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DOI: 10.1039/D2RP00070A (Paper) Chem. Educ. Res. Pract., 2022, 23, 829-843

Examining relationships between chemistry anxiety, chemistry identity, and chemistry career choice in terms of gender: a comparative study using multigroup structural equation modelling

Xipei Guo a, Xuemin Hao ab, Jun Ma b, Hongyan Wang c and Weiping Hu *ad
aMOE Key Laboratory of Modern Teaching Technology, Shaanxi Normal University, Xi’an, Shaanxi, China. E-mail: Weipinghu@163.com
bSchool of Education, Shaanxi Normal University, Xi’an, Shaanxi, China
cXi’an International Studies University, Xi’an, Shaanxi, China
dShaanxi Normal University Branch, Collaborative Innovation Center of Assessment toward Basic Education Quality at Beijing Normal University, China

Received 9th March 2022 , Accepted 18th May 2022

First published on 30th May 2022

Abstract

Although there are numerous chemistry-related careers within the STEM fields, chemistry-related careers are not well regarded. High school is a critical time for developing students’ career choices. Previous studies suggest that anxiety and identity may be predictors of career choice. Therefore, the purpose of this study was to investigate the influence of high school students’ chemistry anxiety (learning anxiety and test anxiety) and chemistry identity (competence/performance beliefs, interest, external recognition, and holistic impression on identity) on chemistry career choices. Guided by the possibility of different hindrances to chemistry career choice for males and females, the study further detected gender-specific patterns of relations between variables. The results of multigroup structural equation modeling firstly showed that different constructs of chemistry identity were positive and significant predictors of chemistry career choice but varied by gender. Specifically, competence/performance beliefs and holistic impression on identity were significantly associated with females’ chemistry career choices. In contrast, interest, external recognition, and holistic impression on identity motivated males’ chemistry career choices. Secondly, the effects of chemistry learning anxiety and test anxiety on chemistry career choice were completely mediated by chemistry identity, whereas the pathways and strength of mediation differed between females and males.

Introduction

In modern times, the cultivation of high-level science, technology engineering, and mathematics (STEM) talents is considered key to enhancing national competitiveness and solving complex human sustainability problems. As a fundamental natural science, chemistry is inextricably linked to many STEM careers, such as medicinal chemistry, materials chemistry, environmental chemistry, bioengineering, and food safety (Buriak and Jillian, 2015; Huryn et al., 2017; Spitzer and Gröger, 2018). However, the reality is that most countries are facing a shortage of talent in the field of STEM, and in particular chemistry-related careers are recognized as an undesirable option (Avargil et al., 2020).

One reason is that many students don’t choose to study chemistry in high school, further contributing to the decline in the number of graduate students who continue to pursue chemistry in college (Ardura and Pérez-Bitrián, 2018; Avargil et al., 2020). Specifically, in many countries, science-related courses are not required in high school, and students can choose whether to learn science-related subjects by themselves. This has an impact on high school students’ science learning, with the most pronounced impact on chemistry and physics (Ardura and Pérez-Bitrián, 2018). However, the subject learning in high school is the foundation and a necessary prerequisite for the selection of a student's major in college. In addition, researchers have noted that adolescence is a critical stage in forming career choices (Dudovitz et al., 2017). Drawing on research associated with social psychology, the career choices formed during adolescence play a decisive role in their career trajectory as adults (Bandura et al., 2001; Riegle-Crumb et al., 2011). Therefore, enhancing high school students’ propensity for chemistry career choice is critical to retaining chemistry talents within STEM fields.

Understanding what factors influence students’ willingness to choose a chemistry career is key to improving talent retention within the chemistry field. According to the previous studies, identity plays a large role in a student's career choice (Robinson et al., 2019), while both identity and career choice are domain-specific. Despite the increasing attention on the relationships between identity and career choice, few studies have investigated the role of chemistry identity in chemistry career choice for high school students (Nauta and Kahn, 2007; Cass et al., 2012; Li et al., 2015; Godwin et al., 2016). Therefore, the first aim of this study was to test whether chemistry identity is related to students’ chemical career choices so that we can influence their career choices by enhancing their chemistry identity.

Furthermore, relations between anxiety and either identity or career choices have been well reported in previous literature, and anxiety is similar to identity and career choice in that they are domain-specific concepts (Pekrun, 2006). Nevertheless, there is a lack of research on the relationship between chemistry identity, chemistry anxiety, and chemistry career choice, especially in the high school student population. More importantly, both chemistry identity and chemistry anxiety contain multiple dimensions, whereas few studies have examined intertwined relations between them and chemistry career choices in a single model. Thus, the second purpose of this study was to explore whether it is possible to increase students’ chemistry identity in multiple dimensions by reducing their different dimensions of chemistry anxiety and further influencing their willingness to choose a career in chemistry.

In addition, studies have shown a gender gap in the chemistry field (Huryn et al., 2017). Drawing on expectancy-value theory and related research, the gender gap in domain-specific related career choices may be explained by gender differences in identity and anxiety (Wang and Degol, 2013). For example, the level of math anxiety and the effect of math anxiety on science-related career interests differed between males and females (Huang et al., 2018). Based on this, we sought to further test whether the relationship between the different variables differed by gender so that we could make valid recommendations for gender-specific students.

Chemistry identity and chemistry-related career choice

Identity is a self-perception and expectation that develops gradually during the interaction between the self and the environment, which is a domain-specific psychological structure (Berzonsky and Kuk, 2000). In education research fields, chemistry identity can be meant by “I think myself as a chemistry person” (Hosbein and Barbera, 2020a).

Eccles’ expectancy-value theory (EVT), as a leading theory of human motivation, provides a theoretical framework for linking identity and career choices (Eccles et al., 2015; Gottlieb, 2018). According to EVT, the interaction of expectancy × value could predict students’ achievement-related choices (e.g., university major choice) and career choices (Nagengast et al., 2011). Expectancy refers to the individual's expected beliefs about the successful completion of a task, involving self-efficacy and competence judgments. Value refers to the individual's subjective judgment of the significance or importance of the outcome of the behavior, including the importance of completing the task, interest, the link between the task and the individual's future goals, and so on (Eccles, 2009). Based on EVT, identity as a motivational structure that consists of self-concept and subjective value perceptions together influences individuals’ career choices by informing individuals of the importance they place on the task and their expectations of success (Eccles, 2009; Wang and Degol, 2013).

