Research on University Student’s self-efficacy scale in science education: A systematic review
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Research in Science Education--Res Sci Educ, and International Journal of Innovation in Science and Mathematics Education IJISME. Res Sci Educ was published by Springer and IJSE
was published by Routledge Taylor and Francis Group. IJISME was published by University of Sidney, Australia. In addition, two of these journals listed in Social Science Citation Index
by Thomson Reuters. In the following step, about eight articles within the last 10 years remained worthy of being read more closely. From the obtained collection, those articles
were selected that present empirical studies, including self-efficacy scale, validity, and reliability in the sense identified above. Some authors stated clearly the set of their fields;
some did not provide any information at all. Finally, the papers were chosen as listed in Table 1.
Table 1 . Papers were selected in this study.
No Author
Title Name of the Journal
Publisher
1 Uzuntiryaki
Aydın 2009
Development and Validation of Chemistry Self-Efficacy Scale for
College Students Research in Science
Education Springer
2 Lin, Liang,
Tsai 2014 Identifying Taiwanese University
Students’ Physics Learning Profiles and Their Role in Physics Learning
Self-Efficacy Research in Science
Education Springer
3 Lin, Liang
Tsai 2014 Conceptions of Memorizing and
Understanding in Learning, and Self-Efficacy Held by University
Biology Majors International Journal of
Science Education Routledge
4 Lindstrøm
Sharma 2011
Self-Efficacy of First Year University Physics Students: Do Gender and
Prior Formal Instruction in Physics Matter?
International Journal of Innovation in Science
and Mathematics Education
University of Sidney
3. Results and discussion
3.1 Results and discussion
Previous researchers developed the self-efficacy item scale in science major and how they analyzed their data. They used a Likert scale questionnaire in their instrument. Most
researchers reported that rating scale as interval data in published research Privitera, 2014. This type of scale is a numeric response scale used to indicate a participant’s level of
agreement or opinion with some statement. Therefore, Self-efficacy scale are discussed here as an interval scale. This situation in-lined with some scholars who noticed that for using
factor analysis of the item at least the variables should be the ordinal level of measurement Brace, et al., 2006.
Granting to the selection of study method above, four papers from science education view were deduced from the empirical data. Each of the paper focuses on
physics, chemistry, and biology; two papers in the physics domain, while others come from the fields of biology and chemistry. In addition, two papers were conducted in university in
Taiwan while the others in Turkey and Australia. Based on Table 2, the previous researchers
develop the self-efficacy item scale in science major and how they analyze their data. Looking
at the number of participants, all researchers used a large sample more than 250. It is intended to obtain the stability of the data. Particularly, Lindstrøm Sharma 2011 used
participant between 122 and 281 each time over a year due to their used Cohort study in their methodology. As a general rule, most item parameters can be estimated with relative
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stability for samples of 200 participants, and so this might consider the minimum number desired Crocker Algina, 1986.
Table 2. Overview about articles analyzed in this review. Study and
Country Major
Level and Total number of
participants Instrument: Questionnaire
Name of Instrument
Dimension Number
of items
Uzuntiryaki Aydin
2008 Turkey
Chemis College first
phase= 363 students
Second phase =353 students
Chemistry Self-Efficacy
Scale CCSS a self-efficacy for
knowledge comprehension-level
skills, b self-efficacy for
higher-order skills, c self-efficacy for
psychomotor skills, d self-efficacy for
everyday applications
Original 22 items,
Finally 21 items after
content validation
Lin, et al 2014a
Taiwan Physics
Undergraduate2 50 students
The physics learning self-
efficacy PLSE
a Conceptual Understanding
5 items b Higher-order
cognitive skills 6 items
c Practical work 7 items
d Everyday application 8 items
e Science communication
6 items 32 items
Lin, et al 2014b
Taiwan Biology
18 -master students; 82
undergraduate level; total 293
students Students’
biology learning self-
efficacy BLSE
a Higher-order cognitive skills, BLSE-
HC 6 items b Everyday
application, BLSE, EA 8 items
c Science communication,
BLSE-SC 6 items d Practical work, BLSE-
PW 7 items 27 items
Lindstrøm Sharma
2011 Australia
Physics Undergraduate
N between 122
and 2814 times in one year to
the 1st year physics cohort
The Physical Self-Efficacy
Questionnaire Only 1 factor
dimension since the author only focused on
Physics self-efficacy items
5 items
Research on University Student’s self-efficacy scale in science education: A systematic review
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Table 3. Summary of data analysis Uzuntiryaki Aydin, 2008. Kind of analysis and the results
Dimension Content
validation Factor Analysis and T
he total percentage of variance extracted
Internal consistency
from Cronbach’s
alpha
a self-efficacy for
knowledge comprehensio
n-level skills SCS
b self-efficacy for higher-
order skills an added
dimension after EFA
c self-efficacy for
psychomotor skills, SPS
d self-efficacy for everyday
applications SEA
11 experts in chemistry,
chemistry education,
educational psychology,
and educational
measurement Exploratory Factor Analysis
EFA for original sample Kaiser–Meyer–Olkin
KMO = 0.92 The bartlett’s test was
significant BTS value=3067.45, p0.001,
The correlation matrix was significantly different
from an identity matrix. The 22 items were factor
analyzed and three factors emerged with
eigenvalues 1.
