Twelve Frequently Asked Questions About Growth Curve Modeling

Curran, Patrick J.; Obeidat, Khawla; Losardo, Diane

doi:10.1080/15248371003699969

Cited by 920 publications

(843 citation statements)

References 57 publications

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“…To examine the effects of baseline stress and self-efficacy over time, as well as the effects of fluctuation (change) in both, we fitted growth curve models within a multilevel modeling framework (e.g., Curran et al 2010; Raudenbush and Bryk 2002), with measurement points (level 1) nested within individuals (level 2). These models let us identify a unique intercept and slope (or growth rate) in life satisfaction for each person.…”

Section: Resultsmentioning

confidence: 99%

The Role of Perceived Stress and Self-Efficacy in Young People’s Life Satisfaction: A Longitudinal Study

Burger

Samuel

2016

J Youth Adolescence

147

104

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Life satisfaction is an important indicator of successful development. However, adolescents' life satisfaction tends to be relatively unsteady, and environmental influences play a critical role in shaping life satisfaction among adolescents in the transition to young adulthood. Given the paramount importance that education plays in adolescents' lives, adolescents' life satisfaction may vary as a function of school-related stress experience. At the same time, coping resources may help reduce adverse effects of stress on life satisfaction. With this in mind, we examined whether, and to what extent, perceived stress in education and general self-efficacy (a resource that facilitates coping) affect the life satisfaction of adolescents in transition to young adulthood. We distinguished between baseline levels of stress and self-efficacy and within-person change in stress and self-efficacy to determine whether life satisfaction is sensitive to fluctuations in stress and self-efficacy when person-specific levels of stress and self-efficacy are taken into account. Estimating growth curve models on data from a panel study on the life trajectories of compulsory-school leavers (n = 5,126, 55.3% female), we found that baseline levels of stress and self-efficacy, as well as within-person change in stress and self-efficacy, affected adolescents' life satisfaction.Moreover, our results showed that baseline self-efficacy mitigated the negative effect of baseline stress on life satisfaction. These findings improve our understanding of two major psychological determinants of adolescents' life satisfaction and extend our knowledge of life satisfaction trajectories during the transition to young adulthood.

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Section: Resultsmentioning

confidence: 99%

The Role of Perceived Stress and Self-Efficacy in Young People’s Life Satisfaction: A Longitudinal Study

Burger

Samuel

2016

J Youth Adolescence

147

104

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“…Noticeably, the control group reaches just the minimum sample size of 100, which is typically preferred for latent growth modeling (Curran, Obeidat, & Losardo, 2010). The total number of person-by-time observations influences statistical power (Curran et al, 2010). Additionally, due to the progressive recruitment of participants into the THBP over a 4-year period, the models estimated are based on extrapolation from an incomplete data set, in which some individuals have only one or two observations over time.…”

Section: Increasing Cognitive Reserve In Older Adultsmentioning

confidence: 99%

The psychology of intelligence analysis: Drivers of prediction accuracy in world politics.

Mellers¹,

Stone²,

Atanasov³

et al. 2015

Journal of Experimental Psychology: Applied

126

130

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This article extends psychological methods and concepts into a domain that is as profoundly consequential as it is poorly understood: intelligence analysis. We report findings from a geopolitical forecasting tournament that assessed the accuracy of more than 150,000 forecasts of 743 participants on 199 events occurring over 2 years. Participants were above average in intelligence and political knowledge relative to the general population. Individual differences in performance emerged, and forecasting skills were surprisingly consistent over time. Key predictors were (a) dispositional variables of cognitive ability, political knowledge, and openmindedness; (b) situational variables of training in probabilistic reasoning and participation in collaborative teams that shared information and discussed rationales (Mellers, Ungar, et al., 2014); and (c) behavioral variables of deliberation time and frequency of belief updating. We developed a profile of the best forecasters; they were better at inductive reasoning, pattern detection, cognitive flexibility, and open-mindedness. They had greater understanding of geopolitics, training in probabilistic reasoning, and opportunities to succeed in cognitively enriched team environments. Last but not least, they viewed forecasting as a skill that required deliberate practice, sustained effort, and constant monitoring of current affairs. A t APA, we understand how challenging it can be for you to stay abreast of the latest and best academic research on psychology. Disciplines Finance and Financial Management | Social and Behavioral Sciences

