The Influencing Factors of Self-evaluation in Adolescents: A Structural Equation Modeling

doi:10.21203/rs.3.rs-89997/v1

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The Influencing Factors of Self-evaluation in Adolescents: A Structural Equation Modeling

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Background: Adolescents’ self-evaluation is an expressive form of their self-concept. Parental bonding, personality traits and psychological status were determining factors of adolescents’ self-evaluation; however, no study has tested the effects of the three variables simultaneously. We aimed to explore these effects by a systematic approach.

Methods: Data came from the Chinese Family Panel Studies (CFPS). The associations of parental bonding (measured by the Parental Bonding Instrument), personality traits (the Responsibility Scale) and psychological status (the Kessler Psychological Distress Scale, K6) with self-evaluation measured in three domains (the Rosenberg Self-Esteem Scale, the Nowicki-Strickland Locus of Control Scale for children and the Self-Discipline Scale) were tested by the structural equation modeling (SEM) analysis.

Results: A total of 892 Chinese adolescents (Mage in years=10.72, SD = 0.89) were included. Signiﬁcant positive correlations were found among self-evaluation, parental bonding, personality traits and psychological status. The overall (direct and indirect) effects of parental bonding on adolescents’ self-evaluation was 0.49 (parental bonding→ self-evaluation, β=0.28; parental bonding→ personality traits and psychological status→ self-evaluation, β=0.21). Personality traits and psychological status were directly associated with adolescents’ self-evaluation (personality trait→ self-evaluation, β=0.78 and K6→ self-evaluation, β=0.39).

Conclusions: The use of SEM offered a detailed analysis of the correlations between parental bonding, personality traits, and psychological status, and a systematic approach to investigate their direct and indirect effects on adolescents’ self-evaluation. The three factors were directly associated with adolescents’ self-evaluation and parental bonding had an indirect effect on adolescents’ self-evaluation that was mediated by both personality traits and psychological status.

Pediatrics

Adolescents

self-evaluation

parental bonding instrument

personality traits

Kessler Psychological Distress Scale (K6)

structural equation modelling

Developing a clear and multifaceted self-concept is an important goal in adolescence. Self-concepts are defined in the developmental literature as self-perceptions related to specific domains, roles, or social contexts [1]. Although some specificity in self-concepts are developed earlier in childhood [2], these self-perceptions become increasingly complex and differentiated across adolescence [1, 3], presumably due to both the cognitive development and social changes experienced during adolescence.

Adolescents’ self-evaluation is an expressive form of their self-concept. A person with a positive cognitive core self-evaluation in his adolescence, probably has confidence and hope for the future life that encourage him to make continuous efforts to solve problems [4]. Recently, the society from all walks of life has attached more importance to self-evaluation issues among adolescents and the factors influencing contemporary adolescent self-evaluation are multidimensional and complex. Here, we nominate three main groups of factors: first, factors related to the family of origin; such as the relationships between the family members [5]. Parents are the earliest social environment for children, and play a unique and significant role in the development of adolescents [6, 7]. Parental bonding was identified as one of the crucial factors in children’ self-evaluation [8]. The second group of factors that affects adolescents’ self-evaluation is related to the personality traits [9, 10] which contribute to the diversity in personal, emotional, motivational and social behaviours of adolescents [11, 12]. Compared to parental bonding and personality traits, little research has been done on the impact of the third group of factors; the psychological status of adolescents on their self-evaluation [13, 14]. Adolescents with negative core beliefs, attachment anxiety, or low self-esteem were described as exhibiting behaviors that reflect a negative self-image and then compensate for that, which implies that they act in accord with negative self-patterns in order to gain positive self-respect [15].

Although studies have respectively investigated the associations of parental bonding, personality traits, and psychological status on adolescents’ self-evaluation, no study has combined all the three factors together nor examined the relations between them so far. Therefore, we aimed in this study to fill this gap and provide a new perspective in examining the associations between these three factors in their pathways to affect the adolescents’ self-evaluation. Based on the evidence presented previously, the hypotheses were formulated as follow:

H1: Parental bonding is directly associated with adolescents’ self-evaluation.

H2: Personality traits are directly associated with adolescents’ self-evaluation.

H3: Psychological status is directly associated with adolescents’ self- evaluation.

H4: Parental bonding is indirectly associated with adolescents’ self-evaluation (mediated by personality traits and psychological status).