An oblique rotation direct oblimin
All items had pattern coefficients higher than 0.3
51 the three factors were
deemed sufficient and
conceptually valid in their
correspondence to the existing
theory. 0.92 for the
SCS, .87 for the SPS, and
0.82 for the SEA,
indicating satisfactory
reliability.
Confirmatory Factor Analysis CFA for final sample
Analysis of moment structures AMOS version 4, Multiple goodness-of-fit tests
including: Normed Fit Index NFI, Comparative Fit Index CFI, and the Root
Mean Square Error Approximation RMSEA.
Results from the CFA: the three-factor structure fit well to the sample data with all
fit indices NFI=0.98; CFI=0.98 indicating a good fit except for RMSEA =0.08, which
indicated a reasonable fit.
All parameters factor loadings were found to be significant, indicating a significant
contribution of each item to the corresponding dimension.
Examining item-total
correlations indicated
that all items in
each dimension
contributed to the
consistency of scores
with item- total
correlations higher than
0.60.
Turning to how the researchers conducted an analysis of data, all researchers used exploratory factor analysis EFA to identify the factor structure of the scale see Tables 3–5.
Some of them continued to confirmatory factor analysis CFA to create cross-validating each item and reliability coefficients to estimate each dimension’s internal consistency. In
conducting a factor analysis, the first set of factor loadings was obtained by using a method that permits convenient calculation of the loadings. These loadings are called initial or
unrotated loadings. Nevertheless, all researchers in these cases used the rotated loading to approximate simple structure. For instance, Uzuntiryaki Aydin 2008 used an oblique
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rotation rather than orthogonal rotation. On the other hand, some researchers used varimax as the most popular method for orthogonal rotation. For example, Lin et al. 2014a and Lin
et al. 2014b used a varimax rotation to gain principal component extraction. Other ways,
some researchers also provided farther content validity evidence. For the purpose of content validation, a group of experts in science view were asked to assess the quality of each item,
verify matching of items to the corresponding dimensions, and provide further suggestions.
Table 4. Summary of data analysis Lin et al., 2014b. Kind of analysis and the results
Dimension Content
valid- ation
Factor Analysis and T he total percentage of
variance extracted Internal consistency
from Cronbach’s alpha
a Conceptual
Understanding 5 items
b Higher-order
cognitive skills 6 items
c Practical work
7 items d
Everyday application 8
items e
Science communication
6 items
EFA Criteria: the retained items
should preferably be weighted greater than 0.4. In other
words, the items with a factor loading of less than 0.4 were
deleted.
Principal component extraction with a varimax rotation
The Cronbach’s alpha coefficient for each scale of
each dimension of the PLSE instrument was calculated
The eigenvalues of the five factors from the principal
component analysis were all larger than one
The total variance
explained was 65.60
. Cronbach’s alpha
coefficients for the five factors were
0.80; 0.80; 0.90; 0.86; and 0.90
respectively.
The overall alpha was .95, indicating that
these factors had high internal
consistency in measuring the
participants’ five dimensions of
physics learning self- efficacy.
a Higher-order
cognitive skills, BLSE-HC
6 items b
Everyday application,
BLSE, EA 8 items
c Science
communicatio, BLSE-SC
6 items d
Practical work, BLSE-PW
7 items Original five-
factor structure The items of
Conceptual learning CL’
were eliminated.
