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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) 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
Description
The purpose of this study was to examine the association between characteristics of the symptomatology change curve (i.e., initial symptomatology, rate of change, curvature) and final treatment outcome. The sample consisted of community clients (N = 492) seen by 204 student therapists at a training clinic. A multilevel approach to account for therapist effects was followed. Linear, quadratic, and cubic trajectories of anxiety and depression symptomatology, as assessed by the Shorter Psychotherapy and Counseling Evaluation (sPaCE; Halstead, Leach, & Rust, 2007), were estimated. The multilevel quadratic trajectory best fit the data and depicted a descending curve (partial “U”-shaped). The quadratic growth parameters (intercept, slope, quadratic) were then used as predictors of both symptom change and reliable improvement in general symptomatology (pre- to post-treatment), as assessed by the Outcome Questionnaire-45.2 (OQ-45.2; Lambert, Hansen, Umpress, Lunen, Okiishi et al., 1996). The quadratic growth parameters of depression and anxiety showed predictive power for both symptom change and reliable improvement in general symptomatology. Patterns for two different successful outcomes (1-change in general symptomatology and 2-reliable improvement) were identified. For symptom change, successful outcomes followed a pattern of low initial levels of depression and anxiety, high initial rates of change (slope), and high (flattening after initial drop) curvature, and the pattern applied to both within- and between-therapist levels. For reliable improvement at within-therapist level, successful outcomes followed a pattern of high initial rate of change (slope) and high curvature. For reliable improvement at between-therapist level, successful outcomes were associated with a pattern of low initial levels of depression and anxiety. Implications for clinical practice are discussed.
ContributorsJimenez Arista, Laura E (Author) / Tracey, Terence (Thesis advisor) / Kinnier, Richard (Committee member) / Bernstein, Bianca (Committee member) / Randall, Ashley K. (Committee member) / Levy, Roy (Committee member) / Arizona State University (Publisher)
Created2018
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 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.
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
Description
Socioeconomic status (SES) is one of the most well researched constructs in developmental science, yet important questions underly how to best model it. That is, are relations with SES always in the same direction or does the direction of association change at different levels of SES? In this dissertation, I conducted a meta-analysis using individual participant data (IPD) to examine two questions: 1) Does a nonmonotonic (quadratic) model of the relations between components of SES (i.e., income, years of education, occupation status/prestige), depressive symptoms, and academic achievement fit better than a monotonic (linear) model? and 2) Is the magnitude of relation moderated by developmental period, gender/sex, or race/ethnicity? I hypothesized that there would be more support for the nonmonotonic model. Moderation analyses were exploratory.
I identified nationally representative IPD from the Inter-university Consortium for Political and Social Research (ICPSR). I included 59 datasets, which represent 23 studies (e.g., Add Health) and 1,844,577 participants. Higher income (β = -0.11; β = 0.10), years of education (β = -0.09; β = 0.13), and occupational status (β = -0.04; β = 0.04) and prestige (β = -0.03; β = 0.04) were associated with a linear decrease in depressive symptoms and increase in academic achievement, respectively. Higher income (β = 0.05), years of education (β = 0.02), and occupational status/prestige (β = 0.02) were quadratically associated with a decrease in depressive symptoms followed by a slight increase at higher levels of income and a diminishing association towards higher levels of education and occupational status/prestige. Higher income was also quadratically associated with academic achievement (β = -0.03). I found evidence that these associations varied between developmental periods and racial/ethnic samples, but I did not find evidence of variation between females and males.
I integrate these findings with three conclusions: (1) more is not always better and (2) there are unique contexts and resources associated with different levels of SES that (3) operate in a dynamic fashion with other cultural systems (e.g., racism), which affect the integrated actions between the individual and context. I outline several measurement implications and limitations for future research directions.
ContributorsKorous, Kevin M. (Author) / Causadias, José M (Thesis advisor) / Bradley, Robert H (Thesis advisor) / Luthar, Suniya S (Committee member) / Levy, Roy (Committee member) / Arizona State University (Publisher)
Created2021