Psychological Mechanism of Subjective Well-Being:A Stable Trait or Situational Variability

Zhihua Li • Xiayun Yin • Sha Jiang • Mengcheng Wang •

Taisheng Cai

Accepted: 16 September 2013� Springer Science+Business Media Dordrecht 2013

Abstract In order to explore the stability and variation of the indicators of subjective

well-being (SWB), the classic Latent State-Trait (LST), Trait-State-Occasion (TSO), and

Indicator-Specific Trait (IT) models were employed to evaluate the stability and variability

of life satisfaction, positive emotions, and negative emotions. The study was conducted

based on a 1-year and three-wave longitudinal study with a sample of 360 college students.

The results show the suitability of the IT model. The consistency coefficients (CO) of all

variables are significantly higher than occasion-specificity coefficients (OSpe) in this

model, indicating that stability is a remarkable feature of SWB. Moreover, the consistency

coefficients of positive affection and negative affection are much higher than that of life

satisfaction, suggesting that the positive and negative emotions of individuals are more

stable than life satisfaction.

Z. Li � T. Cai (&)Medical Psychological Institute, Second Xiangya Hospital, Central South University, #139 Ren-MinZhong Road, Changsha 410011, Hunan, People’s Republic of Chinae-mail: happycareer314@163.com

Z. Lie-mail: lzhua1018@163.com

X. YinInstitute of Education, Hunan University of Science and Technology, Xiangtan 411201,People’s Republic of Chinae-mail: yxy8248@126.com

S. JiangOriental Institute of Science and Technology, Hunan Agricultural University, Changsha 410128,People’s Republic of Chinae-mail: sasa881115@hotmail.com

M. WangCenter for Psychology and Brain Science, Guangzhou University, Guangzhou 510006,People’s Republic of Chinae-mail: wmcheng2006@126.com

123

Soc Indic ResDOI 10.1007/s11205-013-0449-x

Keywords Subjective well-being � Stability � Variation � Latent state-trait

model

1 Introduction

1.1 Disputes About the Trait of Subjective Well-Being

Subjective well-being (SWB) is a core concept in positive psychology and a significant

indicator of quality of life. According to (Diener et al. 1999; Diener 2000), SWB refers to

the cognition and evaluation of individuals in terms of their satisfaction with their lives,

surrounding environments and related events; it also refers to the subjective identification

of emotional experiences in these aspects. Subjective well-being (SWB) consists of three

parts, namely, life satisfaction, high level positive affection, and low level negative

affection. However, some controversies exist with regards the psychological mechanism of

SWB. One of the most dominant disputes is whether SWB changes with time and cir-

cumstance or is a stable and unchangeable trait (Strack et al. 2007). Investigating this issue

is of great significance to the theory and the application of positive psychology, with the

implication that if SWB is a stable trait, then the endeavor of altering life would not

necessarily make people happier. However, if SWB is a state under a specific circum-

stance, then ‘‘specific situations’’ should be taken into consideration to make people happy

(Lyubomirsky 2001).

Some researchers believe the main factors that influence the SWB of individuals are

daily mood and situation, such that improving the mood or changing the situation of the

individual can enhance SWB. For example, Reis et al. (2000) reported that daily mood is

the principal factor influencing SWB. The Broaden-and-Build Theory first proposed by

Fredrickson (2001) suggests that experiencing positive emotions can broaden the

momentary thought-action repertoires of an individual. In turn, these build upon their

enduring personal resources (ranging from physical and intellectual resources to social and

psychological resources), thereby resulting in improved perceptions of well-being. This

theory has been verified by the study of Gross and John (2003), in which the rise of the

index of well-being is significant in the experimental group where people showed more

positive emotions daily than in the control group, where people showed more negative

emotions daily. The studies of Bruce and Alexander (1989) and Angner (2010) revealed

that the main factors influencing SWB are the life events experienced by an individual. If

an individual continuously or frequently experiences negative life events, their well-being

may be reduced to some extent. However, some scholars have pointed out that social

factors do have the greatest impact on individual well-being. This is demonstrated by the

findings of a previous work, which state that a harmonious social environment promotes

the well-being of individuals, whereas undesirable social surroundings weaken their well-

being (Cameron et al. 1971; Michalos 1985). Related studies have shown that an increase

in income can enhance the well-being of an individual (Cummins 2000). Based on these

perspectives, well-being has more state characteristics.

