- Journal Details
- Format
- Journal
- eISSN
- 2444-8656
- First Published
- 01 Jan 2016
- Publication timeframe
- 2 times per year
- Languages
- English
To revise the Chinese version of Planned Happenstance Career Inventory (PHCI), a total of 1161 college students were recruited in the study, among whom 327 were used for item analysis and exploratory factor analysis (EFA), 568 for further confirmatory factor analysis (CFA) and internal consistency reliability analysis, and 266 were retested after a four-week interval for test-retest reliability analysis. Occupational Development Contingency Scale (ODCS), CAAS-China were chosen as the criterion-related indication. The scale consisted of 20 items, and the EFA produced five factors, which accumulatively explained the variance of total variance by 58.15%. The CFA showed that the 5-factor model fits well. The internal consistency reliability of sample measurement was 0.862, retest reliability coefficients was 0.871, and the split-half reliability coefficients was 0.733. The Chinese version of PHCI has acceptable reliability and validity and can be used to measure career planning for Chinese college students.
Keywords
- planned happenstance
- validity and reliability
- career
- mathematical statistics
College is an important period for a person to go from adolescence to adulthood, and it is also an important period for the continuous development of self-awareness. From adolescence to adulthood, college students are faced with many challenges, but in the end, through constant trial and error, they acquire adaptation strategies and develop self-management skills to take on new responsibilities [1]. College is also a prelude for a person to enter society. During this period, in addition to the pressure to study, there is also another source of pressure for college students, that is, the pressure of finding employment. College students should not only find their own career path, but also strive for one with potential employment opportunities [2]. while opportunity is particularly important in college students’ career development [3]. In this study, we tested the reliability and validity of a scale for pursuing opportunities for career development among college students and we investigate whether it is an appropriate tool to measure the impact of contingencies on career planning for Chinese college students. We also examined its positive correlation with occupational development contingency and career adapt ability.
The traditional career theory focuses on the psychological structure and the specific process of its function, and emphasises that individuals can make their own career plans which are suitable for themselves and have practical feasibility by analysing the characteristics of themselves and the environment, such as the Personality-Job Fit Theory of Holland. However, many researchers believe that the traditional career theory may fail to describe the impact of unplanned and unexpected events and experience on career development. They propose that career decision-making and career development are not linear, and that the impact of accidental events on individual career development should be paid closer attention.
In fact, Career development theorists have long been concerned about happenstances and its effect on an individual's career. Miller [4] believed that career choices do not always stem from rational decisions and emphasises that accidental events can also affect career decision-making. Betsworth and Hansen (1996) believed that more than half of the respondents attributed their career decision-making to happenstances. The term ‘planned happenstance’ was first proposed by Mitchell et al. [5]. It is defined as ‘an unexpected, unpredictable accident or happenstance’. Planned happenstance theory advocates the use of happenstance as opportunities through one's positive behaviour. Krumboltz [5] proposed the happenstance learning theory (HLT), which emphasises that, along with the growth of the individual, that they will inevitably encounter contingencies, which may be converted into positive and negative experiences of career development. What matters is that individuals learn to identify or create opportunities, using them to advance their career paths. Krumboltz identified five skills for planned happenstance: curiosity, persistence, flexibility, optimism, and risk taking [6, 15].
Based on the above theories, Kim et al. [7] developed a Career-Related Planned Happenstance Scale (CPHS). CPHS consists of 14 projects, but CPHS shows relatively low reliability in terms of risk taking and flexibility. Kim et al. [7] developed the Planned Happenstance Career Inventory (PHCI), which is a tool to measure the skill of actively coping with happenstance and to measure how reaction to happenstance affects career choice. The PHCI measures five skills, and curiosity is defined as the exploration of new learning opportunities that are relevant to career. Persistence means that one continues to pursue one's career tasks regardless of difficulties. Flexibility examines a person's attitude towards adaptation and progress in different occupational environments. Optimism refers to positive expectations for his or her career. Taking risks refers to challenging behaviour. The higher the PHCI score, the greater the confidence in turning happenstance into career-related opportunities.
Jeong and Lee [8] tested the cross-cultural validity of PHCI, which also supports the 5-factor structure. Other related studies have also shown that PHCI is positively correlated with career involvement [9, 14]. Considering that there is no Chinese version of the PHCI, this study will follow the existing research ideas and revise the Chinese version of the PHCI on the basis of the English version. The reliability and validity of the scale are tested in vocational college students, in order to provide a reliable measurement tool for future research, and it is suggested that the measurement method be used in career development research and career counselling practice.