Data Sources and Participants

The data for this study were obtained from the China Social Science Survey Center, Peking University, which is one of the Chinese Family Panel Studies (CFPS) for which samples were collected from all over China in 2010. The study had collected data over four rounds, however, the data analyzed in this research was only from a single-round collection in 2014. The details of the study protocol and sampling method can be found elsewhere [16, 17]. In brief, the CFPS sample was a multi-stage probability sample extracted by implicit stratification method. The samples of subsampling frame were extracted in three stages. The first-stage sampling (primary sampling units, PSUs) were counties (or county-level cities/districts) listed in descending order by a socioeconomic indicator. The second-stage sampling (Secondary sampling units, SSUs) were resident committees and administrative villages from counties (or county-level cities/districts). The third and final stage sampling (Tertiary sampling units, TSUs) were households from resident committees and administrative villages. At the first two stages in the sampling process, official administrative entities were used. The administrative structure in China has two important features: first, it is strictly hierarchical; and second, at least in theory, it covers the entire population of China exhaustively, without exception. Moreover, to minimize sampling frame errors, in the third stage it was the random starting point of the circular isometric sampling method that was used to extract sample households. The study was designed to include, interview and consider all members over the age of 9 years in a sampled household as core members of the CFPS.

Adolescents in this study were students aged 10 to 15 years old in east, north, south, central, northeast, northwest and southwest regions of China. Among 2519 recruited adolescents from 2229 families, 1627 adolescents from 1358 families were excluded from the analysis because they had not a clear self-concept of themselves and chose the answer “I don't know” when asked questions about self-evaluation. Only 892 adolescents from 871 families included in the study with relatively complete and significant study questionnaire.

Measures

Questionnaire design

To improve the operability of the questionnaire survey, a preliminary survey of 783 adolescents was conducted in Beijing, Shanghai and Guangdong provinces in 2008 and 2009. Those 783 adolescents were excluded from the formal study because the final questionnaire had been modified according to their feedback. The study is based on the CFPS self-administered questionnaire for adolescents aged 10 to 15 years that covered basic information on age, gender, living condition, educational level, type of residence, self-evaluation, parental bonding instrume-nt, personality traits, and psychological status.

Self-evaluation

Adolescents’ self-evaluation consists of three scales: the Rosenberg Self-Esteem Scale [18], the Nowicki-Strickland Locus of Control Scale for children (NLCS-C) [19] and the Self-Discipline Scale [20]. The Rosenberg Self-Esteem Scale consisted of 10 questions and answers were on a 4 -point Likert scale (1) “totally disagree” (2) “disagree” (3) “agree” and (4) “totally agree”, with some questions were reverse scored. A higher final sum score for all the ten items indicated higher self-esteem. The NLCS-C scale consisted of 40 yes/no answered questions with a low score indicating internal locus of control and a high score indicating external locus of control. The Self-Discipline Scale based a 5 -point Likert scale (1) “Disagree strongly” (2) “, Disagree a little” (3) “Neither agree nor disagree” (4) “Agree a little” and (5) “Agree Strongly” of 12 questions, a higher score indicates optimism and higher perceived self-efficacy. However, existing and mature scales from foreign countries have been introduced to China, and the researchers are also faced with the problems of measuring validity and reliability. Because Chinese people tend to be conservative in their responses to the scale, they tend to choose neutral answers rather than the most suitable ones, which leads to the inaccurate measurement results. In dealing with this problem, the researchers of CFPS has added or deleted items and modified their scoring to include (1) “totally disagree” (2) “disagree” (3) "neither agree nor disagree” (4) “agree” and (5) “totally agree” to reduce the probability that respondents improperly choose the middle option [21]. The Cronbach's alphas for respective three scales were 0.88, 0.82 and 0.70.

Parental Bonding Instrument, Personality Traits and Psychological Status

In order to make the scales conform to Chinese people's answering habits and thinking mode, and to reduce the inaccurate measurement results, the researchers of CFPS have modified these scoring scales a little and re-measured the validity and reliability of the scales [21]. The Parental Bonding Instrument introduced by Parker et al [22] was used with a 14-item measure of differences in parental bonding and responses were scored on a 5-point Likert scale ranging from (1) “never” (2) “rarely” (3) “sometimes” (4) “often” and (5) “always”; Cronbach's alpha for the scale was 0.80. The Responsibility Scale that was used to assess the personality traits [21] was derived from a sub-scale of the Big Five Questionnaire-Children produced by Barbaranelli et al [23] with a 12- self-reported items rated from (1) “totally disagree” to (5) “totally agree” and its Cronbach's alpha was 0.77. The psychological status was measured using the Kessler 6 Psychological Distress Scale (K6) by Kessler [24]. The scale consisted of 6 items scored on a 5-point Likert scale ranging from (1) “always” (2) “often” (3) “sometimes” (4) “rarely” and (5) “never”. Some of the items were reversely scored so that the high total score indicates positive psychological status. The Cronbach's alpha for this scale was 0.649.

