Matching Items (5)
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Description
Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS)

Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution (e.g., multivariate normal). FCS, on the other hand, imputes variables one at a time, drawing missing values from a series of univariate distributions. In the single-level context, these two approaches have been shown to be equivalent with multivariate normal data. However, less is known about the similarities and differences of these two approaches with multilevel data, and the methodological literature provides no insight into the situations under which the approaches would produce identical results. This document examined five multilevel multiple imputation approaches (three JM methods and two FCS methods) that have been proposed in the literature. An analytic section shows that only two of the methods (one JM method and one FCS method) used imputation models equivalent to a two-level joint population model that contained random intercepts and different associations across levels. The other three methods employed imputation models that differed from the population model primarily in their ability to preserve distinct level-1 and level-2 covariances. I verified the analytic work with computer simulations, and the simulation results also showed that imputation models that failed to preserve level-specific covariances produced biased estimates. The studies also highlighted conditions that exacerbated the amount of bias produced (e.g., bias was greater for conditions with small cluster sizes). The analytic work and simulations lead to a number of practical recommendations for researchers.
ContributorsMistler, Stephen (Author) / Enders, Craig K. (Thesis advisor) / Aiken, Leona (Committee member) / Levy, Roy (Committee member) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Identifying modifiable causes of chronic disease is essential to prepare for the needs of an aging population. Cognitive decline is a precursor to the development of Alzheimer's and other dementing diseases, representing some of the most prevalent and least understood sources of morbidity and mortality associated with aging. To contribute

Identifying modifiable causes of chronic disease is essential to prepare for the needs of an aging population. Cognitive decline is a precursor to the development of Alzheimer's and other dementing diseases, representing some of the most prevalent and least understood sources of morbidity and mortality associated with aging. To contribute to the literature on cognitive aging, this work focuses on the role of vascular and physical health in the development of cognitive trajectories while accounting for the socioeconomic context where health disparities are developed. The Assets and Health Dynamics among the Oldest-Old study provided a nationally-representative sample of non-institutionalized adults age 65 and over in 1998, with biennial follow-up continuing until 2008. Latent growth models with adjustment for non-random missing data were used to assess vascular, physical, and social predictors of cognitive change. A core aim of this project was examining socioeconomic and racial/ethnic variation in vascular predictors of cognitive trajectories. Results indicated that diabetes and heart problems were directly related to an increased rate of memory decline in whites, where these risk factors were only associated with baseline word-recall for blacks when conditioned on gender and household assets. These results support the vascular hypotheses of cognitive aging and attest to the significance of socioeconomic and racial/ethnic variation in vascular influences on cognitive health. The second substantive portion of this dissertation used parallel process latent growth models to examine the co-development of cognitive and functional health. Initial word-recall scores were consistently associated with later functional limitations, but baseline functional limitations were not consistently associated with later word-recall scores. Gender and household income moderated this relationship, and indicators of lifecourse SES were better equipped to explain variation in initial cognitive and functional status than change in these measures over time. Overall, this work suggests that research examining associations between cognitive decline, chronic disease, and disability must account for the social context where individuals and their health develop. Also, these findings advocate that reducing socioeconomic and racial/ethnic disparities in cognitive health among the aging requires interventions early in the lifecourse, as disparities in cognitive trajectories were solidified prior to late old age.
ContributorsBishop, Nicholas Joseph (Author) / Kronenfeld, Jennie J. (Thesis advisor) / Haas, Steven A. (Committee member) / Eggum, Natalie D. (Committee member) / Arizona State University (Publisher)
Created2011
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Description
Decision trees is a machine learning technique that searches the predictor space for the variable and observed value that leads to the best prediction when the data are split into two nodes based on the variable and splitting value. Conditional Inference Trees (CTREEs) is a non-parametric class of decision trees

Decision trees is a machine learning technique that searches the predictor space for the variable and observed value that leads to the best prediction when the data are split into two nodes based on the variable and splitting value. Conditional Inference Trees (CTREEs) is a non-parametric class of decision trees that uses statistical theory in order to select variables for splitting. Missing data can be problematic in decision trees because of an inability to place an observation with a missing value into a node based on the chosen splitting variable. Moreover, missing data can alter the selection process because of its inability to place observations with missing values. Simple missing data approaches (e.g., deletion, majority rule, and surrogate split) have been implemented in decision tree algorithms; however, more sophisticated missing data techniques have not been thoroughly examined. In addition to these approaches, this dissertation proposed a modified multiple imputation approach to handling missing data in CTREEs. A simulation was conducted to compare this approach with simple missing data approaches as well as single imputation and a multiple imputation with prediction averaging. Results revealed that simple approaches (i.e., majority rule, treat missing as its own category, and listwise deletion) were effective in handling missing data in CTREEs. The modified multiple imputation approach did not perform very well against simple approaches in most conditions, but this approach did seem best suited for small sample sizes and extreme missingness situations.
ContributorsManapat, Danielle Marie (Author) / Grimm, Kevin J (Thesis advisor) / Edwards, Michael C (Thesis advisor) / McNeish, Daniel (Committee member) / Anderson, Samantha F (Committee member) / Arizona State University (Publisher)
Created2023
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Description
Currently, there is a clear gap in the missing data literature for three-level models.