EFA The KMO value 0.95 and the
result of Bartlett’s test x
2
=5329.27, p=0.001 suggested the suitability of conducting
factor analysis of the surveyed responses
Component analysis with orthogonal varimax rotation
to reveal meaningful clusters of factors from the results of
the questionnaires.
Examined the distribution of the data and revealed the
skewness and kurtosis The total
variance explained
was 63.68
The reliability
coefficients Cronbach’s alpha of
the factors in the SLSE questionnaire were
0.89, 0.89, 0.91, and 0.89, respectively.
For the whole questionnaire, the
alpha value achieved 0.96 and represented
sufficient reliability to assess the BLSE of
university students with biology-related
majors.
Research on University Student’s self-efficacy scale in science education: A systematic review
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Table 5. Summary of data analysis Lindstrøm Sharma, 2011. Kind of analysis and the results
Dimension Content
validation Factor Analysis and T
he total percentage of variance extracted
Internal consistency from
Cronbach’s alpha Only one
factor: Physics Self-
Efficacy with 5 items
Three experienced
physics education
experts, one of whom is
also an expert in self-efficacy
and related constructs.
They were asked to
comment on the validity of
the items. Initial trial
EFA using the Statistical Package for the Social
Sciences SPSS version 15.0. Scree plot, clearly indicated
one factor only. Equally there was only one
element, factor rotation did not use
The five items had factor loadings in the range .694 to
0.821
Confirmatory trial
CFA using Amos 7.0 provided evidence for the construct’s
validity values in parentheses indicate
requirements for validity; χ2 = 2.127, p = 0.831 p.05.
Main fit showed a very good model fit: RMSEA =.000 .05
with a 90 confidence interval of [0.000, 0.042];
RMR = 0.009 .05; GFI = 0.998 .95; NFI=0.994
0.95; and CFI = 1.000 0.95.
Final checks on the questionnaire were for
invariance and stability A questionnaire is said to be
invariant if the factor structure for data from
different samples from the population is consistent.
The factor explained
56 of the variance
Cronbach’s α=0.796.
3.2 Implications for the next research
For the future research, Figure 2 summarizes the development process of the science self-efficacy questionnaire. The diagram also compatible for developing general self-efficacy
and an alternative way how to analyze the data. More ever, there are several criteria for conducting data analysis summarized from four papers were discussed:
1
Exploratory factor analysis was
employed to attain the factor structures of the two adopted instruments based on the participants’ responses on the instrument.
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2 The validation criteria of exploratory factor analysis: the retained items should preferably
be weighted greater than 0.4. In other words, the items with a factor loading of less than 0.4 were deleted.
3 T
he condition for factor extraction was based on a combination of Kaiser’s criterion of eigenvalue greater than 1 confirming the intended factor structure at least four-factor
loadings over 0.6. 4
The factor explained 56 of the variance values over 50 were acceptable.
Figure 2. The process of developing science self-efficacy questionnaire.
In addition, Table 6 also described the possibility of dimensions relating to university student self-efficacy in science. In addition, we put two dimensions, namely self-learning
strategy and self-assessment as part of the self-efficacy dimension. The main reason is many researchers explored the relationships between university students and their cognitive and
metacognitive strategy use and science achievement. In terms of metacognitive, university’s students have self-learning strategy and self-assessment. The field of metacognition in
science education is in a state of development and expansion, and that metacognition is increasingly incorporated into research addressing the core aims of science instruction
Zohar Barzilai, 2013. Conceptual understanding of science was found to be one of the key objectives of current metacognition research. Their study employed a wide range of
instructional practices for fostering learners’ metacognition. The most prominent practice is the use of metacognitive cues and prompts through instruction. It means learning strategy
and assessment as part of the course of instruction.
Research on University Student’s self-efficacy scale in science education: A systematic review
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Table 6. The possibility of dimensions relating to university student self-efficacy in science.
No Dimension based on literature review
Dimension author’s perspective
1 Conceptual Understanding
Conceptual Understanding Knowledge comprehension-level skills
2 Higher-order cognitive skills
Higher-order thinking skills 3
Practical work Practical work
Psychomotor skills 4
Everyday application Everyday application
5 Science communication
Science communication 6
Self- Learning Strategy 7
Self-Assessment
4. Conclusion and remarks