First, the data of sample 1 were collected for item analysis and exploratory factor analysis (EFA), and the data of sample 2 were used for confirmatory factor analysis (CFA), convergence validity analysis and discriminant validity analysis. Next, the data of samples 1 and 2 were used to test the internal consistency reliability. Finally, the data of sample 3 retested 4 months later were used for retest reliability test. The statistical analysis was conducted using SPSS 22.0 and AMOS 24.0.
As authorised by the original author, this study translated the scale into Chinese. Keeping in line with the Chinese cultural background and Chinese expression habits, two English teachers translated the items into Chinese while keeping the original meaning of the scale unchanged, with clear expression and easy understanding as the standard. On this basis, four teachers holding English majors translated it back into English, one psychology graduate student compared the translated items with the original items of the scale, and one psychology expert participated in the evaluation discussion. Finally, we adjust the translated 25 items to make them as simple and easy to understand as possible. A 5-point Likert scale was used, which range from 1 (strongly disagree) to 5 (strongly agree). The higher the score, the more confident people are in turning unexpected events into career-related opportunities.
The ODCS (revised by Zhu Zhiwei in 2019) was adopted [10]. The scale contains 20 items in total, including five factors: individual contingency, career contingency, family contingency, learning contingency and financial contingency. The scale required that the degree of influence of the stated events on career choice was taken as the evaluation standard, and a 5-point Likert scale was used from 1 (strongly disagree) to 5 (strongly agree). The higher the score, the more the career was influenced by happenstance. In this paper, the ODCS was used as a positive criterion. The Cronbach's alpha was 0.90 in the original study.
CAAS-China revised by Hou Zhijin in 2012 is adopted [11]. The scale consists of four dimensions and 24 questions in total. A 5-point Likert scale was used, which range from 1 (strongly disagree) to 5 (strongly agree). In this study, the CAAS-China was used as a positive criterion. The Cronbach's alpha was 0.96 in the original study. The higher the score, the more adaptable the individual's career will be.
The overall reliability coefficient of the data from the item analysis and EFA samples was analysed. The results showed that the reliability coefficient of the sample was 0.701. Then, items were deleted according to the test criteria of the overall correlation coefficient <0.30 and the overall reliability coefficient improved after the deletion of the items. The result showed that the correlation coefficient the 3rd item is 0.11, 5th item is 0.287, 11th item is 0.224, 12th item is 0.280 and 22nd item is 0.223. The correlation coefficient of the total score of the3rd item and 5th item was too low to meet the standard and did not belong to the same factor, so they were deleted. The homogeneity test also showed that after the elimination of these two items, the Cronbach's alpha coefficient increased. For the remaining three factors with low total score correlation, considering that the 22nd item has a longer expression that may require higher semantic understanding ability, the 12th item and 11th item belong to the same factor. If they are deleted rashly, it may affect the homogeneity of the scale. Therefore, we try to retain the 11th, 12th and 22nd items, which will be discussed in combination with EFA.
Correlation between the score of each item and the total score (
| 1 | 0.311** | 6 | 0.335** | 11 | 0.224** | 16 | .430** | 21 | 0.347** |
| 2 | 0.361** | 7 | 0.371** | 12 | 0.280** | 17 | 0.474** | 22 | 0.223** |
| 3 | 0.110* | 8 | 0.308** | 13 | 0.372** | 18 | 0.496** | 23 | 0.421** |
| 4 | 0.355** | 9 | 0.386** | 14 | 0.297** | 19 | 0.415** | 24 | 0.399** |
| 5 | 0.287** | 10 | 0.345** | 15 | 0.379** | 20 | 0.386** | 25 | 0.394** |
The Chinese version of PHCI factor items and loads (
| C20 | 0.830 | ||||
| C19 | 0.809 | ||||
| C16 | 0.763 | ||||
| C23 | 0.715 | ||||
| C21 | 0.676 | ||||
| C14 | 0.788 | ||||
| C10 | 0.744 | ||||
| C4 | 0.728 | ||||
| C6 | 0.709 | ||||
| C12 | 0.641 | ||||
| C18 | 0.799 | ||||
| C13 | 0.751 | ||||
| C17 | 0.719 | ||||
| C15 | 0.645 | ||||
| C8 | 0.847 | ||||
| C24 | 0.776 | ||||
| C9 | 0.679 | ||||
| C2 | 0.813 | ||||
| C1 | 0.761 | ||||
| C7 | 0.687 | ||||
PHCI, Planned Happenstance Career Inventory.