To date, the literature has only focused on the theoretical and algorithmic work

required to implement three-level imputation using the joint model (JM) method of

imputation, leaving relatively no work done on fully conditional specication (FCS)

method. Moreover, the literature

Currently, there is a clear gap in the missing data literature for three-level models.

To date, the literature has only focused on the theoretical and algorithmic work

required to implement three-level imputation using the joint model (JM) method of

imputation, leaving relatively no work done on fully conditional specication (FCS)

method. Moreover, the literature lacks any methodological evaluation of three-level

imputation. Thus, this thesis serves two purposes: (1) to develop an algorithm in

order to implement FCS in the context of a three-level model and (2) to evaluate

both imputation methods. The simulation investigated a random intercept model

under both 20% and 40% missing data rates. The ndings of this thesis suggest

that the estimates for both JM and FCS were largely unbiased, gave good coverage,

and produced similar results. The sole exception for both methods was the slope for

the level-3 variable, which was modestly biased. The bias exhibited by the methods

could be due to the small number of clusters used. This nding suggests that future

research ought to investigate and establish clear recommendations for the number of

clusters required by these imputation methods. To conclude, this thesis serves as a

preliminary start in tackling a much larger issue and gap in the current missing data

literature.
ContributorsKeller, Brian Tinnell (Author) / Enders, Craig K. (Thesis advisor) / Grimm, Kevin J. (Committee member) / Levy, Roy (Committee member) / Arizona State University (Publisher)
Created2015
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Description
Published in 1992, “The osteological paradox: problems of inferring prehistoric health from skeletal samples” highlighted the limitations of interpreting population health from archaeological skeletal samples. The authors drew the attention of the bioarchaeological community to several unfounded assumptions in the field of paleopathology. They cautioned that bioarchaeologists needed to expand

Published in 1992, “The osteological paradox: problems of inferring prehistoric health from skeletal samples” highlighted the limitations of interpreting population health from archaeological skeletal samples. The authors drew the attention of the bioarchaeological community to several unfounded assumptions in the field of paleopathology. They cautioned that bioarchaeologists needed to expand their methodological and theoretical toolkits and examine how variation in frailty influences mortality outcomes. This dissertation undertakes this task by 1) establishing a new approach for handling missing paleopathology data that facilitates the use of new analytical methods for exploring frailty and resiliency in skeletal data, and 2) investigating the role of prior frailty in shaping selective mortality in an underexplored epidemic context. The first section takes the initial step of assessing current techniques for handling missing data in bioarchaeology and testing protocols for imputation of missing paleopathology variables. A review of major bioarchaeological journals searching for terms describing the treatment of missing data are compiled. The articles are sorted by subject topic and into categories based on the statistical and theoretical rigor of how missing data are handled. A case study test of eight methods for handling missing data is conducted to determine which methods best produce unbiased parameter estimates. The second section explores how pre-existing frailty influenced mortality during the 1918 influenza pandemic. Skeletal lesion data are collected from a sample of 424 individuals from the Hamann-Todd Documented Collection. Using Kaplan-Meier and Cox proportional hazards, this chapter tests whether individuals who were healthy (i.e. non-frail) were equally likely to die during the flu as frail individuals. Results indicate that imputation is underused in bioarchaeology, therefore procedures for imputing ordinal and continuous paleopathology data are established. The findings of the second section reveal that while a greater proportion of non-frail individuals died during the 1918 pandemic compared to pre-flu times, frail individuals were more likely to die at all times. The outcomes of this dissertation help expand the types of statistical analyses that can be performed using paleopathology data. They contribute to the field’s knowledge of selective mortality and differential frailty during a major historical pandemic.
ContributorsWissler, Amanda (Author) / Buikstra, Jane E (Thesis advisor) / DeWitte, Sharon N (Committee member) / Stojanowski, Christopher M (Committee member) / Mamelund, Svenn-Erik (Committee member) / Arizona State University (Publisher)
Created2021