Common Understanding (CU) is the shared awareness that a cultural domain exists as a category that labels some phenomenon of interest in the world within a particular social group, and this domain is described by a set of terms that set certain boundaries around it. This article deploys a research protocol using: (1) free lists to investigate variation both within and between group CU, and (2) a greedy algorithm to partition CU into covering subsets, or non-redundant configurations of listed terms, to enhance both interpretative meaning and advance further study. An American, ethnographic example from the domain of fabrics is used to illustrate the concept and method. Other potential applications, and an R program for computing the greedy algorithm, are also included.
Conducting behavioral analysis and administering questionnaires at the start of psychotherapy is time consuming and costly. This study explores a new approach linking diagnostic information across disorders to behavioral response categories (BRCs) used in behavioral analysis addressing the research gap concerning the integration of behavioral analysis into questionnaire-based diagnostic methods. Clinical psychologists assigned SCL-90-R items to one of four response variables (emotional, cognitive, physiological, behavioral). Three groups emerged: items unequivocally assigned to one BRC, items assigned to multiple BRCs (combined M var = 296.15), and items that couldn't be assigned due to expert disagreement, ambiguous formulations, or enumerations in items ( M var = 109.97). The feasible approach enables receiving multiple types of diagnostic information while only executing one questionnaire, potentially enabling cost-effective diagnostics, reducing therapy time, and making treatment more accessible to individuals with lower socioeconomic backgrounds. The findings emphasize the importance of semantic clarity in item formulations.
This study addresses enrollment and compliance in ecological momentary assessment (EMA) research by focusing on a sample of older adults with varying levels of technological familiarity. This is noteworthy because contemporary EMA studies predominantly require participants to use smartphones to record data in real time. We employ a two-stage analysis where we estimate the probability of enrollment based on sociodemographic and research design attributes and next use the output from this model to address potential selection bias in a subsequent model estimating compliance rates. We find that smartphone ownership is a major predictor of enrollment and that offering participants a loaner smartphone is insufficient to overcome this barrier. Ultimately, our findings suggest that while there are some potential design strategies that prospective researchers could implement to improve enrollment and compliance, there will likely always be some people whose attributes decrease their probability of enrollment and compliance regardless of intervention.
The growing work on causal modeling has produced as one key insight that we should not give a causal interpretation to estimates of controls. We should only interpret the treatment effect of interest. I show that this guidance that is valid for treatment effects is not necessarily valid for case selection in mixed-methods research when cases are selected based on their residuals. The effect of a control variable needed to identify the treatment effect may not itself be identified. While unproblematic for treatment effect interpretation, the resulting bias undermines the role that residuals are supposed to play for case selection. Guided by the distinction between good, bad, and neutral controls, I explain what identification assumptions must be met in mixed-methods research to use residuals for case selection. I present practical guidance on how to perform residual-based case selection in mixed-methods studies.
Research with families and friends of homicide victims (FFHV) requires researchers to consider how to best protect participants. Due to the nature of this type of research, researchers need to be aware of concerns such as sensitivity due to the emotional weight of sharing about their loved one, mental health, and trauma. Although there has been an increase in research focused on FFHV, no known research has provided guidance on considerations for holding focus groups (FG) with FFHV. Based on several years of holding focus groups with FFHV, this short take provides suggestions that include the following: selecting a research team; confidentiality; victim-informed questions; the research environment; research time and location; mental health concerns; participant burden; support information; concluding the groups; and researcher transparency and accountability.
