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Wendy F Delvecchio - One of the best experts on this subject based on the ideXlab platform.
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the rank order consistency of personality traits from childhood to old age a quantitative review of Longitudinal Studies
Psychological Bulletin, 2000Co-Authors: Brent W Roberts, Wendy F DelvecchioAbstract:The present study used meta-analytic techniques to test whether trait consistency maximizes and stabilizes at a specific period in the life course. From 152 Longitudinal Studies, 3,217 test-retest correlation coefficients were compiled. Meta-analytic estimates of mean population test-retest correlation coefficients showed that trait consistency increased from .31 in childhood to .54 during the college years, to .64 at age 30, and then reached a plateau around .74 between ages 50 and 70 when time interval was held constant at 6.7 years. Analysis of moderators of consistency showed that the Longitudinal time interval bad a negative relation to trait consistency and that temperament dimensions were less consistent than adult personality traits. Do personality traits stop changing at some point during the life course? The answer to this question is critical for both basic and applied psychologists. For personality psychologists, it goes to the heart of how personality traits are conceptualized. At the core of most definitions is the assumption that traits remain consistent over time (West & Graziano, 1989). For example, Tellegen (1988) defines a trait as "a psychological (therefore) organismic structure underlying a relatively enduring behavioral disposition, i.e., a tendency to respond in certain ways under certain circumstances" (p. 622; see also Harkness& Hogan, 1995). If age is strongly related to trait consistency, then the construct validity of trait measurements may be affected by the age of the samples studied. For applied psychologists, whether traits are unchanging pertains to whether change efforts should be attempted and whether age should be factored into this decision. For example, if personality traits change before age 18 and not after, then interventions focused on changing traitlike syndromes, such as leadership style or personality disorders, may be shaped by the age of the client (see, e.g., Hellervik, Hazucha, & Schneider, 1992; Linehan & Kehrer, 1993). Despite its obvious importance, the question of when in the life course personality traits reach their peak consistency has received little systematic empirical and quantitative attention since Bloom's (1964) review in 1964. In the present study, we focus on one aspect of personality change, rank-order consistency, by compiling Longitudinal Studies of personality trait consistency. We address three questions: (a) What is the relation between chronological age and trait consistency, (b) at what age does trait consistency peak, and (c) does trait consistency peak at a level high enough to warrant, as some have
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the rank order consistency of personality traits from childhood to old age a quantitative review of Longitudinal Studies
Psychological Bulletin, 2000Co-Authors: Brent W Roberts, Wendy F DelvecchioAbstract:The present study used meta-analytic techniques to test whether trait consistency maximizes and stabilizes at a specific period in the life course. From 152 Longitudinal Studies, 3,217 test-retest correlation coefficients were compiled. Meta-analytic estimates of mean population test-retest correlation coefficients showed that trait consistency increased from .31 in childhood to .54 during the college years, to .64 at age 30, and then reached a plateau around .74 between ages 50 and 70 when time interval was held constant at 6.7 years. Analysis of moderators of consistency showed that the Longitudinal time interval had a negative relation to trait consistency and that temperament dimensions were less consistent than adult personality traits.
Brent W Roberts - One of the best experts on this subject based on the ideXlab platform.
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the rank order consistency of personality traits from childhood to old age a quantitative review of Longitudinal Studies
Psychological Bulletin, 2000Co-Authors: Brent W Roberts, Wendy F DelvecchioAbstract:The present study used meta-analytic techniques to test whether trait consistency maximizes and stabilizes at a specific period in the life course. From 152 Longitudinal Studies, 3,217 test-retest correlation coefficients were compiled. Meta-analytic estimates of mean population test-retest correlation coefficients showed that trait consistency increased from .31 in childhood to .54 during the college years, to .64 at age 30, and then reached a plateau around .74 between ages 50 and 70 when time interval was held constant at 6.7 years. Analysis of moderators of consistency showed that the Longitudinal time interval bad a negative relation to trait consistency and that temperament dimensions were less consistent than adult personality traits. Do personality traits stop changing at some point during the life course? The answer to this question is critical for both basic and applied psychologists. For personality psychologists, it goes to the heart of how personality traits are conceptualized. At the core of most definitions is the assumption that traits remain consistent over time (West & Graziano, 1989). For example, Tellegen (1988) defines a trait as "a psychological (therefore) organismic structure underlying a relatively enduring behavioral disposition, i.e., a tendency to respond in certain ways under certain circumstances" (p. 622; see also Harkness& Hogan, 1995). If age is strongly related to trait consistency, then the construct validity of trait measurements may be affected by the age