In addition, although “I see myself as a domain-specific person” is considered a single indicator of the holistic impression on identity and is closely related to career choices, educational researchers believe that identity should be conceptualized as a more complex construct (Carlone and Johnson, 2007; Godwin et al., 2016). Specifically, a domain-specific identity has been framed around three key shaping constructs in previous studies: competence/performance beliefs, interest, and external recognition (Carlone and Johnson, 2007; Hosbein and Barbera, 2020b; Verdín, 2021).

The three identity shaping constructs also have been considered when exploring the factors that influence students’ decisions to pursue domain-related careers (Hazari et al., 2010). Competence/performance beliefs refer to students’ beliefs in the ability to understand content knowledge and perform well in applying competencies to solve tasks (Chen et al., 2021). It influences students’ career pursuits primarily by enhancing their self-efficacy (Fouad et al., 2002). Interest represents students’ attitudes toward learning and their desire to learn more and engage in relevant learning activities (Chen and Wei, 2020), and research studies show that higher interest is positively related to student's career choice and persistence (Adams et al., 2006). External recognition refers to teachers’, parents’, and peers’ perceptions of whether a student is a science or chemistry person, which influences the student's self-perception and self-expectations, and in turn, has an impact on the student's career choices (Hazari et al., 2010).

Chemistry anxiety and chemistry career choice

Chemistry anxiety could be defined as an unpleasant emotional state of unease, nervousness and fear toward chemistry or chemistry courses, as well as the observable physiological reactions (i.e., avoidant behavior) that are triggered by those emotions for students (Senocak and Baloglu, 2014).

As mentioned before, EVT treats the “value” as one of the key elements in determining career choice, while “value” depends in part on the judgment of the cost (e.g., time, effort, etc.) to complete the task. When individuals have high anxiety about completing a task, they will perceive that they need to invest a significant amount of cost and thus will be reluctant to choose that task (Eccles, 2009; Degol et al., 2018). Indeed, expanding the focus of the study from career choices to general decisions, researchers similarly point out that higher levels of anxiety lead to negative expectations of outcomes, overestimating and tending to avoid the risks associated with choices. Thus, the higher the level of anxiety, the more it promotes an individual's decision-making avoidance (i.e., not making a choice or postponing making a choice) (Hartley and Phelps, 2012; Arbona et al., 2021).

Although there are few studies conducted to examine the relationship between chemistry anxiety and chemistry career choice, studies in other fields have shown that anxiety is associated with a person's career choices. For example, studies conducted in math fields showed that math anxiety has an impact on students’ math and STEM career choices (Ahmed, 2018; Cribbs et al., 2021). Given that chemistry anxiety is closely related to math anxiety (Eddy, 2000), this study provides information to examine the relationship between chemistry anxiety and career choices.

While the above studies on the relationship between anxiety or math anxiety and career choice provide the empirical and theoretical basis for this study, it is worth noting that chemistry anxiety consists of three dimensions (i.e., evaluation anxiety, learning anxiety, and handling chemicals anxiety) (Senocak and Baloglu, 2014). Whether the chemistry career choices of gender-specific students are influenced by different chemistry anxieties also needs to be further explored.

Chemistry anxiety and chemistry identity

Although no studies focus on examining the relationship between chemistry anxiety and chemistry identity, several experimental studies indicated that anxiety plays a role in identity formation processes and thus determines the final identity state. According to the identity theory of Erikson and Marcia, identity formation is a dynamic process in which the main elements are exploration and commitment (Malees and Chandler, 1992). The exploration process occurs prior to commitment, involves evaluating possible identity choices (including values, career choices, beliefs, etc.), considering the possible consequences of different identity choices, and making trade-offs (Marcia and James, 1966; Malees and Chandler, 1992). The process of commitment refers to an individual making a relatively firm goal choice in a particular domain and being willing to work toward it (Kroger et al., 2010). Regarding anxiety and identity development, Marcia suggested that compared to low-anxiety individuals, high-anxiety individuals tend not to explore and commit, or explore but make only relatively vague commitments (Marcia, 1967).

Based on Marcia's research, Crocetti et al. (2009) further investigated the causal relationship between anxiety and identity in Western adolescents through a five-wave longitudinal study. The results suggested that high-anxiety individuals’ uncertainty level of the initial identity commitment was higher than for low-anxiety individuals. Meanwhile, individuals with high anxiety levels experience a gradual decrease in their commitment levels over time. The reason is that highly anxious individuals are prone to doubt, rethinking, and re-exploring alternative identities and outcomes, so it is difficult for them to make a firm commitment to an identity. In contrast, the identity commitments of adolescents with low levels of anxiety become more solid over time (Crocetti et al., 2009). In short, high levels of anxiety is an important factor that hinders adolescents’ identity development. In addition, Cribbs et al. (2021) also demonstrated that mathematical anxiety was a significant predictor of mathematical identity.

Gender difference in anxiety, identity, and career choice of chemistry

The shortage of talent in STEM-related careers, especially chemistry-related careers, is indeed a pressing challenge that needs to be addressed today (Shwartz et al., 2021). Yet another real problem is the underrepresentation of women in chemistry-related occupational fields or STEM fields (Wang and Degol, 2013). For example, less than 20% of those employed in the field of medicinal chemistry are women (Huryn et al., 2017). This gender disparity may be due to the different factors on women's and men's career choices and the intensity of the various factors (Robinson et al., 2020). Consistent with this perspective, prior research found that parental values socialization predicted males’ STEM career aspirations by influencing their science motivation, but this pathway was not significant for females (Lee et al., 2020). On the other hand, the findings of Sunny et al. (2016) showed that males had lower test anxiety than females. And it is known that there are differences between men and women in terms of science identity (Williams and George-Jackson, 2014; Vincent-Ruz and Schunn, 2018). Therefore, it is reasonable to expect that the pathways and magnitude of the effects of chemistry anxiety and chemistry identity on chemistry career choice vary by gender. Despite gender differences in anxiety, identity, and career choice having been reported in existing studies, it is not clear whether the interrelationships between the different variables would differ by gender in adolescents. In addition, although it is widely acknowledged that men tend to pursue chemistry-related careers, Avargil et al. (2020) found that women were more likely than men to choose chemistry at the high school and college levels in Israel. Therefore, we need to further examine gender differences in chemistry career choices in the context of the present study and whether such gender differences are associated with chemistry anxiety and chemistry identity.