An EFA was based on sample 1. First, principal component analysis and maximum variance method were used to carry out EFA for 23 items in the original scale. The results showed that the KMO value was 0.754, Bartlett's test of sphericity test
Based on The Chinese version of PHCI model obtained from EFA, AMOS 24.0 was used to do CFA on sample 2. The results showed that all the fitting indexes met the statistical standards (see Table 3). The Chinese version of PHCI model with 20 items and five factors fitted well and had good structural validity. The EFA results of this study retained 20 items, which was inconsistent with the structure of the original scale, so we compared the 5-factor model of 20 items with the 5-factor model of 25 items and found that the former produced more stability than the latter (
CFA Model fitting index of the Chinese version of PHCI (n = 568)
| Revised 5 factors (20 items) | 3.135 | 0.925 | 0.901 | 0.926 | 0.926 | 0.912 | 0.056 | 0.062 |
| 5 factors (25 items) | 4.975 | 0.826 | 0.787 | 0.832 | 0.833 | 0.810 | 0.084 | 0.087 |
CFA, confirmatory factor analysis; PHCI, Planned Happenstance Career Inventory.
The external validity was verified by the criterion-related validity. Using 568 valid data of sample 2, the ODCS and CAAS-China were used as the validity criteria of the Chinese version of PHCI. Correlation analysis showed that the total score of the Chinese version of PHCI and the scores of the five factors were significantly positively correlated with the total score of the ODCS and CAAS-China (Table 4). In this study, the composite reliability (CR) and average variance extracted (AVE) of the scale were used for the convergent validity. The results showed that the CR of the Chinese version of PHCI were from 0.82 to 0.95, and AVE were from 0.58 to 0.63, indicating that the scale had good convergent validity. The detailed results are shown in Table 5.
Correlation between the Chinese version of PHCI and efficacy targets (
| Factor 1 | 1 | |||||||
| Factor 2 | 0.722** | 1 | ||||||
| Factor 3 | 0.776** | 0.706** | 1 | |||||
| Factor 4 | 0.309** | 0.196** | 0.298** | 1 | ||||
| Factor 5 | 0.399** | 0.395** | 0.386** | 0.356** | 1 | |||
| The Chinese version of PHCI | 0.879** | 0.847** | 0.846** | 0.497** | 0.636** | 1 | ||
| ODCS | 0.157** | 0.074* | 0.115** | 0.426** | 0.321** | 0.256** | 1 | |
| CAAS-China | 0.711** | 0.682** | 0.639** | 0.186** | 0.360** | 0.723** | 0.142** | 1 |
CAAS, Career Adapt Ability Scale; ODCS, Occupational Development Contingency Scale; PHCI, Planned Happenstance Career Inventory.
Convergence validity test table of the Chinese version of PHCI
| Factor 1 | 0.682–0.823 | 0.58 | 0.87 |
| Factor 2 | 0.704–0.812 | 0.60 | 0.88 |
| Factor 3 | 0.704–0.833 | 0.61 | 0.86 |
| Factor 4 | 0.700–0.838 | 0.60 | 0.82 |
| Factor 5 | 0.748–0.828 | 0.63 | 0.84 |
| Scale as a whole | 0.682–0.838 | 0.60 | 0.97 |
AVE, average variance extracted; CR, composite reliability; PHCI, Planned Happenstance Career Inventory.
A total of 895 valid data of sample 1 and sample 2 were tested for internal consistency reliability. The internal consistency coefficient of the Chinese version of PHCI scale was 0.862, and Cronbach's alpha reliability of the five factors ranged from 0.679 to 0.815.
Test-retest reliability was conducted on 266 valid data in sample 3. The test-retest reliability of the total scale was 0.871 and the test-retest reliability of the five factors was between 0.698 and 0.894. Spearman–Brown coefficient of split-half reliability was 0.773, and the split-half reliability of each factor was between 0.681 and 0.793. (Table 6) It can be concluded that the Chinese version of the PHCI scale has a high reliability.
Reliability analysis table of the Chinese version of PHCI
| Cronbach's alpha | 0.862 | 0.815 | 0.810 | 0.762 | 0.679 | 0.689 |
| Test-retest reliability | 0.871 | 0.766 | 0.798 | 0.750 | 0.698 | 0.726 |
| Split-half reliability | 0.733 | 0.726 | 0.793 | 0.748 | 0.698 | 0.681 |
PHCI, Planned Happenstance Career Inventory.