Statistical analysis

SPSS 25.0 and AMOS V-24.0 were used for data analyses. The participants’ characteristics were presented as number and frequencies. In order to examine the theoretical model which contained the targeted factors, a type of multivariate analysis method, structural equation modeling (SEM) was applied. Initially, a model contained the final scores of parental bonding instrument, personality traits scale and psychological status scale with the adolescents’ self-evaluation domains was designed. Then the estimated value and significance level of each parameter were obtained. After that, the bivariate correlation analysis (BCA) was used to observe the correlation among the included factors. After the analysis results are confirmed, the proposed hypothesis model was verified and fitted with the structural equation modeling (SEM) analysis of AMOS. The proposed model was confirmed by examining the goodness of fit statistics, which indicted a ratio of the chi-square (χ²) to the degree of freedom (df), a goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). The definition of satisfactory goodness of fit was χ²/df < 2.0, GFI > 0.95, AGFI > 0.90, CFI > 0.97, and RMSEA < 0.05, while the standard of acceptable goodness of fit was χ2/df < 3.0, GFI > 0.90, AGFI > 0.85, CFI > 0.95, and RMSEA < 0.08[25]. The chi-square difference test was used to check the nested models and if the complex model fitted a higher degree of the data when compared to the simpler model [26]. By comparing the Akaike information criterion (AIC) value of the models, a nested model is proved to be a significant well-fitting model if it was associated with at least 50 units reduction in the measurement [27, 28, 29].

Baseline characteristics

The baseline characteristics of study participants are shown in Table 1. A total of 892 adolescents from east (15.1%), north (11.3%), south (14.0%), central (19.7%), northeast (10.2%), northwest (16.0%) and southwest (13.6%) China, included 485 (54.4%) males and 407 (45.6%) females were included in the current study. The sample was composed of 365 (40.9%) adolescents living in urban areas and 527 (59.1%) adolescents from rural areas. The average age of total, male and female adolescents was 10.72 (SD = 0.892), 10.71 (SD = 0.832) and 10.72 (SD = 0.959) years respectively.

Table 1

Baseline characteristics of the study participants
Characteristics	N	%
Geographic Location
East China	135	15.1
North China	101	11.3
South China	125	14.0
Central China	176	19.7
Northeast China	91	10.2
Northwest China	143	16.0
Southwest China	121	13.6
Age, years
10	426	47.8
11	346	38.8
12	89	10.0
≥13	31	3.4
Sex
Male	485	54.4
Female	407	45.6
Living Environment
Urban	365	40.9
Rural	527	59.1
Health Status
Excellent	311	34.9
Very good	275	30.8
Good	238	26.7
Fair	56	6.3
Poor	12	1.3
Whether have pocket money
Yes	685	76.8
No	207	23.2
Whether participated in student organizations
Yes	89	10.0
No	613	68.7
No student organization in school	190	21.3

Correlations

The mean, standard deviation (SD) and Pearson correlation coefficients of the targeted variables are shown in Table 2. The results show that the score of parental bonding instrument correlated with scores of the self-evaluation domains, including self-esteem, self-discipline and NLCS-C (r from 0.119 to 0.265, P < 0.01). The personality traits score was reasonably associated with K6 score of psychological status and personality traits score (r = 0.185 and 0.157, P < 0.01, respectively). At the same time, personality traits score was correlated with all self-evaluation domains’ scores (r from 0.156 to 0.364, P < 0.01), with the strongest correlation was observed with self-esteem (r = 0.364). Meanwhile, K6 score (psychological status) was moderately correlated with some self-evaluation domains, which were the self-esteem score (r = 0.250, p < 0.01) and NLCS-C score (r = 0.126, p < 0.01) specifically.

Table 2

Descriptive statistics and Pearson correlations of variables
		Minimum	Maximum	Mean	SD	Parental bonding Instrument	Personality Traits	K6 (Psychological status)	Self-evaluation
		Minimum	Maximum	Mean	SD	Parental bonding Instrument	Personality Traits	K6 (Psychological status)	Self-esteem	Self-discipline	NLCS-C
Parental bonding instrument		21	65	45.22	7.73	1
Personality traits		20	59	43.57	6.4	.185**	1
K6 (Psychological status)		12	30	26.7	3.87	.157**	0.002	1
Self-evaluation	Self-esteem	34	66	50.32	4.84	.265**	.364**	.250**	1
	Self-discipline	21	46	34.36	3.38	.119**	.350**	0.088	.165**	1
	NLCS-C	26	47	37.24	3.41	.133**	.156**	.126**	.314**	.084*	1
, *, represents a significance level of p < 0.05 and p < 0.01, respectively.

