensional construct
that interacts with cognition to influence learning’’ (Taasoobshirazi
and Sinatra, 2011, p. 904). Both cognitive and motivational learner
characteristics interact within a specific learning environment in
order to support or hinder conceptual change (Dole and Sinatra,
1998). Pintrich and his colleagues (1993) have adopted four general
motivational constructs (namely goals, values, self-efficacy, and
control beliefs) as potential mediators of the learner’s conceptual
change and suggested that they may influence science learning
both in the short term and over longer periods of time as well.
In addition, a series of contextual factors, including the class-
room environment, the teacher, the nature of academic tasks,
and the assessment processes have been proposed to moderate
the interactions between motivational constructs and science
learning (Pintrich et al., 1993).
The complex multidimensional nature of motivation has
been brought out via the use of social cognitive theory which
has been systematically employed over the last fifteen years in
order to explain human learning and motivation in terms of
reciprocal interactions involving personal characteristics (e.g.,
intrinsic motivation, self-efficacy, and self-determination),
environmental contexts (e.g., high school), and behaviour
(e.g., enrolling in advanced science or chemistry courses)
(Bandura, 2001; Pintrich, 2003).
Concentrating on the role of motivation in learning science
and more specifically chemistry as a distinct science subject, a
recent study by Zusho et al. (2003) provided evidence that
students’ beliefs in relation to self-efficacy and task-value are
significant predictors of their performance in chemistry. The
same researchers also reported that students’ judgments of their
confidence to do well in the chemistry class decreased over time,
and as a result students’ beliefs about the importance and/or
a
National and Kapodistrian University of Athens, Department of Chemistry,
Panepistimiopolis, 15701 Athens, Greece
b
2nd Model Experimental Upper Secondary School of Athens, Panagi Kyriakou 12,
11521, Athens, Greece
c
Technological Educational Institute (T.E.I.) of Ionian Islands, Department of
Environmental Technology, Neo Ktirio – Panagoula, 29100, Zakynthos, Greece.
E-mail: dkoul@teiion.gr; Fax: +30-26950-24949; Tel: +30-26950-24940
Received 20th September 2014,
Accepted 23rd December 2014
DOI: 10.1039/c4rp00196f
www.rsc.org/cerp
Chemistry Education
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usefulness of the chemistry course tended to deteriorate as well.
Moreover, evidence has been provided that poor motivation to
learn chemistry often leads students to turn away from advanced
chemistry studies and chemistry related careers (Salta et al., 2012).
The constantly increasing interest for the study of students’
motivation to learn science has triggered the development of
several instruments for measuring students’ motivation based
on different theoretical perspectives. From the perspective of
social cognitive theory, Glynn and his colleagues (2011) have
developed and evaluated the Science Motivation Questionnaire II
(SMQ II). This instrument has been found to be a reliable and valid
tool which leads to the assessment of the following five components
of students’ motivation to learn science in college courses in USA
(Glynn et al., 2011): intrinsic motivation, self-determination, self-
efficacy, career motivation, and grade motivation.
The main purpose of the present study has been the
adaptation and validation of the chemistry specific version of
SMQ II (i.e. the Chemistry Motivation Questionnaire II or briefly
CMQ II) with Greek students (thus producing the Greek CMQ II).
The rationale behind this work lies in the need for the availability of
valid measurements which will subsequently allow for cross-
cultural comparisons in order to explore the proposed influence
of the cultural context on the motivational constructs (Pintrich,
2003). Besides the different cultural context (Greece relative to USA),
two additional factors are changed in the current study relative to
the original work of Glynn et al. (2011): (a) students belong to a
younger age group and different educational level (secondary
school relative to college) and (b) the focus is on measuring
motivation to learn a specific science subject which is chemistry.
Thus, the specific research questions that have guided this
study can be summarized as follows:
(1) How valid is the Greek version of the ‘‘Chemistry Motivation
Questionnaire II’’ (Greek CMQ II) for measuring secondary school
students’ motivation to learn chemistry?
Subsequently, if Greek CMQ II proves to be valid,
(2) Which are the motivational characteristics of Greek
secondary school students to learn chemistry?
(3) What are the differences in motivation to learn chemistry
between Greek lower and upper secondary school students?
(4) What are the differences in motivation to learn chemistry
between Greek male and female secondary students?
Theoretical framework
The term ‘‘motivation’’ has been defined ‘‘as the attribute that
‘moves’ us to do or not do something’’ (Gredler, 2001 as cited in
Broussard and Garrison, 2004, p. 106). In education, motivation to
learn is broadly considered as ‘‘the internal state that arouses,
directs, and sustains students’ behaviour’’ (Koballa and Glynn,
2007, p. 85). According to Brophy (1983) ‘‘motivation to learn refers
to the enduring disposition of students to enjoy the process of
learning and take pride in the outcomes of experience involving
knowledge acquisition or skill development’’ (p. 200). Hence, mod-
ern theories of motivation focus mainly ‘‘on the relation of beliefs,
values, and goals with action’’ (Eccles and Wigfield, 2002, p. 110).
Research on motivation associated with academic achievement
and/or development has been conducted via different theoretical
perspectives and has led to the development of an extensive
motivation terminology (Murphy and Alexander, 2000). In fact,
theorists have indicated the need for theoretical integration in
the domain (Eccles and Wigfield, 2002). The main theoretical
perspectives that have been employed in the study and conceptua-
lization of motivation can be briefly summarized as follows.
Achievement goal theory or simply goal theory (Ames, 1992;
Blumenfeld, 1992) relates students’ goals to their achievement
behaviour. The main idea in this perspective is the ‘‘goal
orientations’’ that refer to students’ beliefs about the purpose
of engaging in achievement-related behaviour.
Self-determination theory (Deci and Ryan, 2000) postulated
that an understanding of human motivation requires a consid-
eration of innate psychological needs for competence, autonomy
and relatedness. As reviewed in Deci and Ryan (2000), a social
context which supports these three needs tends to enhance
intrinsic motivation and facilitate the internalization of extrinsic
motivation. Subsequently, enhanced motivation seems to be
associated with positive affective experiences, and high-quality
performance.
The theoretical concept of interest has attracted researchers’
attention over the past few years (Schiefele, 1999; Hidi and
Harackiewicz, 2001), and has been proposed to be connected
with intrinsic motivation to learn by educational psychologists
(Koballa and Glynn, 2007). A distinction between individual
and situational interest is typically made. Particularly, individual
interest is considered as a relatively stable orientation towards
certain domains in contrast to situational interest that is an
emotional state aroused by specific features of a task (Hidi
and Renninger, 2006). Much research on the relation between
individual interest and learning has been conducted (Schiefele,
1999; Renninger et al., 2002). An important finding was the
stronger relationship between interest and deep-learning than
with rote learning (Schiefele, 1999).
Finally, the theoretical perspective of the social cognitive
theory, originally developed by Bandura (1986) and extended by
the same and other researchers (Bandura, 2001; Pajares and
Schunk, 2001; Pintrich, 2003), conceptualizes the process of
learning via a series of reciprocal interactions among personal,
behavioural and social/environmental factors. Within the fra-
mework of social cognitive theory, Pintrich and his colleagues
(1993) proposed a conceptual change model for describing
students’ learning and postulated that the cognitive and moti-
vational constructs influence each other and at the same time
they are influenced by the social context. In turn, both cognitive
and motivational constructs are assumed to influence students’
involvement with their learning and, consequently, achieve-
ment outcomes (Pintrich, 2000).