We clarified the reliability and validity of the Chinese version of the PHCI in higher vocational college students, and the results reported that the new scale had good structural validity, criterion correlation validity, convergent validity and reliability.
The structural validity analysis of this study shows that the 5-factor model of 20 items is more suitable for Chinese college students. However, this result is somewhat different from the research of Kim et al. (2014). Taking Korean college students as samples, each subscale contains five factors, with a total of 25 items. In the EFA, the total correlation coefficient of the 3rd and 5th items were <0.3. The homogeneity test coefficient would increase upon deletion, so these two items were deleted according to the psychometric standard. The 25th, 11th and 22nd items have dual loadings and they were also deleted. The reasons for the poor statistical performance of the deleted items may be due to translation deviation, vocational college students’ misunderstanding of the contents of the items, or different psychological structure of item measurement led by different cultural backgrounds. Therefore, a formal scale with five factors of 20 items was finally determined in this study.
At the same time, CFA showed that all the fitting indices of the 5-factor structure in the revised scale were good, which proved that the scale had good structural validity. Reliability analysis showed that the Chinese version of PHCI had a high homogeneity reliability (>0.70). In this study, retest was conducted four weeks later, and the results showed that the test-retest reliability of the scale and all the subscales ranged from 0.698 to 0.871, indicating that the test-retest reliability of the scale was maintained. The split-half reliability of the scale was 0.733, which also met the requirement of psychometrics, indicating that PHCI Chinese version had good reliability and validity. All these indicate that PHCI of Chinese version has high stability.
In order to study the criterion-related validity, other career measurement tools (ODCS and CAAS-China) were selected as controls in this study. The results showed that the total score of PHCI was positively correlated with both ODCS and CAAS-China, indicating that PHCI and the latter two scales measured similar career influencing factors. The Chinese version of PHCI and all subscale were significantly positively correlated with the sum score of ODCS and CAAS-China. Therefore, the scale has a good correlation validity, which can effectively measure the impact of planned happenstances on the career paths of college students.
To sum up, the Chinese version of PHCI has a high reliability and a good application value for measuring and planning the impact of planned happenstances on the career paths of Chinese college students. It can also provide a scientific empirical research basis for career counselling, career decision-making and career guidance [12, 13]. However, only vocational college students were selected as the research objects in this study, and the applicability to other groups should be further investigated in subsequent studies to evaluate whether the factor structure described in this study is robust for different groups of respondents.
Correlation between the score of each item and the total score (n = 335)
| 1 | 0.311 |
6 | 0.335 |
11 | 0.224 |
16 | .430 |
21 | 0.347 |
| 2 | 0.361 |
7 | 0.371 |
12 | 0.280 |
17 | 0.474 |
22 | 0.223 |
| 3 | 0.110 |
8 | 0.308 |
13 | 0.372 |
18 | 0.496 |
23 | 0.421 |
| 4 | 0.355 |
9 | 0.386 |
14 | 0.297 |
19 | 0.415 |
24 | 0.399 |
| 5 | 0.287 |
10 | 0.345 |
15 | 0.379 |
20 | 0.386 |
25 | 0.394 |
Correlation between the Chinese version of PHCI and efficacy targets (n = 568)
| Factor 1 | 1 | |||||||
| Factor 2 | 0.722** | 1 | ||||||
| Factor 3 | 0.776** | 0.706** | 1 | |||||
| Factor 4 | 0.309** | 0.196** | 0.298** | 1 | ||||
| Factor 5 | 0.399** | 0.395** | 0.386** | 0.356** | 1 | |||
| The Chinese version of PHCI | 0.879** | 0.847** | 0.846** | 0.497** | 0.636** | 1 | ||
| ODCS | 0.157** | 0.074* | 0.115** | 0.426** | 0.321** | 0.256** | 1 | |
| CAAS-China | 0.711** | 0.682** | 0.639** | 0.186** | 0.360** | 0.723** | 0.142** | 1 |
CFA Model fitting index of the Chinese version of PHCI (n = 568)
| Revised 5 factors (20 items) | 3.135 | 0.925 | 0.901 | 0.926 | 0.926 | 0.912 | 0.056 | 0.062 |
| 5 factors (25 items) | 4.975 | 0.826 | 0.787 | 0.832 | 0.833 | 0.810 | 0.084 | 0.087 |
Convergence validity test table of the Chinese version of PHCI
| Factor 1 | 0.682–0.823 | 0.58 | 0.87 |
| Factor 2 | 0.704–0.812 | 0.60 | 0.88 |
| Factor 3 | 0.704–0.833 | 0.61 | 0.86 |
| Factor 4 | 0.700–0.838 | 0.60 | 0.82 |
| Factor 5 | 0.748–0.828 | 0.63 | 0.84 |