of the samples studied. For applied psychologists, whether traits are unchanging pertains to whether change efforts should be attempted and whether age should be factored into this decision. For example, if personality traits change before age 18 and not after, then interventions focused on changing traitlike syndromes, such as leadership style or personality disorders, may be shaped by the age of the client (see, e.g., Hellervik, Hazucha, & Schneider, 1992; Linehan & Kehrer, 1993). Despite its obvious importance, the question of when in the life course personality traits reach their peak consistency has received little systematic empirical and quantitative attention since Bloom's (1964) review in 1964. In the present study, we focus on one aspect of personality change, rank-order consistency, by compiling Longitudinal Studies of personality trait consistency. We address three questions: (a) What is the relation between chronological age and trait consistency, (b) at what age does trait consistency peak, and (c) does trait consistency peak at a level high enough to warrant, as some have
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the rank order consistency of personality traits from childhood to old age a quantitative review of Longitudinal Studies
Psychological Bulletin, 2000Co-Authors: Brent W Roberts, Wendy F DelvecchioAbstract:The present study used meta-analytic techniques to test whether trait consistency maximizes and stabilizes at a specific period in the life course. From 152 Longitudinal Studies, 3,217 test-retest correlation coefficients were compiled. Meta-analytic estimates of mean population test-retest correlation coefficients showed that trait consistency increased from .31 in childhood to .54 during the college years, to .64 at age 30, and then reached a plateau around .74 between ages 50 and 70 when time interval was held constant at 6.7 years. Analysis of moderators of consistency showed that the Longitudinal time interval had a negative relation to trait consistency and that temperament dimensions were less consistent than adult personality traits.
Jurgen Rehm - One of the best experts on this subject based on the ideXlab platform.
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the association between cannabis use and depression a systematic review and meta analysis of Longitudinal Studies
Psychological Medicine, 2014Co-Authors: Shaul Levran, Michael Roerecke, Le B Foll, Tony P George, Kwame Mckenzie, Jurgen RehmAbstract:Background Longitudinal Studies reporting the association between cannabis use and developing depression provide mixed results. The objective of this study was to establish the extent to which different patterns of use of cannabis are associated with the development of depression using meta-analysis of Longitudinal Studies. Method Peer-reviewed publications reporting the risk of developing depression in cannabis users were located using searches of EMBASE, Medline, PsychINFO and ISI Web of Science. Only Longitudinal Studies that controlled for depression at baseline were included. Data on several study characteristics, including measures of cannabis use, measures of depression and control variables, were extracted. Odds ratios (ORs) were extracted by age and length of follow-up. Results After screening for 4764 articles, 57 articles were selected for full-text review, of which 14 were included in the quantitative analysis (total number of subjects = 76058). The OR for cannabis users developing depression compared with controls was 1.17 [95% confidence interval (CI) 1.05–1.30]. The OR for heavy cannabis users developing depression was 1.62 (95% CI 1.21–2.16), compared with non-users or light users. Meta-regression revealed no significant differences in effect based on age of subjects and marginal difference in effect based on length of follow-up in the individual Studies. There was large heterogeneity in the number and type of control variables in the different Studies. Conclusions Cannabis use, and particularly heavy cannabis use, may be associated with an increased risk for developing depressive disorders. There is need for further Longitudinal exploration of the association between cannabis use and developing depression, particularly taking into account cumulative exposure to cannabis and potentially significant confounding factors.
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a systematic review of Longitudinal Studies on the association between depression and smoking in adolescents
BMC Public Health, 2009Co-Authors: Michael Chaiton, Jurgen Rehm, Joanna E Cohen, Jennifer OloughlinAbstract:It is well-established that smoking and depression are associated in adolescents, but the temporal ordering of the association is subject to debate. Longitudinal Studies in English language which reported the onset of smoking on depression in non clinical populations (age 13-19) published between January 1990 and July 2008 were selected from PubMed, OVID, and PsychInfo databases. Study characteristics were extracted. Meta-analytic pooling procedures with random effects were used. Fifteen Studies were retained for analysis. The pooled estimate for smoking predicting depression in 6 Studies was 1.73 (95% CI: 1.32, 2.40; p < 0.001). The pooled estimate for depression predicting smoking in 12 Studies was 1.41 (95% CI: 1.21, 1.63; p < 0.001). Studies that used clinical measures of depression were more likely to report a bidirectional effect, with a stronger effect of depression predicting smoking. Evidence from Longitudinal Studies suggests that the association between smoking and depression is bidirectional. To better estimate these effects, future research should consider the potential utility of: (a) shorter intervals between surveys with longer follow-up time, (b) more accurate measurement of depression, and (c) adequate control of confounding.