As also noted in the Introduction, the social cognitive theory
conceptualizes motivation to learn as a multidimensional
construct which is therefore comprised of several components
(also referred in Glynn et al. (2011) as ‘‘types and attributes of
motivation’’). Many motivational components linked to learning
science have been studied extensively and reviewed in Glynn and
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Koballa (2006), Koballa and Glynn et al. (2007), Pintrich (2003),
and Schunk et al. (2008). Examples of these components are
intrinsic motivation, which involves the inherent satisfaction in
learning science for its own sake (e.g.,Eccleset al.,2006);
extrinsic motivation, which involves learning science as a means
to a tangible end, such as a career or a grade (e.g.,Mazloet al.,
2002); self-determination, which refers to the control students
believe they have over their learning of science (e.g., Black and
Deci, 2000); and self-efficacy, which refers to students’ belief that
they can achieve well in science (e.g.,Lawsonet al.,2007).The
above examples of motivation components are also included in
the list of 20 motivation terms proposed to be relevant to
academic achievement and motivation (Murphy and Alexander,
2000). This ‘‘corpus’’ of terms contains motivation components
associated with all four theoretical perspectives of motivation we
referred to above.
In the following part of this theoretical section a brief
overview of the literature on motivation to learn science with
a focus on chemistry will be made. With regard to students’
motivational characteristics toward a specific science domain,
there is evidence that even in pre-primary school, children
express some differences in their motivation toward different
science disciplines (Mantzicopoulos et al., 2008). Focusing on
chemistry learning, there exist some studies which have
attempted to examine how it may be influenced by students’
motivation (Black and Deci, 2000; Mazlo et al., 2002; Zusho
et al., 2003; Feng and Tuan, 2005; Juris
ˇevic
ˇet al., 2008; Aydin
and Uzuntiryaki, 2009; Taasoobshirazi and Glynn, 2009;
Scherer, 2013). Black and Deci (2000) applied self-determination
theory to investigate the effects of students’ course-specific self-
regulation and their perceptions of their instructors’ autonomy
support on adjustment and academic performance in a college-
level organic chemistry course. Their results have shown that the
students’ perceived instructor autonomy support is related to
positive adjustment and to increased students’ autonomy as the
semester progresses, which is in turn related to better performance.
Zusho and his colleagues (2003) evidenced on one hand an overall
decline in college students’ motivational levels over time and on the
other an increase in students’ use of organizational and self-
regulatory strategies. A diversity of trends was found which were
shown to be dependent on students’ achievement levels. Finally,
the relation of motivation to achievement was evidenced via the
identification of two motivational components, namely self-efficacy
and task value, as ‘‘best predictors of final course performance
even after controlling for prior achievement’’ (Zusho et al.,
2003, p. 1081).
Research conducted by Juris
ˇevic
ˇand her colleagues (2008)
showed that Slovene first year pre-service primary school student
teachers (18.5 years old) are more or less equally motivated for
chemistry as for any other subject, but their intrinsic motivation
decreases as the level of abstraction increases. It has been similarly
established that among the three levels of chemistry learning—
namely, macroscopic, submicroscopic, and symbolic—students
were the least motivated to study concepts at the symbolic level
(Juris
ˇevic
ˇet al., 2008). Taasoobshirazi and Glynn (2009) found that
college students’ self-efficacy influenced strategy use and chemistry
problem solving. Students with relatively high chemistry
self-efficacy tended to use a working-forward strategy and
solve problems successfully while students with relatively low
chemistry self-efficacy tended to use a working-backward strategy
and solve the problems incorrectly. Their findings are consistent
with other studies conducted on students’ chemistry self-efficacy,
such as those of Zusho et al. (2003) and Schraw et al. (2005).
Moreover, Scherer (2013) has provided evidence for the empirical
distinction between the motivation components of self-concept and
self-efficacy within the domain of chemistry. By using data collected
by 459 German high-school students (mean age 16.6 years), he
found that students’ perceptions of themselves within the
academic environment (academic self-concept) are not the
same with their perceptions about their ability to master given
tasks or develop specific competences (academic self-efficacy)
within the domain of chemistry. However, his results indicated
that the two constructs affect each other.
Cultural and contextual effects in motivation to learn science
and chemistry
One of the aims of social cognitive theory is exploring the
influence that different contextual and cultural practices may
have on students’ motivation. According to Brophy (2004), the
psychology of motivation that has been developed by studies in
western countries is reflective of a common human condition
and thus equally applicable everywhere. However, comparative
research has identified interesting contrasts between nations
and world regions. For example, Hufton et al. (2002) found that
Russian adolescents showed patterns of school-related motivation
that contrasted in several respects with those displayed by
American and British adolescents. Particularly, Russian students
tend to maintain task engagement even though their teachers
are short on praise and overly persistent on correction. It is
suggested that this pattern reflects a pervasive Russian cultural
value on becoming an educated person.
Perhaps the most significant cultural differences have been
observed between people from western nations (e.g., the United
States and Western Europe) and people from East Asia (e.g.,
China, Korea, and Japan). Studies have shown that in the
former countries people display a more individualistic attitude
and their self-concepts emphasize uniqueness and independence
from others. On the other hand, people from East Asia display a
more collectivistic attitude with self-concepts which are inter-
dependent (Boekaerts, 1998; Fiske et al., 1998). These general
differences lead to differences on more specific motivational
aspects. In attributing behaviour to causes, for example, East
Asians tend to make fewer references to personal dispositions
but more references to situational factors than westerners do
(Choi et al., 1999; Krull et al.,1999).Otherstudieshaveindicated
that East Asians also tend to be more comfortable setting up
their personal agendas by taking into account those of their
families or groups. For example, East Asian (but not American)
students display increased benefit perception of goal achieve-
ment when they pursue goals in order to please others (e.g.
parents or friends) (Oishi and Diener, 2001). Similarly, East
Asian students seem to show higher levels of intrinsic motivation
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relative to American students ‘‘when choices are made for them
by trusted significant others’’ rather ‘‘than when they make the
choices themselves’’ (Iyengar and Lepper, 1999).
Moreover, also within a specific cultural context, differences
have been observed in students’ motivational patterns depending
on age, gender and family environment. Different grade levels
present somewhat different motivational challenges because of
age-related changes in students’ motivational patterns. Thus, there
is evidence that the enthusiastic start of schooling by most students
is followed by gradual decrease on measures of intrinsic motiva-
tion, curiosity and school-related attitudes (Gottfried et al., 2001;
Gentry et al., 2002; Wigfield and Eccles, 2002). Potvin et al. (2009)
suggested that the first 2 years of high school may be a critical
period when students turn toward or away from a science career
path; whereas Bryan et al. (2011) argued that for those students who
turn towards a science career path the last 2 years of high school
may be a critical period for motivation to learn particular areas/
disciplines of science.
As students develop in our society, they are exposed to
gender role socialization. Thus, certain family and social roles,
occupations, personal attributes, and ways of dressing and
behaving are considered primarily feminine, while others pri-
marily masculine. Most students have certain individuals they
encounter in their personal lives and in the media as role
models. The behaviour expressed by the students reflects the
messages they receive from their parents and peers (and even
sometimes their teachers) (Li, 1999; Tenenbaum and Leaper,
2003). To the extent at which a specific school activity is
associated primarily with one gender, students’ attitudes and
expectations are likely to be affected.
