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DOI: 10.1039/D2RP00243D (Paper) Chem. Educ. Res. Pract., 2023, 24, 369-383

Chinese chemistry motivation questionnaire II: adaptation and validation of the science motivation questionnaire II in high school students

Jiafeng Zhang a and Qing Zhou *b
aSchool of Education, Shaanxi Normal University, Xi’an, China
bSchool of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi’an, China. E-mail: zhouq@snnu.edu.cn; Fax: +86 29 8153 0829; Tel: +86 29 8153 0829

Received 21st August 2022 , Accepted 3rd November 2022

First published on 7th November 2022

Abstract

This study aimed to adapt a new version of the Science Motivation Questionnaire II, the Chinese Chemistry Motivation Questionnaire II, for high school students in China, focusing specifically on chemistry. The sample consisted of 1635 students from four high schools, by stratified random sampling. Data was collected online. Exploratory and confirmatory factor analysis confirmed the original five-component motivation (intrinsic motivation, career motivation, self-determination, self-efficacy and grade motivation) structure after dropping four problematical items, and the factorial invariance was also confirmed across gender, region of residence, and choice of chemistry. Five components were strongly correlated in the Chinese context. Among the five components, students scored the highest in grade motivation. Generally, students who would continue chemistry scored much higher in all five components than those who would discontinue, with boys scoring slightly higher than girls and urban students scoring slightly higher than rural students. Specifically, for students who would continue chemistry, there were medium gender differences in self-efficacy, small gender differences in intrinsic motivation, career motivation, and self-determination, no significant gender differences in grade motivation, and small regional differences in all five components. For students who would discontinue chemistry, there were no significant differences in all five components across gender and region of residence. The internal structure of the questionnaire, correlations among the five components, and group differences in motivation were discussed. Some implications for researchers and practitioners were presented.

Introduction

China has reformed its high school curriculum and college entrance examinations. Before 2017, high school students were required to choose ‘a basket’ of three courses, e.g., a basket of physics, chemistry, and biology, or a basket of politics, geography, and history. Now, they must choose any three of the six courses mentioned, and the final high school grades for those courses are used to determine college entrance admission. The courses chosen in high school also affect students’ selection of courses and majors at university. For example, if a student does not choose chemistry in high school, they will not be able to take chemistry-related majors in college (e.g., clinical medicine science), meaning that chemistry-related future careers will no longer be available to them (e.g., becoming a doctor).

Fewer students are now selecting chemistry because the subject is optional in Chinese high schools. For example, in Zhejiang province, the percentage of students studying chemistry declined from 65% in 2014 to 51% in 2017. The extent of the decrease varies across Chinese provinces along with the pace of reform. A similar phenomenon has been reported in other countries where students lack the desire to study science and pursue science-related careers (Dekkers and de Laeter, 2001; Osborne et al., 2003; Lyons, 2006; DeWitt et al., 2014).

Motivation is an important factor influencing high school students’ course selection. High school students motivated to study chemistry are more likely to aspire to chemistry-related advanced courses and majors in the future (Bryan et al., 2011; Ardura and Perez-Bitrian, 2018). The Science Motivation Questionnaire II (SMQII, Glynn et al., 2011) is widely used in college and secondary schools to measure students’ motivation to learn science. It can also be used for specific domains (e.g., math, physics, chemistry, and biology) and adapted for use across languages and cultures.

The purpose of the present study was adaptation of the SMQII for Chinese high school students. In Chinese high schools, there is no subject termed ‘science’ but chemistry, physics, and biology are specific scientific disciplines that students can study. Students who choose chemistry may select other science subjects—or not—but their selection of chemistry will impact their selection of chemistry-related majors in college. Therefore, it is reasonable to measure students’ motivation to study chemistry specifically rather than measuring their general motivation to learn science.

Theoretical framework

Social-cognitive theory and motivation to learn chemistry

Social-cognitive theory considers that the motivation to learn science is the internal state that arouses, directs, and sustains science-learning behavior (Glynn et al., 2011). It is a multi-component construct with the main components of self-efficacy, self-determination, intrinsic motivation, grade motivation, and career motivation. By extension, the motivation to learn chemistry can be defined similarly as the definition of motivation to learn science. Motivation to learn chemistry should, therefore, have the same underlying constructs as the motivation to learn science.

Social-cognitive theory, developed by Bandura (1986, 2001, 2005, 2006) and extended by others (Pajares, 1996; Schunk, 1999, 2001; Schunk and DiBenedetto, 2020; Woolfolk, 2020), posits that human functioning involves triadic reciprocal causality among behavioral, environmental, and personal factors. Motivation, as a personal factor (Schunk and DiBenedetto, 2020; Woolfolk, 2020), depends on other personal, environmental, and behavioral factors (e.g., gender, socioeconomic status, and level of ability), and changes as individuals mature (Schunk et al., 2014). Differences in motivation, thus, in turn, reflect individual, group, social, and cultural differences (Schunk et al., 2014).

Group differences in motivation to learn chemistry (or science) across chemistry choice, gender, and residence region

According to social cognitive theory, gender and region of residence, as personal factors, may influence students' motivation to learn chemistry, and motivation to learn chemistry may influence students' chemistry choice (behavioral factor). Therefore, this study was interested in the differences in motivation to learn chemistry across choice of chemistry, gender, and region of residence. With its vast territory and large population, regional differences are often of interest to Chinese studies.

The motivation to learn chemistry may be different in high school between students who aspirate chemistry as a subject and those non-aspirates. Students who chose chemistry are typically more motivated to learn the subject than those who do not chose it (Bryan et al., 2011; Palmer et al., 2017; Ardura and Perez-Bitrian, 2018). The effect size may vary depending on the components. The largest one may be career motivation (Ardura and Perez-Bitrian, 2018). However, other researchers found large difference in intrinsic motivation (Lyons, 2006; Sadler et al., 2012; Lent et al., 2018), or self-efficacy (Brown and Cinamon, 2015; Avargil et al., 2020; Shwartz et al., 2021).

The motivation to learn chemistry may be different across gender. In high school, boys tend to have more positive beliefs about science than girls (Meece and Jones, 1996; DeBaker and Nelson, 2000; Meece et al., 2006; Brotman and Moore, 2008; Sunny et al., 2017), though girls reported higher scores than boys in some components for chemistry, such as self-determination (Salta and Koulougliotis, 2015; Ardura and Perez-Bitrian, 2018).

The motivation to learn chemistry may also vary depending on the regions where students live. In China, the culture and socioeconomic status of rural and urban residents can be very different (Murphy and Johnson, 2009; Hao et al., 2014; Wu, 2019). Rural residents engage in agricultural work, while urban residents undertake industrial and commercial work. Generally, people earn more money in industry and business than in agriculture. From the perspective of social-cognitive theory, these differences in culture and socioeconomic status (environmental factors) may influence the motivation (personal factor) of students coming from particular areas. A study about learning English as a foreign language with Chinese students showed the regional difference (higher for urban students).

Science motivation questionnaire II

The SMQII was developed to assess college students’ motivation to learn science. With 25 items, SMQII assesses five components: intrinsic motivation, self-determination, self-efficacy, grade motivation, and career motivation (Glynn et al., 2011).

The construct of five components is based on social-cognitive theory and the findings of earlier studies. Science education researchers have created many motivation constructs and adopted four theoretical orientations: behavioral, cognitive, humanistic, and social. Noteworthy constructs include arousal and anxiety, interest and curiosity, intrinsic and extrinsic motivation, self-determination, goal-directed behavior, self-regulation, self-efficacy, expectations, and strategies (Koballa Jr and Glynn, 2007). Integration of these constructs within the social-cognitive framework gave rise to six key components: intrinsic motivation, extrinsic motivation, personal relevance (student goals), self-determination, self-efficacy, and assessment anxiety. The Science Motivation Questionnaire (SMQ)—the first edition of the questionnaire—was developed to assess these six components (Glynn and Koballa Jr, 2006).

The SMQII—the second edition of the questionnaire—was based on the results of studies using SMQ to assess students’ motivation to learn science. Assessment of non-science major students’ motivation to learn science using SMQ revealed that (1) intrinsic motivation involved personal relevance, and self-efficacy was linked to assessment anxiety, and (2) extrinsic motivation differentiated between grade and career motivation (Glynn et al., 2007, 2009). SMQII was developed by revising the SMQ to assess the five components identified.

Adaptation of SMQII for high school students

The SMQII was developed to measure college students’ motivation to learn science in general, and their motivation to learn specific science disciplines, such as physics, chemistry, or biology (Glynn et al., 2011). Researchers have used the SMQII (original or partially revised) with secondary school students to measure their motivation to learn science or specific scientific disciplines. Covert et al. (2019) and Dixon and Wendt (2021) used the original SMQII with high school students in a science program and a flipped classroom, respectively. Bryan et al. (2011) used parts of the SMQ (intrinsic motivation, self-determination, and self-efficacy) with students (14–16 years old) in an introductory science course. Parts of the SMQII (career motivation, self-efficacy, and self-determination), with four items for each component, were used to evaluate motivation of secondary school students in an inquiry-based classroom (Schmid and Bogner, 2017). Fiorella et al. (2021) used the Mathematics Motivation Questionnaire—adapted from the SMQ by replacing the word ‘science’ with ‘math’—to measure secondary school students’ motivation to learn math.