However, more scholars believe that well-being is a stable trait. Lucas and Diener

(2008), for example, note that SWB is moderately related to personality trait and possesses

cross-situational consistency and stability across time. A survey that includes 1,100 sub-

jects has shown that individual extraversion and neuroticism can significantly predict

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individual well-being. SWB mainly depends on personality traits, which could even

forecast well-being after 10 years (Costa and McCrae 1980). DeNeve and Cooper (1998)

conducted a meta-analysis of the relationship between 137 personality traits and SWB.

They found that personality traits can remarkably predict life satisfaction and positive

feelings. In addition, studies from the field of genetics provide support to the theory of

stable well-being. For example, Lykken and Tellegen (1996), through a 10-year consistent

study of twins, found that 50 % of well-being is decided by genetics. Furthermore, Bartels

and Boomsma (2009) reported that, aside from genes, other innate factors determine well-

being (e.g., gender and age); specifically, these two genetically determined demographic

variables have equal influence on well-being. These studies support the notion that SWB is

influenced by genetics and is closely related to personality traits. Therefore, a change in the

life state or an accidental event can hardly influence the well-being of an individual.

Researchers have employed a variety of methods to solve the dispute of whether SWB is

dictated by personality traits or situational variability. However, the existing studies do not

present a unified conclusion. For instance, a previous study (Fujita and Diener, 2005)

reported that life satisfaction has about 24 % significant variance change within 5 years.

Another work (Lucas and Donnellan 2007) studied the life satisfaction of a population

group with ages 25–60 years and found a 34–38 % variation attributed to stable traits.

They concluded that life satisfaction is a moderate stable variation. Schimmack et al.

(2010) also investigated the stability and variability of SWB and discovered that the

variance contributed by personality traits or by state are more or less the same. However, a

study by Eid and Diener (2004) demonstrated that variance contributed by SWB is much

higher than that contributed by state. They concluded that SWB is characterized by highly

stable traits.

1.2 Latent State-Trait (LST) Model

Hertzog and Nesselroade (1987) pointed out that many psychological attributes are quite

difficult to define as stable traits or variable state. They are more likely to be a mix of the

two characteristics, and the key solution is to identify which parts are traits or state,

respectively. Therefore, scholars have proposed several solutions, including LST theory,

which posits that human cognition, emotion, and behavior are the results of individual

traits, situational characteristics, traits, and situation. Based on this theory, the LST model

consists of three latent variables: Latent Trait variable (T), Latent State variable (S), and

Occasion factor (R). The classic LST model divides the indicators into three components as

follows: Stable Trait (T), State Latent Variable 9 Situational Factors (SR), and Mea-

surement Error (E). The formula is expressed by the following.

Yit ¼ kitT þ ditSRt þ eit ð1Þ

where t represents a measurement occasion, i means the ith index of individual in the

t measurement occasions, kit is the factor loading on traits, and dit is the factor loading on

situational factors (Geiser and Lockhart 2012; Steyer et al. 1999). The path of the classic

LST model is shown in Fig. 1 below.

In Fig. 1, Yit denotes the ith observed variable (indicator) measured at time t, T rep-

resents latent trait factor, and SRt stands for latent state residual factor. Compared with T,

SRt is a random variation. For instance, an individual may feel pleasant when he first

answers the questions in questionnaire but might later feel comparatively anxious. Obvi-

ously, such pleasant or anxious mind can affect how the individual evaluates all questions,

Psychological Mechanism of Subjective Well-Being

123

especially questions about psychological quality (e.g., well-being). The model assumes that

the SRt values are uncorrelated, and that the error variable eit is also uncorrelated to T and

SRt Thus, the variance of the observation index yit can be decomposed as follows:

Var Yitð Þ ¼ kit Var Tð Þ þ d2itVar ðSRtÞ þ eit ð2Þ

According to (2), three significant coefficients in the application of the LST model can

be discerned. These are consistency (CO), occasion-specificity (OSpe), and reliability

(Rel). CO refers to the observed proportion of variance determined by individual stable

characteristics, wherein the higher the coefficient, the greater its impact. OSpe stands for

the proportion of variance determined by the interaction of situation and state. The reli-

ability coefficient is the sum of the two coefficient mentioned above. The respective

formulas are given by the following:

COðYitÞ ¼k2

itVarðTÞVarðYitÞ

; ð3Þ

OSpe(YitÞ¼d2

itVarðSRtÞVarðYitÞ

; ð4Þ

RelðYitÞ ¼k2

itVarðTÞ þ d2itVarðSRtÞ

VarðYitÞ: ð5Þ

The advantage of this model is apparent. Several repeated measurements through a

tracking model can also present trait factors and status factors, as well as determine if these

psychological attributes are traits or state through the relevant coefficient (CO, OSpe;

Geiser and Lockhart 2012; Lagrange and cole (2008); Steyer et al. 1999). Through a

follow-up study, Cole et al. (2005) reported that the classic LST model ignores the time

effect; moreover, they note that the previous measurement may affect the subsequent ones.