Focusing on the role of gender on students’ motivation to
learn science, recent research by Glynn et al. (2011) has
provided evidence for a gender effect favouring college male
students with regard to a specific motivational construct,
namely self-efficacy. On the other hand, a study conducted
among upper secondary school students and in a cross-cultural
context which included four countries namely Malaysia, Slovenia,
Switzerland, and Turkey revealed more complicated relationships
between gender and motivation (Zeyer et al.,2013).
Measuring students’ motivational constructs
A motivational construct generally corresponds to a latent
variable which is a presumed explanatory variable to reflect a
continuum that is not directly observable (Kline, 2011). Thus a
construct may be assessed only indirectly; this is achieved via
measuring certain items which indicate empirically how the
specific construct is conceptualized by the subject. Students’
conceptualizations are important since they tend to influence
their actions (McGinnis et al., 2002; Scott et al., 2007).
Recently, several studies have focused on developing students’
motivation scales to learn science (Tuan et al., 2005; Glynn and
Koballa, 2006; Glynn et al., 2009, 2011; Velayutham et al.,2011).A
Students’ Motivation towards Science Learning (SMTSL) question-
naire was developed by Tuan and her colleagues (2005) and was
validated with junior high school students from central Taiwan.
The researchers identified six motivational constructs: self-efficacy,
active learning strategies, science learning value, performance goal,
achievement goal, and learning environment stimulation. The
SMTSL questionnaire has been used as a tool for measurement
of change in students’ learning motives after the implementation
of specific activities in a chemistry course (Feng and Tuan, 2005).
Recently, the SMTSL questionnaire has been adapted in the Greek
language and has been used in undergraduate student teachers
with reference to physics learning (Dermitzaki et al., 2013). None-
theless, the developers have annotated the conceptualization and
measurement of some constructs as ambiguous and not theoreti-
cally sound (Tuan et al., 2005). Another instrument, the Students’
Adaptive Learning Engagement in Science questionnaire was devel-
oped by Velayutham and her colleagues (2011) based on theoretical
and research underpinnings. The validation process resulted in
four constructs which included goal orientation, task value, self-
efficacy, and self-regulation. The researchers reported evidence for
strong construct validity of the scales, by testing them on Australian
lower secondary students (Velayutham et al., 2011). Another scale
designated as the ‘‘High School Chemistry Self-Efficacy Scale’’ was
developedandvalidatedbyAydinandUzuntiryaki(2009)inorder
to measure Turkish high school students’ self-efficacy from the
perspectives of cognitive and laboratory competencies.
Glynn and his colleagues focused on those motivational
components/constructs that influence self-regulatory learning
to develop and validate the Science Motivation Questionnaire II
(SMQ II) (Glynn and Koballa, 2006; Glynn et al., 2007, 2009,
2011). According to the developers, the items of SMQ II were
designed to serve as empirical indicators of components of
students’ motivation to learn science in college courses. The
following five motivation components (scales) are included in
the final version of SMQ II (Glynn et al., 2011), each one
comprised of 5 items, and constitute the working model of
motivation in the present work as well: intrinsic motivation,
self-determination, self-efficacy, career motivation, and grade
motivation. The scales were found to be useful in assessing the
motivation of both science and non-science majors. The motivation
scales of SMQ II were shown to be positively related, with intrinsic
and career motivation exhibiting the strongest relation. In addition,
the scales were shown to be related to students’ marks, with
strongest relation exhibited by self-efficacy.
The present study
In regard to research on motivation in school settings, most
investigations have been conducted in the mathematics and
science domains. Moreover, the majority of these studies have
been undertaken with college students. Therefore, both from
an academic as well as from a practical point of view (e.g. for
use in the educational policy decision making process), it is
important to investigate the motivation of secondary school
students to learn science and particularly chemistry. In addition,
there is a need to accurately examine how the motivational
constructs might be moderated by different cultural/educational
contexts. The availability of valid instruments for measuring
motivation is a vital necessary requirement in order to engage in
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such a demanding research task. Hence, we anticipate that the
investigation of motivation to learn chemistry among Greek
secondary school students will provide a valuable contribution
to various aspects of research related to motivation. Finally, it is
important to note that in Greece there has been so far no systematic
study which aims directly at measuring students’ motivation to learn
chemistry. A measurement of upper secondary school students’
attitudes toward chemistry reveals a neutral attitude regarding the
interest and a negative attitude regarding the usefulness of the
chemistry course to their future career. Only a few students (about
4%) express the wish to study chemistry at University (Salta and
Tzougraki, 2004). These neutral and negative attitudes are indices of
a low motivation to study and learn chemistry (Koballa and Glynn,
2007). Taking into account the considerations noted above, we
decided to investigate Greek secondary school students’ motivation
to learn chemistry using a translation and adaptation of SMQ II
(Glynn et al., 2011) in the domain of chemistry. This instrument was
selected due to its psychometric features and wide usage. Particu-
larly, SMQ II combines a number of key motivational components in
a single scale consisting of five subscales, one for each construct,
with strong experimental evidence for their validity (Glynn et al.,
2011). Questionnaire validity is an issue of central importance which
is however seldom examined and/or established.
Method
The context
The interpretation of the results of a motivational study is
related to the context of the existing curriculum. Thus, in this
section a short description of the Greek chemistry curriculum is
given. Greek secondary school includes three years of lower
(junior) and three years of upper (senior) secondary school. The
curricula for secondary school are structured in six grades; 7th,
8th, 9th for lower and 10th, 11th, 12th for upper secondary
school respectively. Secondary school science curricula are
centralized. All schools throughout the country must follow a
particular sequence of science courses and use the same
educational materials authorized by the Ministry of Education.
Table 1 demonstrates the weekly distribution of teaching hours
for Science subjects in Greek secondary schools. A number of
1 hour laboratory activities are included in Science Curricula.
Table 2 demonstrates the distribution of laboratory activities
for Science subjects in Greek secondary school.
The subject of chemistry is obligatory in grades 8 to 11 (core
chemistry courses), and optional in the 12th grade (advanced
chemistry course), depending on the direction of studies chosen
by the student. At grades 8 and 9, the chemistry curriculum
follows a macroscopic to microscopic approach. This approach
refers to instructional methods that use examples of real-world
or demonstrations to introduce chemistry topics followed by
microscopic explanations using two-dimensional drawings of
dots and circles to represent atoms, ions, and molecules (Gabel,
1999). At grades 10 to 12, the chemistry curriculum, both in core
and advanced courses, emphasizes a linear development of
chemical concepts. This refers to instructional methods that
start from subjects that introduce first basic theoretical concepts
of atomic theory and bonding on the microscopic level and
proceed to subjects focusing on the macroscopic level (Gabel,
1999). One of the chemistry curriculum objectives is the ability of
students to solve algorithmic chemistry exercises and problems.
Participants
The study was conducted in 4 urban public secondary schools
located in the metropolitan area of the Greek capital, Athens. The
participants were 330 secondary school students (163 males and
167 females) in grade 8 (n=146),andingrade10(n= 184). The
lower secondary school students (grade 8) were 14–15 years old and
the upper secondary school students (grade 10) were 16–17 years
old. Most students in the sample were of middle socioeconomic
status. The students participated voluntarily without extra credit or
compensation for their participation. The students and their
parents were informed about the aim of this study.
