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The stability and change of malaise scores over 27 years: Findings from a nationally representative sample
Personality and Individual Differences 79 (2015) 30–34
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The stability and change of malaise scores over 27 years: Findings from a nationally representative sample Adrian Furnham a,b,⇑, Helen Cheng a,c a
Research Department of Clinical, Educational and Health Psychology, University College London, London, UK Norwegian Business School, Olso, Norway c ESRC Centre for Learning and Life Chances in Knowledge Economies and Societies, Institute of Education, University of London, UK b
a r t i c l e
i n f o
Article history: Received 8 August 2014 Received in revised form 3 January 2015 Accepted 20 January 2015
Keywords: Malaise Minor psychiatric morbidity Intelligence Childhood problems
a b s t r a c t This study investigates the stability and change of malaise scores over a period of 27 years taking account of socio-demographic variables, childhood intelligence and behavioural problems. 6822 cohort members had the complete data on Malaise measured at 23, 33, 42, and 50 years. T-tests showed that there were significant changes of malaise mean scores over the 27 years: malaise scores decreased significantly from 23 to 33 years, but increased significantly from 33 to 42 years, and there were no significant change between 42 and 50 years. The trend showed a similar pattern for men and women, though women scored significantly higher than men on the measure at every time point. Correlational analysis showed that over 27 years malaise scores ranged from r = .41 to r = .52, indicating the relative stability. Malaise was significantly and inversely associated with childhood intelligence and behavioural problems. It was also significantly associated with education in the expected direction. Regression analysis showed that previous malaise scores were significant predictors of the later ones, and the strength of the predictive power decreased over time. Limitations were considered. Ó 2015 Published by Elsevier Ltd.
1. Introduction There have been many studies that examine the stability of psychological characteristics over time such as aggression (Huesmann, Eron, Lefkowitz, & Walder, 1984) and intelligence (Deary, Whalley, Lemmon, Starr, & Crawford, 2000). There have also been a large number of studies of personality traits over time using different data bases (Briley & Tucker-Drob, 2014). The debate about the equivocal nature of both findings and conclusions regarding continuity vs. change revolves around a number of issues, such as the reliability and validity of personality tests used (to account in part for measurement error); the moderator variables considered (like sex, education and ethnicity); the age at which people are measured (adolescents, adults, old age); the time span that shows most change and stability; how change is measured (such as mean level change, rank order, ipsative change); the stability of the environments of people and what, if anything, leads to change (Boyce, Wood, & Powdthavee, 2013; Cramer,
⇑ Corresponding author at: Research Department of Clinical, Educational and Health Psychology, University College London, London, UK. E-mail address: [email protected] (A. Furnham). http://dx.doi.org/10.1016/j.paid.2015.01.027 0191-8869/Ó 2015 Published by Elsevier Ltd.
2003; Haan, Millsap, & Hartka, 1986; Helson, Jones, & Kwan, 2002; Loehlin & Martin, 2001; Lucas & Donnellan, 2011; Martin, Long, & Poon, 2002; Roberts, Caspi, & Moffitt, 2003; Roberts, Walton, & Viechtbauer, 2006; Srivastava, John, Gosling, & Potter, 2003). Deary et al. (2000) reviewing 17 studies on the stability of intelligence over various time periods showed correlations between 0.41 to 0.92 and .63 for their study of people measured at 11 and again at 77 years. The results have similar patterns to them though there inevitably remains many disagreements (Ardelt, 2000; Conley, 1985; McCrae & Costa, 1994). All agree that there is evidence of both stability and change. From these studies it may be possible to draw the following conclusions: Personality seems most stable between the ages of 30 and 60 years, particularly using established big five measures to assess it; there are modest increases in Emotional Stability and Agreeableness over this period with Extraversion and Neuroticism showing least change (both with a slight decline) and Conscientiousness showing most change (an increase); Males seem more stable than females. Various studies have been reported using longitudinal data and cross-lagged correlation coefficients, path analysis and structural equation modelling where the causal ordering in the analysis has not matched the time at which data was gathered (Cheng &
A. Furnham, H. Cheng / Personality and Individual Differences 79 (2015) 30–34
Furnham, 2012; McManus, Keeling, & Paice, 2004). That is, because it is generally accepted that some factors are generally stable over adulthood (like height) it is assumed that when they are measured is relatively unimportant (i.e. people remain very similar in height from 20 to 60 years). It is however generally accepted it is desirable to measure variables according to the causal modelling pattern and also check the reliability of those measures.