The SMQII has also been translated into several languages. In a cross-cultural study, Zeyer et al. (2013) translated the SMQ for upper secondary students in four countries: Malaysia, Slovenia, Switzerland, and Turkey. Schumm and Bogner (2016) translated the SMQII into German for tenth graders at the college preparatory secondary school level, and Shin et al. (2018) translated the SMQII for male Korean high school students. The SMQII was also translated for Chinese high school students by Dong et al. (2020).

Several researchers have adapted the SMQII by replacing ‘science’ with specific disciplines and then translating the tool into their own languages. Ardura and Perez-Bitrian (2018) focused on physics and chemistry and translated the questionnaire into Spanish for use with secondary school students. Salta and Koulougliotis (2015) and de Souza et al. (2022) opted for ‘chemistry,’ and translations into Greek and Brazilian for secondary school students, respectively. Janstova and Sorgo (2019) used the word ‘biology,’ and translated the questionnaire for Czech upper secondary school students.

There is, however, one point of concern. Komperda et al. (2020) tested a modified SMQII with wording variants (‘science’, ‘chemistry’, or ‘biology’) and extension revisions in problematic items found in previous studies, and found that none of the individual scales demonstrated acceptable functioning across all course and wording conditions, although data-model fit for the five-factor structural model was acceptable.

Present study

This study developed the Chinese Chemistry Motivation Questionnaire II (CCMQII) by replacing the word ‘science’ in the SMQII with the word ‘chemistry,’ and then translating the questionnaire into Chinese. Chinese high schools offer specific science subjects, such as chemistry, physics, and biology and motivation to learn science is domain-specific (Hornstra et al., 2016; Salta and Koulougliotis, 2020; Tuominen et al., 2020). Thus, students’ motivation to learn chemistry may differ from their motivation for physics or biology study. When students are asked to describe their motivation to learn science, they may be confused about whether to report motivation for all scientific subjects (chemistry, physics, and biology), or just one (or some) of them. Therefore, examination of student motivation to learn chemistry specifically will yield more relevant results than asking about science in general.

The SMQII was originally developed in English to measure college students’ motivation to learn science. Adaptation was needed to ensure the instrument was suitable for our study, measuring Chinese high school students’ motivation to learn chemistry. Three variables were considered in the process of adaptation and translation: language (English and Chinese), discipline (science and chemistry), and grade (college and high school). When translating, we attempted to conform to the actual situation of chemistry learning in Chinese high schools and use Chinese language appropriate for this context (Gregoire, 2018).

This study also investigated factorial invariance of the CCMQII across groups of interest. Chinese high school students’ motivation to learn chemistry may vary with gender, region of residence, and choice of chemistry. Therefore, we evaluated whether the components of the CCMQII were invariant across groups: gender (boys or girls), region of residence (urban or rural), and choice of chemistry (choosing or abandoning the subject). Factorial invariance would suggest that the CCMQII can be widely used (Byrne, 2016).

Thus, the goal of the study was to adapt the SMQII to measure Chinese high school students’ motivation to learn chemistry. A related goal was to test the factorial invariance across gender, region of residence, and choice of chemistry. The following research questions were associated with these goals:

(1) What is the construct validity (internal structure) and reliability (internal consistency) of the CCMQII in high school students?

(2) Is the CCMQII factorial invariant across groups by gender, region of residence, and choice of chemistry in terms of model-fit-index differences?

(3) What are the similarities and differences in students’ motivation to learn chemistry across groups by gender, region of residence, and choice of chemistry?

Method

Research design. A previous study has provided some evidence for internal structure of SMQII for Chinese high school students (Dong et al., 2020), but some limitations need to be considered. First, the study measured students’ motivation to learn science, but not chemistry. Second, students in the study were only from a single high school.

The present study translated and adapted SMQII to a specific discipline-chemistry. Small changes to item wording could affect instrument functioning (Komperda et al., 2018a). Science is not equivalent to, or includes, chemistry, and the motivation to learn science is not the same as the motivation to learn chemistry. The adaptation of SMQII needs to be first adapted to the practical context of chemistry education.

A broad student body was considered. China has a large number of high school students, and students have different levels of learning. Only one school could not represent the total efficiently. It makes more sense to select students from multiple levels.

After checking the validity and reliability of the CCMQII, an ex-post-facto design was used to investigate group differences across the five motivational components of the three groups: choice of chemistry, gender, and region of residence.

Participants

Using stratified sampling, we studied 1635 tenth graders, aged 14–17, in four different quality high schools (termed Schools A–D) in Hainan Province, China. The mean age of the students was 15.3 years (SD: 0.58). Of this sample, 913 (56%) were girls and 722 (44%) were boys. There were 544 students from School A: 292 girls and 252 boys. From School B there were 236 girls and 178 boys. School C had 392 participants: 239 girls and 153 boys. There were 285 students from School D: 146 girls and 139 boys. The four schools, chosen for convenience but broadly typical of the province, vary in quality and location. Schools A, B, and C are urban schools (Schools A and B in provincial capital cities; School C in a county seat) and School D is rural and based in a town. School A was rated the best of these schools, followed by School B, and then Schools C and D. In China, students are accepted based on their scores on the high school entrance exam. These schools were rated according to their entrance scores of total scores (900): A (759), B (690), C (652), and D (594). In general, students in high-quality schools will perform well in chemistry, and it is therefore likely that average chemistry results will be highest for students in School A, followed by School B, School C and then School D.

The students lived in different regions: 927 (57%) in urban locations (city and county) and 708 (43%) in rural (town and village) settings. In School A, there were 479 urban students and 65 rural students. School B had 226 urban students and 188 rural students. In School C, there were 144 urban students and 248 rural students. School D comprised 78 urban students and 207 rural students.

The students could either choose or abandon chemistry in the second semester of tenth grade. In total, 1100 (67%) students chose chemistry, and 535 (33%) students opted to abandon it. In School A, 451 students chose chemistry and 93 students abandoned it. In School B, 265 students chose chemistry, and 149 students abandoned it. In School C, 202 students chose chemistry and 190 students abandoned it. In School D, 182 students opted to continue with chemistry while 103 decided not to.

The students had previously taken a 1 year chemistry course in ninth grade. In accordance with the Chinese curriculum, ninth graders start taking chemistry courses and the score for that course is used for high school entrance exams.

Questionnaire translation and adaptation

The CCMQII is the chemistry-specific version of the SMQII. All the SMQII items, scoring rubrics, and instructions were translated into Chinese based on guidelines from Muniz et al. (2013) and Gregoire (2018). A double-translation and reconciliation procedure was used. The translation team consisted of three translators, all with knowledge of the English/Chinese languages, English/Chinese culture, and the subject of chemistry. The first translator was a chemistry instructor who had worked in a high school for approximately 13 years, had a master's degree in education and was currently working towards a doctoral degree. The second translator was an English language teacher with a senior professional title, who had worked in a high school for 19 years and had lived in England for 6 months while studying. Consequently, she was familiar with British culture and the English language. The third translator was a chemistry education professor and researcher, working in a prestigious university in China. She was familiar with chemistry and chemistry education, the cultures, and testing principles.

The three translators worked on their translations independently, seeking a balance between accurate/equivalent words in the translations and culturally appropriate language. To facilitate the understanding of meaning, the three translators met before translation to clarify the definitions and indicators of the concepts in the questionnaire. After that, the first and second translators performed an independent translation, focusing on the meaning of the items rather than on literal word-for-word translation. After individual translations, the three translators met to identify and discuss any discrepancies and decide on the most appropriate translations.

The final version was the CCMQII (see Appendix A). Compared to the original version developed by Glynn et al. (2011), the CCMQII used more native expressions, for example, ‘grade’ was translated into ‘score’ because in Chinese high schools scores are usually used but not grade. Compared to the version translated in the study performed by Dong et al. (2020), the CCMQII used simpler expressions for easy understanding to students. Two expressions confused us, one was ‘think about’, and the other was ‘give me a career advantage’. Our translation team ended up giving different translations based on the original meanings.

An online questionnaire was developed for ease of data collection. The questionnaire included three pages. The first page contained three demographic items: gender, age, and region of residence. Page 2 contained the 25 items of the CCMQII. Students could respond to each item on a rating scale of temporal frequency: never (0), rarely (1), sometimes (2), often (3), or always (4). The final page required participants to indicate their choice of chemistry for the future. Participants could complete the questionnaire online or using their mobile phones.

Data collection

Data were collected before students made the decision about their subject choice, at the end of the first semester in tenth grade in 2020. Students received the link to the online questionnaire from their chemistry teachers and completed it online or using their smart mobile phones during class time (40 minutes). The four schools undertook data collection at slightly different times based on the individual arrangements in each school. Students were informed about the study that ‘your participation will help improve chemistry instruction’. They consented and volunteered to complete the online questionnaire anonymously. No other additional credits or compensations were given for participating.