Questionnaire translation and adaptation
The Chemistry Motivation Questionnaire II (CMQ II) is the
chemistry specific SMQ II version in which the word ‘‘chemistry’’
Table 1 Weekly distribution of teaching hours for Science subjects in
Greek secondary school
Grades
Weekly teaching hours per grade
Lower secondary school Upper secondary school
7 8 9 10 11 12
Compulsory subjects
Biology 2 — 2 1 1 1
Chemistry —1122—
Geography 2 2 — — — —
Physics — 2 2 3 2 1
Optional subjects
Biology — — — — — 2
Chemistry ———— 22
Physics — — — — 2 3
Total hours per grade 35 35 35 33 34 30
Table 2 Annual distribution of 1-hour laboratory activities for Science
subjects in Greek secondary school
Grades
Annual distribution of 1-hour
laboratory activities per grade
Lower secondary
school
Upper secondary
school
7 8 9 10 11 12
Compulsory subjects
Biology 5 — 6 3 3 2
Chemistry —4323 —
Physics — 7 9 5 4 1
Optional subjects
Biology — — — — — 2
Chemistry ——— —33
Physics — — — — 3 3
Total lab activities per grade 5 11 18 10 10–16 3–11
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is substituted for the word ‘‘science’’. SMQ II was selected for the
following reasons: (a) it is based on a widely accepted theoretical
formulation of motivational constructs; (b) it functionalizes the
motivational construct with a range of indicators, and (c) it
demonstrates various psychometric properties which render it
acceptable. The translation and adaptation process was con-
ducted by taking into account the Hambleton comments on
International Test Commission (ITC) guidelines for test transla-
tion and adaptation (Hambleton, 2001).
A team of three translators working independently to translate
and adapt the test was used. Scoring rubrics and instructions were
also translated in the Greek language. The translators have knowl-
edge of both languages. Knowledge of the cultures, and at least
general knowledge of the subject matter and testing principles,
made part of the selection criteria for translators. One of the
translators, the second author, is a science researcher who has
lived for five years in USA during his doctoral studies, and he now
lives and works in Greece. Consequently, he is familiar with the
source and the target culture, and the concept of interest. The
second translator is a science teacher with over twenty years of
experience in teaching science subjects at Greek secondary school
and thus he is familiar with the culture of the population that will
be studied. Finally, the third translator is the first author, a
chemistry educator, familiar both with the concept of interest
and with literature on questionnaire development. A professional
English language teacher (a native Greek with postgraduate studies
intheUK)wasalsousedasbacktranslator.
Prior to translation, the translators met to clarify the defini-
tions and indicators of the concepts examined by the selected
questionnaire in order to promote understanding of meanings
and hence facilitate translation. Each team member first made
an independent translation of the questionnaire. During
forward translation, the translators focused on the meaning
of the items rather than on literal word-for-word translation.
After individual translations were made, the team members
met in order to review the translated version, discuss the
discrepancies, and decide on the most appropriate translation
of the items. In the next phase, the back translation from the
target language into the source language was used in order to
identify problems with the forward translation. The team of
translators then reviewed the back translation for comparabil-
ity of meaning with the target language and clarity of wording.
Although Hambleton (2001) states that a back translation
provides little evidence of measurement equivalence, in our
case it was used to assure that the content and meaning of the
original items, instructions and response categories are the
same as the original. The last phase of the instrument prepara-
tion dealt with pre-testing the translated questionnaire for
comprehension and cultural validity. Five 10th grade students
completed the Greek version of CMQ II. The first author
engaged the students in a discussion to determine clarity of
the items using questions such as ‘‘What does the item
mean?’’, and ‘‘Was this an item you felt comfortable respond-
ing?’’ The content of the discussion with the students was
analyzed and a few necessary changes were made to items by
taking into account the students’ suggestions. In fact, a need
for altering the wording of an item came to light. The chemistry
laboratory activities in Greek high school are considered com-
plementary without requiring both preparation of students and
separate examinations or exam questions. Consequently, the
second part ‘‘...and labs’’ of item 16 ‘‘I prepare well for science
tests and labs’’ was considered not applicable for use in the
Greek secondary school context and it was deleted. This action
does not create any problem in the scoring procedure.
The end product of the translation and adaptation work is
the Greek version of the Chemistry Motivation Questionnaire II
(CMQ II). This version is available on line (http://www.coe.uga.
edu/assets/docs/outreach/smqii/SMQII-Translations.pdf) in the
form of Science Motivation Questionnaire II (Greek-SMQ II)
applicable to all science disciplines. The team of translators
attempted to find a balance between a literal and a culturally
specific translation so that the translation and adaptation of
items are appropriate for Greek secondary school students.
Hence, the questionnaire in the target language was finalized
and subjected to further testing of psychometric properties.
Procedure
The questionnaire was administered to students in their chem-
istry classes by their chemistry teacher during March and April
of school year 2012–2013. Students were informed about the
study and they consented to participate. Their responses to all
motivation statements were assessed using a Likert-type scale
ranging from 0 (never) to 4 (always).
Confirmatory factor analysis (CFA) was selected as the procedure
for statistical analysis since the present study aims to test the hypo-
thesized motivational structure/model proposed by Glynn et al.
(2011) for students’ motivation to learn chemistry. In contrast to
Exploratory Factor Analysis (EFA) that may be appropriate for scale
development, CFA is preferred when measurement models have
strong hypothesis regarding the number of latent variables in a
model (Usher and Pajares, 2009). Moreover, CFA provides a rigorous
test of equivalence across the groups. If the assumption of equiva-
lence is rejected, then EFA may be employed to discover where the
anomalies are in the database (Hurley et al., 1997).
The factors of the model correspond to the scales of related
items identified by Glynn et al. (2011) that were hypothesized to
represent the components of students’ motivation to learn
chemistry. Adopting a componential model of motivation
based on the social cognitive theory, we hypothesized that (a)
the students’ responses to the questionnaire can be predicted
by the five specified components; (b) the components are
related because they measure positive, mutually supporting
components of the motivation. Confirmatory factor analysis
was conducted using the Structural Equation Modeling (SEM)
software program AMOS, Version 21.
Results
Model specification
The five-factor model for the SMQ II proposed by Glynn and his
colleagues (2011) was validated for the chemistry domain and
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for Greek high school students. The model included five latent
(unobserved) variables (factors): intrinsic motivation (IM), self-
determination (SD), self-efficacy (SE), career motivation (CM),
and grade motivation (GM). CFA was conducted in a manner
that each latent variable (factor) was ‘‘observed’’ via its specific
set of five items (measured variable indicators). Summarizing,
the model to be confirmed contains 25 measured variables.
Specifically, the factor intrinsic motivation (IM) predicts the
students’ responses to items 01, 03, 12, 17, and 19; the factor
self-determination (SD) predicts the students’ responses to
items 05, 06, 11, 16, and 22; the factor self-efficacy (SE) predicts
the students’ responses to items 09, 14, 15, 18, and 21; the
factor career motivation (CM) predicts the students’ responses
to items 07, 10, 13, 23, and 25; and the factor grade motivation
(GM) predicts the students’ responses to items 02, 04, 08, 20,
and 24. It is also necessary to confirm that the motivational
factors may be intercorrelated. Therefore, the factors were
permitted to covary. Error terms were hypothesized to be
uncorrelated. In each model the first item loading was con-
strained to 1.0 to set the scale of measurement, and no items
were allowed to double load.