1.1. Malaise This study looks at the stability of psychological malaise over time. The concept of malaise includes two related themes. First, a physical condition of general bodily weakness or discomfort, often marking the onset of an illness. Second, a vague feeling of discomfort or unease which is related to psychological distress and depression. It has both state and trait features and may be considered a measure of minor psychiatric morbidity similar to the Langner 22 (Langner, 1962). However there is data to suggest that Malaise is stable over time suggesting it is trait-like. For instance McGee, Williams, and Silva (1986) reported test–retest correlations of .58.63 over a two year period with 836 adult New Zealand women. These results compare favourably with well data from well-established personality tests (Costa, Herbst, McCrae, & Siegler, 2000) suggesting that Malaise may be thought of as a trait, essentially a facet of Neuroticism. This study will investigate its stability and correlates over time. As far as we know there are no other longitudinal studies of malaise. In this study we used the Malaise Inventory (Rutter, Tizard, & Whitmore, 1970) which is 24 ‘yes–no’ items of the inventory designed to cover emotional disturbance and associated physical symptoms. The measure was developed from the Cornell Medical Index Health Questionnaire (CMI) (Brodman, Erdmann, Lorge, Gershenson, & Wolff, 1952). Indeed 14 of the 24 questions are taken directly from the CMI (Rutter et al., 1970). Rutter claimed that ‘the inventory differentiates moderately well between individuals with and without psychiatric disorder’ (Rutter et al., 1970). Using a cut-off score criterion individuals responding ‘yes’ to eight or more of the 24 items are considered to be at risk of depression (Rodgers, Pickles, Power, Collishaw, & Maughan, 1999). Items include: ‘‘Do you feel tired most of the time?’’; ‘‘Do you often get worried about things?’’; ‘‘Do people often annoy and irate you?’’; ‘‘Is your appetite poor?’’; ‘‘Are you constantly keyed up and jittery?’’ and ‘‘Do you often suddenly become scared for no good reason?’’ There have been various positive evaluations of the measure (Hirst, 1983). The internal consistency of the scale has been shown to be acceptable and the validity of the inventory is shown to hold in different socio-economic groups (Rodgers et al., 1999). The scale has been used in both general population studies (McGee et al., 1986; Rodgers et al., 1999; Rutter & Madge, 1976) and in investigations of high-risk groups (Grant, Noland, & Ellis, 1990). It continues to be used in empirical studies (Carr, 2008). Various papers have appeared using the data set reported in this study but using the Malaise score measured at 33 years (Hamilton & Hango, 2008; Hatch, Harvey, & Maughan, 2010) and 42 years (Morgan, Brugha, Fryers, & Stewart-Brown, 2012). In this study we explored a unique data set which involves a large representative sample completing the same test (i.e. Malaise Inventory) at four different time periods over 27 years. This allowed us to look at the stability of the measure over time, much longer than any other study and considerably longer than the average test–retest study. We were also able to investigate possible early markers of malaise including sex, social class, intelligence
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and behavioural problems and seek to see how they related to malaise at the four different time periods. 2. Method 2.1. Participants The National Child Development Study 1958 is a large-scale longitudinal study of the 17,415 individuals who were born in Great Britain in a week in March 1958 (Ferri, Bynner, & Wadsworth, 2003). Parental social class was measured at birth, mothers completed a questionnaire of children’s behavioural problems when participants were at age 11, and children at age 11 completed tests of cognitive ability. Testing took place in school, and written, informed consent was given by the parents. Malaise was measured at 23, 33, 42, and 50 years. At 23 years, participants provided information on their educational qualifications obtained, and at 23, 33, 42, and 50 years participants provided information on their current occupational levels. The analytic sample comprises 6822 cohort members (53 per cent females) for whom complete data were collected at birth, at 11, 23, 33, 42, and 50 years. Bias due to attrition of the sample during childhood has been shown to be minimal (Davie, Butler, & Goldstein, 1972; Fogelman, 1976). 2.2. Measures Family social background includes information on parental social class and parental education. Social Class: Parental social class at birth was measured by the Registrar General’s measure of social class (RGSC). RGSC is defined according to occupational status (Marsh, 1986). Where the father was absent, the social class (RGSC) of the mother’s father was used. RGSC was coded on a 6point scale: I professional; II managerial/technical; IIIN skilled non-manual; IIIM skilled manual; IV semi-skilled; and V unskilled occupations (Leete & Fox, 1977). Scores were reversed in the following analyses. At the time of testing this sample was representative of the population as a whole. Childhood intelligence: This was assessed at age 11 in school using a general ability test (Douglas, 1964) consisting of 40 verbal and 40 non-verbal items which were significantly correlated and could be considered as a robust test of functional memory. More details are available from Cheng, Eysenck, Green, & Furnham, 2015). Child behavioural problems: This was assessed by The Behaviour Adjustment Scale (Rutter et al., 1970) consists of 14 items. It was answered by mothers when participants were 11 years old. Responses (3-point, never = 1, sometimes = 2, frequently = 3) were summed to provide scores on children’s behavioural problems. The Alpha in many studies was around 0.70 and 0.66 in the study. Education: At 23 years, participants were asked about their highest academic or vocational qualifications. Responses are coded to the six-point scale of National Vocational Qualifications levels (NVQ) ranging from ‘none’ to ‘university degree/equivalent’. At 23, 33, 42, and 50 years participants provided information on their occupational levels which are coded according to the RGSC described above, using a 6-point classification. Malaise: This was assessed at 23, 33, 42, and 50 using Rutter Malaise Inventory (Rutter et al., 1970). It comprises of 24 items with Yes/No. The inventory covers emotional disturbance and associated physical symptoms. At 23, 33, and 42 years, all 24 items were included into the questionnaire, and at 50, 9 items were included in the questionnaire. For comparison purpose 9 identical items were selected from measures at 23, 33, and 42 years. The Alpha ranged from .72 to .83 at these time points.