Structural equation model

In the initial model, it was assumed that self-evaluation was affected by the scores of parental bonding instrument, personality traits and psychological status. The unsatisfactory fit of the initial model was revealed by the measures of model fit (χ 2 /df = 12.214, GFI = 0.961, AGFI = 0.908, CFI = 0.793, RMSEA = 0.112), especially whose AIC was 133.929 (Fig. 1).

To optimize the model, several significant paths were added and a revised structural equation model was specified in Fig. 2. The measures of the revised model fit (χ 2 /df = 2.876, GFI = 0.994, AGFI = 0.977, CFI = 0.977, RMSEA = 0.046) indicates that the revised model has a satisfactory fit. Furthermore, given that the AIC for the revised model (47.255) was lower than that of the initial model (133.929); thus, the revised model was more suitable for the data than the initial one.

Significant relationships between observed variables

As the path diagram with the standardized coefficients for each factor was provided in Fig. 2, Table 3 presented the direct, indirect and total effects of the scores of parental bonding instrument, personality traits and K6 (psychological status) on the self-evaluation scores calculated with both unstandardized and standardized coefficients. Using the standardized coefficients, the path coefficients of variables measured at different scales within the same model can be directly compared. In the revised SEM, parental bonding instrument has a great direct and indirect influence on self-evaluation (direct β = 0.28; mediated by personality traits and K6 β = 0.21, and total effect β = 0.49), while K6 was directly related to self-evaluation (β = 0.39). Additionally, personality traits have the strongest positive total (direct) effect on self-evaluation (β = 0.78).

Table 3

Total, direct and indirect effects of the independent variables on the dependent variable.
Dependent variables (self-evaluation and its domains)	Effect	Independent variables
		Parental bonding Personality traits K6			Parental bonding Personality traits K6
		β^a			B^b
Self-evaluation	Total	0.49	0.78	0.39	0.05	0.09	0.08
	Direct	0.28	0.78	0.39	0.03	0.09	0.08
	Indirect	0.21	0	0	0.02	0	0
Self-esteem	Total	0.23	0.37	0.19	0.15	0.28	0.24
	Direct	0	0	0	0	0	0
	Indirect	0.23	0.37	0.19	0.15	0.28	0.24
Self-discipline	Total	0.17	0.28	0.14	0.08	0.15	0.12
	Direct	0	0	0	0	0	0
	Indirect	0.17	0.28	0.14	0.08	0.15	0.12
NLCS-C	Total	0.11	0.17	0.09	0.05	0.09	0.08
	Direct	0	0	0	0	0	0
	Indirect	0.11	0.17	0.09	0.05	0.17	0.08
^a Standardized regression weight (β); ^b Unstandardized regression weight (B)

This is the first empirical study to use the SEM systematic approach to investigate the influence of parental bonding, personality traits and psychological status on self-evaluation of Chinese adolescents, which presented a comprehensive study of mechanisms for the parental, personal and psychological impacts on adolescents’ self-concept. The SEM analysis has demonstrated that parental ponding, personality traits and psychological status played significant positive roles in the self-evaluation of Chinese adolescents.

The current study findings indicated that parental ponding was a determining factor of Chinese adolescents’ self-evaluation. The score of parental bonding instrument was not only directly associated with self-evaluation, but also had an indirect association with self-evaluation mediated by personality traits and K6 scores (total effect = 0.49). During adolescence, adolescents are vulnerable to the harm of adverse stimuli, which would cause some learning and psychological problems and reduce their self-evaluation. In such situations, there is no doubt that the parental effects will not only limit to the role in their children's growth and development, but will also extend to the ways by which the adolescents interact with these harmful stimuli. In a recent study [30], it was indicated that adolescents who perceived no parental support were 4-times likely to have mental health problems compared with those who perceived a strong support from their parents (adjusted odds ratio = 3.80, CI: 2.81–5.13). Personality traits and K6-measured psychological status mediated the association between parental ponding and self-evaluation of adolescents, besides their direct influencing effects on self-evaluation, which affirms the previous study findings that pointed to concrete psychological mechanisms in terms of mentality and personality for parental ponding affecting adolescent self-evaluation [9, 31].

The study found that not only parental ponding had significant effects on self-evaluation among Chinese adolescents but also those adolescents with higher scores on personality traits scale were more likely to have higher self-evaluation (total effect = 0.78). Personality traits are persistent and universal positive or negative attitudes towards people or things [32]. Adolescents with high personality traits score usually have constant affirmation and ability to complete daily tasks [33, 34]; thus, personality traits are a crucial factor in the improvement of adolescents’ self-evaluation, which was consistent with previous studies’ findings [9, 31]. Therefore, in the progress of enhancing self-evaluation, the development of personality traits merits is a major consideration.