Model identification
A model is characterized as ‘‘identified’’ when ‘‘it is theoretically
possible for the computer to derive a unique estimate of every
model parameter’’ (Kline, 2011). For testing model identifi-
cation, the first step is to count the number of data points and
the number of parameters that need to be estimated. The
model to be confirmed has 265 fewer parameters than data
points, so the model may be identified in accordance with the
required condition. Subsequently, the identifiability of the
measurement portion of the model is established by examining
the number of factors and measured variables as well as the
error correlations and factor covariances (Kline, 2011). It is thus
noted that in the testing model, there are five indicators for
each factor. The errors are uncorrelated and each indicator
loads on only one factor. In addition, the covariances between
the factors are not zero. Therefore, this CFA model may be
identified (Kline, 2011).
Model estimation
The model estimation starts with the evaluation of model fit
which means the determination of how well the model explains
the data. Assuming satisfactory model fit, the interpretation of
the parameter estimates can take place (Kline, 2011). Multiple
well-established indices and criteria were used to assess the
goodness of model fit, because each given index evaluates only
particular aspects of the model fit (Byrne, 2010; Kline, 2011).
The analysis was conducted using maximum likelihood (ML)
estimation.
The test of our hypothesis that the Chemistry Motivation
Questionnaire (CMQ II) responses can be explained by five
factors, yielded a w
2
value of 569.31 with 265 degrees of freedom
and a probability of less than 0.001 ( po0.001), thereby
suggesting that the fit of the data to the hypothesized model
is not entirely adequate (Byrne, 2010). One of the chi squared
test limitations, namely the sensitivity of w
2
value to sample
size, is a reason to consider that it may be a poor index of the
quality of the fit when sample sizes are large (Hu et al., 1992).
The test of the w
2
/df ratio was proposed to address this problem
(Byrne, 2010). The obtained value of 2.15 for the w
2
/df ratio
is considered a good fit of our hypothesized model to the
data since it falls within the recommended range of 1.0–3.0
(Glynn et al., 2011).
The following four additional fit indexes, which are among
the most widely reported in the Structural Equation Modeling
(SEM) literature, were also selected to determine model fit
(Hu and Bentler, 1999; Kline, 2011): the Steiger–Lind Root
Mean Square Error of Approximation (RMSEA), the Jo
¨reskog–
So
¨rbom Goodness of Fit Index (GFI), the Bentler Comparative
Fix Index (CFI), and the Standardized Root Mean Square
Residual (SRMR).
The first index, RMSEA, assesses a lack of fit of the popula-
tion data to the estimated model and Hu and Bentler (1999)
have suggested a value less than 0.06 as indicative of a good
fit between the hypothesized model and the observed data.
Moreover, MacCallum et al. (1996) strongly urged the use of
confidence interval in practice, suggesting a very narrow con-
fidence interval as supporting for good precision of the RMSEA
value in reflecting a model fit in the population. The RMSEA
value for our hypothesized model is 0.06, with the 90% con-
fidence interval ranging from 0.05 to 0.07. This indicates that
we can be 90% confident that the true RMSEA value in the
population will fall within the bounds of 0.05 and 0.07, which
represents a good degree of precision (Byrne, 2010).
The second index, GFI, is an alternative to the Chi-Square
test and calculates a weighted proportion of variance in the
sample covariance accounted for by the estimated population
covariance matrix (Tabachnick and Fidell, 2000). A GFI value of
0.90 or higher indicates a good model fit. Although the GFI
value for our hypothesized model (0.88) is slightly lower than
the threshold level, it is generally perceived as ‘‘acceptable’’ for
other indices of fit (Byrne, 2010).
The third index, CFI, takes into account sample size (Byrne,
2010) and performs well even in small samples (Tabachnick
and Fidell, 2000). A value greater than 0.90 is needed in order to
ensure that models are accepted, with values close to 0.95
indicating superior fit (Hu and Bentler, 1999). The CFI value
for our hypothesized model (0.91) is indicative of good fit.
The last index, SRMR, is a measure of the mean absolute
correlation residual, the overall difference between the
observed and predicted correlations. Small values of SRMR
(less than 0.05) indicate good-fitting models (Byrne, 2010),
however values as high as 0.08 are deemed acceptable (Hu
and Bentler, 1999). The SRMR value of our hypothesized model,
equal to 0.06, can be interpreted as meaning that the model
explains the correlations to within an average error of 0.06
(Byrne, 2010). As mentioned above, the model chi-square is
statistically significant, so the exact-fit hypothesis is rejected;
however the values of the other four used fit indices indicate
that our hypothesized model fits the data well, providing
evidence of questionnaire construct validity.
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To examine measurement invariance across different groups,
successive multi-group CFAs were conducted (Vandenberg and
Lance, 2000). Factorial invariance tests were done in a hierarchical
fashion by conducting initially the most basic form of measure-
ment invariance that is configural invariance or equal form
invariance (Model 1). If configural invariance is confirmed, this
indicates that participants belonging to different groups concep-
tualize the constructs in the same way. A stronger form of
measurement invariance is construct-level metric invariance or
equal factor loadings (Model 2), which means that the unstan-
dardized factor loadings of each indicator are equal across the
groups. Metric invariance is important as a prerequisite for
meaningful cross-group comparison (Cheung and Rensvold,
2002). When metric invariance is met, the suggestion is that
different groups respond to the items in the same way. Subse-
quently, constraints to factor loadings and item intercepts are
imposedinordertotestforscalarorstronginvariance(Model3).
Establishing scalar invariance indicates that individuals who have
thesamescoreonthelatentconstruct would obtain the same
score on the observed variable regardless of their group member-
ship. A value of DCFIsmallerthanorequalto0.01indicatesthat
the null hypothesis of invariance should not be rejected (Cheung
and Rensvold, 2002) and it is indicative of model invariance.
Recall that CFAs were conducted on increasingly-restrictive
hierarchical measurement models for each of the two subgroups of
interest: gender and age. To assess how well the CFA models
represented the data, the following criteria were used as cut-offs
for good fit: CFI 40.90 (with 40.95 being ideal), RMSEA and
SRMR o0.08 (with o0.05 being ideal) (Hu and Bentler, 1999).
Because the measurement model showed a modestly good fit for all
foursubgroupsnamelygirls,boys, lower, and upper secondary
school students, (Table 3), we specified the same model for each
subgroup when testing for factorial invariance.
Multiple groups modeling started with testing for configural
invariance. As Table 4 shows, the model gave a modestly good
fit with the data supporting the configural validity across gender
and different age groups. With regard to metric invariance, the
results revealed that the model fits the data very well across gender
and different age groups.
Furthermore, this additional set of constraints did not lead
to a meaningful drop in fit (DCFI = 0.000) between Model 2 and
Model 1, providing support for metric invariance across gender
and age groups. Subsequently, scalar invariance was investi-
gated. The overall goodness-of-fit indices and DCFI value
between Model 3 and Model 2 across gender and age groups
supported scalar invariance. Although the CFI values for all CFA
models are slightly lower than the threshold level, they are
generally perceived as ‘‘acceptable’’ for other indices of fit.