Therefore, on the basis of the classic LST model, Cole et al. proposed the Trait-State-

Occasion (TSO) model (Fig. 2). Schimmack et al. (2010) employed this model to evaluate

Fig. 1 The path graph of theclassic LST model

Z. Li et al.

123

the stability and variability of SWB, and found that the variation ratio of variance con-

tributed by latent trait factors and latent state factors are almost the same. Moreover, the

factors of an individual’s life satisfaction are more consistent and stable. Their research,

however, neglected a significant issue, that is, the TSO model can only measure a single

psychological characteristic, although SWB includes a cognitive element (life satisfaction)

and an emotional element (positive emotion and negative emotion), Results derived from

this model are, therefore, not necessarily reliable (Davern et al. 2007).

But how to handle a psychological structure that contains several types of psychological

characteristics (e.g., SWB). Many researchers (Bonnefon et al. 2007; Eid and Diener 2004)

have developed the Indicator-Specific Trait (IT) model based on the LST model to deal

with this issue (Fig. 3). The formula is as follows:

Yit ¼ ait þ kitTi þ ditSRt þ eit: ð6Þ

When index i is added to latent trait factor T, the model indicates that the trait variable is no

longer the only common factor. In fact, this model includes several special variables that

are related to each other. The IT model applies to situations in which researchers assume

that one psychological structure belongs to a broader psychological structure that contains

several different features such as SWB. Moreover, this broader psychological structure

does not only include cognitive composition but also contains the psychological attributes

of positive emotion and negative emotion. The IT model still assumes that these different

characteristics remain in the same measurement occasions and are affected by the same

situation (Geiser et al. 2012). Eid and Diener (2004) attempted to use the IT model to study

SWB; however, the researchers only made two measurements and these two measurements

were done 40 days apart. This research can provide clues pertaining to the stability and

variability of SWB in certain time nodes; however, the clues are still insufficient and are

easily influenced by the time interval between measurements, thereby yielding extremely

unstable results (Fraley and Roberts 2005).

In conclusion, three-wave longitudinal surveys on the subjective well-being (SWB) of

individuals were conducted for a period of one year. The classic LST model as well as the

TSO and IT models were used to assess SWB with three sets of tracking data. The models

were also employed to determine the optimal model for deriving conclusions regarding

SWB. The CO and OSpe of the most suitable model were used to evaluate the stability and

change the indicators of SWB, such as life satisfaction, positive emotion, and negative

emotion.

2 Methods

2.1 Participants and Procedures

The participants were 406 students from a Chinese university. The longitudinal study

covered three measurement occasions with a time lag of 4 months between the two

measurement occasions. In the following two surveys, 28 questionnaires remained unan-

swered due to the internship or suspension of schooling of the student, and 4 incomplete

questionnaires were removed. Therefore, the complete data sets were available from

N = 360 (175 males, 185 females, between 17 and 23 years of age: M = 19.69;

SD = 2.93). Hence, the drop-out rate (11 %) was relatively small.

Psychological Mechanism of Subjective Well-Being

123

2.2 Measures

2.2.1 Life Satisfaction

General life satisfaction was measured using the Satisfaction With Life Scale (SWLS;

Diener et al. 1985). This scale, which uses a 7-point rating, is considered suitable for

different age groups. Its reliability and validity indexes are comparatively good, and are

used widely. In this study, the values of reliability coefficients a in the three measurements

were 0.71, 0.72 and 0.77, respectively.

2.2.2 Positive Emotions and Negative Emotions

The Positive Emotions and Negative Emotions Questionnaire (PANAS, Watson et al. 1988)

was used in this study. The PANAS is commonly used in studies that focus on well-being. This

Fig. 2 TSO model

Fig. 3 IST model

Z. Li et al.

123

scale uses a 5-point scale and consists of two subscales, namely, positive emotion scale and

negative emotion scale. In the current study, two subscales were used to measure the reliability

coefficients a, and the results for positive emotion scale were 0.86, 0.82 and 0.85, respectively,

whereas those for negative emotion scale were 0.84, 0.87 and 0.83, respectively.

2.2.3 Data Analysis

The Mplus 6.0 was used for data processing and analysis, which consisted of two steps.

The first step evaluated the date of subjective well-being (SWB) from the three-wave

measurement using the three models, namely, the classic LST model as well as the TSO

and the IT model. From these, the most suitable model was identified. The CO, OSpe, and

Rel of SWB in three measurements were calculated according to the optimum model. By

analyzing these coefficients, answers to questions on whether SWB is a stable trait or a

state under circumstance and the extent of their contribution to SWB can be obtained.