As indicated by the results, psychological status, measured by K6 scale was also associated with the self-evaluation in Chinese adolescents (total effect = 0.39). Psychological status acts as a major internal motive for improving psychological and physical health[35, 36] and a few previous studies had investigated this association and reached supporting conclusions that adolescents with poor psychological status were prone to a negative self-evaluation, considering themselves inferior to others [37, 38]. Consequently, those adolescents with distressed psychological status were vulnerable to lose their courage and determination to surmount difficulties and experienced greater emotional instability (higher neuroticism), which can lead to a reduction in individual self-evaluation [39]. Therefore, not coincidentally was the positive correlation between self-evaluation and K6 scores observed in this study. In the view of this, it is essential to ameliorate the psychological status of adolescents for improving their self-evaluation.

From the view of practical significance, with the aim of raising the level of self-evaluation among Chinese adolescents, compared to interventional measures that target a single variable of either parental bonding, personality traits or psychological status of the adolescents, simultaneous interventions that take in consideration the mentioned three variables should be formulated and conduced.

The causal associations between variables cannot be deduced because of the cross-sectional nature of this study. Besides, the associations between the variables may be over- or under-estimated, since the study was conducted by self-reports from only one source. Last, about 60% of the interviewed adolescents did not submit a relatively complete research questionnaire, especially the question about self-evaluations, so they were excluded from the study in the process of selecting the population. Although, those participants were probably not having a clear self-concept of themselves, the characteristics of them did not differ than those of the included adolescents in terms of sociodemographic data, health status, and scores for parental bonding instrument, personality traits, and psychological status scales.

Factors influencing Chinese adolescent’s self-evaluation included parental bonding, personality traits and psychological status. Findings of this research contributed to the scarce empirical evidence regarding the factors determining the adolescent’s self-evaluation. The direct and indirect effects of parental bonding, personality traits and psychological status in the association’s paths towards self-evaluation and its domains (self-esteem, self- discipline and NLCS-C were supported by the SEM (path analyses).

CFPS: the Chinese Family Panel Studies. K6：the Kessler Psychological Distress Scale.

SEM：the structural equation modeling. PSUs: primary sampling units. SSUs: Secondary sampling units. TSUs: Tertiary sampling units. NLCS-C: the Nowicki-Strickland Locus of Control Scale. BCA: the bivariate correlation analysis. χ²: the chi-square; df: the degree of freedom. GFI: goodness of fit index. AGFI: adjusted goodness of fit index. CFI: comparative fit index. RMSEA: root mean square error of approximation.

Acknowledgments

The authors of this study were very grateful to the staffs of China Institute of Social Science Survey and Center for Social Research of Peking University as well as other relevant institutions. The dedication of all the participants of China Family Panel Studies (CFPS) was also appreciated.

Role of funding source

This study was supported by the National Social Science Fund of China in 2015 (grant number: 15BSH057).

Availability of data and material

The public data used to support the findings of this study are available from the Peking University Open Research Data.

https://opendata.pku.edu.cn/dataset.xhtml?persistentId=doi:10.18170/DVN/45LCSO.

Authorship contribution statement

XBZ, LHD, JZ, and YSP did statistical analyses, XBZ drafted the manuscript. XBZ, LHD, Ehab S Eshak, CJ, JZ, MY, JZ, YSP, JHC, and BX revised the final manuscript, BX, and JHC are act as the guarantors for the paper. All authors have read and agree to publish the final manuscript.

Ethics approval and consent to participate

Participants had received explanation before signing the consent to participate and given the right to withdraw from the study whenever they are reluctant to continue, and the analysis was entirely anonymous. The study was approval by the Ethics Committee of Peking University, Institute of Social Science Survey (ISSS).

Consent for publication

Participants (or legal parents or guardians of adolescents) agree that researchers may report their personal data.

Declaration of Competing Interest

The Authors declare that there is no conflict of interest.

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The Influencing Factors of Self-evaluation in Adolescents: A Structural Equation Modeling

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Abstract

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Introduction

Methods

Data Sources and Participants

Measures

Questionnaire design

Self-evaluation

Parental Bonding Instrument, Personality Traits and Psychological Status

Statistical analysis

Results

Baseline characteristics

Correlations

Structural equation model

Significant relationships between observed variables

Discussion

Limitations

Conclusions

Abbreviations

Declarations

References

Status:

Version 1