Latent Markov Factor Analysis: A Mixture Modeling Approach for Evaluating Within- and Between-Person Measurement Model Differences in Intensive Longitudinal Data

SUMMARY DOCTORAL DISSERTATION: Experience sampling methodology, in which participants are repeatedly questioned via smartphone apps, is popular for studying psychological constructs or “factors” (e.g., well-being or depression) within persons over time. The validity of such studies (e.g., concerning treatment decisions) may be hampered by distortions of the measurement of the relevant constructs due to response styles or item interpretations that change over time and differ across persons. In this PhD project, we developed a new approach to evaluate person- and time-point-specific distortions of the construct measurements, taking into account the specific characteristics of (time-intensive) longitudinal data inherent to experience sampling studies. Our new approach, latent Markov factor analysis, extends mixture factor analysis and clusters time-points within persons according to their factor model. The factor model describes how well items measure the constructs. With the new approach, researchers can examine how many and which factor models underlie the data, for which persons and time-points they apply, and thus which observations are validly comparable. Such insights can also be interesting in their own right. In personalized healthcare, for example, detecting changes in response styles is critical for accurate decisions about treatment allocation over time, as response styles may be related to the occurrence of depressive episodes.

Social assimilation and immigrants’ labour market outcomes

We analyse how immigrants’ level of social assimilation is related to their labour market outcomes. More precisely, we estimate the association between assimilation and employment, wages, underemployment, three measures of job satisfaction, overeducation and wages. Using Australian longitudinal data, we find that assimilation is strongly associated with employment and wages as well as a number of job satisfaction measures. We then split our data and repeat the analysis for before and after the financial crisis of 2008–2009. We find important differences in the way assimilation is associated with different measures of labour market outcomes under different economic conditions. Finally, we explore mechanisms that may underlie the results.

Digital Disclosure of Delinquency: Online Peers and the Sharing of Offline Crime

Digital communication poses challenges for scholars interested in the link between peers and crime since youth are often less inhibited online and can more easily share their opinions and experiences with offline activities. Drawing on longitudinal data from middle and high school students, this study explores how online communication impacts the sharing of personal and peer delinquency. Criminogenic risk factors are largely unrelated to the digital disclosure of personal delinquency among those who offend; however, peer online disclosure is related to self-reported delinquency, independent of perceived peer delinquency. These findings suggest cyberspace may extend offline mechanisms of peer influence beyond providing a unique source of online influence.