Method

The present study

As outlined in the theoretical and empirical investigation above, there should be a link between chemistry anxiety, chemistry identity, and chemistry career choice. Firstly, few researchers investigated the relationship between chemistry anxiety, chemistry identity, and chemistry career choices. Research on understanding the relationship between anxiety, identity, and career choices is mostly in math fields, while these variables are domain-specific (Hosbein and Barbera, 2020; Daker et al., 2022). Secondly, previous studies examined identity and anxiety as unidimensional variables (Huang et al., 2018; Cribbs et al., 2021). However, as mentioned earlier, chemistry identity and chemistry anxiety are multidimensional variables (Hazari et al., 2010; Senocak and Baloglu, 2014). Research on the relationship between dimensions of chemistry anxiety and chemistry identity, as well as the different effects they have on chemistry career choices is missing. Thirdly, most of the research on the relationship between different variables ignores gender differences. To contribute to increasing students’ willingness to pursue chemistry-related careers, further studies are needed into whether the relationship between different variables would differ by gender. Thus, the research questions that guides our analyses were as follows:

1. How do high school students’ chemistry anxiety and chemistry identity relate to chemistry career choice?

2. Do the relations between chemistry anxiety, chemistry identity, and chemistry career choice differ by gender?

Given that both chemistry anxiety and chemistry identity are multidimensional variables, we further define the relationship between the different dimensions of each variable. The case of chemistry anxiety mainly includes evaluation anxiety, learning anxiety, and handling chemicals anxiety (Eddy, 2000). However, the above three dimensions of chemistry anxiety mainly target college students, and 10th grade students in China are less exposed to chemistry experiments, so this study only focuses on chemistry learning anxiety and chemistry test anxiety. For chemistry identity, it is recognized as including three shaping constructs (i.e., competence/performance beliefs, interest, and external recognition) and a single indicator (i.e., I see myself as a chemistry person) (Hosbein and Barbera, 2020; Verdín, 2021). The single indicator represents students’ overall perceptions of chemistry identity. Previous research in other fields (e.g., engineering) has demonstrated a positive relationship among the four aspects of identity, i.e., competence/performance beliefs influence students’ overall perception of identity through the mediating role of interest and external recognition, respectively (Godwin et al., 2016; Dou and Cian, 2021; Verdín, 2021) (as shown in Fig. 1). Based on this, we expand our work on how the different sub-constructs of chemistry anxiety and identity are associated with chemistry career choice, with one hypothetical model further presented in Fig. 2.


image file: d2rp00070a-f1.tif
Fig. 1 Relationships between recognition, performance/competence beliefs, interest, and holistic impression on identity based on previous identity framework.

image file: d2rp00070a-f2.tif
Fig. 2 Hypothesized model regard relationships between chemistry anxiety, chemistry identity and chemistry career choice. Notes. CLA chemistry learning anxiety, CTA chemistry test anxiety, Com/Per competence/performance, INT interest, E-Rec external recognition, HICI holistic impression on chemistry identity, Car-Cho chemistry career choice.

Sample

A total of 579 students in Grade 10 were selected from China to participate in this study, with all students aged 14 to 18 years (mean = 15.4; SD = 0.6). A stratified sampling method was used in this study. First, China includes three major economic zones: the eastern coastal region, the central inland region, and the western region. We selected one school in the eastern coastal region and the central inland region, as well as two schools in the western region, for a total of four schools. Second, we divided the students in each school into three levels (high, medium, and low) according to their academic performance. At each level, about 50 students were randomly selected to participate in the survey.

The reasons for selecting the 10th-grade students in China include the following three points. First, these students have already experienced two years of systematic chemistry learning and had some basic understanding of chemistry. Second, these students are about to choose which subjects they would continue to learn in 10th grade. Third, compared to other high school grades, students in grade 10 are under less academic pressure and thus have enough time to fill out the questionnaire carefully. Among them, 249 were males, accounting for 43.0%, and 330 were females, accounting for 57%.

Permissions were obtained from all appropriate authorities prior to the implementation of the survey. In addition, we explained the purpose of the survey to teachers, parents, and students. All students participated voluntarily with the consent of their parents and teachers, and no additional fees or costs were provided to students. We committed to protecting the privacy of our students.

Data analysis procedures and tools

The data analysis tools used in this study include SPSS 21.0 and AMOS 23.0. The main uses of the tool and the research procedures are shown below:

First, the questionnaires used in the present study were adapted from the well-established questionnaire. Therefore, the reliability and validity of the adapted questionnaires need to be examined first. All samples were randomly divided into two groups. An exploratory factor analysis (EFA) was conducted firstly using SPSS 21.0 for sample 1 (Lee et al., 2008). After further revision based on the results of the EFA, sample 2 was subjected to CFA using AMOS 23.0 to verify the validity of the potential variables (Velayutham and Aldridge, 2012). In addition to this, Cronbach's Alpha (α) was performed using SPSS 21.0 to detect the internal consistency of the questionnaires (Hair, 2006). For the EFA results, the number of factors extracted was determined based on parallel analysis. According to O’Conno (2000), if the eigenvalue explained by a factor drawn from the actual data is larger than that explained by the corresponding factor drawn from the eigenvalue from the random data, the factor should be retained. In addition, the factor loading scores for each item should be higher than 0.40 (Wei et al., 2020). The CFA results indicate an acceptable model fit when the following indicators are shown: χ2/df < 5, RMSEA values < 0.08, SRMR < 0.08, as well as CFI and TLI values are greater than 0.9 (Opperman et al., 2013). An excellent model fit is indicated when χ2/df < 3, RMSEA and SRMR values are below 0.05, and CFI and TLI are greater than 0.95 (Brown and Cudeck, 1992).

Second, the direct and indirect effects in the hypothesized model were tested using multigroup structural equation modeling (Multi-group SEM) with AMOS 23.0. Indirect effects refer to the effect of the independent variable on the dependent variable through one or more mediating variables. Hence it is also called the mediating effect. If both mediating and direct effects are significant, the effect of the independent variable on the dependent variable is incompletely mediated; when the mediating effect is significant while the direct effect is insignificant, it means that the relationship between variables is completely mediated (González and Paoloni, 2015). The significance test for the effect was performed using the bias-corrected bootstrap method. It has been shown that the bootstrap method has higher test power than the Sobel test and is not affected by the pattern of data distribution (Edwards and Lambert, 2007; Preacher and Hayes, 2008). 5000 bootstrap samples along with 95% confidence intervals were used to determine the significance of the effect. If the 95% confidence intervals do not include 0, the effect is significant (Hayes, 2015). The fit of the entire hypothetical model was evaluated by the χ2/df, RMSEA, SRMR, GFI, CFI, and TLI.