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A. Furnham, H. Cheng / Personality and Individual Differences 79 (2015) 30–34
3. Results 3.1. Descriptive analysis First we looked at the pattern of the malaise scores from 23 to 50 (see Table 1). Figure 1 shows the trend of malaise over the 27 years. T-test showed that there was a statistically significant decrease between 23 and 33 years (t (df = 6821) = 13.70, p < .001); there was a statistically significant increase between 33 and 42 years (t (df = 6821) = 29.28, p < .001); and there was no significant change between 42 and 50 years. There were also sex differences on malaise. Women scored significantly (p < .001) higher than men at all four time points. Figure 2 shows the trends for men and women over the 27 years from 23 to 50 years. At 23 years the means for malaise were .75 (SD = 1.19) for men and 1.46 (SD = 1.60) for women (t (df = 6821) = 20.92); at 33 years the means were .64 (SD = 1.19) for men and 1.11 (SD = 1.54) for women (t (df = 6821) = 14.38); at 42 years the means for malaise were 1.17 (SD = 1.58) for men and 1.67 (SD = 1.75) for women (t (df = 6821) = 12.36); and at 50 years the means were 1.10 (SD = 1.64) for men and 1.68 (SD = 1.99) for women (t (df = 6821) = 13.07).
Fig. 1. Malaise mean scores (vertical axis) at 23, 33, 42, 50 years in the total sample (N = 6822).
3.2. Correlational analysis Table 1 shows the correlations between the observed variables in the study, and the means and standard deviations of the each measure. Correlations between the Malaise scores at the four time periods ranged from .41.52 but when corrected for attenuation rose to around r = .60 which could be considered strong evidence of stability. Malaises scores measures at 23, 33, 42, and 50 were also significantly associated with socio-demographic variables, as well as intelligence and behavioural problems (p < .001). Overall the size and direction of these correlations were very similar suggesting stability of the relationship over time.
Fig. 2. Malaise mean scores (vertical axis) at 23, 33, 42, 50 years by gender.
accounting for 4 per cent of the total variance. Malaise measured in previous time points were all significantly associated with current malaise scores, accounting, in addition, 32 per cent of the variance. Table 3 shows that using malaise 42 years as the dependent variable, earlier malaise, childhood behavioural problems, as well as sex were significant predictors. Table 4 shows that using malaise measured at 33 years as the dependent variable, earlier malaise, childhood behavioural problems and intelligence, as well as sex were significant predictors. Table 5 shows that using malaise measured at 23 years as the outcome variable childhood behaviour and
3.3. Regression analyses We then performed a series of regressions to investigate the most salient predictors of malaise at the different ages. After each we did a second regression to see to what extent malaise measured the time before explained additional variance. Table 2 shows that using malaise measured at 50 childhood behavioural problems and intelligence, as well as current occupation and sex were significant predictors of the outcome variable,
Table 1 Correlation matrices of the variables used in the study. Measures
Mean (SD)
1
1. Gender 2. Parental social class at birth 3. Childhood intelligence at age 11 4. Childhood behavioural problems at age 11 5. Educational qualifications age 23 6. Occupational levels at age 23 7. Occupational levels at age 33 8. Occupational levels at age 42 9. Occupational levels at age 50 10 Malaise at age 23 11. Malaise at age 33 12. Malaise at age 42 13. Malaise at age 50
53:47 M:F 3.29 (1.22) 103.4 (13.09) 21.8 93.31) 2.41 (1.39) 3.68 (1.14) 3.87 (1.26) 4.01 (1.23) 4.09 91.21) 1.13 (1.47) .89 (1.40) 1.43 (1.69) 1.40 91.86)
– .024 .067 .037 .058 .119 .024 .049 .012 .242 .169 .147 .155
2 – .266 .103 .327 .250 .271 .227 .194 .090 .050 .027 .059
3
4
5
6
7
8
– .494 .533 .501 .462 .205 .147 .103 .115
– .518 .444 .401 .102 .097 .103 .065
– .576 .483 .120 .100 .095 .074
– .625 .121 .112 .074 .083
9
10
11
12
13
– .494 .439 .412
– .520 .407
– .469
–
– .176 .496 .385 .364 .358 .337 .167 .131 .090 .110
– .171 .137 .140 .114 .107 .199 .174 .149 .158
– .131 .088 .088 .072
Note: Standard deviations (SD) are given in parentheses. Correlations were Pearson’s. Variables were scored such that a higher score indicated being female, higher scores on childhood intelligence and behavioural problems, higher scores on educational qualifications, a more professional occupation for the parents and cohort members. Correlations of r = .05 are significant at p < .001. Correlations in bold indicate those with Malaise at the four different measurement times. Correlations in italics are those between Malaise scores at different times.
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A. Furnham, H. Cheng / Personality and Individual Differences 79 (2015) 30–34 Table 2 Predicting malaise at age 50 years using standard multiple regressions. Model 1
* ** ***
Model 2 t
b Sex Parental social class at birth Childhood intelligence at age 11 Childhood behavioural problems at age 11 Educational qualifications age 23 Current occupational levels at age 50 Malaise at age 23 Malaise at age 33 Malaise at age 42 R2
Table 5 Predicting malaise at age 23 years using standard multiple regressions.
t
b
.16 .02 .06 .10
10.24*** 0.97 3.09** 6.26***
.02 .05
1.08 2.84*
Model 1
.02 .03 .14 .20 .38 .364
.043
1.48 1.74 8.73*** 12.61*** 25.23***
p < .05. p < .01. p < .001.