3 Results

3.1 Descriptive Statistics

The mean values of each dimension, standard deviation, and correlation coefficient matrix

in the three tracking measurements are presented in Table 1. Within a year, only slight

fluctuations in the mean value of each dimension and standard deviation in the three

periods are observed. Meanwhile, the coefficients of life satisfaction, negative attitudes,

and positive attitudes in these three periods are closely related. Moreover, positive emotion

and negative emotion are very weakly related; however, these two factors are closely

related to life satisfaction.

3.2 Goodness of Fit

All three models were fit to each data set. Mplus scripts for all analyses were obtained from the

three-wave longitudinal measurement, Table 2 shows the goodness-of-fit statistics for all

models. Following generally accepted practice, we evaluated the fit of each model by

examining multiple fit indices (Kline 2010). We used the Chi square, root-mean-square error

of approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), Tucker-

Lewis index (TLI), comparative fit index (CFI), and Akaike information criterion(AIC),

Sample-Size Adjusted BIC(ABIC). Conventional guidelines suggest that x2/df ratio less than

3.0, RMSEA, SRMR values B0.08 indicate acceptable model fit and B0.05 indicate good

model fit, and CFI, TLI C0.90 indicate adequate model fit, (Kline 2010). In addition, the

smaller the AIC and ABIC are, the better the model fit the data (Burnham and Anderson

2002). According to the Table 2, The IT model fit the data best. Therefore, the IT model was

used to calculate the CO, OSpe, and Rel of each variable. The results are shown in Table 3.

3.3 Consistency Coefficient (CO), Occasion-Specificity Coefficient (OSpe)

and Reliability Coefficient (Rel)

The coefficients in Table 3 are the focus of this study. At time point T1, the Rel of SWLS

is 0.73, indicating that the error of SWLS in the first measurement is 0.27 (1-Rel). This

Psychological Mechanism of Subjective Well-Being

123

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1

Z. Li et al.

123

shows that the variance of the random measurement error is 27 %. The CO is 0.514,

indicating that the variance from the stable characteristics of the individual differences is

51.4 %. The OSpe is only 0.213, indicating that 21.3 % of the variance is neither from

measurement error nor from stable individual differences in characteristics, but from the

influence of specific contexts. The interpretation of other variables is also similar to SWLS

at time point T1. During the three measurements, the SWLS CO values range from 0.46 to

0.61 and the Ospe values range from 0.174 to 0.213. The CO values of PA range from

0.758 to 0.814, and the OSpe values are between 0.121 and 0.191. The CO values of

negative emotions are between 0.676 and 0.711, and the OSpe values range from 0.136 to

0.164. In the three measurements within 1 year, the value of each CO variable is higher

than the OSpe value, but the proportions of negative and positive emotions are higher than

that of life satisfaction.

4 Discussion

Does subjective well-being (SWB) change with time and situation or is it a stable trait?

Many researchers believe that the SWB of an individual both includes unchangeable traits

as well as elements influenced by situation (Lyubomirsky 2001). However, to what extent

do stable traits and state elements contribute to SWB? Due to the limitation of previous

study methods, an accurate answer to this question cannot be provided. In response, the

current study used the LST model to analyze the stability and changes of SWB. The

greatest advantage of the LST model is that it utilizes coefficients (CO, Ospe) obtained

through the model, which are then employed to explain whether a psychological charac-

teristic is a stable trait or a state. This study also compares the goodness-of-fit indices of

three kinds of LST models and identified that the IT model has the best fit in terms of

Table 2 Goodness-of-fit Indices for different latent state-trait models

Model x2 df CFI TLI RMSEA SRMR AIC ABIC

Classic LST model 326.96 32 0.51 0.44 0.16 0.13 20,204.64 20,220.33

TSO model 192.87 31 0.73 0.69 0.12 0.09 20,033.39 20,049.80

IT model 35.58 18 0.97 0.94 0.052 0.035 19,850.84 19,876.53

TSO model Trait-state-occasion model, IT model indicator-specific trait factor model

Table 3 Estimates of CO, OSpe,and Rel (IT model)

Variable CO OSpe Rel

1. T1 SWLS 0.514 0.213 0.73

2. T1 PA 0.758 0.191 0.95

3. T1 NA 0.676 0.164 0.84

4. T2 SWLS 0.460 0.201 0.66

5. T2 PA 0.814 0.150 0.96

6. T2 NA 0.711 0.136 0.85

7. T3 SWLS 0.610 0.174 0.78

8. T3 PA 0.778 0.121 0.90

9. T3 NA 0.704 0.149 0.85

Psychological Mechanism of Subjective Well-Being

123

tracking the measurement date of SWB. This is because this model is more suitable for

SWB, which is a broader psychology structure consisting of remarkably different attri-

butes, including emotional and cognitive components.