Compared to the SEM, the advantage of multi-group SEM is that it not only tests the relationship between variables but more importantly, it also examines whether the relationship between variables is equal across sample groups or whether the parameters have invariance (Lee and Whittaker, 2021). Specifically, we constructed the following four nested models. An unconstrained model was constructed in the first step, which assumed a different model for each gender group. In the second step, we gradually added the constraints of equal structural weights, equal structural covariances, and equal measurement residuals to obtain three constrained models in turn (Orkibi and Ram-Vlasov, 2019). Then, the changes in CFI and chi-square values between the different nested models were used to determine whether the path relationships in Model 1 differed by the gender of the students. If the change in chi-square value is not significant (Δχ2p-value > 0.05) and ΔCFI < 0.01, it indicates that the relationships in the model remain consistent across groups (Cheung and Rensvold, 2002; Kang et al., 2018).

Measurement instruments

Measures of chemistry anxiety. The questionnaire to measure students’ chemistry anxiety levels was adapted from Derived Chemistry Anxiety Rating Scale (DCARS) and Abbreviated Math Anxiety Scale (AMAS). Since chemistry anxiety is defined similarly to math anxiety, the Revised Mathematics Anxiety Rating Scale (RMARS) was used to develop the DCARS. RMARS consists of two factors, learning math anxiety and math evaluation anxiety (Plake and Parker, 1982). Eddy (2000) added the handling-chemicals anxiety to RMARS to develop DCARS for college students. However, the sub-scale of handling-chemicals anxiety aims at measuring college students’ anxiety about using chemicals and laboratory equipment while performing chemical experiments (Eddy, 2000). For example, heating a chemical in the Bunsen Burner flame (Senocak and Baloglu, 2014).

Although 10th grade students in China also perform chemistry experiments, the number of experiments is much less compared to college students. Their chemistry learning is mainly in the classroom, where the teacher will conduct a chemistry experiment demonstration. Thus, only two subscales from the DCARS (learning-chemistry anxiety and chemistry-evaluation anxiety) were used in this study. In addition, Eddy (2000) did not provide adequate reliability and validity statements for the DCARS, and Hopko (2003) demonstrated that the two-factor model fit of RMARS cited by DCARS was poor (Derek and Hopko, 2003). Furthermore, Hopko et al. (2003) censored the RMARS and constructed the Abbreviated Math Anxiety Scale (AMAS) with nine items. The two-factor model of AMAS has been shown to have good reliability and validity, as well as cross-gender measurement invariance in adolescent populations by researchers in several countries, including Germany, Spain, and Italy (Primi et al., 2014; Schillinger et al., 2018; Martin-Puga et al., 2022).

Taken together, we selected the corresponding nine items from the DCARS according to the AMAS to constitute the instrument for measuring chemical anxiety in the present study. Five items measure students’ anxiety about learning chemistry (e.g., having to use the tables in a chemistry book, which was translated into Chinese “image file: d2rp00070a-u1.tif”) and four items measure students’ chemistry-evaluation anxiety (e.g., thinking about an upcoming chemistry test one day before, which was translated into Chinese as “image file: d2rp00070a-u2.tif”). All items were scored on a 5-point Likert-type scale (1 = “not at all”, 2 = “a little bit”, 3 = “moderately”, 4 = “quite a bit”, and 5 = “extremely” anxiety). As for the EFA, the results showed that the revised DCRAS had a two-factor structure (KMO = 0.905, Bartlett's spherical test showed χ2(36) = 1618.148 and p < 0.001) and the two factors explained 70.834% of the total variance. Parallel analysis results showed that the actual data eigenvalues of the first two factors were greater than the random data eigenvalues 95th percentile. The factor loadings of each item in the EFA ranged from 0.442 to 0.909. According to the CFA, the two-factor model fits well: χ2/df = 2.208, RMSEA = 0.065, SRMR = 0.039, CFI = 0.974, and TLI = 0.964.

Measures of chemistry identity. The instrument used for the chemistry identity measure was adapted from the Engineering Identity Measures (EIM) and Student Science Identity (SSI) questionnaire (Godwin et al., 2016; Chen and Wei, 2020). The chemistry identity measurement questionnaire includes three shaping constructs (competence/performance beliefs, interest, and external recognition). The total number of items was 13. Twelve of these items were used to measure competence/performance beliefs, external recognition, and interest, as well as a single indicator (I see myself as a chemistry person) was used to test the overall perceptions of chemistry identity. Students scored using a five-point Likert scale from “Strongly disagree” to “Strongly agree”. Higher scores indicate higher chemistry identity for the students. In terms of the results of EFA about shaping constructs, KMO and Bartlett's test of sphericity (<0.001) indicated that EFA suitability analysis can be performed (KMO = 0.892, Bartlett's spherical test showed χ2(66) = 2384.138 and p < 0.001). Factor loadings for each item were greater than 0.4 (ranged from 0.587 to 0.902) with an initial eigenvalue of 6.133, explaining 73.619% of the total variance. The actual data eigenvalues of the first three factors were greater than the random data eigenvalues 95th percentile. The results from CFA suggested an acceptable model fit, as χ2/df = 2.423, RMSEA = 0.070, SRMR = 0.051, CFI = 0.964, and TLI = 0.953.
Chemistry career interest. We use a single item to measure students’ Chemistry career interest. According to Fuchs and Diamantopoulos (2009), the rationale for using a single item to measure chemistry career interest includes four aspects. First, chemistry career interest aims at measuring students’ overall perceptions of their intentions to choose a chemistry-related career. Further, Fuchs and Diamantopoulos (2009) stated that concrete and unidimensional constructs such as intention and favorability are appropriate to use single-item measures. Third, the participants in this study are a diverse group of students at different learning levels, from different regions of China, and with not identical learning experiences. Fourth, many studies have demonstrated the appropriateness of using a single item to measure students’ interest in a specific career choice (Huang et al., 2018; Robinson et al., 2020). This item was adapted from Riegle-Crumb et al. (2011) that was used to measure adolescents’ willingness to choose a career in mathematics and science. The adapted item is “To what extent do you agree with the following statement? I would like a job that involves using chemistry”. The results are divided into 5 levels, from 1 to 5 representing strongly disagree to strongly agree.