Table 3 Predicting malaise at age 42 years using standard multiple regressions. Model 1
Sex Parental social class at birth Childhood intelligence at age 11 Childhood behavioural problems at age 11 Educational qualifications age 23 Current occupational levels at age 42 Malaise at age 23 Malaise at age 33 R2 * ** ***
Model 2 t
b
.16 .02 .04 .11
10.36*** 1.28 2.12* 6.85***
.03 .01 .01 .04
2.00* 0.94 0.89 2.85**
.04 .01
2.16* 0.53
.01 .01 .24 .39 .303
0.38 0.92 14.88*** 25.04***
b
.040
t
p < .05. p < .01. p < .001.
Table 4 Predicting malaise at age 33 years using standard multiple regressions. Model 1
Sex Parental social class at birth Childhood intelligence at age 11 Childhood behavioural problems at age 11 Educational qualifications age 23 Current occupational levels at age 33 Malaise at age 23 R2 * ** ***
Model 2 t
b .19 .02 .08 .10
12.42*** 1.09 4.60*** 6.77***
.06 .05
3.00** 3.12**
.065
t
b .07 .02 .04 .05 .01 .05 .47 .259
t
b 2.31* 0.45 1.04 1.70
.03 .01 .02 .02
4.89*** 1.32 2.31* 3.50*** 0.34 3.06** 33.53***
p < .05. p < .01. p < .001.
intelligence, current occupational levels, and sex were the significant predictors of the outcome variable. We attempted other analyses like latent variable analysis and SEM but found no clear way to model the data or improve on the above straightforward analysis. 4. Discussion The correlations between the Malaise scores at four different time periods, over 27 years were between .41 and .52. When corrected for disattenuation these correlations increase to well over
Sex Parental social class at birth Childhood intelligence at age 11 Childhood behavioural problems at age 11 Educational qualifications age 23 Current occupational levels at age 23 R2 ***
.26 .01 .10 .13 .11 .03 .120
18.15*** 0.35 5.72*** 8.84*** 5.85*** 1.84
p < .001.
.60 which is in line with Deary et al. (2000) who measured intelligence in a sample at age 11 and 77 years. Indeed studies on the reliability of NEO-PI-R Neuroticism facets for different age groups and periods of time have revealed scores around .50.71 which are taken as evidence of the stability of personality traits over 30 years of age (Terracciano, Costa, & McCrae, 2006). Thus it could be argued that the data from this study on a large N over 27 years and based on a short but reliable and valid measure suggest that malaise has the characteristics of a stable, personality trait. This study therefore demonstrates a reasonable stability of malaise from early to mid- adulthood. Figures 1 and 2 show that whilst there was a dip in malaise from 23 to 33 years the pattern was overall stable and that if anything there was a slight increase over time. It is interesting to note the sex difference in malaise at all four measurement points. This may be explained by the well-established sex difference in neuroticism/adjustment shown in many different studies and using many different questionnaires (Matthews, Deary, & Whiteman, 2009). That is, malaise, is a measure of emotional and psychological distress closely related to trait neuroticism. There is meta-analytic evidence of small to medium effect sizes for gender on trait neuroticism though disagreement about why these differences appear. However longitudinal studies of Neuroticism seem to suggest show a slight decline over adulthood (Roberts et al., 2006). The four regression tables showed three things. First, that various factors such as sex and social class, as well as intelligence and behaviour problems were significantly associated with the malaise score at all four time points. Second, along with education and occupation these factors accounted for between 4% and 12% of the variance with their predictive power declining over time. Third, when the Malaise scores were added to the regressions from time period to time period (i.e. Malaise at 23 predicting Malaise at 33) the amount of variance increased from a quarter to a third of the total variance. In all the regressions, after sex, childhood behavioural problems measured at 11 years is the strongest predictor of malaise at 23, 33, 42 and 50 years. The behavioural problems scale was designed to be completed by either parents or teachers and can ‘‘be employed as a screening instrument to select children likely to show some emotional and behavioural disorder’’ (Rutter, 1967, p9). In this sense this could be seen as an observer measure of neuroticism/ adjustment. It is interesting to note that this observational data measured when a participant was 11 years old significantly predicted their malaise score at 50 years old, some 39 years later. Malaise is consistently related to intelligence, education and occupational status which are logically and positively inter-correlated. The results show that less intelligent people with lower educational qualifications and occupational status tend to have higher malaise scores. It is possible that those with early behavioural problems do not do well at school which affects their educational