Data analysis

Raw data were used in all the analyses. First, the internal consistency of the CCMQII was tested. The questionnaire consisted of five components (sub-questionnaires) and the scores could be grouped in different ways (total sample, choose/abandon, boy/girl, urban/rural). Thus, this study tested the reliabilities of these five sub-questionnaires by each of these groups. McDonald's omega was used to determine the internal consistency since it is a more appropriate single-administration reliability coefficient than Cronbach's alpha (Komperda et al., 2018b). We used the software jamovi (The jamovi project, 2022) to analyze McDonald's omega.

Next, factor analysis was performed and the internal structure of the CCMQII was tested. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used for a new setting (Lewis, 2022). To use cross-validation, the 1635 students in the sample were randomly split into two samples for CFA (n = 817) and EFA (n = 818). EFA was performed using the software jamovi to identify the underlying factors in the 25 items by clustering them into a smaller number of homogeneous sets, each of which constituted a new factor. Factors were extracted via principal axis method based on parallel analysis, followed by direct oblique rotations (Costello and Osborne, 2005). CFA was used because the CCMQII was based on a hypothesized five-factors structure underpinning the motivation to learn science in the SMQII. Since the students in our investigation were supposed to be distinct regarding their motivation to learn chemistry, we also undertook the CFA separately in the total sample and six subgroups: boys, girls, urban, rural, choose, and abandon.

Furthermore, factorial invariance testing was used to examine if the internal structure was consistent across groups of interest: choice of chemistry, gender, and region of residence. We analyzed the model-fit indices, factor loadings, and correlations of the five components using the Analysis of Moment Structure (AMOS, version 24, IBM) with the Maximum Likelihood (ML) method. More details of CFA and factorial invariance testing will be shown below.

Finally, the motivation differences across groups of interest were analyzed. Motivation differences across choice of chemistry reflected the influence of motivation on students’ choice of chemistry, and motivation differences across gender and region of residence reflected the influence of gender and region of residence on motivation. We used a factor-based scale score, which is the mean value of the items within each factor. Using the software jamovi, mean differences in motivation were analyzed with independent samples t-tests. A Cohen's d was calculated to determine the effect size (Cohen, 1992), the value of d was interpreted as small (0.20), medium (0.50), or large (0.80).

Confirmatory factor analysis

Model specification. As for the original SMQII model, the model in the present study consisted of five factors (latent variables): intrinsic motivation (IM), career motivation (CM), self-determination (SD), self-efficacy (SE), and grade motivation (GM). Intrinsic motivation predicted the responses to items Q1, Q3, Q12, Q17, and Q19; career motivation predicted the responses to items Q7, Q10, Q13, Q23, and Q25; the factor self-determination predicted the responses to items Q5, Q6, Q11, Q16, and Q22; self-efficacy predicted the responses to items Q9, Q14, Q15, Q18, and Q21; grade motivation predicted the responses to items Q2, Q4, Q8, Q20, and Q24. The five factors were allowed to be correlated. All errors (measurement residuals) were hypothesized to be uncorrelated. In each factor construct, the last item's factor loading was constrained to be 1.0. No item was allowed to be predicted by two or more factors.
Model fit assessment. We used multiple indices to assess the goodness of fit of the measurement model, because any given index would evaluate only one particular aspect of model fit (Kline, 2016). The chi-square test and Goodness-of-Fit Index (GFI) were not used, although they are commonly applied in research studies of this kind. Instead, we used the Standardized Root Mean Square Residual (SRMR), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Tucker Lewis Index (TLI) as the indices to evaluate model fit because these indices have special features that were important for the study. The following paragraph provides the rationale for the choices made.

The chi-square test is used to assess the extent that a proposed model varies from data and a non-significant p-value is ideal. However, the chi-square statistic depends heavily on sample size, and larger samples yield larger chi-square statistics meaning that the p-value is then always significant (Hoyle, 2012; Kline, 2016). To reduce the effect of sample size on the chi-square statistic, chi-square can be divided by the degrees of freedom (χ2/df). However, the same problem is still experienced to some extent and there is no universally agreed on standard of a good/bad fitting model (Hoyle, 2012; Kline, 2016). The GFI is used to calculate the proportion of variability in the sample covariance accounted for by the estimated covariance matrix. GFI is affected by sample size (Fan et al., 1999; Hoyle, 2012; Byrne, 2016) and the number of times that the true model is rejected (Type I error) increases substantially as the sample size increases (Sharma et al., 2005). For these reasons, we reported the chi-square values and the GFI values for comparison with other studies, but did not use them as indices of goodness of fit in the present study.

SRMR is sensitive to sample size but has little penalty for model complexity (Hoyle, 2012). That is, SRMR is positively biased, and that bias is greater for small sample sizes. RMSEA is also sensitive for small sample sizes, but not for large samples, and penalizes smaller models with relatively few variables (Hoyle, 2012; Kline, 2016). Thus, RMSEA is positively biased and the degree of bias depends on smallness of sample size and, especially, degrees of freedom. A 90% confidence interval can be computed for RMSEA and currently, RMSEA is one of the most popular measures of model fit.

CFI, TLI, and the non-normed Fit Index (NNFI) are all not sensitive for sample size and have a penalty for model complexity (Hoyle, 2012). CFI and TLI are not affected by sample size and penalize models that estimate many parameters. The difference between them is that the penalty of CFI for complexity is lower than for TLI (Kline, 2016), meaning that CFI is always greater than TLI.

The cut-offs (Glynn et al., 2011; Hoyle, 2012; Byrne, 2016; Kline, 2016; Schumacker and Lomax, 2016) suggest that CFI and TLI are goodness of fit indices, with values of 0.95 or higher indicating a good model fit. RMSEA and SRMR are badness-of-fit indices with RMSEA values of less than 0.08 indicating a good model fit, and SRMR values of 0.05 or less indicating good model fit.

Factorial invariance testing

Factorial invariance considered whether or not the model of the CCMQII structure was invariant across the groups of interest. The main groups of interest were gender, region of residence, and choice of chemistry. Testing for factorial invariance encompassed a series of hierarchical steps (Byrne, 2016). First, using the model-fit indices, the baseline model for each group was separately determined. Second, testing for configural invariance was implemented with no equality constraints imposed on any of the parameters. Third, testing for metric invariance was implemented with equality constraints imposed on the unstandardized coefficients. Fourth, based on metric invariance, testing for structural invariance was implemented with additional equality constraints imposed on structural covariances (factor variances and covariances). Finally, based on structural invariance, testing for measurement residual invariance was implemented with additional equality constraints imposed on measurement residuals (error variances). A difference value of between two CFIs (ΔCFI) smaller than or equal to 0.01 indicates that the null hypothesis of invariance should be accepted and is an index of model invariance (Cheung and Rensvold, 2002).

Results

Factor analysis

The results of factor analysis were shown in Table 1. For original 25 items, in EFA, the Kaiser–Meyer–Olkin test measured the sampling adequacy for the analysis (KMO = 0.97). As indicated by Barlett's test of sphericity, χ2(300) = 19[thin space (1/6-em)]873, and p < 0.001, the correlations between items were large enough. Fig. 1 showed five factors extracted. From the perspective of factor loading, four items should be dropped. Items Q1, Q2, Q12, and Q16 showed low relation to their intended scale factor, and evidence of association with more than one factor. In CFA, the values of model fit indices (χ2(265) = 1700.5, GFI = 0.84, TLI = 0.92, CFI = 0.93, SRMR = 0.047, RMSEA = 0.081 (0.078, 0.085)) indicate acceptable model fit. All factor loadings were higher than 0.70 except items Q1 (0.59) and Q2 (0.65). Modification indices for items Q12 and Q16 were 18.54 indicated by CM and 19.90 indicated by SE, respectively. The results of EFA and CFA suggest that those four item should be dropped.
Table 1 Factor loadings of original and modification CCMQII in EFA and CFA (n = 818 for EFA; n = 817 for CFA)
Item Original Modification Statement
EFA CFA EFA CFA
CM SE GM SD IM CM SE GM SD IM
Note: EFA, exploratory factor analysis; CFA, confirmatory factor analysis; IM, intrinsic motivation; CM, career motivation; SD, self-determination; SE, self-efficacy; GM, grade motivation; factor loadings below 0.30 are not shown in EFA.
Intrinsic motivation
Q19 0.47 0.91 0.63 0.94 I enjoy learning chemistry
Q17 0.52 0.83 0.52 0.83 I am curious about discoveries in chemistry
Q12 0.52 0.35 0.83 Item dropped Learning chemistry makes my life more meaningful
Q3 0.57 0.87 0.57 0.87 Learning chemistry is interesting
Q1 0.33 0.59 Item dropped The chemistry I learn is relevant to my life
Career motivation
Q25 0.59 0.85 0.60 0.85 I will use chemistry problem-solving skills in my career
Q23 0.77 0.79 0.82 0.79 My career will involve chemistry
Q10 0.90 0.87 0.90 0.86 Understanding chemistry will benefit me in my career
Q13 0.77 0.88 0.79 0.88 Knowing chemistry will give me a career advantage
Q7 0.78 0.83 0.78 0.83 Learning chemistry will help me get a good job
Self-determination
Q22 0.54 0.87 0.51 0.87 I study hard to learn chemistry
Q16 0.35 0.80 Item dropped I prepare well for chemistry tests and labs
Q11 0.82 0.86 0.80 0.88 I spend a lot of time learning chemistry
Q6 0.57 0.84 0.57 0.84 I use strategies to learn chemistry well
Q5 0.94 0.83 0.92 0.85 I put enough effort into learning chemistry
Self-efficacy
Q21 0.63 0.87 0.61 0.87 I am sure I can understand chemistry
Q18 0.81 0.89 0.78 0.89 I believe I can earn a grade of ‘A’ in chemistry
Q15 0.83 0.92 0.81 0.92 I believe I can master chemistry knowledge and skills
Q14 0.63 0.86 0.65 0.86 I am confident I will do well on chemistry labs and projects
Q9 0.85 0.87 0.84 0.88 I am confident I will do well on chemistry tests
Grade motivation
Q24 0.82 0.89 0.85 0.89 Scoring high on chemistry tests and labs matters to me
Q20 0.83 0.89 0.84 0.89 I think about the grade I will get in chemistry
Q8 0.78 0.88 0.82 0.87 It is important that I get an ‘A’ in chemistry
Q4 0.86 0.83 0.86 0.83 Getting a good chemistry grade is important to me
Q2 0.34 0.40 0.65 Item dropped I like to do better than other students on chemistry tests