Results

Preliminary analysis

The mean and standard deviation of each variable were calculated and presented in Table 1. In addition to this, the skewness and kurtosis of each variable were presented in Table 1 to assure the normality of the variables. According to Kline (2005), the kurtosis and skewness of the variables within ±3 are consistent with normal. As demonstrated in Table 1, the skewness of the variables in the present study ranged from 0.07 to 0.56, and the kurtosis was in the range of −0.10 to 0.96, all of which are inside the threshold value. Table 2 displays the bivariate correlations between variables in this study. The results suggested that the correlations between all variables were significant.
Table 1 Descriptive statistics
Variable Minimum Maximum Mean SD Skewness Kurtosis
Notes. CLA chemistry learning anxiety, CTA chemistry test anxiety, Com/Per competence/performance, INT interest, E-Rec external recognition, HICI holistic impression on chemistry identity, Car-Cho chemistry career choice.
CLA 1 5 2.17 0.85 0.56 −0.10
CTA 1 5 2.98 1.03 0.07 −0.65
Com/per 1 5 3.06 0.82 0.24 0.08
INT 1 5 2.08 0.93 0.26 0.96
E-Rec 1 5 3.60 0.84 0.53 0.36
HICI 1 5 2.31 1.08 0.37 0.56
Car-Cho 1 5 2.56 1.06 0.21 0.40

Table 2 Bivariate correlations between variables
1 2 3 4 5 6 7
Notes. CLA chemistry learning anxiety, CTA chemistry test anxiety, Com/Per competence/performance, INT interest, E-Rec external recognition, HICI holistic impression on chemistry identity, Car-Cho chemistry career choice.
(1) CLA —
(2) CTA 0.58*** —
(3) Com/per −0.46*** −0.43*** —
(4) INT −0.35*** −0.27*** 0.61*** —
(5) E-Rec −0.31*** −0.35*** 0.52*** 0.38*** —
(6) HICI −0.33*** −0.34*** 0.51*** 0.45*** 0.74*** —
(7) Car-Cho −0.28*** −0.28*** 0.48*** 0.44*** 0.57*** 0.60*** —

Next, we performed tests of measurement invariance to ensure that the differences between groups were not due to measurement differences (Wigfield and Harold, 1997). Before the measurement invariance test, we first performed separate CFA analyses on the male and female data, and the results showed that all models fit well (see Table 3). Then, the measurement invariance test is completed in four steps. In the first step, a configured model (baseline model) is built to ensure that the survey items are related to each other in a similar way in all groups. In the second step, the metric model is built based on the configured model with the constraint of equal factor loadings. If the difference between the metric model and the unconstrained model is not significant, it means that there is weak invariance. In the third step, the scalar model is built by restricting the item intercepts to be equal based on the invariance of the metric model. If the intercept invariance holds, the measurement tool has strong measurement invariance. Finally, limiting the error covariances to be equal, the model has strict measurement invariance if the change from the third step is not significant (Robinson et al., 2020; Rocabado et al., 2020). As displayed in Table 3, the results support weak invariance (Δχ2p-value > 0.05, ΔCFI < 0.01, ΔRMSEA < 0.015, ΔSRMR < 0.03) and strong invariance across gender groups (Δχ2p-value > 0.05, ΔCFI < 0.01, ΔRMSEA < 0.015, ΔSRMR < 0.01). The significant change in the chi-square values of the conservative model compared to the scalar model indicates that the two measurement instruments didn’t reach a strict level of measurement invariance (Rocabado et al., 2020).

Table 3 Measurement invariance testing for the chemistry anxiety and chemistry identity
Model χ 2 DF χ 2/df CFI SRMR RMSEA Δχ2p-value ΔCFI ΔSRMR ΔRMSEA
Notes. CLA chemistry learning anxiety, CTA chemistry test anxiety, Com/Per competence/performance, INT interest, E-Rec external recognition, HICI holistic impression on chemistry identity, Car-Cho chemistry career choice.
Chemistry anxiety
Male 62.084 26 2.388 0.963 0.041 0.075
Female 44.547 26 1.713 0.987 0.042 0.047
Configural 106.647 52.000 2.051 0.977 0.041 0.043
Metric (weak) 111.891 59.000 1.896 0.978 0.043 0.039 0.630 0.001 0.002 −0.004
Scalar (strong) 121.848 68.000 1.792 0.977 0.047 0.037 0.354 −0.001 0.004 −0.002
Conservative (strict) 178.206 77.000 2.314 0.957 0.047 0.048 0.000 −0.020 0.000 0.011
Chemistry identity
Male 116.563 51 2.286 0.965 0.051 0.072
Female 139.281 51 2.731 0.962 0.051 0.073
Configural 255.850 102.000 2.508 0.963 0.0509 0.051
Metric (weak) 263.366 111.000 2.373 0.963 0.0505 0.049 0.584 −0.0004 0.000 −0.002
Scalar (strong) 283.936 123.000 2.308 0.961 0.0505 0.048 0.057 0.0000 0.002 −0.001
Conservative (strict) 364.581 135.000 2.701 0.945 0.0521 0.054 0.000 0.0016 0.0016 0.006

Third, the present study investigated the mean differences across gender on each variable through an independent-samples t-test. As shown in Table 4, both chemistry learning anxiety and chemistry test anxiety were significantly higher in females than in males. Regarding the chemistry identity, females’ competence/performance, external recognition, and holistic impression on chemistry identity were lower than males, and the differences were statistically significant. Nevertheless, females’ interest in chemistry was slightly lower than males’, but the difference was not significant. In addition, females’ willingness to choose a chemistry-related career in the future was less likely than males.

Table 4 Mean differences across gender
Mean β t df p
Female Male
Notes. CLA chemistry learning anxiety, CTA chemistry test anxiety, Com/Per competence/performance, INT interest, E-Rec external recognition, HICI holistic impression on chemistry identity, Car-Cho chemistry career choice.
CLA 2.27 2.05 0.13 3.15 577.00 <0.001
CTA 3.22 2.66 0.27 6.68 577.00 <0.001
Com/Per 2.88 3.29 0.25 −6.21 577.00 <0.001
E-Rec 1.90 2.31 0.22 −5.44 577.00 <0.001
INT 3.55 3.67 0.07 −1.58 537.91 0.116
HICI 2.12 2.57 0.21 −5.03 577.00 <0.001
Car-Cho 2.42 2.75 0.15 −3.72 577.00 <0.001

Structural model analysis

Before the multi-group analysis, we first attempted to examine the model fit of the theoretical model for all students, males, and females separately (Fig. 2). The SEM results showed that the fitted indices of the theoretical model are partially not within the acceptable range for all students and females (see Table 5). We found that the direct effects of chemistry learning anxiety and chemistry test anxiety on career choice were not significant in both male and female groups. Therefore, we removed the two direct paths from chemical learning anxiety and test anxiety to career choice (Fig. 3). SEM results indicated that the revised model fitted well in all groups.
Table 5 The fit index of the theoretical model and revised model
Model χ 2/df TLI CFI RMSEA SRMR
Theoretical model
All students 5.956 0.939 0.997 0.093 0.016
Female 5.848 0.884 0.994 0.121 0.021
Male 1.721 0.980 0.999 0.054 0.013
Revised model
All students 2.059 0.987 0.998 0.043 0.016
Female 2.054 0.975 0.996 0.057 0.022
Male 1.386 0.989 0.998 0.039 0.018


image file: d2rp00070a-f3.tif
Fig. 3 Revised model regarding relationships between chemistry anxiety, chemistry identity, and chemistry career choice.