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and occupational outcomes and which in turn increases their malaise. The pattern is however established very early and can be seen in the correlations aged 23 years. It suggests a vicious cycle where proneness to depression and psychiatric morbidity has long term social consequences. This data cannot explain the aetiology of malaise, nor how it could be effectively treated. The question is what explains both the stability and change in malaise over time. It is quite possible that many people in this sample attempted various interventions (therapy, pharmacology) to help them with their malaise, though this data is not available. It would have been very desirable to try to understand why some people remained particularly stable over time and others not, and more particularly what explains these findings. Further, it does not explain the significant ‘‘dip’’ in the scores for both males and females between the ages of 23 and 42 years. Nevertheless the study adds data to those longitudinal studies that have shown the stability of personality functioning over time (Roberts et al., 2006; Srivastava et al., 2003). References Ardelt, M. (2000). Still stable after all these years? Social Psychology Quarterly, 63, 392–405. Boyce, C., Wood, A., & Powdthavee, N. (2013). Is personality fixed? Social Indicators Research, 111, 287–305. Briley, D., & Tucker-Drob, E. (2014). Genetic and environmental continuity in personality development. Psychological Bulletin, 140, 1303–1331. Brodman, K., Erdmann, A., Lorge, I., Gershenson, C., & Wolff, H. (1952). The Cornell Medical Index health questionnaire: 3. The evaluation of emotional disturbances. Journal of Clinical Psychology, 8, 119–124. Carr, J. (2008). Families of 40-year olds with downs syndrome. Journal of Developmental Disabilities, 14, 35–43. Cheng, H., & Furnham, A. (2012). Childhood cognitive ability, education, and personality predict attainment in adult occupational prestige over 17 years. Journal of Vocational Psychology, 81, 218–226. Cheng, H., Eysenck, M., Green, A., & Furnham, A. (2015). Correlates of adult functional memory: Findings from a British Cohort. Intelligence, 47, 134–140. Conley, J. (1985). Longitudinal stability of personality traits. Journal of Personality and Social Psychology, 49, 1266–1282. Costa, P., Herbst, J., McCrae, R., & Siegler, I. (2000). Personality at midlife: Stability, intrinsic maturation, and response to life events. Assessment, 7, 367–380. Cramer, P. (2003). Personality change in later adulthood is predicted by defense mechanism use in early adulthood. Journal of Research in Personality, 37, 76–104. Davie, R., Butler, N., & Goldstein, H. (1972). From birth to seven. London: Longman. Deary, I., Whalley, L., Lemmon, H., Starr, J., & Crawford, J. (2000). The stability of individual differences in mental ability from childhood to old age. Intelligence, 28, 49–58. Douglas, J. W. B. (1964). The home and the school. London: Panther Books. Ferri, E., Bynner, J., & Wadsworth, M. (2003). Changing Britain, changing lives: Three generations at the turn of the century. London: Institute of Education. Fogelman, K. (1976). Britain’s 16-year-olds. London: National Children’s Bureau.
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The stability and change of malaise scores over 27 years: Findings from a nationally representative sample Adrian Furnham a,b,⇑, Helen Cheng a,c a
Research Department of Clinical, Educational and Health Psychology, University College London, London, UK Norwegian Business School, Olso, Norway c ESRC Centre for Learning and Life Chances in Knowledge Economies and Societies, Institute of Education, University of London, UK b
a r t i c l e
i n f o
Article history: Received 8 August 2014 Received in revised form 3 January 2015 Accepted 20 January 2015
Keywords: Malaise Minor psychiatric morbidity Intelligence Childhood problems
a b s t r a c t This study investigates the stability and change of malaise scores over a period of 27 years taking account of socio-demographic variables, childhood intelligence and behavioural problems. 6822 cohort members had the complete data on Malaise measured at 23, 33, 42, and 50 years. T-tests showed that there were significant changes of malaise mean scores over the 27 years: malaise scores decreased significantly from 23 to 33 years, but increased significantly from 33 to 42 years, and there were no significant change between 42 and 50 years. The trend showed a similar pattern for men and women, though women scored significantly higher than men on the measure at every time point. Correlational analysis showed that over 27 years malaise scores ranged from r = .41 to r = .52, indicating the relative stability. Malaise was significantly and inversely associated with childhood intelligence and behavioural problems. It was also significantly associated with education in the expected direction. Regression analysis showed that previous malaise scores were significant predictors of the later ones, and the strength of the predictive power decreased over time. Limitations were considered. Ó 2015 Published by Elsevier Ltd.
1. Introduction There have been many studies that examine the stability of psychological characteristics over time such as aggression (Huesmann, Eron, Lefkowitz, & Walder, 1984) and intelligence (Deary, Whalley, Lemmon, Starr, & Crawford, 2000). There have also been a large number of studies of personality traits over time using different data bases (Briley & Tucker-Drob, 2014). The debate about the equivocal nature of both findings and conclusions regarding continuity vs. change revolves around a number of issues, such as the reliability and validity of personality tests used (to account in part for measurement error); the moderator variables considered (like sex, education and ethnicity); the age at which people are measured (adolescents, adults, old age); the time span that shows most change and stability; how change is measured (such as mean level change, rank order, ipsative change); the stability of the environments of people and what, if anything, leads to change (Boyce, Wood, & Powdthavee, 2013; Cramer,
⇑ Corresponding author at: Research Department of Clinical, Educational and Health Psychology, University College London, London, UK. E-mail address: [email protected] (A. Furnham). http://dx.doi.org/10.1016/j.paid.2015.01.027 0191-8869/Ó 2015 Published by Elsevier Ltd.