Statisticians (Byrne, 2010) frequently point out that fit indexes
can only describe a model’s ‘‘lack of fit’’ (p. 84) and that the
judgment of a model’s adequacy ‘‘rests squarely on the
shoulders of the researcher’’. With this in mind and based on
the majority of the evaluated indexes, we have strong evidence
that: (a) participants belonging to different groups conceptua-
lize the constructs in the same way (configural invariance); (b)
different groups respond to the items in the same way (metric
invariance); (c) individuals who have the same score on the
latent construct would obtain the same score on the observed
variable regardless of their group membership (scalar invar-
iance). Thus, cross-group comparisons are plausible as well.
Table 5 lists each item in the Chemistry Motivation Question-
naire II along with its standardized loading estimate for each of the
five measurement factors. The factor loadings are estimated correla-
tions often referred to as validity coefficients, which indicate how
well a given item measures its corresponding factor. In all analyses,
the standardized factor loadings were significant at the p= 0.05 level
and all, except one which is equal to 0.34 for the lower secondary
group (shown in bold), displayed magnitude in the range between
0.86 and 0.48, and therefore exceed the factor-loading criterion of
0.35 (Tabachnick and Fidell, 2000).
The standardized correlations among the factors shown in
Table 6 are considered corrected (i.e., disattenuated) for
measurement error and thus may be viewed as representing the
true associations among the motivation components represented
by the factors. The five factors showed statistically significant
intercorrelations ranging in magnitude from 0.23 (between career
motivation and self-determination for girls) to 0.77 (between grade
motivation and self-efficacy for upper secondary students).
A thorough psychometric evaluation also includes some form
of reliability evidence. The reliabilities (internal consistencies) of
the subscales for the all groups, assessed by Cronbach’s alphas,
are presented in Table 7.
The values of Cronbach’s alpha of the five factors ranged in
magnitude from 0.71 (intrinsic motivation for lower secondary
school students) to 0.90 (career motivation for upper secondary
school students). According to DeVellis (2003), a coefficient
above 0.80 is ‘‘very good,’’ 0.70 to 0.80 is ‘‘respectable,’’ 0.60
to 0.69 is ‘‘undesirable to minimally acceptable,’’ and below
0.60 is ‘‘unacceptable.’’ The Cronbach’s alpha values of all
25 items ranged from 0.88 (lower secondary school students)
to 0.93 (upper secondary school students). Analogous values of
coefficients were reported by Glynn et al. (2011) for the scales of
SMQ II.
Boys and girls as well lower and upper secondary school
students were compared on their motivation components using
the mean factor-based scale scores (Table 8). We adopted the
factor-based scale scores instead of the estimated factor scores
because the former are more easily interpreted and can also be
used for comparison between studies (Glynn et al., 2011). A two-
way ANOVA was conducted to determine possible differences in
students’ mean scores on the scales of CMQ II according to age
Table 3 Summary of goodness-of-fit statistics for the Chemistry Motiva-
tion Questionnaire II measurement model by subgroup
Subgroup model w
2
df w
2
/df CFI RMSEA SRMR
Girls 499.43 265 1.89 0.86 0.07 (0.06–0.08) 0.07
Boys 444.16 265 1.68 0.90 0.06 (0.05–0.07) 0.07
Lower secondary 456.79 265 1.72 0.84 0.07 (0.06–0.08) 0.07
Upper secondary 474.00 265 1.79 0.91 0.07 (0.06–0.08) 0.07
Total 569.31 265 2.15 0.91 0.06 (0.05–0.07) 0.06
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(lower secondary school and upper secondary school), gender
(boys and girls), and the interaction of these variables.
The ANOVA results revealed a significant main effect of students’
age on their grade motivation scores, F(1, 326) = 9.242, po0.05.
Table 4 Tests for invariance of the Chemistry Motivation Questionnaire II measurement model across gender and age
Model w
2
df w
2
/df CFI RMSEA (90% CI) Model comparison DCFI
Boys vs. Girls
Model 1 (no constrains) 943.588 530 1.780 0.88 0.049 (0.044–0.054)
Model 2 (equal factor loadings) 959.826 550 1.745 0.88 0.048 (0.043–0.053) 2 vs. 1 0.000
Model 3 (equal intercepts) 976.345 565 1.728 0.88 0.047 (0.042–0.052) 3 vs. 2 0.000
Lower vs. upper secondary
Model 1 (no constrains) 930.788 530 1.756 0.89 0.048 (0.043–0.053)
Model 2 (equal factor loadings) 952.122 550 1.731 0.89 0.047 (0.042–0.052) 2 vs. 1 0.000
Model 3 (equal intercepts) 972.279 565 1.721 0.89 0.047 (0.042–0.052) 3 vs. 2 0.000
Table 5 Standardized factor loadings
CMQ II item number
Factor loading
StatementTotal Boys Girls Lower secondary Upper secondary
Factor: grade motivation
24 0.78 0.79 0.79 0.76 0.77 Scoring high on chemistry tests and labs matters to me
4 0.76 0.69 0.84 0.68 0.80 Getting a good chemistry grade is important to me
8 0.75 0.78 0.71 0.79 0.72 It is important that I get an ‘‘A’’ in chemistry
20 0.67 0.66 0.70 0.58 0.71 I think about the grade I will get in chemistry
2 0.57 0.52 0.60 0.53 0.60 I like to do better than other students on chemistry tests
Factor: self-efficacy
15 0.80 0.82 0.78 0.75 0.84 I believe I can master chemistry knowledge and skills
9 0.79 0.77 0.81 0.72 0.83 I am confident I will do well on chemistry tests
18 0.70 0.77 0.60 0.66 0.73 I believe I can earn a grade of ‘‘A’’ in chemistry
14 0.64 0.60 0.70 0.61 0.66 I am confident I will do well on chemistry labs and projects
21 0.56 0.62 0.51 0.49 0.62 I am sure I can understand chemistry
Factor: self-determination
22 0.74 0.75 0.72 0.69 0.78 I study hard to learn chemistry
11 0.70 0.70 0.69 0.70 0.71 I spend a lot of time learning chemistry
16 0.60 0.60 0.60 0.66 0.56 I prepare well for chemistry tests
5 0.60 0.63 0.52 0.50 0.66 I put enough effort into learning chemistry
6 0.56 0.58 0.49 0.52 0.59 I use strategies to learn chemistry well
Factor: career motivation
13 0.78 0.79 0.75 0.66 0.86 Understanding chemistry will benefit me in my career
23 0.78 0.79 0.76 0.79 0.80 My career will involve chemistry
25 0.73 0.73 0.75 0.73 0.77 I will use chemistry problem-solving skills in my career
7 0.72 0.67 0.72 0.50 0.83 Learning chemistry will help me get a good job
10 0.69 0.66 0.76 0.58 0.74 Knowing chemistry will give me a career advantage
Factor: intrinsic motivation
19 0.80 0.78 0.81 0.75 0.84 I enjoy learning chemistry
12 0.71 0.69 0.72 0.67 0.74 Learning chemistry makes my life more meaningful
17 0.67 0.71 0.61 0.58 0.73 I am curious about discoveries in chemistry
3 0.66 0.65 0.65 0.55 0.75 Learning chemistry is interesting
1 0.48 0.55 0.36 0.34 0.59 The chemistry I learn is relevant to my life
Table 6 Standardized factor correlations for all groups
Grade motivation Self-efficacy Self-determination Career motivation
T
a
B
a
G
a
L
a
U
a
T
a
B
a
G
a
L
a
U
a
T
a
B
a
G
a
L
a
U
a
T
a
B
a
G
a
L
a
U
a
GM
SE 0.66 0.65 0.65 0.59 0.77
SD 0.56 0.60 0.53 0.52 0.60 0.45 0.51 0.41 0.40 0.49
CM 0.48 0.53 0.40 0.40 0.52 0.54 0.66 0.41 0.48 0.58 0.37 0.47 0.23 0.46 0.32
IM 0.48 0.47 0.49 0.44 0.53 0.57 0.58 0.59 0.57 0.58 0.53 0.56 0.46 0.53 0.53 0.70 0.69 0.69 0.72 0.67
Note: Minimum and maximum values are shown in bold.
a
T: Total, B: Boys, G: Girls, L: Lower secondary, U: Upper Secondary.
