Search Results
Author: Grimm, Kevin J.
Resulting in 6 citations.
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Grimm, Kevin J. |
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Multivariate Longitudinal Methods for Studying Developmental Relationships Between Depression and Academic Achievement International Journal of Behavioral Development 31,4 (July 2007): 328–339. Also: http://jbd.sagepub.com/cgi/reprint/31/4/328.pdf Cohort(s): Children of the NLSY79 Publisher: Taylor & Francis Keyword(s): Achievement; Behavior Problems Index (BPI); Change Scores; Children, Academic Development; Depression (see also CESD); Methods/Methodology; Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Picture Vocabulary Test (PPVT); Variables, Independent - Covariate Recent advances in methods and computer software for longitudinal data analysis have pushed researchers to more critically examine developmental theories. In turn, researchers have also begun to push longitudinal methods by asking more complex developmental questions. One such question involves the relationships between two developmental processes. In this situation, choosing a longitudinal method is not obvious and should depend on specific hypotheses and research questions. This article outlines three common bivariate longitudinal models, including the bivariate latent growth curve model, the latent growth curve with a time-varying covariate, and the bivariate dual change score growth model, and illustrates their use by modeling how the development of depression is related to the development of achievement. Each longitudinal model is fitted to repeated measurements of children's depression and achievement from the National Longitudinal Survey of Youth (NLSY) data set in order to examine differing developmental relationships, and show how the developmental questions are answered by each longitudinal technique. The results from the longitudinal models appear to be somewhat at odds with one another regarding the developmental relationships between achievement and depression, but the conclusions are actually correct solutions to different developmental questions. These results highlight the need for researchers to match their research questions with model selection. |
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Bibliography Citation
Grimm, Kevin J. "Multivariate Longitudinal Methods for Studying Developmental Relationships Between Depression and Academic Achievement." International Journal of Behavioral Development 31,4 (July 2007): 328–339. A.
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Grimm, Kevin J. Jacobucci, Ross |
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Reliable Trees: Reliability Informed Recursive Partitioning for Psychological Data Multivariate Behavioral Research published online (16 April 2020): DOI: 10.1080/00273171.2020.1751028. Also: https://www.tandfonline.com/doi/full/10.1080/00273171.2020.1751028 Cohort(s): NLSY79 Young Adult Publisher: Taylor & Francis Keyword(s): Depression (see also CESD); Health, Mental/Psychological; Monte Carlo; Statistical Analysis Recursive partitioning, also known as decision trees and classification and regression trees (CART), is a machine learning procedure that has gained traction in the behavioral sciences because of its ability to search for nonlinear and interactive effects, and produce interpretable predictive models. The recursive partitioning algorithm is greedy--searching for the variable and the splitting value that maximizes outcome homogeneity. Thus, the algorithm can be overly sensitive to chance associations in the data, particularly in small samples. In an effort to limit chance associations, we propose and evaluate a reliability-based cost function for recursive partitioning. The reliability-based cost function increases the likelihood of selecting variables that are more reliable, which should have more consistent associations with the outcome of interest. Two reliability-based cost functions are proposed, evaluated through simulation, and compared to the CART algorithm. Results indicate that reliability-based cost functions can be beneficial, particularly with smaller samples and when more reliable variables are important to the prediction, but can overlook important associations between the outcome and lower reliability predictors. The use of these cost functions was illustrated using data on depression and suicidal ideation from the National Longitudinal Survey of Youth. |
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Bibliography Citation
Grimm, Kevin J. and Ross Jacobucci. "Reliable Trees: Reliability Informed Recursive Partitioning for Psychological Data." Multivariate Behavioral Research published online (16 April 2020): DOI: 10.1080/00273171.2020.1751028.
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Grimm, Kevin J. Stegmann, Gabriela |
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Modeling Change Trajectories with Count and Zero-inflated Outcomes: Challenges and Recommendations Addictive Behaviors 94 (July 2019): 4-15. Also: https://www.sciencedirect.com/science/article/pii/S0306460318310177 Cohort(s): NLSY97 Publisher: Elsevier Keyword(s): Adolescent Behavior; Alcohol Use; Modeling; Modeling, Mixed Effects The goal of this article is to describe models to examine change over time with an outcome that represents a count, such as the number of alcoholic drinks per day. Common challenges encountered with this type of data are: (1) the outcome is discrete, may have a large number of zeroes, and may be overdispersed, (2) the data are clustered (multiple observations within each individual), (3) the researchers needs to carefully consider and choose an appropriate time metric, and (4) the researcher needs to identify both a proper individual (potentially nonlinear) change model and an appropriate distributional form that captures the properties of the data. In this article, we provide an overview of generalized linear models, generalized estimating equation models, and generalized latent variable (mixed-effects) models for longitudinal count outcomes focusing on the Poisson, negative binomial, zero-inflated, and hurdle distributions. We review common challenges and provide recommendations for identifying an appropriate change trajectory while determining an appropriate distributional form for the outcome (e.g., determining zero-inflation and overdispersion). We demonstrate the process of fitting and choosing a model with empirical longitudinal data on alcohol intake across adolescence collected as part of the National Longitudinal Survey of Youth 1997. |
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Bibliography Citation
Grimm, Kevin J. and Gabriela Stegmann. "Modeling Change Trajectories with Count and Zero-inflated Outcomes: Challenges and Recommendations." Addictive Behaviors 94 (July 2019): 4-15.