2003; Haan, Millsap, & Hartka, 1986; Helson, Jones, & Kwan, 2002; Loehlin & Martin, 2001; Lucas & Donnellan, 2011; Martin, Long, & Poon, 2002; Roberts, Caspi, & Moffitt, 2003; Roberts, Walton, & Viechtbauer, 2006; Srivastava, John, Gosling, & Potter, 2003). Deary et al. (2000) reviewing 17 studies on the stability of intelligence over various time periods showed correlations between 0.41 to 0.92 and .63 for their study of people measured at 11 and again at 77 years. The results have similar patterns to them though there inevitably remains many disagreements (Ardelt, 2000; Conley, 1985; McCrae & Costa, 1994). All agree that there is evidence of both stability and change. From these studies it may be possible to draw the following conclusions: Personality seems most stable between the ages of 30 and 60 years, particularly using established big five measures to assess it; there are modest increases in Emotional Stability and Agreeableness over this period with Extraversion and Neuroticism showing least change (both with a slight decline) and Conscientiousness showing most change (an increase); Males seem more stable than females. Various studies have been reported using longitudinal data and cross-lagged correlation coefficients, path analysis and structural equation modelling where the causal ordering in the analysis has not matched the time at which data was gathered (Cheng &
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Furnham, 2012; McManus, Keeling, & Paice, 2004). That is, because it is generally accepted that some factors are generally stable over adulthood (like height) it is assumed that when they are measured is relatively unimportant (i.e. people remain very similar in height from 20 to 60 years). It is however generally accepted it is desirable to measure variables according to the causal modelling pattern and also check the reliability of those measures.
1.1. Malaise This study looks at the stability of psychological malaise over time. The concept of malaise includes two related themes. First, a physical condition of general bodily weakness or discomfort, often marking the onset of an illness. Second, a vague feeling of discomfort or unease which is related to psychological distress and depression. It has both state and trait features and may be considered a measure of minor psychiatric morbidity similar to the Langner 22 (Langner, 1962). However there is data to suggest that Malaise is stable over time suggesting it is trait-like. For instance McGee, Williams, and Silva (1986) reported test–retest correlations of .58
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and behavioural problems and seek to see how they related to malaise at the four different time periods. 2. Method 2.1. Participants The National Child Development Study 1958 is a large-scale longitudinal study of the 17,415 individuals who were born in Great Britain in a week in March 1958 (Ferri, Bynner, & Wadsworth, 2003). Parental social class was measured at birth, mothers completed a questionnaire of children’s behavioural problems when participants were at age 11, and children at age 11 completed tests of cognitive ability. Testing took place in school, and written, informed consent was given by the parents. Malaise was measured at 23, 33, 42, and 50 years. At 23 years, participants provided information on their educational qualifications obtained, and at 23, 33, 42, and 50 years participants provided information on their current occupational levels. The analytic sample comprises 6822 cohort members (53 per cent females) for whom complete data were collected at birth, at 11, 23, 33, 42, and 50 years. Bias due to attrition of the sample during childhood has been shown to be minimal (Davie, Butler, & Goldstein, 1972; Fogelman, 1976). 2.2. Measures Family social background includes information on parental social class and parental education. Social Class: Parental social class at birth was measured by the Registrar General’s measure of social class (RGSC). RGSC is defined according to occupational status (Marsh, 1986). Where the father was absent, the social class (RGSC) of the mother’s father was used. RGSC was coded on a 6point scale: I professional; II managerial/technical; IIIN skilled non-manual; IIIM skilled manual; IV semi-skilled; and V unskilled occupations (Leete & Fox, 1977). Scores were reversed in the following analyses. At the time of testing this sample was representative of the population as a whole. Childhood intelligence: This was assessed at age 11 in school using a general ability test (Douglas, 1964) consisting of 40 verbal and 40 non-verbal items which were significantly correlated and could be considered as a robust test of functional memory. More details are available from Cheng, Eysenck, Green, & Furnham, 2015). Child behavioural problems: This was assessed by The Behaviour Adjustment Scale (Rutter et al., 1970) consists of 14 items. It was answered by mothers when participants were 11 years old. Responses (3-point, never = 1, sometimes = 2, frequently = 3) were summed to provide scores on children’s behavioural problems. The Alpha in many studies was around 0.70 and 0.66 in the study. Education: At 23 years, participants were asked about their highest academic or vocational qualifications. Responses are coded to the six-point scale of National Vocational Qualifications levels (NVQ) ranging from ‘none’ to ‘university degree/equivalent’. At 23, 33, 42, and 50 years participants provided information on their occupational levels which are coded according to the RGSC described above, using a 6-point classification. Malaise: This was assessed at 23, 33, 42, and 50 using Rutter Malaise Inventory (Rutter et al., 1970). It comprises of 24 items with Yes/No. The inventory covers emotional disturbance and associated physical symptoms. At 23, 33, and 42 years, all 24 items were included into the questionnaire, and at 50, 9 items were included in the questionnaire. For comparison purpose 9 identical items were selected from measures at 23, 33, and 42 years. The Alpha ranged from .72 to .83 at these time points.