image file: d2rp00243d-f1.tif
Fig. 1 The Scree plot shows the five factors extracted by a parallel analysis based on the ‘principal axis’ extraction method combined with an ‘oblimin’ rotation.

After dropping those four items, EFA and CFA were performed again. For modified 21 items, in EFA, KMO = 0.97, χ2(210) = 16[thin space (1/6-em)]891, and p < 0.001. All factor loadings were above 0.50. In CFA, the values of model fit indices (χ2(179) = 980.40, GFI = 0.90, TLI = 0.95, CFI = 0.95, SRMR = 0.034, RMSEA = 0.074 (0.070, 0.079)) indicate good model fit. All factor loadings were above 0.70.

Confirmatory factor analysis

Model fit. The values of the model-fit indices are shown in Table 2. Only the values of CFI (0.93) and TLI (0.92) in the group ‘Choose’ were slightly lower than the cut-off. The other values of the four model-fit indices (CFI, TLI, SRMR, and RMSEA) were all acceptable, indicating good model fit.
Table 2 Values of the model fit indices
N χ 2 df GFI CFI TLI SRMR RMSEA (90% CI)
Note: values of model-fit indices lower than the cut-off were highlighted in bold.
Total 1635 1660.15 179 0.91 0.96 0.95 0.031 0.071 (0.068–0.074)
Boys 722 939.20 179 0.89 0.95 0.95 0.029 0.077 (0.072–0.082)
Girls 913 970.23 179 0.91 0.96 0.95 0.037 0.070 (0.065–0.074)
Urban 927 1141.71 179 0.89 0.95 0.94 0.033 0.076 (0.072–0.080)
Rural 708 748.53 179 0.91 0.96 0.95 0.032 0.067 (0.062–0.072)
Choose 1100 1285.32 179 0.90 0.93 0.92 0.046 0.075 (0.071–0.079)
Abandon 535 617.17 179 0.90 0.95 0.94 0.040 0.068 (0.062–0.074)

Factor loading. Factor loadings (standardized regression weights) are shown in Table 3. The factor loadings were all above 0.70 except one of item Q23 (0.66) in group ‘abandon’, indicating that each item measured its corresponding factor very well in all groups.
Table 3 Factor loading (standardized regression weights)
Total Boys Girls Urban Rural Choose Abandon
Intrinsic motivation
Q19 0.93 0.94 0.93 0.94 0.92 0.88 0.89
Q17 0.83 0.85 0.81 0.83 0.83 0.79 0.76
Q3 0.87 0.87 0.86 0.86 0.88 0.80 0.81
Career motivation
Q25 0.85 0.87 0.82 0.87 0.81 0.79 0.77
Q23 0.80 0.83 0.78 0.81 0.81 0.74 0.66
Q13 0.87 0.88 0.87 0.88 0.86 0.85 0.82
Q10 0.87 0.87 0.88 0.86 0.89 0.84 0.84
Q7 0.82 0.81 0.82 0.82 0.81 0.74 0.79
Self-determination
Q22 0.87 0.90 0.84 0.86 0.88 0.81 0.79
Q11 0.88 0.88 0.87 0.89 0.86 0.83 0.85
Q6 0.83 0.85 0.80 0.83 0.82 0.77 0.75
Q5 0.86 0.88 0.83 0.87 0.84 0.84 0.80
Self-efficacy
Q21 0.88 0.89 0.86 0.87 0.88 0.83 0.79
Q18 0.88 0.87 0.89 0.89 0.87 0.85 0.79
Q15 0.91 0.91 0.90 0.91 0.91 0.88 0.87
Q14 0.85 0.86 0.82 0.84 0.84 0.83 0.79
Q9 0.86 0.86 0.86 0.87 0.85 0.81 0.79
Grade motivation
Q24 0.90 0.90 0.90 0.91 0.89 0.86 0.87
Q20 0.88 0.89 0.88 0.88 0.89 0.85 0.83
Q8 0.86 0.85 0.86 0.86 0.85 0.76 0.85
Q4 0.83 0.81 0.84 0.84 0.82 0.74 0.81

Factorial invariance

Baseline model. Table 4 shows that in all groups (boys and girls, urban and rural, choose and abandon), the values of model-fit indices (CFI, TLI, SRMR, and RMSEA) were acceptable, indicating good model fit for each group. Table 4 shows that in all groups, the values of factor loadings were all acceptable, demonstrating that each item measured its corresponding factor well. In summary, the good model fit and acceptable factor loadings showed a good baseline model for each group.
Table 4 Values of model-fit indices for factorial invariance tests
Model χ 2 df RMSEA (90% CI) CFI Model comparisons ΔCFI
Girls vs. boys
Model 1 (no constrains) 1909.46 358 0.052 (0.049–0.054) 0.954
Model 2 (equal measurement weights) 1937.85 374 0.051 (0.048–0.053) 0.954 2 vs. 1 0.000
Model 3 (equal structural covariances) 2037.11 389 0.051 (0.049–0.053) 0.951 3 vs. 2 0.003
Model 4 (equal measurement residuals) 2098.10 410 0.050 (0.048–0.052) 0.950 4 vs. 3 0.001
Urban vs. rural
Model 5 (no constrains) 1890.22 358 0.051 (0.049–0.053) 0.955
Model 6 (equal measurement weights) 1925.86 374 0.050 (0.048–0.053) 0.954 6 vs. 5 0.001
Model 7 (equal structural covariances) 1966.10 389 0.050 (0.048–0.052) 0.954 7 vs. 6 0.000
Model 8 (equal measurement residuals) 2014.57 410 0.049 (0.047–0.051) 0.953 8 vs. 7 0.002
Choose vs. abandon
Model 9 (no constrains) 1902.48 358 0.051 (0.049–0.054) 0.938
Model 10 (equal measurement weights) 2002.42 374 0.052 (0.049–0.054) 0.935 10 vs. 9 0.003
Model 11 (equal structural covariances) 2135.09 389 0.052 (0.050–0.055) 0.930 11 vs. 10 0.005
Model 12 (equal measurement residuals) 2388.19 410 0.054 (0.052–0.056) 0.921 12 vs. 11 0.009

Configural invariance. Table 4 shows that in the Models 1, 5, and 9, the values of CFI and RMSEA were all acceptable, indicating that the same configuration (the same number of factors, the same pattern of factor loadings, the same pattern of factor covariances) held across groups (gender, region of residence, and choice of chemistry). Thus, configural invariance was confirmed in each corresponding group.
Metric invariance. Table 4 shows that for the Models 2, 6, and 10, the values of ΔCFI were all smaller than 0.01, indicating that the hypothesis of equal measurement weights (factor loadings) across groups (gender, region of residence, and choice of chemistry) was retained. That is, metric invariance in each corresponding group was confirmed.
Structural invariance. As shown in Table 4, in the Models 3, 7, and 11, the values of ΔCFI were all smaller than 0.01. This supports the hypothesis of equal structural covariances (factor variances and covariances) across groups (gender, region of residence, and choice of chemistry) and, again, structural invariance in each corresponding group was confirmed.
Measurement residual invariance. Table 4 indicates that in the Models 4, 8 and 12, the values of ΔCFI were all smaller than 0.01. This confirms the hypothesis of equal measurement residuals (errors) across groups (gender, region of residence, and choice of chemistry) and the measurement residual invariance in each group.

