Despite their many benefits, longitudinal studies are much less common than one-time data collection or pre-post intervention designs. One reason for their scarcity is that longitudinal studies introduce requirements and challenges that non-longitudinal studies do not. One of the biggest challenges is participant attrition. In order to help…

Imputation or likelihood-based approaches to handle missing data assume the data are missing completely at random (MCAR) or missing at random (MAR). However, little research has examined the missingness pattern before using these imputation/likelihood methods. Three missingness mechanisms -- MCAR, MAR, and not missing at random (NMAR) -- can be…

Preventing panel members from attriting is a fundamental challenge for panel surveys. Research has shown that response behavior in earlier waves (response or nonresponse) is a good predictor of panelists' response behavior in upcoming waves. However, response behavior can be described in greater detail by considering the time until the response is…

While social programs are often assessed using short-term impact studies, longitudinal designs allow evaluators to capture change over time, identify longer-term outcomes, adapt instruments, and better understand participants in transition. A mixed methods design can be critical in understanding these dynamics; yet there is a lack of literature…

This paper focuses on microethical issues concerning interactions in which the process of informed consent occurs. It draws on research analysing the biographies and future hopes of 15- to 17-year-old girls and boys participating in targeted youth/educational programmes in the Helsinki area. The ethical challenge explored here is how to negotiate…

This paper explores the process of going back in qualitative longitudinal research (QLR) to gather later accounts of unfolding personal experiences. The design of interview-based QLR is usually premised on collecting data, over-time, around an unfolding experience or event. This design facilitates the establishment of an on-going research…

Multiple imputation (MI), a two-stage process whereby missing data are imputed multiple times and the resulting estimates of the parameter(s) of interest are combined across the completed datasets, is becoming increasingly popular for handling missing data. However, MI can result in biased inference if not carried out appropriately or if the…