Richard M. Lerner - One of the best experts on this subject based on the ideXlab platform.
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Use of missing data methods in Longitudinal Studies: the persistence of bad practices in developmental psychology.
Developmental psychology, 2009Co-Authors: Helena Jelicic, Erin Phelps, Richard M. LernerAbstract:Developmental science rests on describing, explaining, and optimizing intraindividual changes and, hence, empirically requires Longitudinal research. Problems of missing data arise in most Longitudinal Studies, thus creating challenges for interpreting the substance and structure of intraindividual change. Using a sample of reports of Longitudinal Studies obtained from three flagship developmental journals-Child Development, Developmental Psychology, and Journal of Research on Adolescence-we examined the number of Longitudinal Studies reporting missing data and the missing data techniques used. Of the 100 Longitudinal Studies sampled, 57 either reported having missing data or had discrepancies in sample sizes reported for different analyses. The majority of these Studies (82%) used missing data techniques that are statistically problematic (either listwise deletion or pairwise deletion) and not among the methods recommended by statisticians (i.e., the direct maximum likelihood method and the multiple imputation method). Implications of these results for developmental theory and application, and the need for understanding the consequences of using statistically inappropriate missing data techniques with actual Longitudinal data sets, are discussed.
Karin I Proper - One of the best experts on this subject based on the ideXlab platform.
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the relationship between shift work and metabolic risk factors a systematic review of Longitudinal Studies
American Journal of Preventive Medicine, 2016Co-Authors: Karin I Proper, Daniella Van De Langenberg, Wendy Rodenburg, Roel Vermeulen, Allard J Van Der Beek, Harry Van Steeg, Linda W M Van KerkhofAbstract:Context Although the metabolic health effects of shift work have been extensively studied, a systematic synthesis of the available research is lacking. This review aimed to systematically summarize the available evidence of Longitudinal Studies linking shift work with metabolic risk factors. Evidence acquisition A systematic literature search was performed in 2015. Studies were included if (1) they had a Longitudinal design; (2) shift work was studied as the exposure; and (3) the outcome involved a metabolic risk factor, including anthropometric, blood glucose, blood lipid, or blood pressure measures. Evidence synthesis Eligible Studies were assessed for their methodologic quality in 2015. A best-evidence synthesis was used to draw conclusions per outcome. Thirty-nine articles describing 22 Studies were included. Strong evidence was found for a relation between shift work and increased body weight/BMI, risk for overweight, and impaired glucose tolerance. For the remaining outcomes, there was insufficient evidence. Conclusions Shift work seems to be associated with body weight gain, risk for overweight, and impaired glucose tolerance. Overall, lack of high–methodologic quality Studies and inconsistency in findings led to insufficient evidence in assessing the relation between shift work and other metabolic risk factors. To strengthen the evidence, more high-quality Longitudinal Studies that provide more information on the shift work schedule (e.g., frequency of night shifts, duration in years) are needed. Further, research to the (mediating) role of lifestyle behaviors in the health effects of shift work is recommended, as this may offer potential for preventive strategies.
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is retirement good for your health a systematic review of Longitudinal Studies
BMC Public Health, 2013Co-Authors: Iris Van Der Heide, Rogier M Van Rijn, Suzan J W Robroek, Alex Burdorf, Karin I ProperAbstract:Several Studies regarding the effect of retirement on physical as well as mental health have been performed, but the results thereof remain inconclusive. The aim of this review is to systematically summarise the literature on the health effects of retirement, describing differences in terms of voluntary, involuntary and regulatory retirement and between blue-collar and white-collar workers. A search for Longitudinal Studies using keywords that referred to the exposure (retirement), outcome (health-related) and study design (Longitudinal) was performed using several electronic databases. Articles were then selected for full text analysis and the reference lists of the selected Studies were checked for relevant Studies. The quality of the Studies was rated based on predefined criteria. Data was analysed qualitatively by using a best evidence synthesis. When possible, pooled mean differences and effect sizes were calculated to estimate the effect of retirement on health. Twenty-two Longitudinal Studies were included, of which eleven were deemed to be of high quality. Strong evidence was found for retirement having a beneficial effect on mental health, and contradictory evidence was found for retirement having an effect on perceived general health and physical health. Few Studies examined the differences between blue- and white-collar workers and between voluntary, involuntary and regulatory retirement with regards to the effect of retirement on health outcomes. More Longitudinal research on the health effects of retirement is needed, including research into potentially influencing factors such as work characteristics and the characteristics of retirement.