In summary, across groups—gender, region of residence, and choice of chemistry—the model demonstrated factorial invariance (configural, metric, structural, and measurement residual invariance). The CCMQII can, thus, be used in broad populations.

Correlations among the five components

The correlation coefficients of the five components in the seven groups (total, boy/girl, urban/rural, choose/abandon) are presented in Table 5. Five components were positively and strongly related, and their correlations vary across groups. The correlations between IM, SE, and SD were very strong, ranging from approximately 0.75 to 0.91. The other correlations were relatively small, ranging from approximately 0.56 to 0.85.
Table 5 Factor correlations by student sub-group
F1 F2 Total Boys Girls Urban Rural Choose Abandon
Note: F1, factor 1; F2, factor 2; IM, intrinsic motivation; CM, career motivation; SD, self-determination; SE, self-efficacy; GM, grade motivation.
IM CM 0.79 0.85 0.73 0.79 0.79 0.63 0.75
SD 0.84 0.89 0.79 0.81 0.87 0.76 0.70
SE 0.87 0.91 0.83 0.85 0.89 0.77 0.84
GM 0.76 0.77 0.75 0.73 0.79 0.58 0.69
CM SD 0.75 0.80 0.70 0.76 0.75 0.60 0.66
SE 0.78 0.84 0.72 0.79 0.76 0.64 0.71
GM 0.76 0.79 0.74 0.74 0.78 0.61 0.71
SD SE 0.84 0.89 0.80 0.83 0.84 0.75 0.73
GM 0.73 0.76 0.70 0.70 0.75 0.57 0.64
SE GM 0.73 0.79 0.69 0.70 0.76 0.56 0.67

Reliability

Table 6 shows the McDonald's omega values. These results indicate good internal consistency of the items for the corresponding factors in all groups.
Table 6 Reliability: McDonald's omegas
IM CM SD SE GM
Note: IM, intrinsic motivation; CM, career motivation; SD, self-determination; SE, self-efficacy; GM, grade motivation.
Total 0.91 0.93 0.92 0.94 0.92
Boys 0.92 0.93 0.93 0.94 0.92
Girls 0.90 0.92 0.90 0.94 0.91
Urban 0.91 0.93 0.92 0.94 0.93
Rural 0.91 0.92 0.91 0.94 0.92
Choose 0.86 0.89 0.88 0.92 0.88
Abandon 0.86 0.88 0.88 0.90 0.91

Motivation differences across groups

Choice of chemistry. In Table 7, the ‘Total’ column shows that of the five components, students reported the highest mean score for grade motivation (2.93), followed by intrinsic motivation (2.71), career motivation (2.42), self-efficacy (2.40), and self-determination (2.28).
Table 7 Motivation differences for choice of chemistry (min = 0, max = 4)
Total Choose Abandon t p Cohen's d
Note: IM, intrinsic motivation; CM, career motivation; SD, self-determination; SE, self-efficacy; GM, grade motivation; M, mean; sd, standard deviation.
IM M 2.71 3.16 1.80 31.8 <0.01 1.68
sd 1.04 0.79 0.87
CM M 2.42 2.82 1.58 27.7 <0.01 1.46
sd 1.03 0.85 0.86
SD M 2.28 2.65 1.51 27.9 <0.01 1.47
sd 0.94 0.81 0.72
SE M 2.40 2.81 1.55 28.0 <0.01 1.48
sd 1.04 0.87 0.82
GM M 2.93 3.32 2.12 26.7 <0.01 1.41
sd 1.02 0.75 1.03

Table 7 also shows the differences in motivation between students who would continue chemistry or not. The mean scores of the five components (intrinsic motivation, career motivation, self-determination, self-efficacy, and grade motivation) for the ‘Choose’ group were higher than those for the group ‘Abandon,’ and the differences were all statistically significant. Effect sizes were all very large.

Gender. Table 8 shows the gender differences in motivation. Generally, for the total sample, there were statistically significant gender differences in all five components (higher for boys), the effect size was medium for self-efficacy, and small for the other four components. Specifically, for students who would abandon chemistry, there were no statistically significant gender differences in all five components. In contrast, for students who would choose chemistry, the scores of grade motivation was not statistically significant different. However, the scores of the other four components (intrinsic motivation, career motivation, self-determination, and self-efficacy) were statistically significant for gender difference. Boys’ scores were higher than girls’ for the four components. Effect size was medium for self-efficacy and small for the other three components.
Table 8 Motivation differences by students’ gender in choice of chemistry (min = 0, max = 4)
Total Choose Abandon
Boys Girls t p Cohen's d Boys Girls t p Cohen's d Boys Girls t p Cohen's d
Note: IM, intrinsic motivation; CM, career motivation; SD, self-determination; SE, self-efficacy; GM, grade motivation; M, mean; sd, standard deviation.
IM M 2.87 2.59 5.36 <0.01 0.27 3.28 3.05 4.98 <0.01 0.30 1.74 1.83 −1.16 0.25 −0.10
sd 1.07 0.99 0.79 0.77 0.90 0.85
CM M 2.56 2.30 5.05 <0.01 0.25 2.90 2.75 2.96 <0.01 0.18 1.63 1.55 1.05 0.30 0.09
sd 1.04 1.01 0.85 0.84 0.94 0.81
SD M 2.41 2.17 4.98 <0.01 0.25 2.76 2.54 4.56 <0.01 0.28 1.43 1.55 −1.84 0.07 −0.17
sd 1.03 0.86 0.87 0.73 0.78 0.68
SE M 2.68 2.18 9.97 <0.01 0.50 3.07 2.58 9.60 <0.01 0.58 1.63 1.50 1.73 0.09 0.16
sd 1.04 0.98 0.81 0.86 0.88 0.78
GM M 3.02 2.86 3.09 <0.01 0.15 3.34 3.31 0.58 0.56 0.04 2.15 2.11 0.46 0.65 0.04
sd 1.01 1.02 0.75 0.74 1.12 0.98

Region of residence. Table 9 shows the regional differences in motivation. In general, urban students reported significantly higher scores than their counterparts in rural areas and the effect sizes were small. Specifically, scores of all five components for students who would abandon chemistry did not differ significantly by region. In contrast, a statistically significant regional differences in the scores of all five components were found for students who would choose chemistry. Urban students’ scores were higher than those of rural students, and the effect sizes were small.
Table 9 Motivation differences by students’ region of residence (min = 0, max = 4)
Total Choose Abandon
Urban Rural t p Cohen's d Urban Rural t p Cohen's d Urban Rural t p Cohen's d
Note: IM, intrinsic motivation; CM, career motivation; SD, self-determination; SE, self-efficacy; GM, grade motivation; M, mean; sd, standard deviation.
IM M 2.81 2.59 4.34 <0.01 0.22 3.22 3.08 2.83 0.01 0.17 1.85 1.73 1.62 0.11 0.14
sd 1.02 1.04 0.78 0.79 0.89 0.84
CM M 2.48 2.33 2.88 <0.01 0.14 2.87 2.75 2.35 0.02 0.14 1.56 1.61 −0.71 0.48 −0.06
sd 1.05 1.01 0.85 0.85 0.87 0.84
SD M 2.41 2.10 6.51 <0.01 0.33 2.77 2.48 5.93 <0.01 0.36 1.56 1.45 1.67 0.10 0.14
sd 0.96 0.90 0.80 0.78 0.71 0.72
SE M 2.53 2.23 5.81 <0.01 0.29 2.92 2.66 4.93 <0.01 0.30 1.61 1.49 1.71 0.09 0.15
sd 1.04 1.02 0.86 0.87 0.83 0.81
GM M 3.02 2.82 3.96 <0.01 0.20 3.37 3.26 2.40 0.02 0.15 2.19 2.05 1.56 0.12 0.13
sd 0.99 1.04 0.74 0.75 1.03 1.03

Discussion

The SMQII was originally developed to measure US college students’ motivation to learn science. The present study provides new experimental evidence of the reliability and validity of the CCMQII, adapted from the SMQII, for a specific science discipline (chemistry), education level (high school), and cultural context (China). Factor analysis confirmed the model of the hypothesized structure for motivation to learn chemistry underlying the CCMQII, and factorial invariance across groups: gender, region of residence, and choice of chemistry. Motivation differences across these three groups were also analyzed. Findings open the possibility of using the same or similar questionnaires for cross-cultural, multi-level, and interdisciplinary comparison of students’ motivation to learn chemistry or other science disciplines (e.g., physics, biology).

Internal structure

In the factor analysis, this study confirms the five-factor construct of CCMQII as originally designed. However, five problematic items (Q1, Q12, Q2, and Q16) were found.

Item Q1 was developed to measure intrinsic motivation, but low factor loading indicates poor quality in this study. This result is in line with some previous studies (Salta and Koulougliotis, 2015; Ardura and Perez-Bitrian, 2018; Dong et al., 2020; Komperda et al., 2020). Komperda et al. (2020) found students confused when facing ‘relevant’, and used the phrase ‘world around me’ instead of ‘life’ but still found low factor loading though improved. These results suggest that this item needs further investigation.

