My research interests center around the development and application of advanced statistical methods to improve the measurement and testing of psychological phenomenon. Studying quantitative psychology has afforded me the ability to work in many subfields of psychology and this is one of the reasons I am pursuing a degree in quantitative psychology. I plan on continuing my research and actively pursuing outside support to fund future research, which will involve both undergraduate and graduate student assistants.
Applications of Structural Equation Modeling
My experience with structural equation modeling began with my involvement in research studying a series of U.S. high-school based studies on the relationship between perceptions of intergroup contact and prejudice reduction among adolescents. This research is based on an adaptation of Berry, Trimble, and Olmedo's (1986) acculturation theory, which emphasizes immigrants' dilemma of interacting with the host culture while valuing the traditions of their culture of origin. We adapted a mutual acculturation approach, which adapts both dimensions (which we call outgroup orientation and ethnic identity, respectively), to the study of intergroup prejudice. In multiple studies, we have demonstrated that outgroup orientation is a consistent mediator of the intergroup contact - prejudice relationship, while ethnic identity mediates prejudice for some samples but not others.
My master's thesis at California State University, Northridge compared four of the major ethnic groups studied (i.e. African Americans, Asian Americans, Euro Americans and Latino Americans), in a multi-group path analysis performed through structural equation modeling software (i.e. EQS). I found that the four groups did not significantly differ in terms of the model and this was counter to what was expected. Further studies have looked at comparing the mutual acculturation model to a model based on common ingroup identity (Gaertner & Dovidio, 2000). The results of this study are presented in Wittig, Molina, Giang, and Ainsworth (in press). In another study we separated ethnic identity into separate components of ethnic identity exploration and ethnic identity commitment in order to investigate how each component affects outgroup orientation and prejudice (Whitehead, Wittig & Ainsworth, under review). Additionally, I am working on a study that is an extention of my master's thesis that compares ethnic status groups in a full measurement, multi-group, mean-structure analysis in two different high school intervention programs (Ainsworth, Wittig & Rabinowitz, in preparation).
In a separate line of research I compared two different estimation procedures in utilizing two-level structural equation models. Research subjects are often sampled within existing groups and it is known that this type of sampling needs to be analyzed by methods that take the grouped nature of the data into account. Multilevel structural equation modeling is one of many methods that addresses clustered or hierarchical data designs and like any method in structural equation modeling there is always a question of what estimation procedure to utilize. The Muthèn (1989) approximation to maximum likelihood estimation (MUML) has been a popular estimation method in multi-level SEM because of its simplicity but is not a true maximum likelihood estimator when groups have unequal sizes. My study compares of the accuracy of the Bentler and Liang (2003) full information maximum likelihood estimator (BLML), which is a true maximum likelihood estimator in the unbalanced case, in capturing true parameter estimates when compared to the popular MUML method of estimation. Results favor the BLML in accuracy and efficiency in capturing parameter estimates as well as in the percent of admissible solutions and the percent of rejected models.
I am also involved in two grants that utilize structural equation modeling techniques. One of them is an R01grant applying structural equation modeling to test a mediational model of the utilization of an AIDS vaccine dissemination program. The other is an R34 grant that applies structural equation modeling to test a mediational model adapting group self-management programs, developed and proven effective for persons with chronic medical illness, for patients who also have depression. Both of these grants are in the data collection phase and I have already assisted in the developmental stages as a structural equation modeling expert. Future research will also be tied to these two grants pending the completion of data collection.
Application of Item Response Theory Models
In Reise, Meijer, Ainsworth, Morales, and Hays (in press) we applied group-level parametric and non-parametric item response theory models to the Consumer Assessment of Health Plans Survey (CAHPS®) 2.0 core items in a large sample (35,000+) of Medicaid recipients nested within over 100 health plans. Results indicated that CAHPS® responses are dominated by within health plan variation, and only weakly influenced by between health plan variation. In other words patient views of their health plan vary more within each health plan than they do across health plans; meaning that people are as happy (or unhappy) about their own health plan when compared to members of other health plans. Thus, although the CAHPS® 2.0 survey has acceptable psychometric properties when analyzed at the individual level, large sample sizes are needed to reliably differentiate among health plans. These results illustrate why it is important to study evaluations of health care, such as CAHPS®, at multiple levels of analyses.
I was also recently hired by Telesage to perform IRT analyses for a grant to develop new personality and psychopathology scales (e.g. depression, anxiety, occupational functioning, interpersonal functioning, physical functioning, etc.) intended for use with both "normal" and psychopathological respondents. During phase 1 of the grant I was responsible for performing basic item response functions to establish each scales measurement properties (e.g. unidimensionality, item scale relationship, item information, etc.), which included identifying items from each scale with poor measurement properties. My results were then used to apply for funding for the second phase of the grant in which I performed further tests on just the depression and anxiety scales. In addition to the establishing basic scale properties I performed differential item functioning comparing male and female respondents in order to identify items that are interpreted and utilized in the same way by both genders.
Currently, I am also working on a project whose purpose is to first identify unidimensional subscales of the MMPI-2 adult survey and MMPI-A adolescent survey by utilizing item response models. Secondly, when unidimensional scales are identified and the scales overlap in both the 2 and A versions we will perform differential item functioning analysis in order to identify whether scale items are interpreted and utilized in the same manner in both age groups. It is important to identify differential item functioning so that we can identify whether a scale can be used to track changes in trait level over time or at least cross-sectionally.
Additionally, I have plans for more research involving the mutual acculturation model and prejudice. One line of research will try and address the recent debates concerning the relationship between ethnic identity and prejudice in recent literature. Results from my previous research concerning the mutual acculturation model have pointed out certain discrepancies in terms of the role of ethnic identity in mediating the relationship between contact and prejudice. Other studies have shown that high levels of ethnic identity lead to more prejudice and while still others show that prejudice would be reduced. I have plans for a line of research that is designed to try and uncover moderating and mediating variables that can be contributing to the seemingly contradictory findings. Advanced latent variable models will by utilized to test for 1) latent profiles within a sample of high school respondents and 2) moderated mediation, as further explanations for the previous discrepant results. Another line of research will utilize growth curve analyses to try and 1) track the predictors of change in prejudice over time (e.g. does change in conditions of contact predict change in prejudice while mediated by a change in outgroup orientation) and 2) identify mixtures of responses within a sample in order to identify the types of students for which a prejudice reduction intervention is successful.