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“…LGC modeling is highly flexible as it can handle a variety of methodological issues typically occurring in training research such as partially missing data, non-normally distributed data, or non-linear change trajectories (Curran et al 2010). Further, LGC modeling has the advantage to account for measurement error and to provide separate latent estimates for baseline cognitive performance (i.e., the intercept) and change in training performance (i.e., the slope).…”

Section: The Present Studymentioning

confidence: 99%

“…In the second study (Guye and von Bastian 2017), older adults received a mixed-paradigm WM training intervention, consisting of a memory updating, a binding, and a complex span task. All three interventions were adaptive, with the level of difficulty increasing depending on individuals' performance.To estimate the training trajectories, we fitted LGC models to the data recorded during training.LGC modeling uses structural equation modeling (SEM) to estimate interindividual differences in intraindividual change over time.LGC modeling is highly flexible as it can handle a variety of methodological issues typically occurring in training research such as partially missing data, non-normally distributed data, or non-linear change trajectories (Curran et al 2010). Further, LGC modeling has the advantage to account for measurement error and to provide separate latent estimates for baseline cognitive performance (i.e., the intercept) and change in training performance (i.e., the slope).…”

mentioning

confidence: 99%

Do Individual Differences Predict Change in Cognitive Training Performance? A Latent Growth Curve Modeling Approach

2017

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Cognitive training interventions have become increasingly popular as a potential means to cost-efficiently stabilize or enhance cognitive functioning across the lifespan. Large training improvements have been consistently reported on the group level, with, however, large differences on the individual level. Identifying the factors contributing to these individual differences could allow for developing individually tailored interventions to boost training gains. In this study, we therefore examined a range of individual differences variables that had been discussed in the literature to potentially predict training performance. To estimate and predict individual differences in the training trajectories, we applied Latent Growth Curve models to existing data from three working memory training interventions with younger and older adults. However, we found that individual differences in demographic variables, real-world cognition, motivation, cognitionrelated beliefs, personality, leisure activities, and computer literacy and training experience were largely unrelated to change in training performance. Solely baseline cognitive performance was substantially related to change in training performance and particularly so in young adults, with individuals with higher baseline performance showing the largest gains. Thus, our results conform to magnification accounts of cognitive change.Keywords Working memory training . Individual differences . Latent growth curve modeling Over the past decade, there has been an exploding interest in computer-based commercial Bbrain training^programs and in scientific evidence relating to the effectiveness of such interventions, triggered by promising results of working memory (WM) training gains generalizing to previously untrained cognitive abilities such as intelligence in both younger (e.g., Jaeggi et al. 2008) and older adults (e.g., Borella et al. 2010). Although the idea of improving general cognitive functioning within a few weeks is enticing, there is also accumulating evidence against a generalized effect of WM training (e.g., Clark et al. 2017;De Simoni and von Bastian 2017;Guye and von Bastian 2017;Sprenger et al. 2013). Even on the meta-analytic level, evidence is mixed regarding the effectiveness of cognitive training in both younger and older adults (e.g., Au et al. 2015;Dougherty et al. 2016;Karbach and Verhaeghen 2014;Kelly et al. 2014;Lampit et al. 2014;Melby-Lervåg and Hulme 2013;Melby-Lervåg et al. 2016;Schwaighofer et al. 2015;Soveri et al. 2017). Aside from design and methodological choices potentially explaining the diverging findings (e.g., Noack et al. 2009;Shipstead et al. 2012), many authors increasingly articulated the potentially important influence of individual differences on cognitive training trajectories and outcomes (e.g., Buitenweg et al. 2012;Guye et al. 2016;Könen and Karbach 2015;Shah et al. 2012;von Bastian and Oberauer 2014 Individual differences in cognitive functioning (e.g., Ackerman and Lohman 2006) and learning potential (e.g., Stern ...

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Twelve Frequently Asked Questions About Growth Curve Modeling

Cited by 920 publications

References 57 publications

The Role of Perceived Stress and Self-Efficacy in Young People’s Life Satisfaction: A Longitudinal Study

The Role of Perceived Stress and Self-Efficacy in Young People’s Life Satisfaction: A Longitudinal Study

The psychology of intelligence analysis: Drivers of prediction accuracy in world politics.

Do Individual Differences Predict Change in Cognitive Training Performance? A Latent Growth Curve Modeling Approach

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