Item Q12 was also developed to measure intrinsic motivation, but its loading on career motivation is much greater than on intrinsic motivation in this study. This suggests that students respond to the item mostly as a reason to pursue a career. This result is similar to the result found by Komperda et al. (2020), and the later revised items using the phrase ‘world around me’ performed well in their study.

Item Q2 was developed to measure grade motivation, but with a slightly lower factor loading in this study This result is in line with some previous studies (Salta and Koulougliotis, 2015; Schumm and Bogner, 2016; Ardura and Perez-Bitrian, 2018; Dong et al., 2020; Komperda et al., 2020). Furthermore, this item also has a cross-loading on self-efficacy in this study, in line with the results found by Schumm and Bogner (2016). These results suggest that this item needs further investigation.

Item Q16 was developed to measure self-determination, but the factor loading was low and a cross-loading on self-efficacy was found in this study. Similarly, Komperda et al. (2020) also found the cross-loading of the item on self-efficacy. In contrast, some other studies (Schumm and Bogner, 2016; Schmid and Bogner, 2017; de Souza et al., 2022) had not found this cross-loading. These results suggest that this item needs further investigation.

Correlations between the five components

Career motivation is strongly correlated with intrinsic motivation. This study found that the correlation coefficients for career motivation and intrinsic motivation were large. This suggests that students who rated intrinsic motivation highly also rated career motivation highly. This result aligns with findings from other studies (Bryan et al., 2011; Glynn et al., 2011; Salta and Koulougliotis, 2015; Ardura and Perez-Bitrian, 2018; Dong et al., 2020; de Souza et al., 2022) and the consistent results confirm a strong relationship between career motivation and intrinsic motivation.

Self-efficacy is strongly correlated with intrinsic motivation. This study found that the correlation coefficients between self-efficacy and intrinsic motivation were very large. This indicates that students who rated self-efficacy highly also rated intrinsic motivation highly. This result fits with most other studies (Bryan et al., 2011; Glynn et al., 2011; Salta and Koulougliotis, 2015; Ardura and Perez-Bitrian, 2018; Dong et al., 2020; de Souza et al., 2022), although the degree of correlation vary slightly.

The relationship between self-determination and intrinsic motivation should be further investigated. We found very large correlation coefficients between self-determination and intrinsic motivation. This indicates that This indicates that students who rated intrinsic motivation highly also rated self-determination highly. This result concurs with some studies (e.g., Bryan et al., 2011; Salta and Koulougliotis, 2015; Dong et al., 2020; de Souza et al., 2022), but differs from others (e.g., Glynn et al., 2011; Ardura and Perez-Bitrian, 2018). The mixed findings indicate a need for further research into the relationship between self-determination and intrinsic motivation.

The relationship between self-determination and self-efficacy needs to be researched further. Our study found a very large correlation between self-efficacy and self-determination, suggesting that students who rated self-efficacy highly also rated self-determination highly. This result is in line with some studies (e.g., Bryan et al., 2011; Dong et al., 2020; de Souza et al., 2022), but different to others (e.g., Glynn et al., 2011; Salta and Koulougliotis, 2015; Ardura and Perez-Bitrian, 2018). The inconsistent results suggest that further research into the relationship between self-determination and self-efficacy is also needed.

Motivation differences across groups

Overall, of five components (intrinsic motivation, career motivation, self-efficacy, and grade motivation), grade motivation scores were the highest (compared to intrinsic motivation in total sample in Table 7, t = 10.93, p < 0.01). This result fits with some previous studies (e.g., Glynn et al., 2011; Salta and Koulougliotis, 2015; Ardura and Perez-Bitrian, 2018; de Souza et al., 2022). These consistent results highlight the importance of short-term external rewards (grade motivation) for high school students learning chemistry, suggesting they are even more important than internal interest (intrinsic motivation), and long-term major and career prospects in the future (career motivation).

The differences are large in all five components (intrinsic motivation, career motivation, self-determination, self-efficacy, and grade motivation) between students choosing chemistry and those who will discontinue it in this study. These results were mostly in line with those found by some previous studies (Bryan et al., 2011; Ardura and Perez-Bitrian, 2018), except self-determination in which the effect size was just medium when they studied chemistry and science motivation in Spanish secondary schools and US high schools, respectively. These somewhat inconsistent results on differences in self-determination suggest the need for further research.

For students abandoning chemistry, there are no (or small) gender differences in five components. This study found no statistically significant gender difference, generally in line with those from Ardura and Perez-Bitrian (2018) who found small gender differences in career motivation and self-determination in Spanish secondary school students.

For students choosing chemistry, there are no (or small) gender differences in three components (intrinsic motivation, career motivation, and grade motivation), and mixed results in self-efficacy and self-determination. This study found no significant gender difference in grade motivation, a small gender difference in intrinsic motivation, career motivation, and self-determination, and a medium size gender difference in self-efficacy (higher for boys). Ardura and Perez-Bitrian (2018) found no significant gender difference in intrinsic motivation, career motivation, and self-efficacy, a small gender difference in grade motivation, and a medium size gender difference in self-determination (higher for girls) in Spanish secondary school students. Some researchers found similar results in samples with all students. Salta and Koulougliotis (2015) found no significant gender difference in four components (intrinsic motivation, career motivation, self-efficacy, and grade motivation), and a medium size gender difference in self-determination (higher for girls) in Greek secondary school students. de Souza et al. (2022) found no significant gender difference in intrinsic motivation, and a small gender difference in career motivation, self-determination, and grade motivation (all higher for girls), and self-efficacy (higher for boys) in Brazilian high school students. These results were generally consistent (no or small difference) for intrinsic motivation, career motivation, and grade motivation, but varied (no, small, or medium difference) for self-determination and self-efficacy.

Gender differences in self-determination and self-efficacy are also mixed in science motivation for high school students. For self-determination, this study found a small gender difference. Some studies observed similar results in Korean and China, respectively (Shin et al., 2017; Dong et al., 2020). However, Bryan et al. (2011) found no significant gender difference in students aspirating to continue science, and a small gender difference (higher for girls) in those who aspirate not to in US, and Schumm and Bogner (2016) found the opposite result—girls scored higher and a medium size gender effect was reported in German. For self-efficacy, this study found a medium size gender difference (higher for boys) in line with some studies in Korean and China (Shin et al., 2017; Dong et al., 2020). However, Schumm and Bogner (2016) found a small gender difference in German, and Bryan et al. (2011) reported none in aspirants and small gender difference in non-aspirants in US.

The gender difference in chemistry-related self-efficacy may be influenced by gender-role stereotypes for high school students (Steele, 1997; Meece et al., 2006; Sunny et al., 2017). In general, ‘boys report stronger ability and interest beliefs in mathematics and science, whereas girls have more confidence and interest in language arts and writing’ (Meece et al., 2006). From the perspective of social-cognitive theory, gender-role stereotypes are environmental factors and self-efficacy is a personal factor. Gender-role stereotyping could directly influence self-efficacy, and also indirectly affect it by mediation of behavioral factors (e.g., academic achievement and performance). The gender gaps in chemistry self-efficacy for high school students may be influenced by gender-role stereotyping of chemistry, and the opposite result (no gender gap) could be achieved by contending that girls can perform as well as boys (Else-Quest et al., 2013).

Differences in motivation to study chemistry between students living in urban and rural areas need to be taken more seriously. Differences in rural-urban areas are of particular concern in education and development in the People's Republic of China (Murphy and Johnson, 2009; Wang, 2012). Differences in student motivation to learn are one of the themes of this issue. The present study found no significant or small differences in student motivation to learn chemistry. To the best of our knowledge, for the first time, this study shows the results of no (or small) motivational differences between urban and rural high school students learning chemistry, using CCMQII and similar versions. It would be interesting to perform a comparison for nations and regions with regional difference.

Limitations and further research

Three limitations should be considered in this study. First, China is a nation of more than 1400 million inhabitants, with 56 ethnic groups and 34 provincial-level administrative regions. By virtue of this, it would be empirically interesting to assess student motivation to learn chemistry based on a larger and more heterogeneous sample. Clearly, this would allow one to investigate more deeply the appropriateness of the instrument for measuring student motivation to learn chemistry in a Chinese context. Second, online responses were used in this study for ease of data collection, and there may be limits to this approach. To reduce the effect of the method, the paper-and-pen method can be used in the future to test the consistency of the two methods. Third, CCMQII is a self-reported instrument and some bias will be present. To overcome this shortcoming, some other methods should be considered suggestively, for example, cognitive interviewing to identify what students really think.