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3. Results 3.1. Descriptive analysis First we looked at the pattern of the malaise scores from 23 to 50 (see Table 1). Figure 1 shows the trend of malaise over the 27 years. T-test showed that there was a statistically significant decrease between 23 and 33 years (t (df = 6821) = 13.70, p < .001); there was a statistically significant increase between 33 and 42 years (t (df = 6821) = 29.28, p < .001); and there was no significant change between 42 and 50 years. There were also sex differences on malaise. Women scored significantly (p < .001) higher than men at all four time points. Figure 2 shows the trends for men and women over the 27 years from 23 to 50 years. At 23 years the means for malaise were .75 (SD = 1.19) for men and 1.46 (SD = 1.60) for women (t (df = 6821) = 20.92); at 33 years the means were .64 (SD = 1.19) for men and 1.11 (SD = 1.54) for women (t (df = 6821) = 14.38); at 42 years the means for malaise were 1.17 (SD = 1.58) for men and 1.67 (SD = 1.75) for women (t (df = 6821) = 12.36); and at 50 years the means were 1.10 (SD = 1.64) for men and 1.68 (SD = 1.99) for women (t (df = 6821) = 13.07).
Fig. 1. Malaise mean scores (vertical axis) at 23, 33, 42, 50 years in the total sample (N = 6822).
3.2. Correlational analysis Table 1 shows the correlations between the observed variables in the study, and the means and standard deviations of the each measure. Correlations between the Malaise scores at the four time periods ranged from .41
Fig. 2. Malaise mean scores (vertical axis) at 23, 33, 42, 50 years by gender.
accounting for 4 per cent of the total variance. Malaise measured in previous time points were all significantly associated with current malaise scores, accounting, in addition, 32 per cent of the variance. Table 3 shows that using malaise 42 years as the dependent variable, earlier malaise, childhood behavioural problems, as well as sex were significant predictors. Table 4 shows that using malaise measured at 33 years as the dependent variable, earlier malaise, childhood behavioural problems and intelligence, as well as sex were significant predictors. Table 5 shows that using malaise measured at 23 years as the outcome variable childhood behaviour and
3.3. Regression analyses We then performed a series of regressions to investigate the most salient predictors of malaise at the different ages. After each we did a second regression to see to what extent malaise measured the time before explained additional variance. Table 2 shows that using malaise measured at 50 childhood behavioural problems and intelligence, as well as current occupation and sex were significant predictors of the outcome variable,
Table 1 Correlation matrices of the variables used in the study. Measures
Mean (SD)
1
1. Gender 2. Parental social class at birth 3. Childhood intelligence at age 11 4. Childhood behavioural problems at age 11 5. Educational qualifications age 23 6. Occupational levels at age 23 7. Occupational levels at age 33 8. Occupational levels at age 42 9. Occupational levels at age 50 10 Malaise at age 23 11. Malaise at age 33 12. Malaise at age 42 13. Malaise at age 50
53:47 M:F 3.29 (1.22) 103.4 (13.09) 21.8 93.31) 2.41 (1.39) 3.68 (1.14) 3.87 (1.26) 4.01 (1.23) 4.09 91.21) 1.13 (1.47) .89 (1.40) 1.43 (1.69) 1.40 91.86)
– .024 .067 .037 .058 .119 .024 .049 .012 .242 .169 .147 .155
2 – .266 .103 .327 .250 .271 .227 .194 .090 .050 .027 .059
3
4
5
6
7
8
– .494 .533 .501 .462 .205 .147 .103 .115
– .518 .444 .401 .102 .097 .103 .065
– .576 .483 .120 .100 .095 .074
– .625 .121 .112 .074 .083
9
10
11
12
13
– .494 .439 .412
– .520 .407
– .469
–
– .176 .496 .385 .364 .358 .337 .167 .131 .090 .110
– .171 .137 .140 .114 .107 .199 .174 .149 .158
– .131 .088 .088 .072
Note: Standard deviations (SD) are given in parentheses. Correlations were Pearson’s. Variables were scored such that a higher score indicated being female, higher scores on childhood intelligence and behavioural problems, higher scores on educational qualifications, a more professional occupation for the parents and cohort members. Correlations of r = .05 are significant at p < .001. Correlations in bold indicate those with Malaise at the four different measurement times. Correlations in italics are those between Malaise scores at different times.
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A. Furnham, H. Cheng / Personality and Individual Differences 79 (2015) 30–34 Table 2 Predicting malaise at age 50 years using standard multiple regressions. Model 1
* ** ***
Model 2 t
b Sex Parental social class at birth Childhood intelligence at age 11 Childhood behavioural problems at age 11 Educational qualifications age 23 Current occupational levels at age 50 Malaise at age 23 Malaise at age 33 Malaise at age 42 R2
Table 5 Predicting malaise at age 23 years using standard multiple regressions.
t
b
.16 .02 .06 .10
10.24*** 0.97 3.09** 6.26***
.02 .05
1.08 2.84*
Model 1
.02 .03 .14 .20 .38 .364
.043
1.48 1.74 8.73*** 12.61*** 25.23***
p < .05. p < .01. p < .001.