Multi-group analysis across gender

The multi-group SEM analysis was conducted to test the goodness of fit of the revised models, as well as whether the model varies by gender. Tables 6 and 7 display the results of the multi-group SEM analysis. Table 6 shows that the model fit indices of all unconstrained and constrained models reached a good level (χ2/df < 3, RMSEA < 0.05, SRMR < 0.05, TLI > 0.95, CFI > 0.95), which suggested that the model adequately explains the data.
Table 6 The fit index for the constrained models and unconstrained model from the multigroup analysis
Model χ 2 DF χ 2/df TLI CFI RMSEA SRMR
Unconstrained (A) 10.320 6.000 1.720 0.981 0.997 0.035 0.215
Structural weights (B) 30.700 23.000 1.335 0.991 0.995 0.024 0.271
Structural covariances (C) 45.335 26.000 1.744 0.981 0.988 0.036 0.307
Structural residuals (D) 53.250 31.000 1.718 0.982 0.986 0.035 0.293

Table 7 Structural model invariance test results
Model Δχ2 ΔDF Δχ2p-value ΔCFI
Notes. Δχ2, Δdf, ΔTLI, ΔCFI, and ΔGFI represents the difference between constrained models and the unconstrained model in chi-square, df, Tucker–Lewis fit index (TLI), comparative fit index (CFI), goodness-of-fit index (GFI). Δχ2p-value refers to the significance of the difference between constrained models and the unconstrained model in chi-square.
Structural weights 20.380 17.000 0.255 −0.002
Structural covariances 35.015 20.000 0.020 −0.009
Structural residuals 42.930 25.000 0.014 −0.011

Table 7 further presented the variation between the constrained and unconstrained models. As indicated in Table 7, compared to the unconstrained model (Model A), the constrained models (Model B) with equal structural coefficients had insignificant changes in chi-square (Δχ2p-value = 0.255 > 0.05). This suggested that the path model works for both males and females. However, after adding the restriction of equal structural covariance and the structural weights, Model C changed significantly compared to model A (Δχ2p-value = 0.020 < 0.05). This indicated that the relationship between different variables is not identical for females and males. After further adding the condition of equal variance of structural residual variables in model C, model D also changed significantly compared to model A (Δχ2p-value = 0.014 < 0.05). Accordingly, we used the present accepted unconstrained model to further explore which direct or indirect effects have gender differences (Wu, 2018).

The direct and indirect effects analyses

Fig. 4 and 5 show the significant direct relationships between chemistry anxiety, chemistry identity, and chemistry career choice in females and males. Tables 8 and 9 display how chemistry anxiety influences students’ chemistry career choices through the mediating role of chemistry identity. The 95% bootstrap CI for all indirect effects did not include 0, suggesting that chemistry identity plays a mediating role between chemistry anxiety and chemistry career choice. The significant indirect effects indicate that the influence of chemistry anxiety on chemistry career choice was mediated by chemistry identity. But the specific mediated pathways were different for females and males.
image file: d2rp00070a-f4.tif
Fig. 4 Structural relationships between variables in females. Notes: *p < 0.05, **p < 0.01, ***p < 0.001.

image file: d2rp00070a-f5.tif
Fig. 5 Structural relationships between variables in males. Notes: *p < 0.05, **p < 0.01, ***p < 0.001.
Table 8 The results of indirect path analysis in females
Paths Standardized β SE Bootstrap 95% CI p
Lower Upper
Notes. CLA chemistry learning anxiety, CTA chemistry test anxiety, Com/Per competence/performance, INT interest, E-Rec external recognition, HICI holistic impression on chemistry identity, Car-Cho chemistry career choice.
CLA → INT → HICI → Car-Cho −0.007 0.005 −0.022 −0.001 0.014
CLA → Com/Per → Car-Cho −0.089 0.030 −0.158 −0.039 0.001
CLA → Com/Per → HCSI → Car-Cho −0.019 0.009 −0.043 −0.005 0.006
CLA → Com/Per → INT → HCSI → Car-Cho −0.010 0.005 −0.023 −0.003 0.003
CLA → Com/Per → E-Rec → HCSI → Car-Cho −0.041 0.012 −0.071 −0.022 0.000
CTA → Com/Per → Car-Cho −0.039 0.018 −0.084 −0.012 0.002
CTA → Com/Per → HCSI → Car-Cho −0.008 0.005 −0.022 −0.002 0.005
CTA → Com/Per → INT → HCSI → Car-Cho −0.004 0.003 −0.012 −0.001 0.004
CTA → Com/Per → E-Rec → HCSI → Car-Cho −0.017 0.007 −0.037 −0.006 0.001

Table 9 The results of indirect path analysis in males
Paths Standardized β SE Bootstrap 95% CI p
Lower Upper
Notes. CLA chemistry learning anxiety, CTA chemistry test anxiety, Com/Per competence/performance, INT interest, E-Rec external recognition, HICI holistic impression on chemistry identity, Car-Cho chemistry career choice.
CLA → Com/Per → INT → Car-Cho −0.049 0.022 −0.108 −0.018 0.001
CLA → Com/Per → INT → HCSI → Car-Cho −0.012 0.008 −0.036 −0.002 0.005
CLA → Com/Per →E-Rec → Car-Cho −0.050 0.019 −0.100 −0.021 0.000
CLA → Com/Per → E-Rec → HCSI → Car-Cho −0.026 0.014 −0.064 −0.006 0.006
CTA → E-Rec → Car-Cho −0.073 0.032 −0.153 −0.023 0.003
CTA → E-Rec → HCSI → Car-Cho −0.037 0.02 −0.091 −0.008 0.007
CTA → Com/Per → E-Rec → Car-Cho −0.034 0.015 −0.074 −0.011 0.003
CTA → Com/Per → E-Rec → HCSI → Car-Cho −0.017 0.01 −0.048 −0.004 0.007
CTA → Com/Per → INT → Car-Cho −0.033 0.017 −0.082 −0.008 0.003
CTA → Com/Per → INT → HCSI → Car-Cho −0.008 0.006 −0.027 −0.002 0.006

As for females, chemistry anxiety influenced chemistry career choice through nine indirect pathways, with a total effect of −0.234. The sum of significant indirect effects from chemistry learning anxiety on females’ career choices was −0.166; the total significant indirect effects from chemistry test anxiety on females’ career choices was −0.068. With regard to males, chemistry anxiety influenced chemistry career choice through ten indirect pathways, with a total effect of −0.339. The sum of significant indirect effects of chemistry learning anxiety on males’ career choices was −0.137. The sum of significant indirect effects of chemistry test anxiety on males’ career choices was −0.202.