Conclusion

The advent of chemistry as an optional subject in Chinese high schools has led to declining numbers of students opting to study it. An effective way to solve this problem may be to improve students’ motivation to learn chemistry. To measure the motivation to learn chemistry, a valid and reliable instrument was needed. In this study, the CCMQII, adapted from the SMQII, was used to measure Chinese high school students’ motivation to learn chemistry. The results of the factor analysis confirmed the internal structure of the five components (intrinsic motivation, career motivation, self-determination, self-efficacy, and grade motivation), after dropping four problematic items. Factorial invariance was confirmed across groups by gender, region of residence, and choice of chemistry. The relevance of the five components was strongly correlated in the Chinese context, but these correlations may vary depending on the country or context. Of the five components, students rated external rewards – grades – as the highest, even higher than internal interests and future careers. There were differences in motivation to learn chemistry across groups by gender, region of residence, and choice of chemistry. Overall, there were very large differences in motivation to learn chemistry between students who would go on to study chemistry and those who would not, with boys reporting slightly higher scores than girls and urban students reporting slightly higher scores than their rural counterparts. Specifically, for students who would discontinue chemistry, there were minor differences in motivation to learn chemistry across groups by gender and region of residence. In contrast, for students who would continue chemistry, there were small regional differences in all five components, as well as small gender differences in intrinsic motivation, career motivation, and grade motivation, and in the Chinese context, gender differences were medium for self-efficacy and small for self-determination, but these differences for self-efficacy and self-determination may vary depending on the country or context.

Implications

As a result of this and numerous previous studies that have identified multiple problematic items and numerous other structural issues (Komperda et al., 2020), educational researchers and instructors should carefully consider the use of SMQII and its disciplinary and linguistic variants.

For researchers, a factor analysis should be performed first when deciding on the use of these assessment instruments. Both exploratory and confirmatory factor analysis are encouraged, as only confirmatory factor analysis fails to find some problematic items, as happened in this study. If modification is decided to use, this and some previous studies compiled in this study would be carefully considered and some phrase revisions would be referred to the results investigated by some studies (e.g., Komperda et al., 2020).

For instructors deciding to use these instruments, a version of similar context (discipline and language, secondary school or college) should be preferred, and the problematic items should be dropped out. For example, in the Chinese context of measuring the motivation of high school students to study chemistry, CCMQII should be preferred when problematic items are dropped. Alternatively, collaboration with researchers should be encouraged to first confirm the validity of the instrument and, furthermore, to perform some possible additional modifications.

Instructors should pay special attention to grade motivation and intrinsic motivation, and step up efforts to fulfill student achievement needs and keep students interested and enjoying themselves in chemistry learning. To reduce the gender gap in motivation to learn chemistry, instructors should pay more attention to girls, especially in terms of self-efficacy in learning chemistry, and give girls more opportunities to be more successful and confident.

Conflicts of interest

There are no conflicts to declare.

Appendix

Appendix A. Chinese chemistry motivation questionnaire II and the english version

Item Items in Chinese Items in English
Q1 image file: d2rp00243d-u1.tif The chemistry I learn is relevant to my life
Q2 image file: d2rp00243d-u2.tif I like to do better than other students on chemistry tests
Q3 image file: d2rp00243d-u3.tif Learning chemistry is interesting
Q4 image file: d2rp00243d-u4.tif Getting a good chemistry grade is important to me
Q5 image file: d2rp00243d-u5.tif I put enough effort into learning chemistry
Q6 image file: d2rp00243d-u6.tif I use strategies to learn chemistry well
Q7 image file: d2rp00243d-u7.tif Learning chemistry will help me get a good job
Q8 image file: d2rp00243d-u8.tif It is important that I get an ‘A’ in chemistry
Q9 image file: d2rp00243d-u9.tif I am confident I will do well on chemistry tests
Q10 image file: d2rp00243d-u10.tif Knowing chemistry will give me a career advantage
Q11 image file: d2rp00243d-u11.tif I spend a lot of time learning chemistry
Q12 image file: d2rp00243d-u12.tif Learning chemistry makes my life more meaningful
Q13 image file: d2rp00243d-u13.tif Understanding chemistry will benefit me in my career
Q14 image file: d2rp00243d-u14.tif I am confident I will do well on chemistry labs and projects
Q15 image file: d2rp00243d-u15.tif I believe I can master chemistry knowledge and skills
Q16 image file: d2rp00243d-u16.tif I prepare well for chemistry tests and labs
Q17 image file: d2rp00243d-u17.tif I am curious about discoveries in chemistry
Q18 image file: d2rp00243d-u18.tif I believe I can earn a grade of ‘A’ in chemistry
Q19 image file: d2rp00243d-u19.tif I enjoy learning chemistry
Q20 image file: d2rp00243d-u20.tif I think about the grade I will get in chemistry
Q21 image file: d2rp00243d-u21.tif I am sure I can understand chemistry
Q22 image file: d2rp00243d-u22.tif I study hard to learn chemistry
Q23 image file: d2rp00243d-u23.tif My career will involve chemistry
Q24 image file: d2rp00243d-u24.tif Scoring high on chemistry tests and labs matters to me
Q25 image file: d2rp00243d-u25.tif I will use chemistry problem-solving skills in my career

Acknowledgements

The authors would like to acknowledge the students, teachers and schools involved in the data collection for their help, and the editors and reviewers for their constructive comments and suggestions.