| Scale as a whole | 0.682–0.838 | 0.60 | 0.97 |
The Chinese version of PHCI factor items and loads (n = 327)
| C20 | 0.830 | ||||
| C19 | 0.809 | ||||
| C16 | 0.763 | ||||
| C23 | 0.715 | ||||
| C21 | 0.676 | ||||
| C14 | 0.788 | ||||
| C10 | 0.744 | ||||
| C4 | 0.728 | ||||
| C6 | 0.709 | ||||
| C12 | 0.641 | ||||
| C18 | 0.799 | ||||
| C13 | 0.751 | ||||
| C17 | 0.719 | ||||
| C15 | 0.645 | ||||
| C8 | 0.847 | ||||
| C24 | 0.776 | ||||
| C9 | 0.679 | ||||
| C2 | 0.813 | ||||
| C1 | 0.761 | ||||
| C7 | 0.687 | ||||
Reliability analysis table of the Chinese version of PHCI
| Cronbach's alpha | 0.862 | 0.815 | 0.810 | 0.762 | 0.679 | 0.689 |
| Test-retest reliability | 0.871 | 0.766 | 0.798 | 0.750 | 0.698 | 0.726 |
| Split-half reliability | 0.733 | 0.726 | 0.793 | 0.748 | 0.698 | 0.681 |
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application of constructing football training linear programming based on multiple linear regression equation Research on management evaluation of enterprise sales cash flow percentage method based on the application of quadratic linear regression equations Mathematical simulation analysis of optimal detection of shot-putters’ best path Determination of the minimum distance between vibration source and fibre under existing optical vibration signals: a study Mathematical modelling of enterprise financial risk assessment based on risk conduction model Nonlinear differential equations based on the B-S-M model in the pricing of derivatives in financial markets Mathematical simulation experiment based on optimisation of heat treatment process of aluminium alloy materials Mathematical model of transforming image elements to structured data based on BP neural network Educational reform informatisation based on fractional differential equation 3D Mathematical Modelling Technology in Visual Rehearsal System of Sports Dance MCM of Student’s Physical Health Based on Mathematical Cone Sports health quantification method and system implementation based on multiple thermal physiology simulation Research on visual optimization design of machine–machine interface for mechanical industrial equipment based on nonlinear partial equations Informationisation of teaching model for track and field education based on finite element higher-order fractional differential equation Research on identifying psychological health problems of college students by logistic regression model based on data mining Information technology of preschool education reform of fine arts based on fractional differential equation Information Teaching Model of Preschool Art Education in Colleges and Universities Based on Finite Element Higher-Order Fractional Differential Equation A mathematical model of PCNN for image fusion with non-sampled contourlet transform Application of artificial intelligence algorithm in mathematical modelling and solving College Students’ Mental Health Climbing Consumption Model Based on Nonlinear Differential Equations Communication architecture of power monitoring system based on incidence matrix model Differential equation to verify the validity of the model of the whole-person mental health education activity in Universities Optimisation of Modelling of Finite Element Differential Equations with Modern Art Design Theory Analysis and Prediction of College Students’ Mental Health Based on K-means Clustering Algorithm Mathematical function data model analysis and synthesis system based on short-term human movement Human gait modelling and tracking based on motion functionalisation Analysis and synthesis of function data of human movement Energy-saving technology of BIM green buildings using fractional differential equation Study on the training model of football movement trajectory drop point based on fractional differential equation Financial Accounting Measurement Model Based on Numerical Analysis of Rigid Normal Differential Equation and Rigid Functional Equation User online consumption behaviour based on fractional differential equation Differential equation model of financial market stability based on Internet big data Multi-attribute Decision Method Based on Normal Random Variable in Economic Management Risk Control Children’s cognitive function and mental health based on finite element nonlinear mathematical model Dichotomy model based on the finite element differential equation in the educational informatisation teaching reform model Nonlinear Dissipative System Mathematical Equations in the Multi-regression Model of Information-based Teaching Stock price analysis based on the research of multiple linear regression macroeconomic variables Fractional Linear Regression Equation in Agricultural Disaster Assessment Model Based on Geographic Information System Analysis Technology