Discussion and conclusion

High school is a crucial stage in shaping career choices. Although chemistry-related occupations widely exist in various fields such as medicine, materials, and food safety, chemistry-related careers are not popular among high school students. Identity, as a domain-specific variable, is a significant predictor of individuals’ career choices. Based on this, the first purpose of the present research is to investigate the relationship between chemistry identity and chemistry career choice. In addition, previous studies have proved that students’ academic anxiety is related to their identity and career choices. Therefore, this study further intends to test whether the chemistry anxiety of high school students directly or indirectly through chemistry identity affects their chemistry career choice. In addition to this, this study also found that the effect sizes of different dimensions of chemistry anxiety and chemistry identity on chemistry career choice differed between the male and female groups. This has important implications for deepening our understanding of gender differences within the field of chemistry. Next, we discussed the gender-specific patterns of relationships between the variables.

The effects of chemistry anxiety and chemistry identity on gender-specific students’ chemistry career choice

Consistent with previous studies, students’ chemistry identity plays an important role in chemistry career choice. For instance, Hazari's survey indicated that all three shaping constructs of physics identity were positively associated with physics-related career choices for high school students (Hazari et al., 2010). Robinson et al. (2019) found that students’ science identity significantly predicted their STEM pursuits. However, the above previous studies did not distinguish whether the relationships between different structures of identity (three constructs and holistic impressions on chemistry identity) and career choices differed by gender. The results of this study suggested that only the holistic impression of chemistry identity predicted both females’ and males’ chemistry career choices, while the relationships between the three shaping constructs of chemistry identity and career choices differed across genders.

Specifically, the effect of competence/performance beliefs on chemistry-related career choices was significant in the female group but not in the male group. This result can be partially supported by the study of Bubić and Ivanišević (2016), which showed that adolescents’ career self-efficacy (i.e., beliefs about completing the necessary tasks to achieve desired career outcomes) significantly predicted career decisions only for females, but not for males.

Interest and recognition, however, only significantly predict males’ career choices. According to previous studies, because of gender bias, discrimination, and lack of inclusion for females in STEM fields, females do not choose careers related to STEM fields even if they have a strong interest in them (Cardador et al., 2020). Alternatively, females have the ability and tendency to focus on multiple goals at the same time, as opposed to males who typically focus on one career choice (Maines, 1983; Eccles, 2009). In contrast, higher competence/performance beliefs usually mean that less time or effort may be required at work and they can devote enough time to caring for their families. Therefore, females are more likely to choose their career goals based on their ability/performance beliefs rather than their interests. In addition, the results of the relationship between external recognition and chemistry career choice could be corroborated by the investigation of Lee et al. (2020), which reported a significant parental influence on STEM career aspirations of adolescent males, but a smaller and non-significant influence on females.

A direct and significant correlation between chemistry anxiety and chemistry career choice was not found in the present study. This finding is also in line with a study that there was no significant direct correlation between math anxiety and career choice (Cribbs et al., 2021). Regarding the relationships between chemistry anxiety and chemistry identity, the current research found that female and male students’ higher chemistry learning and test anxiety were linked with lower competence/performance beliefs, which are in line with Li et al.'s (2021) investigation. Chemistry test anxiety was not found to be significantly associated with interest in either females or males, which is consistent with previous research (Lohbeck et al., 2016). Chemistry learning anxiety significantly predicted interest only for females. This is similar to the findings of Huang et al. (2018) that math anxiety was associated with science career interests for females, but not for males. In addition, the present study found that chemistry test anxiety was significantly associated with external recognition for males only.

Based on the fact that chemistry anxiety was significantly correlated with chemistry identity, while chemistry identity was a significant predictor of students’ career choices, the present study further verified the fully mediating role of chemistry identity between chemistry anxiety and chemistry career choice. This finding supports the argument of social cognitive theory and social cognitive career theory (SCCT) that the relationship between anxiety and career choice may be mediated by self-efficacy, outcome expectations, and interest, which overlap with identity (Aydin et al., 2011; Mozahem, 2020; Luo et al., 2021).

Considering the gender differences in the direct correlations, the current research further found similarities and differences in indirect pathways and effect sizes from chemistry anxiety to chemistry career choice (see Tables 8 and 9). From the results of the mediation analysis, we found an interesting phenomenon is that the largest effects of either chemistry learning anxiety or chemistry test anxiety on career choices for females were mediated by competence/performance beliefs. These results suggest that females’ competence/performance beliefs play a relatively important mediating role between chemistry anxiety and chemistry career choice. One possible explanation is that females are less dependent on others’ evaluations (Mustafa Alpaslan, 2019), so their career choices more rely on their perceptions of chemistry learning rather than chemistry tests. In turn, the higher the anxiety experienced in learning chemistry, the more negative self-perception the individual will hold about their chemistry competencies and chemistry performance, ultimately leading to a stronger avoidance of chemistry learning and chemistry-related careers (Hembree, 1990; Ashcraft, 2002).

Nevertheless, the greatest indirect effect of chemistry test anxiety on chemistry career choice was mediated by external recognition of males. In addition, chemistry learning anxiety had a greater impact on males' career choices mainly through two chain mediating paths (competence/performance beliefs and external recognition, as well as competence/performance beliefs and interests). The above results suggest that chemistry anxiety is more likely to influence chemistry career choice through the mediating effect of external recognition in males than in females. This may be because the traditional impression is that males have higher talent in science than females and should perform better (Tenenbaum and Leaper, 2003). Therefore, when males hold a higher level of chemistry anxiety, they may believe that they fall short of others’ expectations and thus have difficulty perceiving others’ recognition and feel less confident in themselves.

Implications of the results

As an important subject related to career fields such as medicine, materials development, and environmental protection, it is crucial to enhance students’ chemistry career choices (Buriak and Jillian, 2015; Huryn et al., 2017). In addition to this, there is a problem of the underrepresentation of women in chemistry-related career fields. Therefore, we expect to propose some feasible recommendations to help enhance the chemistry career choice of gender-specific high school students based on the results of this study.