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Moreover, there were significant main effects of students’
gender on their self-determination scores, F(1, 326) = 13.108,
po0.001, their career motivation scores, F(1, 326) = 6.209,
po0.05, and the intrinsic motivation scores, F(1, 326) =
15.077, po0.001. Nevertheless, no significant interactions
(p40.05) were observed between age and gender for any of the
students’ scores on the scales of CMQ II. This actually means
that the effect of the students’ age on their grade motivation
scores is fairly similar for male and female students. The effect
of gender on self-determination, career motivation, and intrinsic
motivation scores is also similar for lower and upper secondary
students.
The mean factor-based scale scores are shown in Table 8.
Examination of the means in Table 8 and the results of ANOVA show
that lower secondary school students have significantly higher scores
ongrademotivationthanstudentsinuppersecondaryschool.In
regard with gender, statistical analysis indicates the following: (a)
Girls in lower secondary school have significantly higher scores on
self-determination, career motivation and intrinsic motivation rela-
tive to boys of the same age, (b) Girlsinuppersecondaryschoolhave
statistically significant higher score in self-determination relative to
boys of the same age.
Discussion
Validity and reliability of quantitative measurements are the
two most important aspects of educational research. Although
the use of existing instruments is convenient, these instru-
ments should not be used without sufficient evidence of their
ability to produce valid and reliable scores in the desired
context. Only if the instruments’ scores are valid and reliable
they can result in valid interpretations, and can lead to poten-
tially gainful decisions for students as well as for educational
researchers and policy makers. Consequently, access to valid
instruments is crucial for researchers conducting quantitative
research in chemistry education.
Thus the first main contribution of this work is the provision
of solid experimental evidence for the validity and reliability of
the SMQ II instrument, originally developed for measuring
motivation among college students in USA (Glynn et al.,2011),
in a specific science subject (Chemistry) a different cultural
context (Greece) and in students belonging to a younger age
group (14–17 years old) and a different educational level
(secondary school). This opens the possibility for reliable
measurement of students’ motivation within the Greek cultural
context for the first time and in addition for reliable cross-
cultural comparisons of students’ motivation via the use of the
same valid instrument. The second main contribution of this
work is the investigation of Greek secondary school students’
motivation to learn chemistry for the first time.
Confirmatory Factor Analysis (CFA) was used to test the
hypothesized motivational structure proposed by Glynn et al.
(2011) for students’ motivation to learn chemistry, for the Greek
version of the chemistry-specific original questionnaire (Greek-
CMQ II). It is important to note that CFA was employed not only
in order to verify whether the original factor structure could be
validated, but also in order to examine whether the measure-
ment structure underlying motivation to learn chemistry is
equivalent within the (sub)groups corresponding to gender
and age. In previous studies, comparisons between group
means were made based on the assumption that the measures
of motivation are fully applicable within the specific groups as
well. In our study, we first used multi-group CFA in order to
confirm that the measures are invariant within groups and
subsequently conducted comparisons between these groups
(Byrne, 2010; Kline, 2011). The results of statistical analyses
provided evidence supporting the construct validity of Greek
CMQ II, as well as for configural, metric and scalar invariance,
thus allowing meaningful comparisons between groups. Thus
the five component model of motivation consisting of grade
motivation, career motivation, intrinsic motivation, self-efficacy,
and self-determination was also confirmed for the Greek version
of CMQ II. The scales were positively related, consistent with the
view that the components were mutually supporting. In particu-
lar, intrinsic motivation and career motivation were strongly
Table 7 Reliability of internal consistencies
Reliability of internal consistencies
Total
(n= 330)
Boys
(n= 163)
Girls
(n= 167)
Lower
secondary
(n= 146)
Upper
secondary
(n= 184)
GM 0.82 0.81 0.84 0.79 0.83
SE 0.82 0.84 0.80 0.78 0.85
SD 0.77 0.78 0.73 0.75 0.79
CM 0.86 0.85 0.86 0.79 0.90
IM 0.80 0.81 0.76 0.71 0.85
All items 0.91 0.92 0.90 0.88 0.93
Table 8 Descriptive statistics of CMQ II scales among boys and girls
students in lower and upper secondary school
Lower secondary Upper secondary
Boys
(n= 67)
Girls
(n= 79)
Total
(n= 146)
Boys
(n= 96)
Girls
(n= 88)
Total
(n= 184)
Grade motivation (min = 0, max = 20)
M 14.69 15.32 15.03 13.34 13.73 13.53
sd 4.01 4.06 4.03 4.81 4.30 4.56
Self-efficacy (min = 0, max = 20)
M 11.90 11.71 11.80 12.81 12.53 12.68
sd 4.42 3.44 3.91 4.21 3.86 4.04
Self-determination (min = 0, max = 20)
M9.87 11.30 10.64 9.66 11.28 10.43
sd 4.09 3.69 3.93 4.02 3.45 3.84
Career motivation (min = 0, max = 20)
M8.33 10.15 9.32 8.76 9.65 9.19
sd 4.53 4.21 4.44 5.15 5.42 5.29
Intrinsic motivation (min = 1, max = 20)
M11.08 13.30 12.28 11.53 12.73 12.10
sd 3.92 3.37 3.79 4.44 3.96 4.25
Note: The results of the factors that showed statistically significant
differences are shown in bold.
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related, suggesting that intrinsic motivation orientates students
to careers that involve chemistry. Self-efficacy and grade motiva-
tion were also strongly related, supporting that self-efficacy
predicts students’ performance, in line with previous studies’
findings (Zusho et al., 2003; Schraw et al., 2005; Britner, 2008;
Taasoobshirazi and Glynn, 2009). Although most correlations
between scales of CMQ II are similar with those of SMQ II (Glynn
et al., 2011), the correlations between self-efficacy and grade
motivation, as well as between intrinsic motivation and grade
motivation are higher. This finding might be interpreted by the
main idea of self-determination theory that there is not a clear
distinction between intrinsic and extrinsic types of motivation
but motivated behaviours vary in the degree to which they are
autonomous (intrinsically motivated) vs. controlled (career or
grade motivated) (Ryan and Deci, 2000). The reliabilities (internal
consistencies) of the scales of the Greek version of CMQ II for all
foursubgroupsnamelygirls,boys, lower, and upper secondary
school students, assessed by Cronbach’s alpha values, were analo-
gous of those reported by Glynn et al. (2011) for the scales of SMQ
II. Particularly, the reliabilities of the scales for upper secondary
school students are very similar to those reported by Glynn et al.