In the three-wave measurements conducted in this study, the CO values of SWLS range

from 0.46 to 0.61, and the Ospe values range from 0.174 to 0.213. These indicate that in the

period of a year, 46–61 % of the elements are stable in the variable of general life

satisfaction, and about 17.4–21.3 % comprise the situational elements. During the same

period, the stability of the positive affections of one individual ranges from 75.8 to 81.4

and the variabilities under the influence of context range from 12.1 to 19.1 %. The stability

of negative affections ranges from 67.6 to 71.1 % and the variability under the influence of

context is from 13.6 to 16.4 %. The result of the current study shows that, overall, the CO

value of SWB is higher than that of Ospe, thereby supporting the view that SWB is more

likely to be a stable trait within one year. The result of this study with regards life

satisfaction is basically consistent with the results of Fujita (2005) and Lucas and Don-

nellan (2007), who reported that life satisfaction is moderately stable. However, the results

of the current study are much lower than those of Eid and Diener (2004). In their research,

the time interval between two measurements is only 40 days. However, in this study and in

the study of Fujita, the tracking time is longer (one or 5 years). This means that, within a

relatively short period of time, the life satisfaction of an individual is a highly stable

psychology trait. However, life satisfaction fluctuates and changes within a longer period

of time before reaching a moderately stable level.

Basically, the result of this study as regards positive and negative emotions are con-

sistent with the result of Eid and Diener (2004), and the CO coefficient of these two

emotions are much higher than the coefficient of life satisfaction. This can be attributed to

the fact that positive and negative emotions are more related to the genetics and traits of the

individual. Therefore, these two coefficients are quite stable within a certain period of time.

Nevertheless, life satisfaction is not only influenced by the cognitive methods of indi-

viduals but also by their life situation and how they evaluate their lives. Thus, life satis-

faction is more influenced by contextual elements. The conclusion of present study seems

inconsistent with the Davern’s et al. (2007) study which found that 64 % of the variance in

SWB was explained by six Core Affects, thus they believed that SWB was more likely a

affective construct. But they defined the six Core Affects as a enduring and stable char-

acteristic of all individuals, and possibly a predominantly biologically based characteristic.

It is responsible for this stability identifies perhaps the most fundamental determinant of

human happiness. So the conclusion of this study and Davern’s et al. (2007) viewpoint are

actually consistent: stability is a remarkable feature of SWB(Davern et al. 2007).

Many scholars have questioned whether SWB can be improved or changed. This

question is based on two opposite theories (Lyubomirsky et al. 2005). On the one hand,

some theories suggest that SWB is very easy to change by simply changing the context.

However, finding the inherent rules that affect SWB is difficult. On the other hand, some

theories suggest that SWB is a fixed individual trait that cannot be changed according to

context. Nevertheless, these two completely opposite views have a common point: both

believe that any attempt to enhance individual well-being may be futile. From the results of

the current study, these two types of views are deemed undesirable. Each indicator of SWB

not only has stable and unchangeable elements, but also elements that are influenced by

context and account for a greater proportion. Life satisfaction is the part that is relatively

easy to change. Therefore, considering ways by which to enhance the life satisfaction of

individuals is necessary in policy-making activities that aim to explore strategies to

improve the well-being of individuals.

Z. Li et al.

123

Conventional wisdom states that if an individual has more happy emotions, they would

have fewer unhappy emotions. However, this study finds that the correlation between

positive and negative emotions are very weak (close to zero), suggesting that a corre-

sponding negative correlation between happy and unhappy emotions does not exist.

Obviously, this assumption requires further support with neuropsychological evidence. In

fact, some studies have already found that the happy and unhappy emotions of humans are

independent of each other and are each correlated with different factors (Bradburn and

Caplovitz 1965; Diener and Emmons 1984; Diener et al. 2009). Thus, when studying SWB,

positive and negative emotions should be studied as two independent variables (Diener and

Emmons 1984). The results of this study prove this assumption.

This study also has limitations. First, the tracking time of this study is only a year.

However, SWB is influenced by an individual’s long-term life experiences and evaluations.

Therefore, a longer tracking time may be used for future studies. Second, the participants

of the study are university students. Given that the SWB of different groups of people do

not have the same characteristics, future studies must involve a sample of different groups

of people.

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