References

  1. Ardura D. and Perez-Bitrian A., (2018), The effect of motivation on the choice of chemistry in secondary schools: adaptation and validation of the Science Motivation Questionnaire II to Spanish students, Chem. Educ. Res. Pract., 19, 905–918.
  2. Avargil S., Kohen Z. and Dori Y. J., (2020), Trends and perceptions of choosing chemistry as a major and a career, Chem. Educ. Res. Pract., 21, 668–684.
  3. Bandura A., (1986), Social foundations of thought and action: A social cognitive theory, Englewood Cliffs, NJ: Prentice-Hall.
  4. Bandura A., (2001), Social cognitive theory: An agentive perspective, Annu. Rev. Psychol., 1–26.
  5. Bandura A., (2005), Evolution of social cognitive theory, in Smith K. G. and Hitt M. A. (ed.), Great Minds in Management, Oxford: Oxford University Press, pp. 9–35.
  6. Bandura A., (2006), Going global with social cognitive theory: From prospect to paydirt, in Donaldson S. I., Berger D. E. and Pezdek K. (ed.), The Rise of Applied Psychology: New Frontiers and Rewarding Careers, Mahwah, NJ: Erlbaum, pp. 53–70.
  7. Brotman J. S. and Moore F. M., (2008), Girls and science: A review of four themes in the science education literature. J. Res. Sci. Teach., 45(9), 971–1002.
  8. Brown D. and Cinamon R. G., (2015), Choosing a high school major: An important stage in the career development of Israeli adolescents, J. Career Assess., 23, 630–644.
  9. Bryan R. R., Glynn S. M. and Kittleson J. M., (2011), Motivation, achievement, and advanced placement intent of high school students learning science, Sci. Educ., 95, 1049–1065.
  10. Byrne B. M., (2016), Structural equation modeling with AMOS: Basic concepts, applications, and programming, New York: Routledge.
  11. Cheung G. W. and Rensvold R. B., (2002), Evaluating goodness-of-fit indexes for testing measurement invariance, Struct. Equ. Modeling, 9, 233–255.
  12. Cohen J., (1992), A power primer, Psychol. Bull., 112, 155–159.
  13. Costello A. B. and Osborne J., (2005), Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis, Pract. Assess. Res. Eval., 10, 7.
  14. Covert H., Ilunga Tshiswaka D., Ramkissoon I., Sisskin E., Lichtveld M. and Wickliffe J., (2019), Assessing science motivation among high school students participating in a supplemental science programme: the Emerging Scholars Environmental Health Sciences Academy, Int. J. Sci. Educ., 41, 2508–2523.
  15. de Souza R. T. M. P., Dos Santos Barbosa Da Silva M., Barbato D. M. L., Guzzi M. E. R. D. and Kasseboehmer A. C., (2022), Motivation to learn chemistry: a thorough analysis of the CMQ-II within the Brazilian context, Chem. Educ. Res. Pract., 23(4), 799–810.
  16. DeBaker T. K. and Nelson R. M., (2000), Motivation to learn science: Differences related to gender, class type, and ability, J. Educ. Res., 93, 245–254.
  17. Dekkers J. and de Laeter J., (2001), Enrolment trends in school science education in Australia, Int. J. Sci. Educ., 23, 487–500.
  18. DeWitt J., Archer L. and Osborne J., (2014), Science-related aspirations across the primary-secondary divide: evidence from two surveys in England, Int. J. Sci. Educ., 36, 1609–1629.
  19. Dixon K. and Wendt J. L., (2021), Science motivation and achievement among minority urban high school students: an examination of the flipped classroom model, J. Sci. Educ. Technol., 30, 642–657.
  20. Dong Z., Li M., Minstrell J. and Cui Y., (2020), Psychometric properties of Science Motivation Questionnaire II-Chinese version in two waves of longitudinal data, Psychol. Schools, 57, 1240–1256.
  21. Else-Quest N. M., Mineo C. C. and Higgins A., (2013), Math and science attitudes and achievement at the intersection of gender and ethnicity, Psychol. Women Q., 37, 293–309.
  22. Fan X., Thompson B. and Wang L., (1999), Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes, Struct. Equ. Modeling, 6, 56–83.
  23. Fiorella L., Yoon S. Y., Atit K., Power J. R., Panther G., Sorby S., Uttal D. H. and Veurink N., (2021), Validation of the Mathematics Motivation Questionnaire (MMQ) for secondary school students, Int. J. STEM Educ., 8(1), 1–14.
  24. Glynn S. M. and Koballa Jr T. R., (2006), Motivation to learn college science, in Mintzes J. J. and Leonard W. H. (ed.), Handbook of College Science Teaching, Arlington, VA: National Science Teachers Association Press, pp. 25–32.
  25. Glynn S. M., Taasoobshirazi G. and Brickman P., (2007), Nonscience majors learning science: A theoretical model of motivation, J. Res. Sci. Teach., 1088–1107.
  26. Glynn S. M., Taasoobshirazi G. and Brickman P., (2009), Science Motivation Questionnaire: Construct validation with nonscience majors, J. Res. Sci. Teach., 46, 127–146.
  27. Glynn S. M., Brickman P., Armstrong N. and Taasoobshirazi G., (2011), Science Motivation Questionnair II: Validation with science majors and nonscience majors, J. Res. Sci. Teach., 1159–1176.
  28. Gregoire J., (2018), ITC guidelines for translating and adapting tests (second edition), Int. J. Test., 18, 101–134.
  29. Hao L., Hu A. and Lo J., (2014), Two aspects of the rural-urban divide and educational stratification in China: A trajectory analysis, Comp. Educ. Rev., 58, 509–536.
  30. Hornstra L., van der Veen I. and Peetsma T., (2016), Domain-specificity of motivation: A longitudinal study in upper primary school, Learn. Individ. Differ., 51, 167–178.
  31. Hoyle R. H., (2012), Handbook of structural equation modeling, New York: Guilford press.
  32. Janstova V. and Sorgo A., (2019), Evaluation, validation and modification of Science Motivation Questionnaire for upper secondary school, J. Balt. Sci. Educ., 18, 748–767.
  33. Kline R. B., (2016), Principles and practice of structural equation modeling, New York: Guilford Press.
  34. Koballa Jr T. R. and Glynn S. M., (2007), Attidudinal and motivational constructs in science education, in Abell S. K. and Lederman N. (ed.), Handbook of Research in Science Education, Mahwah, NJ: Erlbaum, pp. 75–102.
  35. Komperda R., Hosbein K. N. and Barbera J., (2018a), Evaluation of the influence of wording changes and course type on motivation instrument functioning in chemistry. Chem. Educ. Res. Pract., 19(1), 184–198.
  36. Komperda R., Pentecost T. C. and Barbera J., (2018b), Moving beyond alpha: A primer on alternative sources of single-administration reliability evidence for quantitative chemistry education research, J. Chem. Educ., 95, 1477–1491.
  37. Komperda R., Hosbein K. N., Phillips M. M. and Barbera J., (2020), Investigation of evidence for the internal structure of a modified science motivation questionnaire II (mSMQ II): a failed attempt to improve instrument functioning across course, subject, and wording variants, Chem. Educ. Res. Pract., 21, 893–907.
  38. Lent R. W., Sheu H.-B., Miller M. J., Cusick M. E., Penn L. T. and Truong N. N., (2018), Predictors of science, technology, engineering, and mathematics choice options: a meta-analytic path analysis of the social-cognitive choice model by gender and race/ethnicity, J. Couns. Psychol., 65, 17–35.
  39. Lewis S. E., (2022), Considerations on validity for studies using quantitative data in chemistry education research and practice, Chem. Educ. Res. Pract., 23(4), 764–767.
  40. Lyons T., (2006), The puzzle of falling enrolments in physics and chemistry courses: Putting some pieces together, Res. Sci. Educ., 36, 285–311.
  41. Meece J. L., Glienke B. B. and Burg S., (2006), Gender and motivation, J. Sch. Psychol., 44, 351–373.
  42. Meece J. L. and Jones M. G., (1996), Gender differences in motivation and strategy use in science: Are girls rote learners?, J. Res. Sci. Teach., 33, 393–406.
  43. Muniz J., Elosua P. and Hambleton R. K., (2013), International Test Commission Guidelines for test translation and adaptation: second edition, Psicothema, 25, 151–157.
  44. Murphy, R. and Johnson D., (2009), Education and development in China – Institutions, curriculum and society, Int. J. Educ. Dev., 29 (5), 447–453.
  45. Osborne J., Simon S. and Collins S., (2003), Attitudes towards science: a review of the literature and its implications, Int. J. Sci. Educ., 25, 1049–1079.
  46. Pajares F., (1996), Self-efficacy beliefs in academic settings, Rev. Educ. Res., 66, 543–578.
  47. Palmer T., Burke P. F. and Aubusson P. (2017), Why school students choose and reject science: a study of the factors that students consider when selecting subjects, Int. J. Sci. Educ.39 (6): 645–662.
  48. Sadler P. M., Sonnert G., Hazari Z. and Tai R., (2012), Stability and volatility of STEM career interest in high school: A gender study, Sci. Educ., 96, 411–427.
  49. Salta K. and Koulougliotis D., (2015), Assessing motivation to learn chemistry: adaptation and validation of Science Motivation Questionnaire II with Greek secondary school students, Chem. Educ. Res. Pract., 16, 237–250.
  50. Salta K. and Koulougliotis D., (2020), Domain specificity of motivation: chemistry and physics learning among undergraduate students of three academic majors, Int. J. Sci. Educ., 42, 253–270.
  51. Schmid S. and Bogner F. X., (2017), How an inquiry-based classroom lesson intervenes in science efficacy, career-orientation and self-determination, Int. J. Sci. Educ., 39, 2342–2360.
  52. Schumacker R. E. and Lomax R. G., (2016), A beginner's guide to structural equation modeling, New York: Routledge.
  53. Schumm M. F. and Bogner F. X., (2016), Measuring adolescent science motivation, Int. J. Sci. Educ., 38, 434–449.
  54. Schunk D. H., (1999), Social-self interaction and achievement behavior, Educ. Psychol., 34, 219–227.
  55. Schunk D. H., (2001), Social cognitive theory and self-regulated learning, in Zimmerman B. J. and Schunk D. H. (ed.), Self-Regulated Learning and Academic Achievement: Theoretical Perspectives, Mahwah, NJ: Erlbaum, vol. 2, pp. 125–151.
  56. Schunk D. H. and DiBenedetto M. K., (2020), Motivation and social cognitive theory, Contemp. Educ. Psychol., 60, 101832.
  57. Schunk D. H., Pintrich P. R. and McCcc J. L., (2014), Motivation in education: theory, research and applications, Upper Saddle River, NJ: Pearson.
  58. Sharma S., Mukherjee S., Kumar A. and Dillon W. R., (2005), A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models, J. Bus. Res., 58, 935–943.
  59. Shin S., Lee J.-K. and Ha M., (2017), Influence of career motivation on science learning in Korean high-school students, Eurasia J. Math. Sci. Technol. Educ., 13, 1517–1538.
  60. Shin S., Rachmatullah A., Ha M. and Lee J.-K., (2018), A Longitudinal Trajectory of Science Learning Motivation in Korean High School Students, J. Balt. Sci. Educ., 17, 674–687.
  61. Shwartz G., Shav-Artza O. and Dori Y. J., (2021), Choosing chemistry at different education and career stages: Chemists, chemical engineers, and teachers, J. Sci. Educ. Technol., 30, 692–705.
  62. Steele C. M., (1997), A threat in the air – How stereotypes shape intellectual identity and performance, Am. Psychol., 52, 613–629.
  63. Sunny C. E., Taasoobshirazi G., Clark L. and Marchand G., (2017), Stereotype threat and gender differences in chemistry, Instr. Sci., 45, 157–175.
  64. softwareThe jamovi project (2022), jamovi, (Version 2.3) [Computer Software], Retrieved from https://www.jamovi.org.
  65. Tuominen H., Juntunen H. and Niemivirta M., (2020), Striving for success but at what cost? Subject-specific achievement goal orientation profiles, perceived cost, and academic well-being, Front. Psychol., 11, 557445.
  66. Wang L., (2012), Social exclusion and education inequality: towards an integrated analytical framework for the urban-rural divide in China, Br. J. Sociol. Educ., 33 (3), 409–430.
  67. Woolfolk A., (2020), Educational psychology, Essex: Pearson Education.
  68. Wu X., (2019), Inequality and social stratification in postsocialist China, Annu. Rev. Sociol., 45, 363–382.
  69. Zeyer A., Cetin-Dindar A., Zain A. N. M., Jurisevic M., Devetak I. and Odermatt F., (2013), Systemizing: A cross-cultural constant for motivation to learn science, J. Res. Sci. Teach., 50, 1047–1067.
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