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Grissmer, David W. Grimm, Kevin J. Aiyer, Sophie M. Murrah, William M. Steele, Joel S. |
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Fine Motor Skills and Early Comprehension of the World: Two New School Readiness Indicators Developmental Psychology 46,5 (September 2010): 1008-1017. Also: http://psycnet.apa.org/journals/dev/46/5/1008/ Cohort(s): Children of the NLSY79 Publisher: American Psychological Association (APA) Keyword(s): Behavior Problems Index (BPI); British Cohort Study (BCS); Cross-national Analysis; Early Childhood Longitudinal Study (ECLS-B, ECLS-K); Home Observation for Measurement of Environment (HOME); Methods/Methodology; Motor and Social Development (MSD); Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); School Entry/Readiness; Temperament This paper extends the work of Duncan et al. (2007) that utilized six longitudinal data sets to identify the kindergarten readiness factors best predicting longer term achievement. Their results identified kindergarten math and reading readiness and attention as the primary predictors, while finding no effects from social skills, internalizing, and externalizing behavior. We incorporate motor skill measures from three of the data sets and find that fine motor skills are an additional strong predictor of later achievement. Fine motor skills and attention have similar predictive strength for math, but attention has a somewhat greater effect for reading. Evidence suggests that skills linked to attention and fine motor skills may remain the strongest developmental skills predicting later achievement. |
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Bibliography Citation
Grissmer, David W., Kevin J. Grimm, Sophie M. Aiyer, William M. Murrah and Joel S. Steele. "Fine Motor Skills and Early Comprehension of the World: Two New School Readiness Indicators ." Developmental Psychology 46,5 (September 2010): 1008-1017.
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O'Rourke, Holly P. Fine, Kimberly L. Grimm, Kevin J. MacKinnon, David P. |
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The Importance of Time Metric Precision When Implementing Bivariate Latent Change Score Models Multivariate Behavioral Research published online (1 February 2021): DOI: 10.1080/00273171.2021.1874261. Also: https://www.tandfonline.com/doi/full/10.1080/00273171.2021.1874261 Cohort(s): Children of the NLSY79 Publisher: Taylor & Francis Keyword(s): Modeling; Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis; Test Scores/Test theory/IRT The literature on latent change score models does not discuss the importance of using a precise time metric when structuring the data. This study examined the influence of time metric precision on model estimation, model interpretation, and parameter estimate accuracy in bivariate LCS (BLCS) models through simulation. Longitudinal data were generated with a panel study where assessments took place during a given time window with variation in start time and measurement lag. The data were analyzed using precise time metric, where variation in time was accounted for, and then analyzed using coarse time metric indicating only that the assessment took place during the time window. Results indicated that models estimated using the coarse time metric resulted in biased parameter estimates as well as larger standard errors and larger variances and covariances for intercept and slope. In particular, the coupling parameter estimates--which are unique to BLCS models--were biased with larger standard errors. An illustrative example of longitudinal bivariate relations between math and reading achievement in a nationally representative survey of children is then used to demonstrate how results and conclusions differ when using time metrics of varying precision. Implications and future directions are discussed. |
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Bibliography Citation
O'Rourke, Holly P., Kimberly L. Fine, Kevin J. Grimm and David P. MacKinnon. "The Importance of Time Metric Precision When Implementing Bivariate Latent Change Score Models." Multivariate Behavioral Research published online (1 February 2021): DOI: 10.1080/00273171.2021.1874261.
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Zhang, Zhiyong Hamagami, Fumiaki Wang, Lijuan Nesselroade, John R. Grimm, Kevin J. |
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Bayesian Analysis of Longitudinal Data Using Growth Curve Models International Journal of Behavioral Development 31,4 (July 2007): 374-383. Also: http://jbd.sagepub.com/content/31/4/374.abstract Cohort(s): Children of the NLSY79 Publisher: Taylor & Francis Keyword(s): Bayesian; Growth Curves; Methods/Methodology; Modeling, Growth Curve/Latent Trajectory Analysis; Peabody Individual Achievement Test (PIAT- Reading); Statistical Analysis Bayesian methods for analyzing longitudinal data in social and behavioral research are recommended for their ability to incorporate prior information in estimating simple and complex models. We first summarize the basics of Bayesian methods before presenting an empirical example in which we fit a latent basis growth curve model to achievement data from the National Longitudinal Survey of Youth. This step-by-step example illustrates how to analyze data using both noninformative and informative priors. The results show that in addition to being an alternative to the maximum likelihood estimation (MLE) method, Bayesian methods also have unique strengths, such as the systematic incorporation of prior information from previous studies. These methods are more plausible ways to analyze small sample data compared with the MLE method. Data Data in this example are two subsets from the National Longitudinal Survey of Youth (NLSY).2 The first subset includes repeated measurements of N = 173 children. At the first measurement in 1986, the children were about 6–7 years of age. The same children were then repeatedly measured at 2-year intervals for three additional measurement occasions (1988, 1990, and 1992). Missing data existed for some of the children. The second subset includes repeated measurements of N = 34 children. At their first measurement in 1992, the children were also about 6–7 years of age. The same children were also measured again at an approximate 2-year interval for another three times in years 1994, 1996, and 1998. Missing data also existed for several of the children. The children from both data sets were tested using the Peabody Individual Achievement Test (PIAT) Reading Recognition subtest that measured word recognition and pronunciation ability. The total score for this subtest ranged in value from 0 to 84. In the present study, this score was rescaled by dividing by 10. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Behavioral Development is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts) |
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Bibliography Citation
Zhang, Zhiyong, Fumiaki Hamagami, Lijuan Wang, John R. Nesselroade and Kevin J. Grimm. "Bayesian Analysis of Longitudinal Data Using Growth Curve Models." International Journal of Behavioral Development 31,4 (July 2007): 374-383.
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