Table 3 Predicting malaise at age 42 years using standard multiple regressions. Model 1
Sex Parental social class at birth Childhood intelligence at age 11 Childhood behavioural problems at age 11 Educational qualifications age 23 Current occupational levels at age 42 Malaise at age 23 Malaise at age 33 R2 * ** ***
Model 2 t
b
.16 .02 .04 .11
10.36*** 1.28 2.12* 6.85***
.03 .01 .01 .04
2.00* 0.94 0.89 2.85**
.04 .01
2.16* 0.53
.01 .01 .24 .39 .303
0.38 0.92 14.88*** 25.04***
b
.040
t
p < .05. p < .01. p < .001.
Table 4 Predicting malaise at age 33 years using standard multiple regressions. Model 1
Sex Parental social class at birth Childhood intelligence at age 11 Childhood behavioural problems at age 11 Educational qualifications age 23 Current occupational levels at age 33 Malaise at age 23 R2 * ** ***
Model 2 t
b .19 .02 .08 .10
12.42*** 1.09 4.60*** 6.77***
.06 .05
3.00** 3.12**
.065
t
b .07 .02 .04 .05 .01 .05 .47 .259
t
b 2.31* 0.45 1.04 1.70
.03 .01 .02 .02
4.89*** 1.32 2.31* 3.50*** 0.34 3.06** 33.53***
p < .05. p < .01. p < .001.
intelligence, current occupational levels, and sex were the significant predictors of the outcome variable. We attempted other analyses like latent variable analysis and SEM but found no clear way to model the data or improve on the above straightforward analysis. 4. Discussion The correlations between the Malaise scores at four different time periods, over 27 years were between .41 and .52. When corrected for disattenuation these correlations increase to well over
Sex Parental social class at birth Childhood intelligence at age 11 Childhood behavioural problems at age 11 Educational qualifications age 23 Current occupational levels at age 23 R2 ***
.26 .01 .10 .13 .11 .03 .120
18.15*** 0.35 5.72*** 8.84*** 5.85*** 1.84
p < .001.
.60 which is in line with Deary et al. (2000) who measured intelligence in a sample at age 11 and 77 years. Indeed studies on the reliability of NEO-PI-R Neuroticism facets for different age groups and periods of time have revealed scores around .50
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and occupational outcomes and which in turn increases their malaise. The pattern is however established very early and can be seen in the correlations aged 23 years. It suggests a vicious cycle where proneness to depression and psychiatric morbidity has long term social consequences. This data cannot explain the aetiology of malaise, nor how it could be effectively treated. The question is what explains both the stability and change in malaise over time. It is quite possible that many people in this sample attempted various interventions (therapy, pharmacology) to help them with their malaise, though this data is not available. It would have been very desirable to try to understand why some people remained particularly stable over time and others not, and more particularly what explains these findings. Further, it does not explain the significant ‘‘dip’’ in the scores for both males and females between the ages of 23 and 42 years. Nevertheless the study adds data to those longitudinal studies that have shown the stability of personality functioning over time (Roberts et al., 2006; Srivastava et al., 2003). References Ardelt, M. (2000). Still stable after all these years? Social Psychology Quarterly, 63, 392–405. Boyce, C., Wood, A., & Powdthavee, N. (2013). Is personality fixed? Social Indicators Research, 111, 287–305. Briley, D., & Tucker-Drob, E. (2014). Genetic and environmental continuity in personality development. Psychological Bulletin, 140, 1303–1331. Brodman, K., Erdmann, A., Lorge, I., Gershenson, C., & Wolff, H. (1952). The Cornell Medical Index health questionnaire: 3. The evaluation of emotional disturbances. Journal of Clinical Psychology, 8, 119–124. Carr, J. (2008). Families of 40-year olds with downs syndrome. Journal of Developmental Disabilities, 14, 35–43. Cheng, H., & Furnham, A. (2012). Childhood cognitive ability, education, and personality predict attainment in adult occupational prestige over 17 years. Journal of Vocational Psychology, 81, 218–226. Cheng, H., Eysenck, M., Green, A., & Furnham, A. (2015). Correlates of adult functional memory: Findings from a British Cohort. Intelligence, 47, 134–140. Conley, J. (1985). Longitudinal stability of personality traits. Journal of Personality and Social Psychology, 49, 1266–1282. Costa, P., Herbst, J., McCrae, R., & Siegler, I. (2000). Personality at midlife: Stability, intrinsic maturation, and response to life events. Assessment, 7, 367–380. Cramer, P. (2003). Personality change in later adulthood is predicted by defense mechanism use in early adulthood. Journal of Research in Personality, 37, 76–104. Davie, R., Butler, N., & Goldstein, H. (1972). From birth to seven. London: Longman. Deary, I., Whalley, L., Lemmon, H., Starr, J., & Crawford, J. (2000). The stability of individual differences in mental ability from childhood to old age. Intelligence, 28, 49–58. Douglas, J. W. B. (1964). The home and the school. London: Panther Books. Ferri, E., Bynner, J., & Wadsworth, M. (2003). Changing Britain, changing lives: Three generations at the turn of the century. London: Institute of Education. Fogelman, K. (1976). Britain’s 16-year-olds. London: National Children’s Bureau.
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