(2011) for college students, suggesting the very good fitness of the
scales for these ages.
Our findings suggest that the structure of CMQ II (number
of factors and factor-item association) is not dependent on
gender and age group. In addition, both genders and age
groups have equal strengths of relations between the under-
lying constructs and specific scale items.
Furthermore, our findings revealed interesting statistically
significant differences between specific means of the CMQ II
scales across the genders and the examined age groups. More
specifically, lower secondary students had higher mean score
on grade motivation than upper secondary students. In the
other four scales, there is no evidence for statistically signifi-
cant differences between the two age groups. Previous research
findings support that most students show a progressive dete-
rioration on school-related attitudes and motivation (Gottfried
et al., 2001; Gentry et al., 2002; Wigfield and Eccles, 2002). Our
work provides similar evidence only for the motivation compo-
nent related with grade motivation. Although the decline in
students’ motivation to learn science has been documented
and is evidenced by many science teachers, studies have shown
that this decline is not an attribute of adolescence, but a result
of school and class context (Nolen, 2003; Vedder-Weiss and
Fortus, 2011). A limitation of the present study is the fact that it
is based on a cross-sectional research design. Cross-sectional
samples do not accurately capture true intra-person change and
preclude assessment of measurement invariance over time and
as people age. A longitudinal research design is required in
order to address these concerns.
In regard with differences between genders, secondary
school female students exhibited higher means than male on
self-determination irrespective of age group. Furthermore, the
female students of the younger age group (lower secondary
school) had higher means than corresponding male students
on two additional motivational components: career motivation
and intrinsic motivation. No significant inter-gender differ-
ences were found for the grade motivation and self-efficacy
component scales. The results related with the difference
between boys and girls in self-determination are in line with
findings from previous studies (e.g. Cavallo et al., 2004; Glynn
et al., 2009, 2011). However, our findings contradict those of
Glynn et al. (2011) which indicated that men had higher self-
efficacy than women, but are in line with those of Britner (2008)
which indicate no gender differences in self-efficacy in physical
science. Concentrating on the comparison of this work with the
results of Glynn et al. (2011), since in both cases measurements
were made via the use of equivalent instruments (SMQ II and
Greek CMQ II), the documented differences concerning the
inter-gender comparison of motivation could be attributed to
several factors (acting separately or synergistically) such as:
(a) different age of students, (b) different cultural context,
(c) different educational context, (d) investigation of a specific
science domain (chemistry) in our case. Research on gender
differences in motivation to learn science and mathematics
(Li, 1999; Tenenbaum and Leaper, 2003) points out that such
differences are attributed to social influences (e.g. family,
teachers, friends, media) rather than to natural differences
between boys and girls. Such a social influence that could
possibly justify the herein documented increased motivation
of female lower and/or upper secondary school students rela-
tive to their male classmates with regard to self-determination,
career motivation and intrinsic motivation, is the increased
ratio of female vs. male chemistry teachers in Greek secondary
education. The large presence of female chemistry teachers in
Greek secondary schools may serve as a positive role model for
the female students.
Elaborating more on the comparison of the total scores of
our results with those of Glynn et al. (2011), the following main
comments can be made:
(i) An interesting finding of our work is related with the
especially low mean factor-based scores of the career motiva-
tion scale. As seen in Table 8, the absolute values of these
scores are the lowest relative to the other four motivation
components and below 10 (in a scale from 0–20) for the whole
sample (equal to 9.39/20 and 9.19/20 in the two age groups).
These scores are much lower relative to the one of science
majors in USA (that is equal to 15.95/20) which is not a
surprising finding since the sample of the latter case involves
older students who already pursue studies related with a career
in science. However, the career motivation scores of the Greek
students are also significantly lower relative to the corres-
ponding score of the non-science majors in USA (equal to
11.13). Thus, the present study provides a direct measurement
of the poor motivation of Greek students to pursue a chemistry
related career, a fact which was inferred in the study of Salta
et al. (2012).
(ii) With regard to self-determination, the cross-cultural
comparison reveals a very similar pattern with the one dis-
cussed above for career motivation. In fact, Greek secondary
school students have a quite low mean absolute score in self-
determination (10.64/20 and 10.43/20 in the two age groups)
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which is much lower from the corresponding scores of college
students in USA (equal to 15.20/20 and 13.61/20 for science and
non-science majors respectively). This large documented dif-
ference in self-determination between the two countries could
be partly related with the different educational context of the
examined student samples. A college learning environment is
often more flexibly structured relative to secondary school.
However, the especially low self-determination scores of Greek
secondary school students could also be linked to certain
features of the Greek chemistry curriculum. In fact, as recently
pointed out by Greek researchers in science education (Halkia
and Mantzouridis, 2005), the demanding content of the Greek
chemistry curriculum poses major difficulties to the teachers
since it leaves them very little freedom to use other teaching
resources apart from the provided science textbook. This
chemistry curriculum has to be covered in a small number of
teaching hours (1 or 2 per week depending on grade, see
Table 1). In addition, the possibility for hands-on experimenta-
tion is also very limited (2–4 one-hour lab experiments per year
depending on grade, see Table 2). These structural character-
istics of the Greek educational system are expected to have a
detrimental effect on Greek secondary school students’ sense of
control over their learning chemistry (self-determination) as
well as in the other components of motivation to learn
chemistry.
(iii) The total mean scores of grade motivation of college
students in USA (equal to 16.96/20 and 16.11/20 for science and
non-science) are much higher relative to the one of
upper secondary school Greek students (equal to 13.53/20)
and increased also relative to the one of Greek lower secondary
school students (equal to 15.03/20). This difference could be
most closely related to the different educational context of the
two student samples. College students (irrespective of major)
have already made a conscious choice of pursuing higher level
studies for getting a tertiary degree while high school students
are not in a position to make specific choices over the courses
they have to study for getting their secondary education
certificate.
(iv) With regard to the remaining two motivation compo-
nents, self-efficacy and intrinsic motivation, the mean scores
displayed by the Greek secondary school students are decreased
relative to college science majors but quite similar in magni-
tude with non-science majors in USA. The increased values for
science majors are not surprising taking into account the fact
that these are students who have already chosen a study path
related to science. On the other hand, the display of similar
self-efficacy and intrinsic motivational characteristics between
students who have consciously chosen a non-science major
(USA) and secondary education students (Greece) may be,
among others, related to perceptions most students tend to
have about the nature of the chemistry course irrespective of
country of origin.
Research studies that provide novel empirical and/or theo-
retical content on a science education topic in a novel educa-
tional context are expected to provide significant contributions
to scientific knowledge (Taber, 2012). The availability of a valid
tool, such as the Greek version of Chemistry Motivation Ques-
tionnaire II, for exploring quantitatively the motivation of
Greek secondary school students to learn chemistry opens
several new possibilities for researchers, chemistry educators
as well as educational policy makers in Greece. Its application
to carefully selected student samples could provide valuable
information and feedback which can be employed for increasing
the effectiveness of chemistry curricula, teaching materials and
teaching methodologies. Future research should be focused on
identifying the elements of school culture and teacher practices
which have the strongest influence on students’ motivation for
chemistry learning as well as their in-between interactions.
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