In the classroom, teachers may be able to reduce students’ learning anxiety and increase their competence/performance beliefs through collaborative active learning. On the one hand, active learning satisfies the students’ need for autonomy in the learning process (Daniel, 2016). In such a learning environment, students are more likely to perceive their value in science learning, which further can lead to higher interest and competence beliefs (Cicuto and Torres, 2016; Hendrickson, 2021). Thus, active learning contributes to improving students’ chemistry identity. On the other hand, for high school students, active learning is more difficult than passively receiving knowledge, it may trigger students’ learning anxiety. According to the results of this study, elevated chemical anxiety affects students’ career aspirations by reducing their chemistry identity. Therefore, we need to integrate cooperative learning into active learning, so that students could create interdependent social cohesion by helping each other (Johnson and Maruyama, 1983; Slavin, 2015), which helps reduce students’ learning anxiety (Daniel and Awokoya, 2010). Also, the mutual encouragement among peers and the feeling of being needed by others is crucial to alleviate students’ anxiety and boost their self-confidence (Downing et al., 2020). Thus, teachers should be careful to guide students to respect and praise the efforts of others in the cooperative learning process.

In addition, the present study provides evidence that females’ interest in chemistry was not significantly different from males, but the effect of interest on females’ chemistry career choice intention was not as significant as that of males. In contrast, females’ competence/performance beliefs had a stronger impact on their chemistry career choice intention, but females’ competence/performance beliefs were significantly lower than males. At the same time, learning anxiety has a greater impact on females’ competence/performance beliefs and interests than test anxiety. This suggests that it is crucial to reduce females’ anxiety and enhance their competence/performance beliefs through appropriate strategies during the learning process. In other words, it is more important for females to have positive experiences and feelings about their chemistry learning process rather than chemistry test results. Therefore, teachers should pay special attention to giving positive feedback and evaluating female performance during the learning process so that they can enhance females’ competence/performance beliefs through emotional support (Lou and Noels, 2020). Besides, teachers could help students set appropriate and stage-based learning goals. On the one hand, a clear and explicit learning direction is of benefit to reduce students’ learning anxiety (Law et al., 2010). On the other hand, breaking down a difficult learning goal into stage-based and progressive learning goals allows students to gain confidence through stage-based success, further boosting their competence/performance beliefs to complete more difficult tasks (Chang et al., 2022).

It is important to note that the above results do not imply that enhancing females’ interest and males' competence/performance beliefs are not important. Because females’ interest affects their career choice by enhancing their overall perception of identity, males’ competence/performance beliefs are favorable for enhancing interest and internalizing the external recognition into self-recognition.

Limitations and future directions

The first limitation is the cross-sectional design of this study, so while the results of this study can support a causal hypothesis model based on theory out, future longitudinal studies are needed to examine further validation of the causal relationships between variables.

Second, the measurements in this study were primarily derived from students’ self-reports, which may result in some errors. More diverse and objective measurement methods need to be considered in future studies.

Third, this study has a limited explanation for the differences between males and females in the model. Our interpretation is based on some inferences drawn from previous studies. For example, we conjecture that gender differences in the relationship between gender and career choice are caused by gender bias. However, ours did not investigate whether subjects were influenced by gender bias. Therefore, in future research, we need to further investigate the mechanisms that lead to gender differences through empirical studies.

Forth, this study used a single item for the survey of chemistry-related career interests. However, high school students may not have a complete and deep understanding of chemistry-related careers (Avargil et al., 2020). They may think of chemistry-related careers as chemists in the laboratory (Solano et al., 2011; Avargil et al., 2020). This may be a factor contributing to students’ low interest in chemistry careers. Therefore, educators should pay attention to enhancing career education for high school students to avoid stereotypes about chemistry-related careers.

Funding

This research was supported by the Fundamental Research Funds for the Central Universities (2019TS131; 2021CBWZ001), National Natural Science Foundation of China (31871118; 32171065), Research Program Funds of the Collaborative Innovation Center of Assessment toward Basic Education Quality at Beijing Normal University (2021-05-002-BZGK03), and Social Science Foundation Project of Shaanxi Province (2020P014).

Conflicts of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix

Tables 10 and 11.
Table 10 The direct effects test of the model for females
Path β SE Bootstrap 95% CI p
Lower Upper
CLA → Com/Per −0.367 0.057 −0.475 −0.252 0.000
CTA → Com/Per −0.182 0.058 −0.295 −0.066 0.002
Com/Per → INT 0.541 0.051 0.434 0.635 0.000
Com/Per → E-Rec 0.419 0.046 0.324 0.507 0.000
CLA → INT −0.143 0.064 −0.271 −0.019 0.022
CLA → E-Rec −0.053 0.056 −0.165 0.054 0.339
CTA → E-Rec −0.099 0.058 −0.211 0.016 0.101
CTA → INT 0.032 0.052 −0.073 0.132 0.543
INT → HCSI 0.122 0.043 0.035 0.203 0.008
E-Rec → HCSI 0.635 0.043 0.549 0.713 0.000
Com/Per → HCSI 0.124 0.050 0.026 0.223 0.011
CLA → HCSI −0.036 0.042 −0.121 0.045 0.386
CTA → HCSI 0.035 0.043 −0.047 0.122 0.402
E-Rec → Car-Cho 0.108 0.071 −0.039 0.247 0.145
INT → Car-Cho 0.098 0.053 −0.005 0.202 0.065
HISI → Car-Cho 0.351 0.072 0.212 0.495 0.000
Com/Per → Car-Cho 0.207 0.062 0.084 0.326 0.001

Table 11 The direct effects test of the model for males
Path β SE Bootstrap 95% CI p
Lower Upper
CLA → Com/Per −0.272 0.075 −0.418 −0.125 0.001
CTA → Com/Per −0.223 0.078 −0.373 −0.067 0.006
Com/Per → INT 0.634 0.052 0.523 0.731 0.000
Com/Per → E-Rec 0.428 0.061 0.303 0.545 0.000
CLA → INT −0.037 0.061 −0.158 0.082 0.546
CLA → E-Rec 0.032 0.074 −0.111 0.179 0.651
CTA → E-Rec −0.204 0.073 −0.347 −0.058 0.005
CTA → INT 0.014 0.064 −0.109 0.141 0.810
INT → HCSI 0.196 0.060 0.085 0.316 0.001
E-Rec → HCSI 0.619 0.061 0.490 0.727 0.001
Com/Per → HCSI −0.013 0.081 −0.167 0.150 0.905
CLA → HCSI −0.040 0.058 −0.153 0.076 0.484
CTA → HCSI −0.095 0.062 −0.219 0.027 0.124
E-Rec → Car-Cho 0.330 0.074 0.187 0.479 0.000
INT → Car-Cho 0.217 0.074 0.079 0.368 0.001
HISI → Car-Cho 0.270 0.101 0.060 0.464 0.012
Com/Per → Car-Cho −0.006 0.094 −0.192 0.174 0.943

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