Social Indicators to Explain Response in Longitudinal Studies

Bianchi, Annamaria; Biffignandi, Silvia

doi:10.1007/s11205-018-1874-7

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…Research has identified paradata [74][75][76] and Big Five personality traits [62,[77][78][79] as predictors of panel attrition. These variables could also interact with the mode design indicator.…”

Section: Datamentioning

confidence: 99%

Transitioning an employee panel survey from telephone to online and mixed-mode data collection

Mackeben

Sakshaug

2023

SJI

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Employee panel surveys, which are essential for measuring ongoing labor market developments, are facing significant challenges of respondent recruitment and retention. Even interviewer-administered panel surveys, historically considered the gold standard form of data collection, are facing high costs and nonresponse issues that threaten their sustainability and inferential capabilities. Supplementing interviewer-administration with online data collection is a popular method of reducing costs and may improve contactability and reduce nonresponse in employee surveys. However, the effects of introducing online data collection in an ongoing panel survey of the employed population have received little attention. We address this research gap by analyzing a mode design experiment embedded in the fourth wave of a German employee panel survey. Individuals were randomly assigned to the standard telephone-only design, or a sequential web-telephone mixed-mode design. An invitation letter experiment was also conducted to test the effect of mentioning the telephone follow-ups in the web survey invitation. Introducing the mixed-mode design led to a higher response rate (59.9% vs. 50.1%), similar levels of nonresponse bias, and lower costs compared to the single-mode design. Mentioning the telephone follow-ups had no effect on participation in the web starting mode or the full mixed-mode design. Implications of these findings for survey practice are discussed.

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Section: Datamentioning

confidence: 99%

Transitioning an employee panel survey from telephone to online and mixed-mode data collection

Mackeben

Sakshaug

2023

SJI

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“…Much has been learned about predictors of participation in health and broader research studies in general, suggesting that a wide range of sociodemographic, individual, and social and situational characteristics may be related to participation and attrition [13][14][15]; however, despite the potential value of information on predictors of participation in EMA studies, there has been very little research on this topic specifically. It is important to illuminate predictors of participation in EMA studies as a specific type of research design as it possesses some unique characteristics, including its reliance on the use of smartphone-based technologies, the prolonged and intensive data collection schedule, and the collection of data in the flow of people's daily lives.…”

Section: Overviewmentioning

confidence: 99%

Predicting Participation Willingness in Ecological Momentary Assessment of General Population Health and Behavior: Machine Learning Study

Murray¹,

Ushakova²,

Zhu³

et al. 2023

J Med Internet Res

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Background Ecological momentary assessment (EMA) is widely used in health research to capture individuals’ experiences in the flow of daily life. The majority of EMA studies, however, rely on nonprobability sampling approaches, leaving open the possibility of nonrandom participation concerning the individual characteristics of interest in EMA research. Knowledge of the factors that predict participation in EMA research is required to evaluate this possibility and can also inform optimal recruitment strategies. Objective This study aimed to examine the extent to which being willing to participate in EMA research is related to respondent characteristics and to identify the most critical predictors of participation. Methods We leveraged the availability of comprehensive data on a general young adult population pool of potential EMA participants and used and compared logistic regression, classification and regression trees, and random forest approaches to evaluate respondents’ characteristic predictors of willingness to participate in the Decades-to-Minutes EMA study. Results In unadjusted logistic regression models, gender, migration background, anxiety, attention deficit hyperactivity disorder symptoms, stress, and prosociality were significant predictors of participation willingness; in logistic regression models, mutually adjusting for all predictors, migration background, tobacco use, and social exclusion were significant predictors. Tree-based approaches also identified migration status, tobacco use, and prosociality as prominent predictors. However, overall, willingness to participate in the Decades-to-Minutes EMA study was only weakly predictable from respondent characteristics. Cross-validation areas under the curve for the best models were only in the range of 0.56 to 0.57. Conclusions Results suggest that migration background is the single most promising target for improving EMA participation and sample representativeness; however, more research is needed to improve prediction of participation in EMA studies in health.

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“…The fact that participation willingness could be overall only weakly predicted from sociodemographic, health, and behavior-related respondent characteristics suggests that there are other factors to consider alongside these to improve the prediction of participation propensity. Although previous studies have suggested that participation in research may be predicted by a range of sociodemographic, individual, and situational factors [13,15,66], the available evidence base primarily relates to traditional survey methods, and it is not clear how well this generalizes to EMA studies. Additional predictors to consider for EMA research participation willingness could include factors such as how much and in what ways individuals use and engage with their smartphones, work or life schedules (eg, some occupations may preclude the ability to respond to EMA prompts during the working day), and markers of interest in the specific topic of the EMA study.…”

Section: Overviewmentioning

confidence: 99%

Predicting Participation Willingness in Ecological Momentary Assessment of General Population Health and Behavior: Machine Learning Study (Preprint)

Murray¹,

Ushakova²,

Zhu³

et al. 2022

Preprint

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BACKGROUND Ecological momentary assessment (EMA) is widely used in health research to capture individuals’ experiences in the flow of daily life. The majority of EMA studies; however, rely on non-probability sampling approaches, leaving open the possibility of non-random participation with respect to the individual characteristics of interest in EMA research. Knowledge of the factors that predict participation in EMA research is required to evaluate this possibility and can also inform optimal recruitment strategies. OBJECTIVE The present study aimed to examine the extent to which being willing to participate in EMA research is related to respondent characteristics and to identify the most critical predictors of participation. METHODS We leveraged the availability of comprehensive data on a general young adult population pool of potential EMA participants and used and compared logistic regression, classification and regression trees (CART), and random forest approaches to evaluate respondent characteristic predictors of willingness to participate in the Decades-to-Minutes (D2M) EMA study . RESULTS In unadjusted logistic regression models, gender, migration background, anxiety, ADHD symptoms, stress, and prosociality were significant predictors of participation willingness and in logistic regression models mutually adjusting for all predictors, migration background, tobacco use, and social exclusion were significant predictors. Tree-based approaches also identified migration status, tobacco use, and prosociality as prominent predictors. However, overall, willingness to participate in the D2M EMA study was only weakly predictable from respondent characteristics. Cross-validation areas under the curve (AUCs) for the best models were in the range of .56-.57. CONCLUSIONS Results suggest that migration background is the single most promising target for improving EMA participation and sample representativeness; however, more research is needed to improve prediction of participation in EMA studies in health.

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Social Indicators to Explain Response in Longitudinal Studies

Cited by 8 publications

References 33 publications

Transitioning an employee panel survey from telephone to online and mixed-mode data collection

Transitioning an employee panel survey from telephone to online and mixed-mode data collection

Predicting Participation Willingness in Ecological Momentary Assessment of General Population Health and Behavior: Machine Learning Study

Predicting Participation Willingness in Ecological Momentary Assessment of General Population Health and Behavior: Machine Learning Study (Preprint)

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